Evaluation of water quality in the South-to-North Water Diversion Project of China using the water quality index (WQI) method

Evaluation and source identification of water pollution

Maintaining good surface water quality is essential for protecting ecosystems and human health. Henan Province has long faced challenges related to water scarcity and severe water pollution. To support effective management of water pollution in Henan Province and provide insights for regional water pollution management, we collected extensive water quality monitoring data and applied spatial autocorrelation along with random forest to analyze the sources of heavily polluted areas. Results indicate that the spatial pollution pattern of surface water quality in Henan Province can be generally classified as insignificant pollution in the north, heavy pollution in the central regions, and light pollution in the south. Heavily polluted areas are mainly located in Zhengzhou, Luoyang, and Kaifeng. Key indicators affecting water quality in these regions are chemical oxygen demand (CODMn), dissolved oxygen (DO), ammonia nitrogen (NH3-N), and total phosphorus (TP), with urban sewage and industrial wastewater identified as the main causes of deterioration. These results not only provide a scientific basis for the systematic management of surface water quality pollution in Henan Province but also provide a reference for regional water pollution management.

Combined effects of flood, drought and land use dominate water quality and nutrient exports in Jialing River basin, SW China

Climate change and the associated increase in hydroclimatic extremes necessitate a deeper understanding of the resulting water quality responses. This study investigates the combined impacts of hydroclimatic extremes and land uses on water quality of the Chinese Jialing River, of which the middle and downstream areas experienced a flood in 2021 and a severe drought in 2022. Water Quality Index (WQI) and nutrient loads were assessed using daily data from 22 monitoring stations across the Jialing River and its two tributaries, the Qujiang River and Fujiang River, over 2021-2022. The results indicate a slight upward trend in water quality, as reflected by the WQI, for the tributaries from 2021 to 2022, while a declining trend was observed in the mainstream. Floods had a more pronounced impact on water quality than droughts, particularly on nutrient concentrations, and both dilution and flushing effects were observed as discharge increased in the Jialing River and its tributaries. Notably, water quality deterioration was most pronounced in the downstream areas with land uses dominated by cropland and built-up area, where intensified rainfall distributed and exacerbated nutrient losses in the rainy seasons. Nutrient fluxes, including Chemical Oxygen Demand (COD), Total Phosphorus (TP), and Total Nitrogen (TN), were closely linked to discharge, with hydroclimatic extremes therefore significantly affecting nutrient exports. This study elucidates the complex interactions between land use, extreme weather and water quality in the Jialing River Basin. Our findings underscore the need to strengthen the management of non-point source pollutants in the downstream areas of the Jialing River to address the challenges posed by anticipated increases in extreme rainfall in the near future.

Enhancing water security: Statistical measurement and spatiotemporal analysis

Water security is essential for ensuring energy security, sustainable development, and human survival. However, due to a series of challenges, including rising water demand, environmental pollution, and water resource shortages, the global water security situation remains concerning and poses a threat to global sustainable development. To assess water security in China, this study uses data from 30 provinces in China from 2012 to 2021. A comprehensive evaluation method was applied to determine the level of water resource security in China. The Dagum Gini coefficient, Moran index, and spatial model were used to clarify regional differentiation characteristics and the driving factors. The results indicate that while China's water resource security is relatively low, it has shown steady improvement in recent years. Significant regional disparities exist in water resource security across China, with notable spatial characteristics, and socio-economic factors are the primary causes of these differences. Based on the above research, we put forward policy recommendations from the aspects of water resources management, public participation and inter-regional water resources cooperation, to provide reference for water resources security in developing countries.

Nutrient variations and environmental relationships of lakes and reservoirs before and after the COVID-19 epidemic public lockdown policy elimination: A nationwide comparative view in China

The continuous impact of COVID-19 on aquatic environments has attracted considerable attention, primarily focusing on short-term water quality effects during lockdown, while studies on changes following the lifting of restrictions are relatively limited. Following adjustments to China's pandemic public policy in December 2022, the effects on water quality and nutrient status in lakes and reservoirs remain unclear. In this study, we collected national environmental monitoring data comprising 15 indicators of water quality, meteorology, soil, and economic factors, from 86 lakes and reservoirs across China between March 2021 and December 2023. Total nitrogen (TN), total phosphorus (TP), the mass TN/TP ratio (TN/TP), and ammonia-nitrogen (NH3-N) were selected as representative nutrient indicators. The water quality index (WQI) and multivariate statistical techniques were employed to comprehensively assess national water quality and identify the drivers of nutrient variations in sub-regions. The results show that during the monitoring period from 2021 to 2023, Chinese national water quality consistently fell within the 'good (61-80)' or 'excellent (81-100)' categories, with the lowest water quality observed in the summer of each year. The summer of 2021 recorded the lowest WQI value among all seasons at 75.01. Following the elimination of the COVID-19 epidemic public lockdown policy, concentrations of TN, TP, and NH3-N declined. These findings indicate a general improvement in the water quality of lakes and reservoirs nationwide. Mantel test and multiple stepwise linear regression models revealed significant correlations between nutrients and human activity indicators in central, eastern, and northern China. In northern China, TP showed a significant positive correlation with GDP (0.2 < Mantel's r < 0.5, P < 0.05), with the beta value increasing from 0.27 to 0.38 after the elimination of the COVID-19 epidemic public lockdown policy. In these regions, the influence of rainfall, wind speed, NDVI, surface soil moisture, and water temperature on nutrients shifted from significant to insignificant effects after the elimination of the COVID-19 epidemic public lockdown policy, indicating that human activities have overshadowed natural factors. This study examines the water quality and nutrient status of lakes and reservoirs in China after the elimination of the COVID-19 epidemic public lockdown policy, highlighting the long-term impacts and spatial variations of the pandemic. These findings will inform environmental governance and promote sustainable water resource management in the post-pandemic era.

Assessment of groundwater quality of unconfined aquifers in an urbanized area using the water quality index method

This study performed an evaluation of the groundwater quality of unconfined aquifers in Yangzhou, located in the Yangtze River Delta, eastern China. The study area is the source area of the Eastern Route (ER) of the South-to-North Water Diversion Project (SNWDP), typical of the lower alluvial plain of the lower Yangtze River. Water samples from monitoring wells and domestic wells were collected to analyze common variables (pH, EC), major ions, trace metals, etc. The water quality index method was employed to evaluate groundwater quality in both eastern and western parts of the study area. The study found that the groundwater hydrochemistry is typical of HCO3-Ca and HCO3-Na, similar between the eastern and western parts of the study area. In addition, the eastern part of the study area is more affected by nitrate pollution than the western part due to agricultural activities. The water quality analysis suggests that the groundwater in the western region was slightly better than that in the eastern region. The comparison between different scenarios indicates that the water quality using only major ions can differ significantly from that using all chemical parameters, but may offer some preliminary insights into the groundwater quality useful for conducting further more detailed analysis. Our study shows that using the combination of major ions and heavy metals could provide relatively robust results of the groundwater quality in our study area. Our study has important implications for the assessment of groundwater quality in regions with similar conditions.

