Lentic Research Articles

National scale assessment of the occurrence and risk of trace organic contaminants in Canadian Lake sediments.

In Canada studies on the presence of trace organic contaminants (TrOCs) such as pharmaceuticals, personal care products, pesticides and flame retardants in lakes have primarily focused on the water column at localized scales. To address this gap, the occurrence of 44 TrOCs, representative of various types of human activities, was investigated in surface sediments (0-2cm) from 193 lakes across Canada. A total of 28 targeted TrOCs were detected, with 99.5% of the samples containing at least one detection, and one lake exhibiting up to 12 detections. The most frequently detected contaminants (> 20% of samples) were the insect-repellent diethyltoluamide (DEET), the UV filter oxybenzone, the flame retardants tris(2-butoxyethyl) phosphate (TBEP), tris(2-chloroethyl) phosphate (TCEP), and triphenyl phosphate (TPP), the stimulant caffeine, and cotinine, a metabolite of the stimulant nicotine. Median reported concentrations of the targeted TrOCs ranged from 0.017pgg-1 to 359ngg-1, with a maximum value of 23,700ngg-1 observed for DEET in one lake. The geographic distribution of analyte concentrations varied by compound class: pharmaceuticals and consumer product additives were predominantly found in the more urbanized regions of Ontario and Quebec, whereas personal care products such as DEET and oxybenzone were more frequently detected in the western provinces of Canada. An environmental risk assessment based on an additive model conducted on three aquatic organisms (algae, cladocerans, and fish) revealed that 4% and 6% of the lakes posed a potentially high risk for cladocerans and algae, respectively. A geographical analysis indicated that lakes in the south of the eastern provinces of Canada presented the highest risks for all three species. These findings represent the first large-scale results detailing the extent of contamination caused by TrOCs on Canadian lake sediments. They establish reference levels that can guide future monitoring efforts and inform policy discussions aimed at protecting lake ecosystems.

Production and transfer of essential fatty acids in a man‐made tropical lake ecosystem

Production and transfer of essential fatty acids in a man‐made tropical lake ecosystem

Predicting Chlorophyll-a Concentrations in the World's Largest Lakes Using Kolmogorov-Arnold Networks.

Accurate prediction of chlorophyll-a (Chl-a) concentrations, a key indicator of eutrophication, is essential for the sustainable management of lake ecosystems. This study evaluated the performance of Kolmogorov-Arnold Networks (KANs) along with three neural network models (MLP-NN, LSTM, and GRU) and three traditional machine learning tools (RF, SVR, and GPR) for predicting time-series Chl-a concentrations in large lakes. Monthly remote-sensed Chl-a data derived from Aqua-MODIS spanning September 2002 to April 2024 were used. The models were evaluated based on their forecasting capabilities from March 2024 to August 2024. KAN consistently outperformed others in both test and forecast (unseen data) phases and demonstrated superior accuracy in capturing trends, dynamic fluctuations, and peak Chl-a concentrations. Statistical evaluation using ranking metrics and critical difference diagrams confirmed KAN's robust performance across diverse study sites, further emphasizing its predictive power. Our findings suggest that the KAN, which leverages the KA representation theorem, offers improved handling of nonlinearity and long-term dependencies in time-series Chl-a data, outperforming neural network models grounded in the universal approximation theorem and traditional machine learning algorithms.

Global patterns and drivers of coupling between anammox and denitrification processes across inland aquatic ecosystems

Denitrification and anammox collectively drive nitrogen loss from aquatic ecosystems, yet their global patterns and interactions remain unclear. To fill this gap in knowledge, we compiled a global dataset on anammox and denitrification, encompassing river, lake, wetland, and estuary ecosystems and comprising 2539 observations from 136 peer-reviewed papers. Here, we show that aquatic ecosystems with abundant denitrifying bacteria tend to have abundant anammox bacteria, but the abundance of anammox bacteria is lower than that of denitrifying bacteria. Importantly, we observed that hotspots for denitrification in aquatic ecosystems were also hotspots for anammox, and we explained the variation in anammox (21.55 (95% CI: 8.21–58.90) nmol-N g-1 day-1) and denitrification rates (171.76 (95% CI: 65.40–519.25) nmol-N g-1 day-1) across aquatic ecosystems. These results highlight that anammox should be included in models for accurate nitrogen budget assessment in aquatic ecosystems on a global scale, especially in the context of future climate warming.

