Water Quality Research Articles

Integrating machine learning and data envelopment analysis for reliable reservoir water quality index assessment considering uncertainty

ABSTRACT Water quality assessment is crucial for environmental health and quality of life. This study introduces a novel water quality index (WQI) model for reservoirs, using Wadi Dayqah Dam in Oman as a case study. The model advances water quality assessment using a data-driven approach, reducing reliance on subjective expert opinions. A large dataset of water samples was analysed using machine learning (ML) to select water quality variables (WQVs). Using a bootstrapping and subsampling approach, the proposed WQI was then calculated through sub-indexing, weighting, and aggregating sub-indices. WQV weights were estimated using gradient boosting and rank order centroid techniques, while aggregation involved scoring and data envelopment analysis (DEA). The model effectively captures uncertainty, prioritizes WQVs, and provides solutions to issues such as eclipsing, ranking, and dealing with bad variable values. The results were validated through uncertainty and sensitivity analyses, highlighting the model’s potential for enhancing data-driven decision making in reservoir management.

Hydrochemical evaluation of the strip aquifer in Srivaikundam region, Southern India: implications for drinking and irrigation

The present study focuses on understanding the groundwater geochemistry and hydrochemical characteristics of the Srivaikundam region, Tamil Nadu, with special concern on seasonal and spatial variations in groundwater quality. Groundwater samples were collected during the pre-monsoon (2021) and post-monsoon (2022) seasons, and required water quality parameters were analyzed, including pH, total dissolved solids (TDS), electrical conductivity, and concentrations of major ions such as sodium (Na⁺), calcium (Ca2⁺), magnesium (Mg2⁺), chloride (Cl⁻), bicarbonate (HCO₃⁻), and sulfate (SO₄2⁻). Further ionic ratios, such as Na + /Cl⁻ and Ca2⁺/Mg2⁺, helped to characterize the groundwater quality to understand the ion exchange occurring in areas affected by seawater intrusion. The Gibbs diagram revealed that groundwater chemistry is primarily controlled by rock-water interactions, while the ionic ratios confirmed the significant influence of marine water mixing in the coastal aquifers. In addition to that, the study highlights the role of the strip aquifer system in regulating groundwater flow and storage, which influences the seasonal variations in water quality. The strip aquifer system, consisting of both unconfined and semi-confined aquifers, is highly vulnerable based on the impacts of anthropogenic activities and reflected in the elevated concentrations of salts and other ions. According to the WHO 2017 guidelines, certain areas exhibited high levels of TDS, chloride, and magnesium, which alarm potential risks related to salinity and pollution, particularly due to the effects of human activities in the study region. This study exhibits the need for continuous monitoring and the adoption of effective groundwater management strategies in the study region to mitigate the impacts of seawater intrusion and pollution on the water quality of the study region.

Microbial composition on microplastics mediated by stream impairment

BackgroundStudies into biofilm interactions with microplastic polymers in marine environments are widespread in the literature. Increasing evidence suggests that lotic microplastics are a significant contributor and may accumulate harmful or pathogenic organisms, thereby contributing to the degradation of marine ecosystems where they meet riverine systems. Suboptimal water quality of these riverine systems may influence these biomes. This project compared the microbial diversity of biofilms that developed on microplastics to natural stone substrates in an impaired and unimpaired section of the Quinnipiac River Watershed. In this project, the influence of impairment was studied based on microbial diversity via 16S rRNA gene sequencing while monitoring total colony and fecal coliform colony counts using standard water sampling methods.ResultsTotal coliform colony counts were greater in the impaired Quinnipiac River site than in the unimpaired Honeypot Brook tributary and on the microplastic substrate than the stone substrate. Sequenced features to the class level were dominated by Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria, comprising 75% of the community biome. Simpson’s Diversity indices indicated that within the two substrates, there was little variation between the communities. However, it was noted that microplastic alpha diversity trended slightly lower than the stone. Further analysis of common aquatic enteropathogens showed that the genus Citrobacter was significantly more abundant on the microplastics at both locations.ConclusionsOur results indicate impaired waterbodies with a microplastic burden may retain greater fecal coliform bacterial loads than unimpaired waterbodies. Increased microplastic loads in compromised lotic systems may have an additive impact. Water quality remediation and careful monitoring are recommended to reduce this effect. Comparing this study with environmental community analysis could provide valuable insight into preferential surface attachment of bacteria onto microplastic.

