3.1. Environmental compartments

3.1.1. Introduction

Authors: John Parsons, Steven Droge

In order to understand and predict the effects of chemicals in the environment we need to understand the behaviour of chemicals in specific environments and in the environment as a whole. In order to deal with the diversity of natural systems, we consider them to consist of compartments. These are defined as parts of the physical environment that are defined by a spatial boundary that distinguishes them from the rest of the world, for example the atmosphere, soil, surface water and even biota. These examples suggest that three phases: gas, liquid, and solid, are important but compartments may consist of different phases. For example, the atmosphere consists of suspended liquids (e.g., fog) and solids (e.g., dust) as well as gases. Similarly, lakes contain suspended solids and soils contain gaseous and water-filled pore space. In detailed environmental models, each of these phases may also be considered to be a compartment.

The behaviour and fate of chemicals in the environment is determined by the properties of environmental compartments and the physicochemical characteristics of the chemicals. Together these properties determine how chemicals undergo chemical and biological reactions, such as hydrolysis, photolysis and biodegradation, and phase transfer processes such as air-water exchange and sorption.

In this chapter, we first introduce the most important compartments and their most important properties and processes that determine the behaviour of chemical contaminants: the atmosphere, the hydrosphere, sediment, soil, groundwater and biota. The emissions of chemicals into the environment from either point sources or diffuse sources is discussed and the important pathways and processes determining the fate of chemicals. The partitioning approach to phase-transfer processes is presented with sorption as a specific example. The impact of physicochemical properties on partitioning is also discussed.

Other important environmental processes are discussed in sections on metal speciation, processes affecting the bioavailability of metals and organic contaminants and the transformation and degradation of organic chemicals. These sections also include information on the basic methods to measure these processes.

Finally, approaches that are used to model and predict the environmental fate of chemicals, and thus the exposure of organisms to these chemicals are described in section 3.8.

3.1.2. Atmosphere

Authors: Astrid Manders-Groot

Reviewer: Kees van Gestel, John Parsons, Charles Chemel

Leaning objectives:

You should be able to:

describe the structure of the atmosphere and mention its main components
describe the processes that determine residence time and transport distances of chemicals in air

Keywords: atmosphere, transport distance, residence time

Composition and vertical structure of atmosphere

The atmosphere of the Earth consists of several layers that have limited interaction. The troposphere is the lowermost part of the atmosphere and contains the oxygen that we breathe and most of the water vapor. It contains on average 78% N₂, 20% O₂ and up to 4% water vapor. Greenhouse gases like CO₂ and CH₄ are present at 0.0038 % and 0.0002%, respectively. Air pollutants like ozone and NO₂ have concentrations that are even a factor 1,000-10,000 lower, but are already harmful for the health of humans, animals and vegetation at these concentrations.

The troposphere is 6-8 km high near the poles, about 10 km at mid-latitudes and about 15 km at the equator. It has its own circulation and determines what we experience as weather, with temperature, wind, clouds and precipitation. The lowest part of the troposphere is the boundary layer, the part that is closest to the Earth. Its height is determined by the heating of the atmosphere by the Earth surface and the wind conditions and has a daily cycle, determined by the incoming sunlight. It is not a completely separate layer, but the exchange of air pollutants like O3, NOx, SO2, and xenobiotic chemicals between the boundary layer and the above layers is generally inefficient. Therefore it is also termed the mixing layer.

Above the troposphere there is the stratosphere, a layer that is less strongly influenced by the daily solar cycle. It is very dry and has its owns circulation, with some exchange with the troposphere. The stratosphere contains the ozone layer that protects life on Earth against UV radiation and extends to about 50 km altitude. The layers covering the next 50 km are the mesosphere and thermosphere, which are not directly relevant for the transport of the chemicals considered in this book.

Properties of pollutants in the air

Air pollutants include a wide range of chemicals, ranging from metals like lead and mercury to asbestos fibers, polycyclic hydrocarbons (PAH) and chloroform. These pollutants may be emitted into the atmosphere as a gas or as a particle or droplet with sizes of a few nanometer to tens of micrometers. The particles and droplets are termed aerosol, or, depending on the measurement method, particulate matter. The latter definition is used in air quality regulations. Note that a single aerosol can be composed of several chemical compounds. Once a pollutant is released in the atmosphere, it is transported by diffusion and advection by horizontal and vertical winds and may be ultimately deposited to the Earth’s surface by rain (wet deposition), and by sticking to the surface (dry deposition). Large particles may fall down by gravitational settling, a process also called sedimentation. Air pollutants may interact with each other or with other chemicals, particles and water by physical or chemical processes. All these processes will be explained in more detail below. A summary of the relevant interactions is given in Figure 1.

Figure 1. Overview of the most relevant process in the atmosphere related to release and transport of air pollutants (source: Wilma IJzerman).

It is important to realize that air pollutants can have an impact on meteorology itself, by acting as a greenhouse gas, scattering or absorbing incoming light when in aerosol form, or be involved in the formation of clouds. This aspect will not be discussed here.

Meteorology is relevant for all aspects, ranging from mixing and transport to temperature or light dependent reaction rates and absorption of water. Depending on the removal rates, species may be removed with timescales of seconds, like heavy sand particles, to decades or longer, like halogen (Cl, Br)-containing gases, and be transported over ranges of a few meters to crossing the globe several times. Concentrations of gases are often expressed in volume mixing ratios (parts per billion, ppb) whereas for particulate matter the correct unit is in (micro)gram per cubic meter as there is no molecular weight associated to it. For ultrafine particles, concentrations are expressed as numbers of particles per cubic meter, for asbestos, the number of fibers per cubic meter is used.

Physical and chemical processes determining the properties of air pollutants

The properties of air pollutants, like solubility in water, attachment efficiencies to the Earth’s surface (water, vegetation, soil) and size of particles, are key elements determining the lifetime and transport distances. These properties may change due to the interaction with other chemicals and with meteorology.

The main physical processes are:

Condensation or evaporation with decreasing/increasing temperature. A potentially toxic aerosol may thus be covered by semi-volatile chemicals like ammonium sulfate, whilea gas may condensate on an aerosol and be transported further as part of this aerosol. Some pollutants exist at the same time in aerosol and in gas phase with their partitioning depending on air temperature and relative humidity.
Gases may cluster to form ultrafine particles of a few nanometers (nucleation) that will grow to larger sizes.
Particles may grow by coagulation: rapidly moving small particles bump into large slow-moving particles and remain attached to it.
Particles may take up water (hygroscopicity), leading to a larger diameter.

Chemical conversions include:

Chemical reactions between gas-phase pollutants and ambient gas, which alters the characteristics of an air pollutant or can lead to the formation of pollutants (e.g. NO₂ being directly emitted by combustion, leading to ozone formation).
Chemical reactions between aerosols and gases, often involving water attached to aerosol.
Cloud droplets or water attached to aerosols have their own role in the chemistry of the atmosphere, and gases may diffuse into the water .
Some pollutants may act as a catalyst.
Some air pollutants may be degraded by (UV) light (photodegradation).

Pollutants are characterized by their chemical composition but for aerosols also the size distribution of particles is relevant. Note that the conservation of atoms always applies, but particle size distribution and particle number can be changed by physical processes. This has to be kept in mind when concentrations are expressed in particles per volume instead of mass concentrations.

Transport of air pollutants

Several processes determine the mixing and transport of chemicals in the air:

Diffusion due to the motion of molecules, or Brownian motion of particles (random walk).
Turbulent diffusion: the mixing due to (small-scale) turbulent eddies which have a random nature, but the strength of this diffusion is related to friction in the flow.
Advection: the process of transport with the large-scale flow (wind speed and direction).
Mixing or entrainment of different air masses leads to further mixing of air pollutants over a larger volume. This process is for example relevant when the sun rises, and air in the boundary layer is heated, rises, and mixes with the air in the layer above the boundary layer.

Although the processes of diffusion and transport are well-known, it is not an easy task to solve the equations describing these processes. For stationary point and line sources under idealized conditions, analytical descriptions can be derived in terms of a plume with concentration profile with a Gaussian distribution, but for more realistic descriptions the equations must be solved numerically. For complex flow around a building, computational fluid dynamics is required for an accurate description, for long-range transport a chemistry-transport model must be used.

Wet deposition

Wet deposition comprises the removal processes that involve water:

Scavenging of particles by or dissolution of gases in cloud droplets. These cloud droplets may grow to larger size and rain out, thereby removing the dissolved air pollutants from the atmosphere (in-cloud scavenging).
Particles below the clouds may be scavenged by falling raindrops (below-cloud scavenging).
Occult deposition occurs when clouds are in contact with the surface (mountain areas) and cloud droplets containing air pollutants stick to the surface.

Wet deposition is a very efficient removal mechanism for both small (<0.1 µm diameter) and large aerosols (diameter >1 µm). Aerosols that are hygroscopic can grow in size by absorbing water, or shrink by evaporating water under dry conditions. This affects their deposition rate for wet or dry deposition.

Dry deposition

Dry deposition is partly determined by the gravitational forces on a particle. Heavy particles (≥ 5 µm) fall to the Earth’s surface in a process called gravitational settling or sedimentation. In the lowest layer of the atmosphere, air pollutants can be brought close enough to the surface to stick to it or be taken up. In the turbulent boundary layer, air pollutants are brought close to the surface by the turbulent motion of the atmosphere, up to the very thin laminar layer (laminar resistance, only for gases) through which they diffuse to the surface. Aerosols or gases can stick to the Earth’s surface or be taken up by vegetation, but they may also rebound. Several pathways take place in parallel or in series, similar to an electric circuit with several resistances in parallel and series. Therefore the resistance approach is often used to describe these processes.

Deposition above snow or ice is generally slow, since the atmosphere above it is often stably stratified with little turbulence (high aerodynamic resistance), the surface area to deposit on is relatively small (impactors) and aerosols may even rebound to an icy surface (collection efficiency of impactors) to which it is difficult to attach. On the other hand, forests often show high deposition velocities since they induce stronger turbulence in the lowermost atmosphere and have a large leaf surface that may take up gases by the stomata or provide sticking surfaces for aerosols. Deposition velocities thus depend on the type of surface, but also on the season, atmospheric stability (wind speed, cloud coverage) and ability of stomata to take up gases. When the atmosphere is very dry, for example, plants close their stomata and this pathway is temporarily shut down. For particles, the dry deposition velocity is lowest at sizes of 0.1-1 µm.

Re-emission

Once air pollutants are removed from the atmosphere, they can be part of the soil or water compartments which can act as a reservoir. This is in general only taken into account for a limited number of chemicals. Ammonia or persistent organic pollutants may be re-emitted from the soil by evaporation. Dusty material or pollutants attached to dust may be brought back into the atmosphere by the action of wind. This is relevant for bare areas like agricultural lands in wintertime, but also for passing vehicles that bring up the dust on a road by the flow they induce.

Atmospheric fate modelling

Due to the many relevant processes and interactions, the fate of chemical pollutants in the air has to be determined by using models that cover the most important processes. Which processes need to be covered depends on the case study: a good description of a plume of toxic material during an accident, where high concentrations, strong gradients and short timescales are important, requires a different approach than the chronic small release of a factory. Since it would require too heavy numerical simulations to include all aspects, one has to select the relevant processes to be included. Key input for all transport models are emission rates and meteorological input.

When one is interested in concentrations close to a specific source, next to emission rate the effective emission height is important, and processes that determine dispersion: wind speed, atmospheric stability. Chemical reaction rates and deposition velocities should be included when the time horizon is long or when the reactions are fast or deposition velocities are high.

When one is interested in actual concentrations resulting from releases of multiple sources and species over a large area of interest, like for an air quality forecast, the processes of advection, deposition and chemical conversions become more relevant, and input meteorology needs to be known over the area. Sharp gradients close to the individual sources are, however, no longer resolved. In particular rain can be a very efficient removal mechanism, removing most of the aerosol within one hour. Dry deposition is slower, but results in a lifetime of less than a week and transport distances of less than 1,000 km for most aerosols. For some gaseous compounds like halogens and N₂O deposition does hardly play a role and they are chemically inert in the troposphere, leading to very long lifetimes.

To assess the overall long-term fate of a new chemical to be released to the market, the potential concentrations in air, water and soil have to be determined. Ideally, models for air, soil and water are used together in a consistent way, including their interaction For many air pollutants the atmospheric lifetime is short but determines where and in which form they are deposited onto ground and water surfaces, where they may accumulate. This means that even if a concentration in air is relatively low at a certain distance from a source, the deposition of an air pollutant over a year may still be significant. Figure 2 shows an example of annual mean modelled concentrations and annual total deposition of a hypothetical passive (non-reactive) soot-like tracer that is released at 1 kg/hour at a fictitious site in The Netherlands. Annual mean concentrations are small compared to ambient concentrations of particulate matter, but the footprint of the accumulated deposition is larger than that of the mean concentration, since the surface acts as a reservoir. This implies that re-emission to air can be relevant. It may take several years for soil or water before an equilibrium concentration is reached in these compartments from the deposition input, as different processes and time scales apply. Mountain ranges are visible in the accumulated wet deposition (Alps, Pyrenees), as they are areas with enhanced precipitations.

In addition to spatially explicit modelling, also box models exist that have the advantage that they can make long-term calculations for a continuous release of a species, including interaction between the compartments air, soil and water. They can be used to determine when an equilibrium concentration is reached within a compartment, but these models cannot resolve horizontal concentration gradients within a compartment.

Figure 2. Constant release of a passive tracer from a point source in The Netherlands. Upper panel shows the annual mean concentration, the lower panel shows the accumulated wet and dry deposition over one year. Note the nonlinear colour scale to cover the large range of values. Source: https://doi.org/10.3390/atmos8050084.

Learn more

EU air quality, policy and air quality legislation: http://ec.europa.eu/environment/air/index_en.htm

US hazardous air pollutants, including lists of toxics: https://www.epa.gov/haps

Plume dispersion approach: http://courses.washington.edu/cewa567/Plumes.PDF

Chemistry-transport models: https://www.narsto.org/sites/narsto-dev.ornl.gov/files/Ch71.3MB.pdF

Seinfeld, J., Pandis, S.N. Atmospheric Chemistry and Physics, from air pollution to climate change, Wiley, 2016, covering all aspects.

John, A. C., Küpper, M., Manders-Groot, A. M., Debray, B., Lacome, J. M., Kuhlbusch, T. A. (2017). Emissions and possible environmental implication of engineered nanomaterials (ENMs) in the atmosphere. Atmosphere, 8(5), 84.

3.1.3. Hydrosphere

Authors: John Parsons

Reviewers: Steven Droge, Sean Comber

Leaning objectives:

You should be able to:

describe the most important chemical components and their sources
describe the most important chemical processes in fresh and marine water.
be familiar with the processes regulating the pH of surface water.

Keywords: Hydrogen bonding, carbonates, dissolved salts

The properties and importance of water

Water covers 71% of the earth’s surface and this water, together with the smaller amounts present as gas in the atmosphere, as groundwater and as ice is referred to collectively as the hydrosphere. The bulk of this water is salt water in the oceans and seas with only a minor part of freshwater being present as lakes and rivers (Figure 1).

Figure 1. Global hydrological cycle and water balance (arrows are fluxes of water per year). Adapted from Kayane (1992) and Peixoto (1994) by Steven Droge 2019.

Water is essential for life and also plays a key role in many other chemical and physical processes, such as the weathering of minerals and soil formation and in regulating the Earth’s climate. These important roles of water derive from its structure as a small but very polar molecule arising from the polarised hydrogen-oxygen bonds (Figure 2). As a consequence, water molecules are strongly attracted by hydrogen bonding, giving it relatively high melting and boiling points, heat capacity, surface tension, etc. The polarity of the water molecule also makes water an excellent solvent for a wide variety of ionic and polar chemicals but a poor solvent for large nonpolar molecules.

Figure 2. Hydrogen bonding between water molecules

The freshwater environment

As mentioned above, freshwater is only very small proportion of total amount of water on the planet and most of this is present as ice. Since this water is in contact with the atmosphere and the soils and bedrock of the Earth’s crust, it dissolves both atmospheric gases such as oxygen and carbon dioxide and salts and organic chemicals from the crust. If we compare the relative compositions of cations in the Earth’s crust and the major dissolved species (Table 1) it is clear that these are very different. This difference reflects the importance of the solubility of these components. For ionic chemicals, this depends on both their charge and their size (expressed as z/r², where z is the charge and r the radius of an ion). As well as reflecting the properties of the local crust, the composition of salts is also influenced by precipitation and evaporation and the deposition of sea salt in coastal regions.

Table 1. Comparison of the major cation composition of average upper continental crust and average river water. (*except aluminum and iron from Broecker and Peng (1982))

	*Upper continental crust (mg/kg) (Wedepohl 1995)**	River water (mg/kg) (Berner & Berner 1987*)
Al	77.4	0.05
Fe	30.9	0.04
Ca	29.4	13.4
Na	25.7	5.2
K	28.6	1.3
Mg	13.5	3.4

The pH of surface water is determined by both the dissolution of carbonate minerals and carbon dioxide from the atmosphere. These components are part of the set of equilibrium reactions known as the carbonate system (Figure 3).

Figure 3. The carbonate system of equilibria regulating the pH of surface water. Source: http://butane.chem.uiuc.edu/pshapley/GenChem1/L26/3.html

At equilibrium with the current atmospheric CO₂ concentration and solid calcium carbonate, the pH of surface water is between 7 and 9 but this may reach more acidic values where soils are calcium carbonate (limestone) poor. This is illustrated by the pH values measured in a river in Northern England, where acidic, organic carbon-rich water at the source is gradually neutralised once the river encounters limestone rich bedrock (Figure 4).

Figure 4. Water chemistry in the Malham Tarn area of northern England, showing the relationship between pH, alkalinity and dissolved calcium as water flows from bog on siliceous mudrock to limestone where the pH is buffered around 8 due to once limestone weathering. Redrawn from Fig. 5.6 in Andrews et al. (2004) by Wilma Ijzerman.

As well as these natural processes, there are human influences on the pH of surface water including acidic precipitation resulting from fossil fuel combustion and acidic effluents from mining activities caused by oxidation and dissolution of mineral sulphides. Regions such as Southern Scandinavia with carbonate-poor soils are particularly vulnerable to acidification due to these influences and this is reflected in for example, reduced fish populations in these vulnerable regions (see Figure 5). More recently, reduced coal burning and the decline in heavy industry is resulting in the recovery of pH values in upland areas across Europe.

Figure 5. Average catch size of salmon in seven rivers in southern Norway receiving acid precipitation and 68 other rivers that do not receive acid precipitation. Redrawn with data from Henriksen et al. (1989) by Wilma Ijzerman.

Dissolved oxygen is of course essential to aquatic life and concentrations are in general adequate in well mixed water bodies. Oxygen can become limiting in deep lakes where thermal stratification restricts the transport of oxygen to deeper layers, or in water bodies with high rates of organic matter decomposition. This may result in anoxic conditions with significant ecological impacts and on the behaviour of chemical contaminants.

The marine environment

Freshwater eventually moves into seas and oceans where the concentrations of dissolved species are much higher than in the freshwater environment. This is partly due to the effects of evaporation of water from the oceans but is also be due to specific marine sources of some dissolved components. Estuaries are the transition zones where freshwater and seawater mix. These are highly productive environments where increasing salinity has a major impact on the behaviour of many chemicals, for example on the speciation of metals and the aggregation of colloids as a result of cations shielding the negative surface change of colloidal particles (Figure 6). Increasing salinity also affects organic chemicals, with ionic chemicals forming ion pairs, and even reducing the solubility of neutral organics (the so-called salting-out effect). As well as these chemical effects due to increasing salinity, the lowering of flow rates in estuaries leads to the deposition of suspended particles.

Figure 6. The Electrical Double Layer (EDL), comprising a fixed layer of negative charge on a clay particle (due to isomorphic substitutions and surface acids) and a mobile ionic layer in solution. The latter is caused because positive ions are attracted to the particle surface. Note that with increasing distance from the particle surface the solution approaches electrical neutrality. (Source Steven Droge 2019)

Since the concentrations of pollutants are in general lower in the marine environment than in the freshwater environment, concentrations in estuaries decrease as freshwater is diluted with seawater. Measuring salinity at different locations in estuaries is a convenient way to determine the extent of this dilution. Components that are present in higher concentrations in seawater will of course show an increase with salinity unless. Plotting salinity against the concentrations of chemicals at different locations can yield information on whether they behave conservatively (i.e. only undergoing mixing) or are removed by processes such as degradation or partitioning into the atmosphere or sediments. Figure 7 shows examples of plots expected for conservative chemicals and those that are either removed in the estuary or have local sources there. Models describing the behaviour of chemicals in estuaries can be used with these data to derive the rates of removal or addition of the chemical in the system.

Figure 7. Idealized plots of estuarine mixing illustrating conservative and non-conservative mixing. C_R and C_S are the concentrations of the ions in river and seawater respectively. Redrawn from Figure 6.3 in Andrews et al. (2004) by Wilma Ijzerman.

The open ocean is sufficiently mixed for the composition of major dissolved constituents to be fairly constant, except in local situations as a result of upwelling of deep nutrient-rich waters or the biological uptake of nutrients. In coastal regions the concentrations of chemicals and other components originating from terrestrial sources may also be locally higher. The major components in seawater are listed in Table 2 with their typical concentrations.

Table 2. Major ion composition of freshwater and seawater.

	Seawater (mmol/L) (Broecker and Peng, 1982)	River water (mmol/L) (Berner and Berner, 1987)
Na⁺	470	0.23
Mg²⁺	53	0.14
K⁺	10	0.03
Ca²⁺	10	0.33
HCO₃^-	2	0.85
SO₄^2-	28	0.09
Cl^-	550	0.16
Si	0.1	0.16

These concentrations may be higher in waterbodies that are partly or wholly isolated from the oceans and are impacted by evaporative losses of water (e.g. Mediterranean, Baltic, Black Sea). In extreme case, concentrations of salts may exceed their solubility product, resulting in precipitation of salts in evaporate deposits.

As is the case in freshwater, carbonates play an important role in regulating the ocean pH. The fact that the oceans are supersaturated in calcium carbonate makes it possible for a variety of organisms to have calcium carbonate shells and other structures. The important processes and equilibria involved are illustrated in Figure 8. There is concern that one of the most important effects of increasing atmospheric carbon dioxide will be lowering of ocean pH to values that will result in destabilisation of these carbonate structures.

Figure 8. (a) Schematic diagram to illustrate the buffering effect of CaCO₃ particles (suspended in the water column) and bottom sediments on surface water HCO₃^- concentrations (after Baird and Cann 2012). (b) A sample of the seawater in (a) will have a pH very close to 8 because of the relative proportions of CO₂, HCO₃^- and CO₃^2-, which in seawater is dominated by the HCO₃^- species. Increased CO₂ concentrations in the atmosphere from anthropogenic sources could induce greater dissolution of CaCO₃ sediments including coral reefs. Redrawn from Figure 6.8 in Andrews et al. (2004) by Wilma Ijzerman.

References

Andrews, J.E., Brimblecombe, P., Jicketts, T.D., Liss, P.S., Reid, B.J. (2004). An Introduction To Environmental Chemistry, Blackwell Publishers, ISBN 0-632-05905-2.

Baird, C., Cann, M. (2012). Environmental Chemistry, Fifth Edition, W.H. Freeman and Company, ISBN 978-1429277044.

Berner, E.K., Berner, R.A. (1987). Global water cycle: geochemistry and environment, Prentice-Hall.

Broecker, W.S., Peng, T.S. (1982). Tracers in the Sea, Lamont-Doherty Geol. Obs. Publ.

Henriksen, A., Lien, L., Rosseland, B.O., Traaen, T.S., Sevaldrud, I.S. (1989). Lake Acidification in Norway: Present and Predicted Fish Status. Ambio 18, 314-321

Wedepohl, K.H. (1995). The composition of the continental crust, Geochimica Cosmochimica Acta 59, 1217-1232.

3.1.4. Sediment

In preparation

3.1.5. Soil

Author: Kees van Gestel

Reviewers: John Parsons, Jose Alvarez Rogel

Learning goals

You should be able to

describe the main components of which soils consist
describe how soil composition influences properties that may affect the fate of chemicals in soil

Keywords:

Particle size distribution, Porosity, Minerals, Organic matter, Cation Exchange Capacity, Water Holding Capacity

Introduction

Soil is the upper layer of the terrestrial environment that serves as a habitat for organisms and medium for plant growth. In addition, it also plays an important role in water storage and purification and helps to regulate the Earth's atmosphere (e.g. carbon storage, gas fluxes, …).

Soils are composed of three phases (Figure 1).

Figure 1. Average composition of soil (in volume %).

The solid phase is formed by mineral and organic components. Mineral components appear in different particle sizes from coarse particles (sand), intermediate (silt) and fine (clay) which combination determine soil texture. The particles can be arranged to form porous aggregates; soil pores being filled with air and/or water. The proportion of air in soils depends on soil moisture content. The composition of the soil solid phase may be quite variable.

The gaseous phase has a similar composition as the air, but due to the respiration of plant roots and the metabolic activity of soil microorganisms, O₂ content generally is lower and CO₂ content higher. Exchange of gases between soil pores and atmospheric air takes place by diffusion. Diffusion proceeds faster in dry soil and much slower when soil pores are filled with water.

The liquid phase of the soil, the soil solution or pore water, is an aqueous solution containing ions (mainly Na⁺, K⁺, Ca²⁺, Cl^-, NO₃^-, SO₄^2-, HCO₃^-) from dissolution of a variety of salts, and also contains dissolved organic carbon (DOC, also referred to as dissolved organic matter, DOM). The soil solution is part of the hydrological cycle, which involves input from among others rain and irrigation, and output by water uptake by plants, evaporation, and drainage to ground and surface water. The soil solution acts as a carrier for the transport of chemicals in soil, both to plant roots, soil microorganisms and soil animals and to ground and surface water.

Soil solids

The soil solid phase consists of mineral and organic soil particles. Based on their size, the mineral particles are divided into sand (63-2000 µm), silt (2- 63 µm), and clay (<2 µm). With increasing particle size, the specific surface area decreases, pore size increases and water retention capacity decreases. The sand fraction mainly consists of quartz (SiO₂) and does not have any sorption properties because the quartz crystals are electrically neutral. Sandy soils have large pores, so a low capacity to retain water. In soils with a high silt fraction, smaller pores are better represented, giving these soils a higher water retention capacity. Also the silt fraction has no adsorptive properties. Clays are aluminium silicates, lattices composed of SiO₄ tetrahedrons and Al(OH)₆ octahedrons. Upon the formation of clay particles, isomorphic substitution occurred, a process in which Si⁴⁺ was replaced by Al³⁺, and Al³⁺ by Mg²⁺. Although having similar diameters, these elements have different valences. As a consequence, clay particles have a negative charge, making positive ions to accumulate on their surface. This includes ions important for plant growth, like NH₄⁺, K⁺, Na⁺ and Mg²⁺, but also cationic metals (Figure 2). Many other minerals have pH-dependent charges (either positive or negative) which are also important in binding cations and anions.

Figure 2. Schematic representation of a clay particle. Due to its negative charge, cations will accumulate near the surface of the clay particle.

In addition to mineral particles, soils also contain organic matter, which includes all dead plant and animals remains and their degradation products. Living biota is not included in the soil organic matter faction. Organic matter is often divided into: 1. humin, non-dissolved organic matter associated with clay and silt particles, 2. humic acids having a high degree of polymerization, and 3. fulvic acids containing more phenolic and carboxylic acid groups. Humic and fulvic acids are water soluble but their solubility depends on pH. For example, humic acids are soluble at alkaline pH but not at acidic pH. The dissociation of the phenolic and carboxylic groups gives the organic matter also a negative charge (Figure 3), the density of which increases with increasing soil pH. The soil organic matter acts as a reservoir of nitrogen and other elements, provides adsorption site for cations and organic chemicals, and supports the building of soil aggregates and the development of soil structure.

Figure 3. Proposed structure of a humic acid molecule. The phenolic and carboxylic acid groups on the molecule may dissociate depending on the pH of the soil, giving rise to negative sites on the molecule. With this, humic acid contributes to the Cation Exchange Capacity of the soil. Adapted from Schulten & Schnitzer (1997) by Steven Droge.

The binding of cations to the negatively charged sites on the soil particles is an exchange process. The degree of cation accumulation near soil particles depends on their charge density, the affinity of the cations to the charged surfaces (which is higher for bivalent than for monovalent cations), the concentration of ions in solution (the higher the concentration of a cation in solution, the higher attraction to soil particles), etc. Due to their binding to charged soil particles, cations are less available for leaching and for uptake by organisms. The Cation Exchange Capacity (CEC) is commonly used as a measure of the number of sites available for the sorption of cations. CEC is usually expressed as cmol_c/kg dry soil. Soils with higher CEC have a higher capacity to bind cations, so cationic metals show a lower (bio)availability in high CEC soils (see the Section on metal speciation). CEC depends on the content and type of clay minerals, with montmorillonite having a higher CEC than e.g. kaolinite, organic matter content and pH of the soil. In addition to clay and organic matter, also aluminium and iron oxides and hydroxides may contribute to the binding of cations to the soil.

Soil water

The transport of water through soil pores is controlled by gravity, and by suction gradients which are the result of water retention by capillary and osmotic processes. Capillary binding of water is stronger in smaller soil pores, which explains why clayey soils have higher water retention capacities than sandy soils. The osmotic binding of water increases with increasing ionic strength, and is especially high close to charged soil particles like clay and organic matter where ions tend to accumulate.

The stronger water is retained by soil, the lower its availability is for plants and other organisms. The strength by which water is retained depends on moisture content, because 1. at decreasing moisture content the ionic strength of the soil solution and therefore osmotic binding increases, 2. when soil moisture content decreases the larger soil pores will be emptied first, leading to increasing capillary retention of the remaining water in smaller pores. Water retention curves describe the strength with which water is retained as a function of total water content and in dependence of the composition of the soil. Figure 4 shows pF curves for three different soil types.

Figure 4. pF curves showing the retention of water by three different soil types. pF is the log of the force with which water is retained, expressed in hPa. W.P. = wilting point, F.C. = field capacity. Source: Wilma IJzerman.

