Eutrophication of lake waters in China: cost, causes, and control

Metabolomic analysis reveals contrasting effects of PFOS and PFAS on cyanobacterial bloom and metabolic pathways in eutrophic water

Per- and polyfluoroalkyl substances (PFAS) are widespread in the aquatic environment, and the toxic effects of individual compounds on microalgae have been extensively documented. However, the differences in the impacts of single versus combined PFAS exposures on cyanobacterial blooms are still ambiguous. Herein, we explored the impacts and mechanisms of both single perfluorooctane sulfonate (PFOS) and combined PFAS exposure on Microcystis aeruginosa (M. aeruginosa) bloom under eutrophic conditions. The results indicated that PFOS suppresses algal bloom by diminishing growth rates and photosynthetic capabilities, accompanied by elevated expression of microcystin toxins and the induction of oxidative stress. Conversely, PFAS at environmentally relevant concentrations promote cyanobacteria bloom by enhancing growth rates and photosynthetic activity. Metabolomics analyses revealed that PFOS disrupts cell energy metabolism, reducing energy production and enhancing fatty acid synthesis. Additionally, PFOS disrupts purine metabolism, downregulating pyrimidine and amino acid metabolism. On the other hand, PFAS boosts the tricarboxylic acid cycle, upregulates amino acid synthesis, intensifies nucleotide metabolism, maintains the intracellular antioxidant system, and promotes algal growth. This study highlights the differences in the impacts of various perfluorinated compound exposure patterns on cyanobacterial bloom, offering new insights for improved water environment management.

Ecological threshold responses of fish communities to lake eutrophication

Identifying ecological thresholds, which are critical points at which ecosystems undergo abrupt shifts in structure and function, has become a cornerstone of conservation and management strategies. This study introduces a novel framework, the "ecological threshold spectrum", by integrating exploration, inference, and validation approaches, to address the limitations of existing methods. The effectiveness of this analytical framework is demonstrated through a case study on fish communities in shallow eutrophic lakes located in the middle and lower reaches of the Yangtze River. We analyzed phosphorus loading as a key driver of fish community and identified three critical ranges of total phosphorus (TP) using Gradient Forest, Threshold Indicator Taxa Analysis and Boosted Regression Trees. The first range, TP < 0.08 mg L-1, is characterized by submerged macrophyte dominance and high fish biodiversity. The second range, TP between 0.08 and 0.12 mg L-1, represents a transitional phase with significant species and diversity loss. The third range, TP between 0.12 and 0.17 mg L-1, is dominated by pollution-tolerant species and associated with severe turbidity. We illustrate how the different analytical methods complement one another by capturing both species-specific and community-level responses to environmental gradients, thereby enabling more robust threshold identifications based on multiple response variables. Our framework addresses uncertainties in ecological threshold detection by combining the strengths of multiple methods while accounting for data quality, noise, and species-specific masking effects. The findings emphasize the importance of TP control in maintaining ecological stability and inform targeted restoration strategies at different eutrophication stages. Overall, the ecological threshold spectrum framework offers new insights into state transitions and provides a robust, practical approach to sustainable management.

Spatiotemporal changes in chlorophyll-a concentration in China's lakes and its driving factors

This study provides a comprehensive analysis of the temporal and spatial variations in chlorophyll-a (Chl-a) concentrations across 524 lakes in China from 2007 to 2016, leveraging extensive water quality monitoring data to identify the key drivers of these changes. Our results indicate a general decline in Chl-a concentrations, suggesting a mitigation of eutrophication. However, significant regional and seasonal disparities persist, reflecting varying ecological conditions and anthropogenic influences. Elevated Chl-a concentrations were predominantly observed in regions with intensive agricultural and urban areas, such as the middle and lower reaches of the Yangtze River, the Pearl River Delta, and the North China Plain. In contrast, lower concentrations were recorded in areas with extensive forest and grassland cover, such as the northeast and southwest. Multiple linear regression analysis revealed that ammonia nitrogen (NH3-N) and chemical oxygen demand (CODMn) were the primary factors influencing Chl-a levels during the growing season, while water temperature (W-T), CODMn, and pH had greater impacts in the non-growing season. Categorized according to the key factors influencing Chl-a concentration, 52 lakes were divided into 7 categories, and future Chl-a concentrations were projected using data from the China National Environmental Monitoring Centre and the China Environmental Status Bulletin. Specifically, we estimate a 3.060% annual decrease in CODMn and a 2.748% annual decrease in NH3-N, total nitrogen (TN), and total phosphorus (TP), alongside a 0.350% annual increase in water temperature. These projections suggest that while most lakes are expected to see reductions in Chl-a concentrations, some CODMn-sensitive and water temperature-sensitive lakes may still experience increases, underscoring the complexity of interactions between multiple environmental factors. To address these dynamics, we recommend targeted management strategies, including reducing nutrient and organic matter inputs, enhancing ecological protection measures, and closely monitoring temperature fluctuations to mitigate the risk of algal blooms and maintain lake health.

Challenges and outlooks for the polyoxometalates, metal-organic frameworks (POMs-MOFs) hybrid materials in water treatment technologies

The importance of water for life is undeniable. However, modern industrial and urban practices have led to the pollution of water reservoirs. Efficient wastewater purification is crucial for sustainability, and several materials with specific characteristics have been investigated to improve water quality. The integration of polyoxometalates (POMs) into metal-organic frameworks (MOFs) holds significant potential for water treatment applications due to their complementary properties. POMs are renowned for their high catalytic activity, redox versatility, and resistance to harsh environments, while MOFs offer high porosity, tunable chemical environments, and enhanced stability. When immobilized within MOF structures, POMs can exhibit improved processability and recyclability, overcoming limitations such as leaching and aggregation. The resulting composites maintain the catalytic efficiency of POMs and leverage the structural and adsorptive characteristics of MOFs to target contaminants in water. These hybrid systems are up-and-coming with improved characteristics where the synergy between the POM's catalytic sites and the MOF's porous network can facilitate efficient degradation of organic pollutants, heavy metal sequestration, and enhanced adsorption of micropollutants, paving the way for sustainable water purification technologies. This review encapsulates the latest advancements in POM-MOF composites, discussing the predominant synthesis strategies and their applications, particularly in wastewater treatment. Furthermore, POM-MOF composite nanoplatforms for wastewater treatment are explored based on their high stability and large specific surface area, making them an ideal choice for waste-water treatment.

U-shaped deep learning networks for algal bloom detection using Sentinel-2 imagery: Exploring model performance and transferability

Inland water sources, such as lakes, support diverse ecosystems and provide essential services to human societies. However, these valuable resources are under increasing pressure from rapid climate changes and pollution resulting from human activities. Combining remote sensing technologies with advanced artificial intelligence algorithms enables frequent monitoring of these ecosystems, timely detection of potential threats, and effective conservation measures. This study evaluated U-shaped deep learning (DL) networks, including U-Net, Residual U-Net (RU-Net), Attention U-Net, Attention Residual U-Net (ARU-Net), and SegNet, for detecting and mapping algal blooms using Sentinel-2 satellite imagery. Multitemporal Sentinel-2 imagery spanning different dates was used to construct robust DL models, with ground truth datasets representing both high- and low-density algae formations. The study emphasized the importance of diverse datasets in addressing the limitations of previous models, particularly in detecting low-density blooms and generalizing across temporal and geographical contexts. The models' transferability was assessed using imagery from different dates and geographical locations, including Lake Burdur, Lake Chaohu, and Lake Turawskie. RU-Net and ARU-Net consistently outperformed other models, achieving exceptional F-scores, such as 99.80 % for Lake Burdur, 97.23 % for Lake Chaohu, and 99.61 % for Lake Turawskie. ARU-Net demonstrated superior generalization capabilities, effectively detecting low-density algae, which is critical for comprehensive environmental assessments. These findings underscored the efficacy and transferability of U-shaped DL networks in accurately detecting algal blooms, offering valuable insights for environmental monitoring and management applications.