Impact of Land Use Change on Lake Pollution Dynamics: A Case Study of Sapanca Lake, Turkey

Modeling non-point source pollution dynamics in inland lake basins is essential for safeguarding water quality, maintaining ecosystem integrity, protecting public health, and advancing long-term environmental sustainability. This study explores non-point pollution dynamics in the Sapanca Lake basin, Turkey, in association with the basin’s land use, land cover, hydrology, pollutant sources, and water quality parameters. The required data were gathered via a three-year monitoring program, which was carried out at 12 sampling stations around the lake, as well as using the collecting field measurements and GIS databases. Stepwise multiple regression analysis was employed to determine the best relation between non-point pollutants and land features. The results showed that urbanization and population density have significant correlations with the total nitrogen (TN) and total phosphorus (TP) in the study areas. Rivers crossing pristine areas, such as forests and uncultivated lands, demonstrated better water quality, thereby positively contributing to the lake ecosystem conservation. The highest nutrient loads were observed in streams that flow through highly urbanized sub-basins, followed by predominantly agricultural areas. This is likely due to runoff from urban environments, leaching from cultivated land, and contributions from livestock and tourism facilities. Conversely, densely forested regions exhibited the lowest levels of nutrient loads, highlighting their capacity for nutrient retention. The peak levels of non-point source pollution (TN = 5.22 mg/L and TP = 0.53 mg/L) were recorded in catchments with the highest degree of urbanization, whereas the lowest values (TN = 0.28 mg/L and TP = 0.04 mg/L) were found in the least urbanized areas. These findings emphasize that nutrients primarily impact water quality because of increasing urban and agricultural activities, while forested land plays a vital role in preserving lake water quality. To ensure sustainable water quality in lake basins, it is essential to strike a careful balance between protective measures and utilization policies, prioritizing conservation efforts.

Record-setting cyanobacterial bloom in the largest freshwater lake in northern China caused by joint effects of hydrological variations and nutrient enrichment.

Cyanobacterial blooms represent a significant environmental issue posing widespread threats to global aquatic ecological health. Climate and nutrient enrichment were the most studied factors modulating cyanobacterial blooms in eutrophic lakes. However, in many floodplain lakes, the importance of hydrological variation in driving and predicting cyanobacterial blooms is often overlooked and largely underestimated, which has hampered the effectiveness of lake management. Here, we use a process-based lake ecosystem model (GOTM-WET) to evaluate the potential drivers of the record-setting cyanobacterial bloom during summer 2022 (>70% of lake area) in the largest shallow lake in Northern China (Hulun Lake). The model was calibrated based on a comprehensive field dataset including both remote sensing (surface water temperature) and in-lake observations (water quality). We performed a scenario analysis with various combinations of nutrient loading, hydrological variations and climate change. Our modeling results unravel that the profound water level rise through 2021 to 2022 serves as the main trigger of the severe cyanobacterial bloom in summer 2022, whereas climate factors demonstrate marginal impacts. Our model predicted a decrease of 60.5% in the annual average of cyanobacterial biomass when water level remained stable. In addition, both water level change and nutrient concentration explains 72.5% of the variance in long-term maximum area of cyanobacterial blooms. Our model further reveals that the water level rise drives the cyanobacterial bloom via bringring in excessive nutrient to lake water column from the lake basin. Thus, our results suggest that continuous increase in water level across two years could serve as an early warning signal to cyanobacterial blooms in the consecutive summer. Our findings may be applicable to similar temperate shallow lakes in floodplain areas, especially for those located in agricultural and pasture regions with abundant nutrient legacy, thus may provide a new indicator for cyanobacterial blooms prediction.

Distribution patterns and community assembly processes of bacterial communities across different sediment habitats of subsidence lakes.

