An updated state of the science EQC model for evaluating chemical fate in the environment: application to D5 (decamethylcyclopentasiloxane)

The EQuilibrium Criterion (EQC) model developed and published in 1996 has been widely used for screening level evaluations of the multimedia, fugacity-based environmental fate of organic chemicals for educational, industrial, and regulatory purposes. Advances in the science of chemical partitioning and reactivity and the need for more rigorous regulatory evaluations have resulted in a need to update the model. The New EQC model is described which includes an improved treatment of input partitioning and reactivity data, temperature dependence and an easier sensitivity and uncertainty analysis but uses the same multi-level approach, equations and environmental parameters as in the original version. A narrative output is also produced. The New EQC model, which uses a Microsoft Excel platform, is described and applied in detail to decamethylcyclopentasiloxane (D5; CAS No. 541-02-6). The implications of these results for the more detailed exposure and risk assessment of D5 are discussed. The need for rigorous evaluation and documentation of the input parameters is outlined.     

Simulation of long-term environmental dynamics of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans using the dynamic multimedia environmental fate model and its implication to the time trend analysis of dioxins

Long term environmental fate of polychlorinated dibenzo-p-dioxins (PCDD) and polychlorinated dibenzofurans (PCDF) were studied using the fugacity-based dynamic multimedia environmental fate model. New formulation about soil wind erosion into air was included into the model. Effect of process assumption, sensitivity analysis for parameters, and tentative validation against the measured sediment core analysis was performed. Mass fluxes between compartments were estimated by the dynamic modeling. From the model estimates, major mass fluxes coming from emission source were shown to go to the soil and water through wet/dry deposition, then go to degradation mainly in the soil and sediment. Major mass fluxes of TCDD and OCDD come from the impurities in CNP (Chlornitrofen) and PCP (Pentachlorophenol) directly into the soil. Consideration about multimedia environmental dynamics using the modeled mass fluxes was shown in the discussion.     

Effects of input uncertainty and variability on the modelled environmental fate of organic pollutants under global climate change scenarios

Global climate change (GCC) is expected to influence the fate, exposure and risks of organic pollutants to wildlife and humans. Multimedia chemical fate models have been previously applied to estimate how GCC affects pollutant concentrations in the environment and biota, but previous studies have not addressed how uncertainty and variability of model inputs affect model predictions. Here, we assess the influence of climate variability and chemical property uncertainty on future projections of environmental fate of six polychlorinated biphenyl congeners under different GCC scenarios using a spreadsheet version of the ChemCAN model and the Crystal Ball® software. Regardless of emission mode, results demonstrate: (i) uncertainty in degradation half-lives dominates the variance of modelled absolute levels of PCB congeners under GCC scenarios; (ii) when the ratios of predictions under GCC to predictions under present day climate are modelled, climate variability dominates the variance of modelled ratios; and (iii) the ratios also indicate a maximum of about a factor of 2 change in the long-term average environmental concentrations due to GCC that is forecasted between present conditions and the period between 2080 and 2099. We conclude that chemical property uncertainty does not preclude assessing relative changes in a GCC scenario compared to a present-day scenario if variance in model outputs due to chemical properties and degradation half-lives can be assumed to cancel out in the two scenarios.     

Modelling PCB bioaccumulation in a Baltic food web

A steady state model is developed to describe the bioaccumulation of organic contaminants by 14 species in a Baltic food web including pelagic and benthic aquatic organisms. The model is used to study the bioaccumulation of five PCB congeners of different chlorination levels. The model predictions are evaluated against monitoring data for five of the species in the food web. Predicted concentrations are on average within a factor of two of measured concentrations. The model shows that all PCB congeners were biomagnified in the food web, which is consistent with observations. Sensitivity analysis reveals that the single most sensitive parameter is log K(OW). The most sensitive environmental parameter is the annual average temperature. Although not identified amongst the most sensitive input parameters, the dissolved concentration in water is believed to be important because of the uncertainty in its determination. The most sensitive organism-specific input parameters are the fractional respiration of species from the water column and sediment pore water, which are also difficult to determine. Parameters such as feeding rate, growth rate and lipid content of organism are only important at higher trophic levels.     