Landscape fragmentation of built-up land significantly impact on water quality in the Yellow River Basin

Urbanization development often leads to significant changes in the extent in area and fragmentation of built-up land landscape pattern (BLLP) in river basins, which greatly impact the processes of rainfall runoff and pollutant migration. Understanding the spatial scale effects and driving mechanisms of BLLP changes on water quality in large river basins is a challenging research topic and an international frontier in the interdisciplinary fields of geography and environment. This study analyzes the spatial variations of BLLP and water quality throughout the Yellow River Basin (YRB) during the rainy seasons from 2019 to 2021 (4 h scale). Utilized the random forest model to quantitatively separates the contributions of rainfall processes to surface runoff and water pollution, revealing the scale effects and non-linear driving mechanisms of BLLP impacts on water environment changes. The results indicate that: 1) The YRB exhibits great spatial heterogeneity in terms of both BLLP and water quality, with places with lower water quality displaying bigger areas and higher degrees of BLLP fragmentation. 2) The patch density and built-up land area (PD.B and CA.B) have a major impact on changes in water quality in the YRB, with notable impacts noted in circular buffer zones with radii of 20 km and 5 km, respectively. 3) PD.B is sensitive to water quality in the YRB, explaining 39.1%-49.5% of the variance under different rainfall conditions, and exhibits a significant non-linear relationship, with an impact threshold of 0.38 (n/100 ha). The study suggests that for large-scale regions like the YRB, the degree of BLLP fragmentation is more likely to lead to degradation of water environmental quality compared to its area. BLLP fragmentation due to higher PD.B and CA.B disrupts the original ecosystem and hydrological connectivity, resulting in poorer retention and filtration of pollutants carried by rainfall runoff, while increasing the export of other pollutants. However, once urbanization surpasses a certain threshold, the BLLP fragmentation can enhance water quality by reducing the impermeable surface connectivity, as they are no longer impacted by expanding areas. To achieve ecologically sustainable development, it is necessary to apply rational landscape management and water resource management policies that consider the dual process of how BLLP fragmentation affects the water environment.

Relationships between water quality of a long-distance inter-basin water diversion project and air pollution emissions along the canal: Distributions, lag effects, and nonlinear responses

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators-permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)-and six air pollution indicators-O3, CO, NO2, SO2, 2.5 μm particulate matter (PM2.5), and inhalable particles (PM10)-using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann-Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

A novel coupling interpretable machine learning framework for water quality prediction and environmental effect understanding in different flow discharge regulations of hydro-projects

Machine learning models (MLMs) have been increasingly used to forecast water pollution. However, the "black box" characteristic for understanding mechanism processes still limits the applicability of MLMs for water quality management in hydro-projects under complex and frequently artificial regulation. This study proposes an interpretable machine learning framework for water quality prediction coupled with a hydrodynamic (flow discharge) scenario-based Random Forest (RF) model with multiple model-agnostic techniques and quantifies global, local, and joint interpretations (i.e., partial dependence, individual conditional expectation, and accumulated local effects) of environmental factor implications. The framework was applied and verified to predict the permanganate index (CODMn) under different flow discharge regulation scenarios in the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). A total of 4664 sampling cases data matrices, including water quality, meteorological, and hydrological indicators from eight national stations along the main canal of the MRSNWDPC, were collected from May 2019 to December 2020. The results showed that the RF models were effective in forecasting CODMn in all flow discharge scenarios, with a mean square error, coefficient of determination, and mean absolute error of 0.006-0.026, 0.481-0.792, and 0.069-0.104, respectively, in the testing dataset. A global interpretation indicated that dissolved oxygen, flow discharge, and surface pressure are the three most important variables of CODMn. Local and joint interpretations indicated that the RF-based prediction model provides a basic understanding of the physical mechanisms of environmental systems. The proposed framework can effectively learn the fundamental environmental implications of water quality variations and provide reliable prediction performance, highlighting the importance of model interpretability for trustworthy machine learning applications in water management projects. This study provides scientific references for applying advanced data-driven MLMs to water quality forecasting and a reliable methodological framework for water quality management and similar hydro-projects.

Identifying groundwater characteristics and controlling factors in Jiaozhou Bay's northern coastal region, China: a combined approach of multivariate statistics, isotope analysis, and field empirical investigations

Explicit identification of hydrochemical processes and their controlling factors within groundwater systems is critical for the sustainable utilization of water resources in coastal urban areas. This study was undertaken in the North Coastal Region of Jiaozhou Bay (NCRJB), located in the eastern part of Shandong Province, China, an area grappling with significant issues of groundwater quality degradation and water scarcity. A total of 105 groundwater samples and 34 surface water samples, collected from 2020 to 2024, were analyzed and studied using various hydrogeological tools, multivariate statistical analyses, and water quality assessment methods. These include the Piper diagram, hydrochemical facies evolution diagram (HFE-D), Principal Components Analysis (PCA), correlation analysis, stable isotope analysis, Water Quality Index (WQI), and USSL diagrams. The results indicated that all surface water and pore groundwater samples were categorized as Na-Cl type, exhibiting high Total Dissolved Solids (TDS) and Electrical Conductivity (EC) values, characteristics that render them poor to unsuitable for drinking and irrigation purposes. The fracture groundwater is predominantly of the Ca-Na-Cl mixed type, with average suitability for irrigation and a limited proportion (22.5%) deemed suitable for drinking. Seawater intrusion, primarily through the surface water system, and the impact of human activities were identified as the predominant controlling factors con-tributing to the degradation of the local groundwater environment. Field empirical investigations further validated the results derived from hydrogeological assessments, multivariate statistical analyses, and isotopic approaches. The long-term shifts in hydrochemical properties, along with the latent threat of seawater intrusion, exhibit an upward trend during the dry season and show a certain degree of mitigation during the wet season. This study highlights that field investigations, in conjunction with hydrochemical tools, multivariate statistical analyses, and stable isotope analysis, can successfully furnish reliable insights into the predominant mechanisms governing regional groundwater evolution within the context of long-term and intricate envi-ronmental settings.

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

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

Source apportionment of major ions and trace metals in the lacustrine systems of Schirmacher Hills, East Antarctica

The fabric of the Antarctic lacustrine system has a crucial role in assimilating the anthropogenic inputs and mitigating their long time impacts on climate change. Here, we present the changes in the concentrations of major ions and trace metals in the surface water of the lacustrine system to understand the extent of anthropogenic impacts from the adjacent Schirmacher Hills, East Antarctica. The results show that the land-locked lakes (closed-basin lakes surrounded by topographical barriers such as mountains or bedrock formations) in the region have a moderate enrichment in elemental concentrations compared to the pro-glacial lakes (marginal freshwater bodies that form at the terminus of a glacier or ice sheet). The water quality index (WQI: 7.58-12.63) and pollution evaluation index (PEI: 1.36-2.35) remained normal, indicating that the water in these lake are of good quality. However, a significant correlation between lithogenic elements (Al, Fe) and potentially toxic elements (Cd, Cr, and Ba), suggests an increase in the anthropogenic impacts. Based on the principal component analysis (PCA), the source of trace metals to the lacustrine systems appears to be the surrounding environment, followed by aerosol dust particles. Hierarchical cluster analysis (HCA) revealed that regional topography significantly impacts the supply of major ions/trace metals to these lakes. The present study provides baseline data and can be used to estimate and forecast future local and/or global anthropogenic contaminations in the lacustrine system of Schirmacher Hills, East Antarctica. Moreover, the presence of research stations (Maitri and Novolazarevskaya), tourist activities, and the potential for anthropogenic stressors necessitate continued monitoring and impact assessment programs within the Schirmacher Hills lacustrine systems. These programs are crucial for safeguarding this pristine ecosystem from future environmental disturbances under a changing Antarctic climate, as mandated by the Antarctic Treaty System and the Indian Antarctic Act.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Spatio-temporal evolution mechanism and dynamic simulation of nitrogen and phosphorus pollution of the Yangtze River economic Belt in China