Subsidence lakes, formed due to extensive underground coal mining activities, present both ecological challenges and opportunities for alternative land use practices, such as photovoltaic power generation and aquaculture. However, the ecological consequences of these anthropogenic activities on bacterial communities within subsidence lakes remain largely unexplored. To address this knowledge gap, we conducted a comprehensive investigation of bacterial communities in two typical subsidence lake districts located in Huainan, Anhui Province, China. A total of 44 sediment samples were collected across four distinct habitats: photovoltaic zones (PV), aquaculture zones (AC), photovoltaic-aquaculture zones (PV_AC), and unimpacted zones (Natural). Bacterial communities were investigated using 16S rRNA gene sequencing and various statistical methods. Our results revealed that the α-diversity and network complexity of bacterial communities in PV, PV_AC, and AC habitats are significantly higher than habitats of Natural (P<0.01). Specifically, the α-diversity is the highest in PV habitats, while the network complexity is the highest in AC habitats. The network stability is the highest in PV and PV_AC habitats, while it is the lowest in AC. There were also significant differences in community structure among different habitats, Nitrospirota in PV (31.9%) was higher than that in the other three habitats, the percentage of Firmicute in Natural (21.96%) and PV_AC (13.70%) was higher than other two habitats, Actinobacteriota has the highest proportion in AC (20.93%). Furthermore, stochastic processes dominate community assembly in PV, PV_AC, and AC habitats, it has the highest proportion in PV, particularly on dispersal limitation (DL). However, deterministic processes prevail in Natural habitats, particularly on heterogeneous selection (HeS). Collectively, these findings highlight the significant differences in sediment bacterial communities across various habitats in subsidence lakes. Our study provides valuable insights into understanding the ecological implications of different habitats in subsidence lake ecosystems.

Impacts of climate-induced drought on lake and reservoir biodiversity and ecosystem services: A review.

Intensifying extreme droughts are altering lentic ecosystems and disrupting services provisioning. Unfortunately, drought research often lacks a holistic and intersectoral consideration of drought impacts, which can limit relevance of the insights for adaptive management. This literature review evaluated the current state of lake and reservoir extreme drought research in relation to biodiversity and three ecosystem services. The study findings demonstrated that few articles linked or discussed drought implications with one or more ecosystem services, instead focusing primarily on biodiversity. Drought effects on biodiversity varied among species and taxonomic groups. In the limited literature that included ecosystem service provisioning, droughts had a general negative effect. Drinking water supply can decrease and become more costly. Decreasing water flow and volume can reduce hydropower generation. Degraded water quality can also impact recreation. Future intersectoral collaborations and research on intensifying droughts should support adaptive management efforts in mitigating drought impacts.

Nitrogen fixation may not alleviate stoichiometric imbalances that limit primary production in eutrophic lake ecosystems.

Ecosystem-scale primary production may be proximately limited by nitrogen (N) but ultimately limited by phosphorus (P) because N2 fixation contributes new N that accumulates relative to P at ecosystem scales. However, the duration needed to transition between proximate N limitation and ultimate P limitation remains unknown for most ecosystems, including lakes. Here we present the results of a fully replicated, multi-annual lake mesocosm experiment that permitted full air-water-sediment interactions that mimicked lake ecosystem ecology. We manipulated N supply relative to P to achieve a gradient of N:P stoichiometry. Despite N2 fixation contributing as much as 80% of reactive N in the low N treatments, phytoplankton biomass in these treatments was not different from the unfertilized controls. This suggests that primary production remained N limited in the lowest N treatments, even when N2 fixation was substantial. Although fixed N inputs reduced the N imbalance relative to P in the low N treatments seasonally, fixed N did not accumulate over multiple years. Additionally, reactive N did not readily accumulate in the high N treatments. Instead, water column stoichiometry was proportional to the experimental N and P additions, suggesting a strong influence from external nutrient loading. Thus, we found no evidence that N accumulation from N2 fixation was sufficient to trigger a transition to ultimate P limitation seasonally or across our 3-year experiment. Rather, our results indicate that proximate N limitation perpetuates in eutrophic lakes, likely due to N export being proportional to its inputs. These findings offer new insight regarding the biogeochemical controls on ecosystem stoichiometry and their influence on the timeframe for proximate N limitation and ultimate P limitation in freshwater, marine, and terrestrial ecosystems.