Development of a Dynamic Aquatic Model (DynA Model): Estimating Temporal Emissions of DDT to Lake Maggiore (N. Italy)

- DOI: http://dx.doi.org/10.1065/espr2006.01.009 Background, Aims and Scope Most existing models used to describe the fate of chemicals in surface water and sediment generally consider a 'static scenario', in which a contaminant is discharged at a constant rate and environmental input parameters do not change during the simulation time. This approach is not suitable in environmental scenarios characterized by daily or periodic changes of several input parameters. The aim of this study is to estimate approximate emissions of DDT lo Lake Maggiore using a new surface water model, (DynA Model) that describes the fate of a chemical in a dynamic scenario. Methods The model is developed on the grounds of an existing and validated model (QWASI). A numerical solution was adopted to build the fully dynamic version of the model. Results and Discussion The model was applied to Lake Maggiore emitting DDT at a constant rate until steady-state was reached. Emissions were stopped and later sporadic 'pulse' emissions were added. This was done to calculate the amount of DDT needed to simulate concentrations close to those measured in water and sediments. This allowed the evaluation of the order of magnitude of emissions. An uncertainty analysis for sediment resuspension was also performed, given the lack of measured resuspension rates. Conclusion The model showed the time response of the Lake Maggiore system to varying emission scenarios and provided what are regarded as reasonable estimates of DDT emissions. The model demonstrated the importance of sediment-water exchange. Recommendation and Outlook In order to better calculate DDT concentrations the model should be run with different discharge scenarios to clarify the time trends of concentrations, possibly with the use of different sets of measured data (such as biota and sediment deposition/resuspension rates).     

Development of a black carbon-inclusive multi-media model: application for PAHs in Stockholm

A multi-media model was developed for predicting the fate of organic chemicals in the Greater Stockholm Area, Sweden, and applied to selected polycyclic aromatic hydrocarbons (PAHs). Although urban models have been previously developed, this model is novel in that it includes sorption to pyrogenically-derived particles, commonly termed "black carbon" (BC), within the model structure. To examine the influence of BC sorption on environmental fate of PAHs, two versions of the model were generated and run: one in which sorption to BC was included and one in which BC sorption was excluded. The inclusion of BC sorption did not cause any significant variations to air levels, but it did cause an average 20-30% increase in sediment concentrations related to increased sediment solids partitioning. The model also predicted reduced advective losses out of the model domain, as well as chemical potential to diffuse from sediments, whilst total chemical inventory increased. In all cases, the lighter PAHs were more affected by BC inclusion than their heavier counterparts. We advocate the addition of sorption to BC in future multi-media fate and exposure models, which as well as influencing fate will also alter (lower) chemical availability and, thus, wildlife exposure to hydrophobic chemicals. A quantification of the latter was derived with the help of the soot-inclusive model version, which estimated a lowering of dissolved water concentrations between five and >200 times for the different PAHs of this study.     

What environmental fate processes have the strongest influence on a completely persistent organic chemical's accumulation in the Arctic?

Fate and transport models can be used to identify and classify chemicals that have the potential to undergo long-range transport and to accumulate in remote environments. For example, the Arctic contamination potential (ACP), calculated with the help of the zonally averaged global transport model Globo-POP, is a numerical indicator of an organic chemical's potential to be transported to polar latitudes and to accumulate in the Arctic ecosystem. It is important to evaluate how robust such model predictions are and in particular to appreciate to what extent they may depend on a specific choice of environmental model input parameters. Here, we employ a recently developed graphical method based on partitioning maps to comprehensively explore the sensitivity of ACP estimates to variations in environmental parameters. Specifically, the changes in the ACP of persistent organic contaminants to changes in each environmental input parameter are plotted as a function of the two-dimensional hypothetical “chemical space” defined by two of the three equilibrium partition coefficients between air, water and octanol. Based on the patterns obtained, this chemical space is then segmented into areas of similar parameter sensitivities and superimposed with areas of high default ACP and elevated environmental bioaccumulation potential within the Arctic. Sea ice cover, latitudinal temperature gradient, and macro-diffusive atmospheric transport coefficients, and to a lesser extent precipitation rate, display the largest influence on ACP-values for persistent organic contaminants, including those that may bioaccumulate within the polar marine ecosystems. These environmental characteristics are expected to be significantly impacted by global climate change processes, highlighting the need to explore more explicitly how climate change may affect the long-range transport and accumulation behavior of persistent organic pollutants.     

