Meteorological factors and water quality changes of Plateau Lake Dianchi in China (1990-2015) and their joint influences on cyanobacterial blooms

Ecological restoration reduces greenhouse gas emissions by altering planktonic and sedimentary microbial communities in a shallow eutrophic lake

Ecological restoration is a promising approach to alleviate eutrophication. However, its impacts on greenhouse gas (GHG) emissions and the underlying microbial mechanisms in different habitats of lakes remain unclear. To address this knowledge gap, we measured carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes at both water-air and sediment-water interfaces of eutrophic (Caohai) and restored area (Dapokou) of Dianchi Lake, a typical eutrophic lake in China. Meanwhile, we investigated the responses of planktonic and sedimentary bacterial and fungal communities by high-throughput sequencing. Our results indicated that 6 years of ecological restoration significantly reduced CO2 and N2O fluxes by 1.0-3.6 and 2.2-2.8 folds respectively, with more pronounced variations at the water-air interface than the sediment-water interface. Ecological restoration also shifted the structures of planktonic bacterial and fungal communities remarkably, leading to a significant reduction in the relative abundances of Actinobacteriota (by 70.94%), Bacteroidota (by 61.65%), Planctomycetota (by 74.18%) and Chytridiomycota (by 95.44%). Correlation analyses further suggested that GHG fluxes at the water-air interface were significantly correlated with planktonic microbial community composition (P < 0.05), and the significant reduction of CO2 and N2O fluxes under ecological restoration could be attributed to the decreased abundances of organic matter decomposers (such as hgcI_clade, Sporichthyaceae and Acidibacter) and increased abundances of autotrophs (such as Hydrogenophaga and Cyanobium_PCC-6307) in water. Collectively, our findings verify the importance of ecological restoration in reducing GHG emissions in inland lake ecosystems, providing new insights for addressing global climate change and advancing carbon neutrality.

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.

Spatiotemporal evolution of environmental factors in representative tributaries of the Yellow River: insights from a decade of monitoring data

The Yellow River, recognized as the cradle of Chinese civilization, has experienced significant environmental transformations due to rapid urbanization, which has substantially increased wastewater discharge into its basin, thereby altering its hydrological and water quality dynamics. Based on nearly a decade of monitoring data from over 20 sections within the Dahei River basin, a pivotal tributary at the upper reaches of the Yellow River, this study analyzed the variations in seven key water quality parameters (TN, TP, NH3-N, COD, BOD5, DO, and CODMn) across regions influenced by varying degrees of human activity and differing climatic conditions over wet, normal, and dry years. The results indicated that the most suitable model for fitting the 'hydrological frequency-water quality' relationship over time was linear. The R2 values ranging from 0.82 to 0.99, suggesting strong reliability of the model. Generally, the water quality concentrations exhibited gradual changes over time. Spatially, the preferred model for the 'hydrological frequency-water quality' relationship was exponential, with a greater likelihood of sudden increases in water quality concentrations, supported by R2 values ranging from 0.85 to 0.99, indicating dependable fitting. During various periods, the water quality in high-activity areas consistently exhibited poorer water quality compared to the low-activity areas. Indicators negatively impacting water quality were 1.66-63.25 times higher in high-activity areas than in low-activity areas. Water quality was generally poorer during wet years. For example, total nitrogen (TN) concentrations exceeded the Class V water standard (2 mg/L) in all river sections during wet years, with 81.05% of sections surpassing this threshold in normal years and 90.36% in dry years. These findings offer valuable insights for basin managers, highlighting the need for targeted interventions to mitigate water quality deterioration and enhance pollution control strategies.

Influencing Factors of Phosphorus Mobility and Retention in the Sediment of Three Typical Plateau Lakes

The mechanisms driving changes in the stability of phosphorus (P) in sediments under lake ecosystem degradation remain poorly understood. This study investigated the P-binding forms in sediments from three plateau lakes with different trophic states in Yunnan Province, China, aiming to elucidate the responses of sediment P compositions to human activities, lake trophic status, and dissolved organic matter (DOM) characteristics. The results showed that human activity directly contributed to sediment P retention. The trophic type of lake exerted a discernible effect on P mobility in the sediments, as eutrophic algae-type lakes had a higher content of sediment mobile-P. Moreover, the sediment DOM promoted the adsorption of BD-P and NH4Cl-P. Generally, exogenous pollution caused by human activity leads to lake eutrophication and a decline in lake ecosystem stability. This variation was largely influenced by water depth. A decrease in lake ecosystem stability leads to increased P mobility in sediments, which increases the risk of endogenous pollution. The DOM plays an important role in the mobility of sediment P. These insights offer a novel perspective for understanding how lake ecosystem characteristics are related to endogenous P loads in lakes.

Are more data always better? - Machine learning forecasting of algae based on long-term observations

Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important as they provide early warnings and enable implementation of mitigation strategies. In this context, machine learning (ML) emerges as a promising forecasting tool. However, the performance of ML models is heavily dependent on the availability of appropriate training data. Consequently, it is essential to determine the volume of data necessary to develop reliable ML forecasts. Understanding this will guide future monitoring strategies, optimize resource allocation, and set realistic expectations for management outcomes. In this study, we used 30 years of fortnightly measurements of 13 different parameters from a lake in the English Lake District (UK) to examine the impact of training data duration on the performance of ML models for forecasting chlorophyll-a two weeks in advance. Once training data availability exceeded four years, a Random Forest model was found to consistently outperform naive benchmarks (mean absolute percentage error 16.4 % lower than the best-performing benchmark). With more than 5 years of training data, model performance generally continued to improve, but with diminishing returns. Furthermore, it was found that equivalent and, in some cases, better performance could be achieved by only using a subset of the most important input features. Additionally, it was found that reducing the sampling frequency had negative impacts on performance, both due to the reduced number of training observations available, and increased forecast horizon. Our findings demonstrate that for lakes ecologically similar to the study site, a consistent and regular sampling programme focused on monitoring a limited number of key parameters can provide sufficient observations for generating short-term algae forecasts after approximately five years of data collection. Importantly, this result provides justification for the initiation of new monitoring programmes for sites where algal blooms are a concern, and suggests that there are likely many pre-existing monitoring datasets which would be suitable for training algae forecast models.

Hybrid deep learning based prediction for water quality of plain watershed

Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain watershed with complex hydraulic conditions. This study aims to evaluate the efficacy of three traditional machine learning models versus three deep learning models in predicting the water quality of plain river networks and to develop a novel hybrid deep learning model to further improve prediction accuracy. The performance of the proposed model was assessed under various input feature sets and data temporal frequencies. The findings indicated that deep learning models outperformed traditional machine learning models in handling complex time series data. Long Short-Term Memory (LSTM) models improved the R2 by approximately 29% and lowered the Root Mean Square Error (RMSE) by about 48.6% on average. The hybrid Bayes-LSTM-GRU (Gated Recurrent Unit) model significantly enhanced prediction accuracy, reducing the average RMSE by 18.1% compared to the single LSTM model. Models trained on feature-selected datasets exhibited superior performance compared to those trained on original datasets. Higher temporal frequencies of input data generally provide more useful information. However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Overall, the proposed hybrid deep learning model demonstrates an efficient and cost-effective method for improving water quality prediction performance, showing significant potential for application in managing water quality in plain watershed.

Endogenous phosphorus release from plateau lakes responds significantly to temperature variability over the last 50 years

The ecological environment of plateau lakes is very sensitive to temperature changes. Higher temperatures accelerate the cycling processes between lake sediments and water nutrients. Quantitatively investigating the influence mechanism of regional climate change and sediment phosphorus release over a long time series is difficult in revealing the causes of eutrophication in plateau lakes. This paper quantitatively reveals the long-term response mechanism of endogenous phosphorus release to temperature change in Dianchi, the largest plateau eutrophic lake in China, based on nearly 50 years of temperature and sediment phosphorus data from 1964 to 2013, and taking advantage of the Random Forest machine learning algorithm for deep processing of long time series and nonlinear relation. The results showed that: (1) Over the past 50 years, endogenous phosphorus release and temperature showed no trend for 22 years, followed by a consistent, significant increase in both after 1986. (2) Random Forest analysis showed that before the increase of temperature, the contribution to the phosphorus release was weak, while after the mutation, the contribution reached 52.6%, and typically was concentrated from March to August each year. (3) The response relationship between temperature and endogenous phosphorus release had non-linear variation with a threshold interval of 18.3 °C-19.2 °C. This research aims to explore the theoretical scientific knowledge of endogenous phosphorus release processes and complex mechanisms in plateau lakes under changing environments, and further explores the effects of long-term temperature variability on endogenous phosphorus loading in plateau lakes. That is, long-term temperature mutations can alter the internal cycling processes of sedimentary phosphorus by stimulating algal growth, which have a more drastic effect than short-term temperature variations.