A pF value of 2.2-2.5 corresponds with a binding strength of 200 to 300 hPa. This is called field capacity; water is readily available for plant uptake. At pF 4.2 (15,000 hPa), water is strongly bound in the soil and no longer available for plant uptake; this is called the wilting point. For soil organisms, not the total water content of a soil is of importance but rather the content of available water. Water retention curves may be important to describe the availability of water in soil. Toxicity tests with soil organisms are typically performed at 40-60% of the water holding capacity (WHC) of the soil, which corresponds with field capacity.

References/further reading

Schulten, H.-R., Schnitzer, M. (1997). Chemical model structure for soil organic matter and soils. Soil Science 162, 115-130.

Blume, H.-P., Brümmer, G.W., Fleige, H., Horn, R., Kandeler, E., Kögel-Knabner, I., Kretzschmar, R., Stahr, K., Wilke, B.-M. (2016). Scheffer/Schachtschabel Soil Science, Springer, ISBN 978-3-642-30941-0

3.1.6. Groundwater

(draft)

Author: Thilo Behrends

Reviewer: Steven Droge, John Parsons

Leaning objectives:

You should be able to:

understand the significance of redox reactions for the fate of potentially toxic compounds in groundwaters and aquifers.
apply the Nernst equation to assess the feasibility of redox reactions.

Keywords: Aquifer, Nernst equation, electron transfer, redox potential, half reactions

Introduction

Some definitions conceive all water beneath the earth’s surface as groundwater while others restrict the definition to water in the saturated zone. In the saturated zone the pores are completely filled with water in contrast to the undersaturated zone in which some pores are filled with gas and capillary action are important for moving water. Geological formations, which host groundwater in the saturated zone, can be classified as ‘aquifer’, ‘ aquitard’, or ‘aquifuge’ depending on their permeability. In contrast to aquitard and aquifuge, which have a low permeability, an aquifer permits water to move in significant rates under ordinary field conditions. Aquifers typically have a high porosity and the pores are well connected with each other. Examples of aquifers include sedimentary layers of sand or gravel, carbonate rocks, sandstones, volcanic rocks and fractured igneous rocks. The redox chemistry discussed in this chapter is focusing on aquifers in sedimentary formations.

Groundwaters are an important source for drinking water and the quality of groundwater is, therefore, of high importance for protecting human health. However, aquifers also represent a habitat for bacteria and aquatic invertebrates and are, therefore, also an object for ecotoxicological studies. Furthermore, groundwater can act as a transportation pathway connecting different environmental compartments e.g. soils with rivers or oceans. Groundwater thus plays a role in the distribution of contaminants in the environment.

Transport of contaminants in aquifers

The movement of a chemical in groundwater is controlled by three processes: advection, dispersion and reaction. Advection is the transport of a chemical in dissolved form together with the groundwater flow. When a chemical is released from a point source into groundwater with a constant flow direction, a plume is forming downstream of the source. The spreading of the chemical is due to dispersion. There are two reasons for this spreading: First, molecular diffusion causes transport of the chemical independently from advection; Second, differences in groundwater velocities at different scales causes mixing of the groundwater (mechanical dispersion) in the direction of groundwater flow but also perpendicular to it. Several process can retard the transport of chemicals or can cause its removal from the system (e.g. degradation). For the mobility of a chemical, the distribution between immobile solid phase and moving liquid phase is of key importance in groundwater (see chapter 3.4). There are several processes which can lead to the degradation of a compound in aquifers. Microbial activity can contribute to the degradation of chemicals but also abiotic reactions can be of importance. For some chemical, redox reactions are relevant which are discussed in the following section.

Redox reactions in aquifers

Many elements are redox-sensitive under environmental conditions. This means they occur naturally in different ‘redox states’. For example oxidation or reduction of carbon plays a pivotal role in the energy metabolism of living organisms and carbon occurs in oxidation states from +IV in CO₂ (because the two oxygen atoms both count as –II ((because oxygen is more electronegative than carbon)), and the total molecule should balance out) to -IV in CH₄ (because each H-atom counts as +I ((because hydrogen is less electronegative than carbon))). Also potentially toxic elements, such as arsenic, are found in nature in different oxidation states. Important oxidation states of arsenic are +V, (e.g. AsO₄^3-, arsenate), + III (e.g. AsO₃^-3, arsenite), 0 (elemental arsenic or arsenic associated with sulfide as in FeAsS, arsenopyrite). Arsenic can also have negative oxidation states when it forms arsenides such as FeAs₂ (löllingite). Bioavailability, toxicity and mobility of redox sensitive elements are usually strongly dependent on their oxidation state. For example, arsenite tends to be more toxic and more mobile than arsenate. For this reason, assessing the redox state of potentially toxic elements is an important element of environmental risk assessment of groundwater.

Organic contaminants can also undergo redox transformations. At the earth surface, when oxygen is present, (photo-)oxidation is an important degradation pathway for organic contaminants. In subsurface environments, when oxygen concentrations are often very low (anoxic conditions), reduction can play an important role in degradation pathways. For example, the reductive dehalogenation of chlorinated hydrocarbons or the reduction of nitroaromatic compounds have been extensively investigated. The reduction of these compounds can be mediated by microorganisms but they can also occur abiotically on solid surfaces present in the subsurface. In any case, reduction of organic contaminants is only possible when the reaction is thermodynamically feasible. For this reason it is necessary to know the redox conditions in, for example, an aquifer.

Quantitative assessment of redox reactions

As the name indicates, redox reactions combine oxidation of one constituent in the system with the reduction of another and, hence, involve electron transfer. The oxidation of arsenite with elemental oxygen to arsenate has following stoichiometry:

\(AsO_3^{3-} \ +\ 0.5\ O_2\ \leftrightarrow AsO_4^{3-}\)

It is important that the stoichiometries of redox reactions are not only charge- and mass-balanced but also electron-balanced. Here, arsenic releases two electrons when going from oxidation state +III to +V (arsenite becomes oxidized to arsenate) while one oxygen atom takes up the two electrons and goes from oxidation state 0 to -II (elemental oxygen becomes reduced). For this reaction an equilibrium constant can be obtained and based on the activities (or concentrations) of dissolved reactants and products it can be evaluated whether the reaction is in equilibrium or in which direction the reaction is thermodynamically favorable.

When a natural system contains several different redox-active constituents, a large number of possible redox reactions can be formulated and evaluated separately. In this situation it is more convenient to formulate and compare half reactions. For examples, the oxidation of arsenite with oxygen can be split up into the reactions of arsenic and oxygen.

\(H_3SO_4\ +\ 2\ e^-\ +\ 2\ H^+\ \leftrightarrow H_3SO_3\ +\ H_2O\) \(E_h^\circ\ = 0.575\ V\)

\(O_2\ +\ 4\ e^-\ +\ 4\ H^+\ \leftrightarrow 2\ H_2O\) \(E_h^\circ\ = 1.229\ V\)

Half reactions are typically formulated as reduction reactions (electrons are on the left hand side of the reaction). The E_h^o is the standard redox potential and represents the electrical potential, which would be measured in a standardized electrochemical cell which contains on one side, H₃AsO₄, H₃AsO₃ and H⁺, all with activities of 1 mol l^-1, and a solution containing 1 mol l^-1 H⁺ in equilibrium with H₂ gas with a pressure of 1 bar, on the other side.

In natural environments the pH is usually not 0 and the activities of arsenite and arsenate are not 1 mol l^-1. The redox potential, E_h under these conditions can be calculated using the Nernst equation:

\(E_h\ =\ E_h^\circ\ +\ {RT\over zF}\ ln\ {ox\over red}\ = 0.575\ V\ + {{8.314\ J\ mol^{-1}\ K^{-1}\ 298\ K}\over {2\ *\ 96485\ mol\ C^{-1}}}\ ln\ {\{H_3AsO_4\}\{H^+\}^2\over\{H_3AsO_3\}} \)

where:

R is the ideal gas constant ( 8.314 J mol^-1 K^-1),

T the temperature in K,

z is the number of electrons which are transferred in the reaction,

F the Faraday constant (96485 mol C^-1).

In the ratio ox/red, ‘ox’ represents the activities or pressures of the constituents on the right hand side of the half reaction, whereby the stoichiometric factor becomes the corresponding exponent, while ‘red’ represents the right hand side of the half reaction.

The redox potentials of different half reactions can be compared:

The half reaction with the higher redox potential provides the electron acceptor in the thermodynamically favorable redox reaction,
The half reaction with the lower potential provides the electron donor.

In other words, it is thermodynamically favorable that the half reaction with the high potential proceeds from left to right and the half reaction with the low potential from right to left.

Redox conditions in aquifers

The redox conditions in an aquifer depends on the inherited inventory of oxidants and reductants during the formation of the geological formation and the processes which have been occurring throughout its history. Oxidants and reductants can have entered the aquifer by diffusion or with the infiltrating water and slowly progressing redox reaction can have modified the assemblage of oxidants and reductants. In the absence of (microbial) catalysis redox reactions often have very slow kinetics. Furthermore, due to photosynthesis, redox reactions are not in equilibrium at the earth’s surface and the upper part of the underlying subsurface. As a consequence, the redox conditions in an aquifer can usually not be represented in one unique redox potential. This implies that values obtained for groundwaters with electrochemical measurements, e.g. potentiometric measurements using redox electrodes, might be not representative for the redox conditions in the aquifer. Furthermore, relevant half reactions in the aquifer often involve solids (heterogeneous reactions) with low solubility, implying that the concentrations in solution (for example of Fe³⁺) are too low to be detected. Hence, evaluating the redox conditions in subsurface environments is often challenging.

Oxygen concentrations in groundwaters are often virtually zero, as oxygen in infiltrating rain water or entering the subsurface by molecular diffusion is often consumed before it can reach the aquifer. Hence, ‘reducing conditions’ typically prevail in aquifers. The redox potential measured in a system may reflect the dominant electron acceptors besides oxygen that are present in the system (Figure 1).

Figure 1. Redox potential scale, ranging from oxic to anoxic conditions. Even when oxygen is absent, the concentrations of available alternative oxidant materials (NO^3- and Fe²⁺) may render a positive redox potential. Methanogenesis (CH₄ formation) only initiates at very low negative redox potentials. (Source: Steven Droge)

In sediments or sedimentary rocks, redox reactions after deposition are predominately driven by the oxidation of organic matter, which entered the sediment during its deposition. However, the aquifer might also have received dissolved organic matter via infiltrating water. The oxidation of organic matter is predominately microbially mediated and predominately coupled to the reduction of elemental oxygen (if present). However, when elemental oxygen is depleted, which is usually the case, other electron acceptors are used by microorganisms. Relevant electron acceptors (see Figure 1) in anoxic environments include:

Nitrate (in dissolved form),
Mn(IV) (as solid surface),
Mn(III) (as solid surface),
Fe(III) (as solid surface),
Sulphate (in dissolved form).

Nitrate and sulphate can be present in dissolved form while Mn(IV), Mn(III), Fe(III) occur as solids with low solubility. The (hydr)oxide solids of these metals, such as goethite (FeOOH) or manganite (MnOOH) are mostly accessible for microbial reduction while Mn(III) or Fe(III) in silicates can only be partially reduced or are not bioavailable for reduction. When also these electron acceptors run short, methanogenesis can be initiated.

Microorganisms, which reduce sulphate, Mn or Fe(III), can use the products of fermentative organisms. These fermentative organisms produce short-chain fatty acids, such as acetate or lactate, but often also release hydrogen gas. That is, hydrogen concentrations in groundwater reflect a steady state of hydrogen production and consumption, and are typically limited by the rates of production. As a consequence, hydrogen concentrations in groundwater are often at the physiological limit of the consuming organism. The concentrations are just sufficient to allow the organism to conserve energy from oxidizing the hydrogen. This limit increases according to the sequence of electron acceptors (Figure 1): nitrate reduction < Mn reduction < Fe reduction < sulphate reduction < methanogenesis when the corresponding compounds are present in relevant amounts or concentrations. For this reason, concentrations of dissolved hydrogen can be a useful indicator to identify the dominant, anaerobic respiration pathway in an aquifer. For example, one can determine whether sulphate reduction is enabled or methanogenesis has set in. The hydrogen concentrations in the groundwater can also directly be used to assess whether the microbial reduction of metals, metalloids, chlorinated hydrocarbons, nitro aromatic compounds or other organic contaminants is feasible.

The reduction of Fe(III)(hydr)oxides are sulphate leads to the formation of Fe(II) and sulphide, which, in turn, typically results in the precipitation of ferrous solids such as FeCO₃ (siderite), FeS (mackinawite) or FeS₂ (pyrite). These Fe(II) containing minerals often play an important role in the abiotic reduction of organic or inorganic contaminants in aquifers. When the composition of the groundwater and the mineral assemblage is known, the Nernst equation can be used to calculate the redox potential of relevant half reactions in the aquifer. These redox potential can be then used for evaluating whether reduction of potentially toxic compounds is possible or not. For example, the half reaction for the reduction of an amorphous ferric iron hydroxide coupled to the precipitation of siderite is given by:

\(Fe(OH)_3 (s)+ e^-+H^++H_2 CO_3↔FeCO_3 (s)+H_2 O\) \(E_h^\circ = 0.579\ V\)

At given pH and carbonic acid concentration, the corresponding redox potential can be calculated using the Nernst equation. This redox potential can be compared to that obtained from the Nernst equation for the reductive dichlorination of tetrachloroethylene (Cl₂C=CCl₂)

\(Cl_2 C=C Cl_2 + 2e^-\ +\ H^+\ ↔\ Cl_2 C=CHCl\ +\ Cl^- \) \(E_h^\circ = 0.70\ V\)

With this approach the feasibility of redox reactions involving potentially organic and inorganic compounds can be evaluated in aquifers. That does, however, not imply that the corresponding reactions also occur within the relevant time scale. For this the kinetics of the reaction have to be known and have been studied for many reactions of potential relevance in aquifer systems. However, the kinetics of redox reactions are not subject of this section.

References

Sparks, D. (2002). Environmental Soil Chemistry, Second Edition, Academic Press, Chapters 5 and 8, ISBN 978-0126564464.

Essington, M.E. (2004). Soil and Water Chemistry: An Integrative Approach, Chapters 7 and 9, CRC Press, ISBN 978-0849312588

3.1.7. Biota

(draft)

Author: Steven Droge,

Reviewer: Nico van der Brink, John Parsons

Leaning objectives:

You should be able to:

explain the effect of cell and body composition on toxicokinetics of compounds
describe the role of biota in the environmental fate of chemicals

Keywords: cellular composition, body composition, exposure routes, absorption, distribution

Introduction

Just like soil, water, and air, the organic tissue of living organisms can also be regarded as a compartment of the ecosystem where chemical pollutants can accumulate or can be broken down. The internal concentration in living organisms provide important information on chemical exposure and ultimately determines the environmental risk of pollution, but it is important to understand the key features of tissue that influence chemical partitioning into organisms. Chemical accumulation in the tissue of living organisms is a series of chemical and biological processes, briefly based on:

- chemical uptake (mostly permeation from bulk media over certain membranes into cells);

- internal distribution (e.g. via blood flows through organs);

- metabolism (e.g. biotransformation processes in for instance the liver).

- excretion (e.g. through urine and feces, but also via gills, sweat, milk, or hairs)

These four processes are the basis of toxicokinetic modeling, and are often summarized as Absorption, Distribution, Metabolism, and Excretion, or “ADME”. These ADME processes can strongly vary for different polluting compounds due to the properties of the chemical structure. These ADME processes can also strongly vary for different organisms, because of:

- the physiological characteristics (e.g. having gills, lungs, or roots, availability of specific chemical uptake mechanisms, presence of specific metabolic enzymes, size-related properties like metabolic rate),

- the position in the polluted environment (flying birds or midge larvae living in sediment),

- the interaction with the polluted environment (living in soil or water, food choice, etc.)

- the behaviour in the polluted environment (being sessile or able to move (temporarily) away from a polluted spot).

More details of these toxicokinetic processes are presented in section 4.1 on Toxicokinetics and bioaccumulation. The current module aims to provide a summary of the key features of different tissue components that explain the internal distribution of chemicals (distribution), the different types of contact between pollutants and organisms (exposure-absorption), and temporal changes in physiology that may affect internal exposure (e.g. excretion, which includes examples such as release of POPs via lactation, and increasing POP concentrations during starvation). Before we discuss how chemicals are taken up into biota, it is important to first define the key chemical properties and the molecular composition of tissue that influence the way chemicals are absorbed from the surrounding environment and distributed throughout an organism.

Absorption-distribution: Tissue building blocks

All organisms are composed of cells, which are composed of a cell membrane, surrounding the largely watery solution filled with inner organelle membranes, protein structures, and DNA/RNA. Prokaryote organisms such as bacteria, but also algae, fungi and plants have reinforced membranes with cell walls to prevent water leaking by high osmotic pressures, and to protect the cell membrane. Metabolic energy is stored in large molecules such as fatty esters and sugars. Remarkably, for all existing living organism species, these tissue components are mostly structures made out of relatively simple and repetitive molecular building blocks, with minor variations on side chains. See examples in Figure 1. The composition of organs, as a collection of specific cells, in terms of the percentage of lipids, proteins and carbohydrates is important for the overall toxicokinetics of chemicals in the whole organism.

Figure 1. the key molecular components and partial building blocks of living tissue.

Cell walls are mostly made from highly polar polysaccharides, e.g.:

cellulose, a polymer of sugary molecules, and chitin in fungi, which is highly polar and therefore permeable for water.
peptidoglycan semi-crystal structures surrounding bacteria (90% of the dry weight of Gram-positive bacteria but only 10% of Gram-negative strains), a mixed polymer between N-acetylglucosamine (alike chitine) and short interconnecting 4 or 5 amino acid chains.
lignin, a polar (~30% oxygen) but more hydrophobic supra-structure of polymerized phenolic molecules lining the main plant vessels that transport water.

The specific algae group of diatoms have a cell wall composed of biogenic silica (hydrated silicon dioxide), typically as two valves that overlap each other surrounding the unicellular species. Diatoms generate about 20 percent of the oxygen annually produced on the planet, and contribute nearly half of the organic material found in the oceans. With their specific cell wall structure, diatoms take in over 6.7 billion metric tons of silicon each year from the waters in which they live, which creates huge deposits when they die off.

Cell membranes are made up mostly of a phospholipid bilayer, with each phospholipid molecule basically having a polar and ionized headgroup connected to two long alkyl chains (Figure 1 example with POPC type phospholipid). The outer sides of a phospholipid bilayer are hydrophilic (water-loving), the inside is hydrophobic (water-fearing). Ions (inorganic salts, nutrients, metals, strong acids and ionized biomolecules) do not readily permeate through such a membrane passively, and require specific transport proteins that can transport as well as regulate ions in and out of the cell interior. Cholesterol molecules stabilize the fluidity of the membrane bilayers in cells of most organisms, but for example not in most Gram negative bacteria. Dissolved neutral chemicals may passively diffuse through phospholipid bilayers into and out of cells.

Proteins are chains of a variety of amino acids, 21 of which are known to be genetically coded, and of which humans can only produce 12. The other nine must be consumed, and are therefore called essential amino acids (coded H, I, L, K, M, F, T, W, V). Proteins form complex 3 dimensional structures that allow for enzymatic reactions to occur effectively and repeatedly. There are two amino acids with side chains that carry a positive charge at neutral pH: Arginine (pKa 12) and Lysine (pKa 10.6), and two amino acids with side chains that carry a negative charge at neutral pH: Aspartic acid (pKa 3.7) and Glutamic acid (pKa 4.1). Some amino acids carry typical hydrophobic side chains: amongst others Leucine and Phenylalanine. Cysteine has a thiol (SH) moiety that can form strong connective disulfide interactions with spatially nearby cysteine side groups in the 3D structure. The key blood transport protein albumin, for example, contains about 98 anionic amino acids, and 83 cationic amino acids, and about 35 cysteine residues.

DNA and other genetically encoding chains are composed of 4 different nucleotides that form a double helix of two opposing strands, held together by hydrogen bonds connecting the complementary bases: A and T (or A and U in RNA), and G and C. DNA can be densely packed around histone proteins, and is either or part of the cellular cytoplasm (in prokaryotic species) or separated within a membrane (in eukaryotic species). DNA is not a critical accumulation phase for chemicals, but of course it is a cellular structure where pollutants can strongly impact all kinds of cellular processes when they react with DNA components or affect the structural organisation otherwise.

Storage fat provides for many animals and fruits of plants an important energy reserve, but also insulates warm-blooded animals in cold climates, lubricates joints to move smoothly, and protects organs from shocks (e.g. eyes and kidneys). Seeds and nuts may contain up to 65% (walnuts) of fatty components, which of course provides energy for initial growth, but from which also oil can be pressed. Storage fat in most animals is present in the form of triglycerides, and as such neutral and very hydrophobic phases within tissue. Polyunsaturated fatty acid esters like omega-6 and omega-3 fatty acids are abundant in fish (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)) and in seeds and plants (mostly alpha-linolenic acid (ALA), but algae also contain EPA and DHA). The high intake of algae by fish in aqueous food webs based on algae, results in the high EPA/DHA levels in many fish species, as they mostly cannot make it themselves (https://www.pufachain.eu). Humans can make some EPA and DHA from ALA.

The average composition of living organisms based on the key tissue components lipid, protein, and carbohydrate can range widely, as illustrated in Table 1.

Table 1. Tissue structure composition of the average dry weight of different organisms.

Organism	lipid % of d.w.	protein % of d.w.	carbohydrate % of d.w.
Grass	0.5-4	15-25	60-84
Phytoplankton	20	50	30
Zooplankton	15-35	60-70	10
Oyster	12	55	33
Midge larvae	10	70	20
Army cutworms (moth larvae)	72% of body
Pike filet	3.7	96.3	0
Lake trout	14.4	85	0
Eel (farmed for 1.5y)	65	~34	1
Deer game meat	10	90	0

Table 2. Estimates on the tissue structure composition of a woman (BW= 60 kg, H = 163 cm, BMI = 22.6 kg/m²), (taken from Goss et al., 2018). Bones are not included.

Organ	Total organ volume (mL)	moisture content	phospholipid % of d.w.	storage lipid % of d.w.	protein % of d.w.
Adipose	22076	26.5%	0.3%	93.6%	6.1%
Brain	1311	80.8%	35.4%	22.6%	42.1%
Gut	1223	81.8%	9.9%	22.0%	68.1%
Heart	343	77.3%	19.1%	17.7%	63.2%
Kidneys	427	82.4%	16.5%	5.9%	77.7%
Liver	1843	79.4%	19.4%	8.0%	72.6%
Lung	1034	94.5%	13.1%	13.7%	73.2%
Muscle	19114	83.7%	2.6%	2.5%	95.0%
Skin	3516	71.1%	2.6%	22.9%	74.5%
Spleen	231	83.5%	5.6%	2.4%	92.0%
Gonads	12	83.3%	18.8%	0.0%	81.3%
Blood	4800	83.0%	2.5%	2.4%	95.1%
total	55929	60.2%	1.7%	71.0%	27.3%

Different organs in a single species can also largely differ in their composition, as well as their contribution to the overall body, as shown for a human in Table 2. Most organs have a moisture content >75%, but overall the moisture content is considerably lower, due to the low moisture content of bones and adipose tissue. Adipose tissue is by far the largest repository of lipids, but made up mostly of storage lipid, while the brain is also particularly rich in both lipids, but particularly enriched in phospholipids of cell membranes. Muscles and blood contain relatively high protein content.

Absorption-distribution: Chemical properties

The influence of chemical structure on the accumulation of chemicals in the biotic compartments is largely dependent on their bioavailability, as discussed in more detail in section 4.1 on Toxicokinetics and bioaccumulation, as well as on the basic binding properties as the results of the chemical’s hydrophobicity and volatility (section 3.4 on Partitioning and partitioning constants) and ionization state (section 2.2.6 on Ionogenic organic chemicals). In brief, the more non-polar the composition of a chemical, the more hydrophobic it is, the higher its affinity to partition from dissolved phases (both externally as well as internally) into poorly hydrated tissue phases such as storage fat and cell membranes. For this reason, the main issue with classical organic pollutants such as dioxins, DDT, and PCBs, is often their high hydrophobicity which results in strong accumulation in tissue. Such chemicals often take very long times to excrete from the tissue if they are not made less hydrophobic via biotransformation processes. This leads to foodweb accumulation and specific acute or chronic toxic effects at a certain organism level (section 4.1.6 on Food chain transfer). Proteins and sugary carbohydrates are mostly comprised of extended series of polar units and thus strongly hydrated, and bind hydrophobic chemicals to a much lower extent. Proteins may have three dimensional pockets that could fit either hydrophilic of hydrophobic chemicals, and as such act as transport proteins in blood throughout the body (transporting fatty acids for example), based on the specific binding affinity. Many protein based receptors are also based on a specific binding affinity, and in many cases this involves (combinations of) polar and electrostatic interactions that also have an optimum three-dimensional fitting space. Volatile chemicals are more abundantly present in the gas phase rather than being dissolved, and are more readily in contact with biota via gas-exchange on the extensive surfaces of lungs of animals and leaves of plants.

In order to be taken up into cells, or into organs, chemicals have to permeate through membranes. For most organic pollutants, the passive diffusion through phospholipid bilayers has an optimum at a certain hydrophobicity. The high accumulation in the membrane ensures desorption into the adjacent cellular solution. It is assumed that ionized chemicals have a passive permeation rates that are either negligible or at least orders of magnitude lower than that of corresponding neutral chemicals. For this reason, all kinds of molecular intra-extracellular gradients can be readily maintained, for example for protons (H⁺) or sodium-potassium (Na⁺/K⁺). The movement of very polar and ionic chemicals can be tightly regulated by transport proteins protruding the membrane bilayer. Specific molecules can be actively excreted from cells (e.g. certain drugs) or reabsorbed (e.g. in the kidneys back into the blood stream). This again is based on three dimensional fitting in the transport pocket and stepwise movement through the protein structure, and costs energy. For acids and bases with a very small fraction of neutral species at physiological pH, the passive permeation over membranes may still be dominated by the neutral species.

Exposure: Contact between biota and various environmental compartments

There are multiple routes by which chemicals can enter the tissue of biota, for example via respiratory organs, through digestion of contaminated food, or dermal contact. Most animals need to take in a more or less constant flux of oxygen and water, and periodically food, to release nutrients and energy from the food. Of course they also need to release CO₂ (and other chemicals) as waste. Pollutant chemicals are taken up alongside these basic processes, and it depends on the chemical properties and the efficiency of the uptake route how much the organism will take in from these different exposure routes.

Plants need plenty of water and during daytime photosynthesis need CO₂, but also require oxygen during the night. High algal densities can deplete the oxygen levels in shallow aquatic systems during the night, and replenish oxygen levels during daytime. Oxygen is plentiful in air (200,000 parts per million in the air), but it is considerably less accessible in water (15 parts per million in cool, flowing water), and often depleted below the first few mm of sediment. To obtain sufficient oxygen, water and food, aquatic organisms have to pass large volumes of waters through their gills. Sediment-dwelling organisms either have hemoglobin to bind oxygen, or constantly pump fresh overlying water through burrows created in sediment, often lined with mucus. Living organisms are thus constantly in contact with water dissolved pollutants, and air breathing organism are readily exposed to air pollutants. To simplify the domain of living organisms as part of this module about the biotic compartment and how they get into contact with chemicals relevant to Environmental Toxicology, they can for example be divided in:

, which often take in large amounts of water from their surroundings via their roots, driven by the evaporation of water at the leaves and resulting internal flows.
water breathing organisms, which pass large amounts of water through gills or gill like structures (tubules or other thin skin structures close to where water is passing) in order to take in enough oxygen and reduce the built up of CO₂. Filter feeders like oysters and mussels, that populate enormous surfaces as reefs or banks, can turnover a huge volume of water on a daily basis, and thus allow dissolved chemicals get in close contact to the outer membranes.
air breathing organisms, which can effectively exchange large quantities of volatile and gaseous chemicals with the air (oxygen but also organic compounds), but typically take in less/non-volatile chemicals via food and require active excretion and metabolism for the emission of less/non-volatile chemicals.

Figure 2. Left panel: Plant water transport (adapted from https://en.wikipedia.org/wiki/Xylem), a schematic view of the waxy upper layers on leaves (adapted from Moeckel et al. 2008)). Middle panel: water flushing along gill lamellae replenishes oxygen in blood. Top Right panel: Many benthic organisms living in anoxic sediments require overlying oxygen rich water, and feed on dissolved particles. Lower Right Panel: Earthworms take up oxygen via diffusion through their skin, but also metals and organic pollutants, as shown in studies where the mouth part has been glued shut during exposure (adapted from Vijver et al., 2003).

Plants

Nearly all plants have roots below ground, a sturdy structure of stem and branches above ground, and leaves. Along with soil pore water, soluble chemicals are readily transported from roots of the plant in the internal circulation stream through xylem cells, which are lined with water impenetrable lignin (see Figure 2). Moderately hydrophobic chemicals (K_ow of 1-1000) are rapidly transported from roots to shoots to leaves, while hydrophobic chemicals may be strongly retained on the membranes and cell walls and mostly accumulate in root sections, limiting transport to above-ground plant tissues. Roots may also actively release considerable quantities of chemicals to influence the immediate surrounding media of the roots (rhizosphere), e.g. to stimulate microbial processes or pH in order to release nutrients. These plant root ‘exudates’ can be ions, small acids, amino acids, sterols, etc. Chemicals that enter plants via leaves, such as pesticides or semi-volatile organic pollutants, can be redistributed to other plant parts via the phloem streams.

The transport through xylem up to higher plant tissues occurs via capillary forces and is enhanced by three passive phenomena:

the high sugar content in the phloem causing osmotic pressure to attract water from other parts.
the evaporation of water creates surface tension on thousands of cells that pulls water from the soil through the xylem system.
the osmotic pressure of root cells compared to soil pore water. Root pressure is highest in the morning before the stomata open and allow transpiration to begin.

As a result of the capillary forces needed to pull water up against gravity, and a certain maximum diameter of the vessels to do so, there is a maximum possible plant height of 122-130 m (Koch et al., 2004) which compares to Redwood trees (Sequoias) reaching a maximum height of 113 m.