Microbial community structure and causal analysis in sediments of shallow eutrophic freshwater lakes under heavy metal compound pollution.

Heavy metals, due to their toxicity, persistence, and non-biodegradability, have become some of the most severe environmental pollutants globally. Their accumulation in lake sediments can significantly impact aquatic ecosystems' biogeochemical cycles by altering the ecological dynamics of microbial communities. To further elucidate the mechanisms underlying microbial responses to complex heavy metal pollution in lake sediments, sediment samples were collected from Nan Yi Lake, and their physicochemical properties and microbial composition were systematically analyzed. The results demonstrated that the sediments of Nan Yi Lake were significantly contaminated with heavy metals, which were identified as the predominant factors shaping microbial community structure. Heavy metals influenced microbial richness and distribution patterns along sediment depth gradients, driving the establishment of optimal ecological niches. Meanwhile, other physicochemical factors indirectly affected microbial communities by modulating the concentration of heavy metals. Furthermore, the microbial co-occurrence network was closely associated with the concentrations of Fe and As, with sediment particle size also playing a contributing role. This study highlights the intricate interactions between physicochemical factors and microorganisms, offering critical insights into the multifaceted impacts of heavy metal compound pollution on lake ecosystems. It provides a scientific foundation for effective management of lake environmental pollution and ecological restoration efforts.

Two decades of bacterial ecology and evolution in a freshwater lake.

Ecology and evolution are considered distinct processes that interact on contemporary time scales in microbiomes. Here, to observe these processes in a natural system, we collected a two-decade, 471-metagenome time series from Lake Mendota (Wisconsin, USA). We assembled 2,855 species-representative genomes and found that genomic change was common and frequent. By tracking strain composition via single nucleotide variants, we identified cyclical seasonal patterns in 80% and decadal shifts in 20% of species. In the dominant freshwater family Nanopelagicaceae, environmental extremes coincided with shifts in strain composition and positive selection of amino acid and nucleic acid metabolism genes. These genes identify organic nitrogen compounds as potential drivers of freshwater responses to global change. Seasonal and long-term strain dynamics could be regarded as ecological processes or, equivalently, as evolutionary change. Rather than as distinct interacting processes, we propose a conceptualization of ecology and evolution as a continuum to better describe change in microbial communities.

Water regulation weakens the relationship between biodiversity and ecosystem multifunctionality: Insights from a highly managed Chinese lake.

Water diversions can mitigate water scarcities by strategically reallocating water resources. Despite their benefits, these interventions may profoundly affect biodiversity and multiple ecological functions ("multifunctionality") within highly managed lake systems. However, the specific impact of such interventions on the relationship between biodiversity and multifunctionality remains elusive, which limits our grasp of how water regulation shapes the dynamics of managed lake ecosystems. In this study, we investigated the differences in biodiversity and ecological functions between periods with and without water regulation in a highly managed lake, and extended this analysis by calculating diversity and multi-diversity indices and assessing the ecosystem's multifunctionality, with the goal of improving our understanding of how water regulation affects the ecological integrity of such lakes. We found that the diversity and multi-diversity indices both decreased markedly during the regulation period, indicating a negative impact on biodiversity. Conversely, multifunctionality remained stable. To clarify these responses, we developed linear mixed-effects models that incorporated multi-diversity indices and multifunctionality. Our analysis uncovered a robust positive correlation between multi-richness and multifunctionality. However, this relationship weakened during the periods with water regulation. Finally, we established structural equation models based on trophic cascade theory to pinpoint the key biological groups that sustained multifunctionality. We found that plants and omnivorous fish were instrumental in supporting multifunctionality during the non-regulation period, whereas planktivorous fish played a crucial role during the regulation period. These findings significantly enhance our understanding of how water regulation influences both biodiversity and multifunctionality, and provide insights for future management of regulated lakes.