Influence of climate extremes on long-term changes in cyanobacterial blooms in a eutrophic and shallow lake

Climate change and human activities have crucial effects on the variations in phytoplankton blooms in lakes worldwide. A record-breaking heatwave and drought event was reported in the middle and lower reaches of the Yangtze River during the summer of 2022, but only little is known about how cyanobacterial blooms in lakes respond to such climate extremes. Here, we utilized MODIS images to generate the area, occurrence, and initial blooming date (IBD) of cyanobacterial blooms in Lake Chaohu from 2000 to 2022. We found that the area and occurrence of cyanobacterial blooms were largely reduced. At the same time, the IBD was delayed in 2022 compared with the previous 20 years. The annual occurrence and mean area of cyanobacterial blooms in 2022 were 17 % and 23.1 km2, respectively, which were the lowest reported levels since the 21st century. The IBD in 2022 was four months late compared with the IBD in 2020. The high wind speed in spring delayed the spring blooms in 2022. The record-breaking heatwaves and drought from June to August reduced the blooms by influencing the growth of cyanobacteria and reducing the flow of nutrients from the watershed into the lake. This study highlights the compound impact of heatwave and drought climate events on reducing cyanobacterial blooms in a long-term period, enhancing additional understanding of the changes in phytoplankton blooms in lakes.

Exploring Spatiotemporal Patterns of Algal Cell Density in Lake Dianchi with Explainable Machine Learning

The escalating global occurrence of algal blooms poses a growing threat to ecosystem services. In this study, the spatiotemporal heterogeneity of water quality parameters was leveraged to partition Lake Dianchi into three clusters. Considering water quality parameters and both the delayed and instantaneous effects of meteorological factors, ensemble learning, and quasi-Monte Carlo methods were employed to predict daily algal cell density (AD) between January 2021 and January 2024. Consistently, superior predictive accuracy across all three clusters was exhibited by the Stacking-Elastic-Net regularization model. Furthermore, the minimum combination of drivers that achieved near-optimal accuracy for each cluster was identified, striking a balance between accuracy and cost. The ranking of the effect of drivers on AD varied by cluster, while the delayed effect of meteorological factors on AD generally outweighed their instantaneous effect for all clusters. Additionally, the heterogeneous or homogeneous thresholds and responses between drivers and AD were explored. These findings could serve as a scientific and cost-effective basis for government agencies to develop regional sustainable strategies for managing water quality.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3-N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Dynamics of algal blooms in typical low-latitude plateau lakes: Spatiotemporal patterns and driving factors

The alpine lakes distributed on the plateau are crucial for the hydrological, and biogeochemical cycle, and also serve as a guarantee for regional economic development and human survival. However, under the influence of human interference and climate fluctuations, lakes are facing problems of eutrophication and subsequent algal blooms (ABs) with acceleration, and the development and driving factors of this phenomenon need to be considered as a whole. In this study, ten lakes located on the Yunnan-Guizhou Plateau were selected as the study area to analyze the spatiotemporal distribution of ABs and possible controlling forces. The FAI (Floating Algae Index) derived from multiple MODIS products and water quality data under high-frequency monitoring were selected as the data sources for characterizing ABs. Three nutrient parameters and five meteorological variables were used to explore the driving factors affecting ABs. Various methods of trend detection and correlation analysis have been applied. The main results are as follows: (1) Dianchi Lake (in lake area) and Xingyun Lake (in area proportion) are the two lakes with the most serious ABs in the historical period; (2) ABs are mainly distributed on the shoreline and northern edge of lakes, and tend to stay away from the lake center during high-temperature periods of the day; (3) Six lakes show a decreasing trend in ABs, especially after 2018, while other lakes (including Fuxian, Chenghai, Yangzong, and Erhai) are increasing, not only in peak value but also in duration; (4) Lakes with severe ABs are all P-restricted lakes, the minimum temperature is the most sensitive meteorological factor, while the impact of precipitation against ABs has a time lag; (5) Establishing a warning system of temperature and nutrient concentration is critical in ABs adaptive strategy. This study is expected to provide scientific references for regional water management and the restoration of the eutrophic aquatic ecosystem.

Characteristics of microbial community structure and influencing factors of Yangcheng Lake and rivers entering Yangcheng Lake during the wet season

Yangcheng Lake, a typical fishery lake in the middle and lower reaches of the Yangtze River, is threatened by eutrophication. As the main performers of biogeochemical cycles, microorganisms affect the ecological stability of the lake. To study the structural characteristics of the microbial community in Yangcheng Lake and rivers entering Yangcheng Lake and the response relationship with environmental factors, the microbial community was categorized based on the contour of Yangcheng Lake, the major rivers entering Yangcheng Lake, and the pollution sources. The distribution characteristics of seven physicochemical indices were analyzed, including total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), water temperature (WT), pH, dissolved oxygen (DO), and ratio of total nitrogen to total phosphorus (TN/TP). Characterization of microbial community structure based on 16S rRNA high-flux sequencing technology and ANOSIM analysis were used to explore the differences in the relative abundance of microorganisms at each sampling point in the lake and rivers, and redundancy analysis (RDA) was used to analyze the relationship between the microbial community and physicochemical factors. The results showed that the dominant phyla, genera of microorganisms, and the total number of OTUs in the lake and rivers were similar. The dominant phyla included Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, and Verrucomicrobia; the dominant genera included the hgcI clade, CL500-29 marine group, Microcystis PCC-7914, Chloroplast_norank, Clade III_norank, and Flavobacterium. ANOSIM analyses revealed that the microbial community of Yangcheng Lake exhibited an association with geographical space, while the microbial community in the rivers that was linked to the type of pollution source. Redundancy analysis (RDA) indicated that dissolved oxygen (DO), total nitrogen (TN), and pH were significantly correlated with the dominant phyla in Yangcheng Lake (p < 0.05), while total nitrogen (TN), water temperature(WT), and the ratio of total nitrogen to total phosphorus (TN/TP) were significantly related with the dominant genera in Yangcheng Lake (p < 0.05). Total nitrogen (TN) was also significantly linked to the dominant phyla and genera of the tributaries (p < 0.05). Despite the structural similarities in microbial communities between Yangcheng Lake and its inflowing rivers, environmental factors demonstrated significant associations with these communities, providing crucial data support for pollution prevention and the ecological restoration of Yangcheng Lake.

Feature multi-level attention spatio-temporal graph residual network: A novel approach to ammonia nitrogen concentration prediction in water bodies by integrating external influences and spatio-temporal correlations

Accurate prediction of ammonia nitrogen concentration in water is of great significance for urban water quality management and pollution early warning. In order to improve the prediction accuracy of ammonia nitrogen concentration in water, this study developed a novel model based on graph neural networks called Feature Multi-level Attention Spatio-Temporal Graph Residual Network (FMA-STGRN). The FMA-STGRN model utilizes external influencing factors such as meteorological factors and point of interest data, as well as the spatio-temporal correlation information of ammonia nitrogen concentration between water quality monitoring stations, to accurately predict the concentration of ammonia nitrogen in water. The model consists of four main components: feature multi-level attention module, spatial graph convolution module, temporal-domain residual decomposition module, and feature fusion and output module. Through the organic combination of these four modules, FMA-STGRN can more effectively explore the complex spatio-temporal correlation relationships between water quality monitoring stations and more accurately integrate and utilize external influencing factors, thereby improving the prediction accuracy of ammonia nitrogen concentration in water. Experimental results show that the FMA-STGRN model outperforms other benchmark models such as RF, MART, MLP, LSTM, GRU, ST-GCN, and ST-GAT in various aspects. In addition, a series of feature ablation experiments were conducted to further reveal the key contributions of meteorological factors and point of interest data to the model performance. Overall, our research provides a powerful and practical tool for water quality monitoring and urban water management, with broad application prospects.