Most leaves are covered with a waxy layer, to prevent damage and water evaporation. This wax layer may be 0.3-4.6 µm thick (Moeckel et al., 2008). Large forests provide enormous hydrophobic surfaces to which semi-volatile organic chemicals (SVOC) can bind out of air, which influences the global distribution of chemicals such as PCBs. Partitioning of SVOCs on the vegetation of extended grasslands contaminates the base of the food chain, as well as agricultural and cattle sectors used by humans. The grass/corn-cattle-milk/beef food chain accounts for the largest portion of background exposure of the European and North American population to many persistent SVOCs. The absorption rate of chemicals on the leaves often also depends on the air boundary layer surrounding leaves, which limits diffusion into the leave surfaces. Of course, all kinds of other factors such as wind speed, canopy formation and cuticle thickness also control exchange between leaves and gas phase (see also section 3.1.2 on the Atmosphere). Tiny openings or pores on the lower side of the leaves, called stomata, allow furthermore for gas exchange. In warm conditions, stomata can close to prevent water evaporation, but gas exchange is needed in many plant types to allow for CO₂ to be metabolized and the release of O₂ that is produced. The waxy layer on leaves can trap gaseous organic chemicals. Many plants, like coniferous trees, produce resins to provide effective defense against insects and diseases, and these resins release large amounts and structurally highly diverse organic volatiles such as terpenes and isoprenes (Michelozzi, 1999). These plant-produced volatiles can even contribute to ozone formation. Plants thus accumulate chemicals from their environment, but also release chemicals into the environment. It thus also matters for the exposure of grazing organisms to certain types of pollutants whether they eat roots, shoots, leaves, seeds or fruits of plants living in contaminated environments.

Organisms using dissolved oxygen

The ‘gill’ movements of water breathers create a constant flux of chemicals dissolved in bulk water along outer cell membranes (or mucus layers surrounding cell membranes) of gills. A 1 kg rainbow trout fish ventilates about 160 mL/min, so 230 L/day (Consoer et al., 2014). The total gill surface area in a fish depends on species behaviour and weight (active large fish require a lot of oxygen), and equals to about 1-6 cm² per g fish (Palzenberger & Pohla, 1992). For a 1 kg fish of ~20 cm length, the ~1000 cm² gill area compares to a ~500 cm² outer body surface. This results in an effective partitioning of chemicals between water and cell membranes. Within the gills, the cells are in close contact with the blood system of the organism, and the build-up of chemical concentrations in the outer cells provides an effective exchange with the blood stream (or other internal fluids, Figure 2) flushing along that redistribute chemicals to the inner organs. The reverse equally occurs: chemicals dissolved in blood stream coming from organs will also rapidly exchange with bulk external water if concentrations are lower.

Of course, many pollutants can also enter water breathing organisms via food, but the gills-water exchange is very effective in controlling the distribution of chemicals. The salinity of water plays a strong role in the need of water breathing organisms to “drink” water, and hence take in contaminants via this route.

BOX 1. Osmoregulation (MSc level)

Most aquatic vertebrate animals are osmoregulators: their cells contain a concentration of solutes that is different than the water around them. Fish living in freshwater typically have a cellular osmotic level (300 millOsmoles per Liter, mOsm/L) that is higher than the bulk fresh water (~20-40 mOsm/L), so a lot of water flows passively via the gills (not via the skin) into the tissue of fish. They are thus constantly taking in water (water molecules only) via the gills, which needs to be controlled, e.g. by strongly diluting the urine. They do also take in some water in their gastro-intestinal tract (GIT). Marine fish have similar cellular osmotic levels as freshwater fish, but the salty water (1000 mOsm/L) causes water to move out of the gill tissue through the linings of the fish’s gills by osmosis, which needs to be replenished by active intake of salty water, and separate excretion of the salts. Most invertebrate organisms in oceans have an internal overall concentration of dissolved compounds comparable to the water they live in, so that they don’t suffer from strong osmotic pressures on their soft tissue (osmoconformers).

A single adult oyster can cleanse about 200 liters of water per day (https://www.cbf.org/about-the-bay/more-than-just-the-bay/chesapeake-wildlife/eastern-oysters/oyster-fact-sheet.html). Plans to re-populate the harbour of New York with 1 billion oysters on artificial substrates can have enormous impacts on chemical redistribution. A single 2 cm zebra mussel (Dreissena polymorpha) that inhabits the shallow Lake IJsselmeer (6.05x10¹² L) in can filter about 1 L per day, and the high densities of these and related species in this fresh water lake can turn over the lake volume once or twice per month (Reeders et al., 1989).

Even many soil organism are constantly in contact with wet soil surfaces, and contact between soil pore water and the outer surfaces (gills/soft skin areas) dominates the routes of chemical exchange for many chemicals. Earthworms, for example, do not have lungs and they exchange oxygen through their skin. Earthworms eat bacteria and fungi that grow on dead and decomposing organic matter, and thus act as major organic matter decomposers and recycling of nutrients. Earthworms dramatically alter soil structure, water movement, nutrient dynamics, and plant growth. It is estimated that earthworms turn over the top 15 cm of soil in ten to twenty years (LINK), and so they also are able to mix surface bound pollution into a substantial soil layer. In terms of biomass, earthworms dominate the world of soil invertebrates, including arthropods. In order to better understand how much contamination earthworms take in via food or via their skin, several studies have used earthworms in exposure tests with part of the organisms having their mouth parts sealed with surgical glue (Vijver et al., 2003). Uptake rates of the metals Cd, Cu and Pb in sealed and unsealed earthworms exposed to two contaminated field soils were similar (Vijver et al., 2003), indicating main uptake through the skin of the worms. The uptake rates as well as the maximum accumulation level for several organic contaminants from artificially contaminated soil were also comparable between sealed and non-sealed worms (Jager et al., 2003). The dermal route is thus a highly important uptake route for organic chemicals too. Dermal uptake by soil organisms is generally from the pool of chemicals in the soil pore water, hence the distribution of chemicals between solid particles and organic materials in the soil and the soil pore water is extremely important in driving the dermal uptake of chemicals by earthworms (See section 3.4 on Partitioning and partitioning constants, section 3.5 on Metal speciation and 3.6 on Availability and bioavailability).

Air breathing organisms

Air breathing organisms typically take in less/non-volatile chemicals via food and require active excretion and metabolism for the elimination of these chemicals via e.g. feces and urine. Dermal uptake is generally assumed to be negligible, while the intake of chemicals via air particles can be relatively high, for example of pollutants present in house dust, or contaminants on aerosols. The excretion via air particles is clearly not a dominant route. The chemicals in the food matrix inside the gastro-intestinal tract often first need to be fully dissolved in gut fluids, before they can pass the mucus layers and membranes lining the gastrointestinal tract and enter blood streams for redistribution. However, ‘endocytosis’ may also result in the uptake of (small) particulate chemicals in cells by first completely surrounding the particle by the membrane, after which the encapsulating membrane buds buds off inside the cell and forms a vesicle. Notwithstanding this endocytosis, chemical fractions of pollutants strongly sorbed to non-digestible parts may not always automatically be taken up from food. Grazing animals typically require microbial conversion in their gut to digest plant material like cellulose and lignin into chemical components that can be taken up as energy source.

Many aquatic foodwebs are structured such that they begin with aquatic plants being eaten by water breathing organisms, with air breathing marine animals or birds on top of the food chain. These air breathing top predators take in pollutants largely through their diet, but lack the effective blood-membrane-water exchange through gills. The blood-membrane-air partitioning in lungs is far less effective in removing chemicals via passive partitioning. For this reason, many top predators of foodwebs have the highest concentrations of pollutants. The chemical distribution in foodwebs will be discussed in more detail in section 4.1.6.

References

Palzenberger & Pohla 1992, Reviews in Fish Biology and Fisheries, 2, 187-216.

Jager, T.; Fleuren, R.H.L.J.; Hogendoorn, E.A.; de Korte, G. 2003. Elucidating the routes of exposure for organic chemicals in the earthworm, Eisenia andrei (Oligochaeta). Environ. Sci. Technol. 37 (15), 3399-3404

Koch et al. 2004, Nature (428) 851–854.

Moeckel et al. 2008, Environ. Sci. Technol. 42, 100–105.

Michelozzi 1999, Defensive roles of terpenoid mixtures in conifers, Acta Botanica Gallica 146 (1), 73-84.

Consoer et al. 2014, Aquatic Toxicology 156, 65–73.

Reeders et al. 1989, Freshwater Biology 22 (1), 133-141.

Vijver et al. 2003, Soil Biology and Biochemistry 35, 125-132.

Goss et al. 2018, Chemosphere 199, 174-181.

3.2. Sources of chemicals

Author: Ad Ragas

Reviewer: Kees van Gestel

Leaning objectives

You should be able to:

characterize the origins of environmental pollutants;
explain the relevance of emission assessment;
characterize emission sources;
explain how emission sources can be quantified.

Keywords: environmental pollutant, life cycle, point and diffuse sources, emission factor, emission database

Chemicals can be released into the environment in many different ways. In the professional field, this release is called the emission of the chemical, and the origin of the release is referred to as the emission source. The strength and nature of the emission source(s) are important determinants of the ultimate environmental exposure and thus of the resulting risk. This section explains the most important characteristics of emission sources and familiarizes you with the most common terms used in the field of emission assessment. It starts with a brief introduction on the origin of pollutants in the environment, followed by an explanation of the relevance of emission assessment, how an emission can be characterized, and how data on emissions can be gathered or estimated.

Origin of pollutants

Pollutants in the environment can originate from different processes. Within this context, we here distinguish between three types of chemicals:

natural chemicals that are naturally present in the environment, like (heavy) metals and natural toxins, and that can either be released into the environment by natural processes (e.g. the eruption of a volcano) or by human-induced processes (e.g. resource extraction and subsequent use and dispersal);
synthetic chemicals that are intentionally produced and used by society because of their useful characteristics, e.g. (most) pharmaceuticals, pesticides and plastics;
chemicals that are unintentional byproducts of human activities and production processes, e.g. dioxins and disinfection byproducts.

The latter category can overlap with the first, since the reaction products of natural processes, such as many combustion processes, can be considered natural chemicals. Polycyclic aromatic hydrocarbons (PAHs), for example, can be released from natural (e.g. a forest fire) as well as human-induced processes (e.g. a power plant). This emphasizes the role of the emission process in defining an environmental pollutant. When human activities are involved in either the production or the release of a chemical into the environment, this chemical is considered to be an environmental pollutant. Some synthetic chemicals are also naturally present in the environment as this is a specific field of research in organic chemistry, i.e. the chemical synthesis of natural products.

The relevance of emission assessment

Emission assessment of chemicals is the process of characterizing the emission of a chemical into the environment. Knowledge on the emission can be relevant for different purposes. The most obvious purpose is to assess the exposure and risks of a chemical in the vicinity of the emission source. This is typically done when a facility requires an environmental permit to operate, e.g. a discharge permit for surface water or a permit that involves the emission of pollutants into air from a smoke stack. Such assessments are typically performed locally.

At a higher scale level, e.g. national or global, one might be interested in all emissions of a certain compound into the environment. One should then map all sources through which the chemical can be released into the environment. For synthetic chemicals, this implies the mapping of all emissions throughout the life cycle of the chemical. This life cycle is typically divided into three phases: production, use and waste (Figure 1). Between the production and use of a chemical there may be various intermediate steps, such as the uptake of the chemical in a formulation or a product. And after a chemical – or the product it is contained in – has been used, it may be recycled. The life cycle of chemicals can be illustrated with the simple example of pharmaceuticals. These can be released into the environment during: (1) their production process, e.g. the effluent of a production plant that is being discharged into a nearby river, (2) their use, e.g. the excretion of the parent compound via urine and feces into the sewer system and subsequently the environment, or (3) their waste phase, e.g. when unused pharmaceuticals are flushed through the toilet or dumped in a dustbin and end up with the solid waste in a landfill.

Figure 1. The three main life cycle phases of a chemical: production, use and waste. After production, chemicals can be applied in a formulation or product. After the chemical (or product) becomes waste, it can be recycled to be used again in production, the formulation/product or use phase.

Instead of focusing on the life cycle of an individual chemical, it is more common in environmental assessments to focus on the life cycle of products and services. The life cycle of products and services has an extra phase, i.e. resource extraction. The focus on products or services is particularly useful when one wants to select the most environmentally friendly option from a number of alternatives, e.g. the choice between putting milk in glass or carton. This requires that not only emissions of chemicals are being included in the life cycle assessment, but also other environmental impacts such as the use of non-renewable resources, land use, the emission of greenhouse gases and disturbance by noise or odor. Similar techniques to assess and compare the environmental impacts of human activities include material flow analysis, input/output analysis and environmental impact assessment. The quantification of chemical emissions into the environment is an important step in all these assessment techniques.

Characteristics of the emission source

Emission sources can be characterized based on their properties. An important distinction that is often made is the distinction between point sources and diffuse sources. Point sources are emission sources that are relatively few in number and emit relatively large quantities of chemicals. The smoke stacks of power plants and the discharge pipes of wastewater treatment plants (WWTPs) are typical examples of point sources. Diffuse sources are many in number and emit relatively small amounts of chemicals. Exhaust emissions from cars and volatilization of chemicals from paints are typically considered diffuse emissions. The distinction between point sources and diffuse sources can sometimes be a bit arbitrary and is particularly relevant within regulatory contexts since point sources are generally more easy to control than diffuse sources.

Another important characteristic of emission sources is the compartment to which the chemical is being emitted, in combination with the matrix in which the chemicals are contained. Two important emission types are chemicals in (waste)water discharged into surface waters and chemicals in (hot) air released through a smoke stack. Other common entry pathways of chemicals into the environment are the spaying of pesticides (emission into air, soil and water), the application of manure containing veterinary medicines, the dumping of polluted soils (in soils or water), the dispersal of polluted sediments, and leaching of chemicals from products. Chemicals emitted to air, and to a lesser extent also water, will typically disperse faster into the environment than chemicals emitted to soils. An important aspect that influences dispersal is whether the chemical is dissolved in the matrix or bound to a phase in the matrix, like organic matter, suspended matter or soil particles. The fate of chemicals in the environment is further discussed in Sections 3.3 and 3.4.

The temporal dimension of the emission source is another important characteristic. Distinction is often made between continuous and intermittent sources. Wastewater treatment plants (WWTPs) and power plants are typical examples of continuous sources, whereas the application of pesticides is a typical example of an intermittent emission source. The strength of an emission source may vary over time. Distinction can be made between sources with: (1) a constant emission, (2) a regularly fluctuating emission and (3) a irregularly fluctuating emission. For example, WWTPs typically have a continuous emission, but the amount of a chemical in the WWTP effluent may show a distinct regular pattern over 24 hours, reflecting the diurnal and nocturnal activities of people. Production plants that only operate during the day typically show a block pattern, whereas pesticide emissions typically follow a more irregular pattern fluctuating with the season and the emergence of pest species. Irregular emissions such as from pesticides are typically characterized by peak emissions, i.e. the release of relatively large amounts with a relatively short time frame. Other typical examples of peak emissions are the release of chemicals after industrial accidents or intense rain events, e.g. pesticide runoff from agricultural fields after a long period of drought or combined sewer overflows (CSOs).

Emission data

Considering the importance of emission assessment for assessing the environmental impacts of human activities, it is not surprising that a lot of effort is put in the quantification of emission sources. Emission sources can be quantified in different ways. An important distinction is that between measurement and estimation. The continuous measurement of an emission source is also referred to as monitoring. Measurement often involves the separate determination of two dimensions of the emission, i.e. (1) the concentration of the chemical in the matrix that is being emitted, and (2) the flow of the matrix into the environment, e.g. the volume of wastewater or polluted air released per unit of time. The emission load (i.e. mass of chemical released per unit of time) is subsequently calculated by multiplying the concentration in the matrix by the flow of the matrix.

Measurement is often costly and takes a lot of time. It is therefore not surprising that approaches have been developed to estimate emissions. These estimations are often in essence based on measurement data, but these are then generalized or extrapolated to come to more large scale emission estimations. For example, measurements on the exhaust emissions of a few cars can be extrapolated to an entire country or continent if you know the number of cars. A rather coarse approach that is widely used for emission estimation is the use of emission factors. An emission factor quantifies the fraction of a chemical being used and that ultimately reaches the environment. It is often a conservative value that is based a worst case interpretation of the available measurement data or data on the processes involved in the release of the chemical. A related but more detailed approach is to estimate the emission of a chemical based on proxies such as the amount produced, sold or used in combination with specific data on the release process. Pharmaceuticals can again serve as a good example. If you know the amount of a pharmaceutical that is being sold in a particular country, you can calculate the average per capita use. You can then estimate the amount of pharmaceutical that is being discharged by a particular WWTP if you know: (1) the number of people connected to the WWTP; (2) the fraction of the pharmaceutical that is being excreted by the patient into the sewer system through urine and feces, and (3) how much of the compound is being degraded in the WWTP. You can even further refine this estimation by accounting for (1) demographic characteristics of the population since older people tend to use more pharmaceuticals than young people, and (2) the fractions that are not used by the patient and are either: (a) flushed through the toilet, (b) dumped in the dustbin, or, preferably, (c) returned to the pharmacy.

Emission data can be a valuable source of information for risk assessors. Data gathered locally may be relevant to obtain a picture of national or even global emissions. This insight has led authorities to set up databases for the registration of emissions. Examples of such databases include:

The European Pollutant Release and Transfer Register (E-PRTR), containing data reported by EU member states on releases to air, water and land as well as the transfers of pollutants in waste water for 91 substances and across 65 industrial sub-sectors, and the transfer of waste from these industrial facilities;
Waterbase, containing data on the status and quality of Europe's rivers, lakes, groundwater bodies and transitional, coastal and marine waters, on the quantity of Europe's water resources, and on the emissions to surface waters from point and diffuse sources of pollution;
The Toxics Release Inventory of the United States Environmental Protection Agency, containing data on how much of each chemical is released to the environment and/or managed through recycling, energy recovery and treatment as reported by different industry sectors;
The data from California's pesticide use reporting (PUR) program, containing data on pesticide use in California;
The Dutch Watson database (in Dutch), reporting measurements of chemicals in the influent and effluent of Dutch WWTPs.

3.3. Pathways and processes determining chemical fate

Authors: Dik van de Meent, Michael Matthies

Reviewer: John Parsons

Leaning objectives:

You should be able to

explain how emission of chemicals into the environment leads to exposure of ecosystems, populations, and organisms including man.
understand how quantitative knowledge of process kinetics is of use in mathematical modeling of environmental fate
understand the concept of characterization of fate processes in terms of rate constants and half-lives

give examples of relevant fate processes and briefly describe them

Keywords: fate processes, degradation, transport, partitioning

Characterizing ‘fate’

Chemicals can escape during all steps of their life cycle, e.g. manufacturing, processing, use, or disposal. Release of chemicals into the environment necessarily leads to exposure of ecosystems, populations, and organisms including man. Exposure assessment science seeks to analyze, characterize, understand and (quantitatively) describe the pathways and processes that link releases to exposure. Chemicals in the environment undergo various transport, transfer and degradation processes, which can be described and quantified in terms of loss rates, i.e. the rates at which chemicals are lost from the environmental compartment into which they are emitted or transferred from adjacent compartments. Exposure assessment science aims to capture the ‘environmental fate’ of chemicals in process descriptions that can be used in mass balance modeling, using mathematical expressions borrowed from thermodynamic laws and chemical reaction kinetics (Trapp and Matthies, 1998).

The ‘fate’ of a chemical in the environment can be viewed of as the net result of a suite of transport, transfer and degradation processes (see Section 3.4 on partitioning and partitioning constants, Section 3.6 on availability and bioavailability, Section 3.7 on degradation) that start to act on the chemical directly after its emission (see Section 3.2 on sources of emission) and during the subsequent environmental distribution. Environmental fate modeling (see Section 3.8 on multimedia mass balance modelling) builds on this knowledge by implementing the various degradation, transfer and transport processes derived in exposure assessment science in mathematical models that simulate ‘fate of chemicals in the environment’.

First-order kinetics

In chemical reaction kinetics, the amount of chemical in a ‘system’ (for instance, a volume of surface water) is described by mass balance equations of the kind:

\({dm\over dt}= i - k\ m\) (eq. 1)

where \(dm\over dt\) is the rate of change (kg.s^-1) of the mass (kg) of chemical in the system over time t (s), i is the input rate (kg.s^-1) and k (s^-1) is the reaction rate constant. Mathematically, this equation is a first-order differential equation in m, meaning that the loss rate of mass from the systemis proportional to the first power of m. Equation 1 is widely applied in description and characterization of environmental fate processes: environmental fate processes generally obey first-order kinetics, and can generally be characterized by a first-order reaction rate constant k^1st:

\({dm\over dt}= -k^{1st} m\) (eq. 2)

Such loss rated equations can also be formulated in integral format, which is obtained by integration of equation (2) over time t with initial mass m₀ = m(0):

\(m_t = m_0\ e^{-k^{1st}\ t}\) (eq. 3)

Figure 1. Graphical representation of equation 3. Decrease of relative mass of a chemical in an environmental compartment would follow the blue curve when the loss process is given by a first-order differential loss equation. Loss processes that obey first-order kinetics have constant half-lives (here time \(t_{1⁄2}\) =40 days).

As shown in Figure 1, first-order loss processes are expected to result in exponential decrease of mass from which concentration can be calculated by dividing m with the compartment volume. Using the value \(t_{1⁄2}\) for t in equation 3, it follows directly that the value of \(t_{1⁄2}\) is inversely proportional to the first-order loss rate constant \(k^{1st}\):

\({m_t\over m_0} = {1\over 2} = e^{-k^{1st}\ t_{1/2}} → ln({1\over 2}) = {-k^{1st}\ t_{1⁄2}} → t_{1/2} = {ln⁡(2)\over k^{1st}}\) (eq. 4)

which shows that half-life time constant, i.e. independent of the concentration of the chemical considered. This is the case for all environmental loss processes that obey first-order kinetics. First-order loss processes can therefore be sufficiently characterized by the time required for disappearance of 50% of the amount originally present.

The disappearance time DT50 is often used in environmental regulation but is only identically with the half-life if the loss process is of first order. Note that the silent assumption of constancy of half-life implies that the process considered is assumed to obey first-order kinetics.

Abiotic chemical reactions

Occurrence of true first-order reaction kinetics in chemistry is rare (see Section 3.7 on degradation). It occurs only when substances degrade spontaneously, without interaction with other chemicals. A good example is radio-active decay of elements, with a reaction rate proportional to the (first power of) the concentration (mass) of the decaying element, as in equation 3.

Most chemical reactions between two substances are of second order:

\({dm\over dt} = -k^{2nd}\ m_1\ m_2\) (eq. 5)

or, when a chemical reacts with itself:

\({dm\over dt} = -k^{2nd}\ m_1^2\) (eq. 6)

because the reaction rate is proportional to the concentrations (masses) of both of the two reactants. It follows directly from equation 2. As the concentrations (masses) of both reactants decrease as a result of the reaction taking place, the reaction rate decreases during the reaction, more rapidly so at high initial concentrations. When second-order kinetics applies, half-life is not constant, but increases with ongoing reaction, when concentrations decrease. In principle, this is the case for most chemical reactions, in which the chemical considered is transformed into something else by reaction with a transforming chemical agent.

In the environment, the availability of second reactant (transforming agent) is usually in excess, so that its concentration remains nearly unaffected by the ongoing transformation reaction. This is the case, for oxidation (reaction with oxygen) and hydrolysis (reaction with water). In these cases, the rate of reaction decreases with the decreasing concentration of the first chemical only:

\({dm\over dt} = -(k^{2nd}\ m_2)\ m_1 = k^{1st}\ m_1\) (eq. 7)

and reaction kinetics become practically first-order: so-called pseudo first-order kinetics. Pseudo first-order kinetics of chemical transformation processes is very common in the environment.

Biotic chemical reaction

Chemical reactions in the biosphere are often catalyzed by enzymes. This type of reaction is saturable and the kinetics can be described by the Michael-Menten kinetic model for single substrate reactions. At low concentrations, there is no effect of saturation of the enzyme and the reaction can be assumed to follow (pseudo) first order kinetics. At concentrations high enough to saturate the enzyme, the rate of reaction is independent of the concentrations (masses) of the reactants, thus constant in time during the reaction, and the reaction obeys zero-order kinetics. This is true for catalysis, where the reaction rate depends only on the availability of catalyst (usually the reactive surface area):

\({dm\over dt} = -k^{zero}\ m^0\) = constant. (eq. 8)

One could say that the rate is proportional to the zero-th power of the mass of reactant present. In case of zero-order kinetics, the half-life times are longer for greater initial concentrations of chemical.

An example of zero-order reaction kinetics is the transformation of alcohol (ethanol) in the liver. It has been worked out theoretically and experimentally that human livers remove alcohol from the blood at a constant rate, regardless the amount of alcohol consumed.

Microbial degradation

Microbial degradation (often referred to as biodegradation) is a special case of biotic transformation kinetics. Although this is an enzymatically catalysed process, the microbial transformation process can be viewed of as the result of the encounter of molecules of chemical with microbial cells, which should result in apparent second-order kinetics (first order with respect to the number of microbial cells present, and first order with respect to the mass of chemical present):

\({dm\over dt} = -(k^{2nd}\ m_{bio})\ m = -k_{deg}\ m\) (eq. 9)

where m_bio stands for the concentration (mass) of active bacteria present in natural surface water, and k_deg represents a pseudo-first-order degradation rate constant.

Advective and dispersive transport

Chemicals can be moved from one local point to another by wind and water currents. Advection means transport along the current axis whereas dispersion is the process of turbulent mixing in all directions. Advective processes are driven by external forces such as wind and water velocity, or gravity such as rain fall and leaching in soil. In most exposure models these processes are described in a simplified manner, e.g. the dispersive air plume model. An example of a first-order advective loss process is the outflow of a chemical from a lake:

\({dm\over dt}=-{Q\over V} m=-k_{adv}\ m\) (eq. 10)

where Q stands for the flow rate of lake water [m³/s] and V is the lake volume [m³]. Q/V is known as the renewal rate constant k_adv of the transport medium, here water. More sophisticated hydrological, atmospheric, or soil leaching models consider detailed spatial and temporal resolution, which require much more data and higher mathematical computing effort (see sections 3.1.2 and 3.8.1).

Transfer and partitioning

Due to Fick’s first law the rate of transfer through an interface between two media (e.g. water and air, or water and sediment) is proportional to the concentration difference of the chemical in the two media (see section 3.4 on partitioning, and Schwarzenbach et al., 2017 for further reading). As long as the concentration in one media is higher than in the other, the more molecules are likely to pass through the interface. Examples are volatilization of chemicals from water (to air) and gas absorption from air (to water or soil), adsorption from water (to sediments, suspended solids and biota) and desorption from sediments and other solid surfaces.

When two environmental media are in direct contact, (first-order) transfer can take place in two directions, in the case of water and air by volatilization and gas absorption: Each at a rate proportional to the concentration of chemical in the medium of origin and each with a (first-order) rate constant characteristic of the physical properties of the chemical and of the nature of the interface (area, roughness). This is known as physical intermedia partitioning (see section 3.4 on partitioning), usually represented by a chemical reaction formula:

\([M]_{water} \ {exchange\over \leftrightarrow}\ [M]_{air}\) (eq. 11)

where [M] stands for a (mass) concentration (unit mass per unit volume) and the double arrow represents forward and reverse transport. Intermedia partitioning proceeds spontaneously until the two media have come to thermodynamic equilibrium. In the state of equilibrium, forward and backward rates (here: volatilization from water to air and gas absorption from air to water) have become equal. At equilibrium, the total (Gibbs free) energy of the system has reached a minimum: the system has come to rest, so that

\(k_{abs} [M]_{air} = k_{volat}\ [M]_{water} → K_{AW} = {[M]_{air}\over [M]_{water}} = {k_{volat}\over k_{abs}} \) (eq. 12)

and the ratio of concentrations of the chemical in the two media has reached its (thermodynamic) equilibrium value, called equilibrium constant or partition coefficient (see section 3.4 on partitioning).

Challenge

Challenge to environmental chemists is to describe and characterize the various processes of chemical and microbial degradation and transformation, of intra-media transport and intermedia transfer rate constants and of equilibrium constants, in terms of (i) physical and chemical properties of the chemicals considered and (ii) of the properties of the environmental media.

References

Schwarzenbach, R.P., Gschwend, P.M., Imboden, D.M. (2017). Environmental Organic Chemistry, Third Edition, Wiley, ISBN 978-1-118-76723-8.

Trapp, S., Matthies, M. (1998). Chemodynamics and Environmental Modeling. An Introduction. Springer, Heidelberg, ISBN 3-540-63096-1.

3.4. Partitioning and partitioning constants

3.4.1. Relevant chemical properties

Authors: Joop Hermens, Kees van Gestel

Reviewer: Steven Droge, Monika Nendza

Learning Objectives

You should be able to:

define the concept of hydrophobicity and to explain which chemical properties affect hydrophobicity.
define which properties of a chemical affect its tendency to evaporate from water.
calculate fractions ionized for acids and bases.

Keywords: Hydrophobicity, octanol-water partition coefficients, volatility, Henry’s Law constant, ionized chemicals

Introduction

Different processes affect the fate of a chemical in the environment. In addition to the transfer and exchange between compartments (air-water-sediment/soil-biota), also degradation determines the concentration in each of these compartments (Figure 1).

Figure 1. Environmental fate: exchange between compartments and degradation affect the concentration in each compartment.

Some of these processes are discussed in other sections (see sections on Sorption and Environmental degradation of chemicals). Some chemicals will easily evaporate from water to air, while others remain mainly in the aqueous phase or sorb to sediment and accumulate into biota.

These differences are related to only a few basic properties:

Hydrophobicity (tendency of a substance to escape the aqueous phase)
Volatility (tendency of a substance to vaporize)
Degree of ionization

Hydrophobicity

Hydrophobicity means fear (phobic) of water (hydro). A hydrophobic chemical prefers to “escape from the aqueous phase” or in other words “it does not like to dissolve in water”. Water molecules are tightly bound to each other via hydrogen bonds. For a chemical to dissolve in water, a cavity should be formed in the aqueous phase (Figure 2) and this will cost energy.

Figure 2. The formation of a cavity in water for chemical X.

