Mass-Balance Modeling of Metal Loading Rates in the Great Lakes

Spatiotemporal and multi-isotope assessment of metal sedimentation in the Great Lakes

This study investigates spatiotemporal dynamics in metal sedimentation in the North American Great Lakes and their underlying biogeochemical controls. Bulk geochemical and isotope analyses of n = 72 surface and core sediment samples show that metal (Cu, Zn, Pb) concentrations and their isotopic compositions vary spatially across oligotrophic to mesotrophic settings, with intra-lake heterogeneity being similar or higher than inter-lake (basin-scale) variability. Concentrations of Cu, Zn, and Pb in sediments from Lake Huron and Lake Erie vary from 5 to 73 mg/kg, 18-580 mg/kg, and 5-168 mg/kg, respectively, but metal enrichment factors were small (<2) across the surface- and core sediments. The isotopic signatures of surface sediment Cu (δ65Cu between -1.19‰ and +0.96‰), Zn (δ66Zn between -0.09‰ and +0.41‰) and Pb (206/207Pb from 1.200 to 1.263) indicate predominantly lithogenic metal sourcing. In addition, temporal trends in sediment cores from Lake Huron and Lake Erie show uniform metal concentrations, minor enrichment, and Zn and Pb isotopic signatures suggestive of negligible in-lake biogeochemical fractionation. In contrast, Cu isotopic signatures and correlation to chlorophyll and macronutrient levels suggest more differentiation from source variability and/or redox-dependent fractionation, likely related to biological scavenging. Our results are used to derive baseline metal sedimentation fluxes and will help optimize water quality management and strategies for reducing metal loads and enrichment in the Great Lakes and beyond.

The Ins and Outs of Per- and Polyfluoroalkyl Substances in the Great Lakes: The Role of Atmospheric Deposition

As part of the Integrated Atmospheric Deposition Network, precipitation (n = 207) and air (n = 60) from five sites and water samples (n = 87) from all five Great Lakes were collected in 2021-2023 and analyzed for 41 per- and polyfluoroalkyl substances (PFAS). These measurements were combined with other available data to estimate the mass budget for four representative compounds, PFBA, PFBS, PFOS, and PFOA for the basin. The median Σ41PFAS concentrations in precipitation across the five sites ranged between 2.4 and 4.5 ng/L. The median Σ41PFAS concentration in lake water was highest in Lake Ontario (11 ng/L) and lowest in Lake Superior (1.3 ng/L). The median Σ41PFAS concentration in air samples was highest in Cleveland at 410 pg/m3 and lowest at Sleeping Bear Dunes at 146 pg/m3. The net mass transfer flows were generally negative for Lakes Superior, Michigan, and Huron and positive for Lakes Erie and Ontario, indicating that the three most northern lakes are accumulating PFAS and the other two are eliminating PFAS. Atmospheric deposition is an important source of PFAS, particularly for Lake Superior.

Spatiotemporal ecological risk evaluation and source identification of heavy metals and nutrients in the water and lake surface sediment in a protected catchment area of a volcanic lake

Indonesia has numerous lakes; however, research on the spatiotemporal sediment quality and source identification in lakes remains limited. The overaccumulation of heavy metals and nutrients in lakes severely threatens aquatic ecosystems. This study aims to identify potential sources of metallic deposits (Cu, Pb, Cr, Fe, Al, and Cd) and nutrients (TN and TP) in lake-surface sediment, utilizing enrichment factors (EF), geoaccumulation indices (Igeo), potential ecological risk indices (Er), and risk indices (RI). Multivariate statistical analyses, including principal component analysis (PCA) and Pearson's correlation analysis, were conducted to pinpoint pollution sources linked to land use. Eight sampling sites for surface sediment and water were examined in both wet and dry seasons at Menjer Lake, chosen for its diverse applications in tourism, hydropower, floating net cages, and extensive agriculture in its catchment. Correlation and PCA results indicated that Pb, Fe, and Al mainly originate from tourism, while Al, Fe, TN, and TP are associated with agriculture. The highest average loading from land use was observed in agriculture (> 0.8), floating net cages (> 0.76), and tourism (> 0.68). Furthermore, the highest loading from nutrients and all metals were TP (> 0.71) and all metals (> 0.35), respectively. Ecological risk assessment revealed low to moderately polluted EFs and Igeo in the dry season. However, Menjer Lake's Er and RI for heavy metals were generally classified as unpolluted.