Research on sustainable development strategies based on basin ecological health assessment: a case study of the Fan River.

Basins play a crucial role in regional ecosystems and the supply of water resources. However, owing to regional constraints and insufficient research resources, the ecological health of small basins in county-level areas has not been adequately studied. To explore a simple and practical method for assessing the ecological health of small county-level basins, this study uses the Fan River Basin as a case study to construct a dual spatial scale evaluation framework that encompasses both aquatic and terrestrial ecosystems. The evaluation indicators for the aquatic ecosystem include the Water Quality Condition Index, River Connectivity Index, and Macrobenthic Diversity Composite Index, alongside eight additional indicators. The evaluation of the terrestrial ecosystem incorporates nine core indicators, such as the Forest Coverage Index, Water Source Containment Function Index, and Important Habitat Conservation Index. The entropy weighting method was employed to determine the weights of the indicators, followed by quantitative analysis. The results indicate that the aquatic ecosystem of the Fan River Basin generally falls into the "Moderate" level; however, water quality and species diversity are facing a certain degree of pressure. In the terrestrial ecosystem, forest coverage and soil conservation functions performed well, while habitat conservation and water source containment functions were relatively weak and significantly affected by human activities. Based on the evaluation results, optimization strategies for basin management and sustainable development have been proposed, providing valuable references for decision-makers to promote ecological civilization and sustainable development.

Chemical characteristics of groundwater and surface water affected by human activities in the upper Jinzi River Basin, China

Water resources, as critical ecological and environmental assets, are essential to the social and economic development of countries and regions worldwide. The Jinzi River valley functions as a discharge area for industrial and domestic sewage from mining operations and residential communities along both banks. However, due to human activities, both groundwater and surface water in the region have been contaminated to varying extents. Multiple gold mines are located in the research area. Due to years of unregulated mining, this has had a serious impact on the local ecological environment and water quality. However, research on groundwater and surface water in this region, which are crucial components of the ecological environment, remains limited. This study integrates local socio-economic and hydrogeological conditions, employing methods such as multivariate statistical analysis, the Piper trilinear diagram, the Gibbs model, and ionic ratio relationships to analyze the characteristics and origins of major ions in the region. (1) The primary hydrochemical type of surface water was HCO3-Ca·Na, while groundwater was predominantly of the HCO3-Ca·Mg type. This hydrochemical pattern was consistent across the region, with ion concentrations significantly higher in areas dominated by carbonate rock compared to those with silicate rock. (2) Using principal component analysis, water–rock interaction modeling, and ion source analysis, it was determined that groundwater chemistry was primarily influenced by the weathering of diorite and carbonate rock, along with inputs from domestic and agricultural wastewater. In contrast, surface water chemistry was largely controlled by the weathering of carbonate rocks and the discharge of industrial wastewater. (3) Components such as SO42−, NO3−, Cl−, and total dissolved solids in surface water exhibited a pronounced sensitivity to human activities, with their concentrations significantly exceeding those in groundwater. This indicates that surface water is more heavily impacted by human activities, particularly from industrial, agricultural, and domestic wastewater sources.

Rock mineral wool-based green roofs to improve the quality of urban water runoff.

Green roofs are nature-based solutions that allow greenery to be integrated into the building envelope, making it possible to re-nature cities while providing multiple benefits. However, whether green roofs are a source or sink of pollution in the urban environment is still a controversy. One of the causes of the possible deterioration of the quality of runoff water from green roofs is the substrate. Green roofs based on rock mineral wool (RMW) growing media require thinner substrate layers or can even be substrate-less. In the present study, four green roof systems based on RMW have been studied over the course of 2years. Their performance, in terms of leachate quality, has been compared with two traditional roofs, a green roof with pozzolana as a draining material and a gravel-ballasted conventional flat roof. Limit values for wastewater quality from international regulations were considered benchmark. The main conclusions were that after the first flush, which was observed for all solutions, generally exceeding the limit values, RMW-based solutions performed better than traditional solutions. Furthermore, the average values of leachates from all tested green roofs and especially those from RMW solutions fall within the limits set by international regulations.