Quantifying cumulative changes in water quality caused by small floodgates in Taihu Lake Basin - A case in Wuxi

Small floodgates in the river network area own some characteristics: considerable quantity, wide range and short adjustment time, and intercepts the one-dimensional constant flow of rivers, which induce a great impact on riverine water quality. In this study, a typical urban floodgate-controlled reach was selected, and analyzed through the monthly data of four pollutant indicators TN, TP, CODMn and NH3-N at six sampling sites S1-S6 in 2016-2018. The principal component analysis and correlation analysis showed that TP was a representative indicator and there was a positive correlation between various pollutants. The difference test and linear regression showed that the concentration of pollutants at different sampling points varied greatly, and the pollutant concentrations in the longitudinal direction of the river showed a cubic-linear regression. The cluster system and CCME WQI showed that the water quality in the urban floodgate-controlled reach is "marginal" state, and TN and NH3-N are severely exceeding the standard. The "cumulative changes" of the floodgate on the pollutant input to the environment appeared spatial heterogeneity.

Spatio-temporal study of water quality variables in the Rio de Ondas Hydrographic Basin, west of Bahia, Brazil using multivariate analysis

Water bodies are containers that receive a large load of water quality variables through the release of domestic, industrial, and agricultural effluents. With this focus, this work aimed to conduct a temporal-spatial variability study in the Rio de Ondas Hydrographic Basin through multivariate statistical analysis. For this, seventeen collection sites were established in four stations along the Rio de Ondas and its tributaries between 2017 and 2018. Ionic chromatography with suppressed conductivity was used for ions determination, while ICP-OES determined metals' total concentrations. The land use and occupation assessment between 1985 and 2021 was using data from MapBiomas were used and the descriptive and multivariate analysis of the data using version free of the Statistica software. The results showed that, in 30 years, there was a growth of 569% of agricultural activities in the watershed area, with significant suppression of native vegetation, favoring the transport of contaminants to rivers. Ca2+, PO42-, Al, Cu, and Zn concentrations showed a statistically significant difference between the seasons, with higher medians in the rainy season. Rainy season influenced the formation of three groups in the PCA, consisting of electrical conductivity, salinity, TDS, and PO42- (group 1); temperature, Fe, SO42-, and Cl- (group 2); and Ca2+, Mg2+, Na+, and HCO3- (group 3). The strong correlation between parameters of each group indicates anthropic influence on the watershed's water quality. However, levels are within the potability standard.

Urbanization shifts long-term phenology and severity of phytoplankton blooms in an urban lake through different pathways

Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi-source remote sensing data and integrated the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long-term (2000-2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011-2021 to 2000-2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km2 ) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change.

Significant decline of water pollution associated with inland fishery across China

Water pollution seriously threatens the sustainable development of fisheries in China. To inform effective pollution control policies, a comprehensive understanding of the fishery environment status is needed. However, nationwide data on the temporal changes of major pollutants in the fishery waters of China are scarce. This study collected data on the major water pollutants, including total nitrogen, total phosphorus, heavy metals, and total petroleum hydrocarbons (TPHs), from 2003 to 2017 to evaluate dynamic changes in the inland fishery water environment across China. We discovered that the levels of four heavy metals (Cu, Zn, Pb, and Cd) and TPH decreased during the 15-year period, corresponding to the reduced national discharge of pollution sources from 2003 to 2015. However, nitrogen and phosphorus levels in the inland fishery waters showed no significant changes during this period. A comparative analysis of water quality in different periods indicated that these improvements were highly associated with effective measures for water pollution control in China. In addition, the decline in pollution was consistent among the three regions of China (north, west, and southeast) from 2003 to 2017, while southeast China exhibited the weakest pollution mitigation among the three regions. These findings suggest that the inland fishery water quality improved during 2003-2017, but still faced eutrophication risk.

Environmental DNA metabarcoding revealed the impacts of anthropogenic activities on phytoplankton diversity in Dianchi Lake and its three inflow rivers

Phytoplankton diversity is closely related to environmental variables and has been widely used in ecological health assessment of rivers and lakes. Combining advantages of DNA-based identification and high-throughput sequencing technology, environmental DNA (eDNA) metabarcoding permits a new measurement for biodiversity monitoring in aquatic ecosystems. However, it had rarely been used to explore the variability and similarity of phytoplankton diversity between lake and its inflow rivers and the effects of environmental variables on phytoplankton. This study utilized eDNA metabarcoding to investigate the spatial distribution of phytoplankton and the impacts of environmental variables on the phytoplankton diversity in Dianchi Lake (one of the most polluted urban lakes in China) and its main inflow rivers (Panlong River, Baoxiang River, and Chai River). A total of 243 distinct phytoplankton taxa were detected, covering 9 phyla, 30 classes, 84 orders, and 132 families, and the taxonomic richness of rivers was higher than that of Dianchi Lake. Distinct biodiversity patterns (e.g., community structure, dominant taxon, ɑ-diversity) were exhibited among Dianchi Lake and its three inflow rivers, but similar biodiversity patterns were also observed in Dianchi Lake and the estuarine sites. The patterns of phytoplankton diversity were closely related to environmental variables, which were associated with pollution sources from different anthropogenic activities (e.g., urbanization, water diversion, industrial and agricultural activities). The primary environmental variables correlated with phytoplankton diversity varied in different habitats. The total phosphorus (TP) and chemical oxygen demand (COD) positively correlated with the phytoplankton community structures in Dianchi Lake, whereas negatively correlated in Panlong River and Baoxiang River. The total nitrogen (TN) positively correlated with the phytoplankton community structures in Baoxiang River and Chai River but negatively correlated in Dianchi Lake. Overall, this study provides insights on the phytoplankton diversity monitoring and the conservation of its diversity and healthy management of Dianchi Lake.

Estimating the contribution of environmental variables to water quality in the postrestoration littoral zones of Taihu Lake using the APCS-MLR model

Water quality monitoring is one of the most important aspects of postrestoration assessments because it affects water pollution control and the development of sustainable management strategies. However, a comprehensive understanding of potential water pollution and source apportionment in restoration projects is still lacking. In this study, the water quality variables of three restored national wetland parks with different cofferdam systems (i.e., an eco-layered cofferdam, a fully enclosed cofferdam, and open water) in the littoral zone of Taihu Lake were monitored monthly for three years (2019-2021). Hydrochemical and meteorological variables were used as auxiliary parameters for multivariate statistics, including principal component analysis (PCA) and absolute principal component score-multiple linear regression (APCS-MLR), to accurately estimate the source apportionment of the potential factors influencing the water environment. PCA extracted three or four potential sources, accounting for 64.71 %, 65.40 %, and 63.85 % of the total variance. The APCS-MLR results showed that wind direction and volatile suspended solids were the primary sources affecting water quality in open water, with a sum of the mean source contributions of 40.7 %. In fully enclosed cofferdam systems, the dire state of endogenous pollution was the greatest potential source affecting water quality, with a mean contribution of 41.2 %. The eco-layered cofferdam alleviated the contributions of suspended solids (mean contribution of 23.7 %) and nutrients in the water column (mean contribution of 30.8 %); however, the contribution of organic matter in the cofferdam was relatively high (mean contribution of 13.4 %). Based on these results, eco-layered cofferdams play a positive role in eutrophication control and ecological restoration in the littoral zone of large shallow lakes. Meanwhile, adding meteorological variables to assist hydrochemical variables in multivariate statistics may improve the accuracy and certainty of pollution source apportionment and support decision-makers in developing water quality protection and management strategies for postrestoration projects in littoral zones.

