Development of a dynamic model for estimating the food web transfer of chemicals in small aquatic ecosystems

A dynamic combined fate and food web model was developed to estimate the food web transfer of chemicals in small aquatic ecosystems (i.e. ponds). A novel feature of the modeling approach is that aquatic macrophytes (submerged aquatic vegetation) were included in the fate model and were also a food item in the food web model. The paper aims to investigate whether macrophytes are effective at mitigating chemical exposure and to compare the modeling approach developed here with previous modeling approaches recommended in the European Union (EU) guideline for risk assessment of pesticides. The model was used to estimate bioaccumulation of three hypothetical chemicals of varying hydrophobicity in a pond food web comprising 11 species. Three different macrophyte biomass densities were simulated in the model experiments to determine the influence of macrophytes on fate and bioaccumulation. Macrophytes were shown to have a significant effect on the fate and food web transfer of highly hydrophobic compounds with log K_OW > = 5. Modeled peak concentrations in biota were highest for the scenarios with the lowest macrophyte biomass density. The distribution and food web transfer of the hypothetical compound with the lowest hydrophobicity (log K_OW = 3) was not affected by the inclusion of aquatic macrophytes in the pond environment. For the three different hypothetical chemicals and at all macrophyte biomass densities, the maximum predicted concentrations in the top predator in the food web model were at least one order of magnitude lower than the values estimated using methods suggested in EU guidelines. The EU guideline thus provides a highly conservative estimate of risk. In our opinion, and subject to further model evaluation, a realistic assessment of dynamic food web transfer and risk can be obtained using the model presented here.     

Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks

Benthic macroinvertebrate communities in stream ecosystems were assessed hierarchically through two-level classification methods of unsupervised learning. Two artificial neural networks were implemented in combination. Firstly, the self-organizing map (SOM) was used to reduce the dimension of community data, and secondly, the adaptive resonance theory (ART) was subsequently applied to the SOM to further classify the groups in different scales. Hierarchical grouping in community data efficiently reflected the impact of the environmental factors such as topographic conditions, levels of pollution, and sampling location and time across different scales. New community data not included in the training process were used to test the trained network model. The input data were appropriately grouped at different hierarchical levels by the trained networks, and correspondingly revealed the impact of environmental disturbances and temporal dynamics of communities. The hierarchical clusters based on a two-level classification method could be useful for assessing ecosystem quality and community variations caused by environmental disturbances.     

Indirect effects of contaminants in aquatic ecosystems

Contaminants such as petroleum hydrocarbons, heavy metals and pesticides can cause direct toxic effects when released into aquatic environments. Sensitive species may be impaired by sublethal effects or decimated by lethality, and this ecological alteration may initiate a trophic cascade or a release from competition that secondarily leads to responses in tolerant species. Contaminants may exert direct effects on keystone facilitator and foundation species, and contaminant-induced changes in nutrient and oxygen dynamics may alter ecosystem function. Thus, populations and communities in nature may be directly and/or indirectly affected by exposure to pollutants. While the direct effects of toxicants usually reduce organism abundance, indirect effects may lead to increased or decreased abundance. Here we review 150 papers that reference indirect toxicant effects in aquatic environments. Studies of accidental contaminant release, chronic contamination and experimental manipulations have identified indirect contaminant effects in pelagic and benthic communities caused by many types of pollutants. Contaminant-induced changes in behavior, competition and predation/grazing rate can alter species abundances or community composition, and enhance, mask or spuriously indicate direct contaminant effects. Trophic cascades were found in 60% of the manipulative studies and, most commonly, primary producers increased in abundance when grazers were selectively eliminated by contaminants. Competitive release may also be common, but is difficult to distinguish from trophic cascades because few experiments are designed to isolate the mechanism(s) causing indirect effects. Indirect contaminant effects may have profound implications in environments with strong trophic cascades such as the freshwater pelagic. In spite of their undesirable environmental influence, contaminants can be useful manipulative tools for the study of trophic and competitive interactions in natural communities.     