Aluminum hydroxide-coated pumice and biochar enhance biosand filter defluoridation and water treatment.

Chronic exposure to high fluoride levels in drinking water can cause serious health effects, including skeletal and dental fluorosis. Although conventional biosand filters (BSFs) can provide safe drinking water at the household scale, it has a low fluoride adsorption capacity. In this study, fluoride removal in BSF was enhanced by using aluminum hydroxide coated materials, such as pumice and biochar in place of sand. Bench-scale BSFs were set up with (a) uncoated sand as a control, (b) coated pumice, and (c) coated biochar. BSFs were charged with fluoride spiked surface water (≥ 5mg/L) for more than a year. While fluoride breakthrough was observed in the sand column on day 49, fluoride concentrations remained below the World Health Organization limit (< 1.5mg/L) for 313 and 418days for coated biochar and coated pumice, respectively. Columns with coated media also effectively removed turbidity, fecal indicator bacteria, and organic matter and maintained acceptable filtration rates (> 0.25m/h) without requiring frequent cleaning. Although pumice had a higher fluoride adsorption capacity (1.1mg/g) than biochar (0.94mg/g),filtered water pH in the pumice column was unacceptable (4.18 ± 0.25). However, apost-treatmentprocess, using an oyster shell-filled column, restored pH to within drinking water standards without compromising water quality.

Hydrothermal Synthesis of Nitrogen-Doped Carbon Dots for Selective Fe3+ Detection.

Carbon dots (CDs) have recently regarded as an attractive fluorescent sensor for water quality detection. However, the actual detection application of CDs is limited owing to the low fluorescence quantum yield (QY) and unclear ion detection mechanism. Here, nitrogen doped CDs (N-CDs) with abundant amide groups, which were easy to prepare using urea and citric acid under mild reaction conditions and displayed great light absorption and strong fluorescence performance with a fluorescence QY of up to 26.6%. More importantly, N-CDs could achieve selective detection of iron ion (Fe3+) and its concentrations as low as 0.47 µM in water. Meanwhile, N-CDs exhibited a good linear relationship within the concentration range of 0.5-500 µM of Fe3+. This mainly because the introduction of amide groups of N-CDs not only could serve as both donors (NH-), but also acceptors (C = O) for hydrogen bonding, which consequently increased multifunctional fluorescent recognition sites and formed coordination interactions with metal ions, and ultimately achieved fluorescence detection of ions. Therefore, the preparation of N-CDs rich in amide groups provides a promising strategy for rapid and efficient detection of Fe3+ ions for water quality detection applications.

Temporal-spatial estimation of the water quality variation in the middle and down streams of Han River using a modified ordinary-indicator kriging approach

Temporal-spatial estimation of the water quality variation in the middle and down streams of Han River using a modified ordinary-indicator kriging approach

Assessment of water and sediment quality in intermittent streams across different land uses in the Brazilian semiarid region: implications for sustainable irrigation and resource management.

Water scarcity is a critical global issue, especially in semiarid regions where limited water resources are increasingly pressured by socioeconomic vulnerabilities and agricultural demands. In this study, we evaluated the quality of water and sediment from intermittent streams in a watershed located in Brazil's semiarid northeastern region, assessing their suitability for irrigation while considering the influence of different land uses. Based on the Brazilian Environmental Council classification, we found that 82% of the sampled sites were categorized as freshwater, indicating general suitability for irrigation. However, the hydrochemical and sediment analyses revealed significant anthropogenic impacts, particularly at agricultural and urban sites, where higher levels of salinity, nutrients, heavy metals, and other pollutants were observed. Moderate salinization risks were noted at 70% of the sites, primarily linked to anthropogenic land-use practices, with total dissolved solids, chlorides, and bicarbonates emerging as key indicators of degradation. Riparian forests demonstrated a notable buffering effect, reducing salinity and nutrient levels, although elevated concentrations of manganese and iron were also detected, likely due to natural geochemical mobilization and upstream contamination. These results highlight the critical role of riparian vegetation in mitigating environmental impacts and emphasize the need for targeted soil and water management strategies, such as crop selection, irrigation practices, and land-use planning. By addressing the combined effects of land use and natural pressures on water quality, we provide essential insights for sustainable irrigation and resource management in semiarid regions, supporting both agricultural productivity and ecological preservation.