Hydrophobicity mainly depends on two molecular properties:

Molecular size
Polarity / ability to interact with water molecules, for example via hydrogen bonding

It will take more energy for a chemical with a larger size to create the cavity making the chemical more hydrophobic, while interactions of the chemical with water will favour its dissolution making it less hydrophobic. Figure 3 shows chemicals with increasing hydrophobicity with increasing size and a decreasing hydrophobicity by the presence of polar groups (amino or hydroxy).

Figure 3. The effect of size and presence of polar groups on the hydrophobicity of chemicals. Increasing molecular size increases hydrophobicity; the introduction of polar groups leads to a decrease in hydrophobicity.

Most hydrophobic chemicals are non-polar organic micro pollutants. Well-known examples are the chlorinated hydrocarbons, such as polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs). Water solubility of these chemicals in general is rather low (in the order of a few ng/L up to a few mg/L).

The hydrophobic nature mainly determines the distribution of these chemicals in water and sediment or soil and their uptake across cell membranes. Additional Cl- or Br-atoms in a chemical, as well as additional (CH)_x units, increase the molecular size, and thus a chemical’s hydrophobicity. The increased molecular volume requires a larger cavity to dissolve the chemical in water, while they only interact with water molecules via VanderWaals interactions.

Polar groups, such as the -OH and -NH units on the aromatic chemicals in Figure 3, can form hydrogen-bonds with water, and therefore substantially reduce the hydrophobicity of organic chemicals. The hydrogen bonding of hydroxy-substituents works in two ways: The oxygen of -OH bridges to the H-atoms of water molecules, while the hydrogen of –OH can form bridges to the O-atoms of water molecules. Nearly all molecular units consisting of some kind of (carbon-oxygen) combination reduce the hydrophobicity of organic contaminants, because even though they increase the molecular volume they interact via hydrogen bonds (H-bonds) with surrounding water molecules. Additional polar groups in a chemical typically decrease a chemical’s hydrophobicity.

Octanol-water partition coefficient:

A simple measure of the hydrophobicity of chemicals, originating from pharmacology, is the octanol-water partition coefficient, abbreviated as K_ow (and sometimes also called P_ow or P_oct): this is the ratio of concentrations of a chemical in n-octanol and in water, after establishment of an equilibrium between the two phases (Figure 4). The -OH group in n-octanol does allow for some hydrogen bonding between octanol-molecules in solution, and between octanol and dissolved molecules. However, the relatively long alkyl chain only interacts through VanderWaals interactions, and therefore the interaction strength between octanol-molecules is much smaller than that between water-molecules, and it is energetically much less costly to create a cavity to dissolve any molecule.

Figure 4. Distribution of chemical X between octanol and water and an example of a chemical with log K_ow of 5.0.

Experimentally determined K_ow values were used in pharmacological research to predict the uptake and biological activity of pharmaceuticals. Octanol was selected because it appears to closely mimic the nonionic molecular properties of most tissue components, particularly phospholipids in membranes. Since the beginning of the 1970s, K_ow values have also been used in environmental toxicology to predict the hazard and environmental fate of organic micro pollutants. Octanol may partially also mimic the nonionic molecular properties of most organic matter phases that sorb neutral organic chemicals in the biotic and abiotic environment.

Not unexpectedly, water solubility is negatively correlated with octanol-water partition coefficients.

In practice, three methods can be used to determine or estimate the K_ow:

Equilibration methods

In the shake-flask method (Leo et al., 1971) and the 'slow-stirring' method (de Bruijn et al., 1989), the distribution of a chemical between octanol and water is determined experimentally. For highly lipophilic chemicals (log K_ow > 5-6), the extremely low water solubility, however, hampers a reliable analytical determination of concentrations in the water phase. For such chemicals, these experimental methods are not suitable. During the last two decades, the use of generator columns has allowed for quantification of higher K_ow values. Generator columns are columns packed with a sorbing material (e.g. Chromosorb^®) onto which an appropriate hydrophobic solvent (e.g. octanol) is coated that contains the compound of interest. In this way, a large interface surface area between the lipophilic and water phases is created, which allows for a rapid establishment of equilibrium. When a large volume of (octanol-saturated) water (typically up to 10 litres) is passed slowly through the column, an equilibrium distribution of the compound is established between the octanol and the water. The water leaving the column is passed over a solid sorbent cartridge to concentrate the compound and allow for a quantification of the aqueous concentration. In this way, it is possible to more reliably determine log K_ow values up to 6-7.

Chromatography

K_ow values may also be derived from the retention time in a chromatographic system (Eadsforth, 1986). The use of reversed-phase High Performance Liquid Chromatography (HPLC), thin-layer chromatography or gas chromatography results in a capacity factor (relative retention time; retention of the compound relative to a non-retained chemical species), which may be used to predict the chemical distribution over octanol and water. HPLC systems have shown most successful, because they consist of stationary and mobile phases that are liquid. As a consequence, the nature of the phases can be most closely arranged to resemble the octanol-water system. Of course, this requires calibration of the capacity factors by applying the chromatographic method to a number of chemicals with well-known K_ow values. Chromatographic methods may reliably be applied for estimations of log K_owvalues in the range of 2-8. For more lipophilic chemicals, also these methods will fail to reliably predict K_ow values (Schwarzenbach et al., 2003).

Calculation

K_ow values may also be calculated or predicted from parameters describing the chemical structure of a chemical. Several software programs are commercially available for this purpose, such as KOWWIN program of the US-EPA. These programs make use of the so-called fragment method (Leo, 1993; Rekker and Kort, 1979). This method takes into account the contribution to K_ow of different chemical groups or atoms in a molecule, and in addition corrects for special features such as steric hindrance or other intramolecular interactions (equation 1):

log K_ow = Ʃ f_n + Ʃ F_p (eq.1)

in which f_n quantifies the contributions of each fragment n in a particular chemical (see e.g. Table 1) and F_p accounts for any special intramolecular interaction p between the fragments.

This fragment approach has been improved during the last decades and is available in the EPISUITE program from the US Environmental Protection Agency. Other programs for the calculation of K_ow values are: ChemProp, and ChemAxon from Chemspider.

Table 1. Fragment constants (K_ow) for a few fragments. (from the EPISUITE program)

Fragment	Fragment constant (f)^a
-CH₃ aliphatic carbon	0.5473
Aromatic Carbon	0.2940
-OH hydroxy, aromatic attach	-0.4802
-N aliphatic N, one aromatic attach	-0.9170

Note: the above calculations are given for non-ionized chemicals. The hydrophobicity of ionic chemicals is also highly affected by the degree of ionization (see below).

K_ow values can also be retrieved from databases like echemportal or ECHA and others.

Volatility

Volatility of a chemical from the aqueous phase to air (see Figure 5) is expressed via the Henry’s law constant (K_H).

Figure 5. Evaporation of chemical X from water to air.

Henry’s law constant (K_H, in Pa⋅m³/mol) is the chemical distribution between the gas phase and water, as

\(K_H = {P_i\over C_{aq}} \) (eq.2)

where in an equilibrated water-gas system:

C_aq is the aqueous concentration of the chemical (units in mol/m³), and P_i is the partial pressure of the chemical in air (units in Pascal, Pa), which is the pressure exerted by the chemical in the total gas phase volume (occupied by the mixture of gases the gas-phase above the water solution of the chemical). Note that Pi is a measure of the concentration in the gas phase, but not yet in the same units as the dissolved concentration (discussed below)!

For compounds that are slightly soluble in water, K_H can be estimated from:

\(K_H = {V_p\over S_w}\) (eq.3)

where:

K_H:Henry’s law constant (Pa⋅m³/mol), V_p is the (saturated) vapor pressure (Pa), which is the pressure of the chemical above the pure condensed (liquid) form of the chemical, and S_w is the maximum solubility in water (mol/m³).

The advantage of equation 3 is that both V_p and S_w can be experimentally derived or estimated. The rationale behind equation 3 is that two opposite forces will affect the evaporation of a chemical from water to air:

(i) the vapor pressure (V_p) of the pure chemical - high vapor pressure means more volatile, and

(ii) solubility in water (S_w) - high solubility means less volatile.

Benzene and ethanol (see Table 2) are good illustrations. Both chemicals have similar vapor pressure, but the Henry’s law constant for benzene is much higher because of its much lower solubility in water compared to ethanol; benzene is much more volatile from an aqueous phase.

Table 2. Air-water partition coefficients (K_air-water) calculated for five chemicals (ranked by aqueous solubility) by equation 3.

Chemical	Vapor pressure (Pa)	Solubility (mol/m³)	K_H (Pa.m³/mol)	K_air-water (L/L, or m³/m³)
Ethanol	7.50⋅10³	1.20⋅10⁴	6.25⋅10^-1	2.53⋅10^-4
Phenol	5.50⋅10¹	8.83⋅10²	6.23⋅10^-2	2.52⋅10^-5
Benzene	1.27⋅10⁴	2.28⋅10¹	5.57⋅10²	2.25⋅10^-1
Pyrene	6.00⋅10^-4	6.53⋅10^-4	9.18⋅10¹	3.71⋅10^-4
DDT	2.00⋅10^-5	2.82⋅10^-6	7.08	2.86⋅10^-3

Note: all chemicals at equilibrium have a higher concentration (in e.g. mol/L) in the aqueous phase than in the gas phase. Of these five, benzene is the chemical most prone to leave water, with an equilibrated air concentration about 4 times lower (22.5%) than the dissolved concentration.

Equations 2 and 3 are based on the pressure in the gas phase. Environmental fate is often based on partition coefficients, in this case the air-water partition coefficient (K_air-water). These partition coefficients are more or less ‘dimensionless’, because the concentrations are based on equal volumes (such as L/L), while K_H has the unit Pa⋅m³/mol or something equivalent to the applied units (equation 4).

\(K_{air-water} = {C_{air}\over C_{aq}} \) (eq.4)

where:

C_air is the concentration in air (in e.g. mol/m³) and C_aq is the aqueous concentration (in e.g. mol/m³).

K_air-water can be calculated from K_H according to equation 5:

\(K_{air-water}={K_H\over RT}\) (eq.5)

where R is the gas constant (8.314 m³⋅Pa⋅K⁻¹⋅mol⁻¹), and T is the temperature in Kelvin (Kelvin = ^oCelsius + 273).

This use of “RT” converts this gas phase concentration to a volume based metric, and applies the ideal gas law which relates pressure (P, in Pa) to temperature (T, in K), volume (V, in m³), and amount of gas molecules (n, in mol), according to the gas constant (R: 8.314 m³⋅Pa⋅K^-1⋅mol^-1):

P⋅V = n⋅R⋅T (note that the units of both terms will cancel out) (eq.6)

At 25^oCelcius (298 K), the product RT equals 2477 m³⋅Pa⋅K^-1⋅mol^-1.

Examples of calculated values for K_air-water are presented in Table 2.

The influence of the chemical structure on volatility of a chemical from a solvent fully depends on the cost of creating a cavity in the solvent (interactions between solvent molecules) and the interactions between the chemical and the solvent molecules. For partitioning processes, the gas phase is mostly regarded as an inert compartment without chemical interactions (i.e. gas phase molecules hardly ever touch each other).

The molecules of a strongly dipolar solvent such as water that contain atoms that can interact as hydrogen acceptor (the O in an OH group) and hydrogen donor (the H in an OH group) strongly interact with each other, and it costs much energy to create a cavity. This cost increases strongly with molecular size, for nearly all molecules more than the energy regained by interactions with the surrounding solvent molecules. As a result, for most classes of organic chemicals, affinity with water decreases and volatility out of water into air slightly increases with molecular volume. For chemicals that are not able to re-interact via hydrogen bonding, e.g. alkanes, the overall volatility is much higher than for chemicals that do have specific interactions with water molecules besides van der Waals.

Degree of ionization

Acids and bases can be present in the neutral (HA and B) or ionized form (A^- and BH⁺, respectively). For acids, the neutral form (HA) is in equilibrium with the anionic form (A^-) and for bases the neutral form (B) is in equilibrium with the cationic form (BH⁺). The degree of ionization depends on the pH and the acid dissociation constant (pKa). Table 3 shows the equations to calculate the fraction ionized for acids and bases and examples of two acids (phenols) are presented in Table 4.

Table 3. Calculation of the fraction ionized for acids and bases.

Acids

Bases

\(fraction\ ionized = {1\over 1\ +\ 10^{(pK_a-pH)}}\)

\(fraction\ ionized = {1\over 1\ +\ 10^{(pH-pK_a)}}\)

pK_a = - log K_a, where K_a is dissociation constant of the acidic form (HA or BH⁺).

The degree of ionization is thus determined by the pH and the pKa value and more examples for several organic chemicals are presented elsewhere (see Chapter Ionogenic organic compounds).

Table 4. The degree of ionization of two phenolic structures (acids).

Pentachlorophenol	Phenol

pKa = 4.60	pKa = 9.98
% ionized versus pH	% ionized versus pH
at pH of 7.0: 99.6 % ionized	at pH of 7.0: 0.1 % ionized

Examples for several organic chemicals are presented elsewhere (see section on Ionogenic organic compounds).

The fate of ionic chemicals is very different from that of non-ionic chemicals. The sediment-water sorption coefficient of the anionic species is substantially (>100x) lower than that of the neutral species. If the percentage of ionization is less than ~99 % (at a pH 2 units above the pKa), the sorption of the anion may be neglected (K_d is still dominated by the >1% neutral species) (Schwarzenbach et al., 2003). The reason for the low sorption affinity of the anionic acid form is twofold: anions are much better water soluble, but also most sediment particles (clay, organic matter, silicates) are negatively charged, and electrostatically repulse the similarly charged chemical. In that case the sorption coefficient K_d can be calculated from the sorption coefficient of the non-ionic form and the fraction of the non-ionized form (α):

\(K_d = α\ K_d\ (neutral\ form)\) (eq. 7)

In environments where the pH is such that the neutral acid fraction <1% (when pH >2 units above the pKa), the sorption of the anionic species to soil/sediment may significantly contribute to the overall “distribution coefficient” of both acid species.

For basic environmental chemicals of concern, among which many illicit drugs (e.g. amphetamine, cocaine) and non-illicit drugs (e.g. most anti-depressants, beta-blockers), the protonated forms are positively charged. These organic cations are also much more soluble in water than the neutral form, but at the same time they are electrostatically attracted to the negatively charged sediment surfaces. As a result, the sorption affinity of organic cations to sediment should not be considered negligible relative to the neutral species. The sorption processes, however, may strongly differ for the neutral base species and the cationic base species. Several studies have shown that the sorption affinity of cationic base species to DOM or sediment is even stronger than that of the neutral species.

References

De Bruijn, J., Busser, F., Seinen, W., Hermens, J. (1989). Determination of octanol/water partition coefficients for hydrophobic organic chemicals with the "slow-stirring" method. Environmental Toxicology and Chemistry 8, 499-512.

Eadsforth, C.V. (1986). Application of reverse-phase HPLC for the determination of partition coefficients. Pesticide Science 17, 311-325.

Leo, A., Hansch, C., Elkins, D. (1971). Partition coefficients and their uses. Chemical Reviews 71, 525-616.

Leo, A.J. (1993). Calculating log P(oct) from structures. Chemical Reviews 93, 1281-1306.

Rekker, R.F., de Kort H.M. (1979). The hydrophobic fragmental constant; an extension to a 1000 data point set. Eur.J. Med. Chem. - Chim. Ther. 14:479-488.

Schwarzenbach RP, Gschwend PM, Imboden DM (Eds.) 2003. Environmental organic chemistry. Wiley, New York, NY, USA.

3.4.2. Sorption

Authors: Joop Hermens

Reviewers: Kees van Gestel, Steven Droge, Philipp Mayer

Leaning objectives:

You should be able to:

understand why information on sorption is important for risk assessment
give examples that illustrate the importance of sorption for risk assessment
understand the concept of sorption isotherms
be familiar with different sorption isotherms (linear, Freundlich, Langmuir).

Keywords: Sorption isotherm, absorption and adsorption, organic matter, Freundlich model, Langmuir model, organic carbon content

Introduction

Sorption processes have a major influence on the fate of chemicals in the environment (Box 1). In general, sorption is defined as the binding of a dissolved or gaseous chemical (the sorbate) to a solid phase (the sorbent) and this may involve different processes, including:

(i) binding of dissolved chemicals from water to sediments and soils

and (ii) binding of gaseous phase chemicals from air to soils, plants, and trees.

Information about sorption is relevant because of a number of reasons:

sorption controls the actual fate and thereby the risk of (many) organic and inorganic contaminants in the environment,
sorbed chemicals cannot evaporate, are not available for (photo)chemical or microbial breakdown, are not as easily transported as dissolved/vapor phase chemicals, and are not available for uptake by organisms,
sorption also plays an important role in toxicity tests, affecting exposure concentrations.

Box 1.

The Biesbosch is a wetland area in the Netherlands, an area in between the Rivers Rhine and Meuse and estuaries that are connected to the North Sea. The water flow is relatively low and as a consequence there is a strong sedimentation of particles from the water to the sediment. Chemicals present in the water strongly sorb to these particles, which in the past were polluted with hydrophobic organic contaminants such as dioxins and PCBs. The concentrations of these organic compounds in sediment are still relatively high because they are highly persistent. The reason for this persistence of these compounds is that these sorbed compounds are not easily available for degradation by bacteria. Also, the concentrations in organisms that live close to or in the sediment are high. These concentrations are so high that fishing on eel, for example, is not allowed in the area. This example shows the importance of sorption processes on fate, but also on effects in the environment.

Figure 1. Measurement of sorption coefficients.

Measurement of sorption is a simple procedure. A chemical X is spiked (added) to the aqueous phase in the presence of a certain amount of the solid phase (sediment or soil). The chemical sorbs to the solid phase and when the system is in equilibrium, the concentrations in the sediment (C_s) and in the aqueous phase (C_a) are measured. The solid phase is collected via centrifugation or filtration.

The sorption coefficient K_p (equation 1 and box 2) gives information about the degree of sorption of a chemical to sediment and is defined as:

\(K_p = {C_s \over C_a}\) (1)

Box 2:

The concentration of a chemical X in sediment (C_s) is 30 mg/kg and the concentration in the aqueous phase (C_a) is 0.1 mg/L.

The sorption coefficient K_p = C_s / C_a = 30 mg/kg / 0.1 mg/L = 300 L/kg

Note the units of a sorption coefficient: L/kg

In the environmental risk assessment of chemicals, it is very useful to understand the fraction of the total amount of chemical (A_total) in a system that is sorbed (f_sorbed) or dissolved (f_dissolved) (e.g. due to an accidental spill in a river):

f_dissolved= A_dissolved / A_total, and thus f_sorbed = 1 - f_dissolved

This is related to the sorption coefficients of X and the volume of the solvent and the volume of the sorbent material. The equation derived for calculating f_dissolved is based on the mass balance of chemical A, which relates the concentration of X (C) to the amount of X (A) in each volume (V):

C = A / V, and thus A = C ⋅ V

which for a system of water and sediment (air not included for simplification) relates to:

A_total = A_dissolved + A_sorbed = C_water ⋅ V_water + C_sediment ⋅ V_sediment = C_water ⋅ V_water + (K_p ⋅C_water)⋅V_sediment

f_dissolved= A_dissolved / A_total = C_water ⋅ V_water / (C_water ⋅V_water + K_p ⋅C_water⋅V_sediment)

This way of separating out C_sediment from the equation using K_p can result, after rearranging (by dividing both parts of the ratio by C_water ⋅ V_water) to the following simplified equation:

f_dissolved= 1 / (1 + K_p⋅(V_sediment / V_water))

in this equation, ‘sediment’ can be replaced by any sorbent, as long as the appropriate sorption coefficient is used.

Let’s try to calculate with chemical X from above, in a wet sediment, where 1L wet sediment contains ~80% water and 20% dry solids. The dissolved fraction of X with K_p = 300 kg/L, is only 0.013 in this example. Thus, with 1.3% of X actually dissolved, this indicates that 98.7% of X is sorbed to the sediment.

Sorption processes

There are two major sorption processes (see Figure 2):

Absorption - partitioning (“dissolution”) of a chemical into a 3-D sorbent matrix. The concentration in the sorbing phase is homogeneous.
Adsorption - binding of a chemical to a 2-D sorbent surface. Because the number of sorption sites on a surface is limited, sorption levels off at high concentrations in the aqueous phase.

A sorption isotherm gives the relation between the concentration in a sorbent (sediment) and the concentration in the aqueous phase and the isotherm is important in identifying a sorption process.

Figure 2. Two sorption processes: absorption and adsorption.

Absorption of a chemical is similar to its partitioning between two phases and comparable to its partitioning between two solvents. Distribution of a chemical between octanol and water is a well-known example of a partitioning process (see Section 3.4.1 on Relevant chemical properties for more detailed information on octanol-water partitioning). The isotherm for an absorption process is linear (Figure 3A) and the slope of the y-x plot is the sorption coefficient K_p.

Figure 3. Sorption isotherms for absorption: linear model (3A), and for adsorption: Langmuir model (3B) or Freundlich model (3C).

In an adsorption process, where the sorbing phase is a surface with a limited number of sorption sites, the sorption isotherm is non-linear and may reach a maximum concentration that is adsorbed when all sites are occupied. A mechanistic model for adsorption is the Langmuir model. This model describes adsorption of molecules to homogeneous surfaces with equal adsorption energies, represented by the adsorption site energy term (b) and a limited number of sorption sites (C_max) that can become saturated (Figure 3B). The Langmuir adsorption coefficient (K_ad) is equal to the product (b ⋅ C_max) at relatively low aqueous concentrations, where the product (b ⋅ C_aq) << 1 (note that the denominator term will then be ~1). Indeed, the isotherm curve on a double log scale plot shows a slope of 1 at such low concentrations, indicating linearity.

Another mathematical approach to describe non-linear sorption is the Freundlich isotherm (Figure 3C), where K_F is the Freundlich sorption constant and n is the Freundlich exponent describing the sorption process non-linearity. Using logarithmic values for aqueous and sorbed concentrations, the Freundlich isotherm can be rewritten as:

Log C_s = n ⋅ log C_aq + log K_F (eq. 2)

This conveniently yields a linear relationship (just as y = a⋅x + b) between log Cs and log Caq, with a slope equal to n and the abscissa (crossing point with the Y-axis) equal to log K_F. This allows for easy fitting of linear trend lines through experimental data sets. When n = 1, the isotherm is linear, and equals the one for absorption. In case of saturation of the sorption sites on the solid phase, 1/n will be smaller than 1. The Freundlich isotherm can, however, also yield a 1/n value > 1; this may occur for example if the chemical that is sorbed itself forms a layer that serves as a new sorbing phase and examples are described for surfactants.

Sorption phases

Soils and sediments may show large variations in composition and particle size distribution. The major components of soils and sediments are:

Sand	63 – 2 mm
Silt	2 – 63 µm
Clay	<2 µm
Organic matter	includes e.g. detritus, humic acids, especially associated with the clay and silt fractions
CaCO₃

Figure 4 gives a schematic picture of a sediment or soil particle. In addition to the presence of clay minerals and (soil or sediment) organic matter (SOM), sediment and soil may contain soot particles (a combustion residue).

Figure 4. Structure of a soil or sediment particle showing the major components: organic matter and clay mineral and soot. Modified from Schwarzenbach et al. (2003) by Steven Droge.

Organic matter is formed upon decomposition of plant material and dead animal or microbial tissues. Upon decomposition of plant material, the first organic groups to be released are phenolic acids, some of which have a high affinity for complexation of metals. One example is salicylic acid (o-hydroxybenzoic acid), which occurs in high concentrations in leaves of willows, poplar and other deciduous trees. Further decomposition of plant material may result in the formation of humic acids, fulvic acids and humin. Humic and fulvic acids contain a series of functional groups, such as carboxyl- (COOH), carbonyl- (=C=O), phenolic hydroxyl- (-OH), methoxy- (-OCH3), amino- (-NH2), imino (=NH) and sulfhydryl (-SH) groups (see for more details the section on Soil).

Hydrophobic organic chemicals mainly sorb to organic matter. Because organic matter has the characteristics of a solvent, the sorption is clearly an absorption process and the sorption isotherm is linear. Because binding is mainly to organic matter, the sorption coefficient (K_p) depends on the fraction of organic matter (f_om) or the fraction of organic carbon (f_oc) present in the soil or sediment. Please note that as a rule of thumb, organic matter contains 58% organic carbon (f_oc = 0.58⋅f_om). Figure 5A shows the increase in sorption coefficient with increasing fraction organic carbon in soils and sediments. In order to arrive at a more intrinsic parameter, sorption coefficients are often normalized to the fraction organic matter (K_om) or organic carbon (K_oc). These K_oc or K_om values are less dependent of the sediment or soil type (Figure 5B).

\(K_{om} = {K_p \over f_{om}}\) (3)

\(K_{oc} = {K_p \over f_{oc}}\) (4)

Figure 5. The relationship between the sorption coefficient (K_p) (left) and the organic carbon normalized sorption coefficient (K_oc) (right) and the fraction organic carbon (f_oc). Data from Means et al. (1980). Drawn by Wilma Ijzerman.

Hydrophobic chemicals can have a very high affinity to soot particles relative to the affinity to SOM. If a sediment contains soot, K_pvalues are often higher than predicted based on the fraction organic carbon in the organic matter (Jonker and Koelmans, 2002).

References

Schwarzenbach, R.P., Gschwend, P.M., Imboden, D.M. (2003). Environmental Organic Chemistry. Wiley, New York, NY, USA.

Means, J.C., Wood, S.G., Hassett, J.J., Banwart, W.L. (1980). Sorption of polynuclear aromatic-hydrocarbons by sediments and soils. Environmental Science and Technology 14, 1524-1528.

Jonker, M.T.O., Koelmans, A.A. (2002). Sorption of polycyclic aromatic hydrocarbons and polychlorinated biphenyls to soot and soot-like materials in the aqueous environment mechanistic considerations. Environmental Science and Technology 36, 3725-3734.

3.4.3. Quantitative Structure Property Relationships (QSPRs)

Author: Joop Hermens

Reviewer: Steven Droge, Monika Nendza

Learning objectives:

You should be able to:

indicate which properties of chemicals are applied in a QSPR
list different techniques to derive a QSPR
indicate which interactions may occur between molecules.

Keywords: Quantitative structure-property relationships (QSPR), quantitative structure-activity relationships (QSAR), octanol-water partition coefficients, hydrogen bonding, multivariate techniques.

Introduction

Risk assessment needs input data for fate and effect parameters. These data are not available for many of the existing chemicals and predictions via estimation models will provide a good alternative to actual testing. Examples of estimation models are Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs). The term "activity" is often used in relation to models for toxicity, while "property" usually refers to physical-chemical properties or fate parameters.

In a QSAR or QSPR, a certain environmental parameter is related to a physical-chemical or structural property, or a combination of properties.

The elements in a QSPR or QSAR are shown in Figure 1 and include:

the parameter for which the estimation model has been developed: the Y-variable (upper right),
the properties of the chemical or chemical parameter: the X-variable (upper left),
the model itself (center), and
the prediction of a fate or effect parameter from the chemical properties (bottom).

Figure 1. The principle of a QSPR or QSAR. See text for explanation.

The Y-variable

Estimation models have been developed for many endpoints such as sorption to sediment, humic acids, lipids and proteins, chemical degradation, biodegradation, bioconcentration and ecotoxic effects.

The X-variable

An overview of the chemical parameters (the X-variable) used in estimation models is given in Table 1. Chemical properties are divided in three categories: (i) parameters related to hydrophobicity, (ii) parameters related to charge and charge distribution in a molecule and (iii) parameters related to the size or volume of a molecule. Hydrophobicity is discussed in more detail in the section on Relevant chemical properties.

Other QSPR approaches use large number of parameters derived from chemical graphs. The CODESSA Pro software, for example, generates molecular (494) and fragment (944) descriptors, classified as (i) constitutional, (ii) topological, (iii) geometrical, (iv) charge related, and (v) quantum chemical (Katritzky et al. 2009). Some models are based on structural fragment in a molecule. The polyparameter linear free energy relationships (pp-LFER) use parameters that represent interactions between molecules (see under pp-LFER).

Table 1. Examples of parameters related to hydrophobicity and electronic and steric parameters (the X variable).

Hydrophobic parameters

Aqueous solubility

Octanol-water partition coefficient (K_ow)

Hydrophobic fragment constant π

Electronic parameters

Atomic charges (q)

Dipole moment

Hydrogen bond acidity (H bond-donating)

Hydrogen bond basicity (H bond-accepting)

Hammett constant σ

Steric parameters

Total Surface Area (TSA)

Total Molecular Volume (TMV)

Taft constant for steric effects (Es)

The model

Most models are based on correlations between Y and X. Such a relationship is derived for a “training set” that consists of a limited number of carefully selected chemicals. The validity of such a model should be tested by applying it to a "validation set", i.e. a set of compounds for which experimental data can be compared with the predictions. Different techniques can be used to develop an empirical model, such as:

graphical presentations,
linear or non-linear equations between Y and X,
linear or non-linear equations based on different properties (Y versus X₁, X₂, etc.),
multivariate techniques such as Principal Component Analysis (PCA) and Partial Least Square Analysis (PLS).

Linear equations take the form:

Y(i) = a₁X₁(i) + a₂X₂(i) + a₃X₃(i) + ... + b (1)

where Y(i) is the value of the dependent parameter of chemical i (for example sorption coefficients); X₁-X₃(i) are values for the independent parameters (the chemical properties) of chemical i; a₁-a₃ are regression coefficients (usually 95% confidence limits are given); b is the intercept of the linear equation. The quality of the equation is presented via the correlation coefficient (r) and the standard error of estimate (s). The closer r is to 1.0, the better the fit of the relationship is. More information about the statistical quality of models can be found under “limitation of QSPR”.

The classical approach in QSAR and QSPR studies is the Hansch approach that was develop in the 1960s. The Hansch equation (Hansch et al., 1963) describes the influence of substituents on the biological activity in a series of parent compounds with a certain substituent (equation 2). Substituents are for example a certain atom or chemical group (Cl, F, B,. OH, NH₂) attached to a parent aromatic ring structure.

log 1/C = c π + c' σ + c'' E_s + c''' (2)

in which:

C is the molar concentration of a chemical with a particular effect,

π is a substituent constant for hydrophobic effects,

σ is a substituent constant for electronic effects, and

E_s is a substituent constant for steric effects.

c are constants that are obtained by fitting experimental data

For example, the hydrophobic substituent constant is based on K_ow and is defined as is defined as:

π (X) = log K_ow (RX) - log K_ow (RH) (3)

where RX and RH are the substituted and unsubstituted parent compound, respectively.