Enhanced Microcystis Aeruginosa removal and novel flocculation mechanisms using a novel continuous co-coagulation flotation (CCF)

Co-coagulation flotation (CCF) is a novel flotation technology that renders more efficient algal removal compared to traditional mechanical coagulation flotation (MCF) due to a short residence time (< 30 s) and fast rising behavior of algal flocs (> 250 m·h^-1). This study compared the algal removal performance using continuous CCF and MCF using water samples taken from Lake Dianchi with severe Microcystis aeruginosa blooms. Removal efficiency, dosage of coagulant/flocculant, rising velocity and structural characteristics of the resulting flocs in the two processes were systematically compared. The results show that CCF could save >50 % polyaluminum chloride (PAC) and polyacrylamide (PAM) compared with MCF when the removal efficiency was both over 95 %. The average rising velocity of flocs in CCF could reach 254.3 m·h^-1, much higher than that in MCF (154.5 m·h^-1). In the respective optimal coagulation conditions, the flocs formed in CCF (G = 164.8 s^-1) were larger (1843 ± 128 μm) and more spherical with a higher fractal dimension (D_f = 1.85 ± 0.01) than those generated in MCF (G = 34.1 s^-1). The Stokes's Law was found to correctly predict the rising velocity of spherical flocs with large fractal dimensions (D_f > 1.7). In contrast, the Haarhoff and Edzwald's extended equation was more suitable for calculating the rising velocity of irregular flocs with small fractal dimension. This study provides new insights into the mechanisms of the enhanced algal removal by CCF and lays foundation for developing cost-efficient algal mitigation processes.

Predicting ammonia nitrogen in surface water by a new attention-based deep learning hybrid model

Ammonia nitrogen (NH₃-N) is closely related to the occurrence of cyanobacterial blooms and destruction of surface water ecosystems, and thus it is of great significance to develop predictive models for NH₃-N. However, traditional models cannot fully consider the complex nonlinear relationship between NH₃-N and various relative environmental parameters. The long short-term memory (LSTM) neural network can overcome this limitation. A new hybrid model BC-MODWT-DA-LSTM was proposed based on LSTM combining with the dual-stage attention (DA) mechanism and boundary corrected maximal overlap discrete wavelet transform (BC-MODWT) data decomposition method. By introducing attention mechanism, LSTM could selectively focus on the input data. BC-MODWT could decompose the input data into sublayers to determine the main swings and trends of the input feature series. The BC-MODWT-DA-LSTM hybrid model was superior to other studied models with lower average prediction errors. It could maintain NASH Sutcliffe efficiency coefficient (NSE) values above 0.900 under the lead time up to 7 days, and the area under the receiver operating characteristic (ROC) curve could reach 0.992. The hybrid model also had higher prediction accuracies at the peak spots, indicating that it was capable of early warning when sudden high NH₃-N pollution occurred. The high forecasting accuracy of the suggested hybrid method proved that further improving LSTM model without introducing more complex topologies was a promising water quality prediction method.

A systematic review and quantitative meta-analysis of the relationships between driving forces and cyanobacterial blooms at global scale

The global expansion of cyanobacterial blooms poses a major risk to the safety of freshwater resources. As a result, many explorations have been performed at a regional scale to determine the underlying impact mechanism of cyanobacterial blooms for one or several waterbodies. However, two questions still need to be answered quantitatively at a global scale to assist the water management. One is to specify which factors were often selected as the driving forces of cyanobacterial blooms, and the other is to estimate their quantitative relationships. For that, this paper applied a systematic literature review for 41 peer-reviewed studies published before May 2021 and a statistical meta-analysis based on the Pearson's or Spearman's correlation coefficients from 27 studies. These results showed that the water quality, hydraulic conditions, meteorological conditions and nutrient levels were often considered the driving forces of cyanobacterial blooms in global freshwater systems. Among these, meteorological conditions and nutrient level had the highest probability of being chosen as the driving force. In addition, knowledge of the quantitative relationships between these driving forces and cyanobacterial blooms was newly synthesized based on the correlation coefficients. The results indicated that, at a global scale, meteorological conditions were negatively related to cyanobacterial blooms, and other driving forces, such as water quality, hydraulic conditions and nutrient levels, were positively related to cyanobacterial blooms. In addition, the measurement indicators of these driving forces had diverse forms. For example, the nutrient level can be measured by the concentration of different forms of nitrogen or phosphorus, which may lead to different results in correlation analysis. Thus, a subgroup meta-analysis was necessary for the subdivided driving forces and cyanobacterial blooms, which had a better accuracy. Overall, the synthesized knowledge can help guide advanced cyanobacteria-centered water management, especially when the necessary cyanobacterial data of targeting waterbodies are inaccessible.

Multivariable integrated risk assessment for cyanobacterial blooms in eutrophic lakes and its spatiotemporal characteristics

Climate change has catalyzed the global expansion of cyanobacterial blooms in eutrophic , lakes and threatens water security. In most studies, the cyanobacterial bloom risk levels in lakes were evaluated using field-collected data from multiple indicators or spatially continuous data from one cyanobacteria-related indicator. Nevertheless, the occurrence of cyanobacterial blooms in lakes has clear spatial heterogeneity and is affected by numerous factors. Therefore, we developed a multivariable integrated risk assessment framework for cyanobacterial blooms in lakes using five spatially continuous datasets to estimate the risk level of cyanobacterial blooms at the pixel scale (250 m). The spatial and temporal variations in cyanobacterial bloom risk levels from May 1, 2002, to October 31, 2020, were investigated for three typical eutrophic lakes in China: Lakes Taihu, Chaohu, and Dianchi. Seasons and regions of high cyanobacterial bloom risk were identified for each lake. Environmental characteristics were discussed. A long-term investigation revealed that owing to its warm climate, the cyanobacterial risk levels in summer and autumn were much higher than those in the other two seasons. At the synoptic scale, Lake Taihu had a lower cyanobacterial bloom risk than Lakes Chaohu and Dianchi. A further comparison found that precipitation, wind speed, and temperature were responsible for the differences in cyanobacterial bloom risk levels among the three lakes. At the pixel scale, the risk map indicated that the cyanobacterial bloom risk levels of Lake Taihu were unevenly distributed, and the cyanobacterial bloom risk of the lakeshore was higher than that of the other subregions. Nutrient levels played the most critical role in the regional differences in cyanobacterial bloom risk levels in a lake. While the differences of cyanobacterial bloom risk levels in three lakes were resulted by the climates. Bloom events were defined and classified as "long-term bloom" or "flash bloom" according to their duration (over or below a year). Overall, this study can assist in advanced water management with a pixel-scale evaluation of cyanobacterial bloom risk levels.

Change of the structure and assembly of bacterial and photosynthetic communities by the ecological engineering practices in Dianchi Lake

Cyanobacterial bloom challenges the aquatic ecosystem and ecological restoration is an effective approach for cyanobacterial bloom control, but the change of aquatic community after ecological restoration is still unclear. Dianchi Lake is an eutrophic lake with frequent cyanobacterial blooms in China, and recent ecological restoration projects in Caohai (north part) have a satisfactory performance. In this study, we collected 249 water samples at 23 sites from Dianchi Lake to explore the relationships between water physicochemical variables and aquatic microbial communities. Water physicochemical variables in Waihai (south part) intensively changed along time, whereas those in Caohai did not. Photoautotrophic communities were significantly divergent between Caohai and Waihai. Waihai had a lower diversity of photoautotrophic community, containing higher abundance of Cyanophyceae (89.9%) than Caohai (42.7%). Nutrient level and Cyanophyceae only exhibited strong correlations in Wahai (p < 0.05). Redundancy analysis and microbial ecological network suggested that microbial communities in Caohai had a higher stability. Deterministic process dominated the microbial assembly (50-80% for bacteria and >90% for photoautotrophs), and particularly in Caohai. Our results unraveled that the structure and assembly of bacterial and photoautotrophic communities significantly changed after ecological restoration, offering valuable suggestions that photosynthetic diversity should be focused for other ecological restoration projects.

Life history and population ecology of Radix swinhoei (Lymnaeidae) in nearshore regions of a hypereutrophic plateau lake

Accurate assessment of life history and population ecology of widespread species in ultra-eutrophic freshwater lakes is a prerequisite for understanding the mechanisms by which widespread species respond to eutrophication. Freshwater pulmonate (Radix swinhoei) is widespread and abundant in many eutrophic water bodies in Asia. Despite its key roles in eutrophic lake systems, the information on life history and population ecology of R. swinhoei is lacking, especially in ultra-eutrophic freshwater plateau lakes. Here, we conducted a 1-year survey of R. swinhoei with monthly collections to measure the life history traits (life span and growth), annual secondary production, and population size structure of R. swinhoei in nearshore regions with a high seasonally variation of nutrients in Lake Dianchi, a typic hypereutrophic plateau lake in Southwest China. Our results showed that R. swinhoei had the highest biomass in autumn and had the lowest in winter. Its maximum potential life span was 2.5 years, with three recruitment periods (November, March, and July) within a year. Its annual secondary production and P/B ratio were 137.19 g WW/m2 and 16.05, respectively. Redundancy analysis showed that eutrophication-related environmental factors had weak correlations with population size structure of R. swinhoei. Our results suggested that R. swinhoei is a typical r-strategist with high secondary production and thrive in eutrophic environment. Our study can help better understand the mechanisms for widespread species to survive eutrophication and could also be relevant for biodiversity conservation and management of eutrophic ecosystems.