Water Quality Index and the quality of freshwater resource uMhlathuze river, Kwazulu-Natal, South Africa: A Review

Abstract uMhlathuze River in KwaZulu-Natal, South Africa, is a critical freshwater resource supporting industrial, agricultural, and domestic activities. However, its water quality is under threat from various anthropogenic pressures. This review synthesises existing literature to assess the river’s water quality, focusing on key contaminants, spatial and temporal trends, ecological impacts, and socio-economic implications. Key pollutants identified include elevated levels of nitrates (&gt;10 mg/L), phosphates (&gt;2 mg/L), heavy metals such as lead (Pb &gt; 0.01 mg/L) and chromium (Cr &gt; 0.05 mg/L) (Nsibande et al. Afr. J. Aquat. Sci. 2024, 262–274, 3.), and microbial contaminants with faecal coliforms exceeding 1,000 CFU/100 mL (Mthembu et al., Afr. J. Microbiol. Res. 2012, 2020–2026, 9.). Seasonal variations exacerbate these issues, with higher pollutant loads observed during rainy periods due to increased runoff from industrial, agricultural, and urban areas. Figures illustrating contaminant trends by season or location could complement these findings. The degraded water quality has led to declines in biodiversity, including fish and aquatic macroinvertebrates, and poses significant public health risks for communities reliant on the river for drinking water and sanitation. Maps of pollution hotspots and biodiversity loss would aid in visualizing these impacts. Key knowledge gaps include inconsistent sampling methodologies, insufficient long-term data, and the absence of a region-specific Water Quality Index (WQI). This review highlights the need for standardised WQI frameworks, advanced monitoring technologies, and pollution tracking mechanisms to address these challenges. Additionally, socio-economic studies and community-based conservation initiatives are recommended to mitigate pollution impacts and improve water resource sustainability. These findings serve as a foundation for targeted research and policy actions to protect and sustainably manage the uMhlathuze River system.

Resolving complaints about the quality of drinking water among water supply operators in the Czech Republic

ABSTRACT According to a survey, 12% of the Czech population does not drink tap water. The main reasons given are poor aesthetic quality of water (38%) and health concerns (14%). The latter reason does not appear to be objectively based and must therefore have other causes, such as how well consumers are informed and how effectively their complaints are dealt with. Therefore, a questionnaire survey was carried out among water supply operators focusing on these aspects. The survey showed that only 62% of them have a Complaints Code and that only 53% of the operators record complaints about water quality. The number of recorded complaints per 100,000 consumers varied by several orders of magnitude. This difference was not only due to different water quality but also primarily due to different efficiency of complaint collection and, most importantly, to very different internal definitions of complaints. Under the new EU Drinking Water Directive, water suppliers will be obliged to publish information on complaints, but without the term ‘complaint’ being defined. If the numbers of published complaints are used by consumers or the media as an indicator for utility benchmarking, we may get a very false picture from which the public will draw incorrect conclusions.

Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach.

Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach.

Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods.

This study explores a low-cost sensor system for real-time algae bloom detection and water management. Harmful algal blooms (HABs) threaten water quality, ecosystems, and public health. Traditional detection methods, like satellite imagery and unmanned aerial vehicle (UAV), are expensive and not always suited for real-time monitoring. The proposed system uses sunlight and illuminance sensors to predict algae blooms and water level fluctuations. Data from these sensors are analyzed using support vector machines (SVM) and logical algorithms, with labeling based on sensor readings (e.g., "algae", "sunny", "shade", "aqua"). A multiple linear regression (MLR) model is also developed to predict chlorophyll-a (Chl-a) concentrations. Both SVM and logical algorithms proved effective. The classification using SVM with four sensor values achieved an accuracy of 92.6%, while applying principal component analysis (PCA) before SVM classification resulted in 91.0% accuracy. In contrast, applying a sequential logical sequence to the boundary conditions of a single SVM model improved accuracy to 95.1%, and incorporating PCA-transformed SVM boundary conditions achieved 100.0% accuracy. This surpassed the performance of nonlinear decision models such as random forest and gradient boosting, which achieved 99.2% accuracy. The MLR model successfully predicted Chl-a levels with a 14.3% error rate for values above 5mg/L. The developed system is an efficient alternative to traditional methods, enhancing real-time monitoring in water quality management.