A dynamic compartmental food chain model of radiocaesium transfer to Apodemus sylvaticus in woodland ecosystems

A study was undertaken to quantify the activity concentrations of 137Cs in Apodemus sylvaticus (the woodmouse) in two woodland sites, Lady Wood and Longrigg Wood, adjacent to British Nuclear Fuels Ltd. (BNFL) Sellafield, Cumbria, UK. A deterministic dynamic compartmental food chain model was also constructed to predict 137Cs activity concentration [Bq kg(-1) dry weight (dw)] in A. sylvaticus on a seasonal basis given the activity concentrations in its diet. Within the coniferous woodland site (Lady Wood), significant differences were found between seasons (P < 0.05, summer vs. autumn cohort; P < 0.001, spring vs. autumn cohort), with an autumn peak in activity concentration (geometric mean = 140 x/divided by 2.3 Bq kg(-1) dw) being attributed to mycophagy. Fungal concentrations ranged from 2-3213 Bq kg(-1) dw. The modelled activity concentrations fell between the confidence intervals of the observed data in four of the six seasonal cohorts sampled. Disparities between predicted and observed activity concentrations are attributed to uncertainties surrounding the fundamental feeding ecology of small mammals.     

住宅小区水生态保持设计

中房&#183;高尔夫家园项目,其湖体位于整个小区的中心．是在原有自然水体的基础上经部分人工改造形成。包括三个部分,中间水体面积最大,是整个水景的主题。在主题水景的旁边各有一个面积较小呈带状的水体。小区内建筑依水而建。     

An emission-weighted proximity model for air pollution exposure assessment

Background

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates.

Method

To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO₂) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a ‘Geo-statistical EWPM’. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO₂ exposure risk was validated with 10 virtual cases in prospective exposure scenarios.

Results

Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO₂ exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and ‘Geo-statistical EWPM’ are much smaller than those between TPM and ‘Geo-statistical TPM’ (5.12 vs. 24.63).

Conclusion

EWPM appears to more accurately portray individual exposure relative to TPM. The ‘Geo-statistical EWPM’ effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.     

Modeling the ecological impact of heavy metals on aquatic ecosystems: a framework for the development of an ecological model

In this paper, an ecological model is proposed to predict the effects of heavy metals on aquatic ecosystems. The bioavailable concentration of metals and a concept of toxicity strength (TS) are combined. The integrated ecological model relates the transport, distribution and speciation of heavy metals and their toxicity, and the effect of environmental variability on metal toxicity. It also emphasizes the link between physical and chemical processes of heavy metals in rivers and ecological effects. Based on the data obtained from research in the CERP project (Co-operative Ecological Research Project), the ecological impact of heavy metals on the aquatic ecosystem of the Le An River (polluted by heavy metals from a copper mine) was predicted. The results show that the estimated values of toxicity strength for surface water are in agreement with the percentage inhibition for the test organism (P. phosphoreum) and that the predicted ecological effect of polluted sediment is consistent with natural variability in aquatic ecosystems.     

Mortality rates of autochthonous and fecal bacteria in natural aquatic ecosystems

Bacterial mortality has been investigated in freshwater (River Seine) and in marine (North Sea) systems using a method based on following the disappearance of radioactivity from the DNA of assemblages of bacteria previously labeled with tritiated thymidine. Measurement of bacterial mortality of autochthonous and various types of fecal bacteria allowed direct comparisons between their respective first-order mortality rates. Mortality rates obtained for the different types of bacteria in the River Seine were, respectively, 7.9-33.9 x 10(-3)h(-1) for Escherichia coli, 12.2-29.2 x 10(-3)h(-1) for S. faecium and 7.0-18.3 x 10(-3)h(-1) for the autochthonous bacteria. In the Belgian coastal waters, these rates were 4.6-27.3 x 10(-3)h(-1) for E. coli, 6.0-22.0 x 10(-3)h(-1) for S. typhimurium, 10.0-18.9 x 10(-3)h(-1) for S. faecium and 1.0-13.9 x 10(-3)h(-1) for autochthonous bacteria. In both environments, the overall mortality rates of autochthonous and the different fecal bacteria were in the same order of magnitude and overall mortality rates of E. coli were on average about twice as high for autochthonous bacteria. Grazing by protozooplankton was the dominant process of mortality for fecal and autochthonous bacteria in both environments. Except in a few situations, grazing by protozooplankton was responsible for more than 90% of the overall mortality rate of fecal and autochthonous bacteria in the river and in the coastal area.     

Translocation of inorganic/organic mercury in a model aquatic system

The volatile organic mercury at subnanogram level was determined by steam distillation-Magos treatment and flameless atomic absorption spectrophotometry after preconcentration with silvered sand. By this technique, down to 0.1 ng of methyl mercury can be determined with good precision (±5%). Application of the technique to our aquatic model system made clear that much higher ratio of organic mercury (nearly 8%) to the total one was in water overlying organic sediment than the water sample overlying sandy sediment (approx. 3.5%). In natural water, therefore, it would seem that the content of organomercurials are less than 10% to that of total mercury.     

