Mass balance environmental modeling

For the assessment of the contribution of the emission categories to the observed NMVOC concentrations the Chemical Mass Balance (CMB) modelling technique, version 8 from United States Environmental Protection Agency (Watson et al., 1998 Watson et al., 2001) was selected. The method uses source specific ratios between the emission rates of certain set of compounds and aims to recognise these fingerprints, or soiuce profiles, in the profile measured at the receptor point. As a result the CMB model delivers contributions from each source type to the total ambient NMVOC and individual hydrocarbon species at receptor points and their uncertainties. 

Although LCA contains elements of the tools discussed earlier in this chapter—mass balance, multimedia modeling of environmental fate and transport, and risk characterization— it is a distinct discipline with its own jargon, precepts, and limitations. 

Export processes are often more complicated than the expression given in Equation 7, for many chemicals can escape across the air/water interface (volatilize) or, in rapidly depositing environments, be buried for indeterminate periods in deep sediment beds. Still, the majority of environmental models are simply variations on the mass-balance theme expressed by Equation 7. Some codes solve Equation 7 directly for relatively large control volumes, that is, they operate on "compartment" or "box" models of the environment. Models of aquatic systems can also be phrased in terms of continuous space, as opposed to the "compartment" approach of discrete spatial zones. In this case, the partial differential equations (which arise, for example, by taking the limit of Equation 7 as the control volume goes to zero) can be solved by finite difference or finite element numerical integration techniques. 

MASS BALANCE MODELS OF CHEMICAL FATE 1.5.1 Evaluative Environmental Calculations... 

To assess the relative importance of the volatilisation removal process of APs from estuarine water, Van Ry et al. constructed a box model to estimate the input and removal fluxes for the Hudson estuary. Inputs of NPs to the bay are advection by the Hudson river and air-water exchange (atmospheric deposition, absorption). Removal processes are advection out, volatilisation, sedimentation and biodegradation. Most of these processes could be estimated only the biodegradation rate was obtained indirectly by closing the mass balance. The calculations reveal that volatilisation is the most important removal process from the estuary, accounting for 37% of the removal. Degradation and advection out of the estuary account for 24 and 29% of the total removal. However, the actual importance of degradation is quite uncertain, as no real environmental data were used to quantify this process. The residence time of NP in the Hudson estuary, as calculated from the box model, is 9 days, while the residence time of the water in the estuary is 35 days [16]. 

Pollutants emitted by various sources entered an air parcel moving with the wind in the model proposed by Eschenroeder and Martinez. Finite-difference solutions to the species-mass-balance equations described the pollutant chemical kinetics and the upward spread through a series of vertical cells. The initial chemical mechanism consisted of 7 species participating in 13 reactions based on sm< -chamber observations. Atmospheric dispersion data from the literature were introduced to provide vertical-diffusion coefficients. Initial validity tests were conducted for a static air mass over central Los Angeles on October 23, 1968, and during an episode late in 1%8 while a special mobile laboratory was set up by Scott Research Laboratories. Curves were plotted to illustrate sensitivity to rate and emission values, and the feasibility of this prediction technique was demonstrated. Some problems of the future were ultimately identified by this work, and the method developed has been applied to several environmental impact studies (see, for example, Wayne et al. ). 

In this paper the PLS method was introduced as a new tool in calculating statistical receptor models. It was compared with the two most popular methods currently applied to aerosol data Chemical Mass Balance Model and Target Transformation Factor Analysis. The characteristics of the PLS solution were discussed and its advantages over the other methods were pointed out. PLS is especially useful, when both the predictor and response variables are measured with noise and there is high correlation in both blocks. It has been proved in several other chemical applications, that its performance is equal to or better than multiple, stepwise, principal component and ridge regression. Our goal was to create a basis for its environmental chemical application. 

Thus we see that environmental modeling involves solving transient mass-balance equations with appropriate flow patterns and kinetics to predict the concentrations of various species versus time for specific emission patterns. The reaction chemistry and flow patterns of these systems are sufficiently complex that we must use approximate methods and use several models to try to bound the possible range of observed responses. For example, the chemical reactions consist of many homogeneous and catalytic reactions, photoassisted reactions, and adsorption and desorption on surfaces of hquids and sohds. Is global warming real [Minnesotans hope so.] How much of smog and ozone depletion are manmade [There is considerable debate on this issue.]... 

As a third step, the relations between the various model components have to be specified in terms of mathematical expressions, once the model structure is fixed. In contrast to the common chemical reaction models which describe the reaction kinetics under laboratory conditions (e.g., in a test tube), environmental models usually contain two kinds of processes (1) the familiar reaction processes discussed in Parts II and III of this book, and (2) the transport processes. These processes are linked by the concept of mass balance. 