Excess nitrogen and phosphorus inputs are the main causes of aquatic environmental deterioration. Accurately quantifying and dynamically assessing the regional nitrogen and phosphorus pollution emission (NPPE) loads and influencing factors is crucial for local authorities to implement and formulate refined pollution reduction management strategies. In this study, we constructed a methodological framework for evaluating the spatio-temporal evolution mechanism and dynamic simulation of NPPE. We investigated the spatio-temporal evolution mechanism and influencing factors of NPPE in the Yangtze River Economic Belt (YREB) of China through the pollution load accounting model, spatial correlation analysis model, geographical detector model, back propagation neural network model, and trend analysis model. The results show that the NPPE inputs in the YREB exhibit a general trend of first rising and then falling, with uneven development among various cities in each province. Nonpoint sources are the largest source of land-based NPPE. Overall, positive spatial clustering of NPPE is observed in the cities of the YREB, and there is a certain enhancement in clustering. The GDP of the primary industry and cultivated area are important human activity factors affecting the spatial distribution of NPPE, with economic factors exerting the greatest influence on the NPPE. In the future, the change in NPPE in the YREB at the provincial level is slight, while the nitrogen pollution emissions at the municipal level will develop towards a polarization trend. Most cities in the middle and lower reaches of the YREB in 2035 will exhibit medium to high emissions. This study provides a scientific basis for the control of regional NPPE, and it is necessary to strengthen cooperation and coordination among cities in the future, jointly improve the nitrogen and phosphorus pollution tracing and control management system, and achieve regional sustainable development.

A multidirectional pairwise coupling approach with spectral features unmixing to quantify total phosphorus, total nitrogen, and chlorophyll-a in urban rivers

Comprehensive and effective water quality monitoring is vital to water environment management and prevention of water quality from degradation. Unmanned aerial vehicle (UAV) remote sensing techniques have gradually matured and prevailed in monitoring water quality of urban rivers, posing great opportunity for more effective and flexible quantitative estimation of water quality parameter (WQP) than satellite remote sensing techniques. However, current UAV remote sensing methods often entail large quantities of cost-prohibitive in-situ collected training samples with corresponding chemical analysis in different monitoring watersheds, laying time and fiscal pressure on local environmental protection department. They suffer relatively low calculation accuracy and stability and their applicability in various watersheds is constrained. This study developed a unified two-stage method, multidirectional pairwise coupling (MDPC) with information sharing and delivery of different modeling stages to efficiently predict concentrations of WQPs including total phosphorus (TP), total nitrogen (TN), and chlorophyll-a (Chl-a) from hyperspectral data. MDPC incorporates exterior and interior feature interaction and gravity model variant to improve prediction accuracy and stability with consideration of mutual effect in the proximity. The structure design and workflow of MDPC ensure high robustness and application prospect due to achievement of good performance with less training samples, improving applicability and feasibility. The experiments show that MDPC has achieved good performance on retrieval of WQPs concentrations including TP, TN, and Chl-a, the results mean absolute percent error (MAPE) and coefficient of determination (R2) ranging from 6.34 % to 11.94 % and from 0.74 to 0.93. This study provides a systematic and scientific reference to formulate a feasible and efficient water environment management scheme.

Spatial consistency of co-exposure to air and surface water pollution and cancer in China

Humans can be exposed to multiple pollutants in the air and surface water. These environments are non-static, trans-boundary and correlated, creating a complex network, and significant challenges for research on environmental hazards, especially in real-world cancer research. This article reports on a large study (377 million people in 30 provinces of China) that evaluated the combined impact of air and surface water pollution on cancer. We formulate a spatial evaluation system and a common grading scale for co-pollution measurement, and validate assumptions that air and surface water environments are spatially connected and that cancers of different types tend to cluster in areas where these environments are poorer. We observe "dose-response" relationships in both the number of affected cancer types and the cancer incidence with an increase in degree of co-pollution. We estimate that 62,847 (7.4%) new cases of cancer registered in China in 2016 were attributable to air and surface water pollution, and the majority (69.7%) of these excess cases occurred in areas with the highest level of co-pollution. The findings clearly show that the environment cannot be considered as a set of separate entities. They also support the development of policies for cooperative environmental governance and disease prevention.

Comprehensive monitoring of contamination and ecological-health risk assessment of potentially harmful elements in surface water of Maroon-Jarahi sub-basin of the Persian Gulf, Iran

The increase in heavy metal concentration in water bodies due to rapid industrial and socio-economic development significantly threatens ecological and human health. This study evaluated metal pollution and related risks to ecology and human health in the Maroon-Jarahi river sub-basin in the Persian Gulf and Oman Sea basin, southwest Iran, using various indicators. A total of 70 water samples were taken from the sampling sites in the Maroon, Allah, and Jarahi sub-basins and analyzed for nine heavy metals. According to the results, the mean concentration of metals in the sampling locations across the entire sub-basin of Maroon-Jarahi was observed as follows Iron (528.22 µg/L), zinc (292.62 µg/L), manganese (56.47 µg/L), copper (36.23 µg/L), chromium (11.78 µg/L), arsenic (7.09 µg/L), lead (3.43 µg/L), nickel (3.23 µg/L), and cadmium (1.38 µg/L). Most of the metals were detected at the highest concentration in the sub-basin of the Jarahi River. The Water Quality Index (WQI) index in the basin varied from 18.74 to 22.88, indicating well to excellent quality. However, the investigation of the pollution status at the monitoring stations, based on the classification of Degree of Contamination (CD) and Heavy Metal Pollution Index (HPI) indices, revealed that they are in the category of relatively high pollution (16 < CD < 32) to very high (32 ≤ CD), and in the low pollution category (HPI < 15) to high pollution (HPI < 30), respectively. According to the three sub-basins, the highest amount of WQI, HPI, and Cd was observed in the stations located in the sub-basins of the Jarahi River. The calculation of Heavy Metal Evaluation Index (HEI) also indicated that only 10% of the monitoring stations are in moderate pollution (10 < HEI < 20), while in other monitoring stations the HEI level is less than 10. The Potential ecological risk factors ( E r i ) of an individual metal was obtained as follows: Cd (173.70) > As (131.99) > Zn (57.52) > Cu (55.39) > Ni (48.98) > Cr (21.57) > Pb (0.71), revealing that Cd and As are the main elements responsible for creating ecological risk in the studied area. The Maroon-Jarahi watershed included areas with ecological risks that ranged from low (PERI ≤ 150) to very high (PERI ≥ 600). HI and ILCR health indicators indicated that consumption and long-term contact with river water in the study area can cause potential risks to human health, especially children. Moreover, the findings, the highest level of pollution and health risk for both children and adults, considering both exposure routes, occurred in the Jarahi River sub-basin, suggesting that those who live in the vicinity of the Jarahi River are likely to face more adverse health effects. In addition, the findings of the evaluation of the relationship between land use patterns and water quality in the studied basin showed that agricultural lands acts as a main source of pollutants, but forest lands play an important role in the deposition of pollutants and the protection of water quality at the basin scale. In general, the results of pollution indicators, risk assessment, and statistical techniques suggest that the lower sub-basin, the Jarahi area, and the Shadegan wetland are the most polluted areas in the investigated sub-basin due to excessive discharge of agricultural runoff, industrialization, and rapid urbanization. Thus, special measures should be considered to reduce the risks of HMs pollution in the sub-basin of the Maroon-Jarahi watershed, especially its downstream and the impact of agricultural land use on water quality should be taken into consideration in basin management plans.