Contrasting copper concentrations and isotopic compositions in two Great Lakes watersheds

Copper (Cu) stable isotopes can elucidate the biogeochemical controls and sources governing Cu dynamics in aquatic environments, but their application in larger rivers and catchments remains comparatively scarce. Here, we use major and trace element hydrogeochemical data, Cu isotope analyses, and mixing modeling, to assess Cu loads and sources in two major river systems in Ontario, Canada. In both the Spanish River and Trent River catchments, aqueous hydrochemical compositions appeared reasonably consistent, but Cu concentrations were more variable spatially. In the Spanish River, waters near (historic) industrial mining activities displayed positive Cu isotope compositions (δ65CuSRM-976 between +0.75 ‰ and +1.01 ‰), but these signatures were gradually attenuated downstream by mixing with natural background waters (δ65Cu -0.65 ‰ to -0.16 ‰). In contrast, Trent River waters exhibited more irregular in-stream Cu isotope patterns (δ65Cu from -0.75 ‰ to +0.21 ‰), beyond the variability in Cu isotope signatures observed for adjacent agricultural soils (δ65Cu between -0.26 ‰ and +0.30 ‰) and lacking spatial correlation, reflective of the more diffuse sourcing and entwined endmember contributions to Cu loads in this catchment. This work shows that metal stable isotopes may improve our understanding of the sources and baseline dynamics of metals, even in large river systems.

Determining heavy metal pollution in sediments from the largest impounded lake in the eastern route of China's South-to-North Water Diversion Project: Ecological risks, sources, and implications for lake management

The eastern route of the South-to-North Water Diversion Project (ER-SNWDP) is a major human health project designed to alleviate the water scarcity in the Beijing-Tianjin-Hebei region in China. Impounded lake water security is directly related to the water diversion project effectiveness. At present, there is not a thorough understanding of the sediment heavy metals in Lake Hongze, the largest impounded lake of the ER-SNWDP. Consequently, this study reports a distribution analysis of Cu, Zn, Pb, Cr, Cd, As, Hg, and Ni in 101 sediment samples from Lake Hongze; we, utilized the enrichment factor, geoaccumulation index, and potential ecological risk index for the are to determine the ecological risk of heavy metals. The heavy metal source was examined with correlation analysis and principal component analysis-multiple linear regressions. The results showed that the average heavy metal content (Cu, Zn, Pb, Cr, Cd, As, Hg, Ni) were 0.03-1.57 times greater than the Jiangsu Province background values. Cd, As, and Hg were the main contributors to the Lake Hongze ecological risk. Spatially, the open water area was the most polluted among the four lake parts, and most of the flushing area had a low ecological risk. Chengzi bay and the western lake area have similar risk profiles, but are lower than the open water area risk. Source analysis showed that nonpoint-source agricultural pollution and industrial production were important pollution sources, while a considerable portion of the heavy metal content came from atmospheric deposition and natural sources. This study identified the main contamination areas and revealed the possible sources of each heavy metal; as such, this study can serve as a reference for the remediation and management of Lake Hongze to ensure the water safety of the ER-SNWDP.

A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins.

Loads and elimination of trace elements in wastewater in the Great Lakes basin

The growing use of trace elements in industrialized societies is driving an increase in the occurrence of trace elements in anthropogenic waste streams globally. Yet, the large-scale sources of many trace elements to wastewater and their elimination during treatment remain poorly understood and potential environmental impacts on freshwater systems therefore unclear. We screened 42 wastewater treatment facilities in the North American Great Lakes basin and deployed a black-box approach to calculate representative estimates for average per-capita trace element loads and basin-scale effluent discharge rates, as well as trace element removal efficiencies across different treatment technologies. Our results show different removal of specific groups of trace elements during wastewater treatment: average removal efficiencies were 25% for alkali metals, 50% for alkaline earth metals, 74% for transition metals, and 85% for rare earth elements. Higher elimination of the majority of trace elements was generally achieved by more advanced, tertiary treatment types. Elemental loads generally followed natural abundance patterns, but anomalous loading rates were observed for various trace elements across the sampled facilities. By examining geospatial attributes of the sampled sewersheds, trends in select trace element loads were qualitatively tied to possible point sources and diffuse sources. Overall, these results illustrate the potential of wastewater surveillance to inform environmental management of emerging trace element contaminants.