Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation

Identifying factors influencing reservoir eutrophication using interpretable machine learning combined with shoreline morphology and landscape hydrological features: A case study of Danjiangkou Reservoir, China

Reservoir nearshore areas are influenced by both terrestrial and aquatic ecosystems, making them sensitive regions to water quality changes. The analysis of basin landscape hydrological features provides limited insight into the spatial heterogeneity of eutrophication in these areas. The complex characteristics of shoreline morphology and their impact on eutrophication are often overlooked. To comprehensively analyze the complex relationships between shoreline morphology and landscape hydrological features, with eutrophication, this study uses Danjiangkou Reservoir as a case study. Utilizing Landsat 8 OLI remote sensing data from 2013 to 2022, combined with a semi-analytical approach, the spatial distribution of the Trophic State Index (TSI) during flood discharge periods (FDPs) and water storage periods (WSPs) was obtained. Using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), explained the relationships between landscape composition, landscape configuration, hydrological topography, shoreline morphology, and TSI, identified key factors at different spatial scales and validated their reliability. The results showed that: (1) There is significant spatial heterogeneity in the TSI distribution of Danjiangkou Reservoir. The eutrophication levels are significant in the shoreline and bay areas, with a tendency to extend inward only during the WSPs. (2) The importance of landscape composition, landscape configuration, hydrological topography, and shoreline morphology to TSI variations during the FDPs are 25.12 %, 29.6 %, 23.09 %, and 22.19 % respectively. Besides shoreline distance, the Landscape Shape Index (LSI) and Hypsometric Integral (HI) are the two most significant environmental variables overall during the FDPs. Forest and grassland areas become the most influential factors during the WSPs. The influence of landscape patterns and hydrological topography on TSI varies at different spatial scales. At the 200 m riparian buffer zone, the increase in cropland and impervious areas significantly elevates eutrophication levels. (3) Morphology complexity, shows a noticeable threshold effect on TSI, with complex shoreline morphology increasing the risk of eutrophication.

Harvesting of cyanobacteria and phosphorus by electrocoagulation-flocculation-flotation: Role of phosphorus precipitation in cell separations and organics destabilization

A high level of phosphate triggers the excretion of algogenic organic matter (AOM) during algae blooming, leading to disinfection by-products (DBPs) formation. The presence of phosphate could impact cyanobacteria harvesting and AOM separations by electrocoagulation. This study aims to investigate the role of phosphate in cell separations and AOM destabilization by Al-based electrocoagulation-flocculation-flotation (EFF) for harvesting of cyanobacteria and phosphate. The Al-based EFF was conducted to harvest Microcystis aeruginosa (MA) with varied phosphate (0-10 mg/L) at 5 mA/cm2 and pH 8. Fluorescent organic fractions, molecular weight distributions, the properties of flocs and DBPs formation potential were fully investigated. The results showed that the EFF at a low level of phosphate (1 mg/L) effectively improves the harvesting of MA cells, phosphate and the reduction in dissolved organic matter (DOC) up to 99.5 %, 95 % and 50 %, respectively. However, the presence of concentrated phosphate (10 mg/L) alleviates cell harvesting and worsens AOM separations due to ineffective floc formation induced by the fast formation of inactive AlPO4 precipitates along with limited Al(OH)3. At such a condition, it worsens DBPs precursors minimization owing to AOM release from MA cells. The increase in the current density during EFF can compensate for cell harvesting efficiency even though at concentrated phosphate, but it further induces AOM release. It is concluded that Al-based EFF demonstrates an efficient harvesting of cyanobacteria, phosphorus and AOM separations from algae-laden water under phosphate impact.