Biogeochemical dynamics of particulate organic phosphorus and its potential environmental implication in a typical "algae-type" eutrophic lake

Organic phosphorus (P_o) plays a very important role in the process of lake eutrophication, but there is still a lack of knowledge about the internal cycle of P_o in suspended particulate matter (SPM) dominated by algal debris. In this study, the characterization of bioavailable P_o by sequential extraction and enzymatic hydrolysis showed that 45% of extracted TP was P_o in SPM of Lake Dianchi, and 43-98% of total P_o in H₂O, NaHCO₃ and NaOH fractions was enzymatically hydrolyzable P_o (EHP, H₂O-EHP: 31-53%). Importantly, labile monoester P was the main organic form (68%) of EHP, and its potential bioavailability was higher than that of diester P and phytate-like P. According to the estimation of P pools in SPM of the whole lake, the total load of P_i plus EHP in the H₂O extract of SPM was 74.9 t and had great potential risk to enhance eutrophication in the lake water environment. Accordingly, reducing the amount of SPM in the water during the algal blooming period is likely to be a necessary measure that can successfully interfere with or block the continuous stress of unhealthy levels of P on the aquatic ecosystem.

Variation in Sediment Available-Phosphorus in Dianchi Lake and Its Impacts on Algal Growth

Sediment phosphorus (P) is an active component of the P cycle in lakes and its availability and movement could play an important role in eutrophication. Sediments from different depths in five representative sites of Dianchi Lake China, i.e., Haigeng, Dounan, Luojiacun, Xinjie, and Kunyang, were studied from January to December to evaluate the spatial and temporal dynamics in available-P and soluble-P and their impacts on algal growth. The results showed that the average sediment available-P and soluble-P were 41.2 mg kg^-1 and 0.27 mg kg^-1, respectively. Sediment available-P and soluble-P concentrations varied significantly among seasons, sites, and layers, with the following order: surface (0-5 cm) > middle (5-10 cm) > bottom (10-20 cm). The release and transformation characteristics of available-P and soluble-P at the sediment-water interface varied among sites. The Haigeng site, with the most severe algae blooms, had significantly higher average available-P and soluble-P in sediment than other sites. This indicated that phosphorus release and availability were associated with algal growth, and that the sediment surface (0-5 cm) is an important internal source that supports algal growth in Dianchi Lake.

Responses of dissolved organic matter (DOM) characteristics in eutrophic lake to water diversion from external watershed

Eutrophication is an important water environment issue facing global lakes. Diversion of water from external watersheds into lakes is considered as effective in ameliorating eutrophication and reducing algal blooms. Nevertheless, the changes in lake water environment caused by external water diversion, especially the influence of water diversion on the characteristics of dissolved organic matters (DOM), are still poorly understood. We therefore used a combination of EEM-PARAFAC, Principal Component Analysis (PCA), and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to investigate the effects of water diversion from the Niulan River on DOM characteristics in Lake Dianchi. The results showed that the water diversion from the Niulan River significantly improved the water quality of Lake Dianchi, the concentrations of TN, TP, COD and Chla decreased rapidly, and the degree of humification of dissolved organic matter (DOM) increased, which was in sharp contrast with that of pre-implementation. Firstly, the diversion of water from the Niulan River mainly led to changes in the structure of pollution sources. The load of influent rivers and sewage treatment plants rich in lignin and tannins increased, and the input of terrestrial humus increased. Second, the improved water quality reduced algal enrichment and frequency of blooms, and reduced the release of lipid- and protein-riched algal-derived DOM. Finally, the hydraulic retention time of Lake Dianchi caused by water diversion was shortened, the hydrodynamic conditions were significantly improved, and the dissolved oxygen (DO) level gradually recovered, which played a positive role in improving the humification degree of DOM. Our findings provide new insights for exploring the improvement of eutrophic lake eco-environmental quality caused by water diversion projects.

Spatiotemporal variation evaluation of water quality in middle and lower Han River, China

As the water source for the middle route of the South-to-North Water Transfer Project, the Han River in China plays a role of the world’s largest inter-basin water transfer project. However, this human-interfered area has suffered from over-standard pollution emission and water blooms in recent years, which necessitates urgent awareness at both national and provincial scales. To perform a comprehensive analysis of the water quality condition of this study area, we apply both the water quality index (WQI) and minimal WQI (WQI_min) methods to investigate the spatiotemporal variation characteristics of water quality. The results show that 8 parameters consisting of permanganate index (PI), chemical oxygen demand (COD), total phosphorus (TP), fluoride (F-), arsenic (As), plumbum (Pb), copper (Cu), and zinc (Zn) have significant discrepancy in spatial scales, and the study basin also has a seasonal variation pattern with the lowest WQI values in summer and autumn. Moreover, compared to the traditional WQI, the WQI_min model, with the assistance of stepwise linear regression analysis, could exhibit more accurate explanation with the coefficient of determination (R²) and percentage error (PE) values being 0.895 and 5.515%, respectively. The proposed framework is of great importance to improve the spatiotemporal recognition of water quality patterns and further helps develop efficient water management strategies at a reduced cost.

Monitoring and control methods of harmful algal blooms in Chinese freshwater system: a review

Harmful algal blooms (HABs) are a worldwide problem with substantial adverse effects on the aquatic environment as well as human health, which have prompted researchers to study measures to stem and control them. Meanwhile, it is key to research and develop monitoring methods to establish early warning HABs. However, both the current monitoring methods and control methods have some shortcomings, making the field application limited. Thus, we need to improve current approaches for monitoring and controlling HABs efficiently. Based on the freshwater system features in China, we review various monitoring and control methods of HABs, summarize and discuss the problems with these methods, and propose the future development direction of monitoring and control HABs. Finally, we envision that it can combine physical, chemical, and biological methods to inhibit HAB expansion in the future, complementing each other with advantages. Further, we promise to establish a long-term strategy of controlling HABs with various algicidal bacteria co-cultivate for field applications in China. Efforts in studying algicidal bacteria must be increased to better control HABs and mitigate the risks of aquatic ecosystems and human health in China.

Long-Term Study of Monitoring History and Change Trends in Surface Water Quality in China

To investigate the monitoring history and long-term change trends in surface water quality in China since the reform and opening up, the history of surface water environment monitoring is summarized, including monitoring scope, monitoring methods, and technical requirements. Temporal and spatial patterns of surface water quality in China were analyzed based on the monitoring results. In the past 40 years, the monitoring targets for surface water quality have been continuously improved, the frequency of monitoring has become more science-based, and the monitoring indicators are now comprehensive. Overall, the temporal change trend in surface water quality has followed a “fluctuating changes stage—rapid deterioration stage—fluctuations stalemate stage—rapid improvement stage” pattern. However, the current regional surface water quality is still in a polluted status, and there is a gap between surface water quality status and the goal of building a well-off society. At present, China’s surface water pollution is prone to high numbers of incidents and the treatment of surface water pollution has entered a crucial stage. The potential for the continuous reduction of major pollutant discharges has become more challenging, and the marginal cost for pollution control has increased. It is very difficult to comprehensively solve the outstanding water environment problems. In addition to strengthening the existing work on surface water quality control, it is also necessary to strengthen the work of risk identification, early warning, and regulation implementation of the surface water environment. During the 14th year plan period (2021–2025), the overall planning on water resources, water ecology, and water quality will be implemented, and beautiful rivers and lakes will be created.