The Hammett and Taft constants are derived in a similar way.

Multivariate techniques may be very useful to develop structure-activity relationships, in particular in cases where a large number of chemical parameters is involved. Principal Component Analysis (PCA) can be applied to reduce the number of variables into a few principal components. The next step is to find a relationship between Y and X via, for example, Partial Least Square (PLS) analysis. The advantage of PCA and PLS is that it can deal with a large number of chemical descriptors and that is can also cope with collinear (correlated) properties. More information on these multivariate techniques and examples in the field of environmental science are given by Eriksson et al. (1995).

Poly-parameter Linear Free Energy Relationship (pp-LFER)

The pp-LFER approach has a strong mechanistic basis because it includes the different types of interactions between molecules (Goss and Schwarzenbach, 2001). For example, the sorption coefficient of a chemical from an aqueous phase to soil or to phospholipids (the sorbent) depends on the interaction of a chemical with water and the interaction with the sorbent phase. One of the driving forces behind sorption is the hydrophobicity. Hydrophobicity means fear (phobia) for water (hydro). A hydrophobic chemical prefers to “escape from the aqueous phase” or in other words “it does not like to dissolve in water”. Water molecules are tightly bound to each other via hydrogen bonds. For a chemical to dissolve, a cavity should be formed in the aqueous phase (Figure 2) and this will cost energy. More hydrophobic compounds will often have a stronger sorption (see more information in the section on Relevant chemical properties).

Hydrophobicity mainly depends on two molecular properties:

Molecular size
Polarity / ability to interact with water molecules, for example via hydrogen bonding

Figure 2. The formation of a cavity in water for chemical X and the interaction with another phase (here, a soil particle).

In the interaction with the sorbent (soil, membrane lipids, storage lipids, humic acids), major interactions are van der Waals interactions and hydrogen bonding (Table 2). Van der Waals interactions are attractive and occur between all kind of molecules and the strength depends on the contact area. Therefore, the strength of van der Waals interactions are related to the size of a molecule. A hydrogen bond is an electrostatic attraction between a hydrogen (H) and another electronegative atom bearing a lone pair of electrons. The hydrogen atom is usually covalently bound to a more electronegative atom (N, O, F). Table 2 lists the interactions with examples of chemical structures.

A pp-LFER is a linear equation developed to model partition or sorption coefficients (K) using parameters that represent the interactions (Abraham, 1993). The model equation is based on five descriptors:

\(log⁡ K=c+e∙E+s∙S+a∙A+b∙B+v∙V\) (2)

with:

E	excess molar refraction
S	dipolarity/polarizability parameter
A	solute H-bond acidity (H-bond donor)
B	solute H-bond basicity (H-bond acceptor)
V	molar volume

The partition or sorption coefficient K may be expressed as the sum of five interaction terms, with the uppercase parameters describing compound specific properties. E depends on the valence electronic structure, S represents polarity and polarizability, A is the hydrogen bond (HB) donor strength (HB acidity), B the HB acceptor strength (HB basicity), V is the so-called characteristic volume related to the molecule size, and c is a constant. The lower-case parameters express the corresponding properties of the respective two-phase system, and can thus be taken as the relative importance of the compound properties for the particular partitioning or sorption process. In this introductory section, we only focus on the volume factor (V) and the two hydrogen bond parameters (A and B).

Numerous pp-LFERs have been developed for all kinds of environmental processes and an overview is given by Endo and Goss (2014).

Table 2. Types of interactions between molecules and the phase to which they sorb with examples of chemicals (Goss and Schwarzenbach, 2003).

Compound^a)	Interactions	Examples
Apolar	only van der Waals	alkanes, chlorobenzenes, PCBs
Monopolar	van der Waals + H-acceptor (e-donor)	alkenes, alkynes, alkylaromatic compounds ethers, ketones, esters, aldehydes
Monopolar	van der Waals + H-donor (e-acceptor)	CHCl₃, CH₂Cl₂
Bipolar	van der Waals + H-donor + H-acceptor	R–NH₂, R2–NH, R–COOH, R–OH

^a) Apolar: no polar group present; mono/dipolar: one or two polar groups present in a molecule

Examples of QSPR for bioconcentration to fish

K_ow based model

Predictive models for bioconcentration have a long history. The octanol-water partition coefficient (K_OW) is a good measure for hydrophobicity and bioconcentration factors (BCF’s) are often correlated to K_ow (see more information in section on Bioaccumulation). The success of these K_OW based models was explained by the resemblance of partitioning in octanol and bulk lipid in the organisms, at least for neutral hydrophobic compounds. A well-known example of a linear QSAR model for the log BCF (Y variable) based on the log K_OW (X variable) (Veith et al., 1979):

log BCF = 0.85 log K_OW - 0.70 (5)

Figure 3 gives a classical example of such a correlation for BCF to guppy of a series of chlorinated benzenes and polychlorinated biphenyls. When lipophilic chemicals are metabolised, the relation shown in Figure 3 is no longer valid and BCF will be lower than predicted based on K_OW. Another deviation of this BCF-K_ow relation can be found for highly lipophilic chemicals with log K_ow>7. For such chemicals, BCF often decrease again with increasing Kow (see Figure 3). The apparent BCF curve with Kow as the X variable tends to follow a nonlinear curve with an optimum at log K_ow 7-8. This phenomenon may be explained from molecular size: molecules of chemicals like decachlorobiphenyl may be so large that they have difficulties in passing membranes. A more likely explanation, however, is that for highly lipophilic chemicals aqueous concentrations may be overestimated. It is not easy to separate chemicals bound to particles from the aqueous phase (see box 1 in the section on Sorption) and this may lead to measured concentrations that are higher than the bioavailable (freely dissolved) concentration (Jonker and van der Heijden 2007; Kraaij et al. 2003). For example, at a dissolved organic carbon (DOC) concentration of 1 mg-DOC/L, a chemical with a log Koc of 7 will be 90% bound to particles, and this bound fraction is not part of the dissolved concentration that equilibrates with the (fish) tissue. This shows that these models are also interesting because they may show trends in the data that may lead to a better understanding of processes.

Figure 3. The relationship between bioconcentration factors in guppy and the octanol-water partition coefficients with data from (Bruggeman et al., 1984; Könemann and Leeuwen, 1980).

Examples of QSPR for sorption to lipids

K_owbased models are successful because octanol probably has similar properties than fish lipids. There are several types of lipids and membrane lipids have different properties and structure than for example storage lipids (see Figure 4, and more details in the section on Biota). More refined BCF models include separation of storage and membrane lipids and also proteins as separate sorptive phases (Armitage et al. 2013). pp-LFER is a very suitable approach to model these sorption or partitioning processes and results for two large data sets are presented in Table 3. The coefficients e, s, b and v are rather similar. The only parameter that is different in these two models is coefficient a, which represents the contribution of hydrogen bond (HB) donating properties (A) of chemicals in the data set. This effect makes sense because the phosphate group in the phospholipid structure has strong HB accepting properties. This example shows the strength of the pp-LFER approach because it closely represents the mechanism of interactions.

Figure 4. Structure of a phospholipid and a triglyceride. Note the similar glycerol part in both lipids.

Table 3. LFERs for storage lipid-water partition coefficients (K_SL-W) and membrane lipid-water partition coefficients (K_ML-W(liposome)). Listed are the parameters (and standard error), the number of compounds with which the LFER was calibrated (n), the correlation coefficient (r²), and the standard error of estimate (SE). log K = c + eE + sS + aA + bB + vV.

Para- meter	c	e	s	a	b	v	n	r²	SE
K_SL-W	-0.07 (0.07)	0.70 (0.06)	-1.08 (0.08)	-1.72 (0.13)	-4.14 (0.09)	4.11 (0.06)	247	0.997	0.29
From (Geisler et al. 2012)
K_ML-W (liposome)	0.26 (0.08)	0.85 (0.05)	-0.75 (0.08)	0.29 (0.09)	-3.84 (0.10)	3.35 (0.09)	131	0.979	0.28
From (Endo et al. 2011)

K_SL-W: storage lipid partition coefficients are mean values for different types of oil. Raw data and pp-LFER (for 37 ^oC) reported in (Geisler et al. 2012).

K_ML-W (liposome): data from liposomes made up of phosphatidylcholine (PC) or PC mixed with other membrane lipids. Raw data (20-40 ^oC) and pp-LFER reported in (Endo et al. 2011).

Examples of QSPR for sorption to soil

Numerous QSPRs are available for soil sorption (see section on Sorption). Also the organic carbon normalized sorption coefficient (K_oc) is linearly related to the octanol-water partition coefficient (see Figure 5).

Figure 5. Correlation between the organic carbon normalized sorption coefficient to soil (K_oc) and the octanol-water partition coefficient (K_ow) for data from (Sabljic et al. 1995).

The model in Figure 5 is only valid for neutral, non-polar hydrophobic organic chemicals such as chlorinated aromatic compounds, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyl (PCBs) and chlorinated insecticides or, in general, compounds that only contain carbon, hydrogen and halogen atoms. It does not apply to polar and ionized organic compounds nor to metals. For polar chemicals, also other interactions may influence sorption and a pp-LFER approach would also be useful.

The sorption of ionic chemicals is more complex. For the sorption of cationic organic compounds, clay minerals can be an equally important sorption phase as organic matter because of their negative surface charge and large surface area. The sorption of organic cations is mainly an adsorption process that reaches a maximum at the cation exchange capacity (CEC) of a particle (see section on Soil). Also models for the prediction of sorption of cationic compounds are more complicated and first attempts have been made recently (Droge and Goss, 2013). Major sorption mechanism for anionic chemicals is sorption into organic matter. The sorption coefficient of anionic chemicals is substantially lower than for the neutral form of the chemical, roughly a factor 10-100 for K_OC (Tülp et al. 2009). In case of weakly dissociating chemicals such as carboxylic acids, the sorption coefficient can often be estimated from the sorption coefficient of the non-ionic form and the fraction of the chemical that is present in the non-ionized form (see section on Relevant chemical properties).

Reliability and limitations of QSPR

Predictive models have limitations and it is important to know these limitations. There is not one single model that can predict a parameter for all chemicals. Each model will have a domain of applicability and it is important to apply a model only to a chemical within that domain. Therefore, guidance has to be defined on how to select a specific model. It is also important to realize that in many computer programs (such as fate modeling programs), estimates and predictions are implicitly incorporated in these progams.

Another aspect is the reliability of the prediction. The model itself can show a good fit (high r²) for the training set (the chemicals used to develop the model), but the actual reliability should be tested with a separate set of chemicals (the validation set) and a number of statistical procedures can be applied to test the accuracy and predictive power of the model. The OECD has developed a set of rules that should be applied in the validation of QSPR and QSAR models.

References

Abraham, M.H. (1993). Scales of solute hydrogen-bonding - their construction and application to physicochemical and biochemical processes. Chemical Society Reviews 22, 73-83.

Armitage, J.M., Arnot, J.A., Wania, F., Mackay, D. (2013). Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish. Environmental Toxicology and Chemistry 32, 115-128.

Bruggeman, W.A., Opperhuizen, A., Wijbenga, A., Hutzinger, O. (1984). Bioaccumulation of super-lipophilic chemicals in fish. Toxicological and Environmental Chemistry 7, 173-189.

Droge, S.T.J., Goss, K.U. (2013). Development and evaluation of a new sorption model for organic cations in soil: Contributions from organic matter and clay minerals. Environmental Science and Technology 47, 14233-14241.

Endo, S., Escher, B.I., Goss, K.U. (2011). Capacities of membrane lipids to accumulate neutral organic chemicals. Environmental Science and Technology 45, 5912-5921.

Endo, S., Goss, K.U. (2014). Applications of polyparameter linear free energy relationships in environmental chemistry. Environmental Science and Technology 48, 12477-12491.

Eriksson, L., Hermens, J.L.M., Johansson, E., Verhaar, H.J.M., Wold, S. (1995). Multivariate analysis of aquatic toxicity data with pls. Aquatic Sciences 57:217-241.

Geisler, A., Endo, S., Goss, K.U. (2012). Partitioning of organic chemicals to storage lipids: Elucidating the dependence on fatty acid composition and temperature. Environmental Science and Technology 46, 9519-9524.

Goss, K.-U., Schwarzenbach, R.P. (2001). Linear free energy relationships used to evaluate equilibrium partittioning of organic compounds. Environmental Science and Technology 35, 1-9.

Goss, K.U., Schwarzenbach, R.P. (2003). Rules of thumb for assessing equilibrium partitioning of organic compounds: Successes and pitfalls. Journal of Chemical Education 80, 450-455.

Hansch, C., Streich, M., Geiger, F., Muir, R.M., Maloney, P.P., Fujita, T. (1963). Correlation of biological activity of plant growth regulators and chloromycetin derivatives with hammett constants and partition coefficients. Journal of the American Chemical Society 85, 2817-&.

Jonker, M.T.O., van der Heijden, S.A. (2007). Bioconcentration factor hydrophobicity cutoff: An artificial phenomenon reconstructed. Environmental Science and Technology 41, 7363-7369.

Katritzky, A.R., Slavov, S., Radzvilovits, M., Stoyanova-Slavova, I., Karelson, M. (2009). Computational chemistry approaches for understanding how structure determines properties. Zeitschrift Fur Naturforschung Section B-a Journal of Chemical Sciences 64:773-777.

Könemann, H., Van Leeuwen, K. (1980). Toxicokinetics in fish: Accumulation and elimination of six chlorobenzenes by guppies. Chemosphere 9, 3-19.

Kraaij, R., Mayer, P., Busser, F.J.M., Bolscher, M.V., Seinen, W., Tolls, J. (2003). Measured pore-water concentrations make equilibrium partitioning work - a data analysis. Environmental Science and Technology 37, 268-274.

Sabljic, A., Güsten, H., Verhaar, H.J.M., Hermens, J.L.M. (1995). Qsar modelling of soil sorption. Improvements and systematics of log k_oc vs. Log k_ow correlations. Chemosphere 31, 4489-4514.

Tülp, H.C., Fenner, K., Schwarzenbach, R.P., Goss, K.U. (2009). pH-dependent sorption of acidic organic chemicals to soil organic matter. Environmental Science and Technology 43, 9189-9195.

Veith, G.D., Defoe, D.L., Bergstedt, B.V. (1979). Measuring and estimating the bioconcentration factor of chemicals in fish. Journal of the Fisheries Research Board of Canada 36, 1040-1048.

3.5. Metal speciation

Author: Martina Vijver, John Parsons

Reviewers: Kees van Gestel, Ronny Blust, Steven Droge

Learning objectives:

You should be able to:

describe the reactions involved in the speciation of metals in the aquatic and soil environments.
explain the equilibrium approach to modelling metal speciation.
identify which water and soil properties impact the fate of metals
describe how processes such as competition and sorption impact metal bioavailability
explain why the environmental fate of metals is dynamic

Keywords: Metal complexation, redox reactions, equilibrium reactions, water chemistry, soil properties.

Introduction

Metals occur in different physical and chemical forms in the environment, for example as the element (very rare in the environment), as components of minerals, as free cations dissolved in water (e.g. Cd²⁺), or bound to inorganic or organic molecules in either the solid or dissolved phases (e.g. HgCH₃⁺ or AgCl²⁺) (Allen 1993). The distribution of a metal over these different forms is referred to as metal speciation. Physical processes may also affect the mobility and bioavailability of metals, for example the electrostatic attraction of metal cations to negatively charged mineral surfaces. These processes are in general not referred to as metal speciation in the strict sense but they are discussed here.

Metal speciation reactions

The speciation of metals is controlled by both the properties of the metals (see the section Metals and metalloids) and the properties of the environment in which they are present, such as the pH, redox potential and the presence and concentrations and properties of molecules that could form complexes with the metals. These complex forming molecules are often called ligands and these can vary from relatively simple anions in solution, such as sulphate or anions of soluble organic acids, to more complex macromolecules such as proteins and other biomolecules. The adsorption of metals by covalent bond formation to oxide and hydroxide surfaces of minerals, and oxygen- or nitrogen-containing functional groups of solid organic matter, is also referred to as complexation. Since these metal-binding functional groups are often either acidic or basic, the pH is an important environmental parameter controlling complexation reactions.

In natural systems the speciation of metals is of great complexity and determines their mobility in the environment and their bioavailability (i.e. how easily they are taken up by organisms). Metal speciation therefore plays a key role in determining the potential bioaccumulation and toxicity of metals and should therefore be considered when assessing their ecological risks. Metal bioavailability and transport are in particular strongly related to the distribution over solid and liquid phases of the environmental matrix.

The four main chemical reactions determining metal speciation, so the binding of metal ions to ligands and their presence in solid and liquid phases, are (Bourg, 1995):

adsorption and desorption processes
ion exchange and dissolution reactions
precipitation and co-precipitation
complexation to inorganic and organic ligands

The complexity of these reactions is illustrated in Figure 1.

Figure 1. Metals (M) speciation in the environment is determined by a number of reactions, including complexation, precipitation and sorption. These reactions affect the partitioning of metals across solid and liquid phases, hence their mobility as well as their bioavailability. Drawn by Evelin Karsten-Meessen.

Adsorption, desorption and ion exchange processes take place with the reactive components present in soils, sediments and to lower extent in water. These include:

clay minerals
hydroxides (e.g. FeOH₃) and/or carbonates (e.g. CaCO₃)
organic matter

Metal ions react with these reactive components in different ways. In soils and sediments, cationic metals bind reversibly to clay minerals via cation-exchange processes (see section on Soil). Metal ions also form complexes with so-called functional groups (mainly carboxylic and phenolic groups) present in organic matter (see section on Soil). In aquatic systems similar binding processes occur, in which dissolved organic matter (or carbon) (DOM or DOC) plays a major role. The "dissolved" fraction of organic matter is operationally defined as the fraction passing a 0.45 µm filter and is often referred to fulvic and humic acids.

As mentioned above and in the section on Shttps://maken.wikiwijs.nl/147644/Environmental_Toxicology__an_open_online_textbook#!page-5415168oil, negatively charged surfaces of the reactive mineral and organic components present in soil, sediment or water attract positively-charged atoms or molecules (cations, e.g. Cd²⁺), and allow these cations to exchange with other positively charged ions. The competition between cations for binding sites is driven by the binding affinity of each metal species, as well as the concentration of each metal species. Cation-exchange capacity (CEC) is a property of the sorbent, and defined as the density of available negatively charged sites per mass of environmental matrix (soil, sediment). In fact, it is a measure of how many cations can be retained on solid surfaces. CEC usually is expressed in cmol_c/kg soil (see section on Soil). Increasing the pH (i.e. decreasing the concentration of H⁺ ions) increases the variable charge of most sorbents (more types of protonated groups on sorbent surfaces release their H⁺), especially for organic matter, and therefore also increases the cation exchange capacity. Protons (H⁺) also compete with metal ions for the same binding sites. Conversely, at decreasing pH (increasing H⁺ concentrations), most sorbents lower their CEC.

Modelling metal speciation

Metal speciation can be modelled if we have sufficient knowledge of the most important reactions involved and the environmental conditions that control these reactions. This knowledge is expressed in the form of equilibria expressing the most important complexation and/or redox reactions. For example, in the general case of a complexation reaction between metal M and ligand L described by the equilibrium:

aM^m+ (aq) + bL^n- (aq) ↔ M_aL_b^q+ (aq) (where q = am-bn)

The relationship between the concentrations (or more accurately the activities) of the species is given by:

\(K_f = {[M_a\ L_b^{q+}]\over {[M^{m+}]^a\ +\ [L^{n-}]^b}}\)

If we know the value of K_f, either from experimental measurements or by estimation, we can calculate the relative concentrations or activities of the free and complexed metal ions. The actual concentrations can be calculated if we either measure one of these concentrations and that of the ligand directly or measure the total concentration of the metal present (and of the ligand) and apply a mass balance model. The copper speciation in a salt water without any DOC, for instance, depends on pH as was described by Blust et al. (1991), see Figure 2. At pH 7.5, most Cu is bound to CO₃^2-, but at pH 6 the Cu is mainly present as the free Cu²⁺ ion and in complexes with chloride (as CuCl⁺) and sulphate (as CuSO₄).

Figure 2. Copper speciation in salt water without DOC. Redrawn from Blust et al. (1991) by Wilma Ijzerman.

If redox reactions are involved in speciation, we can use the Nernst equation to describe the equilibrium between reduced and oxidised states of the metal:

\(E_h=E_h^0-{2.303RT\over nF} log {{(Red))}\over (Ox)}\)

Where E_h is the redox potential, E_h⁰ the standard potential of the redox pair (relative to the hydrogen electrode), R the molar gas constant, T the temperature, n the number of transferred electrons, F the Faraday constant and {Red/Ox} the activity (or concentration) ratio of the reduced and oxidized species. Since many redox reactions involve the transfer of H⁺, the value of {Red/Ox} for these equilibria will depend on the pH. Note that the redox potential is often expressed as pe which is defined as the negative logarithm of the electron activity (pe = - log {e^-}).

Using these comparatively simple equations for all the relevant reactions involved it is possible to construct models to describe metal speciation as a function of ligand concentrations, pH and redox potential. As an example, Table 1 presents the relevant equilibria for the speciation of iron in water.

Table 1. Equilibrium reactions relevant for Fe in water (adapted from Essington, 2003)

Boundary	Equilibrium reaction
(1)	Fe³⁺ + e^- D Fe²⁺	pE^Θ= 13.05
(2)	Fe(OH)₃(s) + 3H⁺ D Fe³⁺ + 3H₂O	K_sp = 9.1 x 10³ L² mol^-2
(3)	Fe(OH)₂(s) + 2H⁺ D Fe²⁺ + 2H₂O	K^*_sp = 8.0 x 10¹² L mol^-1
(4)	Fe(OH)₃(s) + H⁺ + e^- D Fe(OH)₂(s) + H₂O
(5)	Fe(OH)₃(s) + 3H⁺ + e^- D Fe²⁺ + 3H₂O

Using these we can derive equations defining the conditions of pH and pe at which the activity or concentration ratio is one for each equilibrium. These are shown as the continuous boundary lines in Fig. 3. In this Pourbaix or pe-pH (or pE-pH) diagram, the fields separated by the boundary lines are labelled with the dominant species present under the conditions that define the fields. (NB. The dotted lines define the conditions of pe and pH under which water is stable.)

Figure 3. A pe-pH diagram for Fe in water showing the dominant species present under different conditions assuming a maximum soluble Fe(II) or (III) concentration of 10^-5 mol L^-1 (adapted from Essington, 2003).

Environmental effects on speciation

In the environment there is, however, in general no equilibrium. This means that the speciation and hence also fate of metals is highly dynamic. Large scale alterations occur when land use changes, e.g. when agricultural land is abandoned and becomes nature. Whereas agricultural soil often is ‘limed’ (addition of CaCO₃) to maintain near-neutral pH and crop is removed by harvesting, in natural ecosystems all produced organic matter remains in the system. Therefore natural soils show an increase in soil organic matter content, while due to microbial decomposition processes soil pH tends to decrease. As a result, DOC concentration in the soil porewater will increase, while metal mobility also is increased by the decreasing soil pH (Cu²⁺ is more mobile than CuCO₃). This may cause historical metal pollution to suddenly become available (the “chemical time bomb” effect). Large scale reconstruction of rivers or deep soil digging for land planning and development may also affect environmental conditions in such a way that metal speciation may change. An example of this is the change in arsenic speciation in groundwater due to the drilling of wells in countries like Bangladesh; the introduction of oxygen and organic matter into the deeper groundwater caused a change of arsenic speciation, enhancing its solubility in water and therefore increasing human exposure (see section on Metals and metalloids).

Dynamic conditions do not only occur on large spatial and temporal scales, nature is also dynamic on smaller scales. Abiotic factors such as rain and flooding events, weather conditions, and redox status may alter metal speciation. In addition, biotic factors may affect metal speciation. An example of the latter is the bioturbation by sediment-dwelling organisms that re-suspend particles into water, or earthworms that by their digging activities aerate the soil and excrete mucus that may stimulate microbial activity (see Figure 4A). These activities of soil and sediment organisms alter the environmental conditions and hence affect metal speciation (see Figure 4B). The production of acidic root exudates by plants may also have similar effects on metal speciation. Another process that alters metal speciation is the uptake of metals. Since the ionic metal form seems most prone to root uptake, or active intake over cell membranes, this process may affect metal partitioning over different species.

Figure 4. Illustration of small scale processes that alter metal speciation. (A) different bioturbation activities by various organisms, (B) the burrowing activities of a chironomid (midge) larvae can alter the environmental conditions and with that affect metal speciation in the sediment. Source: Martina Vijver.

References

Allen, H.E. (1993). The significance of trace metal speciation for water, sediment and soil quality criteria and standards. Science of the Total Environment 134, 23-45.

Andrews, J.E., Brimblecombe, P., Jickells, T.D., Liss P.S., Reid, B. (2004) An Introduction to Environmental Chemistry, 2^nd Edition, Blackwell, ISBN 0-632-05905-2 (chapter 6).

Bourg, A.C.M. (1995) Speciation of heavy metals in soils and groundwater and implications for their natural and provoked mobility. In: Salomons, W., Förstner, U., Mader, P. (Eds.). Heavy Metals. Springer, Berlin. p. 19-31.

Blust, R., Fontaine, A., Decleir, W. (1991) Effect of hydrogen ions and inorganic complexing on the uptake of copper by the brine shrimp Artemia franciscana. Marine Ecology Progress Series 76, 273-282.

Essington, M.E. (2003) Soil and Water Chemistry, CRC Press, ISBN 0-8493-1258-2 (chapters 5, 7 and 9).

Sposito, G. (2008) The Chemistry of Soils, 2^nd Edition, Oxford University press, ISBN 978-0-19-531369-7 (chapter 4).

Sparks, D.L. (2003) Environmental Soil Chemistry, 2^nd Edition, Academic Press, ISBN 0-12-656446-9 (chapters 5, 6 and 8).

3.6. Availability and bioavailability

3.6.1. Definitions

Authors: Martina Vijver

Reviewer: Kees van Gestel, Ravi Naidu

Leaning objectives:

You should be able to:

understand that bioavailability consists of three principle processes.
understand that bioavailability is a dynamic concept.
understand why bioavailability is important to explain uptake and effects, essential for a proper risk assessment of chemicals.

Keywords: Chemical availability, actual and potential uptake, toxico-kinetics, toxico-dynamics.

Introduction:

Although many environmental chemists, toxicologists, and engineers claim to know what bioavailability means, the term eludes a consensus definition. Bioavailability may be defined as that fraction of chemical present in the environment that is or may become available for biological uptake by passage across cell membranes.

Figure 1. Bioavailability relates to a series of processes, ranging from processes external of organisms, towards internal tissues, and fully internal to the biological response site. Redrawn from Ortega-Calvo et al. (2015) by Wilma IJzerman.

Bioavailability generally is approached from a process-oriented point of view within a toxicological framework, which is applicable to all types of chemicals (Figure 1).

The first process is chemical availability which can be defined as the fraction of the total concentration of chemicals present in an environmental compartment that contributes to the exposure of an organism. The total concentration in an environmental compartment is not necessarily involved in the exposure, as a smaller or larger fraction of the chemical may be bound to organic or inorganic components of the environment. Organic matter and clay particles, for instance, are important in binding chemicals (see section on Soil), while also the presence of cations and pH are important factors modifying the partitioning of chemicals between different environmental phases (see section on Metal speciation).

The second process is the actual or potential uptake, described as the toxicokinetics of a substance which reflects the development with time of its concentration on, and in, the organism (see section on Bioconcentration and kinetics modelling).

The third process describes the internal distribution of the substance leading to its interaction(s) at the cellular site of toxicity activation. This is sometimes referred to as toxico-availability and also includes the biochemical and physiological processes resulting from the effects of the chemical at the site of action.

Details on the bioavailability concept described above as well as how the physico-chemical interactions influencing each process are described in the sections on Metal speciation and Bioconcentration and kinetics modelling.

Figure 2. Bioavailability relates to a series of time frames, particularly in external processes (according to Ortega-Calvo et al. 2015).

Kinetics are involved in all three basic processes. The timeframe can vary from very brief (less than seconds) to very long in the order of hundreds of years. Figure 2 shows that some fractions of pollutants are present in soil or sediment, but may never contribute to the transport of chemicals that could reach the internal site during an organism’s lifespan. The fractions with different desorption kinetics may relate to different experimental techniques to determine the relevant bioavailability metric.

Box 1: Illustration of how bioavailability influences our human fitness

Iron deficiency occurs when a body has not enough iron to supply its needs. Iron is present in all cells of the human body and has several vital functions. It is a key component of the hemoglobin protein, carrying oxygen to the tissues from the lungs. Iron also plays an important role in oxidation/reduction reactions, which are crucial for the functioning of the cytochrome P450 enzymes that are responsible for the biotransformation of endogenic as well as xenobiotic chemicals. Iron deficiency therefore can interfere with these vital functions, leading to a lack of energy (feeling tired) and eventually to malfunctioning of muscles and the brain.

In case of iron deficiency, the medical doctor will prescribe Fe-supplements and iron-rich food such as red meat and green leafy vegetables like spinach. Although this will lead to a higher intake of iron (after all exposure is higher), it does not necessarily lead to a higher uptake as here bioavailability becomes important. It is advised to avoid drinking milk or caffeinated drinks together with eating iron-rich products or supplements because both drinks will prevent the absorption of iron in the intestinal tract. Calcium ions abundant in milk will compete with iron ions for the same uptake sites, so excess calcium will reduce iron uptake. Carbonates and caffeine molecules, but also phytate (inositol polyphosphate) present in vegetables, will strongly bind the iron, also reducing its availability for uptake.

Figure 3. Bioavailability correction to estimate the HC5 copper concentration in relation to properties like dissolved organic carbon content and pH (Table 1) in order to estimate its risk in different water types (according to Vijver et al. 2008), in comparison to the 1.5 µg/L total dissolved Cu in surface waters as the current generic Dutch standard (horizontal line). Redrawn from Vijver et al. (2008) by Wilma Ijzerman

Bioavailability used in Risk Assessment

For regulatory purposes, it is necessary to use a straightforward approach to assess and prioritize contaminated sites based on their risk to human and environmental health. The bioavailability concept offers a scientific underpinned concept to be used in risk assessment. Examples for inorganic contaminants are the derived 2^nd tier models such as the Biotic Ligand Models, while for organic chemicals the Equilibrium Partitioning (E_qP) concept (see Box 2 in the section on Sorption) is applied.

