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• W1553722433 abstract "Models are essential tools for our understanding of the fate of chemicals in the environment (MacLeod et al. 2010). Models allow us to interpolate between concentration data measured in the field (and to extrapolate beyond measured data), to establish a mechanistically based interpretation of measured concentrations, and to run scenarios under a range of different conditions, for example for a world with a warmer climate. There are many different types of models for chemicals in the environment, such as models for chemicals in plumes released from smokestacks or other point sources, hydrological models for transport of chemicals in groundwater, atmospheric transport models, models for chemical uptake by plants, and many more. The focus of this Learned Discourse is on multicompartment or multimedia mass-balance models. Models of this type have been used since the late 1970s in a wide range of applications (Mackay 1979). To illustrate how these models work, we first consider the simplest possible case of one chemical in one environmental compartment (called by modelers a box): carbon tetrachloride (CCl4) in the troposphere. If we assume that emissions and losses are equal (steady state) and that the troposphere is a well-mixed reservoir of CCl4, the mass-balance equation is E = k·c·V. Here E (t/y) is the continuous emission of CCl4 into the troposphere, k (1/y) is the first-order loss rate constant of CCl4 in the troposphere, V (m3) is the volume of the troposphere, and c is the concentration of CCl4 in tropospheric air. Approximate values for the three parameters are E = 80 kt/y (in the 1990s), k = 0.0333 1/y, and V = 5·1018 m3 (k is the inverse of the tropospheric lifetime of CCl4, which is approximately 30 years). Solving for c yields c = 3.11·10−9mol/m3, which is equivalent to 76 ppt. Measured concentrations of CCl4 in the troposphere are approximately 90 ppt, and for such a simple model the agreement between modeled and measured concentrations is good. Next, the CCl4 case above can be expanded to include a second environmental compartment or box, in this case seawater. The mass-balance equation then reads, E = ka·ca·Va+kw·cw·Vw, with the subscripts “a” and “w” denoting air and water, respectively. Now also the volume of the water compartment, Vw, and the rate constant for CCl4 loss from water, kw, have to be known, and in addition to the mass-balance equation a second equation is needed to calculate the two unknowns, ca and cw. This equation defines the ratio of the CCl4 concentrations in air and water as is prescribed by the chemical's air–water partition coefficient, Kaw: Kaw = ca/cw. The Kaw is a chemical-specific property that needs to be known in addition to Vw and kw before the two-box model can be solved. In general, we can now see that four pieces of information are needed for a reliable estimate of a chemical's concentration in an environmental system: the emission source strength, the volumes of the compartments that the chemical enters, the main loss process or processes removing the chemical from the system, and the partitioning properties of the chemical. On this basis, the box-model concept has been extended from simple unit-world models, which represent the global troposphere, seawater, and topsoil by one box each, to complex models including many boxes and processes. Examples are Globo-POP (Wania and Mackay 1999) and CliMoChem (by Scheringer and coworkers; ETH Zurich) as two global models consisting of latitudinal zones, and BETR-Global (MacLeod et al. 2010) as a global model with 288 grid cells. Although these models capture many different aspects of the global environmental fate of organic chemicals, their performance strongly depends on the quality of only four types of data. These are, as mentioned above, the emission rate, compartment volumes, main loss processes, and partitioning properties for the important compartments. In the models, these data are integrated with the additional knowledge that is contained in the models: the law of mass conservation, the laws of chemical kinetics, Fick's laws of diffusion, and extensive empirical data sets on the composition of the environment (temperature, precipitation rates, organic carbon concentration in soil, concentration of OH radicals and aerosol particles in air, etc.). Generally, these latter components of the models are available with relatively low uncertainty, and this makes the models a valid platform for the assessment of the environmental fate of organic chemicals. If there is a substantial discrepancy between model results and measured concentrations, it is in most cases caused by inaccurate data for emissions and/or main loss process or processes and phase partitioning. Over the last 15 years, multimedia mass balance models have been used to investigate the fate of a wide range of organic chemicals in the global environment, including comparison to concentrations measured in the field. In most cases, the agreement between modeled and measured concentrations is fully satisfactory (Wania and Mackay 1999; Becker et al. 2011). In many studies, the long-term fate of long-lived chemicals such as persistent organic pollutants (POPs) was investigated. However, it is also possible to set up multimedia mass-balance models for less persistent chemicals and highly dynamic local or regional systems. This is illustrated by a modeling study into the fate of the herbicide, diuron, in a catchment in the coastal plains of Queensland, Australia (Camenzuli et al. 2012). Diuron is used in this region mainly on sugarcane, and a mass-balance model was used to estimate the amounts of diuron that are flushed from the agricultural soil into the water of the Great Barrier Reef lagoon. This is a source of concern because the region has extremely high rainfall in the wet season (up to 200 mm per day) and diuron washed out from the soils and transferred to seawater may affect the wildlife in the lagoon. The model for this case consisted of agricultural and nonagricultural soil, seawater, sediment underneath the water, and air. It was parameterized with actual precipitation, temperature, and wind speed data from the region. The diuron emissions were assumed to enter the agricultural soil and were derived from actual application rates reported for sugarcane in the region. Figure 1 shows the model results (line) in combination with diuron concentrations measured in seawater of the lagoon (dots). Shown are results for the years 2008 to 2011; wet seasons are indicated in gray. The model results include uncertainty ranges (dashed lines), which were derived from the uncertainties of individual model parameters by means of error propagation. The results show higher concentrations in the wet seasons, when runoff of diuron is higher because of strong rainfall, and demonstrate that the model can reproduce the pronounced seasonal pattern that is visible in the measured data. The model also makes it possible to quantify the amounts of diuron present in each compartment of the system at any time point in a model run, to determine the diuron fluxes to seawater, and to evaluate how diuron concentration in seawater respond if the use of diuron in the region is restricted. For all these aspects, see Camenzuli et al. (2012). In conclusion, multimedia mass-balance models provide highly useful tools for estimating concentrations of chemicals in the environment, checking the consistency of emission data, chemical property data, and concentrations measured in the field, and evaluating scenarios of different environmental conditions or chemical use. Because of their flexible structure, it is always possible to “update” these models; current multimedia mass-balance models accommodate many new findings for individual environmental processes. The models can be set up for different spatial and temporal scales and for a wide range of chemicals; recently, models that take into account the specific properties of engineered nanoparticles have also been presented (Praetorius et al. 2012)." @default.
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• W1553722433 date "2014-12-26" @default.
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• W1553722433 title "Multimedia mass-balance models for chemicals in the environment: Reliable tools or bold oversimplifications?" @default.
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• W1553722433 doi "https://doi.org/10.1002/ieam.1591" @default.
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