Hysteresis effects of meteorological variation-induced algal blooms: A case study based on satellite-observed data from Dianchi Lake, China (1988-2020)

As one of three top-priority eutrophic lakes in China, Dianchi Lake has received national attention due to its severe eutrophication in recent decades. Meteorological factors are the main factors driving the formation and persistence of algae blooms. In addition, meteorological variation-induced algal blooms usually have a hysteresis effect. However, there have been few quantitative studies on this hysteresis effect. In the present study, Landsat images were used to extract the dynamic characteristics of changes in algal blooms in Dianchi Lake from 1988 to 2020. The hysteresis effect of meteorological factors driving algal blooms was studied by employing the modified lag-correlation method. The results showed that the algal blooms in Dianchi Lake were most severe between 1998 and 2008. During the periods of algal blooms, the values of air temperature (AT) and precipitation (PP) were significantly higher, while those wind velocity (WV) and sunshine duration (SSD) were obviously lower, than the corresponding annual mean values. AT and PP were significantly positively correlated with algal bloom factors in both the formation and persistence stages of algal blooms, while SSD and WV both promoted their regression, but these effects were less significant in the persistence period than in the formation period. Moreover, rainfall led to a decrease in SSD and WV, indirectly contributing to algal blooms. Furthermore, AT, PP and SSD are the main factors impacting the duration of persistent blooms. The time periods during which each meteorological factor was most influential were as follows: 1) AT - 25-30 days before the maximum bloom. 2) PP - within the first 10 days before the maximum bloom. 3) Both SSD and WV - 15-20 days before the maximum bloom. The results of this study support the prediction of algal blooms in Dianchi Lake.

Environmental controls of harmful cyanobacterial blooms in Chinese inland waters

Harmful cyanobacterial blooms (CyanoHABs) are expanding world-wide, adversely affecting aquatic food production, recreational and tourism activities and safe drinking water supplies. China's inland waters have been increasingly threatened by CyanoHABs during the past several decades. The environmental factors controlling CyanoHABs are highly variable in space and time in China due to significant variations in climate, geography, geological and geochemical conditions among its many regions. Here, we synthesize diverse examples among Chinese water bodies regarding interactive effects of anthropogenic, climatic and geographic drivers influencing CyanoHAB potentials and dynamics in lakes and reservoirs; in order to provide a perspective and integrative approach to mitigating CyanoHABs. In China's many shallow water bodies, water quality is highly susceptible to human activity and to changing climatic and hydrological conditions, when compared to deeper lakes. Rapid increases in population, economic activity, and wastewater have accelerated CyanoHABs in China since 1980s, especially in the heavily urbanized, agricultural and industrial regions in the middle and lower Yangtze River basins. Climatic changes have provided an additional catalyst for expansion of CyanoHABs. In particular, rising spring temperatures have accelerated the onset and proliferation of Microcystis spp, blooms in the middle and lower reaches of Yangtze River basin. Large hydroelectric and water supply projects, like the Three Gorges Reservoir (TGR), have altered hydrological regimes, and have led to an increase of CyanoHABs in reservoirs and tributaries due to increases in water residence times. Manipulating water level fluctuations in the TGR may prove useful for controlling CyanoHAB in its tributary bays. Overall,CyanoHAB mitigation strategies will have to incorporate both N and P input reductions in these shallow systems. Furthermore, nutrient reduction strategies must consider climate change-induced increases in extreme weather events, including more intense rainfall and protracted heat waves and droughts, which can extend the magnitudes and duration of CyanoHABs. Ensuring the maintenance of natural hydrologic connectivity between lakes and rivers is of utmost importance in mitigating CyanoHABs throughout China.

Atmospheric Stilling Promotes Summer Algal Growth in Eutrophic Shallow Lakes

Simple Summary

The variability of chlorophyll a yields per unit nitrogen (Chla/TN), or phosphorus (Chla/TP) and its influencing factors were evaluated in eutrophic shallow Lake Taihu, China. The results indicated warming and longer sunshine hours promoted Chla/TN and Chla/TP in winter months from 2005 to 2017, which may cause severer blooms in winter and spring. However, a more stable water column due to atmospheric stilling and water level elevation mainly led to the increasing Chla/TN and Chla/TP in remaining months from 2005 to 2017, allowing algae to grow better. The results also indicated that water stability promotes algal growth mainly due to improved light availability. As atmospheric stilling is an aspect of global climate changes, this study would affect future algal bloom mitigation efforts in shallow lakes worldwide.

Abstract

Algal blooms are environmental challenges confronting lakes worldwide and are significantly influenced by chlorophyll a yields per unit phosphorus (Chla/TP), or nitrogen (Chla/TN). Here, the influence of inter-annual hydrometeorological variations on Chla/TP and Chla/TN were evaluated in eutrophic shallow Lake Taihu, China. Our results demonstrated significant increases (p < 0.001) in both Chla/TN and Chla/TP from 2005 to 2017, and increased Chla yields during the winter months were mainly correlated with higher water temperature and longer sunshine hours, which may cause severer blooms in winter and spring. In remaining months from 2005 to 2017, typical associations between atmospheric stilling (or water level elevation) and higher Chla yields were observed. The results also indicate that atmospheric stilling and water level elevation significantly (p < 0.001) decreased background turbidity and promoted buoyant cyanobacterial biomass, alleviating phytoplankton light limitation. Given the subtropical location, eutrophic status, and high background turbidity of Lake Taihu, light may be the critical limiting factor for summer phytoplankton growth; thus, improved light availability would promote Chla yields until self-shading caused further light limitations. If the mechanism is general, promoting the effect of atmospheric stilling on annual peak Chla in shallow lakes may be greatly underestimated, and our finding will affect future bloom mitigation efforts in such systems.

Available acid consumption capacity of sediments in six water bodies in the Yangtze River Basin in China

Acid-base reactivity is a fundamental property of sediments and is responsible for sediments' multiple roles in aquatic ecosystems. However, little information currently exists about the composition, magnitude, and change of the available acid consumption capacity (AACC) of sediments. To optimize reaction conditions, we developed operational procedures to determine AACC using base titration to recover surplus acid in suspensions. We characterized the sediment AACC of Dianchi Lake (DL), Daduhe River (DR), Tuojiang River (TR), Honghu Lake (HL), Wuhan Donghu Lake (DhL), and Taihu Lake (TL) in the Yangtze River Basin, China. The procedure demonstrated that reacting 40 mL 0.1 M HCl with fresh sediments equivalent to 1.0 g dry weight for 4 h and recovering surplus acid in the suspension by NaOH titration to an endpoint pH of 3.0 could determine sediment AACC. Sediment AACC in the Yangtze River Basin had high regional variability. The mean magnitude of AACC among sites was ranked DL > DR > DhL > TR > HL > TL, which is extremely similar to their geographical location from the upper to lower reaches of the Yangtze River Basin. Qualitative results from acid titration curves showed that more components contributed to AACC in DL, DR, TR, and DhL sediments than to those in HL and TL sediments. The correlation between AACC and the total amount of multivalent cations released indicated that AACC depended significantly on labile acid-soluble minerals that contain multivalent cations (Fe³⁺, Fe²⁺, Ca²⁺, Al³⁺, Mg²⁺, and Mn²⁺) (p < 0.01). Based on the contribution percentages of multivalent cations to AACC, sediment AACC of six water bodies were divided into two types: Ca-Mg dominated (DL, DR, and TR) and Fe-Al dominated (HL, DhL, and TL). We suggest that sediment AACC complexing with pH can contribute to a better description of the acid-base characteristics of sediments.

Assessment of Water Quality in A Tropical Reservoir in Mexico: Seasonal, Spatial and Multivariable Analysis

Agricultural activities are highly related to the reduction of the availability of water resources due to the consumption of freshwater for crop irrigation, the use of fertilizers and pesticides. In this study, the water quality of the Adolfo López Mateos (ALM) reservoir was evaluated. This is one of the most important reservoirs in Mexico since the water stored is used mainly for crop irrigation in the most productive agricultural region. A comprehensive evaluation of water quality was carried out by analyzing the behavior of 23 parameters at four sampling points in the period of 2012-2019. The analysis of the spatial behavior of the water quality parameters was studied by spatial distribution graphs using the Inverse Distance Weighting interpolation. Pearson correlation was performed to better describe the behavior of all water quality parameters. This analysis revealed that many of these parameters were significantly correlated. The Principal Components Analysis (PCA) was carried out and showed the importance of water quality parameters. Ten principal components were obtained, which explained almost 90% of the total variation of the data. Additionally, the comprehensive pollution index showed a slight water quality variation in the ALM reservoir. This study also demonstrated that the main source of contamination in this reservoir occurs near sampling point one. Finally, the results obtained indicated that a contamination risk in the waterbody and further severe ecosystem degradations may occur if appropriate management is not taken.