Correlations between catchment-scale farm infrastructure densities and stocking rate to stream nutrient concentrations in dairy-dominant catchments

Nitrogen and phosphorus pollution is a critical environmental issue that causes eutrophication of water bodies. High concentrations of these nutrients primarily come from agricultural areas and are driven by catchment characteristics such as climate, hydrology, topography, geology, land use, and land cover. In addition to these factors, specific farming practices - particularly, the use of dairy farm infrastructure and management of stocking rate - also influence stream nutrient concentrations. However, the extent of the influence of specific farming practices and their relative importance in determining nutrient concentrations in waterways remain unknown. In this paper, we used data from an agriculturally-intensive dairy farming region to investigate these relationships. We used statistical analyses and modelling to determine relationships between concentrations of ammonium (NH4+), filterable reactive phosphorus (FRP), nitrogen oxides (NOx), total phosphorus (TP), and total nitrogen (TN) with 26 predictors which include farm infrastructure density and stocking rate. We found that farm infrastructure and operational characteristics such as effluent pond density, dairy shed density, and stocking rate are consistently important predictors that influence concentrations of NH4+, FRP, NOx, TP, and TN during both wet and dry weather periods. This paper has shown that in addition to established factors such as land use and land cover, specific farming practices also play a role in influencing stream nutrient concentrations. By identifying key infrastructure and stocking rate as drivers of stream nutrient concentrations, this research emphasized the need for targeted management strategies to mitigate the impacts of agricultural activities on water quality.

Dominant and keystone genera of microorganisms dominate the multi-trophic aquatic ecological integrity of the Yangtze finless porpoise reserve

Aquatic ecological integrity is a critical attribute to sustain a vibrant ecosystem, which reflects of the intricate balance among various trophic levels, ensuring the prosperity of the aquatic fauna. The Yangtze finless porpoise (Neophocaena asiaeorientalis), a flagship species in the Yangtze River ecosystem, has been grappling with the degradation of its aquatic habitat due to anthropogenic activities, but the current state of the ecosystem remains uncertain since the implementation of the initial 10-year period of the fishery ban. In this study, we evaluated the ecological integrity of the Yangtze finless porpoise Tongling Nature Reserve (including core zone, experimental zone, buffer zone, and semi-natural zone (artificial feeding zone)) by using a multi-trophic index of biological integrity (MT-IBI) involving phytoplankton, zooplankton, and microorganism. We found that the ecological status was relatively favorable both in experimental and core zones. However, the ecological conditions in the buffer zone and artificial feeding zone were significantly poorer. Hydraulic conditions, including flow velocity and water depth, along with nutrient concentrations such as chemical oxygen demand, and total nitrogen, were critical drivers influencing the MT-IBI values. It was particularly worth noting that the integrity of the aquatic ecosystem is largely affected from the presence of key microbial genera Pseudarthrobacter and Rhodococcus, which play a pivotal role in shaping polytrophic communities. This study provides a foundational understanding for the conservation of Yangtze finless porpoise habitat, and underscore the potential of the MT-IBI as a valuable tool for assessing ecosystem health across various aquatic environments in the future.

Short-Term Fertilization with the Nitrogen-Fixing Bacterium (NFB) Kosakonia radicincitans GXGL-4A Agent Can Modify the Transcriptome Expression Profiling of Cucumber (Cucumis sativus L.) Root

The application of nitrogen-fixing bacteria (NFB) as a biofertilizer can greatly reduce or even avoid environmental pollution caused by the excessive use of chemical nitrogen fertilizers. To explore the effect of short-term fertilization of GXGL-4A on the expression of functional genes in the roots of the cucumber (Cucumis sativus L.) cultivar "Xintaimici", this study used transcriptome sequencing technology combined with fluorescent quantitative RT-PCR (qRT-PCR) verification to compare the gene transcription profiles of GXGL-4A-treated and control (sterile-water-treated) groups. A total of 418 differentially expressed genes (DEGs) were detected. The transcription levels of genes Csa5G161290 and Csa3G027720, which encode nitrate transporters, showed significant up-regulation (3.04- and 2.27-fold, respectively) in roots inoculated with GXGL-4A. The genes CsaV3_5G006200, encoding cytokinin dehydrogenase involved in the biosynthesis of zeatin, CsaV3_1G011730, encoding a wound-responsive protein, and CsaV3_6G015610, encoding a heat stress transcription factor, were significantly up-regulated at the transcriptional level (p < 0.05). However, the transcription of nitrogen cycling functional genes CsaV3_3G036500, CsaV3_1g008910, and CsaV3_3G018610, which encode nitrate reductase, high-affinity nitrate transporter (NRT), and ferredoxin-nitrite reductase, respectively, showed significant down-regulation (p < 0.05). Only the KEGG pathway of phenylpropanoid biosynthesis reached a significant level (p < 0.05). This study contributes to a deeper understanding of the interaction between NFB and plants and provides theoretical guidance for the development of GXGL-4A as a mature biological agent for sustainable agricultural production under drought stress.

Boosting the phosphate adsorption of calcite by low Mg2+-Doping

Calcite is a promising material choice for adsorbing phosphates because of its abundance and environmentally benign nature. However, the slow adsorption kinetics and hence low adsorption capacity within a short time frame hinders its practical application. In this work, we solve these problems by presenting a low Mg2+-doped calcite adsorbent, Mg-10. With a 3.75 wt% of Mg2+ doping, Mg-10 exhibits a remarkable adsorption capacity of 157.7 mg P/g. It also demonstrates a substantial boost in the adsorption kinetics, achieving a sixfold increase in adsorption capacity within 24 h compared to the undoped calcite. Meanwhile, Mg-10 not only offers improved adsorption selectivity but also maintains a stable effluent pH, underscoring its environmental compatibility. By conducting soil column experiments, we find that Mg-10 quickly captures the excess phosphates during the mimicking fertilization process, and slowly releases the nutrient afterwards, which can increase the feralization efficiency. These results provide alternative strategies for managing phosphate pollution originated from fertilization, and underscores the potential of Mg-10 in sustainable agriculture and environmental remediation.

How heatwaves impact microalgae in the presence of environmentally relevant PFAS concentration: Metabolic shifts and challenges posed

Per- and polyfluoroalkyl substances (PFAS) are widely distributed in the aquatic environment. While increasing studies have investigated the effects of specific PFAS exposure on microalgae, the impact of environmentally relevant PFAS concentrations, particularly during extreme weather events like heatwaves, remains unclear. For Microcystis aeruginosa, a cyanobacteria causing harmful algal blooms, PFAS exposure promoted growth and photosynthesis by accelerating the TCA cycle, intensifying carbon/nitrogen and nucleotide metabolism, and enhancing antioxidant expression. Moreover, although heatwave exposure alone adversely affected algal growth, co-exposure to PFAS and heatwaves paradoxically enhanced algal growth. This co-exposure also enhanced the expression of photosynthetic pigments and metabolites involved in alanine, aspartate and glutamate metabolism, as well as arginine and proline metabolism (compared to PFAS exposure alone). Nevertheless, co-exposure intensified oxidative stress, leading to differential expression of antioxidants, which may consequently affect the synthesis of membrane lipids. In addition, PFAS adsorption and uptake are primarily influenced by the varying strengths of PFAS molecules in binding with proteins and notably boosted by heatwaves. This study highlights the role of diverse PFAS in microalgae blooms and the influence of heatwave events on pollutant responses, providing scientific foundations for aquatic ecosystem protection against climate and pollution challenges.

A novel phosphate detection sensor: From FTIR to EC-QCL

Excessive discharges of industrial and domestic sewage containing high concentrations of phosphorus are causing damage to the environment, so the detection of these pollutants in bodies of water is extremely critical. External cavity quantum cascade laser (EC-QCL) spectroscopy is a novel measurement technology that surpasses conventional infrared spectroscopy techniques. In this research, we propose a transmission detection system with a long optical path based on an EC-QCL for the detection of phosphate concentration in water bodies. Linear regression models were established based on this detection system, with all determination coefficients higher than 0.98 and a minimum detection limit smaller than 5.1 ppm. Additionally, the high-power spectral density of the EC-QCL permits the construction of a model using the raw radiation intensity data, as opposed to the conventional technique which depends on a reference background. The results show that the overall performance of the model based on the raw radiation intensity is similar to that of the model based on absorbance data. The EC-QCL detection system proposed in this study can ensure accuracy in the detection of pollutants, and the advantage of miniaturization provides a novel idea for the following development of portable phosphate in-situ sewage detection sensors.