A quantitative example is given for copper in different water types in Figure 3 and Table 1, in which water chemistry is explicitly accounted for to enable estimating the available copper concentration. The current Dutch generic quality target for surface waters is 1.5 µg/L total dissolved copper. The bioavailability-corrected risk limits (HC5) for different water types, in most cases, exceeded this generic quality target.

Table 1. Bioavailability adjusted Copper 5% Hazardous Concentration (HC5, potentially affecting <5% of relevant species) for different water types.

Water type description	no.	DOC (mg/L)	pH	Average HC5 (µg/L)
Large rivers	1	3.1 ± 0.9	7.7 ± 0.2	9.6 ± 2.9
Canals, lakes	2	8.4 ± 4.4	8.1 ± 0.4	35.0 ± 17.9
Streams, brooks	3	18.2 ± 4.3	7.4 ± 0.1	73.6 ± 18.9
Ditches	4	27.5 ± 12.2	6.9 ± 0.8	64.1 ± 34.5
Sandy springs	5	2.2 ± 1.0	6.7 ± 0.1	7.2 ± 3.1

When the calculated HC5 value is lower, this means that the bioavailability of copper is higher and hence at the same total copper concentration in water the risk is higher. The bioavailability-corrected HC5s for Cu differ significantly among water types. The lowest HC5 values were found for sandy springs (water type V) and large rivers (water type I), which appear to be sensitive water bodies. These differences can be explained from partitioning processes (chemical availability) and competition processes (the toxicokinetics step) on which the BLMs are based. Streams and brooks (water type III) have rather high total copper concentrations without any adverse effects, which can be attributed to the protective effect of relatively high dissolved organic carbon (DOC) concentrations and the neutral to basic pH causing a high binding of Cu to the DOC.

For risk managers, this water type specific risk approach can help to identify the priority in cleanup activities among sites having elevated copper concentrations. It remains possible that, for extreme environmental situations (e.g., extreme droughts and low water discharges or extreme rain fall and high runoff), combinations of the water chemistry parameters may result in calculated HC5 values that are even lower than the calculated average values. For the latter (important) reason, the generic quality target is more strict.

References

Hamelink, J., Landrum, P.F., Bergman, H., Benson, W.H. (1994) Bioavailability: physical, chemical, and biological interactions, CRC Press.

Ortega-Calvo, J.J., Harmsen, J., Parsons, J.R., Semple, K.T., Aitken, M.D., Ajao, C., Eadsforth, C., Galay-Burgos, M., Naidu, R., Oliver, R., Peijnenburg, W.J.G.M., Römbke, J., Streck, G., Versonnen, B. (2015) From bioavailability science to regulation of organic chemicals. Environmental Science and Technology 49, 10255−10264.

Vijver, M.G., de Koning, A., Peijnenburg, W.J.G.M. (2008) Uncertainty of water type-specific hazardous copper concentrations derived with biotic ligand models. Environmental Toxicology and Chemistry 27, 2311-2319.

3.6.2. Assessing available concentrations of organic chemicals

Author: Jose Julio Ortega-Calvo

Reviewers: John Parsons, Gerard Cornelissen

Learning objectives:

You should be able to:

define the concept of freely dissolved concentrations and fast-desorbing fractions of organic chemicals in soil and sediment, as indicators of their bioavailability
understand how to determine bioavailable concentrations with the use of passive sampling
understand how to determine fast-desorbing fractions with desorption extraction methods.

Keywords: Bioavailability, Freely-dissolved concentration, Desorption, Passive sampling, Infinite sink

Introduction: Bioavailability through the water phase

In many exposure scenarios involving organic chemicals, ranging from a bacterial cell to a fish, or from a sediment bed to a soil profile, the organisms experience the pollution through the water phase. Even when this is not the case, for example when uptake is from sediment consumed as food, the aqueous concentration may be a good indicator of the bioavailable concentration, since ultimately a chemical equilibrium will be established between the solid phase, the aqueous phase (possibly in the intestine), and the organism. Thus, taking an aqueous sample from a given environment, and determining the concentration of a certain chemical with the appropriate analytical equipment seems a straightforward approach to assess bioavailability. However, especially for hydrophobic chemicals, which tend to remain sorbed to solid surfaces (see sections on Relevant chemical properties and Sorption of organic chemicals), the determination of the chemicals present in the aqueous phase, as a way to assess bioavailability, has represented a significant challenge to environmental organic chemistry. The phase exchange among different compartments often leads to equilibrium aqueous concentrations that are very low, because most of the chemicals remain associated to the solids, and after sustained exposure, to the biota. These freely dissolved concentrations (C_free) are very useful to determine bioavailability, as they represent the “tip of the iceberg” under equilibrium exposure, and are what organisms “see” (Figure 1, left). Similarly to the balance between gravity and buoyancy forces leading to iceberg flotation up to a certain level, C_free is determined by the equilibrium between sorption and desorption, and connected to the concentration of the sorbed chemical (C_sorbed) through a partitioning coefficient.

Biological uptake may also result in the fast removal of the chemical from the aqueous phase, and thus in further desorption from the solids, so equilibrium is never achieved, and actual aqueous concentrations are much lower than the equilibrium C_free (or even close to zero). In these situations, bioavailability is driven by the desorption kinetics of the chemical. Usually, desorption occurs as a biphasic process, where a fast desorption phase, occurring during a few hours or days, is followed by a much slower phase, taking months or even years. Therefore, for scenarios involving rapid exposures, or for studies on coupled desorption/biodegradation, the fast-desorbing fraction of the chemicals (F_fast) can be used to determine bioavailability. This fraction is often referred to as the bioaccessible fraction. Following the iceberg analogy (Figure 1, right), F_fast would constitute the upper iceberg fraction rapidly melting by sun irradiation, with a very minimal “visible” surface (representing the desorbed chemical in the aqueous solution, which is quickly removed by biological uptake). The slowly desorbing –or melting- fraction, F_slow, would remain in the sorbed state, within a given time span, having little interactions with the biota.

Figure 1. The magnitude of C_free, determined by the sorption/desorption equilibrium (similarly to a floating iceberg, left), can correspond to a minimal fraction of the total pollutant mass, but may constitute the main driver for bioavailability (and risk) in equilibrium exposure scenarios. In non-equilibrium conditions (right, in analogy, a melting iceberg exposed to irradiation to sun), the fraction of sorbed chemical that can be rapidly mobilized, F_fast, can be taken as an estimate of bioavailability.

Determining bioavailability with passive sampling methods

C_free can be determined with a passive sampler, in the form of polymer-coated fibers or sheets (membranes) made of a variety of polymers, which establish an additional sorption equilibrium with the aqueous phase in contact with the soil or sediment (Jonker et al., 2018). Depending on the analytes of interest, different polymers, such as polydimethylsiloxane (PDMS) or polyethylene (PE), are used in passive samplers. The passive sampler, enriched in the analyte (similarly to the floating iceberg in Figure 1, left, where C_sorbed in this case is the concentration in the passive sampler), can be used in this way to determine indirectly the pollutant concentration present in the aqueous phase, even at very low concentrations, though the appropriate distribution ratio between sampler and water. In bioavailability estimations, passive sampling is designed for equilibrium and non-depletive conditions. This means that the amount of chemical sampled does not alter the solid-water equilibrium, i.e., it is essential that C_free is not affected significantly by the sampler. Equilibrium achievement is critical, and it may take days or weeks.

C_free can be calculated from the concentration of the pollutant in the passive sample polymer at equilibrium (C_p), and the polymer-to-water partitioning coefficient (K_pw):

\(C_{free} = {C_p\over K_{pw}} \)

C_free values can be the basis of predictions for bioaccumulation that use the equilibrium partitioning approach, either directly or through a bioconcentration factor, and for sediment toxicity in conjunction with actual toxicity tests. Passive sampling methods are well suited for contaminated sediments, and they have already been implemented in regulatory environmental assessments based on bioavailability (Burkhard et al., 2017).

Determining bioavailability with desorption extraction methods

The determination of F_fast can be achieved with the use of methods that trap the desorbed chemical once it appears in the aqueous phase. Far from equilibrium conditions, desorption is driven to its maximum rate by placing a material in the aqueous phase that acts as an infinite sink (comparable to the sun irradiation of a melting iceberg in Figure 1, right). The most accepted materials for these desorption extraction methods are Tenax, a sorptive resin, and cyclodextrin, a solubilizing agent (ISO, 2018). These methods allow a permanent aqueous chemical concentration of almost zero, and therefore, sorption of the chemical back to the soil or sediment can be neglected. Several extraction steps can be used, covering a variable time span, which depends on the environmental sample.

The following first-order, two-compartment kinetic model can be used to analyze desorption extraction data:

\({St\over S0} = F_{fast}\ e^{-k_{fast}\ t} + F_{slow}\ e^{- k_{slow}\ t} \)

In this equation, S_t and S_o (mg) are the soil-sorbed amounts of the chemical at time t (h) and at the start of the experiment, respectively. F_fast and F_slow are the fast- and slow-desorbing fractions, and k_fast and k_slow (h^-1) are the rate constants of fast and slow desorption, respectively. To calculate the values of the different constants and fractions (F_fast, F_slow, k_fast, and k_slow) exponential curve fitting can be used. The ln form of the equation can be used to simplify curve fitting.

Once the desorption kinetics are known, the method can be simplified for a series of samples, by using single time point-extractions. A time period of 20 h has been suggested as a sufficient time period to approximate F_fast. It is highly convenient for operational reasons (ISO, 2018), but indicative at best, since the time needed to extract Ffast tends to vary between chemicals and soils/sediments.

References

Burkhard, L.P., Mount, D.,R., Burgess, R.,M. (2017). Developing Sediment Remediation Goals at Superfund Sites Based on Pore Water for the Protection of Benthic Organisms from Direct Toxicity to Nonionic Organic Contaminants EPA/600/R 15/289; U.S. Environmental Protection Agency Office of Research and Development: Washington, DC.

ISO (2018). Technical Committee ISO/TC 190 Soil quality — Environmental availability of non-polar organic compounds — Determination of the potentially bioavailable fraction and the non-bioavailable fraction using a strong adsorbent or complexing agent; International Organization for Standardization: Geneva, Switzerland.

Jonker, M.T.O., van der Heijden, S.A., Adelman, D., Apell, J.N., Burgess, R.M., Choi, Y., Fernandez, L.A., Flavetta, G.M., Ghosh, U., Gschwend, P.M., Hale, S.E., Jalalizadeh, M., Khairy, M., Lampi, M.A., Lao, W., Lohmann, R., Lydy, M.J., Maruya, K.A., Nutile, S.,A., Oen, A.M.P., Rakowska, M.I., Reible, D., Rusina, T.P., Smedes, F., Wu, Y. (2018) Advancing the use of passive sampling in risk assessment and management of sediments contaminated with hydrophobic organic chemicals: results of an international ex situ passive sampling interlaboratory comparison. Environmental Science & Technology 52 (6), 3574-3582.

3.6.3. Assessing available metal concentrations

Authors: Kees van Gestel

Reviewer: Martina Vijver, Steve Lofts

Leaning objectives:

You should be able to:

mention different methods for assessing chemically available metal fractions in soils and sediments.
indicate the relative binding strengths of metals extracted with the different methods or in different steps of a sequential extraction procedure.
explain the pros and cons of chemical extraction methods for assessing metal (bio)availability in soils and sediments.

Keywords: Chemical availability, actual and potential uptake, toxicokinetics, toxicodynamics.

Introduction:

Total concentrations are not very informative about the availability of metals in soils or sediments. Fate and behavior of metals – in general terms mobility – as well as biological uptake and toxicity is highly determined by their speciation. Speciation describes the partitioning of a metal among the various forms in which it may exist (see section on Metal speciation). For assessing the risk of metals to man and the environment, speciation therefore is highly relevant as it may determine their availability for uptake and effects in organisms. Several tools have been developed to determine available metal concentrations or their speciation in soils and sediments. As indicated in the section on Availability and bioavailability, such chemical methods are just indicative, and to a large extent ignore dynamics of availability. Moreover, availability is also influenced by biological processes, with abiotic-biotic interactions influencing the bioavailability process being species- and often even life-stage specific. Nevertheless, chemical extractions may provide useful information to predict or estimate the potential risks of metals and therefore are preferred over the determination of total metal concentrations.

The available methods include:

Porewater extraction
Extractions with water
Extractions with diluted salts
Extractions with chelating agents
Extractions with diluted acids
Sequential extractions using a series of different extraction solutions
Passive sampling methods

Porewater extraction probably best approaches the readily available fraction of metals in soil, which drives mobility and is the fraction of metals experienced directly by many organisms exposed. In general, pore water is extracted from soil or sediment by centrifugation, and filtration over a 0.45 µm (or 0.22 µm) filter to remove larger particles and perhaps some of the dissolved organic matter. Filtration, however, will not remove all complexes, making it impossible to determine solely the dissolved metal fraction in the pore water. Nevertheless, porewater metal concentrations have been shown to have significant correlations with metal uptake (e.g. for copper uptake by barley and tomato by Zhao et al., 2006) and to be useful for predicting toxic threshold concentrations of metals, with correction for pH (e.g. for nickel toxicity to tomato and barley by Rooney et al., 2007).

Extraction with water simulates the immediately available fraction, so the fraction present in the soil solution or pore water. By extracting soil with water, the pore water however, is diluted, which on one hand may facilitate metal analysis by creating larger volumes of solution, but on the other hand may lead to differences between measured and actual metal concentrations in the pore water as it may impact chemical equilibria.

Extraction with diluted salts aims to determine the fraction of metal that is easily available or may become available as it is in the exchangeable form. This refers to cationic metals that may be bound to the negatively charged soil particles (see section on Soil). Buffered salt solutions, for instance 1 M NH₄-acetate at pH 4.8 (with acetic acid) or at pH 7, may under- or overestimate available metal concentrations because of their interference with soil pH. Unbuffered salt solutions therefore are more widely used and may for instance include 0.001 or 0.01 M CaCl₂, 0.1 M NaNO₃ or 1 M NH₄NO₃ (Gupta and Aten, 1993; Novozamsky et al., 1993). Gupta and Aten (1993) showed good correlations between the uptake of some metals in plants and 0.1 M NaNO₃ extractable concentrations in soil, while Novozamsky et al. (1993) found similar well-fitting correlations using 0.01 M CaCl₂. The latter method also seemed well capable of predicting metal uptake in soil invertebrates, and therefore has been more widely accepted for predicting metal availability in soil ecotoxicology. Figure 1 (Zhang et al., 2019) provides an example with the correlation between Pb toxicity to enchytraeid worms in different soils and 0.01 M CaCl₂ extractable concentrations.

Extractions with water (including porewater) and dilute salts are most accurately described as measures of the chemical solubility of the metal in the soil. The values obtained can be useful indicators of the relative metal reactivity across soils, but tend to be less useful for bioavailability assessment, unless the soils under consideration have a narrow range of soil properties. This is because the solutions obtained from such soils themselves have varying chemical properties (e.g. pH, DOC concentration) which are likely to affect the availability of the measured metal to organisms.

Figure 1. Effects of Pb(NO₃)₂ on the reproduction of Enchytraeus crypticus after three weeks exposure in six natural soils. Pb concentrations are expressed as total (A) and 0.01 M CaCl₂ extractable concentrations in soil (B). Lines show the fit of a logistic dose-response curve. When expressed on the basis of 0.01 M CaCl₂ extrable concentrations, dose-response curves did not significantly differ and a single curve is shown. Data taken from Zhang et al. (2019).

Extraction with chelating agents, such as EDTA (0.01-0.05 M) or DTPA (0.005 M) (as their sodium or ammonium salts), aims at assessing the availability of metals for plants. Many plants have the ability to actively affect metal speciation in the soil by producing root exudates. These extractants may form very stable water-soluble complexes with many different polyvalent cationic metals. It should be noted that the large variation in plant species and corresponding physiologies as well as their interactions with symbiotic microorganisms (e.g. mycorrhizal fungi) make that there is no single extraction method is capable of properly predicting metal availability to all plant species.

Extraction with diluted acids has been advocated for predicting the potentially available fraction of metals in soils, so the fraction that may become available in the long run. It is a quite rigorous extraction method that can be executed in a robust way. Metal concentrations determined by extracting soils with 0.43 M HNO₃ showed very good correlation with oral bioaccessible concentrations (Rodrigues et al., 2013), probably because it to some degree simulates metal release under acidic stomach conditions.

Both extraction methods with chelating agents and diluted acid may also dissolve solids, such as carbonates and Fe- and Al-oxides. This raises concerns as to the interpretation of results of these extraction systems, and especially to their generalization to different soil-plant systems (Novozamsky et al., 1993). The extractions with chelating agents and dilute acids are considered methods to estimate the ‘geochemically active’ metal in soil - the pool of adsorbed metal that can participate in solid-solution adsorption/desorption and exchange equilibria on timescales of hours to days. This pool, along with the basic soil properties such as pH etc., also controls the readily available concentrations obtained with water/weak salt/porewater extraction. From the bioavailability point of view, these extractions tend to be most useful as inputs to bioavailability/toxicity models such as that of Lofts et al. (2014), which take further account of the effects of metal speciation and soil chemistry on metal bioavailability to environmental organisms.

Sequential extraction brings together different extraction methods, and aims to determining either how strongly metals are retained or to which components of the solid phase they are bound in soils or sediments. This allows to determine how metals are bound to different fractions within the same soil or sediment, and allows interpretation to the bioavailability dynamics. By far the most widely used method of sequential extraction is the one proposed by Tessier et al. (1979). Five fractions are distinguished, indicating how metals are interacting with soil or sediment components: see Figure 2.

Where the Tessier method aims at assessing the environmental availability of metals in soils and sediments, similar sequential extraction methods have also been developed for assessing the potential availability of metals for humans (bioaccessibility) following gut passage of soil particles (see e.g. Basta and Gradwohl, 2000).

Figure 2. Schematic presentation of the sequential extraction of soil or sediment samples following the method of Tessier et al. (1979). The fractions obtained give an indication of the sites where metals are bound in the soil or sediment, and represent also an increasing binding strength, going from exchangeable to residual. Source: Kees van Gestel.

Passive sampling may also be applied to assess available metal concentrations. The best known method is that of Diffusive Gradients in Thin films (DGT), developed by Zhang et al. (1998). In this method, a resin (Chelex) with high affinity for metals is placed in a device and covered with a diffusive gel and a 0.45 µm cellulose nitrate membrane (Figure 3). The membrane is brought into contact with the soil. Metals dissolved in the soil solution will diffuse through a membrane and diffusive gel and bind to the resin. Based on the thickness of the membrane and gel and the contact time with the soil, the metal concentration in the pore water can be calculated from the amount of metal accumulated in the resin. The method may be indicative of available metal concentrations in soils and sediments, but can only work effectively when soil is sufficiently moist to guarantee optimal diffusion of metals to the resin. For the same reasons, the method probably is better suited for assessing the availability of metals to plants than to invertebrates, especially for animals that are not in continuous contact with the soil solution.

Figure 3. Device used in the Diffusive Gradients in Thin film (DGT) method for determining available metal concentrations in soil and sediment (adapted from Zhang et al., 1998). The device is placed on the soil or sediment in such a way that the membrane filter makes contact with the porewater. Metals may diffuse from the porewater to the resin layer. See text for further explanation.

Several of the above described methods have been adopted by the International Standardization Organization (ISO) in (draft) standardized test guidelines for assessing available metal fractions in soils, sediments and waste materials, e.g. to assess the potential for leaching to groundwater or their potential bioaccessibility. This includes e.g. ISO/TS 21268-1 (2007) “Soil quality - Leaching procedures for subsequent chemical and ecotoxicological testing of soil and soil materials - Part 1: Batch test using a liquid to solid ratio of 2 l/kg dry matter”, ISO 19730 (2008) “Soil quality -Extraction of trace elements from soil using ammonium nitrate solution” and ISO 17586 (2016) “Soil quality -- Extraction of trace elements using dilute nitric acid”.

References:

Basta, N., Gradwohl, R. (2000). Estimation of Cd, Pb, and Zn bioavailability in smelter-contaminated soils by a sequential extraction procedure. Journal of Soil Contamination 9, 149-164.

Gupta, S.K., Aten, C. (1993). Comparison and evaluation of extraction media and their suitability in a simple model to predict the biological relevance of heavy metal concentrations in contaminated soils. International Journal of Environmental Analytical Chemistry 51, 25-46.

Lofts, S., Spurgeon, D.J., Svendsen, C., Tipping, E. (2004). Deriving soil critical limits for Cu, Zn, Cd, and Pb: A method based on free ion concentrations. Environmental Science and Technology 38, 3623-3631.

Novozamsky, I., Lexmond, Th.M., Houba, V.J.G. (1993). A single extraction procedure of soil for evaluation of uptake of some heavy metals by plants. International Journal of Environmental Analytical Chemistry 51, 47-58.

Rodrigues, S.M., Cruz, N., Coelho, C., Henriques, B., Carvalho, L., Duarte, A.C., Pereira, E., Römkens, P.F. (2013). Risk assessment for Cd, Cu, Pb and Zn in urban soils: chemical availability as the central concept. Environmental Pollution 183, 234-242.

Rooney, C.P., Zhao, F.-J., McGrath, S.P. (2007). Phytotoxicity of nickel in a range of European soils: Influence of soil properties, Ni solubility and speciation. Environmental Pollution 145, 596-605.

Tessier, A., Campbell, P.G.C., Bisson, M. (1979). Sequential extraction procedure for the speciation of particulate trace metals. Analytical Chemistry 51, 844-851.

Zhang, H., Davison, W., Knight, B., McGrath, S. (1998). In situ measurements of solution concentrations and fluxes of trace metals in soils using DGT. Environmental Science and Technology 32, 704-710.

Zhang, L., Verweij, R.A., Van Gestel, C.A.M. (2019). Effect of soil properties on Pb bioavailability and toxicity to the soil invertebrate Enchytraeus crypticus. Chemosphere 217, 9-17.

Zhao, F.J., Rooney, C.P., Zhang, H., McGrath, S.P. (2006). Comparison of soil solution speciation and diffusive gradients in thin-films measurement as an indicator of copper bioavailability to plants. Environmental Toxicology and Chemistry 25, 733-742.

3.7. Degradation

3.7.1. Chemical and photochemical degradation processes

Authors: John Parsons

Reviewers: Steven Droge, Kristopher McNeill

Leaning objectives:

You should be able to:

understand the role of chemical and photochemical reactions in the removal of organic chemicals from the environment
understand the most important chemical and photochemical reactions in the environment
understand the role of direct and indirect photodegradation

Keywords: Environmental degradation reactions, Hydrolysis, Reduction, Dehalogenation, Oxidation, Photodegradation

Introduction

Transformation of organic chemicals in the environment can occur by a variety of reactions. These may be purely chemical reactions, such as hydrolyses or redox reactions, photochemical reactions with the direct or indirect involvement of light, or biochemical reactions. Such transformations can change the biological activity (toxicity) of a molecule; it can change the physico-chemical properties and thus change its environmental partitioning processes; it can change its bioavailability, for example facilitating biodegradation; or it may contribute to the complete removal (mineralization) of the chemical from the environment. In many cases, chemicals may be removed by combinations of these different processes and it is sometimes difficult to unequivocally identify the contributions of the different mechanisms. Indeed, combinations of different mechanisms are sometimes important, for example in cases where microbial activity is responsible for creating conditions that favour chemical reactions. Here we will focus on two types of reactions: Abiotic (dark) reactions and photochemical reactions. Biodegradation reactions are covered elsewhere (see section on Biodegradation).

Chemical degradation

Hydrolytic reactions are important chemical reactions removing organic contaminants and are particularly important for chemicals containing acid derivatives as functional groups. Common examples of such chemicals are pesticides of the organophosphate and carbamate classes such as parathion, diazinon, aldicarb and carbaryl. Organophosphate chemicals are also used as flame retardants and are widely distributed in the environment. Some examples of hydrolysis reactions are shown in Figure 1.

Figure 1 Examples of hydrolyses of esters and carbamates (redrawn after Van Leeuwen and Vermeire, 2007).

As the name suggests, hydrolysis reactions involve using water (hydro-) to break (-lysis) a bond. Hydrolyses are reactions with water to produce an acid and either an alcohol or amine as products. Hydrolyses can be catalysed by either OH^- or H⁺ ions and their rates are therefore pH dependent. Some examples of pH-dependent ester hydrolysis reactions are shown in Figure 2.

Halogenated organic molecules may also be hydrolysed to form alcohols (releasing the halogen as a halide ion). The rates of these reactions vary strongly depending on the structure of the organohalogen molecule and the halogen substituent (with Br and I being substituted more rapidly than Cl, and much more rapidly than F) and in general the rates of these reactions are too slow to be of more than minor importance except for tertiary organohalogens and secondary organohalogens with Br and I (Schwarzenbach et al. 2017).

Figure 2. Examples of pH dependent ester hydrolysis reactions (Schwarzenbach et al. 2017). Note that the y-axis is half-life (on a logarithmic scale), meaning high values correspond to slow reactions. Redrawn by Wilma Ijzerman.

In some cases, other substitution reactions not involving water as reactant may be important. Some examples include Cl^– in seawater converting CH₃I to CH₃Cl and reaction of thiols with alkyl bromines in anaerobic groundwater and sediment porewater under sulfate-reducing conditions (Schwarzenbach et al. 2017)

Redox (reduction and oxidation) reactions are another important reaction class involved in the degradation of organic chemicals. In the presence of oxygen, the oxidation of organic chemicals is thermodynamically favourable but occurs at insignificant rates unless oxygen is activated in the form of oxygen radicals or peroxides (following light absorption for example, see below) or if the reaction is catalysed by transition metals or transition metal-containing enzymes (see the sections on Biodegradation and Xenobiotic metabolism and defence).

Reduction reactions are important redox reactions for environmental contaminants in anaerobic environments such as sediment and groundwater aquifers. Under these conditions, organic chemicals containing reducible functional groups such as carboxylic acids and nitro groups undergo reduction reactions (Table 1).

Table 1: Examples of chemical redox reactions that may occur in the environment (adapted from Schwarzenbach et al. 2017)

Organohalogens may also undergo reductions reactions with hydrogen where halogen substituents are replaced by hydrogen. These reactions are referred to as reductive dehalogenations and electron donors in these reaction can be inorganic oxidation reactions (such as the oxidation of Fe(II) minerals) or biochemical oxidation of organic chemicals. In fact, biological processes are also involved indirectly as the environmental redox conditions which determine which redox reactions can take place are in turn determined by microbial activity. Natural organic matter is often involved in environmental redox reactions as a catalyst enhancing electron transfer (Schwarzenbach et al. 2017). As an example, Figure 3 shows reductive dehalogenation reactions of hexachlorobenzene.

Figure 3. Reductive dehalogenation of hexachlorobenzene to less hydrophobic dechlorinated products (redrawn after Van Leeuwen and Vermeire, 2007).

Photodegradation

Sunlight is an important source of energy to initiate chemical reactions and photochemical reactions are particularly important in the atmosphere. Aromatic compounds and other chemicals containing unsaturated bonds that are able to absorb light in the frequency range available in sunlight become exited (energized) and this can lead to chemical reactions. These reactions lead to cleavage of bonds between carbon atoms and other atoms such as halogens to produce radical species. These radicals are highly reactive and react further to remove hydrogen or OH radicals from water to produce C-H or C-OH bonds or may react with themselves to produce larger molecules. Well known examples of atmospheric photochemical stratospheric reactions of CFCs that have had a negative impact on the so-called ozone layer and photochemical oxidations of hydrocarbons that are involved in the generation of smog.

In the aquatic environment, light penetration is sufficient to lead to photochemical reactions of organic chemicals at the water surface or in the top layer of clear water. The presence of particles in a waterbody reduces light intensity through light scattering as does dissolved organic matter through light absorption. Photodegradation contributes significantly to removing oil spills and appears to favour the degradation of longer chain alkanes compared to the preferential attack of linear and small alkanes by biodegradation (Garrett et al., 1998). Cycloalkanes and aromatic hydrocarbons are also removed by photodegradation (D’Auria et al., 2009). There is comparatively little known about the role photodegradation of other organic pollutants in the marine environment although there is, for example, evidence that triclosan is removed by photolysis in the German Bight area of the North Sea (Xie et al., 2008). In the soil environment, there is some evidence that photodegradation may contribute to the removal of a variety of organic chemicals such as pesticides and chemicals present in sewage sludge that is used as a soil amendment but the significance of this process is unclear. Similarly, chemicals that have accumulated in ice, for example as a result of long range transport to polar regions, also seem to be susceptible to photodegradation. Some examples of photodegradation reactions are shown in Figure 4.

Figure 4. Some examples of photodegradation reactions (redrawn after Van Leeuwen and Vermeire, 2007). (Steven Droge 2019)

An important category of photochemical reactions are indirect reactions in which organic chemicals react with photochemically produced radicals, in particular with reactive oxygen species such as OH radicals, ozone and singlet oxygen. These reactive species are present at very low concentrations but are so reactive that under certain conditions they can contribute significantly to the removal of organic chemicals. Products of these reactions are a variety of oxidized derivatives which are themselves radicals and therefore react further. OH radicals are the most important of these photochemically produced species and can react with organic chemicals by removing hydrogen radicals, reacting with unsaturated bonds in alkenes, aromatics etc. to produce hydroxylated products. In water, natural organic matter absorbs light and can participate in indirect photodegradation reactions. Other constituents in surface water, such as nitrogen oxides and iron complexes may also be involved in indirect photodegradation reactions.