Hourly remote sensing monitoring of harmful algal blooms (HABs) in Taihu Lake based on GOCI images

The increasingly serious harmful algal blooms (HABs) in Taihu Lake has brought huge losses to the local economy and people's life in Taihu Lake. Satellite remote sensing technology has become one of the most important monitoring methods for HAB disasters due to its large-scale and long-term advantages. GOCI image has become the new data source of HAB monitoring because of its large size and high time resolution. Due to the low spatial resolution (500 m) and the existence of mixed pixels, the error of HAB area obtained by the NDVI method is large. In this paper, the linear mixing model (LMM) and the normalized difference vegetation index (NDVI) threshold method are combined to extract the HAB area from GOCI images with 500-m spatial resolution. Compared with the results of the HAB area extracted by Landsat8 OLI and MODIS data, three small areas in the study area were selected to verify the accuracy of the HAB area extracted from the GOCI image on October 2, 2015. The results show that when the NDVI threshold is 0.1, the area error of HABs is the smallest when the extracted HAB pixels mask the decomposition results of mixed pixels; besides, the area error of HABs extracted from the GOCI image is smaller than that from MODIS image; finally, GOCI image can extract the spatial dynamic distribution of HABs in Taihu Lake within 8 h a day, which has higher temporal resolution than the MODIS image. Compared with the NDVI threshold method and LMM method, the inversion accuracy is greatly improved, and the accuracy is stable in different regions. It can provide technical support for the decision-making and assessment of HAB ecological disasters.

Addressing algal blooms by bio-pumps to reduce greenhouse gas production and emissions with multi-path

The collapse of dense algal blooms is identified as a significant source of methane (CH₄) emissions. When flocculation is used for algae removal, algal carbon is often turned into CH₄ and carbon dioxide (CO₂). Here, we established a "bio-pump" to control algal blooms and reduce greenhouse gas (GHG) emissions by the introduction of submerged macrophytes to the aquatic ecosystem and combination of flocculation and capping. The results suggested that this strategy contributed to an approximately 98% algae removal and sustainably improved dissolved oxygen (DO) in the water and sediment after the 40-day incubation. The aerobic condition at the sediment-water interface and deeper oxygen penetration in the sediment inhibited the abundance of microorganisms related to anaerobic CH₄ production, then changed the metabolic pathway and fate of algal carbon. After the 40-day incubation, compared with flocculation-capping treatments, the bio-pump reduced 69.07% CH₄ and 77.57% CO₂ emissions, which was jointly contributed by the inhibition of anaerobic CH₄ production, aerobic oxidation of CH₄ and carbon sequestration of submerged macrophytes. This was also demonstrated from the finding of a decrease in methyl coenzyme M reductase (mcrA) gene, an increase in particulate methane monooxygenase (pmoA) gene and the absorption of ¹³C-labeled from algae biomass by submerged macrophytes at the end of incubation. Therefore, the bio-pump established in the present study can improve DO in algal blooms water and turn algal-derived organic matter into the plant biomass, which supplied a sustainable method for algae removal and GHG reduction.

Multi-factor analysis of algal blooms in gate-controlled urban water bodies by data mining

Intense human disturbance has made algal bloom a prominent environmental problem in gate-controlled urban water bodies. Urban water bodies present the characteristics of natural rivers and lakes simultaneously, whose algal blooms may manifest multi-factor interactions. Hence, effective regulation strategies require a multi-factor analysis to understand local blooming mechanisms. This study designed a holistic multi-factor analysis framework by integrating five data mining techniques. First, the Kolmogorov-Smirnov test was conducted to screen out the possible explanatory variables. Then, correlation analyses and principal component analyses were performed to identify variable collinearity and mutual causality, respectively. After collinearity and mutual causality were treated prudently by using orthogonalization and instrumental variables, multilinear regression can be properly conducted to quantify factor contributions to algae growth. Lastly, a decision tree was used innovatively to depict the limiting threshold curves of each driving factor that restricts algae growth under different circumstances. The driving factors, their contributions, and the limiting threshold curves compose the complete blooming mechanisms, thus providing a clear direction for the targeted regulation task. A typical case study was performed in Suzhou, a Chinese city with an intricate gate-controlled river network. Results confirmed that climatic factors (i.e., water temperature and solar radiation), hydrodynamic factors (i.e., flow velocity), nutrients (i.e., phosphorus and nitrogen), and external loadings contributed 49.3%, 21.7%, 21.3%, and 7.7%, respectively, to algae growth. These results indicate that a joint regulation strategy is urgently required. Future studies can focus on coupling the revealed mechanisms with an ecological model to provide a comprehensive toolkit for the optimization of an adaptive joint regulation plan under the background of global warming.

Quantification of the Coordination Degree between Dianchi Lake Protection and Watershed Social-Economic Development: A Scenario-Based Analysis

Dianchi Lake is the largest freshwater lake on the Yunnan–Guizhou Plateau near Kunming City, China. As one of the most polluted lakes in China, although billions of U.S. dollars have been spent trying to clean it up, water pollution and eutrophication are still a bottleneck for regional sustainable development. This research established an integrated approach for the evaluation of the coupling coordination degree to support future planning of the Dianchi Lake basin. Ten future scenarios for possible development directions of Dianchi Lake basin were designed to find the best balance between development and protection. Among these scenarios, a high protection–medium development scenario is the most suitable scenario for future development planning. To further improve the coordination degree, economic growth control and non-point source governance were the most effective and feasible approaches. Furthermore, a water quality model was used to verify the coordination degree. It was found that the high protection–medium development scenario can reach the water quality target in 2025. The coordination degree evaluation could be a practical link to help equilibrate the socio-economic development and environmental protection of the Dianchi Lake basin.

Effects of basin nutrient discharge variations coupled with climate change on water quality in Lake Erhai, China

In Lake Erhai, water quality was affected by the basin nutrient discharge and climate change. To analyze the relationships between the water quality (total nitrogen [TN], total phosphorus [TP], chemical oxygen demand [CODmn], ammonia [NH₄], and trophic level index [TLI]) and basin nutrient discharge (TNd, TPd, and CODd) combined with climate changes (air temperature [AT], precipitation [pre], wind speed [wind], and sunshine hours [SHs]), the generalized additive model (GAM) was employed to explore the nonlinear relationships with their interactions using data sets ranging from 1999 to 2012. Our findings revealed that the water quality in Lake Erhai deteriorated in the early twentieth century, and the basin discharge and AT appeared significant (p < 0.05) rising trends in a long time, while the precipitation decreased significantly (p < 0.05) in the study period. Single-factor GAM results indicated that the basin nutrient discharge was the main explanatory factor for the variations of TN and TP in lake, while precipitation was the main driver for CODmn and NH₄. Besides, the water quality displayed nonlinear responses to the basin discharge, but all of the water quality variables went up as the emission levels increased in the lower range. The results showed that the water quality deteriorated in the lower rainfall, and TN rose as the AT increases, while TP was elevated accompanied by the ascending SHs there. The GAM interaction results suggested that the increase of AT and TPd had a promoting effect on TP in Lake Erhai. Stricter nutrient management measures should be implemented when the impacts of climate change are taken into account.

Multidecadal water quality deterioration in the largest freshwater lake in China (Poyang Lake): Implications on eutrophication management

Poyang Lake is the largest freshwater lake in China and a globally important wetland with various functions. Exploring the multidecadal trend of water quality and hydroclimatic conditions is important for understanding the adaption of the lake system under the pressure from multiple anthropogenic and meteorological stressors. The present study applied the Mann-Kendall trend analysis and Pettitt test to detect the trend and breakpoints of hydroclimatic, and water quality parameters (from the 1980s to 2018) and the trend of monthly-seasonal ammonia (NH₄-N) and total phosphorus (TP)concentrations (from 2002 to 2018) in Poyang Lake. Results showed that Poyang Lake had undergone a highly significant warming trend from 1980 to 2018, with a warming rate of 0.44 °C/decade in terms of annual daily mean air temperature. The wind speed and water level of the lake presented a highly significant decreasing trend, whereas no notable trend was detected for precipitation variations. The annual mean total nitrogen (TN), NH₄-N, TP, and permanganate index (COD_Mn) concentrations showed significant upward trends from the 1980s to 2018. Remarkable abrupt shifts were detected for TN, NH₄-N, and COD_Mn in around 2003. They were in accordance with the water level breakpoint of the lake, thus implying the important role of hydrological conditions in water quality variations in floodplain lakes. A significant increasing trend has been detected for Chl-a variations during wet season from 2008 to 2018, which could be attributed to the increasing trend of nutrient concentration during the nutrient-limited phase of Poyang Lake. These hydroclimatic and water quality trends suggest a high risk of increasing phytoplankton growth in Poyang Lake. This study thus emphasizes the need for adaptive lake eutrophication management for floodplain lakes, particularly the consideration of the strong trade-off and synergies between hydroclimatic conditions and water quality variations.