A model for the analysis of data-driven innovation and value generation in smart cities' ecosystems

Smart cities are key elements to cope with certain of the largest challenges facing society, such as overpopulation, transport, pollution, sustainability, security, health, and the creation of new firms. Smart cities' portals offer a great amount of data that can be used by the private and public entities to create new services. These data are also a valuable source for the deployment of big data businesses. In this article, a model is presented demonstrating how the data released by the smart cities creates value for the citizens and society. The model operates using three stages. The first one shapes the release of data by the smart city, and it includes several of the dimensions that make data appealing for reuse. The second stage analyses the mechanisms to create innovative products and services. The last stage explains how these products and services impact its society.     

A laboratory model for evaluating the behavior of heavy metals in an aquatic environment

A dynamic biological system capable of simultaneously distinguishing between bioaccumulation and biomagnification through successive trophic levels in an aquatic ecosystem is described. The organisms used in the system were algae, predominantly Scenedesmus sp., Daphnia magna, fresh water mussels (Ligumia sp. and Margaritifera sp.) and the fathead minnow, Pimephales promelas.Thiosulfate complexed mercury and silver were each studied at two concentrations. The water and organisms were analyzed for the metals periodically during the 10-week tests. Both mercury and silver can be bioaccumulated by fish. The concentration factors for mercury are greater than for silver. Further, mercury is biomagnified by fish whereas silver is not. Fresh water mussels were found to be poor indicator species for metal contamination.     

Application of an ecosystem model for aquatic ecological risk assessment of chemicals for a Japanese lake.

The Lake Suwa version of the comprehensive aquatic systems model (CASM-SUWA) was developed using field data from Lake Suwa and evaluated to examine the utility of CASM-SUWA for assessing the ecological risk of chemicals for aquatic ecosystems. The calibration of the parameters for the model provided that the established reference model simulation could reproduce complex seasonal biomass behavior of populations that were not significantly different from the general seasonal pattern for the Lake Suwa ecosystem. The sensitivity analyses revealed the potential importance of indirect effects and demonstrated that the parameter values of all the trophic levels were important in determining the biomass of each trophic level in the model. The risk estimation of linear alkylbenzene sulfonates (LAS) demonstrated that the model estimated the risks of direct toxic effects on each population and the indirect ecological effects that propagate through the food-web in the model ecosystem. The CASM-SUWA-derived benchmark levels were approximately one order of magnitude less than the field-derived NOECs in literature. The analyses of the comparison implied that the model could provide a good basis in determining an ecological protective level of a chemical of concern in aquatic ecosystem. This modeling study demonstrated that the model can be used to provide additional information for the decision-making process in the management of the aquatic ecological risk of chemicals.     

Integrated modelling of eutrophication and organic contaminant fate & effects in aquatic ecosystems. A review

Eutrophication and contamination with micropollutants have been major problems in water quality management. Both problems have been subjected to extensive research and modelling but traditionally are treated separately. Traditional simulation models for aquatic systems can be categorised as eutrophication models, contaminant fate models, food web models and food chain bioaccumulation models. Because they are single issue models, many interactions and feedbacks between the food web, nutrient and toxicant cycles are missed. Linking these models is essential to evaluate the fate and risks of contaminants in systems with changing nutrient loading, to assess the natural attenuation of contaminants or to understand the selfpurifying capacity of ecosystems. Combination of the single issue models requires inclusion of 'interaction processes' to account for the coupling between the (sub-) model types, such as organic carbon cycling. toxicity, transport and accumulation of organic contaminants in the food chain, and bottom up versus top down control of primary production and nutrient cycling. This review first provides a brief overview of traditional approaches in modelling eutrophication, contaminant fate, food web dynamics and food chain bioaccumulation. Second, five existing integrated eutrophication, fate and/or effects models are reviewed. Third, the gaps and limitations in modelling the four types of interaction processes are discussed. It is concluded that these models are invaluable tools to focus attention to feedback mechanisms that are often overlooked, to identify dominating processes in ecosystems, to formulate counterintuitive hypotheses on ecosystem functioning, or to assess short term risks of acutely toxic stressors. However, the potential of integrated models for long term simulations of contaminant exposure. food chain bioaccumulation and effects to higher trophic levels remains limited, mainly because of principal limitations in food web modelling. In contrast, the potential of integrated models for long term simulations of contaminant fate is better because the environmental distribution of contaminants is mainly determined by the major abiotic compartments and by biotic compartments at the base of the food chain.     