References

Schwarzenbach, R.P., Gschwend, P.M., Imboden, D.M. (2017). Environmental Organic Chemistry, Third Edition, Wiley, ISBN 978-1-118-76723-8

van Leeuwen, C.J., Vermeire, T.G. (2007). Risk Assessment of Chemicals: An Introduction (2nd ed.), Springer, ISBN 978-1-4020-6101-1

3.7.2. Biodegradation

Author: John Parsons

Reviewers: Steven Droge, Russell Davenport

Leaning objectives:

You should be able to:

the contribution of biochemical reactions in removing chemicals from the environment
explain the differences between biotransformation, primary biodegradation and mineralization
describe the most important biodegradation reactions under aerobic and anaerobic conditions

Keywords: Primary biodegradation, mineralisation, readily biodegradable chemicals, persistent chemicals, oxygenation reactions, reductions reactions

Introduction:

Biodegradation and biotransformation both refer to degradation reactions that are catalyzed by enzymes. In general, biodegradation is usually used to describe the degradation carried out by microorganisms and biotransformation often refers to reactions that follow the uptake of chemicals by higher organisms. This distinction is important and arises from the role that bacteria and other microorganisms play in natural biogeochemical cycles. As a result, microorganisms have the capacity to degrade most (perhaps all) naturally occurring organic chemicals in organic matter and convert them to inorganic end products. These reactions supply the microorganisms with the nutrients and energy they need to grow. This broad degradative capacity means that they are able to degrade many anthropogenic chemicals and potentially convert them to inorganic end products, a process referred to as mineralisation.

Although higher organisms are also able to degrade (metabolise) many anthropogenic chemicals, these chemicals are not taken up as source of nutrients and energy. Many anthropogenic chemicals can disturb cell functioning processes, and the biotransformation process has been proposed as a detoxification mechanism. Undesirable chemicals that may accumulate to potentially harmful levels are converted to products that are more rapidly excreted. In most cases, a polar and/or ionizable unit is attached to the chemical in one or two steps, making the compound more soluble in blood and more readily removed via the kidneys to the urine. This also renders most hazardous chemicals less toxic than the original chemical. Such biotransformation steps always costs energy (ATP, or through the use of e.g. NADH or NADPH in the enzymatic reactions) from the organism. Biotransformation is sometimes also used to describe degradation by microorganisms when this is limited to a conversion of a chemical into a new product.

Biodegradation is for many organic contaminants the major process that removes them from the environment. Measuring the rates of biodegradation therefore is a prominent aspect of chemical risk assessment. Internationally recognized standardised protocols have been developed to measure biodegradation rates of chemicals. Well know examples of these are the OCED Guidelines. These guidelines include screening tests designed to identify chemicals can be regarded as readily (i.e. rapidly) biodegradable as well as more complex tests to measure biodegradation rates of chemicals that degrade slowly in a variety of simulated environments. For more complex mechanistic studies, microorganisms able to degrade specific chemicals are isolated from environmental samples and cultivated in laboratory systems.

In principle, biodegradation of a chemical can be determined by either following the concentration of the chemical during the test or by following the conversion to end products (in most cases by either measuring oxygen consumption or CO₂ production). Although measuring the concentration gives the most directly relevant information on a chemical, it requires the availability or development of analytical methods which is not always within the capability of routine testing laboratories. Measuring the conversion to CO₂ is comparatively straightforward but the production of CO₂ from other chemicals present in the test system (such as soil or dissolved organic matter) should be accounted for. This can be done by using ¹⁴C-labelled chemicals in the tests but not all laboratories have facilities for this. The main advantage of this approach is that demonstration of quantitative conversion of a chemical to CO₂ etc. means that there is no concern about the accumulation of potentially toxic metabolites.

Since it is an enzymatically catalysed process, the rates of biodegradation should be modelled using the Michaelis Menten kinetics, or Monod kinetics if growth of the microorganisms is taken into account. In practice, however, first order kinetics are often used to model biodegradation in the absence of significant growth of the degrading microorganisms. This is more convenient that using Michaelis Menten kinetics but there is some justification for this simplification since the concentrations of chemicals in the environment are in general much lower than the half saturation concentrations of the degrading enzymes.

Table 1. Influence of molecular structure on the biodegradability of chemicals in the aerobic environment.

Type of compounds or substituents	More biodegradable	Less biodegradable
hydrocarbons	linear alkanes < C₁₂	linear alkanes > C₁₂
	alkanes with not too high molecular weight	high molecular weight alkanes
	linear chain	branched chain
	-C-C-C-	-C-O-C-
	aliphatic	aromatic
aliphatic chlorine	Cl more than 6 carbons from terminal C	Cl at less than 6 carbons from terminal C
Substituents to an aromatic ring	-OH	-F
	-CO₂H	-Cl
	-NH₂	-NO₂
	-OCH₃	-CF₃

Whether expressed as terms of first order kinetics or Michaelis Menten parameters, rates of biodegradation vary widely for different chemicals showing that chemical structure has a large impact on biodegradation. Large variations in biodegradation rates are however often observed for the same chemical in different experimental systems. This shows that environmental properties and conditions also play a key role in determining removal by biodegradation and it is often almost impossible to distinguish the effects of chemical properties from those of environmental properties. In other words, there is no such thing as an intrinsic biodegradation rate of a chemical. Nevertheless, we can derive some generic relationships between the structure and biodegradability of chemicals, as listed in Table 1. Examples are that branched hydrocarbon structures are degraded more slowly than linear hydrocarbon structures, and cyclic and in particular aromatic chemicals are degraded more slowly than aliphatic (non-aromatic) chemicals. Substituents and functional groups also have a major impact on biodegradability with halogens and other electron withdrawing substituents having strongly negative effects. It is therefore no surprise than the list of persistent organic pollutants is dominated by organohalogen compounds and in particular those with aromatic or alicyclic structures.

It should be recognized that biodegradation rates have often been observed to change over time. Long term exposure of microbial communities to new chemicals has often been observed to lead to increasing biodegradation rates. This phenomenon is called adaptation or acclimation and is often the case following repeated application of a pesticide at the same location. An example is shown for atrazine in Figure 2 where degradation rates increase following longer exposure to the pesticide.

Figure 1. Effect of chlorination on (aerobic) biodegradation rates. Adapted from Janssen et al. (2005) by Steven Droge.

Figure 2. Comparison of the atrazine removal rates with (days 43 and 105) or without the addition of carbon and nitrogen sources (day 274). Redrawn from Zhou et al. (2017) by Wilma IJzerman.

Another recent example is the differences in biodegradation rates of the builder L-GLDA (tetrasodium glutamate diacetate) by activated sludge from different waste water treatment plants in the USA. Sludge from regions where L-GLDA was not or only recently on the market required long lag time before degradation started whereas sludge from regions where L-GLDA –containing products had been available for several months required shorted lag phases.

Figure 3. Biodegradation as a function of time following initial shipment of L-GLDA-containing products. Redrawn from Itrich et al. (2015) by Wilma Ijzerman.

Adaptation can results from i) shifts in composition or abundances of species in a bacterial community, ii) mutations within single populations, iii) horizontal transfer of DNA or iv) genetic recombination events, or combinations of these.

Biodegradation reactions and pathways

Biodegradation of chemicals that we regard as pollutants takes place when these chemicals are incorporated into the metabolism of microorganisms. The reactions involved in biodegradation are therefore similar to those involved in common metabolic reactions, such as hydrolyses, oxidations and reductions. Since the conversion of an organic chemical to CO₂ is an overall oxidation reaction, oxidation reactions involving molecular oxygen are probably the most important reactions. These reactions with oxygen are often the first but essential step in degradation and can be regarded as activation step converting relatively stable molecules to more reactive intermediates. This is particularly important for aromatic chemicals since oxygenation is required to make aromatic rings susceptible to ring cleavage and further degradation. These reactions are catalysed by enzymes called oxygenases of which there are broadly speaking two classes. Monoxygenases are enzymes catalysing reactions in which one oxygen atom of O₂ reacts with an organic molecule to produce a hydroxylated product. Examples of such enzymes are the cytochrome P450 family and are present in all organisms. These enzymes are for example involved in the oxidation of alkanes to carboxylic acids as part of the “beta-oxidation” pathway, which shortens linear alkanoic acids in steps of C₂-units, as shown in Figure 4.

Figure 4. Typical oxidation steps of an alkane to an alkanoic acid, and subsequent beta-oxidation pathway from dodecanoic acid to decanoic acid, involving Coenzym-A. Redrawn from Schwarzenbach et al. (2003) by Steven Droge.

Dioxygenases are enzymes catalysing reactions in which both oxygen atoms of O₂ react with organic chemicals and appear to be unique to microorganisms such as bacteria. Examples of these reactions are shown for benzene in Figure 5. Similar reactions are involved in the degradation of more complex aromatic chemicals such as PAHs and halogenated aromatics.

Figure 5. Examples of bacterial dioxygenation reactions and ring cleavage of toluene and benzene. Redrawn from Van Leeuwen and Vermeire (2007) by Steven Droge.

The absence of oxygen in anaerobic environments (sediments and groundwater) does not preclude oxidation of organic chemicals. Other oxidants present (nitrate, sulphate, Fe(III) etc) may be present in sufficiently high concentrations to act as oxidants and terminal electron acceptors supporting microbial growth. In the absence of oxygen, activation relies on other reactions, the most important reactions seem to be carboxylation or addition of fumarate. Figure 6 shows an example of the degradation of naphthalene to CO₂ in sediment microcosms under sulphate-reducing conditions.

Figure 6. Proposed anaerobic oxidation pathway of naphthalene (redrawn from Kleeman and Merckenstock 2017). This involves two initial reaction mechanisms: carboxylation to naphthoic acid, methylation to 2-methylnaphthalene. Addition of fumarate (process b) could follow methylation and produce another way of transforming to 2-naphthoic acid. In both cases, 2-naphthoic acid is oxidized to CO₂.

Other important reactions in anaerobic ecosystems (sediments and groundwater plumes) are reductions. This affects functional groups, for example reduction of acids to aldehydes to alcohols, nitro groups to amino groups and, particularly important, substitution of halogens by hydrogen. The latter reactions can contribute to the conversion of highly chlorinated chemicals, that are resistant to oxidative biodegradation, to less chlorinated products which are more amenable to aerobic biodegradation. Many examples of these reductive dehalogenation reactions have been shown to occur in, for example, tetrachloroethene-contaminated groundwater (e.g. from dry-cleaning processes) and PCB-contaminated sediment. These reactions are exothermic under anaerobic conditions and some microorganisms are able to harvest this energy to support their growth. This can be considered to be a form of respiration based on dechlorination and is sometimes referred to as chlororespiration.

As is the case for abiotic degradation, hydrolyses are also important reactions in biodegradation pathways, particularly for chemicals that are derivatives of organic acids, such as carbamate, ester and organophosphate pesticides where hydrolyses are often the first step in their biodegradation. These reactions are similar to those described in the section on Chemical degradation.

References

Itrich, N.R., McDonough, K.M., van Ginkel, C.G., Bisinger, E.C., LePage, J.N., Schaefer, E.C., Menzies, J.Z., Casteel, K.D., Federle, T.W. (2015). Widespread microbial adaptation to L-glutamate-N,N,-diacetate (L-GLDA) following its market introduction in a consumer cleaning product. Environmental Science & Technology 49, 13314-13321.

Janssen, D. B., Dinkla, I. J. T., Poelarends, G. J., Terpstra, P. (2005). Bacterial degradation of xenobiotic compounds: evolution and distribution of novel enzyme activities, Environmental Microbiology 7, 1868-1882.

Kleemann, R., Meckenstock, R.U. (2017). Anaerobic naphthalene degradation by Gram-positive, iron-reducing bacteria. FEMS Microbial Ecology 78, 488-496.

Schwarzenbach, R.P., Gschwend, P.M., Imboden, D.M. (2017). Environmental Organic Chemistry, Third Edition, Wiley, ISBN 978-1-118-76723-8

Van Leeuwen, C., Vermeire, T.G. (2007). Risk Assessment of Chemicals: An Introduction (2nd ed.), Springer, ISBN 978-1-4020-6101-1

Zhou, Q., Chen, L. C., Wang, Z., Wang, J., Ni, S., Qiu, J., Liu, X., Zhang, X., Chen, X. (2017). Fast atrazine degradation by the mixed cultures enriched from activated sludge and analysis of their microbial community succession. Environmental Science & Pollution Research 24, 22152-22157.

3.7.3. Degradation test methods

Authors: John Parsons

Reviewers: Steven Droge, Russell Davenport

Leaning objectives:

You should be able to:

explain the strategy used in standardised biodegradability testing
describe the most important aspects of standard biodegradability testing protocols
interpret the results of standardised biodegradability tests

Keywords: Environmental fate, chemical degradation, photochemical degradation, biodegradation, mineralisation, degradation rate

Introduction

Many experimental approaches are possible to measure the environmental degradation of chemicals, ranging from highly controlled laboratory experiments to environmental monitoring studies. While each of these approaches has its advantages and disadvantages, a standardised and relatively straightforward set of protocols has clear advantages such as suitability for a wide range of laboratories, broad scientific and regulatory acceptance and comparability for different chemicals.

The system of OECD test guidelines (see links in the reference list of this chapter) is the most important set of standardised protocols although other test systems may be used in other regulatory contexts. As well as tests covering environmental fate processes, they also cover physical-chemical properties, bioaccumulation, toxicity etc. These guidelines have been developed in an international context and are adopted officially after extensive validation and testing in different laboratories. This ensures their wide acceptance and application in different regulatory contexts for chemical hazard and risk assessment.

Chemical degradation tests

The OECD Guidelines include only two tests specific for chemical degradation. This might seem surprising but it should not be forgotten that chemical degradation could also contribute to the removal observed in biodegradability tests. The OECD Guidelines for chemical degradation are OECD Test 111: Hydrolysis as a Function of pH (OECD 2004) and OECD Test 316: Phototransformation of Chemicals in Water – Direct Photolysis (OECD 2008). If desired, sterilised controls may also be used to determine the contribution of chemical degradation in biodegradability tests.

OECD Test 111 measures hydrolytic transformations of chemicals in aquatic systems at pH values normally found in the environment (pH 4 – 9). Sterile aqueous buffer solutions of different pH values (pH 4, 7 and 9) containing radio-labelled or unlabelled test substance (below saturation) are incubated in the dark at constant temperature and analysed after appropriate time intervals for the test substance and for hydrolysis products. The preliminary test is carried out for 5 days at 50°C and pH 4.0, 7.0 and 9.0, this is known as a first tier test. Further second tier tests study the hydrolysis of unstable substances and the identification of hydrolysis products and may extend for 30 days.

OECD Test 316 measures direct photolysis rate constants using xenon arc lamp capable of simulating natural sunlight in the 290 to 800 nm or natural sunlight, and extrapolated to natural water. If estimated losses are superior or equal to 20%, the transformation pathway and the identities, concentrations, and rate of formation and decline of major transformation products are identified.

Biodegradability tests

Biodegradation is in general considered to be the most important removal process for organic chemicals in the environment and it is therefore no surprise that biodegradability testing plays a key role in the assessing the environmental fate and subsequent exposure risks of chemicals. Biodegradation is an extensively researched area but data from standardised tests are favoured for regulatory purposes as they are assumed to yield reproducible and comparable data. Standardised tests have been developed internationally, most importantly under the auspices of the OECD and are part of the wider range of tests to measure physical-chemical, environmental and toxicological properties of chemicals. An overview of these biodegradability tests is given in Table 1.

The way that biodegradability testing is implemented can vary in detail depending on the regulatory context but in general it is based on a tiered approach with all chemicals being subjected to screening tests to identify chemicals that can be considered to be readily biodegradable and therefore removed rapidly from wastewater treatment plants (WWTPs) and the environment in general. These tests were originally developed for surfactants and often use activated sludge from WWTPs as a source of microorganisms since biodegradation during wastewater treatment is a major conduit of chemical emissions to the environment. The so-called ready biodegradability tests are designed to be stringent with low bacterial concentrations and the test chemical as the only potential source of carbon and energy at high concentrations. The assumption is that chemicals that show rapid biodegradation under these unfavourable conditions will always be degraded rapidly under environmental conditions. Biodegradation is determined as conversion to CO₂ (mineralisation), either by directly measuring CO₂ produced, or the consumption of oxygen, or removal of dissolved organic carbon, as this is the most desirable outcome of biodegradation. The results that have to be achieved for a chemical to be considered readily biodegradable vary slightly depending on the test, but as an example in the OECD 301D test (OECD 2014), the consumption of oxygen should reach 70% of that theoretically required for complete mineralisation within 28 days.

Table 1. The OECD biodegradability tests

OECD TEST GUIDELINE	PARAMETER MEASURED	REFERENCE
Ready biodegradability tests
301A: DOC Die-away test	DOC	OECD 1992a
301B: CO₂ evolution test	CO₂	OECD 1992a
301C: Modified MITI(I) test	O₂	OECD 1992a
301D: Closed bottle test	O₂	OECD 1992a
301E: Modified OECD screening test	DOC	OECD 1992a
301F: Manometric respirometry test	O₂	OECD 1992a
306: Biodegradability in seawater	DOC	OECD 1992c
310: Test No. 310: Ready Biodegradability - CO₂ in sealed vessels (Headspace Test).	CO₂	OECD 2014

Inherent biodegradability tests
302A: Modified Semi-continuous Activated Sludge (SCAS) test	DOC	OECD 1981b
302B: Zahn-Wellens test	DOC	OECD 1992b
302C: Modified MITI(II) test	O₂	OECD 2009

Simulation tests
303A: Activated sludge units	DOC	OECD 2001
303B: Biofilms	DOC	OECD 2001
304A: Inherent biodegradability in soil	¹⁴CO₂	OECD 1981a
307: Aerobic and anaerobic transformation in soil	¹⁴CO₂/CO₂	OECD 2002a
308: Aerobic and anaerobic transformation in aquatic sediment systems	¹⁴CO₂/CO₂	OECD 2002b
309: Aerobic mineralization in surface water	¹⁴CO₂/CO₂	OECD 2004b
311: Anaerobic biodegradability of organic compounds in digested sludge: by measurement of gas production	CO₂ and CH₄	OECD 2006
314: Simulation tests to assess the biodegradability of chemicals discharged in wastewater	Concentration of chemical, ¹⁴CO₂/CO₂	OECD 2008a

These test systems are widely applied for regulatory purposes but they do have a number of issues. These include the fact that there are practical difficulties when applied to volatile or poorly soluble chemicals, but probably the most important is that for some chemicals the results can be highly variable. This is usually attributed to the source of the microorganisms used to inoculate the system. For many chemicals, there is a wide variability in how quickly they are degraded by activated sludge from different WWTPs. This is probably the result of different exposure concentrations and exposure periods to the chemicals, and may also be caused by dependence on the ability of small populations of degrading microorganisms, which may not always be included in the sludge samples used in the tests. These issues are not dealt with in any systematic way in biodegradability testing. It has been suggested that a preliminary period of exposure to the chemicals to be tested would allow sludge to adapt to the chemicals and may yield more reproducible test results. Further suggestions include using a higher, more environmentally relevant, concentration of activated sludge as the inoculum.

Failure to comply with the pass criteria in ready biodegradability tests does not necessarily mean that the chemical is persistent in the environment since it is possible that slow biodegradation may occur. These chemicals may therefore be tested further in higher tier tests, for what is referred to as inherent biodegradability in tests performed under more favourable conditions or in simulation tests representing specific compartments, to determine whether biodegradation may contribute significantly to their removal. These tests are also standardised (see Table 1). Simulation tests are designed to represent environmental conditions in specific compartments, such as redox potential, pH, temperature, microbial community, concentration of test substance and occurrence and concentration of other substrates.

The criteria used in classifying the biodegradability of chemicals depend on the regulatory context. Biodegradability tests can be used for different purposes: in the EU this includes 3 distinct purposes; classification and labelling, hazard/persistent assessment, and environmental risk assessment Recently regulatory emphasis has shifted to identifying hazardous chemicals, and therefore those chemicals that are less biodegradable and likely to persist in the environment. Examples for the classification as PBT (persistent, bioaccumulative and toxic) or vPvB (very persistent and very bioaccumulative) chemicals are shown in Table 2. As well as the results of standardised tests, other data such as the results of environmental monitoring data or studies on the microbiology of biodegradation can also be taken into account in evaluations of environmental degradation in a so-called weight of evidence approach.

Table 2. Criteria used to classify chemicals as PBT or vPvB (van Leeuwen & Vermeire 2007)

Property	PBT criteria	vPvB criteria
Persistence	T_1/2 >60 days in marine water, or T_1/2 >40 days in fresh/estuarine water, or T_1/2 >180 days in marine sediment, or T_1/2 >120 days in fresh/estuarine sediment, or T_1/2 >120 days in soil.	T_1/2 >60 days in marine, fresh or estuarine water, or T_1/2 >180 days in marine, fresh or estuarine sediment, or T_1/2 >180 days in soil
Bioaccumulation	BCF > 2000 L/kg	BCF > 5000 L/kg
Toxicity	- NOEC < 0.01 mg/L for marine or freshwater organisms, or - substance is classified as carcinogenic, mutagenic, or toxic for reproduction, or - there is other evidence of chronic toxicity, according to Directive 67/548/EEC

The results of biodegradability tests are sometimes also used to derive input data for environmental fate models (see section on Multicompartment modeling). It is however not always straightforward to transfer data measured in what is sometimes a multi-compartment test system into degradation rates in individual compartments as other processes (e.g. partitioning) need to be taken into account.

References

OECD, 1981a. OECD Guidelines for the Testing of Chemicals. Test No. 304A: Inherent Biodegradability in Soil.

OECD, 1981b. OECD Guidelines for the Testing of Chemicals. Test No. 302A: Inherent Biodegradability: Modified SCAS Test.

OECD, 1992a. OECD Guidelines for the Testing of Chemicals. Test No. 301: Ready Biodegradability.

OECD, 1992b. OECD Guidelines for the Testing of Chemicals. Test No. 302B: Inherent Biodegradability: Zahn-Wellens/ EVPA Test.

OECD, 1992c. OECD Guidelines for the Testing of Chemicals. Test No. 306: Biodegradability in Seawater.

OECD, 2001. OECD Guidelines for the Testing of Chemicals. Test No. 303: Simulation Test - Aerobic Sewage Treatment - A: Activated Sludge Units; B: Biofilms.

OECD, 2002a. OECD Guidelines for the Testing of Chemicals. Test No. 307: Aerobic and Anaerobic Transformation in Soil.

OECD, 2002b. OECD Guidelines for the Testing of Chemicals. Test No. 308: Aerobic and Anaerobic Transformation in Aquatic Sediment Systems.

OECD, 2004a. OECD Guidelines for the Testing of Chemicals. Test No. 111: Hydrolysis as a Function of pH.

OECD, 2004b. OECD Guidelines for the Testing of Chemicals. Test No. 309: Aerobic Mineralisation in Surface Water – Simulation Biodegradation Test

OECD, 2006. OECD Guidelines for the Testing of Chemicals. Test No. 311: Anaerobic Biodegradability of Organic Compounds in Digested Sludge: by Measurement of Gas Production.

OECD, 2008a. OECD Guidelines for the Testing of Chemicals. Test No. 314: Simulation Tests to Assess the Biodegradability of Chemicals Discharged in Wastewater

OECD, 2008b. OECD Guidelines for the Testing of Chemicals. Test No. 316: Phototransformation of Chemicals in Water – Direct Photolysis.

OECD, 2009. OECD Guidelines for the Testing of Chemicals. Test No. 302C: Inherent Biodegradability: Modified MITI Test (II).

OECD, 2014. OECD Guidelines for the Testing of Chemicals. Test No. 310: Ready Biodegradability - CO₂ in sealed vessels (Headspace Test).

Van Leeuwen, C.J., Vermeire, T.G. (2007). Risk Assessment of Chemicals: An Introduction (2nd ed.), Springer, ISBN 978-1-4020-6101-1

3.8. Modelling exposure

3.8.1. Multicompartment modeling

Authors: Dik van de Meent and Michael Matthies

Reviewer: John Parsons

Learning objectives:

You should be able to

explain what a mass balance equation is
describe how mass balance equations are used in multimedia fate modeling
explain the concepts of thermodynamic equilibrium and steady state

give some examples of the use of multimedia mass balance modeling

Keywords: mass balance equation, environmental fate model

The mass balance equation

Multicompartment (or multimedia) mass balance modeling starts from the universal conservation principle, formulated as balance equation. The governing principle is that the rate of change (of any entity, in any system) equals the difference between the sum of all inputs (of that entity) to the system and the sum of all outputs from it. Environmental modelers use the balance equation to predict exposure concentrations of chemicals in the environment by deduction from knowledge of the rates of input- and output processes, which can be understood easiest from considering the mass balance equation for one single environmental compartment (Figure 1):

\({dm^{i,j}\over dt} =∑^j input^{i,j} -∑^j output^{i,j} \) (eq. 1)

where dm^i,j/dt represents the change of mass of chemical i in compartment j (kg) over time (s), and input^i,j and output^i,j denote the rates of input and output of chemical to and from compartment j, respectively.

Figure 1. The mass of a chemical in a lake is like the mass of water in a leaking bucket: both can be described with the universal (mass) balance equation, which says differences in inputs and outputs make amounts held up in systems change: \({dm\over dt} = ∑inputs\ -\ ∑outputs\).

One compartment model

In multimedia mass balance modeling, mass balance equations (of the type shown in equation 1) are formulated for each environmental compartment. Outflows of chemical from the compartments are often proportional to the amounts of chemical present in the compartments, while external inputs (emissions) may often be assumed constant. In such cases, i.e. when first-order kinetics apply (see section 3.3 on Environmental fate of chemicals), mass balance equations take the form of equation 1 in section 3.3. For one compartment (e.g. a lake, as in Figure 1) only:

\({dm\over dt} = I-k\ m\) (eq. 2)

in which dm/dt (kg.s^-1) is the rate of change of the mass (kg) of chemical in the lake, I (kg.s^-1) is the (constant) emission rate, and the product k.m (kg.s^-1) denotes the first-order loss rate of the chemical from the lake. It is obvious that eventually a steady state must develop, in which the mass of chemical in the lake reaches a predictable maximum

\({dm\over dt} =I-k\ m =0→m_∞= {I\over k}\) (eq. 2a)

A very intuitive result: mass (thus concentration) at steady state is proportional to the rate of emission (twice the emission yields twice the mass or concentration); steady-state mass is inversely proportional to the rate (constant) of degradation (more readily degrading chemicals reach lower steady-state masses). It can be demonstrated mathematically (not here) that the general (transient) solution of equation 2 exists and can be found (Figure 2):

Figure 2. For one compartment only, in case loss processes obey first-order kinetics and emissions are constant (i.e. not varying with time), the mass of chemical is expected to increase exponentially from its initial mass m₀ to its steady-state level \(I\), which will be reached at ininite time \(t_∞\). After Van de Meent et al. (2011).

\(m_t=m_0\ e^{-k\ t}+{I\over k}\ (1-e^{-k\ t}),\ with\ {I\over k} =m_∞\) (eq. 3)

When the input rate (emission) is constant, i.e. that it does not vary with time, and is independent of the mass of chemical present, the mass of chemical in the systems is expected to increase exponentially, from its initial value at \(t_0\), to a steady level at \(t_∞\). According to equation 3, a final mass level equal to \(I\over k\) is to be expected.

Multi-compartment model

The prefix ‘multi’ indicates that generally (many) more than one environmental compartment is considered. The Unit World (see below) contains air, water, biota, sediment and soil; more advanced global modeling systems may use hundreds of compartments. The case of three compartments (typically one air, one water, one soil) is schematically worked out in Figure 3.

\({dm_1\over dt}=I_1-(k_1+k_{1,2}+k_{1,3})\ m_1+ k_{2,1}\ m_2+ k_{3,1}\ m_3\)

\({dm_2\over dt}=I_2+k_{1,2}\ m_1-(k_2+k_{2,1}+k_{2,3})\ m_2+ k_{3,2}\ m_3\)

\({dm_3\over dt}=I+ k_{1,3}\ m_1+k_{2,3}\ m_2-(k_3+k_{3,1}+k_{3,2})\ .m_3\)

Figure 3. Three-compartment mass balance model. Arrows represent mass flows of chemical to and from compartments. Losses from source compartments (negative signs) become gains to receiving compartments (positive sign). The model consists of three differential mass balance equations, with (nine) known rate constants k_i,j (for flows out of source compartments i, into receiving compartments j, in s^-1), and (three) unknown masses m_i (kg). From Van de Meent et al. (2011), with permission.

Each compartment can receive constant inputs (emissions, imports), and chemical can be exported from each compartment by degradation or advective outflow, as in the one-compartment model. In addition, chemical can be transported between compartments (simultaneous import-export). All mass flows are characterized by (pseudo) first-order rate constants (see section 3.3 on Environmental fate processes). The three mass balance equations eventually balance to zero at infinite time:

\(balance_1=0=I_1-(k_1+k_{1,2}+k_{1,3})\ m_1^*+ k_{2,1}\ m_2^*+ k_{3,1}\ m_3^*\)

\(balance_2=0=I_2+k_{1,2}\ m_1^*-(k_2+k_{2,1}+k_{2,3})\ m_2^*+ k_{3,2}\ m_3^*\)

\(balance_3=0=I_3+ k_{1,3}\ m_1^*+k_{2,3}\ m_2^*-(k_3+k_{3,1}+k_{3,2})\ m_3^*\) (eq. 4)

where the symbols \(m_i^*\) denote mass in compartments i at steady state. Sets of n linear equations with n unknowns can be solved algebraically, by manually manipulating equations 4, until clean expressions for each of the three m_i values are obtained, which inevitably becomes tedious as soon as more than two mass balance equations are to be solved – this did not withhold one of Prof. Mackay’s most famous PhD students from successfully solving a set of 14 equations! An easier way of solving sets of n linear equations with n unknowns is by means of linear algebra. Using linear algebraic vector-matrix calculus, the equations 4 can be rewritten into one linear-algebraic equation:

\({dm ̅ \over dt} =0=I ̅+A\ m ̅\) (eq. 5)

in which in which \(m ̅\) is the vector of masses in the three compartments, \(A\) is the model matrix of known rate constants and \(e ̅\) is the vector of known emission rates:

\(m ̅= \begin{bmatrix} m_1 \\ m_2 \\ m_3 \end{bmatrix},\ I ̅= \begin {bmatrix} I_1 \\ I_2 \\ I_3 \end{bmatrix},\ A= \begin {bmatrix} -(k_1+k_{1,2}+k_{1,3}) & k_{2,1} & k_{3,1} \\ k_{1,2} & -(k_2+k_{2,1}+k_{2,3}) & k_{3,2} \\ k_{1,3} & k_{2,3} & -(k_3+k_{3,1}+k_{3,2} \end{bmatrix}\)

The solution of equation 5 is

\(m ̅^*=-A^{-1}\ I ̅\)

in which \(m ̅^* \) is the vector of masses at steady state and \(A^{-1}\) is the inverse of model matrix \(A\). The linear algebraic method of solving linear mass balance equations is easily carried with spreadsheet software (such as MS Excel, LibreOffice Calc or Google Sheets), which contain built-in array functions for inverting matrices and multiplying them by vectors.