Land use and river-lake connectivity: Biodiversity determinants of lake ecosystems

Lake ecosystems confront escalating challenges to their stability and resilience, most intuitively leading to biodiversity loss, necessitating effective preservation strategies to safeguard aquatic environments. However, the complexity of ecological processes governing lake biodiversity under multi-stressor interactions remains an ongoing concern, primarily due to insufficient long-term bioindicator data, particularly concerning macroinvertebrate biodiversity. Here we utilize a unique, continuous, and in situ biomonitoring dataset spanning from 2011 to 2019 to investigate the spatio-temporal variation of macroinvertebrate communities. We assess the impact of four crucial environmental parameters on Lake Dongting and Lake Taihu, i.e., water quality, hydrology, climate change, and land use. These two systems are representative of lakes with Yangtze-connected and disconnected subtropical floodplains in China. We find an alarming trend of declining taxonomic and functional diversities among macroinvertebrate communities despite improvements in water quality. Primary contributing factors to this decline include persistent anthropogenic pressures, particularly alterations in human land use around the lakes, including intensified nutrient loads and reduced habitat heterogeneity. Notably, river-lake connectivity is pivotal in shaping differential responses to multiple stressors. Our results highlight a strong correlation between biodiversity alterations and land use within a 2-5 km radius and 0.05-2.5 km from the shorelines of Lakes Dongting and Taihu, respectively. These findings highlight the importance of implementing land buffer zones with specific spatial scales to enhance taxonomic and functional diversity, securing essential ecosystem services and enhancing the resilience of crucial lake ecosystems.

Identify the seasonal differences in water quality and pollution sources between river-connected and gate-controlled lakes in the Yangtze River basin

The river-connected Dongting Lake (DT) and Poyang Lake (PY), and the gate-controlled Taihu Lake (TH) and Chaohu Lake (CH) are the four important lakes in the Yangtze River Basin. The comprehensive Water Quality Index (WQI), the Eutrophication Integrated Index (TLI(Σ)), and the Positive Matrix Factorization (PMF) model were employed to evaluate water quality and the contribution of pollution sources for these lakes. The results show that WQI for all lakes indicated generally good water quality, with DT scoring 73.52-86.18, the highest among them. During the wet season, the eutrophication degree of river-connected lake was medium, and that of gate-controlled lakes was high. The surface runoff and agricultural non-point sources are the main pollution sources for both types of lakes, but their impact is more pronounced in gate-controlled lakes during the wet season. The study provides evidence support for scientific understanding of water quality problems and management strategies in these areas.

Spatial-temporal distribution characteristics of surface water pollutants and their potential sources in Ngari, China

The Ngari region has many important rivers and is critical to water resource security and water resource continuity in China and even in adjoining Asian countries. However, the spatial distribution and monthly variation in local water quality have been poorly understood until recently. In this study, the spatial-temporal variations of 12 water quality parameters, including pH, dissolved oxygen (DO), permanganate index (IMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NNH3), total nitrogen (Ntotal), total phosphorus (Ptotal), copper (Cu), fluoride (F), arsenic (As) and cadmium (Cd), were determined from samples collected monthly at 22 water cross-sectional sites in the Ngari region in 2020. The surface water pollution in the southern Ngari region was the most serious, and the water pollution level in winter was higher than that in the other seasons. As (0.0781 ~ 0.6154 mg/L) and F (1.05 ~ 4.64 mg/L) were the main exceedance factors derived from the recharge of high arsenic and fluoride geothermal water and weathering of As and F-bearing minerals. The hazard quotient and carcinogenic risk for As and F at the five contaminated sampling sites indicated potential health risks and even carcinogenicity to local populations. The hydrochemistry types of the lakes and rivers in the Ngari region were mainly chloride water and carbonate water. The results from this study can provide a scientific basis for the prevention and control of surface water pollution in the Ngari region and contribute to subsequent research on the ecology of water bodies.

Assembly mechanism and stability of zooplankton communities affected by China's south-to-north water diversion project

Water diversion can effectively alleviate water resource shortages and improve water environmental conditions, while also causing unknown ecological consequences, in particular, the assembly mechanism of zooplankton communities in the affected areas will become more complex after long-term water transfer. Taking Nansi Lake, the second largest impounded lake along the eastern route of China's South to North Water Diversion Project (SNWDP), as an example, the composition and diversity of zooplankton communities in the lake area and estuaries during the water diversion period (WDP) and non-water diversion period (NWDP) were studied. The potential assembly process of zooplankton communities was further explored, and the stability of communities in different regions during different periods was compared. The related results indicated that the changes in water quality conditions induced by water diversion had a relatively weak impact on the zooplankton communities. In the assembly mechanism of zooplankton communities, stochastic process played a more important role during both WDP or NWDP, and the proportion of deterministic process was relatively higher during NWDP, which may be related to the greater role of total nitrogen (TN) in the assembly of the zooplankton communities. The network analysis and cohesion calculation results showed that the stability of the zooplankton communities in the lake area sites was higher than that in the estuary sites, and the stability during NWDP was higher than that during WDP. In sum, the stability of zooplankton communities displayed a degree of change affected by water diversion activities, but the community assembly was not significantly influenced by the water quality fluctuations after about relatively long-term water diversion. This study provides an in-depth understanding of the ecological effects of water diversion on the biological communities in the affected lake, which is beneficial to the management and regulation of long-term water diversion projects.

Quantitative Effects of Anthropogenic and Natural Factors on Heavy Metals Pollution and Spatial Distribution in Surface Drinking Water Sources in the Upper Huaihe River Basin in China

The water quality of sources in the Huaihe River Basin significantly affects the lives and health of approximately 16.7% of China's population. Identifying and quantifying pollution sources and risks is essential for effective water resource management. This study utilized Monte Carlo simulations and Geodetector to assess water quality and eutrophication, as well as to evaluate the sources of heavy metals and the associated health risks for both adults and children. The results showed that eutrophication of water sources in Huaihe River was severe, with an overall EI value of 37.92; 67.8% of the water sources were classified as mesotrophic and 32.2% classified as eutrophic. Water quality and eutrophication levels in the southern mountainous regions were better than those in the densely populated northern areas. Adults were found to have a higher carcinogenic risk than children, whereas children faced a higher noncarcinogenic risk than adults. Cr presented the highest carcinogenic risk, affecting more than 99.8% of both adults and children at levels above 1 × 10-6 but not exceeding 1 × 10-4. The noncarcinogenic risk from metals did not surpass a level of 1, except for Pb. As was primarily influenced by agricultural activities and transportation, whereas Cd, Cr, and Pb were mainly affected by industrial activities, particularly in local textile industries such as knitting and clothing manufacturing. The analysis demonstrated that the influence of anthropogenic factors on heavy metal distribution was significantly enhanced by indirect natural factors. For example, the explanatory power of Precipitation and Road Network Density on As was 0.362 and 0.189, respectively, whereas their interaction had an explanatory power as high as 0.673. This study indicates that the geodetector method is effective in elucidating the factors influencing heavy metal distribution in water, thereby providing valuable insights into pollution sources in global drinking water.