Applying 224Ra and 223Ra to Trace Lateral Groundwater Discharge into Lake Qinghai, China

AbstractQuantifying lacustrine groundwater discharge (LGD) is important for understanding the dynamics of lake ecosystems and their expansion. This study focuses on Lake Qinghai, employing radium isotope models to evaluate the contributions of both shallow and deep groundwater. The data indicate that the activity of 223Ra and 224Ra demonstrates a pronounced gradient, decreasing from the shoreline to the center of Lake Qinghai. Additionally, vertical stratification characteristics were observed. The spatial distribution of radium isotope activity suggests that there is discharge of both shallow and deep groundwater into the lake. Deep groundwater migrates slowly and its apparent age reflects the time elapsed since the water became enriched in Ra and was isolated from the source, in the study system this age is estimated to be 10.1 d. In contrast, shallow groundwater displayed varied apparent ages in different regions: 7.9 d in the north, 13.1 d in the south, and 7.4 d in the southeastern area of the lake. The LGDs of shallow groundwater discharge in the north, south, and southeast areas of Lake Qinghai were estimated by 224Ra as 1.89 × 106 to 2.69 × 106 m3/d, 3.25 × 106 to 3.99 × 106 m3/d, and 4.51 × 106 to 6.33 × 106 m3/d, respectively. For deep groundwater, the LGD was 0.16 × 106 to 0.29 × 106 m3/d. Annually, the total LGD fluxes of shallow and deep groundwater are 27.86 × 108 to 37.59 × 108 m3/year and 0.58 × 108 to 1.06 × 108 m3/year, respectively. This study is the first to evaluate shallow and deep groundwater discharge around the lake. Understanding these discharge dynamics is essential for developing effective management strategies to preserve lake environments.

Integrating machine learning and remote sensing for long-term monitoring of chlorophyll-a in Chilika Lagoon, India.

Chlorophyll-a (Chla) is recognized as a key indicator of water quality and ecological health in aquatic ecosystems, offering valuable insights into ecosystem dynamics and changes over time. This study aimed to to develop and validate a robust ML model for estimating Chla using Landsat data, produce a time series of Chl a maps, and analyze the spatiotemporal variability of Chla in Chilika Lagoon, Asia's largest brackish water lagoon. Nine ML regression models, including Extreme Gradient Boost, Support Vector Regression, Random Forest, and Bagging Regression, were evaluated using Landsat imagery and field data. After extensive hyperparameter tuning, the Bagging Regression model achieved thehighest estimation accuracy, with an R2 of 0.8776 and a Root Mean Square Error of 0.9190µg/L. This optimized model was subsequently applied to generate a time series of Chla maps for Chilika Lagoon from 2014 to 2023, revealing notable seasonal and spatial variability. Chla concentrations peaked during summer months and were generally higher in the lagoon's northwestern region, gradually decreasing towards the southern area. This approach holds promise for precise Chla monitoring in diverse lagoon environments and may aid in the assessment and management of similar coastal and inland lake ecosystems worldwide.

Environmental risk assessment of the surface sediments based on trace elements analysis from the largest freshwater lake in the southern slope of the Himalaya, Nepal.

Freshwater ecosystems, including high-altitude lakes, can be affected by trace metal pollution derived from a mix of natural sources and anthropogenic activities. These pollutants often collect in surface sediments, with notable concentrations in the deeper areas of lakes. To evaluate the environmental risk associated with metal contaminated sediment in Rara Lake, southern Himalaya, surface sediment samples were systematically collected in November 2018, with a subsequent specific emphasis on determinations of trace element concentrations. Subsequent analysis revealed nine elements exhibiting a descending mean concentrations order: iron (Fe) > manganese (Mn) > chromium (Cr) > rubidium (Rb) > nickel (Ni) > strontium (Sr) > cobalt (Co) > copper (Cu) > cadmium (Cd), of 7205.55mgkg-1, 2290.34mgkg-1, 176.29mgkg-1, 153.78mgkg-1, 51.86mgkg-1, 44.61mgkg-1, 38.89mgkg-1, 29.11mgkg-1, and 0.10mgkg-1, respectively. Comparisons to sediment quality guidelines highlight that Mn, Cr, Cu, and Cd as significant threats to the aquatic ecosystem in Rara Lake. To assess the impact of metal pollution, enrichment factor (EF), geo-accumulation index (Igeo), pollution load index (PLI), and contamination factor (CF) were computed. All metals (except Cd) had Igeo value exceeding 5, displaying strong contamination. EF values for Mn, Cr, Co, and Ni metals were > 10, indicating severe effects of anthropogenic influences. CF and PLI values also indicated significant pollution for most of the investigated sites. Elevated trace element concentrations have the potential to adversely affect water, sediment, and aquatic life, also potentially impacting nutrient cycling and microbial activity. This study enhances our understanding of the metal compositions within Rara Lake sediments and provides a basis for more effective lake management and pollution control strategies. Urgent action by regional governing bodies is crucial to address the early stages of metals pollution, including identification and controlling of pollution sources, by appropriate regulations, optimizing industrial practices, and remediating existing pollution to prevent further contamination and protect the lake ecosystem.