Drinking water safety improvement and future challenge of lakes and reservoirs

To meet the Sustainable Development Goal (SDG) target 6.1, China has undertaken significant initiatives to address the uneven distribution of water resources and to enhance water quality. Since 2000, China has invested heavily in the water infrastructure of numerous reservoirs, with a total storage capacity increase of 4.704 × 1011 m3 (an increase of 90.8%). These reservoirs have significantly enhanced the available freshwater resources for drinking water. Concurrently, efforts to improve water quality in lakes and reservoirs, facilitated by nationwide water quality monitoring, have been successful. As a result, an increasing lakes and reservoirs are designated as centralized drinking water sources (CDWSs) in China. Among the 3,441 CDWSs across all provinces, 40.8% are sourced from lakes and reservoirs, 32.6% from rivers, and 26.6% from groundwater in 2023. Notably, from 2016 to 2023, the percentage of lakes and reservoirs categorized as CDWSs has increased consistently across all 29 provinces. This progress has enabled 561.4 million urban residents to access improved drinking water sources in 2022, compared to 303.4 million in 2004. Our findings underscore the pivotal role of water infrastructure construction and water quality improvement jointly promoting lakes and reservoirs as vital drinking water sources. Nevertheless, the nationwide occurrence of algal blooms has surged by 113.7% from the 2000s to the 2010s , which is a considerable challenge to drinking water safety. Fortunately, algal blooms have been markedly alleviated in past four years. However, it is still crucial to acknowledge that lakes and reservoirs face the challenges of algal blooms, and associated toxic microcystin and odor compounds.

Transgenerational effects of extracts containing Microcystin-LR exposure on reproductive toxicity and offspring growth inhibition in a model organism zebrafish

Cyanobacteria cell lysates release numerous toxic substances (e.g., cyanotoxins) into the water, posing a serious threat to human health and aquatic ecosystems. Microcystins (MCs) are among the most abundant cyanotoxins in the cell lysates, with microcystin-LR (MC-LR) being one of the most common and highly toxic congeners. In this study, zebrafish (Danio rerio) were exposed to different levels MC-LR that from extracts of Microcystis aeruginosa. Changes in the MC-LR accumulations, organ coefficients, and antioxidant enzyme activities in the zebrafish were analyzed. Transgenerational reproductive toxicity of MC-LR in the maternal and paternal generations was further investigated, as well as the influences of extracts containing MC-LR exposures of the F1 on the growth of zebrafish. The study found that high levels of MC-LR could be detected in the major organs of adult zebrafish, particularly in spleen. Notably, concentration of MC-LR in the spermary was significantly higher than that in the ovarium. MC-LR could induce oxidative damage by affecting the activities of catalase and superoxide dismutase. Inherited from F0, MC-LR led to impaired development in the F1 generation. Difference in offspring survival rates could be observed in the groups with different MC-LR levels of maternal and paternal exposures. This study reveals transgenerational effects of MC-LR on the reproductive toxicity and offspring growth inhibition to the aquatic organisms, which should be emphasized in the future ecological risk assessment.

Predicting and analyzing the algal population dynamics of a grass-type lake with explainable machine learning

Algal blooms, exacerbated by climate change and eutrophication, have emerged as a global concern. In this study, we introduce a novel interpretable machine learning (ML) workflow tailored for investigating the dynamics of algal populations in grass-type lakes, Liangzi lake. Utilizing seven ML methods and incorporating the covariance matrix adaptation evolution strategy (CMA-ES), we predict algal density across three distinct time periods, resulting in the construction of a total of 30 ML models. The CMA-ES-CatBoost model consistently demonstrates superior predictive accuracy and generalization capability across these periods. Through the collective validation of various interpretable tools, we identify water temperature and permanganate index as the two most critical water quality parameters (WQIs) influencing algal density in Liangzi Lake. Additionally, we quantify the independent and interactive effects of WQIs on algal density, pinpointing key thresholds and trends. Furthermore, we determine the minimum combination of WQIs that achieves near-optimal predictive performance, striking a balance between accuracy and cost-effectiveness. These findings offer a scientific and economically efficient foundation for governmental agencies to formulate strategies for water quality management and sustainable development.