Application of System Dynamics technique to simulate the fate of persistent organic pollutants in soils

Persistent organic pollutants (POPs) are within the most dangerous pollutants released into the environment by human activities. Due to their resistance to degradation (chemical, biological or photolytic), it is critical to assess the fate and environmental hazards of the exchange of POPs between different environmental media.System Dynamics enables to represent complex systems and analyze their dynamic behavior. It provides a highly visual representation of the structure of the system and the existing relationships between the several parameters and variables, facilitating the understanding of the behavior of the system. In the present study the fate of γ-hexachlorocyclohexane (lindane) in a contaminated soil was modeled using the Vensim® simulation software.Results show a gradual decrease in the lindane content in the soil during a simulation period of 10 years. The most important route affecting the concentrations of the contaminant was the biochemical degradation, followed by infiltration and hydrodynamic dispersion. The model appeared to be highly sensitive to the half-life of the pollutant, which value depends on environmental conditions and directly affects the biochemical degradation.     

Calibration and validation of a dynamic water model in agricultural scenarios

A dynamic aquatic model (DynA model) was previously developed to predict the fate of a chemical in aquatic scenarios characterized by daily or periodic changes in several input parameters. DynA model is here calibrated with data obtained from the literature in specific unsteady state scenarios, such as those of rice fields. The results obtained for two herbicides (cinosulfuron and pretilachlor) in rice paddy scenarios revealed the capability of the model to accurately predict water and sediment concentrations, as shown by some statistical indicators. Modelling efficiency (EF) values of 0.86-0.99 for the water compartment and of 0.77-0.84 for sediment show the good agreement between predicted and measured concentrations. An "external validation" was performed using measured data for a different herbicide (molinate) applied in a Portuguese paddy rice scenario. A sensitivity analysis for this volatile chemical revealed the influence of some climatic parameters (e.g. temperature) to the model outcomes, such as water and sediment concentrations. This confirmed the capability of DynA model as an efficient tool for the pesticide risk assessment in dynamic scenarios.     

A dynamic contaminant fate model of organic compound: a case study of Nitrobenzene pollution in Songhua River, China

A one-dimensional dynamic contaminant fate model, coupling kinematic wave flow option with advection-dispersion-reaction equation, has been applied to predict Nitrobenzene pollution emergency in Songhua River, China that occurred on November 13, 2005. The model includes kinetic processes including volatilization, photolysis and biodegradation, and diffusive mass exchange between water column and sediment layer as a function of particles settling and resuspension. Four kinds of quantitative statistical tests, namely Nash-Sutcliffe efficiency, percent bias, ratio of root-mean-square to the standard deviation of monitoring data and Theil’s inequality coefficient, are adopted to evaluate model performance. The results generally show that the modeled and detected concentrations exhibit good consistency. Flow velocity in the river is most sensitive parameter to Nitrobenzene concentration in water column based on sensitivity analysis of input parameters. It indicates flow velocity has important impact on both distribution and variance of contaminant concentration. The model performs satisfactory for prediction of organic pollutant fate in Songhua River, with the ability to supply necessary information for pollution event control and early warning, which could be applied to similar long natural rivers.     

Evaluation and comparison of multimedia mass balance models of chemical fate: application of EUSES and ChemCAN to 68 chemicals in Japan

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 significantly affected 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 inflow of contaminants in air.     

Uncertainty in Nonpoint Source Pollution Models and Associated Risks

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. The specific objective is to demonstrate how the usability of a flow and transport model (here: MACRO) can be enhanced by developing and analyzing its output probability distributions based on input variability. This infiltration-based model was designed to investigate preferential flow effects on pollutant transport. A statistical sensitivity analysis is used to identify the most uncertain input parameters based on model outputs. Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments. Uncertainty of model outputs is investigated using a Latin hypercube sampling scheme (LHS) with restricted pairing for model input sampling. Probability density functions (pdfs) are constructed for water flow rate, atrazine leaching rate, total accumulated leaching, and atrazine concentration in percolation water. Results indicate that consideration of input parameter uncertainty produces a 20% higher mean flow rate along with two to three times larger atrazine leaching rate, accumulated leachate, and concentration than that obtained using mean input parameters. Uncertainty in predicted flow rate is small but that in solute transport is an order of magnitude larger than that of corresponding input parameters. Macropore flow is observed to contribute to the variability of atrazine transport results. Overall, the analysis provides a quantification of prediction uncertainty that is found to enhance a user's ability to assess risk levels associated with model predictions.     