Enhancement of Fertilizer Efficiency Through Chinese Milk Vetch and Rice Straw Incorporation

The incorporation of rice straw (RS) and Chinese milk vetch (CMV) with reduced chemical fertilizers (CFs) is a viable solution to reduce the dependency on CF. However, limited research has been conducted to investigate the impact of CMV and RS with reduced CF on rice production. A field trial was conducted from 2018 to 2021 with six treatments: CK (no fertilizer), F100 (100% NPK fertilizer (CF)), MSF100 (100% CF+CMV and RS incorporation), MSF80 (80% CF+CMV+RS), MSF60 (60% CF+CMV+RS), and MSF40 (40% CF+CMV+RS). The results revealed that compared with the F100, the MSF80 treatment maintained a significantly higher mean grain yield over the four years, with an increase of 5.8~24.5%. MSF80 treatment also improved nitrogen (N), phosphorus (P), and potassium (K) use efficiencies, sustainable yield index, and partial factor productivity. Soil organic matter (SOM), total nitrogen (TN), ammonium N (NH4+-N), nitrate N (NO3--N), available phosphorus (AP), and available potassium (AK) contents were significantly enhanced under MSF80 across different growth stages in both 2020 and 2021 seasons over F100. Pearson correlation analysis revealed a strong positive correlation among SOM, TN, NH4+-N, AP, AK, and rice yield. Additionally, Partial Least Squares Path Modeling (PLS-PM) demonstrated significant relationships between organic amendments, soil nutrients, nutrient uptake, and yield. The above findings suggest that combining RS returning with CMV incorporation is a long-term sustainable strategy for maintaining soil health, and it could reduce fertilizer addition by 20% without prejudicing rice grain yield under a rice-green manure rotation system.

Classifying eutrophication spatio-temporal dynamics in river systems using deep learning technique

Eutrophication is a major cause of water quality degradation in South Korea, owing to severe algal blooms. To manage eutrophication, the South Korean government provided the Trophic State Index (TSIko), which was revised according to Carlson's TSI. The TSIko levels were simulated using mechanistic water quality modeling. However, the computational complexity of model parameter calibration and the nonlinearity of water quality kinetics complicate analyzing accurate eutrophication conditions. Deep learning models have been considered alternatives to numerical model approaches because they directly extract water quality variables without prior knowledge. In particular, the convolutional neural network (CNN) model showed robust feature extraction from the complex datasets. This study constructed and optimized a CNN model using water quality data from the Han, Guem, Yeongsan, and Nakdong Rivers in South Korea over nine years from 2014 to 2022 to classify the TSIko. The CNN model provided validation results using the statistical measurement of classification accuracy, known as the F1 score, which is the harmonic mean of precision and recall. The F1 scores were 0.922, 0.950, 0.964, and 0.896 for oligotrophic, mesotrophic, eutrophic, and hypertrophic statuses, respectively. The CNN model outperformed conventional machine learning models. Subsequently, a eutrophication map for the four major rivers was generated using the CNN model to simulate the spatial and temporal variations of the eutrophication index, mimicking high spatio-temporal eutrophic dynamics with respect to the mainstream and tributaries of the Yeongsan and Nakdong Rivers. Therefore, this study demonstrates the capability of the CNN model to analyze eutrophication conditions at various spatial and temporal scales of major rivers in South Korea.

Inter-algal associations and nutrients linked by Scenedesmus and Desmodesmus structure eukaryotic algal communities

Eutrophication reduces the variability of the community composition of plankton. However, the mechanisms underlying the diversity and restructuring of eukaryotic algal communities remain unknown. This study analysed the diversity and compositional patterns of algal communities in shallow eutrophic lakes. It investigated how these communities were modified by key genera through mediating inter-algal associations under the influence of abiotic factors. Inter-algal associations explained more variance in algal communities than environmental variables, and variation in composition and diversity was primarily derived from Scenedesmus, Desmodesmus and Cryptomonas, rather than nutrients. Scenedesmus and Desmodesmus were positively correlated with the genera of Chlorophyta and formed the hub of the algal association network. When the relative abundance of Scenedesmus and Desmodesmus increased from 0.41% to 13.74%, communities enriched in biomarkers of Bacillariophyta, Chrysophyceae and Cryptophyta transitioned to communities enriched in biomarkers of Chlorophyta. Moreover, negative associations between the Chlorophyta hub genera and other non-Chlorophyta genera increased. High concentrations of total phosphorus altered the composition of algal communities by increasing the abundance of Scenedesmus and Desmodesmus, which in turn had cascading effects through inter-algal associations. Additionally, algal communities with higher abundances of Scenedesmus and Desmodesmus were more susceptible to the effects of total phosphorus. Our study suggested that inter-algal associations, centred on Scenedesmus and Desmodesmus, had a greater influence on algal diversity and community structure than other factors. Nutrient levels were not a direct driver of algal diversity and community structure adjustments, but acted indirectly by enhancing the influence of Scenedesmus and Desmodesmus.

Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

Combined measures in lake restoration - A powerful approach as exemplified from Lake Groote Melanen (the Netherlands)

Controlling lake eutrophication is a challenge. A case-specific diagnostics driven approach is recommended that will guide to a suite of measures most promising in restoration of eutrophic lakes as exemplified by the case of the shallow lake Groote Melanen, The Netherlands. A lake system analysis identified external and internal nutrient load as main reasons for poor water quality and reoccurring cyanobacterial blooms in the lake. Based on this analysis, a package of restoration measures was implemented between January 2015 and May 2016. These measures included fish removal, dredging, capping of peat rich sediment with sand and an active barrier (lanthanum-modified bentonite), diversion of two inlet streams, reconstruction of banks, and planting macrophytes. Dredging and sand capping caused temporarily elevated turbidity and suspended solids concentrations, while addition of the lanthanum-modified clay caused a temporary exceedance of the Dutch La standard for freshwaters. Diversion of inflow streams caused 35 % less water inflow and larger water level fluctuations, but the lake remained water transporting with strongly improved water quality as was revealed by comparing five years pre-intervention water quality data with five years' post-intervention data. Total phosphorus concentration in the water column was reduced by 93 % from 0.47 mg P l-1 before the intervention to 0.03 mg P l-1 after the intervention, total nitrogen by 66 % from 1.27 to 0.21 mg N l-1, total chlorophyll-a by 75 % from 68 to 16 µg l-1, cyanobacteria chlorophyll-a by 88 % from 32 to 4 µg l-1. Turbidity had declined by 58 % from 23.5 FTU to on average 9.9 FTU. No cyanobacteria blooms were recorded over the entire post-intervention monitoring period (2016-2021). Submerged macrophytes increased from complete absence before intervention to around 10 %-15 % coverage after intervention. Repeated fish removal lowered the fish stock to below 100 kg ha-1 with 12 % of bream and carp remaining. Hence, the package of cohesive measures that was based on a thorough diagnosis resulted in rapidly, strongly and enduringly improved water quality. This case provides evidence for the power of combining measures in restoring eutrophic lakes.

Effects of water replenishment on lake water quality and trophic status: An 11-year study in cold and arid regions

Water replenishment is an important measure for maintaining and improving the aquatic environmental quality of lakes. The problems of water quality deterioration and water shortage can be alleviated by introducing water of higher quality. However, the mechanism of water replenishment in the improvement of the water quality and trophic status of lakes remains unclear. This study investigated water replenishment in Wuliangsuhai Lake (WLSHL) from 2011 to 2021 by collecting seasonal water samples and conducting laboratory analyses. Water replenishment was found to be capable of significantly improving lake water quality and alleviating eutrophication. It is worth noting that single long-term water replenishment measures have limitations in improving the water quality and trophic status. The whole process was divided into three stages according to the water quality and trophic status, namely the buffer period, decline period, and stable period. During the buffer period, the water quality and trophic status showed only slight improvement because of the small amount of water replenishment and the low proportion of higher-quality water from the Yellow River. In the decline period, with increasing water replenishment, the proportion of higher-quality water from the Yellow River gradually increased, leading to the most significant and stable degree of improvement. In the stable period, increases in the amount of water replenishment had little effect on improving the water quality and trophic status, which is attributable to the balance between internal pollutants (lake water-sediment), and the balance between internal-external pollutants (lake water-irrigation return flow + Yellow River water). On the premise of stable water quality, with eutrophication control as the management goal, the optimal water replenishment would be approximately 10.58 ×108 m3. Further necessary measures for solving aquatic environmental problems include the combination of sediment dredging, optimization of the water replenishment route, and implementation of quality management in water replenishment.