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3-72.0 and 56.4-67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

Health impact assessment of the surface water pollution in China

China suffers from severe surface water pollution. Health impact assessment could provide a novel and quantifiable metric for the health burden attributed to surface water pollution. This study establishes a health impact assessment method for surface water pollution based on classic frameworks, integrating the multi-pollutant city water quality index (CWQI), informative epidemiological findings, and benchmark public health information. A relative risk level assignment approach is proposed based on the CWQI, innovatively addressing the challenge in surface water-human exposure risk assessment. A case study assesses the surface water pollution-related health impact in 336 Chinese cities. The results show (1) between 2015 and 2022, total health impact decreased from 3980.42 thousand disability-adjusted life years (DALYs) (95 % Confidence Interval: 3242.67-4339.29) to 3260.10 thousand DALYs (95 % CI: 2475.88-3641.35), measured by total cancer. (2) The annual average health impacts of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancers added up to 2621.20 thousand DALYs (95 % CI: 2095.58-3091.10), revealing the significant health impact of surface water pollution on digestive cancer. (3) In 2022, health impacts in the Beijing-Tianjin-Hebei and surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River added up to 1893.06 thousand DALYs (95 % CI: 1471.82-2097.88), showing a regional aggregating trend. (4) Surface water pollution control has been the primary driving factor to health impact improvement, contributing -3.49 % to the health impact change from 2015 to 2022. It is the first city-level health impact map for China's surface water pollution. The methods and findings will support the water management policymaking in China and other countries suffering from water pollution.

Water quality evaluation based on water quality index and multiple linear regression: A research on Hanyuan Lake in southern Sichuan Province, China

This study aims to understand the changes in the water quality of Hanyuan Lake and to show these changes over time. In this study, monthly sampling was conducted at three sampling sites in Hanyuan Lake, and water samples were measured for water quality indicators in the laboratory according to the methods specified in the Environmental Quality Standards for Surface Water (GB3838-2002). Based on the monitoring data from January to December 2019, the WQI comprehensive evaluation method was used to conduct multiple linear stepwise regression analysis, extract key indicators, and establish the WQImin model. The results show that according to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, and the grade is excellent. The overall water quality of Hanyuan Lake is excellent, and most of the water quality indexes reach the Class I standard in the Environmental Quality Standards for Surface Water (GB3838-2002). WQImin1 (R2 = 0.86, p < 0.001, PE = 4.28) as the best WQImin model. In this study, a model with fewer parameters was established by multiple linear regression method, which is conducive to better monitoring of water quality at monitoring stations while saving costs. PRACTITIONER POINTS: According to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, the rating is excellent. From January 2019 to September 2020, the monthly change trend of each section is roughly the same, showing a trend of first decreasing, then rising, then decreasing, and finally rising and flattening. The WQImin model was developed to completely describe the change in the water body.

Water quality drives the reconfiguration of riverine planktonic microbial food webs

The food web is a cycle of matter and energy within river ecosystems. River environmental changes resulting from human activities are increasingly threatening the composition and diversity of global aquatic organisms and the multi-trophic networks. How multiple environmental factors influence food web patterns among multi-trophic microbial communities in rivers remains largely unknown. Using water quality evaluation and meta-omics techniques, we investigated the composition, structure and interaction characteristics, and drivers of food webs of microorganisms (archaea, bacteria, fungi, protists, metazoa, viridiplantae and viruses) at multiple trophic levels in different water quality environments (Classes II, III, and IV). First, water quality deterioration led to significant changes in the composition of the microbial community at multiple trophic levels, which were represented by the enrichment of Euryarchaeota in the archaeal community, the increase of r-strategists in the bacterial community, and the increase of the proportion of predators in the protist community. Second, deteriorating water quality resulted in a significant reduction in the dissimilarity of community structure (homogenization of community structure in Class III and IV waters). Of the symbiotic, parasitic, and predatory networks, the community networks in Class II water all showed the most stable symbiotic, parasitic, and predatory correlations (higher levels of modularity in the networks). In Class III and IV waters, nutrient inputs have led to increased reciprocal symbiosis and decreased competition between communities, which may have the risk of a positive feedback loop driving a system collapse. Finally, inputs of phosphorus and organic matter could be the main drivers of changes in the planktonic microbial food web in the Fen River. Overall, the results indicated the potential ecological risks of exogenous nutrient inputs, which were important for aquatic ecosystem conservation.

Characterizing the water resource-environment-ecology system harmony in Chinese cities using integrated datasets: A Beautiful China perspective assessment

Integrated management and synergistic improvement of the water system is a topic of widespread concern. This study innovatively integrates three functions of quality assessment, synergy evaluation, and driving influence determination to establish a systematic framework assessing water system harmony. A case study of 336 Chinese cities is further performed by combining multi-scale and multi-source datasets. The results show China's water system quality has improved from 2015 to 2022. Development in the water resource, environment, and ecology subsystems have been differentiated, with 0.05 %, 4.33 %, and -1.64 % changes, respectively. The degradation of water ecology and the weak synergy with the other two subsystems have limited China's water system harmony. Water environment improvement played a contributive role in improving the water system quality. The contribution structure of water resources, environment, and ecology has shifted towards equilibrium in recent years. We found and highlighted the north-south differentiation of water system harmony in Chinese cities. The Beijing-Tianjin-Hebei and its surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River are identified as priority regions for water system harmony improvement. The primary contribution of this study is to propose an assessing concept of water resource-environment-ecology system harmony, establish well-structured assessment methods, and integrate the multiple data sources. The novel methods and findings, including the indicator system, application of data mining and decomposing methods, and the city-level water system harmony map, deconstruct and quantify the complex and diverse water system, supporting clearer and more efficient water management policymaking.

The dynamic changes in phytoplankton and environmental factors within Dongping Lake (China) before and after the South-to-North Water Diversion Project

Dongping Lake is one of the most important regulation and storage lakes along the eastern route of the South-to-North Water Diversion Project in China, the water quality condition of which directly influences the safety of water diverting, because it serves as a Yangtze River water redistribution control point. However, the changes in algae, and in environmental factors affecting their community structures, before and after the water diversion project are rarely reported. In this study, the temporal variations of phytoplankton abundance were examined based on monthly samples collected at three stations from May 2010 to April 2022. The total abundance of algae greatly decreased after the water diversion project was implemented, with a relatively stable biodiversity and evenness before and after the water translocation. Multiple statistical methods were used together with the water quality indices (WQIs) and the nutrient status index (TSIM) to evaluate overall water condition and analyse relationships among environmental factors. The WQIs demonstrated a general "Good" water quality with a seasonal differentiation, and that water conditions during water transfer periods were better than during non-water transfer periods, which may be ascribed to the improved hydraulic conditions and purified water environment during water transfer periods. Redundancy analysis showed that water temperature, ammonia nitrogen, water transparency, and total phosphorus were the most important environmental factors, with relatively decreased contribution rates towards phytoplankton communities after the water translocation. Importantly, some dominant phytoplankton genera of Chlorophyta, Bacillariophyceae, and Cyanophyceae were similarly affected by water transparency, and nitrogen and phosphorus nutrients in summer after the water translocation. These research findings helped us gain a comprehensive understanding of the changing patterns of water quality and microalgae and their relationships before and after the water diversion project, providing a guidance for future lake management in regulating hydraulic conditions and improving water quality of Dongping Lake.