We restrict our discussion to those systems of n linear differential equations that evolve from the construction of mass balance models for one or several chemicals in one or several environmental compartments (boxes). Such systems are always of the form ... 

Bar-Yosef, B., Chang, A.C. and Page, A.L. (2005) Mass balance modeling of arsenic processes in cropland soils. Environmental Geochemistry and Health, 27(2), 177-84. 

Diamond, M.L. (1995) Application of a mass balance model to assess in-place arsenic pollution. Environmental Science... 

The discussion above provides a brief qualitative introduction to the transport and fate of chemicals in the environment. The goal of most fate chemists and engineers is to translate this qualitative picture into a conceptual model and ultimately into a quantitative description that can be used to predict or reconstruct the fate of a chemical in the environment (Figure 27.1). This quantitative description usually takes the form of a mass balance model. The idea is to compartmentalize the environment into defined units (control volumes) and to write a mathematical expression for the mass balance within the compartment. As with pharmacokinetic models, transfer between compartments can be included as the complexity of the model increases. There is a great deal of subjectivity to assembling a mass balance model. However, each decision to include or exclude a process or compartment is based on one or more assumptions—most of which can be tested at some level. Over time the applicability of various assumptions for particular chemicals and environmental conditions become known and model standardization becomes possible. 

The construction of a mass balance model follows the general outline of this chapter. First, one defines the spatial and temporal scales to be considered and establishes the environmental compartments or control volumes. Second, the source emissions are identified and quantified. Third, the mathematical expressions for advective and diffusive transport processes are written. And last, chemical transformation processes are quantified. This model-building process is illustrated in Figure 27.4. In this example we simply equate the change in chemical inventory (total mass in the system) with the difference between chemical inputs and outputs to the system. The inputs could include numerous point and nonpoint sources or could be a single estimate of total chemical load to the system. The outputs include all of the loss mechanisms transport... 

There are many tricks and shortcuts to this process. For example, rather than compiling all of the transformation rate equations (or conducting the actual kinetic experiments yourself), there are many sources of typical chemical half-lives based on pseudo-first-order rate expressions. It is usually prudent to begin with these best estimates of half-lives in air, water, soil, and sediment and perform a sensitivity analysis with the model to determine which processes are most important. One can return to the most important processes to assess whether more detailed rate expressions are necessary. An illustration of this mass balance approach is given in Figure 27.5 for benzol a Ipyrene. This approach allows a first-order evaluation of how chemicals enter the environment, what happens to them in the environment, and what the exposure concentrations will be in various environmental media. Thus the chemical mass balance provides information relevant to toxicant exposure to both humans and wildlife. 

Lansari A, Lindstrom AB, Templeman BD, et al. 1992. Multizonal mass balance modeling of benzene dispersion in a private residence. Proceedings of the 1992 US Environmental Protection Agency/Air and Waste Management Association Symposium on Measurement of Toxic and Related Air Pollutants, May 1992. 

The range in EF is from zero to unity, with a racemic value of 0.5. Enantiomer fractions are preferred to ERs, as the EF range is bounded, and a deviation from the racemic value in one direction is the same as that in the other. For example, if the (—)-enantiomer is twice the concentration as its antipode, the EF is 0.333, which is the same deviation (0.167) from a racemic EF of 0.5 as the opposite case of the (+)-enantiomer at twice the concentration as the (—)-enantiomer (EF = 0.667). The respective ERs would be 0.5 and 2. The corresponding deviations of 0.5 and 1, respectively, are not the same deviation from the racemic ER of 1. Thus, ERs can produce skewed data inappropriate for statistical summaries such as sample mean and standard error [109]. As a result, EEs are more amenable compared to ERs for graphical representations of data, mathematical expressions, mass balance determination, and environmental modelling [107, 109]. Individual ER and EF measurements can be converted [107, 108] ... 

The multimedia urban model (MUM) is a fugacity-based mass balance model that treats the movement of POPs in an urban environment and links emissions to ambient chemical concentrations, and thus outdoor exposure (Diamond et al., 2001). MUM considers longterm, average conditions of chemical transport and transformation among six environmental compartments in urban areas (air, soil, surface water, sediment, vegetation and surface film see Figure 6.1) shows a concepmal version of the model). The model does not estimate event-specihc processes as do meteorological-based air or stormwater models. 

Fehrenbacher, M. C., and Hummel, A. A. (1996). Evaluation of the mass balance model used by the Environmental Protection Agency for estimating inhalation exposure to new chemical substances. Am Ind Hyg Assoc J 57, 526-536. 

Mass Balance Modeling of a Simple Environmental Problem... 

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