Effective remediation of agricultural drainage at three influent strengths by bioaugmented constructed wetlands filled with mixture of iron‑carbon and organic solid substrates: Performance and mechanisms

Agricultural drainage containing a large quantity of nutrients can cause quality deterioration and algal blooming of receiving water bodies, thus needs to be effectively remediated. In this study, iron‑carbon (FeC) composite-filled constructed wetlands (Fe-C-CWs) were employed to treat farmland drainage at three pollution levels, and organic solid substrates (walnut shells) and phosphate-accumulating denitrifying bacteria (Pseudomonas sp. DWP1) were supplemented to enhance the treatment performance. The results showed that the Fe-C-CWs exhibited notably superior removal efficiency for total nitrogen (TN, 52.0-58.2 %), total phosphorus (TP, 67.8-70.2 %) and chemical oxygen demand (COD, 56.7-70.4 %) than the control systems filled solely with gravel (28.5-32.5 % for TN, 33.2-40.5 % for TP and 30.2-55.0 % for COD) at all influent strengths, through driving autotrophic denitrification, Fe-based dephosphorization, and organic degradation processes. The addition of organic substrates and functional bacteria markedly enhanced pollutant removal in the Fe-C-CWs. Furthermore, use of FeC and organic substrates and denitrifier inoculation decreased CO2 and CH4 emissions from the CWs, and reduced global warming potential of the CWs at low influent strength. Pollutant removal efficiencies in the CWs were only marginally impacted by the increasing influent loads except for NO3--N, and pollutant removal mass was largely increased with the increase of influent strengths. The microbial community in the FeC composite-filled CWs exhibited distinct distribution patterns compared to the gravel-filled CWs regardless of the influent strengths, with obviously higher proportions of dominant genera Trichococcus, Geobacter and Ferritrophicum. Keystone taxa associated with pollutant removal in the Fe-C-filled CWs were identified to be Pseudomonas, Geobacter, Ferritrophicum, Denitratisoma and Sediminibacterium. The developed augmented Fe-C-filled CWs show great promises for remediating agricultural drainage with varied pollutant loads.

Eutrophication evolution of lakes in China: Four decades of observations from space

The lake eutrophication is highly variable in both time and location, and greatly restricts the sustainable development of water resources. The lack of national eutrophication evaluation for multi-scale lakes limits the pertinent governance and sustainable management of water quality. In this study, a remote sensing approach was developed to capture 40-year dynamics of trophic state index (TSI) for nationwide lakes in China. 32% of lakes (N = 1925) in China were eutrophic and 26% were oligotrophic, and a longitudinal pattern was discovered, with the 40-year average TSI of 62.26 in the eastern plain compared to 23.72 in the Tibetan Plateau. A decreasing trend was further observed in the past four decades with a correlation of -0.16, which was mainly discovered in the Tibetan Plateau lakes (r > -0.90, p < 0.01). The contribution of climate change and human activities was quantified and varied between lake zones, with anthropogenic factors playing a dominant role in the east plain lakes (88%, N = 473) and large lakes are subject to a more complex driving mechanism (≥ 3 driving factors). The study expands the spatiotemporal scale for eutrophication monitoring and provides an important base for strengthening lake management and ecological services.

The mediatory role of water quality on the association between extreme precipitation events and infectious diarrhea in the Yangtze River Basin, China

Extreme precipitation is exacerbating the burden of infectious diarrhea in the context of climate change, it is necessary to identify the critical and easy-to-intervene intermediate factors for public health strategies. Water quality may be the most important mediator, while relevant empirical evidence is limited. This study aimed to examine the role of water quality in the process of infectious diarrhea caused by extreme precipitation. Weekly infectious diarrhea cases, meteorological factors and water quality data in Yangtze River Basin in China between October 29, 2007 to February 19, 2017 were obtained. Two-stage statistical models were used to estimate city-specific extreme precipitation, water quality and infectious diarrhea relationships that were pooled to derive regional estimates. A causal mediation analysis was used to assess the mediation effect of water quality. In Yangtze River Basin, extreme precipitation events had a significant impact on infectious diarrhea (Incidence Rate Ratios [IRR]: 1.027, 95% Confidence Interval [CI]: 1.013∼1.041). After extreme precipitation events, the dissolved oxygen (DO) in surface water decreased (-0.123 mg/L, 95%CI: -0.159 mg/L∼-0.086 mg/L), while the un-ionized ammonia (NH(3)-N) increased (0.004 mg/L, 95%CI: 0.001 mg/L∼0.006 mg/L). The combined overall effect of DO and NH(3)-N on infectious diarrhea showed that both low and high concentrations were associated with an increased risk of infectious diarrhea. The causal mediation analysis showed that the mediation proportion of the two water quality indexes (DO and NH(3)-N) is 70.54% (P < 0.001). To reduce the health effects of extreme precipitation, in contrast to current population-oriented health strategies, those that take into account more direct and easy-to-intervene water quality indicators should be encouraged by future policies.

Novel assessment tools for inland aquaculture in the western Godavari delta region of Andhra Pradesh

The production of fisheries and shrimp has been twice every 10 years for the previous five decades, making it the most rapidly expanding food industry. This growth is due to intensive farming and the conversion of agriculture into aquaculture in many parts of South Asia. Furthermore, intensive aquaculture generates positive economic growth but leads to environmental degradation without proper monitoring. Unfortunately, technical innovation is less in aquaculture than agricultural and manufacturing industries. The advent of remote sensing and soft computing has expanded various opportunities for utilizing and integrating technological advances in civil and environmental disciplines. This paper presents the aquaculture scenario in the western Godavari delta region of Andhra Pradesh and proposes various novel assessment tools to monitor the aquaculture environment. An experimental investigation was carried out on the physicochemical characteristics of the inland aquaculture ponds to evaluate water quality in the aquaculture ponds. Furthermore, to assess the intensity of inland aquaculture, the current work concentrates on the potential application of remote sensing and soft computing approaches. Geospatial models of kriging and inverse distance weighing (IDW) show higher performance in estimating ammonia levels in the intensive aquaculture groundwaters with coefficient of determination (R2) values of 0.947 and 0.901, respectively. Teaching learning-based optimization (TLBO) and adaptive particle swarm optimization (APSO), two of the five soft computing techniques utilized in the study, perform better than the others. Additionally, it was found that remote sensing-based assessment tools and soft computing prediction models were both trustworthy, accurate, and easy to use. Furthermore, these methods could assist in the real-time evaluation of inland aquaculture waters by stakeholders and policymakers.

Development of a basin-scale total nitrogen prediction model by integrating clustering and regression methods

Nutrient runoff into rivers caused by human activity has led to global eutrophication issues. The Nakdong River in South Korea is currently facing significant challenges related to eutrophication and harmful algal blooms, underscoring the critical importance of managing total nitrogen (T-N) levels. However, traditional methods of indoor analysis, which depend on sampling, are labor-intensive and face limitations in collecting high-frequency data. Despite advancements in sensor allowing for the measurement of various parameters, sensors still cannot directly measure T-N, necessitating surrogate regression methods. Therefore, we conducted T-N predictions using a water quality dataset collected from 2018 to 2022 at 157 observatories within the Nakdong River basin. To account for the water quality characteristics of each location, we employed a clustering technique to divide the basin and compared a Gaussian mixture model with K-means clustering. Moreover, optimal regressor for each cluster was selected by comparing multiple linear regression (MLR), random forest, and XGBoost. The results showed that forming four clusters via K-means clustering was the most suitable approach and MLR was reasonably accurate for all clusters. Subsequently, recursive feature elimination cross-validation was used to identify suitable parameters for T-N prediction, thus leading to the construction of high-accuracy T-N prediction models. Clustering was useful not only for improving the regressors but also for spatially analyzing the water quality characteristics of the Nakdong River. The MLR model can reveal causal relationships and thus is useful for decision-making. The results of this study revealed that the combination of a simple linear regression model and clustering method can be applied to a wide watershed. The clustering-based regression model showed potential for accurately predicting T-N at the basin level and is expected to contribute to nationwide water quality management through future applications in various fields.

