Regional differences in chemical fate model outcome

Webster, Eva; Mackay, Don; Guardo, Antonio Di; Kane, David; Woodfine, David

doi:10.1016/j.chemosphere.2003.10.061

Cited by 49 publications

(27 citation statements)

References 34 publications

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“…These studies and others (e.g. Webster et al 10 ) indicate the need to incorporate variability in environmental properties as well as substance properties to improve the quality and reliability of model output, especially for models that represent large geographical regions. This recognition is one factor that has encouraged the development of spatially-explicit models including several at a European scale.…”

Section: Introductionmentioning

confidence: 99%

Empirical evaluation of spatial and non-spatial European-scale multimedia fate models: results and implications for chemical risk assessment

et al. 2007

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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.

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Section: Introductionmentioning

confidence: 99%

Empirical evaluation of spatial and non-spatial European-scale multimedia fate models: results and implications for chemical risk assessment

et al. 2007

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“…The inclusion of geographical specified data has already been proposed by several authors (Potting 1994;Huijbregts et al 2001Huijbregts et al , 2003Webster et al 2004;Mackay and Reid 2008). The main difference between those methods and the zone classification is schematised on Figs.…”

Section: Zone Classification and Existing Lca-localised Approachesmentioning

confidence: 98%

Classification of chemicals into emission-based impact categories: a first approach for equiprobable and site-specific conceptual frames

Ventura

2010

Int J Life Cycle Assess

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Purpose The classification step is systematically neglected in the calculation of life cycle impact assessment indicators: for several direct contribution of one pollutant to several impact categories, the distribution between these impact categories is not accounted. The proposed approaches of non-redundant classification are based on the probability that an emitted substance and its chemically degraded forms are involved into several environmental impacts. Methods Two types of repartitions are examined in this paper: (1) one called equiprobable classification, based on identical probability of contributions to several impact categories, and (2) and one called zone classification based. Both methods are based on a classification coefficient alpha and require categorization of chemical pathways (reactive, suspensive, direct, indirect). The first method, the equiprobable classification, is a quick method that allows avoiding multiple counting of pollutants. The second method, the zone classification method, is based on two steps: (1) the first step requires defining an impacted zone around the source, inside which the emitted chemicals are expected to majorly diffuse or spread, and (2) in the second step, the score of the chemical is set according to the occurrence of the chemical target inside the impacted zone. Results Both methods are applied on a case study of NOx emissions in Paris. Results highlight big differences (33% to 47%) when using or not the classification coefficient. Coefficients calculated using equiprobable or zone classifications are of the same order of magnitude except for indirect contributions. DiscussionThe zone classification method is compared to other site-specific life cycle assessment methods. The choices of impacted zone and of chemical targets are discussed with the aim to meeting paradoxical constraints: local parameters must be sufficiently detailed in the majority of cases, but generic enough to avoid time consuming researches. The availability of data is also discussed as well as the possibility to include indirect impacts into the environmental system. Conclusions The conceptual frame suggested for classification of pollutants into emission-based impact categories is a first approach for a scientific question that was raised in the 90s. Equiprobable classification is easy to apply but not entirely satisfying from a physical and chemical background. Zone classification relies upon the specificity of emission compartments and occurrence of chemical targets in the impacted zone. It still needs improvements. It has important consequences in terms of data collection, system boundaries, and only practice will show its actual feasibility.

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“…As constant atmospheric pressure is assumed, the amount of imported air equals the amount of exported air. This amount is estimated based on the residence time of air in the upstream, midstream and downstream area, calculated using the annual average wind speed, the distance across the area in each of the eight compass directions, and the frequencies of wind directions, following the method described by Webster et al (2004).…”

Section: Air and Water Transport Flowsmentioning

confidence: 99%

BasinBox: A Generic Multimedia Fate Model for Predicting the Fate of Chemicals in River Catchments

et al. 2006

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Regional differences in chemical fate model outcome

Cited by 49 publications

References 34 publications

Empirical evaluation of spatial and non-spatial European-scale multimedia fate models: results and implications for chemical risk assessment

Empirical evaluation of spatial and non-spatial European-scale multimedia fate models: results and implications for chemical risk assessment

Classification of chemicals into emission-based impact categories: a first approach for equiprobable and site-specific conceptual frames

BasinBox: A Generic Multimedia Fate Model for Predicting the Fate of Chemicals in River Catchments

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