Assessment of Lake Gusinoe Ecosystem Based on Macrophyte Analysis (Republic of Buryatia)

Lake Gusinoye is the largest lake in the Baikal natural territory after Lake. Baikal, around which the Gusnoozersky industrial complex is formed. In the macrophyte flora of the lake. 27 species from 21 families have been identified. The spatial structure of vegetation is determined by the morphometric characteristics of the lake's littoral zone. The northern and southern sectors of the lake are the most overgrown. Compared to previous years of research, an increase in the area of charophyte and moss thickets was revealed. The progressive overgrowing of the lake helps to maintain the processes of self-purification of the ecosystem, removes pollutants and nutrients from circulation and ensures the sustainable development of the ecosystem. The ability of the lake ecosystem to maintain the overall level of productivity due to restructuring of the structure is also evidenced by data on other communities of hydrobionts (zooplankton, zoobenthos and ichthyocenoses). The results of our studies of the chemical composition of macrophytes of Lake. Gusinoe showed, in comparison with the data of the 90s, the concentrations of chemical elements in macrophytes decreased. The recorded ecological state of the lake is determined by a complex of factors, both natural and natural (water level, temperature rise, littoral morphometry, chemical composition of water).

Community Assembly Mechanisms of nirK- and nirS-type Denitrifying Bacteria in Sediments of Eutrophic Lake Taihu, China.

Denitrifying bacteria, particularly nirK- and nirS-type, are functionally equivalent and could occupy different niches, but their community assembly mechanisms and responses to environmental heterogeneity are poorly understood in eutrophic lakes. In this study, we investigated the community assembly mechanisms of nirK- and nirS-type denitrifying bacteria and clarified their responses to sediments environmental factors in Lake Taihu, China. The quantitative real-time PCR and Illumina HiSeq-based sequencing revealed that the abundance and composition of two types of denitrifyingbacterial communities varied among different sites in the sediments of Lake Taihu. The functions of these two types of denitrifying bacteria were assigned to mainly nitrogen cycling along with carbon, oxygen, and sulfur cycling, indicating their diverse ecosystems functions. Neutral community model showed that majority of nirK- and nirS-type denitrifying bacteria were neutrally distributed, while dispersal and selection were the dominant drivers in shaping community assembly of nirK-type bacteria. The community assembly of nirS-type was mainly driven by homogeneous selection. We found complex network interactions between nirK- and nirS-type denitrifying bacteria with other bacterial communities, indicating the importance of otherbacterial coexistence for ecosystem functions by denitrifying bacteria in lake sediments. Keystone taxa of other bacteria showed the highest interactions with denitrifying bacteria; further, a strong significant correlation between keystone taxa with environmental factors and sediment enzyme content revealed by Mantel tests. Specially, total phosphorous was the key environmental factor determining the composition and diversity of nirK and nirS-type denitrifying bacteria in lake sediments, whereas NAR, AmoA, and NIR were the key reductase enzymes directly or indirectly affected to them. Our results provide significant insights into understanding the effects of changing nirK- and nirS-type denitrifying bacterial diversities and underlying community assembly mechanisms under changing environmental conditions in eutrophic lake ecosystems.