Low-cost IoT based system for lake water quality monitoring

Water quality monitoring is a critical process in maintaining the well-being of aquatic ecosystems and ensuring growth of the surrounding environment. Clean water supports and maintains the health, livelihoods, and ecological balance of the ecosystem as a whole. Regular assessment of water quality is essential to ensure clean and reliable water is available to everyone. This requires regular measurement of pollutants or contaminants in water that can be monitored in real-time. Hence, this research showcases a system that consists of low-cost sensors used to measure five basic parameters of water quality that are: turbidity, total dissolved solids, temperature, pH, and dissolved oxygen. The system incorporates electronics and IoT technology that are powered by a solar charged lead acid battery. The data gathered from the sensors was stored locally on a micro-SD card with live updates that could be viewed on a mobile device when in proximity to the system. Data was gathered from three different bodies of water over a span of three weeks, precisely during the seasonal transition from autumn to winter. We adopted a water sampling technique since our low-cost sensors were not designed for continuous submersion. The results show that the temperature drops gradually during this period and an inversely proportional relationship between pH and temperature could be observed. The concentration of total dissolved solids decreased during rainy periods with a variation in turbidity. The deployed system was robust and autonomous that effectively monitored the quality of water in real-time with scope of adding more sensors and employing Industry 4.0 paradigm to predict variations in water quality.

Purification Effect of Water Eutrophication Using the Mosaic System of Submerged-Emerged Plants and Growth Response

Aquatic plants play a crucial role in the sustainable management of eutrophic water bodies, serving as a valuable tool for water purification. However, the effectiveness of using aquatic plants for improving water quality is influenced by landscape considerations. In practical applications, challenges arise concerning low purification efficiency and compromised aesthetic appeal when utilizing plants for water purification. To address these issues, this study aimed to examine the impact of aquatic plants on the purification of simulated landscape water bodies, specifically focusing on the effectiveness of the mosaic system of submerged-emerged plants in remediating eutrophic water bodies. Our findings indicated that individual aquatic plants exhibited limited efficacy in pollutant (total nitrogen, total phosphorus, ammonia nitrogen, and chemical oxygen demand) removal. However, when combined in appropriate proportions, submerged plants could enhance species growth and improve the purification efficiency of polluted water bodies. Notably, the mosaic system of submerged-emerged plants neither significantly promoted nor inhibited the growth of each other, but it effectively removed pollutants from the simulated water bodies and inhibited turbidity increase. The comprehensive evaluation ranked the purification capacity as Canna indica-submerged plants combination (C + S) > Thalia dealbata-submerged plants combination (T + S) > Iris pseudacorus-submerged plants combination (I + S) > Lythrum salicaria-submerged plants combination (L + S). Both C + S and T + S configurations effectively mitigated the rise of water turbidity and offered appealing landscape benefits, making them viable options for practical applications in urban landscape water bodies. Our study highlights that a submerged-emerged mosaic combination is a means of water purification that combines landscape aesthetics and purification efficiency.

Eutrophication diminishes bacterioplankton functional dissimilarity and network complexity while enhancing stability: Implications for the management of eutrophic lakes

Eutrophication is a growing environmental concern in lake ecosystems globally, significantly impacting the structures and ecological functions of bacterioplankton communities and posing a substantial threat to the stability of lake ecosystems. However, the patterns of functional dissimilarity, network complexity, and stability within bacterioplankton communities across different trophic states, along with the underlying mechanisms through which eutrophication influences these aspects, are not well-understood. To bridge this knowledge gap, we collected 88 samples from 34 lakes spanning trophic gradients and investigated bacterioplankton communities using network analysis and multiple statistical methods. Our results reveal that eutrophication, progressing from mesotrophic to hyper-eutrophic states, reduces the putative functional dissimilarity of bacterioplankton, particularly affecting the relative proportions of functional groups such as oxygenic photoautotrophy, phototrophy, and photoautotrophy. Network complexity exhibited a unimodal pattern across increasing trophic states, peaking at mesotrophic states and then decreasing towards hyper-eutrophic conditions, while stability exhibited the opposite pattern (U-shaped), indicating a variation in response to trophic state changes. In essence, eutrophication diminishes network complexity but enhances network stability. Collectively, these findings shed light on the ecological impact of eutrophication on bacterioplankton communities and elucidate the potential mechanisms by which eutrophication drives functional dissimilarity, network complexity and stability within bacterioplankton communities. These insights carry significant implications for the ecological management of eutrophic lakes.

Cooperation of ferrous ions and hydrated ferric oxide for advanced phosphate removal over a wide pH range: Mechanism and kinetics

Excessive phosphate loading leads to eutrophication problems in rivers or lakes and causes serious environmental and economic damages, urging new technologies to reduce effluent phosphate at ultra-low levels. As a promising candidate, adsorption over metal oxides is restricted by the released hydroxide anions (OH-) through ligand exchange, which elevates pH and suppresses further adsorption. In this contribution, we found ferrous ions (Fe2+) significantly enhance phosphate removal over hydrated ferric oxide (HFO) in a wide pH range via a cooperation of adsorption and precipitation, and clarified the synergistic mechanism by a series of characterizations and the modified models of adsorption isotherms and pseudo second-order kinetics. The combination of Fe2+and HFO removed up to 51.7 mg/g of phosphate at pH 4.0, with 43.6 and 8.1 mg/g attributing to adsorption and precipitation, respectively. In comparison to HFO alone, HFO/Fe2+ system achieved 2.2-fold increase in phosphate removal, 1.9-fold increase in phosphate adsorption capacity, and 3.4-fold increase in phosphate removal rate. The enhancement is understood by that hydroxide anions released from ligand exchange over HFO are neutralized by protons produced from the oxidative precipitation of ferrous ions. The HFO/Fe2+ combining system is promising to realize advanced removal of low concentration phosphate containing wastewater, and these findings bring new insights for the development of novel phosphate removal technologies through a rational design of a combination process.

What drives the changing characteristics of phytoplankton in urban lakes: Climate, hydrology, or human disturbance?

Phytoplankton in shallow urban lakes are influenced by various environmental factors. However, the long-term coupling effects and impact pathways of these environmental variables on phytoplankton remain unclear. This is an emerging issue due to high urbanization and the resultant complex climate, lake hydrology and morphology, human interference, and water quality parameter changes. This study used Tangxun Lake, the largest urban lake in the Yangtze River Economic Belt, as an example to assess for the first time the individual contributions and coupled effects of four environmental variables and fourteen indicators on chlorophyll-a (Chla) concentrations under two scenarios from 2000 to 2019. Additionally, the influence pathways between the environmental variables and Chla concentration were quantified. The results indicated that the Chla concentration was most affected by lake hydrology and morphology, as were the total nitrogen, total phosphorus, and transparency. Especially after urbanization (2015-2019), the coupling effect of human interference, lake hydrology and morphology, and water quality parameters was strongest (18%). This is mainly due to fluctuations in the lake water level and an increase in the shape index of lake morphology, large amounts of nutrients were input, which reduced lake transparency and indirectly changed the Chla content. In addition, due to the rapid development of Wuhan city, the expansion of construction land has led to an increase in impervious surface area and a decrease in lake area. During periods of intense summer rainfall, a substantial amount of pollutants entered the lakes through surface runoff, resulting in decreased lake transparency, and elevated concentrations of nitrogen and phosphorus, indirectly increasing the Chla content. This study provides a scientific basis for aquatic ecological assessment and pollution control in urban shallow lakes.

Increased invasion of submerged macrophytes makes native species more susceptible to eutrophication in freshwater ecosystems

Invasion and eutrophication are considered to pose serious threats to freshwater biodiversity and ecosystem function. However, little is known about the synergistic effects of invasion density and nutrient concentration on native submerged macrophytes. Here, we selected a common invasive species (Elodea nuttallii) and two native plants (Hydrilla verticillata and Potamogeton maackianus) to elucidate the effects of invasion density and eutrophication on native submerged plants. We found that (1) high nutrient concentrations inhibited the growth of both invasive and native species, but E. nuttallii, with a wide ecological niche, was more tolerant to eutrophication than the two native species. (2) High invasion density had a remarkable negative effect on the growth of the two native species under the medium and high nutrient concentrations. (3) Medium and high invasion densities of E. nuttallii made native macrophytes more susceptible to eutrophication. (4) The two native macrophytes had species-specific responses to medium and high invasion densities under medium and high nutrient concentrations. Specifically, a high invasion density of E. nuttallii significantly delayed the growth of H. verticillata rather than P. maackianus. Thus, it is necessary to consider the synergistic effects of invasion with eutrophication when assessing invasion in freshwater ecosystems. And our results implied that invasion with eutrophication was a powerful factor determining the results of interspecific competition among submerged macrophytes, which could change the biodiversity, community structure and functions of freshwater ecosystems.