Restorative aquaculture stimulates cultural and ecological benefits: Centering sugar kelp, indigenous stewardship, and a New York estuary

AbstractIn the face of intensifying climate change and growing shoreline industrialization, coastal socio‐ecological systems can benefit from resource development strategies that provide important environmental benefits. One such development strategy (i.e., coastal nature‐based solution) involves restorative aquaculture, which improves coastal health through the continual delivery of climate and ecological services. Through seaweed cultivation, humans directly benefit from small‐scale resource output and improved environmental conditions like water quality. Aquaculture systems also play a large role in the delivery of cultural benefits and can strengthen place‐based connections through access to significant resource areas. Small‐scale restorative aquaculture systems have received little academic attention despite the crucial role they play in sustainable coastal resource cultivation. In this piece, we explain how the Shinnecock Kelp Farmers, an Indigenous and women‐led restorative aquaculture system, have long been leaders in this field. Situated in the Northeastern United States, the Shinnecock Kelp Farmers culture and develop a native seaweed, namely, sugar kelp. As more Northeastern United States coastal programs and conservation agencies explore restorative aquaculture systems to satisfy coastal development goals, they should work with Indigenous communities in decision‐making seats.

Innovative Multimedia Filtration for Effective Microplastic Removal in Mangrove Ecosystems: A Sustainable Approach to Environmental Health

Microplastic contamination posed a significant threat to mangrove ecosystems, impacting biodiversity and water quality. This study evaluated the effectiveness of a multimedia filtration system using silica sand, zeolite, activated carbon, blood clam shells, and gravel in reducing microplastic levels in mangrove waters. Water samples were collected from the Wonorejo Mangrove Ecotourism in Surabaya, Indonesia, and were treated using two filtration reactors: Reactor 1 with sand media and Reactor 2 with clamshell media. The downward-flow filtration system demonstrated promising results, with Reactor 1 achieving a 54-60% microplastic removal efficiency and Reactor 2 showing superior performance with a 61-65% efficiency. Fiber-type microplastics were most effectively removed, with Reactor 2 achieving a 67% reduction. The findings highlighted the potential of clamshell media in enhancing filtration efficiency and promoting environmental sustainability. While the system offered a viable solution for mitigating microplastic pollution in aquatic environments, challenges such as scalability, cost-effectiveness, and long-term maintenance required further research. Future studies should focus on optimizing filtration media and assessing real-world applicability for broader environmental conservation efforts.

Place and infrastructure in the energy transition: planning with community ties and environmental social value

Abstract The energy transition from predominantly fossil- to non-fossil resources will significantly reshape land use and the built environment, and with it, people’s relationships with place. Especially during the infrastructure-intensive “mid-transition” period during which fossil and non-fossil energy systems operate simultaneously with overlapping functions, understanding how people adapt to, value, or resent living with these infrastructures can enable value-informed design and planning efforts. Here, we use conventional qualitative coding methods with machine learning validation to investigate survey responses from people in the US and Australia who already share space with large-scale energy production regarding their natural and social environments. We focus on 1) how individuals use measures of environmental quality to describe not just their natural or physical environment, but also their community; and 2) how and whether shared natural and social spaces facilitate feelings of well-being and belonging, with the goal of informing consultation and planning for the peak infrastructure mid-transition period. We find a blended understanding of nature and culture, with respondents noting tradeoffs from hosting powerful local industry, but also the value of nature as an antidote to deteriorated or low-quality social spaces. We observe a resistance to change paired with a keen sense of loss of local control such that industry is perceived as an outside force, even when formative for local culture and landscapes. Green spaces and good quality air and water are not just measures of how well the environment is doing, but also a benchmark people use to describe how well their community is doing. Themes are notably consistent across the communities included in this analysis, diverse in their size, resource endowment, and industrial history. This work suggests attention to safeguarding and expanding the shared spaces, institutions, and economies that foster strong social ties could improve community outcomes during transition.