Climate displaces deposition as dominant driver of dissolved organic carbon concentrations in historically acidified lakes

Climate and atmospheric deposition interact with watershed properties to drive dissolved organic carbon (DOC) concentrations in lakes. Because drivers of DOC concentration are inter-related and interact, it is challenging to assign a single dominant driver to changes in lake DOC concentration across spatiotemporal scales. Leveraging forty years of data across sixteen lakes, we used structural equation modeling to show that the impact of climate, as moderated by watershed characteristics, has become more dominant in recent decades, superseding the influence of sulfate deposition that was observed in the 1980s. An increased percentage of winter precipitation falling as rain was associated with elevated spring DOC concentrations, suggesting a mechanistic coupling between climate and DOC increases that will persist in coming decades as northern latitudes continue to warm. Drainage lakes situated in watersheds with fine-textured, deep soils and larger watershed areas exhibit greater variability in lake DOC concentrations compared to both seepage and drainage lakes with coarser, shallower soils, and smaller watershed areas. Capturing the spatial variability in interactions between climatic impacts and localized watershed characteristics is crucial for forecasting lentic carbon and nutrient dynamics, with implications for lake ecology and drinking water quality.

Vertical distribution characteristics of microplastics and bacterial communities in the sediment columns of Jianhu lake in China.

Microorganisms change the properties of microplastics, at the same time, microplastics can also affect the distribution of microorganisms. To investigate this issue, we chose to study Jianhu Lake, a plateau lake in southwestern China, by collecting data at three sampling locations. The microplastics and bacterial communities in the sediment columns of Jianhu Lake were sampled within a 0 to 60cm profile, and the basic characteristics of microplastic abundance, shape, color, size, and polymer type were determined accordingly, via their collection, separation, extraction, and identification. The bacterial community in the sediment samples were identified using 16S rRNA high-throughput sequencing, and we assessed whether those microplastic characteristics influenced the community composition and structure. We found the abundance of microplastics ranged from 624 to 3050 particles/kg (dw [dry weight]) in the three sediment columns. Line microplastics accounted for the largest proportion and these were found distributed in each layer. The polymer types present in the largest proportions were rayon (RY), polyester terephthalate (PET) and low-density styrene-butadiene-styrene (SBS). Among the bacterial communities in the sediment columns, the dominant phyla were Chloroflexi, Sva0485, Acidobacteriota, etc. The co-occurrence network analysis between the bacterial community and microplastic features in the sediment columns of Jianhu Lake revealed that there was a correlation between them, and the network were more complex at a depth of 20-40cm. Our results demonstrate that microplastics can affect the diversity and structural characteristics of microbial communities in a lake ecosystem.

Spatiotemporal Analysis and Risk Prediction of Water Quality Using Copula Bayesian Networks: A Case in Qilu Lake, China

Lake water pollution under anthropogenic influences exhibits characteristics of high uncertainty, rapid evolution, and complex control challenges, presenting substantial threats to ecological systems and human health. Quantitative risk prediction provides crucial support for water quality deterioration prevention and management. This study employed the Copula Bayesian Network model to conduct a comprehensive risk assessment of water quality in Qilu Lake, China (2010–2020), incorporating inter-indicator correlations and multiple uncertainty sources. Analysis revealed generally “worse” water quality conditions (5.10 ± 1.35) according to established index classifications, with predicted probabilities of reaching “deteriorated” status ranging from 11.80% to 47.90%. Significant spatial and temporal variations in water quality and pollution risk were observed, primarily attributed to intensive agricultural non-point source loading and water resource deficiency. The study established early warning thresholds through key indicator concentration predictions, particularly for the southern region where “deteriorated” risk levels corresponded to specific ranges: TN (3.42–8.43 mg/L), TP (0.07–1.29 mg/L), and CODCr (27.75–67.19 mg/L). This methodology effectively characterizes lake water quality evolution while enabling risk prediction through key indicator monitoring. The findings provide substantial support for water pollution control strategies, risk management protocols, and regulatory decision-making for lake ecosystem administrators.