Ecological risk assessment, source identification and spatial distribution of organic contaminants in terms of mucilage threat in streams of Çanakkale Strait Basin (Türkiye)

In this study, the spatial distributions of organic contamination stressors in water of fluvial habitats in the Çanakkale Strait (ÇS) watershed were investigated and the data were assessed in terms of human health and mucilage threat. Seven significant riverine ecosystems flowing into the ÇS were defined in the basin. Water samples were taken in the spring season (2023), when the phytoplankton communities reach their highest densities. Then they were tested for a total of 8 limnological parameters. The Nutrient Pollution Index (NPI) and Water Quality Index (WQI) were applied to assess the comprehensive quality characteristics of waters. The Hazard Quotient (HQ) and Hazard Index (HI) were applied to indicate the prospective non-carcinogenic human health risks of organic stressors. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to categorize the investigated habitats and define the sources of investigated contamination parameters. Also, Geographic Information System (GIS) was used to make an effective assessment through visualization. The determined spatial mean values of the measured variables in ÇS watershed as follows: 18.21 °C for temperature, 8.51 mg/L for DO, 4.57 NTU for turbidity, 3.95 mg/L for suspended solids, 1.11 mg/L for NO3-N, 0.012 mg/L for NO2-N; 0.173 mg/L for PO4-P and 2.32 mg/L for BOD. It has been determined that the organic pollution loads and water temperature values of the investigated sub-basins increase from the upstream to the downstream locations and Çanakkale Stream was recorded as the riskiest fluvial habitat for the ÇS watershed. According to the results of health risk assessment indices, non-carcinogenic risks of organic pollutants would not be expected for all age groups.

Integrated evaluation of the impact of water diversion on water quality index and phytoplankton assemblages of eutrophic lake: A case study of Yilong Lake

Water diversion has been widely utilized to enhance lake water quality and mitigate cyanobacterial blooms. However, previous studies have mainly focused on investigating the effects of water diversion on water quality or aquatic ecological health. Consequently, there is limited research investigating the combined impact of water diversion on the water quality and the ecological health of eutrophic lakes, and whether the WQI and phytoplankton assemblages demonstrate similar patterns following water diversion. In this study, the effects of water diversion on the ecosystem health of eutrophic lakes were comprehensively evaluated based on the WQI indices and phytoplankton assemblages during the NWDP-21 and WDP-22. The results showed that the annual mean of WQI increased from 52.02 to 54.36 after water diversion, which improved the water quality of the lake, especially NH3-N and TN decreased by 58.6% and 15.2%, respectively. The phytoplankton assemblages changed significantly before and after water diversion, and we observed that the total biomass of phytoplankton decreased by 12.3% and phytoplankton diversity indices (Shannon-Wiener diversity, Pielou evenness, and Simpson index) increased by 8.6%-8.9% after water diversion, with an improvement in the connectivity and stability of the phytoplankton. Notably, enhanced adaptations of rare sub-communities for resource use in water diversion environments, and water diversion inhibited the dispersal ability of dominant functional groups, and the effects of hydrological disturbances on the structure of phytoplankton assemblage favored the ecological health of eutrophic lakes. VPA analysis further reveals that water diversion alters the drivers of phytoplankton functional group biomass and phytoplankton diversity. The results of the PLS-PM analysis clarify that water diversion indirectly impacts the total phytoplankton biomass and phytoplankton diversity primarily by modifying light availability. Significant correlations are observed between the dominant functional groups biomass and diversity indices of WQI. The trends in changes observed in water quality indices and phytoplankton following water diversion align with the evaluation of water ecological health. This study provides valuable guidance for the ecological management of the diversion project in Yilong Lake and serves as a reference for similar projects in other lakes.

Long-term (2003-2021) evolution trend of water quality in the Three Gorges Reservoir: An evaluation based on an enhanced water quality index

The degradation of water quality induced by the construction of large-scale hydraulic projects is one of the primary public concerns; however, it is rarely addressed with long-term field observation data. Here, we reported the long-term (2003-2021) trends, seasonal patterns, and overall condition of water quality of the Three Gorges Reservoir (TGR) with an enhanced water quality index (WQI). Specifically, to emphasize the importance of the biological role in water quality assessment, chlorophyll-a (Chla) was incorporated into WQI, and then a novel workflow using machine learning approach based on Random Forest (RF) model was constructed to develop a minimal water quality index (WQImin). The enhanced WQI indicated an overall "good" water quality condition, exhibiting a gradually improving trend subsequent to the reservoir impoundment in 2003. Meanwhile, the assessment revealed that the water quality has discernible seasonal patterns, characterized by poorer conditions in the spring and summer seasons. Furthermore, the RF model identified Chla, dissolved oxygen (DO), ammonium nitrogen (NH4-N), water temperature (WT), pH, and total nitrogen (TN) as key parameters for the WQImin, with Chla emerging as the most important factor in determining WQImin in our study. Moreover, weighted WQImin models exhibited improved performance in estimating WQI. Our study emphasizes the importance of biological parameters in water quality assessment, and introduces a systematic workflow to facilitate the development of WQImin for accurate and cost-efficient water quality assessment. Furthermore, our study makes a substantial contribution to the advancement of knowledge regarding long-term trends and seasonal patterns in water quality of large reservoirs, which provides a foundational basis for guiding water quality management practices for reservoirs worldwide.

Ecological evolution of algae in connected reservoirs under the influence of water transfer: Algal density, community structure, and assembly processes

Reservoir connectivity provides a solution for regional water shortages. Understanding the water quality of reservoirs and the response of algal communities to water transfer could provide the basis for a long-term evolutionary model of reservoirs. In this study, a water-algal community model was established to study the effects of water transfer on water quality and algal communities in reservoirs. The results showed that water transfer significantly decreased total nitrogen and nitrate concentrations. However, the water transfer resulted in an increase in the CODMn concentration and conductivity in the receiving reservoir. Additionally, the algal density and chlorophyll-a (chl-a) concentration showed an increase with water transfer. Bacillariophyta, Cyanophyta, and Chlorophyta were the dominant algal phyllum in all three reservoirs. Water transfer induced the evolution of the algal community by driving changes in the chemical parameters of the receiving reservoir and led to more complex relationships within the algal community. The effects of stochastic processes on algal communities were also enhanced in the receiving reservoirs. These results provide specific information for water quality safety management and eutrophication prevention in connected reservoirs.

Adaptive strategies to enhance water security and resilience in low- and middle-income countries: A critical review

The water sector is facing unprecedented pressures as increased environmental and anthropogenic challenges, such as climate change and rapid urbanization, impact the availability and predictability of safe drinking water. There is a need for practitioners and policymakers to integrate water security and resilience (WS&R) factors into programming to sustain investments in drinking water systems to support associated economic, security, and public health benefits. In response to intensifying impacts from WS&R risks, communities around the world are developing adaptive strategies, and a critical review of these strategies may provide lessons that can be implemented at scale. In this critical review, we systematically screened over 9000 peer-reviewed and grey literature articles and extracted data from relevant studies that propose, pilot, and/or evaluate adaptations in low- and middle-income countries (LMICs) and evaluated the suitability of each adaptation for different contexts. We created a portfolio of adaptive strategies from over 75 LMICs to inform practitioners and policymakers in enhancing the resilience of drinking water systems. Over 20 adaptations were identified, including strategies such as stormwater management, wastewater reuse, non-revenue water reductions, water pricing, and public awareness campaigns. We categorized adaptations by function (improving water management, augmenting existing supplies, reducing water demand) and scale (household, municipal, regional) to provide recommendations tailored to local needs. For each adaptation, we highlighted associated strengths, weaknesses, barriers to adoption, and enabling environments for successful implementation. We propose a novel decision-support tool, called STEP WS&R, that provides a consistent and replicable process for informing high-level investment and policy choices around WS&R. This critical review presents recommendations for practitioners and policymakers to invest in WS&R adaptations, catered to shared risks and contexts.