STEMS-Air: a simple GIS-based air pollution dispersion model for city-wide exposure assessment

Current methods of air pollution modelling do not readily meet the needs of air pollution mapping for short-term (i.e. daily) exposure studies. The main limiting factor is that for those few models that couple with a GIS there are insufficient tools for directly mapping air pollution both at high spatial resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model (STEMS-Air) has been developed for PM₁₀ to meet these needs with the option to choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses the grid-based FOCALSUM function in ArcGIS in conjunction with a fine grid of emission sources and basic information on meteorology to implement a simple Gaussian plume model of air pollution dispersion. STEMS-Air was developed and validated in London, UK, using data on concentrations of PM₁₀ from routinely available monitoring data. Results from the validation study show that STEMS-Air performs well in predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM₁₀. For daily modelling, STEMS-Air achieved r² values in the range 0.19-0.43 (p < 0.001) based solely on traffic-related emissions and r² values in the range 0.41-0.63 (p < 0.001) when adding information on ‘background’ levels of PM₁₀. For annual modelling of PM₁₀, the model returned r² in the range 0.67-0.77 (P < 0.001) when compared with monitored concentrations. The model can thus be used for rapid production of daily or annual city-wide air pollution maps either as a screening process in urban air quality planning and management, or as the basis for health risk assessment and epidemiological studies.     

Water body and riparian buffer strip characteristics in a vineyard area to support aquatic pesticide exposure assessment

The implementation of a geodata-based probabilistic pesticide exposure assessment for surface waters in Germany offers the opportunity to base the exposure estimation on more differentiated assumptions including detailed landscape characteristics. Since these characteristics can only be estimated using field surveys, water body width and depth, hydrology, riparian buffer strip width, ground vegetation cover, existence of concentrated flow paths, and riparian vegetation were characterised at 104 water body segments in the vineyard region Palatinate (south-west Germany).Water body segments classified as permanent (n = 43) had median values of water body width and depth of 0.9 m and 0.06 m, respectively, and the determined median width:depth ratio was 15. Thus, the deterministic water body model (width = 1 m; depth = 0.3 m) assumed in regulatory exposure assessment seems unsuitable for small water bodies in the study area. Only 25% of investigated buffer strips had a dense vegetation cover (> 70%) and allow a laminar sheet flow as required to include them as an effective pesticide runoff reduction landscape characteristic. At 77 buffer strips, bordering field paths and erosion rills leading into the water body were present, concentrating pesticide runoff and consequently decreasing buffer strip efficiency. The vegetation type shrubbery (height > 1.5 m) was present at 57 (29%) investigated riparian buffer strips. According to their median optical vegetation density of 75%, shrubberies may provide a spray drift reduction of 72 ± 29%.Implementing detailed knowledge in an overall assessment revealed that exposure via drift might be 2.4 and via runoff up to 1.6 fold higher than assumed by the deterministic approach. Furthermore, considering vegetated buffer strips only by their width leads to an underestimation of exposure by a factor of as much as four. Our data highlight that the deterministic model assumptions neither represent worst-case nor median values and therefore cannot simply be adopted in a probabilistic approach.     

Environmental impacts and metal exposure of aquatic ecosystems in rivers contaminated by small scale gold mining: the Puyango River basin, southern Ecuador.

Gold mining in the Portovelo-Zaruma district in southern Ecuador is causing considerable environmental impacts; the most important ones are related to the discharge of cyanide, mercury and metal rich tailings into rivers of the Puyango catchment area. Cyanide and metal levels in rivers regularly exceed environmental quality criteria. The contamination impacts biodiversity, with cyanide causing a direct lethal effect on biota close to source and metal contaminants considerably reducing aquatic biodiversity further downstream. It is shown that the prevailing neutral or slightly alkaline conditions of the rivers ensure that metals are mainly associated with sediment. However, elevated metal levels in bottom living larvae collected from contaminated sites suggest that these sediment bound metals are readily bioavailable. Leaching experiments indicate that the relative ease by which metals are taken up by larvae is related to the speciation of sediment associated metals. It is further shown that large amounts of metals, which are bound to suspended sediment under ambient pH conditions, enter the dissolved and directly bioavailable state in more acidic conditions. Metal levels in carnivorous fish were found to be modestly elevated only, with the exception of mercury. Mercury levels exceeded 0.5 mg/kg in fish from both contaminated and uncontaminated sites, showing that both methylation and bioaccumulation of mercury are occurring in the Puyango river basin.     