Benzoic acid facilitates ANF in monocot crops by recruiting nitrogen-fixing Paraburkholderia

Associative nitrogen fixation contributes large portion of N input to agro-ecosystems through monocot-diazotrophic associations. However, the contribution of associative nitrogen fixation is usually neglected in modern agriculture, and the underlying mechanisms of association between monocot and diazotrophs remain elusive. Here, we demonstrated that monocot crops employ mucilage and associated benzoic acid to specially enrich diazotrophic partners in response to nitrogen deficiency, which could be used for enhancing associative nitrogen fixation in monocot crops. To be specific, mucilage and benzoic acid induced in sugarcane roots by nitrogen deficiency mediated enrichment of nitrogen-fixing Paraburkholderia through specific recruitment whereas other bacteria were simultaneously repelled. Further studies suggest maize employs a similar strategy in promoting associations with diazotrophs. In addition, our results also suggest that benzoic acid application significantly increases copy numbers of the nifH gene in soils and enhances associative nitrogen fixation in maize using 15N enrichment assay. Taken together, these results reveal a mechanism regulating the association between monocot crops and nitrogen-fixing bacteria, and, thereby point towards ways to harness these beneficial microbes in efforts to increase nitrogen efficiency in monocot crops through pathways regulated by a specific signaling molecule.

Occurrence of polyhalogenated carbazoles and the combined effects with heavy metals on variation in bacterial communities in estuarine sediments

Carbazole (CZ) and eight polyhalogenated carbazoles (PHCZs) were quantified by GC-MS in sediments of 12 estuaries, the interface linking large industrial and living areas to the Bohai Sea, China. These pollutants, heavy metals, and environmental factors caused integrated exposure to sediment bacteria. Four PHCZ congeners were detectable, with ΣPHCZs ranging from 0.56 to 15.94 ng/g dw. The dominant congeners were 3,6-dichlorocarbazole (36-CCZ) and 3-chlorocarbazole (3-CCZ), with a mean contribution of 72.6 % and 20.2 %. Significant positive correlations were found between 36-CCZ and both total organic carbon and heavy metals. Redundancy analysis of microbial variation implicated no impacts from PHCZs. Correlation analysis demonstrated an increase in abundance of Rhodocyclaceae but a decrease in Bacteroides-acidifaciens-JCM-10556 with presence of PHCZs, suggesting that these bacteria can be used as potential contamination indicators. The combined exposure of heavy metals, nutrients, and PHCZs may also increase toxicity and biological availability, adversely affecting the ecosystem and human health.

A novel total phosphorus concentration retrieval method based on two-line classification in lakes and reservoirs across China

Phosphorus is widely recognized as a nutrient that restricts growth and is the primary contributor to eutrophication in 80 % of water bodies. Consequently, the Chinese government has consistently prioritized monitoring and controlling total phosphorus (TP) levels. The remote estimation of TP in lakes and reservoirs at a national scale is a challenging task due to TP being a non-optically active parameter. Currently, there is a lack of developed TP inversion models specifically designed for lakes and reservoirs in China. For solving this problem, a novel two-line classification method drawn on scatter plots based on the natural logarithm of TP (Ln(TP)) and B33/B9 was proposed and used to classify 1211 measured samples obtained from field cruises in 105 lakes and reservoirs across China from 2012 to 2022 into three categories, Class 1, Class 2, and Class 3. Results demonstrate that the proposed classification method has the ability to enhance the correlation between Ln(TP) and 43 basic potential single band and band combinations. Specifically, the correlation range improved from (-0.31,0.15) to (-0.77,0.24) in Class 1, (-0.81, 0.36) in Class 2, and (-0.74, 0.52) in Class 3. Additionally, the classification method also improved the correlation range between Ln(TP) and 820 band ratios, from (-0.32, 0.32) to (-0.83, 0.82) in Class 1, (-0.86, 0.86) in Class 2, and (-0.86, 0.86) in Class 3. These datasets were subsequently utilized as input for eXtreme Gradient Boosting (XGBoost) models. Finally, well performing XGBoost models in Class 1 (R2 = 0.76, RMSE = 0.3, MAPE = 12 %), Class 2 (R2 = 0.84, RMSE = 0.49, MAPE = 38 %), and Class 3 (R2 = 0.74, RMSE = 0.46, MAPE = 14 %) were used to map TP of 563 large lakes and reservoirs (≥20 km2) across China using MODIS images from 2005, 2010, 2015, and 2020. This study presents a novel approach for estimating non-optically active parameters through remote sensing on a national scale.

Integrating ecosystem services closely related to human well-being into the restoration and management of deep lakes facing multiple stressors: Lessons from long-term practice in Qiandao Lake, China

Deep-lake (reservoir) ecosystems provide valuable ecosystem services (ES) and generate significant ecosystem service values (ESV); however, reservoir ecosystems have suffered great losses from environmental changes and human activities. Currently, studies on ES and its correlations with stressors remain insufficient and the integration of ES into ecological restoration and management poses numerous challenges. Here, we combined four types of stressors with six ES closely related to human well-being to discuss their interactions in Qiandao Lake (a representative deep lake in China). Our results indicate that all ESV showed a consistent growth trend throughout the study period, reaching 5203.8 million CNY in 2018, and the cultural service value surpassed the provisioning service value for the first time in 2004. Almost all the ESV were limited during the cyanobacterial bloom in Qiandao Lake. Redundancy analysis and partial least squares structural equation modeling jointly revealed that socioeconomic development was the most important direct driver of the increase in ESV (0.770) and that hydro-meteorological conditions (0.316) and pollutant loads (0.274) positively affected ESV by mediating lake trophic status. The trophic status of the lake is the result of the interaction of multiple stressors, which has a negative impact on ESV. Therefore, to continuously protect the provisioning and cultural service values of deep-lake ecosystems from damage, the government must rationally formulate SED goals and reduce pollutant loads during lake development, operation, and utilization. This work provides valuable insights into the interactions between ES, which are closely related to human well-being, and stressors in deep-lake ecosystems.

An operational approach for large-scale mapping of water clarity levels in inland lakes using landsat images based on optical classification

Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the ad(443)/ap(443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the Rrs(443)/Rrs(655) ratio was used for water type 1 SDD estimation, and the band combination of (Rrs(443)/Rrs(655) - Rrs(443)/Rrs(560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R2 of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R2 = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP.

Revealing the hidden burden for lake management: the sediment phosphorus storage pools in Eastern Plain Lake Zone, China

As one of the essential components in ecosystems, lakes play a major role in the global phosphorus (P) cycle. It is helpful for further understanding of the inside lake P geochemical cycle to research P pollution and storage in lakes, which is of positive significance for lake eutrophication restoration. In this study, we investigated the total phosphorus concentrations (TPC) of water and sediments from 37 lakes in the Eastern Plain Lake Zone (EPL) of China, evaluated the P pollution degree of lakes, and estimated P storage in lake sediments with quantitative data of lake area and number. The results indicate that the total phosphorus concentrations of water (TPCW) and total phosphorus concentrations of the surface sediments (0-1 cm, TPCSS) in EPL were high, the mean values were 0.11 mg·L-1 and 869.85 mg·kg-1 respectively, with obvious differences between urban and rural areas, as well as between different river basins. Over half (56.76% and 70.27% respectively) of the lakes reached severe pollution levels in water and surface sediments. There were 16224 lakes (> 0.01 km2) with a total area of 21662.37 km2 in the EPL, and the P storage in the lake sediments (0-30 cm) was about 4.87 ± 2.08 Tg (1 Tg = 1 × 1012 g), accounting for about 2.74% of the basin soil. TPCW and TPCSS of lakes in the EPL were significantly positively correlated, may suggest a close nutrient cycling relationship between the lake water and the sediment. During periods of high winds and waves, the stored P in the top sediments in the EPL may continue to participate in the internal P geochemical cycle and migrate to the overlying water, posing a potential pollution hazard. Therefore, it is crucial to take into account the sediment P pools when formulating effective lake phosphorus management strategies.

