Calculation of environmental concentration and comparison of output for existing chemicals using regional multimedia modeling (original) (raw)
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Chemosphere, 2001
The European Union System for Evaluation of Substances (EUSES) and the ChemCAN chemical fate model are applied to describe the fate of 68 chemicals on two spatial scales in Japan. Emission information on the chemicals has been obtained from Japan's Pollutant Release and Transfer Registry and available monitoring data gathered from government reports. Environmental concentrations calculated by the two models for the four primary environmental media of air, water, soil and sediment agree within a factor of 3 for over 70% of the data, and within a factor of 10 for over 87% of the data. Reasons for certain large discrepancies are discussed. Concentrations calculated by the models are generally consistent with the lower range of concentrations that are observed in the environment. Agreement between modeled and observed concentrations is considerably improved by including an estimate of the advective input of chemicals in air from outside Japan. The agreement between the EUSES and ChemCAN models suggests that results of individual chemical assessments are not likely to be signi®cantly aected by the choice of chemical fate model. Primary sources of discrepancy between modeled and observed concentrations are believed to be uncertainties in emission rates, degradation half-lives, and the lack of data on advective in¯ow of contaminants in air. Ó
Environmental Toxicology and Chemistry, 2009
The relative influence of substance properties and of environmental characteristics on the variation in the environmental fate of chemicals was studied systematically and comprehensively. This was done by modeling environmental concentrations for 200 sets of substance properties, representative of organic chemicals used, and 137 sets of environmental characteristics, representative of regions in Europe of 250 ϫ 250 km. Since it was expected that the model scale has an influence on the predicted concentration variations, the calculations were repeated for regions with a 100 ϫ 100 km and 50 ϫ 50 km area. Stepwise multiple regression analysis was performed to determine the contribution of each of the individual input parameters on the total concentration variation. Depending on the scenario, the range in predicted environmental concentrations spreads from two up to nine orders of magnitude. In accord with earlier studies, variation in the fate of chemicals in the environment appeared to depend mainly on substance-specific partition coefficients and degradation rates. For the estimation of soil and water concentrations with direct emissions to these compartments, however, the influence of spatial variation in environmental characteristics can mount up to two orders of magnitude, a range that can be significant to account for in certain model applications. Concentration differences in water and soil are predicted to be larger if a smaller region is applied in the model calculations, and the relative influence of environmental characteristics on the total variation increases on a more detailed spatial scale. It is argued that the influence of environmental characteristics as predictors of exposure concentrations of chemicals deserves better attention in comparative risk assessment with conventional nonspatial multimedia box models.
Journal of Environmental Monitoring, 2007
Multimedia environmental fate models are commonly-applied tools for assessing the fate and distribution of contaminants in the environment. Owing to the large number of chemicals in use and the paucity of monitoring data, such models are often adopted as part of decision-support systems for chemical risk assessment. The purpose of this study was to evaluate the performance of three multimedia environmental fate models (spatially-and non-spatially-explicit) at a European scale. The assessment was conducted for four polycyclic aromatic hydrocarbons (PAHs) and hexachlorobenzene (HCB) and compared predicted and median observed concentrations using monitoring data collected for air, water, sediments and soils. Model performance in the air compartment was reasonable for all models included in the evaluation exercise as predicted concentrations were typically within a factor of 3 of the median observed concentrations. Furthermore, there was good correspondence between predictions and observations in regions that had elevated median observed concentrations for both spatiallyexplicit models. On the other hand, all three models consistently underestimated median observed concentrations in sediment and soil by 1-3 orders of magnitude. Although regions with elevated median observed concentrations in these environmental media were broadly identified by the spatially-explicit models, the magnitude of the discrepancy between predicted and median observed concentrations is of concern in the context of chemical risk assessment. These results were discussed in terms of factors influencing model performance such as the steady-state assumption, inaccuracies in emission estimates and the representativeness of monitoring data.