Comparative study of E. coli survival in two aquatic ecosystems

The survival of E. coli in samples of aquatic environments is measured by means of a simple test procedure which allows to deduce the effect of various factors upon the E. coli die-off or aftergrowth. The results of the survival tests are transformed to a log No/Nt form and expressed as die-off indexes. In the Watersportbaan, a β-mesosaprobic lake, fluctuations of the die-off indexes were governed in major part by the seasonal variations in insolation and the concomitant changes in pH and in temperature. In the Coupure, a heavily polluted β-polysaprobic biotope, the indexes correlated primarily with the insolation and the temperature and to a minor extent with the dissolved oxygen content of the water. The seasonal fluctuations of the indexes were much less pronounced in the sludge-water interphases of these biotopes. In the β-mesosaprobic water, the bacterial populations constituted the dominant biotic component of the overall community homeostasis. In the β-polysaprobic biotope however, the die-off turned out in major part to be the result of the activity of thermolabile toxins at one hand and of bacteriophages on the other hand. The significance of the BOD-tolerance limit of aquatic environments with regard to the occurrence of microbial imbalances is discussed.     

Validation of the exposure assessment for veterinary medicinal products

Under the EU Directive 2004/28/EC, an environmental risk assessment of new veterinary medicinal products is required. Given the nature of risk assessment for new applications, there is a need to model exposure concentrations. Critical evaluations are essential to ensure that the use of models by regulators does not result in the propagation of misleading information. The empirical validations of soil exposure models, previously discussed in this journal, indicate that it is impossible to analyse the contribution of every model parameter to the variability in the predictions. In particular, the prediction of the slurry concentration is challenged by uncertainties concerning dilution, mixing and dissipation of residues. Surface water and groundwater models generated highly deviating results compared to the field results, questioning the usefulness of the available screening models. Animal husbandry, slurry handling and environmental conditions throughout Europe are considered in order to define realistic worst case scenarios, to be used in conjunction with distribution models for the environmental risk assessment of veterinary medicinal products at registration. Given the variability in manure management practice throughout Europe, a deterministic approach for the manure-to-soil model was selected. Both worst case and best case scenario were developed. Several modelling assumptions applied in the surface water exposure model for fish nursery effluent were validated against newly available data. Since the available data give no proof that a settling tank contributes to the removal of pesticides from waste water, it is recommended for risk assessment purposes to consider the contribution of the settling tank to removal of pesticides and medicines to be negligible. Surface water dilution factors may be considered to be rather small, a factor of 2, for low flow situations.     

A predictive model for the home outdoor exposure to nitrogen dioxide

The aim of this study is to find and test a predictive model that could be suitable to estimate the outdoor NO(2) concentrations at individual level, by integrating ecological measurements recorded by local monitoring stations with individual information collected by a questionnaire. For this purpose, the data from the Italian centres of the European Community Respiratory Health Survey II (ECRHS II) has been used. Outdoor NO(2) concentrations were measured using NO(2) passive sampling tubes (PS-NO(2)), exposed outdoor for 14 days, between January 2001 and January 2003. Simultaneously, average NO(2) concentrations were collected from all the monitoring stations of the three centres (MS-NO(2)). Individual measurements carried out with passive samplers were compared with the corresponding NO(2) 2-week concentrations obtained as the average of all local (background and traffic) monitoring stations (MS-NO(2)). A multiple linear regression model was fitted to the data using the 2-week PS-NO(2) concentrations as the response variable and questionnaire information and MS-NO(2) concentrations as predictors. The model minimizing the root mean square error (RMSE), obtained from a ten-fold cross validation, was selected. The model with the best predictive ability included centre, season of the survey, MS-NO(2) concentrations, type and age of building, residential area and reported intensity of heavy-duty traffic and explained the 68.9% of the variance. The non-parametric correlation between PS-NO(2) and the concentrations estimated by the model is 0.81 (95% CI: 0.77-0.85). This study shows that over short periods (2 weeks) a good prediction of home outdoor exposure to NO(2) can be achieved by simply combining routinely collected ecological data with dwelling characteristics and self-reported intensity of heavy traffic. Further studies are needed to extend this prediction to long-term exposure.