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework - which we call HydroGraphs - for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

Polymer-based nanocomposite adsorbents for resource recovery from wastewater

Developing mitigation mechanisms for eutrophication caused by the uncontrolled release of nutrients is in the interest of the scientific community. Adsorption, being operationally simple and economical with no significant secondary pollution, has proven to be a feasible technology for resource recovery. However, the utility of adsorption often lies in the availability of effective adsorbents. In this regard, polymer-based nanocomposite (PNC) adsorbents have been highly acclaimed by researchers because of their high surface area, multiple functional groups, biodegradability, and ease of large-scale production. This review paper elaborates on the functionality, adsorption mechanisms, and factors that affect the adsorption and adsorption-desorption cycles of PNC adsorbents toward nutrient resources. Moreover, this review gives insight into the application of recovered nutrient resources in soil amendment.

Long-term spatial-temporal monitoring of eutrophication in Lake Burdur using remote sensing data

Lake eutrophication continues to be one of the major environmental challenges in lakes worldwide. This study is about using a remote sensing approach to monitor long-term chlorophyll-a (Chl-a) concentrations and the trophic state index (TSI) in Lake Burdur, Türkiye. Surface reflectance from Landsat 5, Landsat 7, and Landsat 8 satellite image collections were evaluated with an empirical equation to estimate a 38-year-long (1984-2021) time series of the lake's median Chl-a concentrations. Pixel quality masking was applied to retain only acceptable quality information and exclude images affected by cloud cover, icing, and other environmental factors from the analysis. The results were analyzed based on annual, seasonal, and monthly temporal averages. Over a period of 38 years, Chl-a concentrations were found to vary between 58.7 and 196.4 mg/m³. Notably higher concentrations were recorded in 2010 and 2021, while lower concentrations were observed between 1991 and 1993. A substantial increase in Chl-a concentration was evident from 2002 to 2010. The maximum concentration of Chl-a during the first two decades of the analysis was 130 mg/m³, which increased to 200 mg/m³ during the last two decades. Landsat-derived TSI values suggested that Lake Burdur has been mostly hypertrophic for the entire study period.

Phylogeography and genetic diversity of the Scapholeberis kingii species complex (Cladocera: Daphniidae) in China

There is increasing interest in the diversity and phylogeography of aquatic invertebrate zooplankton in the Eastern Palearctic, yet this topic remains largely unexplored in China. Here, we investigated the lineage diversity and phylogeography of an important cladoceran taxon, the Scapholeberis kingii (Cladocera: Daphniidae) species complex, members of which live in the surface layers of freshwater ecosystems. We identified only the S. smirnovi morphospecies from this species complex in 29 of 491 Chinese water bodies examined. Its phylogenetic position was verified using both a mitochondrial (mitochondrial cytochrome c oxidase subunit I; COI) and a nuclear marker (the nuclear large subunit ribosomal RNA gene; 28S). Pronounced geographical separation among three S. smirnovi mitochondrial lineages was observed in China: only a single lineage (Lineage A) was present in the Eastern Plain, whereas Lineages B and C were restricted to the Inner Mongolia-Xinjiang Plateau and the Qinghai-Tibetan Plateau respectively. This deep mtDNA divergence and the substantial genetic differentiation among S. smirnovi populations from different regions is likely a result of the rapid uplift of the Qinghai-Tibetan Plateau and associated ecological changes. This study contributes to an understanding of the genetic diversity of the S. kingii complex, a key component of neustonic zooplankton.

Beneath the surface: spatial and temporal trends in water quality and its impacts on algal community composition in the Albemarle Sound, North Carolina

Urban and agricultural expansion and intensification pose a critical threat to water quality and aquatic ecosystems. Increased nutrient loading into waterways combined with warming temperatures due to climate change have increased eutrophication and algal blooms. The relationship between land use, nutrient availability, and algal growth can vary dramatically across space and time, but few studies have captured this variation. The goal of this research is to assess water quality across time and disparate land uses, and its influence on algal community composition in the Albemarle Sound, a brackish water estuary in North Carolina. We collected water quality data from 21 sites across the sound, visiting six sites in Chowan County biweekly and 15 other sites twice between June and August 2020. Water samples from each site were tested for nitrate, phosphate, ammonia, bicarbonate, and total phosphorus (TP). Preserved algal samples from the six Chowan County sites were enumerated under a microscope to estimate genus richness and biomass. In the Chowan County sites, phosphorus increased and nitrate decreased over the course of the summer. Across all sites, TP increased with development and agricultural land use. These results suggest that sources of nitrogen and phosphorus in the sound differ. Algal richness increased with nitrate concentration and decreased with precipitation while biomass increased with water temperature. Our results indicate that climate change impacts, particularly increasing temperatures and extreme precipitation, influence how land use, water quality, and algal community composition interact. These data demonstrate the co-benefits of mitigating climate change in developing management strategies to reduce algal blooms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10452-023-10008-y.

Environment-friendly nitrogen management practices in wetland paddy cultivation

A large amount of nitrogen (N) fertilizer is required for paddy cultivation, but nitrogen use efficiency (NUE) in paddy farming is low (20–40%). Much of the unutilized N potentially degrades the quality of soil, water, and air and disintegrates the functions of different ecosystems. It is a great challenge to increase NUE and sustain rice production to meet the food demand of the growing population. This review attempted to find out promising N management practices that might increase NUE while reducing the trade-off between rice production and environmental pollution. We collected and collated information on N management practices and associated barriers. A set of existing soil, crop, and fertilizer management strategies can be suggested for increasing NUE, which, however, might not be capable to halve N waste by 2030 as stated in the “Colombo Declaration” by the United Nations Environment Program. Therefore, more efficient N management tools are yet to be developed through research and extension. Awareness-raising campaign among farmers is a must against their misunderstanding that higher N fertilizer provides higher yields. The findings might help policymakers to formulate suitable policies regarding eco-friendly N management strategies for wetland paddy cultivation and ensure better utilization of costly N fertilizer.

Microbial Fuel Cell-Based Biosensors and Applications

The sustainable development of human society in today's high-tech world depends on some form of eco-friendly energy source because existing technologies cannot keep up with the rapid population expansion and the vast amounts of wastewater that result from human activity. A green technology called a microbial fuel cell (MFC) focuses on using biodegradable trash as a substrate to harness the power of bacteria to produce bioenergy. Production of bioenergy and wastewater treatment are the two main uses of MFC. MFCs have also been used in biosensors, water desalination, polluted soil remediation, and the manufacture of chemicals like methane and formate. MFC-based biosensors have gained a lot of attention in the last few decades due to their straightforward operating principle and long-term viability, with a wide range of applications including bioenergy production, treatment of industrial and domestic wastewater, biological oxygen demand, toxicity detection, microbial activity detection, and air quality monitoring, etc. This review focuses on several MFC types and their functions, including the detection of microbial activity.

Spatiotemporal variation and driving factors of water quality in Yunnan-Guizhou plateau lakes, China

The Yunnan-Guizhou Plateau (YGP) lakes are the typical plateau rift lakes and an important water source in southwest China. However, there is a scarcity of research on its spatiotemporal water quality variations and driving factors, especially on long-term scales. Herein, multiple water quality indicators for 11 natural lakes on the YGP were measured from 2005 to 2020. In this study, the effects of natural lake attributes, human activities, and meteorological conditions on water quality were also analyzed. The results showed that the water quality of the YGP lakes tended to degrade, and was divided into heavy, medium, and light pollution types. Total phosphorus (TP), total nitrogen (TN), permanganate index (COD_Mn), and biochemical oxygen demand (BOD₅) increased by 14.69%, 14.44%, 22.61%, and 11.26%, respectively, from 2005 to 2020. Natural attributes of lakes and land use types were the main reasons for the spatial heterogeneity of water quality in YGP. In contrast, the temporal evolution of lake water quality was mainly related to human activities and climatic conditions. The smaller the water/ terrestrial area ratio, water storage capacity, and water depth, the easier the eutrophication and the worser the water quality of YGP lakes. Land degradation accelerated the deterioration of water quality in plateau lakes, while ecological land played an improving role. This study summarizes the water quality changes and influencing factors in YGP lakes over the past 15 years, which can provide a scientific database reference for water environment protection in YGP.