A generative model-based coevolutionary training framework for noise-tolerant softsensors in wastewater treatment processes

Data-driven softsensors have gained widespread application in process monitoring and quality prediction, offering advantages over traditional measurement techniques by mitigating their limitations and costs. However, the effectiveness of softsensor models is often hindered by noise in data acquisition, posing significant challenges for model training. To tackle this issue, this study introduces a coevolutionary training framework based on generative models to mitigate the impact of noise corruption. The framework employs a denoising variational autoencoder to extract global and local features from auxiliary data, enhancing population distribution and constructing a deep nonlinear representation to counter noise effects. Additionally, a dual population coding method inspired by evolutionary computation is proposed, enabling the coevolution of network parameters and structure. The proposed multiobjective evolutionary network optimization with denoising strategy (MENO-D) demonstrated exceptional performance in various experiments. On a water quality prediction dataset, the MENO-D-trained softsensor model achieved the lowest prediction error under 10% and 20% noise interference. Further, on the WWTP benchmark dataset across three weather conditions, MENO-D-trained softsensor model exhibited competitive accuracy and robustness.

Seasonal dynamics of trace elements in water and sediments (suspension and bottom) in the Madeira River Basin, Western Amazon.

Trace elements are ubiquitous global pollutants in modern society and are present in water and biota associated with natural and anthropogenic sources. In this context, this study reports the dynamics of trace elements in the Upper Madeira River Basin, a region that has undergone major environmental changes in the past decades, such as deforestation, mining, and the installation of two large hydroelectric power plants. Bottom sediment, water, and suspended particulate matter (SPM) were collected to determine water quality parameters and trace elements concentrations, quantified by ICP-OES. The average electrical conductivity of water was higher in the Madeira River (110.6 ± 44.4 µS cm-1) than in its tributaries (59.0 ± 49.1 µS cm-1). The Madeira River water has a pH close to neutral (7.0 ± 0.8), compared to the slightly acidic water of the tributaries (6.3 ± 0.9). The average dissolved oxygen in the Madeira River water samples was 6.9 mg L-1, significantly higher than in the tributaries (3.9 mg L-1). The sediment from the Madeira River has higher concentrations of As, Cu, Mn, Ni, Sr, and Zn than the tributaries but lower concentrations of Ba, Mn, Pb, Sr, V, and Zn in the SPM. Trace elements concentrations in the SPM remained relatively constant during the study period and were controlled by seasonality. The average concentrations of trace elements in water, suspended solids, and bottom sediments of the Madeira River Basin were comparable to other non-impacted aquatic ecosystems and can be considered regional environmental background values. In addition, concentrations were below the limits established by the World Health Organization and Brazilian regulations, for human consumption and conservation of aquatic life except, for occasional peaks of Ni and Pb concentrations.

Leveraging invasive mussel contaminant survey data for stepwise prioritization of chemicals of potential concern in the Great Lakes basin.

Historical and ongoing anthropogenic activities coupled with advancements in analytical techniques have led to the detection of large numbers of contaminants in the Laurentian Great Lakes. Consequently, identifying and prioritizing chemicals likely to cause ecological harm represents a challenge for natural resource managers. Previous prioritization efforts have focused on contaminants in sediment, water, and passive samplers, which may not be representative of compounds that bioaccumulate in aquatic organisms. Consequently, the present study adopted a stepwise method to prioritize chemicals of potential concern detected in dreissenid mussels from samples collected across the Great Lakes from 2009-2018. The stepwise method considered environmental fate, detection frequency and exceedance of toxicity quotients based on ecotoxicological effect concentrations. Overall, a total of 153 compounds out of 267 analyzed were detected in dreissenid mussels, 47 of which had water quality effect concentrations, 56 had apical effect concentrations (Tier 1 ECOTOX or apical screening), 17 had non-apical effect concentrations (Tier 2 ECOTOX, Cytotoxic Burst, and ToxCast) and 33 had estimated effect concentrations (QSAR, estimated screening and pharmacological potency). Of the compounds with water quality effect concentrations, 9 were designated as high priority including the herbicide atrazine and 5 polycyclic aromatic hydrocarbons (PAHs) that were previously identified as potentially hazardous within other matrices. Similar contaminants were identified as high priority in a related study of native unionid mussels in the Great Lakes. A total of 27 compounds were low priority, suggesting that these contaminants do not warrant further action based on this dataset. Overall, these findings will facilitate the development of management strategies to mitigate the effects of contaminants on aquatic organisms within the Great Lakes.