Environmental fate prediction methods

As probabilistic exposure and risk assessment methods are developed and become more frequently used for environmental fate and effects assessment, OPP increasingly needs distributions of environmental fate values rather than single point estimates, and quantitation of error and uncertainty in measurements. Probabilistic models currently being developed by the OPP require distributions of environmental fate and effects parameters either by measurement, extrapolation or a combination of the two. The models predictions will allow regulators to base decisions on the likelihood and magnitude of exposure and effects for a range of conditions which vary both spatially and temporally, rather than in a specific environment under static conditions. This increased need for basic data on environmental fate may increase data collection and drive development of less costly and more precise analytical methods. 

The need to balance costs against benefits both in the public and private sectors resulted in a search for methods of predicting the fate and effects of chemicals in the environment. Actual field testing of all cases of interest is both too costly and too dangerous to perform. Mathematical models, therefore, have been developed to provide descriptive tools and predictive approaches to this problem. At the symposium on which this book is based, a collection of user-oriented information was presented and covered the following aspects of environmental fate modeling ... 

The ability to predict the behavior of a chemical substance in a biological or environmental system largely depends on knowledge of the physical-chemical properties and reactivity of that compound or closely related compounds. Chemical properties frequently used in environmental assessment include melting/boiling temperature, vapor pressure, various partition coefficients, water solubility, Henry s Law constant, sorption coefficient, bioconcentration factor, and diffusion properties. Reactivities by processes such as biodegradation, hydrolysis, photolysis, and oxidation/reduction are also critical determinants of environmental fate and such information may be needed for modeling. Unfortunately, measured values often are not available and, even if they are, the reported values may be inconsistent or of doubtful validity. In this situation it may be appropriate or even essential to use estimation methods. 

This book describes numerous methods for the prediction of toxicity (Chapters 8, 9,12, and 13), environmental fate (Chapters 14 to 16), and the effects of chemicals in humans (Chapters 8, 10, 11, 13, and 17). In addition to those models reviewed in these chapters there are many more available in the open literature (see the next section). Despite these predictive models, animal tests are still being performed to assess toxicity and fate. The question then becomes, How are we to use predictions The simple answer to this question is cautiously . 

Estimation methods for reductive transformations (e.g., dehalogenation or nitro reduction reactions) are limited because it is not yet possible to predict the rates of reductive transformations quantitatively. The choice of appropriate descriptors is complicated by the variability in rate-limiting steps with contaminant structure and environmental conditions. Most QSARs for reduction reactions have been developed as diagnostic tools to determine reduction mechanisms and pathways. So far, only a few of these QSARs provide sufficiently precise predictions and are sufficiently general in scope that they might be useful to predict environmental fate (Tratnyek et al. 2003). They mostly use LFER-type correlations or quantum-chemically derived parameters (e.g., Peijnenburg et al., 1991 Rorije et al., 1995 Scherer et al., 1998 Tratnyek and Macalady, 2000) and many of them are compiled in a recent review by Tratnyek et al. (2003). 

The EPA uses QSARs to predict a large number of ecological effects, as well as for environmental fate within the PMN process. The EPA s website (www.epa.gov) provides a valuable source of further information on all these predictive methods, as well as a database and aquatic toxicity values and detailed information on how the models have been validated. Many of the predictive models have been brought together into the EPISUITE software (see Table 19.2 for a listing of the models available). This includes the OPPT s models used for the prediction of physical and chemical properties for new chemical substances. The EPISUITE software is downloadable free of charge (www.epa.gov/oppt/exposure/docs/episuitedl.htm). This provides not only an excellent resource for the development of QSARs, but also a transparent mechanism for the assessment of PMNs. 

This describes one of the most basic kinds of calculations, which nonetheless can be quite powerful. More sophisticated methods, which take account of transport times within the environment and breakdown rates of chemicals are also available. Such models are widely used in predicting the environmental fate of organic chemicals. 

Due to the need for premanufacture hazard evaluation and the high costs of field sampling, an increasing emphasis is being placed on predictions of environmental fate and exposure. The methods used to predict the environmental fate of chemicals can be categorized into three basic approaches ... 

Degradation by the action of microorganisms is one of the major processes that determines the fate of organic chemicals in the environment. Quantitative Structure-Activity Relationships (QSAR) methods can be applied to biodegradation. Such relationships, often referred to as Quantitative Structure-Biodegradability Relationships (QSBRs), relate the molecular structure of an organic chemical to its biodegradability and consequently aid in the prediction of environmental fate. 

Accurate computational methods to predict the solubihty of crystalline organic molecules in aqueous solutions are highly sought after in many fields of the biomo-lecular sciences and industry. For example, predictions of solubility are required in the pharmaceutical and agrochemical industries to assess the bioavailability of de novo designed drugs and the environmental fate of potential pollutants, respectively [1 ]. Due in part to the requirements of industry, interest in the prediction of solubility has risen dramatically in recent years, with hundreds of articles published in the last decade alone. 

This book intends to provide a starting point for those interested in the prediction of the toxicity and fate of chemicals to humans and the environment. SARs and, more frequently, quantitative structure-activity relationships (QS ARs) provide methods to predict these endpoints. A brief history of the area, the driving forces, and basis of the topic is provided in this chapter. Further chapters (2 to 7) describe the methods to develop predictive models the application of models to human health endpoints (Chapters 8 to 11) their application to environmental toxicity and fate (Chapters 12 to 17) and the use of predictive models (Chapter 19), adoption by the regulatory authorities (Chapter 19), and validation (Chapter 20). 

Portier RJ. 1985. Comparison of environmental effect and biotransformation of toxicants on laboratory microcosm and field microbial communities. In ASTM Spec Tech Publ Validation and Predictability of Laboratory Methods for Assessing the Fate Effects of Contaminants in Aquatic Ecosystems, Philadelphia, PA, 865 14-30. 

Laws regulating toxic substances in various countries are designed to assess and control risk of chemicals to man and his environment. Science can contribute in two areas to this assessment firstly in the area of toxicology and secondly in the area of chemical exposure. The available concentration ( environmental exposure concentration ) depends on the fate of chemical compounds in the environment and thus their distribution and reaction behaviour in the environment. One very important contribution of Environmental Chemistry to the above mentioned toxic substances laws is to develop laboratory test methods, or mathematical correlations and models that predict the environ-... 

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