Unit World modeling

In the late 1970s, pioneering environmental scientists at the USEPA Environmental Research Laboratory in Athens GA, recognized that the universal (mass) balance equation, applied to compartments of environmental media (air, water, biota, sediment, soil) could serve as a means to analyze and understand differences in environmental behavior and fate of chemicals. Their ‘evaluative Unit World Modeling’ (Baughman and Lassiter, 1978; Neely and Blau, 1985) was the start of what is now known as multimedia mass balance modeling. The Unit World concept was further developed and polished by Mackay and co-workers (Neely and Mackay, 1982; Mackay and Paterson, 1982; Mackay et al., 1985; Paterson and Mackay, 1985, 1989). In Unit World modeling, the environment is viewed of as a set of well-mixed chemical reactors, each representing one environmental medium (compartment), to and from which chemical flows, driven by ‘departure from equilibrium’ – this is chemical technology jargon for expressing the degree to which thermodynamic equilibrium properties such as ‘chemical potential’ or ‘fugacity’ differ (Figure 4). Mackay and co-workers used fugacity in mass balance modeling as the central state variable. Soon after publication of this ‘fugacity approach’ (Mackay, 1991), the term ‘fugacity model’ became widely used to name all models of the ‘Mackay-type’, which applied ‘Unit World mass balance modeling’, even though most of these models kept using the more traditional chemical mass as a state variable.

Figure 4. Unit World mass balance modeling as described by Mackay and co-workers. The environment is viewed of as a set of well-mixed chemical reactors (A), for which mass balance equations are formulated. Chemical flows from one environmental compartment to another, driven by ‘departure from equilibrium’, until a steady (= not changing) state has been reached. This may be understood by regarding the hydraulic equivalent of chemical mass flow (B). Figure redrawn after Mackay (1991).

Complexity levels

While conceptually simple (environmental fate is like a leaking bucket, in the sense that its steady-state water height is predictable from first-order kinetics), the dynamic character of mass balance modeling is often not so intuitive. The abstract mathematical perspective may best suit explain mass balance modeling, but this may not be practical for all students. In his book about multimedia mass balance modeling, Mackay chose to teach his students the intuitive approach, by means of his famous water tank analogy (Figure 4B).

According to this intuitive approach, mass balance modeling can be done at levels of increasing complexity, where the lowest, simplest, level that serves the purpose should be regarded as the most suitable. The least complex is level I assuming no input and output. A chemical can freely (i.e. without restriction) flow from one environmental compartment to another, until it reaches its state of lowest energy: the state of thermodynamic equilibrium. In this state, the chemical has equal chemical potential and fugacity in all environmental media. The system is at rest; in the hydraulic analogy, water has equal levels in all tanks. This is the lowest level of model complexity, because this model only requires knowledge of a few thermodynamic equilibrium constants, which can be reasoned from basic physical substance properties.

The more complex modeling level III describes an environment in which flow of chemical between compartments experiences flow resistance, so that a steady state of balance between outputs and inputs is reached only at the cost of permanent ‘departure from equilibrium’. Degradation in all compartments and advective flows, e.g. rain fall or wind and water currents, are also considered. The steady state of level III is one in which fugacities of chemical in the compartments are unequal (no thermodynamic equilibrium); in the hydraulic analogy, water in the tanks rest at different heights. Naturally, solving modeling level III requires detailed knowledge of the inputs (into which compartment(s) is the chemical emitted?), the outputs (at what rates is the chemical degraded in the various compartments?) and the transfer resistances (how rapid or slow is the mass transfer between the various compartments?). Level III modelers are rewarded for this by obtaining more realistic model results.

The fourth complex level of multimedia mass balance modeling (level IV, not shown in Figure 4B) produces transient (time dependent) solutions. Model simulations start (t = 0) with zero chemical (m = 0; empty water tanks). Compartments (tanks), fill up gradually until the system comes to a steady state, in which generally one or more compartments depart from equilibrium, as in level III modeling. Level IV is the most realistic representation of environmental fate of chemicals, but requires most detailed knowledge of mass flows and mass transfer resistances. Moreover, time-varying states are least easy to interpret and not always most informative of chemical fate. The most important piece of information to be gained from level IV modeling is the indication of time to steady state: how long does it take to clear the environment from persistent chemicals that are no longer used?

Mackay describes an intermediate level of complexity (level II), in which outputs (degradation, advective outflows) balance inputs (as in level III), and chemical is allowed to freely flow between compartments (as in level I). A steady state develops in level II and there is thermodynamic equilibrium at all times. Modeling at level II does not require knowledge of mass transfer resistances (other than that resistances are negligible!), but degradation and outflow rates increase the model complexity compared to that of level I. In many situations, level II modeling yields surprisingly realistic results.

Use of multimedia mass balance models

Soon after publication of the first use of ‘evaluative Unit World modeling’ (Mackay and Paterson, 1982), specific applications of the ‘Mackay approach’ to multimedia mass balance modeling started to appear. The Mackay group published several models for the evaluation of chemicals in Canada, of which ChemCAN (Mackay et al., 1995) is known best. Even before ChemCAN, the Californian model CalTOX (Mckone, 1993) and the Dutch model SimpleBox (Van de Meent, 1993) came out, followed by publication of the model HAZCHEM by the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC, 1994) and the German Umwelt Bundesamt’s model ELPOS (Beyer and Matthies, 2002). Essentially, all these models serve the very same purpose as the original Unit World model, namely providing standardized modeling platforms for evaluating the possible environmental risks from societal use of chemical substances.

Multimedia mass balance models became essential tools in regulatory environmental decision making about chemical substances. In Europe, chemical substances can be registered for marketing under the REACH regulation only when it is demonstrated that the chemical can be used safely. Multimedia mass balance modeling with SimpleBox (Hollander et al., 2014) and SimpleTreat (Struijs et al, 2016) plays an important role in registration.

While early multimedia mass balance models all followed in the footsteps of Mackay’s Unit World concept (taking the steady-state approach and using one compartment per environmental medium), later models became larger and spatially and temporally explicit, and were used for in-depth analysis of chemical fate.

In the late 1990s, Wania and co-workers developed a Global Distribution Model for Persistent Organic Pollutants (GloboPOP). They used their global multimedia mass balance model to explore the so-called cold condensation effect, by which they explained the occurrence of relatively large amounts of persistent organic chemicals in the Arctic, where no one had ever used them (Wania, 1999). Scheringer and co-workers used their CliMoChem model to investigate long-range transport of persistent chemicals into Alpine regions (Scheringer, 1996; Wegmann et al., 2005). MacLeod and co-workers (Toose et al., 2004) constructed a global multimedia mass balance model (BETR World) to study long-range, global transport of pollutants.

References

Baughman, G.L., Lassiter, R. (1978). Predictions of environmental pollutant concentrations. In: Estimating the Hazard of Chemical Substances to Aquatic Life. ASTM STP 657, pp. 35-54.

Beyer, A., Matthies, M. (2002). Criteria for Atmospheric Long-range Transport Potential and Persistence of Pesticides and Industrial Chemicals. Umweltbundesamt Berichte 7/2002, E. Schmidt-Verlag, Berlin. ISBN 3-503-06685-3.

ECETOC (1994). HAZCHEM, A mathematical Model for Use in Risk Assessment of Substances. European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels.

Hollander, A., Schoorl, M., Van de Meent, D. (2016). SimpleBox 4.0: Improving the model, while keeping it simple... Chemosphere 148, 99-107.

Mackay, D. (1991). Multimedia Environmental Fate Models: The Fugacity Approach. Lewis Publishers, Chelsea, MI.

Mackay, D., Paterson, S. (1982). Calculating fugacity. Environmental Science and Technology 16, 274-278.

Mackay, D., Paterson, S., Cheung, B., Neely, W.B. (1985). Evaluating the environmental behaviour of chemicals with a level III fugacity model. Chemosphere 14, 335-374.

Mackay, D., Paterson, S., Tam, D.D., Di Guardo, A., Kane, D. (1995). ChemCAN: A regional Level III fugacity model for assessing chemical fate in Canada. Environmental Toxicology and Chemistry 15, 1638–1648.

McKone, T.E. (1993). CALTOX, A Multi-media Total-Exposure Model for Hazardous Waste sites. Lawrence Livermore National Laboratory. Livermore, CA.

Neely, W.B., Blau, G.E. (1985). Introduction to Exposure from Chemicals. In: Neely, W.B., Blau, G.E. (Eds). Environmental Exposure from Chemicals Volume I, CRC Press, Boca Raton, FL., pp 1-10.

Neely, W.B., Mackay, D. (1982). Evaluative model for estimating environmental fate. In: Modeling the Fate of Chemicals in the Aquatic Environment. Ann Arbor Science, Ann Arbor, MI, pp. 127-144.

Paterson, S. (1985). Equilibrium models for the initial integration of physical and chemical properties. In: Neely, W.B., Blau, G.E. (Eds). Environmental Exposure from Chemicals Volume I, CRC Press, Boca Raton, FL., pp 218-231.

Paterson, S., Mackay, D. (1989). A model illustrating the environmental fate, exposure and human uptake of persistent organic chemicals. Ecological Modelling 47, 85-114.

Scheringer, M. (1996). Persistence and spatial range as endpoints of an exposure-based assessment of organic chemicals. Environmental Science and Technology 30, 1652-1659.

Struijs, J., Van de Meent, D., Schowanek, D., Buchholz, H., Patoux, R., Wolf, T., Austin, T., Tolls, J., Van Leeuwen, K., Galay-Burgos, M. (2016). Adapting SimpleTreat for simulating behaviour of chemical substances during industrial sewage treatment. Chemosphere 159:619-627.

Toose, L., Woodfine, D.G., MacLeod, M., Mackay, D., Gouin, J. (2004). BETR-World: a geographically explicit model of chemical fate: application to transport of alpha-HCH to the Arctic Environmental Pollution 128, 223-40.

Van de Meent D. (1993). SimpleBox: A Generic Multi-media Fate Evaluation Model. National Institute for Public Health and the Environment. RIVM Report 672720 001. Bilthoven, NL.

Van de Meent, D., McKone, T.E., Parkerton, T., Matthies, M., Scheringer, M., Wania, F., Purdy, R., Bennett, D. (2000). Persistence and transport potential of chemicals in a multimedia environment. In: Klecka, g. et al. (Eds.) Evaluation of Persistence and Long-Range Transport Potential of Organic Chemicals in the Environment. SETAC Press, Pensacola FL, Chapter 5, pp. 169–204.

Van de Meent, D., Hollander, A., Peijnenburg, W., Breure, T. (2011). Fate and transport of contaminants. In: Sánches-Bayo, F., Van den Brink, P.J., Mann, R.M. (eds.),Ecological Impacts of Toxic Chemicals, Bentham Science Publishers, pp. 13-42.

Wania, F. (1999). On the origin of elevated levels of persistent chemicals in the environment. Environmental Science and Pollution Research 6, 11–19.

Wegmann, F., Scheringer, M., Hungerbühler, K. (2005). First investigations of mountainous cold condensation effects with the CliMoChem model. Ecotoxicology and Environmental Safety 63, 42-51.

3.8.2. Metal speciation models

Authors: Wilko Verweij

Reviewers: John Parsons, Stephen Lofts

Learning objectives:

You should be able to

Understand the basics of speciation modeling
Understand the factors determining speciation and how to calculate them
Understand in which types of situations speciation modeling can be helpful

Keywords: speciation modeling, solubility, organic complexation

Introduction

Speciation models allow users to calculate the speciation of a solution rather than to measure it in a chemical way or to assess it indirectly using bioassays (see section 3.5). As a rule, speciation models take total concentrations as input and calculate species concentrations.

Speciation models use thermodynamic data about chemical equilibria to calculate the speciation. This data, expressed in free energy or as equilibrium constants, can be found in the literature. The term ‘constant’ is slightly misleading as equilibrium constants depend on the temperature and ionic strength of the solution. The ionic strength is calculated from the concentrations (C) and charges (Z) of ions in solution using the equation:

\(1 = {1\over 2} \displaystyle\sum_{i=1}^{n} C_iZ_i^2\)

For many equilibria, no information is available to correct for temperature. To correct for ionic strength, many semi-empirical methods are available, none of which is perfect.

How these models work

For each equilibrium reaction, an equilibrium constant can be defined. For example, for the reaction

Cu²⁺ + 4 Cl^- ⇌ CuCl₄^2-

the equilibrium constant can be defined as

\(β = {{[CuCl_4^{2-}]}\over {[Cu^{2+}] * [Cl^-]^4}}\)

Consequently, when the concentrations of free Cu²⁺ and free Cl^- are known, the concentration of CuCl₄^2- can be easily calculated as:

[CuCl₄^2-] = β * [Cu²⁺] * [Cl^-]⁴

In fact, the concentrations of free Cu²⁺ and free Cl^- are often NOT known, but what is known are the total concentrations of Cu and Cl in the system. In order to find the speciation, we need to set up a set of mass balance equations needs to be set up, for example:

[total Cu] = [free Cu²⁺] + [CuOH⁺] + [Cu(OH)₂] + [Cu(OH)₃^-] (..) + [CuCl⁺] + [CuCl₂] (..) etc.

[total Cl] = (..)

Each concentration of a complex is a function of the free concentrations of the ions that make it up. So we can say that if we know the concentrations of all the free ions, we can calculate the concentrations of all the complexes, and then we can calculate the total concentrations. A solution to the problem cannot be found by rearranging the mass balance equations, because they are non-linear. What a speciation model does is to repeatedly estimate the free ion concentrations, on each loop adjusting them so that the calculated total concentrations more closely match the known totals. When the calculated and known total concentrations all agree to within a defined precision, the speciation has been calculated. The critical part of the calculation is adjusting the free ion concentrations in a sensible and efficient way to find the solution as quickly as possible. Several more or less sophisticated methods are available to solve this, but usually a Newton-Raphson method is applied.

Influence of temperature and ionic strength

In fact the explanation above is too simple. Equilbrium constants are valid under specific conditions for temperature and ionic strength (for example the standard conditions of 25^oC and [endif]--> and need to be converted to the temperature and ionic strength of the system for which speciated is being calculated. It is possible to adapt the equilibrium constants for non-standard temperatures, but this requires knowledge of heat capacity (ΔH) data of each equilibrium. That knowledge is often not available. Constants can be converted from 25°C to other temperatures using the Van ‘t Hoff-equation:

\(ln({K_2\over K_1})=-{∆H\over R} ({1\over T_2} - {1\over T_1})\)

where K₁ and K₂ are the constants, T₁ and T₂ the temperatures, ΔH is the enthalpy of a reaction and R is the gas constant.

Equilbrium constants are also valid for one specific value of ionic strength. For conversion from one value of ionic strength to another, many different approaches may be used. This conversion is quite important, because already at relatively low ionic strengths, deviations from ideality become significant, and the activity of a species starts to deviate from its concentration. Hence, the intrinsic, or thermodynamic, equilibrium constants (i.e. constants at a hypothetical ionic strength of zero) are no longer valid and the activity a of ions at non-zero ionic strength needs to be calculated from the concentration and the activity coefficient:

a = γ * c

where γ is the activity coefficient (dimensionless; sometimes also called f) and c is the concentration; a and c are in mol/liter.

The first solution to calculate activity coefficients for non-zero ionic strength was proposed by Debye and Hückel in 1923. The Debye-Hückel theory assumes ions are point charges so it does not take into account the volume that these ions occupy nor the volume of the shell of ligands and/or water molecules around them. The Debye-Hückel gives good approximations, up to circa 0.01 M for a 1:1-electrolyte, but only up to circa 0.001 M for a 2:2-electrolyte. When the ionic strength exceeds these values, the activity coefficients that the Debye-Hückel approximation predicts deviate significantly from experimental values. Many environmental applications require conversions for higher ionic strengths making the Debye-Hückel-equation insufficient. To overcome this problem, many researchers have suggested other methods, like the extended Debye-Hückel-equation, the Güntelberg-equation and the Davies-equation, but also the Bromley-equation, the Pitzer-equation and the Specific Ion Interaction Theory (SIT).

Many programs use the Davies-equation, which calculates activity coefficients γ as follows:

\(log⁡\ γ= -0.5079\ z^2\ ({√I\over (1+ √I)} -0.3\ I)\)

where z is the charge of the species and I the ionic strength. Sometimes 0.2 instead of 0.3 is used. Basically all these approaches take the Debye-Hückel-equation as a starting point, and add one or more terms to correct for deviations at higher ionic strengths. Although many of these methods are able to predict the activity of ions fairly well, they are in fact mainly empirical extensions without a solid theoretical basis.

Solubility

Most salts have a limited solubility; in several cases the solubility is also important under conditions that occur in the environment. For instance, for CaCO₃ the solubility product is 10^-8.48, which means that when [Ca²⁺] * [CO₃^2-] > 10^-8.48, CaCO₃ will precipitate, until [Ca²⁺] * [CO₃^2-] = 10^-8.48. But it also works the other way around: if solid CaCO₃ is present in a solution where [Ca²⁺] * [CO₃^2-] < 10^-8.48(note the ‘<’-sign), solid CaCO₃ will dissolve, until [Ca²⁺] * [CO₃^2-] = 10^-8.48. Note that the Ca and CO₃ in the formula here refer to free ions. For example, a 10^-13 M solution of Ag₂S will lead to precipitation of Ag₂S. The free concentrations of Ag and S are 6.5*10^-15 M and 1.8*10^-22 M resp. (which corresponds with the solubility product of 10^-50.12, but the dissolved concentrations of Ag and S are 7.1*10^-15 M and 3.6*10^-15 M resp., so for S seven order of magnitude higher. This is caused by the formation of S-complexes with protons (HS^- and H₂S (aq)) and to a lesser extent with Ag.

Complexation by organic matter

Complexation with Dissolved Organic Carbon (DOC) is different from inorganic complexation or complexation with well-defined compounds such as acetate or NTA. The reasons for that difference are as follows.

DOC is very heterogeneous; DOC isolated at two sites may be very different (not to mention the difficulty of selecting isolation procedures).
Complexation with DOC generally shows a continous range of equilibrium constants, due to chemical and steric differences in neighbouring groups.
Increased cation binding and/or the ionic strength of the solution change electrostatic interactions among the functional groups in DOC-molecules, which influences the equilibrium constants.
In addition, changing electrostatic interactions may cause conformational changes of the molecules.

Among the most popular models to assess organic complexation are Model V (1992), VI (1998) and VII (2011), also known as WHAM, written by Tipping and co-authors (Tipping & Hurley, 1992; Tipping, 1994, 1998; Tipping, Lofts & Sonke, 2011). All these models assume that two types of binding occur: specific binding and accumulation in the diffuse double layer. Specific binding is the formation of a chemical bond between an ion and a functional group (or groups) on the organic molecule. Diffuse double layer accumulation is the accumulation of ions of opposite electrical charge adjacent to the molecule, without formation of a chemical bond (the electrical charge is usually negative, so the ions that accumulate are cations).

For specific binding, all these models distinguish fulvic acids (FA) and humic acids (HA) which are treated separately. These two classes of DOC are typically the most abundant components of natural organic matter in the environment – in surface freshwaters, the fulvic acids are typically the most abundant. For each class, eight different discrete binding sites are used in the model. The sites have a range of acid-base properties. Metals bind to these sites, either to one site alone (monodentate), to two sites (bidentate) or, starting with Model VI, to three (tridentate). A fraction of the sites is allowed to form bidentate complexes. Starting with Model VI, for each bidentate and tridentate group three sub-groups are assumed to be present – this further increases the range of metal binding strengths.

Binding constants depend on ionic strength and electrostatic interactions. Conditional constants are calculated in the same way in Model V, VI and VII, as follows:

\(K(z)=K\ *\ e^{2wZ}\)

where:

Z is the charge of the organic acid (in moles per gram organic matter);
w is calculated by :

\(w=P\ *\ log_{10} (I)\)

where:

P is a constant term (different for FA and HA, and different for each model);
I is the ionic strength.

Therefore, the conditional constant depends on the charge on the organic acids as well as on the ionic strength. For the binding of metals, the calculation of the conditional constant occurs in a similar way.

The diffuse double layer is usually negatively charged, so it is usually populated by cations, in order to maintain electric neutrality. Calculations for the diffuse double layer are the same for Model V, Model VI and Model VII. The volume of the diffuse double layer is calculated separately for each type of acid, as follows:

\(V_D= {10N_{Av}\over M}\ *\ {4π\over 3}\ *\ [(r+ {1\over κ})^3-r^3 ]\)

where:

N_Av is Avogadro's number;
M is the molecular weight of the acid;
r is the radius of the molecule (0.8 nm for fulvic acids, 1.72 for humic acids);
κ is the Debye-Hückel parameter, which is dependent on ionic strength.

Simply applying this formula in situations of low ionic strength and high content of organic acid would lead to artifacts (where volume of diffuse layer can be calculated to be more than 1 liter/liter). Therefore, some "tricks" are implemented to limit the volume of the diffuse double layer to 25% of the total.

In case the acid has a negative charge (as it has in most cases), positive and neutral species are allowed to enter the diffuse double layer, just enough to make the diffuse double layer electrically neutral. When the acid has a positive charge, negative and neutral species are present.

The concentration of species in the diffuse double layer is calculated by assuming that the concentration of that species in the diffuse double layer depends on the concentration in the bulk solution and the charge.

In formula:

\({[X^Z]_{DDL}\over [X^Z]_{solution}} = R^{∣Z∣}\)

where R is calculated iteratively, to ensure the diffuse double layer is electrically neutral.

Applications

Speciation models can be used for many purposes. Basically, two groups of applications can be distinguished. The first group consists of applications meant to understand the chemical behaviour of any system. The second group focuses on bioavailability.

Chemical behaviour; laboratory situations

Speciation models can be helpful in understanding chemical behaviour in either laboratory situations or field situations. For instance, if you want to add EDTA to a solution to prevent metals from precipitation, the choice of the EDTA-substance also determines the pH of the final solution. Figure 1 shows the pH of a 1 mM solution of EDTA for five different EDTA-salts. This shows that if you want to end up with a near neutral solution, the best choice is to add EDTA as the Na₃HEDTA-salt. Adding a different salt requires adding either acid or base, or more buffer capacity, which in turn will influence the chemical behaviour of the solution.

Figure 1. pH of a 1 mM EDTA-solution for different EDTA-salts. Data obtained using speciation program CHEAQS Next.

If you have field measurements of redox potential, speciation models can help to predict whether iron will be present as Fe(II) or Fe(III), which is important because Fe(II) behaves quite different chemically than Fe(III) and also has a quite different bioavailability. The same holds for other elements that undergo redox equilibria like N, S, Cu or Mn.

Phase reactions can be predicted with speciation models, for example the dissolution of carbonate due to the gas solution reaction of CO₂. Another example is the speciation in Dutch Standard Water (DSW), a frequently used test medium for ecotoxicological experiments, which is oversaturated with respect to CaCO₃ and therefore displays a part of Ca as a precipitate. The fraction that precipitates is very small (less than 2% of the Ca) so it seems unimportant at first glance, but the precipitate induces a pH-shift of 0.22, a factor of almost two in the concentration of free H⁺.

Many metals are amphoteric and have therefore a minimum solubility at a moderate pH, while dissolving more at both higher and lower pH-values. This can easily be seen in the case of Al: Figure 2 shows the concentration of dissolved Al as a function of pH (note log-scale for Y-axis). Around pH of 6.2, the solubility is at its minimum. At higher and lower pH-values, the solubility is (much) higher.

Figure 2. Soluble Al as function of pH. Data obtained using speciation program CHEAQS Next.

Speciation models can also help to understand differences in the growth of organisms or adverse effects on organisms, in different chemical solutions. For example, Figure 3 shows that changes in speciation of boron can be expected only between roughly pH 8 and 10.5, so when you observe a biological difference between pH 7 and 8, it is not likely that boron is the cause. Copper on the other hand (see Figure 4) does display differences in speciation between pH 7 and 8 so is a more likely cause of different biological behaviour.

Figure 3. Speciation of B as function of pH; concentration of boron was 1x10^-6 M. At higher concentrations, complexes with 2, 3, 4 or 5 B-ions can be formed at significant concentrations. Data obtained using speciation program CHEAQS Next.

Figure 4. Speciation of copper(II) as a function of pH; concentration of copper was 3x10^-8 M. At higher concentrations, complexes with 2 or 3 Cu(II)-ions can be formed at significant concentrations. Data obtained using speciation program CHEAQS Next.

Chemical behaviour: field situations

In field situations, the chemistry is usually much more complex than under laboratory conditions. Decomposition of organisms (including plants) results in a huge variety of organic compounds like fulvic acids, humic acids, proteins, amino acids, carbohydrates, etc. Many of these compounds interact strongly with cations, some also with anions or uncharged molecules. In addition, metals easily adsorb to clay and sand particles that are found everywhere in nature. To make it more complex, suspended matter can contain a high content of organic material which is also capable of binding cations.

For complexation by fulvic and humic acids, Tipping and co-workers have developed a unifying model (Tipping & Hurley, 1992; Tipping, 1994, 1998; Tipping, Lofts & Sonke, 2011). The most recent version, WHAM 7 (Tipping, Lofts & Sonke, 2011), is able to predict cation complexation by fulvic acids and humic acids over a wide range of chemical circumstances, despite the large difference in composition of these acids. This model is now incorporated in several speciation programs.

Suspended matter may be of organic or of inorganic character. Inorganic matter usually consists of (hydr)oxides of metals, such as Mn, Fe, Al, Si or Ti, and clay minerals. In practice, the (hydr)oxides and clays occur together, but the mutual proportions may differ dramatically depending on the source. Since the chemical properties of these metal (hydr)oxides clays are quite different, there is a huge variation in the chemical properties of inorganic suspended matter in different places and different times. As a consequence, modeling interactions between dissolved constituents and suspended inorganic matter is challenging. Only by measuring some properties of suspended inorganic matter, can modeling be applied successfully. For suspended organic matter, the variation in properties is also large and modelling is challenging.

Bioavailability

Speciation models are useful in understanding and assessing the bioavailability of metals and other elements in test media. Test media often contain substances like EDTA to keep metals in solution. EDTA-complexes in general are not bioavailable, so in addition to keeping metals in solution they also change their bioavailability. Models can calculate the speciation and help you to assess what is actually happening in a test medium. An often forgotten aspect is the influence of CO₂. CO₂ from the ambient atmosphere can enter a solution or carbonate in solution (if in excess over the equilibrium concentration) can escape to the atmosphere. The degree to which this exchange takes place, influences the pH of the solution as well as the amount of carbonate that stays in solution (carbonates are often poorly soluble).

Similarly, in field situations models can help to understand the bioavailability of elements. As stated above, the influence of DOC can nowadays be assessed properly in many situations, the influence of suspended matter remains more difficult to assess. Nevertheless models can deliver insights in seconds that otherwise can be obtained only with great difficulty.

Models

There are many speciation programs available and several of them are freely available. Usually they take a set of total concentrations as input, plus information about parameters such as pH, redox, concentration of organic carbon etc. Then the programs calculate the speciation and present them to the user. The equations cannot be solved analytically, so an iterative procedure is required. Although different numerical approaches are used, most programs construct a set of non-linear mass balance equations and solve them by simple or advanced mathematics. A complication in this procedure is that the equilibrium constants depend on the ionic strength of the solution, and that this ionic strength can only be calculated when the speciation is known. The same holds for the precipitation of solids. The procedure is shown in Figure 5.

Figure 5. Typical flow diagram of a speciation program.

Limitations

For modeling speciation, thermodynamic data is needed for all relevant equilibrium reactions. For many equilibria, this information is available, but not for all. This hampers the usefulness of speciation modeling. In addition, there can be large variations in the thermodynamic values found in the literature, resulting in uncertainty about the correct value. A factor of 10 between the highest and lowest values found is not an exception. This of course influences the reliability of speciation calculations. For many equilibria, the thermodynamic data is only available for the standard temperature of 25°C and no information is available to assess the data at other temperatures, although the effect of temperature can be quite strong. Also ionic strength has a high impact on equilibrium ‘constants’; there are many methods available to correct for the effect of ionic strength, but most of them are at best semi-empirical. Simonin (2017) recently proposed a method with a solid theoretical basis; however, the data required for his method are available only for a few complexes so far.

More fundamentally, you should realize that speciation programs typically calculate the equilibrium situation, while some reactions are very slow and, more inportant, nature is in fact a very dynamic system and therefore never in equilibrium. If a system is close to equilibrium, speciation programs can often make a good assessment of the actual situation, but the more dynamic a system, the more care you should take in believing the programs’ results. Nevertheless it is good to realise that a chemical system will always move towards the equilibrium situation, while organisms may move them away from equilibrium. Phototrophs are able to move a system away from its equilibrium situation whereas decomposers and heterotrophs generally help to move a system towards its equilibrium state.

References

Simonin , J.-P. (2017). Thermodynamic consistency in the modeling of speciation in self-complexing electrolytes. Ind. Eng. Chem. Res. 56, 9721-9733.

Tipping, E., Hurley, M.A. (1992). A unifying model of cation binding by humic substances. Geochimica et Cosmochimica Acta 56, 3627 - 3641.

Tipping, E. (1994). WHAM - A chemical equilibrium model and computer code for waters, sediments, and soils incorporating a discrete site/electrostatic model of ion-binding by humic substances. Computers & Geosciences 20, 973 - 1023.

Tipping, E. (1998). Humic Ion-Binding Model VI: An Improved Description of the Interactions of Protons and Metal Ions with Humic Substances. Aquatic Geochemistry 4, 3 - 48.

Tipping, E., Lofts, S., Sonke, J.E. (2011). Humic Ion-Binding Model VII: a revised parameterisation of cation-binding by humic substances. Environmental Chemistry 8, 228 - 235.

3.8.3. Modeling exposure at ecological scales

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