Assessment of drinking water quality and health risk using water quality index and multiple computational models: a case study of Yangtze River in suburban areas of Wuhan, central China, from 2016 to 2021

Water quality, increasingly recognized for its significant impact on health, is garnering heightened attention. Previous studies were limited by the number of water quality indicators and the duration of analysis. This study assessed the drinking water quality and its associated health risk in suburban areas of Wuhan, a city in central China, from 2016 to 2021. We collected 368 finished water samples and 1090 tap water samples and tested these for 37 different indicators. The water quality was evaluated using the water quality index, with trends over time analyzed via the Mann-Kendall test. Furthermore, an artificial neural network model was employed for future water quality prediction. Our findings indicated that the water quality in rural Wuhan was generally good and had an improvement from 2016 to 2021. The qualification and excellent rates were 98.91% and 86.81% for finished water, and 97.89% and 78.07% for tap water, respectively. The drinking water quality was predicted to maintain satisfactory in 2022 and 2023. Additionally, principal component analysis revealed that the primary sanitary issues in the water were poor sensory properties, elevated metal contents, high levels of dissolved solids, and microbial contamination. These issues were likely attributable to domestic and industrial waste discharge and aging water pipelines. The health risks associated with the long-term consumption of this water have been steadily decreasing over the years, underscoring the effectiveness of Wuhan's ongoing water management efforts.

Assessment of water quality based on statistical analysis of physical-chemical, biomonitoring and land use data: Manso River supply reservoir

Preserving the quality of surface water has become increasingly difficult due to the intensification of human activities in watersheds. This study assessed the water quality of the Manso River reservoir, which supplies water to Brazil's third largest metropolitan region. The integration of >10,000 secondary data, comprising physico-chemical parameters, metals and microbiological indicators, together with biomonitoring and land use and occupation data, were analyzed by using statistical tools, the Water Quality Index (WQI) and the Trophic State Index (TSI). The results showed higher concentrations for solids and metals (Fe and Mn) characteristic of local geochemistry and also related to the mining activity in the region. Parameters associated with organic pollution, such as total phosphorus and Escherichia coli, were also elevated, probably due to contamination by untreated or insufficiently treated domestic sewage. The water at the tributary watercourses presented worse quality, predominantly medium WQI, compared with the stations inside the reservoir (predominantly good WQI). The TSI indicated a predominance of ultra-oligotrophic conditions for stations located in the lotic environment and mesotrophic conditions for those located in the lentic environment. In general, the same pattern was observed for the occurrence of the phytoplankton and zooplankton classes, indicating the relationship between the degree of trophy and the composition of these groups. In quantitative terms, for phytoplankton, the Euchlorophyceae and Cyanophyceae classes stood out, mainly in the rainy period (summer), whereas for zooplankton, the Crustacea and Monogonta classes were dominant. Regarding land use and occupation in the reservoir sub-basin, the positive impact of the surrounding forest cover was observed. It was also identified the effect of seasonality on the quality of aquatic environments. The integrated evaluation of the results proved to be efficient in assessing the environmental conditions of the reservoir and the tributaries, providing information for better management of these water resources.

Impacts of the Middle Route of China's South-to-North Water Diversion Project on the water network structure in the receiving basin

The Middle Route of South-to-North Water Diversion Project (MRSNWD) is the main skeleton of China's National Water Network, its construction has changed the structure of the original water network, and analyzing the topological change of the water network in context with MRSNWD is significant for water network planning and management. In this study, the overall network characteristics of the water network in 2010 and 2020 were analyzed based on the small-world and scale-free characteristics of complex network theory. The topological changes of the water network from a node perspective were examined using three network centrality indexes: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC), while assessing the important nodes of the water network and recognizing functional areas of cold-hot spots. The results show that the water network's centrality in the study area improved after the project construction, with the average degree of the water network increasing from 2.39 to 2.42 and the average path length decreasing from 111.81 to 97.08. The propagation efficiency and network stability also increased, with a rise in important node proportion from 9.8 to 14.4%. The nodes in the DC hotspot zone along the project route have increased by 1.5%, implying an increase in the connectivity of the water network, while MRSNWD optimizes its north-south hub propagation path. "Small-worldness" indicates that most nodes of a network can be accessed and connected over shorter paths. The water network has a significant "small-worldness" and has been enhanced by the MRSNWD's construction. Approximating the water network as a scale-free network can impact its security by identifying critical nodes. The results of this research can provide the necessary technical support and reference significance for China's National Water Network.

Salinization of shallow groundwater in the Jiaokou Irrigation District and associated secondary environmental challenges

Understanding groundwater salinization of irrigation areas and related secondary environmental challenges is important for ensuring sustainable development. However, the mechanism under which groundwater salinization forms under the influence of long-term anthropogenic activities remains unclear. Therefore, this study analyzed the spatiotemporal variation in groundwater salinization and the underlying mechanism, and discussed the secondary environmental challenges in an irrigation area. The Jiaokou Irrigation District, North China, was adopted as a case study. The results showed a slight downward trend in groundwater salinity over the past two decades at a rate of 0.0229 g/L/y. Higher groundwater salinity was observed in areas with shallow groundwater depth. This correlation was mainly attributed to evaporative concentration, with secondary processes including natural weathering, depth of water-table, and fertilizer leaching. Drainage ditches may reduce groundwater salinity. Groundwater was transformed from freshwater to salt water and then to brackish water during the runoff process. The former transformation is mainly related to evaporation and fertilization. The latter transformation could be related to the inverse relationship between the distance to the Wei River and sediment permeability, with sediment permeability positively related to groundwater flow and leading to the discharge of salt into the Wei River. The secondary environmental challenges related to groundwater salinization in irrigation areas, mainly manifested in deterioration of irrigation water quality, soil salinization, and increased fluorine concentration. This study can act as a theoretical and practical reference for the development and utilization of water resources, ecological protection, and soil salinization in typical irrigation districts.

A new framework for water quality forecasting coupling causal inference, time-frequency analysis and uncertainty quantification

Accurate forecasting of water quality variables in river systems is crucial for relevant administrators to identify potential water quality degradation issues and take countermeasures promptly. However, pure data-driven forecasting models are often insufficient to deal with the highly varying periodicity of water quality in today's more complex environment. This study presents a new holistic framework for time-series forecasting of water quality parameters by combining advanced deep learning algorithms (i.e., Long Short-Term Memory (LSTM) and Informer) with causal inference, time-frequency analysis, and uncertainty quantification. The framework was demonstrated for total nitrogen (TN) forecasting in the largest artificial lakes in Asia (i.e., the Danjiangkou Reservoir, China) with six-year monitoring data from January 2017 to June 2022. The results showed that the pre-processing techniques based on causal inference and wavelet decomposition can significantly improve the performance of deep learning algorithms. Compared to the individual LSTM and Informer models, wavelet-coupled approaches diminished well the apparent forecasting errors of TN concentrations, with 24.39%, 32.68%, and 41.26% reduction at most in the average, standard deviation, and maximum values of the errors, respectively. In addition, a post-processing algorithm based on the Copula function and Bayesian theory was designed to quantify the uncertainty of predictions. With the help of this algorithm, each deterministic prediction of our model can correspond to a range of possible outputs. The 95% forecast confidence interval covered almost all the observations, which proves a measure of the reliability and robustness of the predictions. This study provides rich scientific references for applying advanced data-driven methods in time-series forecasting tasks and a practical methodological framework for water resources management and similar projects.