Integrated transcriptomic and metabolomic analysis of Microcystis aeruginosa exposed to artemisinin sustained-release microspheres

Artemisinin sustained-release microspheres (ASMs) have been shown to inhibit Microcystis aeruginosa (M. aeruginosa) blooms. Previous studies have focused on inhibitory mechanism of ASMs on the physiological level of M. aeruginosa, but the algal inhibitory mechanism of ASMs has not been comprehensively and profoundly revealed. The study proposed to reveal the toxicity mechanism of ASMs on M. aeruginosa based on transcriptomics and metabolomics. After exposure to 0.2 g·L^-1 ASMs for 7 days, M. aeruginosa biomass was significantly inhibited, with an inhibition rate (IR) of 47 % on day 7. Transcriptomic and metabolomic results showed that: (1) 478 differentially expressed genes (DEGs) and 251 differential metabolites (DMs) were obtained; (2) ASMs inhibited photosynthesis by blocking photosynthetic pigment synthesis, destroying photoreaction centers and photosynthetic carbon reactions; (3) ASMs reduced L-glutamic acid content and blocked glutathione (GSH) synthesis, leading to an imbalance in the antioxidant system; (4) ASM disrupted nitrogen metabolism and the hindered synthesis of various amino acids; (5) ASMs inhibited glyoxylate cycle and TCA cycle. This study provides an important prerequisite for the practical application of ASMs and a new perspective for the management of harmful algal blooms (HABs).

Cooperative interactions between nitrogen fixation and phosphorus nutrition in legumes

Legumes such as soybean are considered important crops as they provide proteins and oils for humans and livestock around the world. Different from other crops, leguminous crops accumulate nitrogen (N) for plant growth through symbiotic nitrogen fixation (SNF) in coordination with rhizobia. A number of studies have shown that efficient SNF requires the cooperation of other nutrients, especially phosphorus (P), a nutrient deficient in most soils. During the last decades, great progress has been made in understanding the molecular mechanisms underlying the interactions between SNF and P nutrition, specifically through the identification of transporters involved in P transport to nodules and bacteroids, signal transduction, and regulation of P homeostasis in nodules. These studies revealed a distinct N-P interaction in leguminous crops, which is characterized by specific signaling cross talk between P and SNF. This review aimed to present an updated picture of the cross talk between N fixation and P nutrition in legumes, focusing on soybean as a model crop, and Medicago truncatula and Lotus japonicus as model plants. We also discuss the possibilities for enhancing SNF through improving P nutrition, which are important for high and sustainable production of leguminous crops.

Eutrophication induces functional homogenization and traits filtering in Chinese lacustrine fish communities

Rapid anthropogenic nutrient enrichment has caused widespread ecological problems in aquatic ecosystems and the resulting eutrophication has dramatically changed fish communities throughout the world. However, few studies addressed how fish communities responded to eutrophication in terms of multidimensional functional and taxonomic structure, especially how eutrophication acted as an environment filter on functional traits. The aim of the present study was to investigate the effects of eutrophication on fish species composition, community metrics and species functional traits in 26 shallow lakes from the middle reaches of Yangtze River basin, China. This study validated that eutrophication is an important factor shaping the fish community structure. Regression analyses showed that eutrophication favored higher total biomass and lower functional diversity of fish communities but had little effect on species richness. Despite the fact that some pelagic zooplanktivorous species were more abundant in the most eutrophic lakes, multivariate analyses of the relationships between species traits and environmental variables revealed weak relationships between feeding traits and eutrophication. In contrast, species with a benthic life stage were negatively associated with eutrophication while those with a large body size and high absolute fecundity showed the opposite trend. Due to demersal habitat degradation, and to a lesser degree, to changes in trophic resources availability, eutrophication caused functional simplification of fish communities by increasing functional traits homogeneity among the most tolerant species. Some relationships between functional traits and eutrophication well established in the western palearctic have not been observed in this study, emphasizing the importance of biases resulting from specific evolutionary histories. This work will provide useful insights on on-going restoration and management of shallow lakes in the Yangtze River basin.

Analysis of water quality indexes and their relationships with vegetation using self-organizing map and geographically and temporally weighted regression

Natural vegetation has been proved to promote water purification in previous studies, while the relevant laws has not been excavated systematically. This research explored the relationships between vegetation cover and water quality indexes in Liaohe River Basin in China combined with self-organizing map (SOM) and geographically and temporally weighted regression (GTWR) innovatively and systematically based on the distributing heterogeneity of water quality conditions. Results showed that the central and northeast regions of the study area had serious organic and nutrient pollution, which needed targeted treatment. And SOM verified that high vegetation coverage with retention potential of organic and inorganic pollutants as well as nutrients improved water quality to some degree, while the excessive discharges of pollutants still had serious threats to nearby water environment despite the purification function of vegetation. GTWR indicated that the waterside vegetation was beneficial for dissolved oxygen increasing and contributed to the decreasing of organic pollutants and inorganic pollutants with reducibility. Natural vegetation also obsorbed nutrients like TN and TP to some degree. However, the retential potential of nitrogen and organic pollutants became not obvious when there were heavy pollution, which demonstrated that pollution sources should be controlled despite the purification function of vegetation. This study implied that natural vegetation purified water quality to some degree, while this function could not be revealed when there was too heavy pollution. These findings underscore that the pollutant discharge should be controlled though the natural vegetation in ecosystem promoted the purification of water bodies.

Polymer-based nanocomposite adsorbents for resource recovery from wastewater

Adsorption is alternative technique for recovery of nutrient resources with no/less secondary pollution. PNC adsorbents are effective for removal and recovery of nutrient resources, and reusing nutrients as fertilizer could prevent eutrophication.

Enhanced soil erosion threatens fluvial tufa landscapes after an Ms 7.0 earthquake in the Jiuzhaigou World Heritage Site, southwestern China

Tufa is a porous freshwater deposit comprising primarily calcite (CaCO₃) and organic matter. Massive tufa depositions can spread for up to several kilometers, forming tufa landscapes that have been recognized as national parks and World Heritage Sites. Previous studies have suggested that enhanced soil erosion owing to human activities (e.g., deforestation and agriculture) is one of the major causes of fluvial tufa decline in many places worldwide. In 2017, an Ms 7.0 earthquake occurred in Jiuzhaigou, which greatly increased soil erosion in the catchment. We compared the water chemistry and tufa deposition before and after the earthquake to understand the impact of soil erosion on tufa landscapes in Jiuzhaigou. After the earthquake, we found that high turbidity greatly reduced the aesthetic value of the lakes. Enhanced soil erosion increased NO₃^-, dissolved organic carbon (DOC), and PO₄^3- concentrations in surface water, which may worsen the problems of increased algal biomass and marsh development. Enhanced soil erosion reduced alkalinity, HCO₃^-, and the saturation index of calcite (SIc), thereby decreasing the potential to generate new calcite. Enhanced soil erosion may also increase the annual tufa deposition rates by increasing the soil and organic materials in the sediment. In addition, the tufa sediment affected by enhanced soil erosion was loose, highly porous, and contained numerous diatoms. This study provides observational data to explain the impact mechanisms of soil erosion on tufa landscapes and assess the necessity and achievements of artificial soil erosion control.

Optimization of constructed wetlands on purifying black-odorous water and their potential purification mechanism

Black-odorous water has become a common and widespread problem in recent decades. In this study, nine constructed wetlands (CWs) with different flow types, filters, plants, and hydraulic loadings were designed according to an orthogonal array (L₉ (3⁴), and were used for the purification of black-odorous water in summer and winter. The results showed that CWs are regarded as effective to purify black-odorous water in both seasons. Microbial degradation is the major removal pathway of pollutants in CWs during summer, while the joint effect of biodegradation and adsorption is the main treatment route during winter. Flow type and hydraulic loading appear to be the most important factors impacting the purification performance of CWs, by changing the redox condition of systems and retention time of contaminants, respectively. 'Vertical flow-zeolite filter-high loading' is proposed as the best parameter selection for CWs on the purification of black-odorous water: among them, CWs with vertical flow have better oxygen transport capacity that is conductive to aerobic processes of pollutants, zeolite substrates may adsorb more nitrogen via ion exchange, higher hydraulic loadings can extend the contact time between contaminants and filters, and regulate the water temperature for microbial activity.