A Mini Review on Microcystins and Bacterial Degradation

Microcystins (MCs) classified as hepatotoxic and carcinogenic are the most commonly reported cyanobacterial toxins found in the environment. Microcystis sp. possessing a series of MC synthesis genes (mcyA-mcyJ) are well documented for their excessive abundance, numerous bloom occurrences and MC producing capacity. About 246 variants of MC which exert severe animal and human health hazards through the inhibition of protein phosphatases (PP1 and PP2A) have been characterized. To minimize and prevent MC health consequences, the World Health Organization proposed 1 µg/L MC guidelines for safe drinking water quality. Further the utilization of bacteria that represent a promising biological treatment approach to degrade and remove MC from water bodies without harming the environment has gained global attention. Thus the present review described toxic effects and bacterial degradation of MCs.

Identification and characterization of the dominant Microcystis sp. cyanobacteria detected in Lake Dong Ting, China

The presence of cyanobacteria in drinking water, aquatic foods and bathing water has created a significant major problem to global public health as these toxins induce damage in various organ including liver, cardiovascular, intestinal and central nervous systems. Although the morphologic, phylogenetic and toxicogenetic characteristics of cyanobacteria were identified in several lakes in China, many freshwater sources such as Dong Ting Lake, Hunan Province, China remain to be determined. Since the presence of these cyanobacteria may potentially affect human health, the aim of this study was to isolate, identify and characterize the most frequent occurring bloom-forming cyanobacteria in Dong Ting Lake, Hunan Province, China, which can provide information on the safety of utilization of this water source for drinking water, agriculture and recreation. Samples collected from the surface water of Dong Ting Lake were subjected to serial dilution in the lab for morphological analysis. Data demonstrated the morphological features were 2-5 µm diameters with rounded shapes and green color resembling Microcystis sp. The isolated cyanobacterial strain obtained from surface water samples in Dong Ting Lake was termed Microcystis sp. YFM2. The MC concentration was detected by enzyme-linked immunosorbent assay (ELISA) and found to be 92.88 µg/10⁷ cells in Microcystis sp. YFM2. By polymerase chain reaction (PCR) results indicated that Microcystis sp. YFM2 isolated from Dong Ting Lake contained synthetase genes (mcyA-C). Our findings indicated that the dominant cyanobacteria Microcystis sp. YFM2 isolated from the freshwater Dong Ting Lake demonstrated morphologic, phylogenetic and toxicogenetic properties resembling a toxin generating cyanobacterium. Based upon this knowledge, it is essential to monitor the use of this Lake for future domestic, agricultural and recreational purposes.

MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

Algal blooms in eutrophic lakes have been a global issue to environmental ecology. Although great progress on prevention and control of algae have been made in many lakes, systematic research on long-term temporal-spatial dynamics and drivers of algal blooms in a plateau Lake Dianchi is so far insufficient. Therefore, the algae pixel-growing algorithm (APA) was used to accurately identify algal bloom areas at the sub-pixel level on the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2018. The results showed that algal blooms were observed all year round, with a reduced frequency in winter–spring and an increased frequency in summer–autumn, which lasted a long time for about 310–350 days. The outbreak areas were concentrated in 20–80 km2 and the top three largest areas were observed in 2002, 2008, and 2017, reaching 168.80 km2, 126.51 km2, and 156.34 km2, respectively. After deriving the temporal-spatial distribution of algal blooms, principal component analysis (PCA) and redundancy analysis (RDA) were applied to explore the effects of meteorological, water quality and human activities. Of the variables analyzed, mean temperature (Tmean) and wind speed (WS) were the main drivers of daily algal bloom areas and spatial distribution. The precipitation (P), pH, and water temperature (WT) had a strong positive correlation, while WS and sunshine hours (SH) had a negative correlation with monthly maximum algal bloom areas and frequency. Total nitrogen (TN) and dissolved oxygen (DO) were the main influencing factors of annual frequency, initiation, and duration of algal blooms. Also, the discharge of wastewater and the southwest and southeast monsoons may contribute to the distribution of algal blooms mainly in the north of the lake. However, different regions of the lake show substantial variations, so further zoning and quantitative joint studies of influencing factors are required to more accurately understand the true mechanisms of algae in Lake Dianchi.

Phosphorus mitigation remains critical in water protection: A review and meta-analysis from one of China's most eutrophicated lakes

The processes of urbanization and industrialization within geological phosphorus-rich mountains (GPMn) have resulted in water degradation within southwest China. Lake Dianchi, one of the most eutrophicated lakes in China, has epitomized this issue. Clear understandings of phosphorus (P) mitigation efforts, the evolution of P budgets, and possible risks in the Dianchi system will benefit future eutrophication control, providing valuable lessons for other plateau freshwater lakes. In this study, we applied systematic review methodology to investigate the above questions, and then compared the results with other lakes worldwide. Generally, meta-analytical approaches have indicated P levels remain a key factor in causing algal blooms. Post-2015, the P budget of the Dianchi system, especially in Caohai section, was modified. However, it's still experiencing high pressures from P enrichment (Caohai: 0.4 mg·l^-1; Waihai: 0.2 mg·l^-1). The flux of P in Dianchi remains high, both through the external P load (556 ton·a^-1), and an internal cycle (304 ton·a^-1 associated with the absorption, deposition and removal of algae biomass; and 380 ton·a^-1 associated with sediment exchange). Meanwhile, significant P retention has been observed in the lake, in particular within the Waihai section (211 ton·a^-1). Currently, water diversion (from external watersheds), sewage diversion, and sediment-dredging projects have benefited Dianchi. However, continuous urbanization and GPMn ecological degradation could introduce hundreds of tons of additional P, leading to subsequent algal blooms. Furthermore, beyond Lake Dianchi, other lakes and reservoirs in southwest China are facing similar issues regarding P mitigation, especially in GPMn regions, though corresponding knowledge is still limited. Therefore, effective and flexible sub-regional protection strategies and research related to external and internal P mitigations have become key requirements for Lake Dianchi management. Meanwhile, ecologically sensitive approaches to GPMn regions, as well as city development within basin and market driven treatments, should be incorporated into regional water source protection for southwest China.

Reducing the Phytoplankton Biomass to Promote the Growth of Submerged Macrophytes by Introducing Artificial Aquatic Plants in Shallow Eutrophic Waters

Harmful cyanobacterial blooms frequently occur in shallow eutrophic lakes and usually cause the decline of submerged vegetation. Therefore, artificial aquatic plants (AAPs) were introduced into enclosures in the eutrophic Dianchi Lake to investigate whether or not they could reduce cyanobacterial blooms and promote the growth of submerged macrophytes. On the 60th day after the AAPs were installed, the turbidity, total nitrogen (TN), total phosphorous (TP), and the cell density of phytoplankton (especially cyanobacteria) of the treated enclosures were significantly reduced as compared with the control enclosures. The adsorption and absorption of the subsequently formed periphyton biofilms attached to the AAPs effectively decreased nutrient levels in the water. Moreover, the microbial diversity and structure in the water changed with the development of periphyton biofilms, showing that the dominant planktonic algae shifted from Cyanophyta to Chlorophyta. The biodiversity of both planktonic and attached bacterial communities in the periphyton biofilm also gradually increased with time, and were higher than those of the control enclosures. The transplanted submerged macrophyte (Elodea nuttallii) in treated enclosures recovered effectively and reached 50% coverage in one month while those in the control enclosures failed to grow. The application of AAPs with incubated periphyton presents an environmentally-friendly and effective solution for reducing nutrients and controlling the biomass of phytoplankton, thereby promoting the restoration of submerged macrophytes in shallow eutrophic waters.