Uncertainty in Nonpoint Source Pollution Models and Associated Risks

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. The specific objective is to demonstrate how the usability of a flow and transport model (here: MACRO) can be enhanced by developing and analyzing its output probability distributions based on input variability. This infiltration-based model was designed to investigate preferential flow effects on pollutant transport. A statistical sensitivity analysis is used to identify the most uncertain input parameters based on model outputs. Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments. Uncertainty of model outputs is investigated using a Latin hypercube sampling scheme (LHS) with restricted pairing for model input sampling. Probability density functions (pdfs) are constructed for water flow rate, atrazine leaching rate, total accumulated leaching, and atrazine concentration in percolation water. Results indicate that consideration of input parameter uncertainty produces a 20% higher mean flow rate along with two to three times larger atrazine leaching rate, accumulated leachate, and concentration than that obtained using mean input parameters. Uncertainty in predicted flow rate is small but that in solute transport is an order of magnitude larger than that of corresponding input parameters. Macropore flow is observed to contribute to the variability of atrazine transport results. Overall, the analysis provides a quantification of prediction uncertainty that is found to enhance a user's ability to assess risk levels associated with model predictions.     

Assessment of the environmental persistence and long-range transport of endosulfan

Concentrations of the insecticide endosulfan (α- and β-isomer) and its degradation product endosulfan sulfate in air, seawater and soil are calculated with the global environmental fate model CliMoChem. As model input, physicochemical properties of all three compounds were assembled and a latitudinally and temporally resolved emission inventory was generated. For concentrations in air, model and measurements are in good agreement; a bimodal seasonality with two peaks in spring and fall as it is observed in Arctic air is reproduced by the model. For seawater, the agreement of model and measurements depends on the values of the hydrolysis activation energy of endosulfan used in the model; with relatively high values around 100 kJ/mol, model results match field data well. The results of this assessment of the levels, persistence, and global distribution of endosulfan are also relevant for the evaluation of endosulfan as a Persistent Organic Pollutant under the Stockholm Convention.     

Uncertainty Analysis of Hydrologic and Water Quality Predictions for a Small Watershed Using SWAT2000

Hydrologic and water quality (H/WQ) models are being used with increasing frequency to devise alternative pollution control strategies. It has been recognized that such models may have a large degree of uncertainty associated with their predictions, and that this uncertainty can significantly impact the utility of the model. In this study, ARRAMIS (Advanced Risk & Reliability Assessment Model) software package was used to analyze the uncertainty of the SWAT2000 (Soil and Water Assessment Tool) outputs concerning nutrients and sediment losses from agricultural lands. ARRAMIS applies Monte Carlo simulation technique connected with Latin hypercube sampling (LHS) scheme. This technique is applied to the Warner Creek watershed located in the Piedmont physiographic region of Maryland, and it provides an interval estimate of a range of values with an associated probability instead of a point estimate of a particular pollutant constituent. Uncertainty of model outputs was investigated using LHS scheme with restricted pairing for the model input sampling. Probability distribution functions (pdfs) for each of the 50 model simulations were constructed from these results. Model output distributions of interest in this analysis were stream flow, sediment, organic nitrogen (organic-N), organic phosphorus (organic-P), nitrate, ammonium, and mineral phosphorus (mineral-P) transported with water. Developed probability distribution functions for the model provided information with desirable probability. Results indicate that consideration of input parameter uncertainty produces 64% less mean stream flow along with approximately 8.2% larger sediment loading than obtained using mean input parameters. On the contrary, mean of outputs regarding nutrients such as nitrate, ammonia, organic-N, and organic-P (but not mineral-P) were almost the same as the one using mean input parameters. The uncertainty in predicted stream flow and sediment loading is large, but that for nutrient loadings is the same as that of the corresponding input parameters. This study concluded that using a best possible distribution for the input parameters to reflect the impact of soils and land use diversity in a small watershed on SWAT2000 model outputs may be more accurate than using average values for each input parameter.     

Priority assessment of toxic substances in life cycle assessment. Part II: assessing parameter uncertainty and human variability in the calculation of toxicity potentials

Toxicity potentials are standard values used in life cycle assessment (LCA) to enable a comparison of toxic impacts between substances. This paper presents the results of an uncertainty assessment of toxicity potentials that were calculated with the global nested multi-media fate, exposure and effects model USES-LCA. The variance in toxicity potentials resulting from input parameter uncertainties and human variability was quantified by means of Monte Carlo analysis with Latin Hypercube sampling (LHS). For Atrazine, 2,3,7,8-TCDD and Lead, variation, expressed by the ratio of the 97.5%-ile and the 2.5%-ile, ranges from about 1.5 to 6 orders of magnitude. The major part of this variation originates from a limited set of substance-specific input parameters, i.e. parameters that describe transport mechanisms, substance degradation, indirect exposure routes and no-effect concentrations. Considerable correlations were found between the toxicity potentials of one substance, in particular within one impact category. The uncertainties and correlations reported in the present study may have a significant impact on the outcome of LCA case studies.