Ecological Modelling, 1989
Exposure and ecotoxicity estimation for environmental chemicals (E4CHEM): application of fate models for surface water and soil. Ecol. Modelling, 47: 115-130. The E4CHEM (Exposure and Ecotoxicity Estimation for Environmental CHEMicals) model system was developed for exposure and hazard assessment of environmental chemicals. Two E4CHEM fate models, EXWAT and EXSOL, for surface waters and soil, respectively, are tested and validated by comparing experimental with calculated results. The concentrations of a volatile compound (tetrachloroethene) in the river Main can be predicted by EXWAT, taking into account the average consumption values along the river and an empirically derived proportionality factor for the release rate (0.6% for tetrachloroethene). A sensitivity analysis shows the dominance of volatilisation over dilution. The transport and fate of the herbicide, 2,4,5-trichlorophenoxyacetic acid, are simulated for four German soils under various climatic conditions. Downward movement is underestimated by laboratory sorption measurements. Sorption coefficients derived from field trials have lower values and lower variabilities than those from laboratory sorption studies.
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Chemosphere, 2007
This paper presented a framework for analysis of chemical concentration in the environment and evaluation of variance propagation within the model. This framework was illustrated through a case study of selected organic compounds of benzo[a]pyrene (BAP) and hexachlorobenzene (HCB) in the Great Lakes region. A multimedia environmental fate model was applied to perform stochastic simulations of chemical concentrations in various media. Both uncertainty in chemical properties and variability in hydrometeorological parameters were included in the Monte Carlo simulation, resulting in a distribution of concentrations in each medium. Parameters of compartmental dimensions, densities, emissions, and background concentrations were assumed to be constant in this study. The predicted concentrations in air, surface water and sediment were compared to reported data for validation purpose. Based on rank correlations, a sensitivity analysis was conducted to determine the influence of individual input parameters on the output variance for concentration in each environmental medium and for the basin-wide total mass inventory.
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Environmental Science and Pollution Research, 1994
Most of the existing chemicals of high priority have been released into the environment for many years. Risk assessments for existing chemicals are now conducted within the framework of the German Existing Chemicals Program and by the EC Regulation on Existing Substances. The environmental assessment of a chemical involves: a) exposure assessment leading to the derivation of a predicted environmental concentration (PEC) of a chemical from releases due to its production, processing, use, and disposal. The calculation of a PEC takes into account the dispersion of a chemical into different environmental compartments, elimination and dilution processes, as well as degradation. Monitoring data are also considered. b) effects assessment. Data obtained from acute or long-term toxicity tests are used for extrapolation on environmental conditions. In order to calculate the concentration with expectedly no adverse effect on organisms (Predicted No Effect Concentration, PNEC) the effect values are divided by an assessment factor. This assessment factor depends on the quantity and quality of toxicity data available. In the last step of the initial risk assessment, the measured or estimated PEC is compared with the PNEC. This "risk characterization" is conducted for each compartment separately (water, sediment, soil, and atmosphere). In case PEC > PNEC an attempt should be made to revise data of exposure and/or effects to conduct a refined risk characterization. In case PEC is again larger than PNEC risk reduction measures have to be considered.
Environmental hazard assessment of existing chemicals
Science of The Total Environment, 1993
The environmental hazard assessment of existing chemicals in Germany consists of the following steps: 1. Collection of all available data on effects and exposure for high production volume chemicals. 2. Validation of this information. 3. Exposure assessment using information on the manufacturing process, emission factors, and use pattern and by applying the distribution model of Mackay (Level 1). Predicted environmental concentrations (PEC) are determined by applying special scenarios which differ according to the compartment and the kind of exposure. 4. For those compartments which have been identified in the exposure analysis as being of relevance, an effect assessment is performed. 5. A safety factor F is derived depending on the quality and the density of data, as well as on information on biodegradability and bioaccumulation. F is compared to the ratio Q of LCs0 (ECs0) or NOEC of the most sensitive species and PEC. The values of F and Q can be changed by further information or tests. 6. If Q remains below F, emissions need to be reduced.