Establishment risk of invasive golden mussel in a water diversion project: An assessment framework

Inter-basin water diversion projects have led to accelerated colonization of aquatic organisms, including the freshwater golden mussel (Limnoperna fortunei), exacerbating global biofouling concerns. While the influence of environmental factors on the mussel's invasion and biofouling impact has been studied, quantitative correlations and underlying mechanisms remain unclear, particularly in large-scale inter-basin water diversion projects with diverse hydrodynamic and environmental conditions. Here, we examine the comprehensive impact of environmental variables on the establishment risk of the golden mussel in China's 1432-km-long Middle Route of the South-to-North Water Diversion Project. Logistic regression and multiclass classification models were used to investigate the environmental influence on the occurrence probability and reproductive density of the golden mussel. Total nitrogen, ammonia nitrogen, water temperature, pH, and velocity were identified as crucial environmental variables affecting the biofouling risk in the project. Logistic regression analysis revealed a negative correlation between the occurrence probability of all larval stages and levels of total nitrogen and ammonia nitrogen. The multiclass classification model showed that elevated levels of total nitrogen hindered mussel reproduction, while optimal water temperature enhanced their reproductive capacity. Appropriate velocity and pH levels were crucial in maintaining moderate larval density. This research presents a quantitative analytical framework for assessing establishment risks associated with invasive mussels, and the framework is expected to enhance invasion management and mitigate biofouling issues in water diversion projects worldwide.

Reversing the damage: ecological restoration of polluted water bodies affected by pollutants due to anthropogenic activities

Aquatic ecosystems provide a large number of cultural, regulating, and supporting services to humans and play a pivotal role in sustaining freshwater-dependent ecosystems. However, an increase in human population coupled with economic growth in the last few decades has severely affected their functioning and ecological health. This has led to an increase in concentrations of pollutants originating from anthropogenic activities such as heavy metals, plastics, semi-volatile organic compounds, and endocrine disruptors. These pollutants provoke deleterious impacts on aquatic biodiversity and affect the water quality and functioning. In this paper, we discuss the sources and impacts of such pollutants as well as restoration techniques for reducing their impact on aquatic ecosystems. Several physical and chemical ecological restoration techniques, such as dredging, sediment capping, water diversion, adsorption, aeration, and flushing, can be employed to improve the water quality of water bodies. Additionally, biological techniques such as phytoremediation, phycoremediation, the use of biomembranes, and the construction of ecological floating beds can be employed to increase the population of aquatic organisms and improve the overall ecological health of aquatic ecosystems. Restoration techniques can effectively reduce the concentrations of suspended solids and dissolved phosphorus and increase the levels of dissolved oxygen. The restoration techniques for improving the ecological health of water bodies should not be limited to simply improving the water quality but should also focus on improving the biological processes and ecosystem functioning since it is essential to mitigate the adverse effects of pollutants and restore the vital ecosystem services provided by water bodies for future generations.

An IoT framework for quality analysis of aquatic water data using time-series convolutional neural network

Water quality monitoring and analysis in fish farms are of paramount importance for the aquaculture sector; however, traditional methods can pose difficulties. To address this challenge, this study proposes an IoT-based deep learning model using a time-series convolution neural network (TMS-CNN) for monitoring and analyzing water quality in fish farms. The proposed TMS-CNN model can handle spatial-temporal data effectively by considering temporal and spatial dependencies between data points, which allows it to capture patterns and trends that would not be possible with traditional models. The model calculates the water quality index (WQI) using correlation analysis and assigns class labels to the data based on the WQI. Then, the TMS-CNN model analyzed the time-series data. It produces high accuracy of 96.2% in analysis of water quality parameters for fish growth and mortality conditions. The proposed model accuracy is higher than the current best model MANN, which has only had an accuracy of 91%.

Hydrochemical characteristics and water quality assessment of natural water in the South China Mountains: the case in Lianzhou

South China Mountain Region has a well-developed water system with the most abundant water in China. Untreated natural water is the main source of drinking water for the local people. This study aimed to investigate the hydrochemical characteristics and trace element concentrations of natural water in the mountainous regions of South China. In this study, 116 water samples were collected. Traditional hydrochemical methods, water quality index (WQI), hazard index (HI), and nutrient speciation of trace elements (NSTE) were used for analysis. In general, the hydrochemical type was mainly Ca-HCO3- type. The hydrochemical characteristics were mainly influenced by the weathering of calcite and silicate rocks. Overall total dissolved solids (TDS) were low, indicating mainly soft and very soft water. The water that met the standards for mineral water had an average concentration of 59.69 mg/L for Sr (strontium) and an average concentration of 0.46 mg/L for H2SiO3 (silicic acid). Although the water quality index (WQI) indicated that 91.3% of the water samples in the study area were of good quality (WQI < 25), 2.58% of the water samples had significant non-carcinogenic risk (HI > 1) due to the high As and Pb concentrations. The water in the study area contributed significantly to human intake of Sr, Cr, and V, accounting for 8.4, 8.3, and 7.7% of the required daily intake for adults, respectively. It is recommended that a comprehensive water quality evaluation system be constructed to ensure that mountain water is managed for development and safe to drink.

Alleviating sample imbalance in water quality assessment using the VAE-WGAN-GP model

Water resources are essential for sustaining human life and promoting sustainable development. However, rapid urbanization and industrialization have resulted in a decline in freshwater availability. Effective prevention and control of water pollution are essential for ecological balance and human well-being. Water quality assessment is crucial for monitoring and managing water resources. Existing machine learning-based assessment methods tend to classify the results into the majority class, leading to inaccuracies in the outcomes due to the prevalent issue of imbalanced class sample distribution in practical scenarios. To tackle the issue, we propose a novel approach that utilizes the VAE-WGAN-GP model. The VAE-WGAN-GP model combines the encoding and decoding mechanisms of VAE with the adversarial learning of GAN. It generates synthetic samples that closely resemble real samples, effectively compensating data of the scarcity category in water quality evaluation. Our contributions include (1) introducing a deep generative model to alleviate the issue of imbalanced category samples in water quality assessment, (2) demonstrating the faster convergence speed and improved potential distribution learning ability of the proposed VAE-WGAN-GP model, (3) introducing the compensation degree concept and conducting comprehensive compensation experiments, resulting in a 9.7% increase in the accuracy of water quality assessment for multi-classification imbalance samples.

Guiding the landscape patterns evolution is the key to mitigating river water quality degradation

Consensus has emerged that landscape pattern evolution significantly impacts the river environment. However, there remains unclear how the landscape pattern evolves possible to achieve a balance between land resource use and water conservation. Thus, simulating future landscape patterns under different scenarios to predict river eutrophication level is critical to propose targeted landscape planning programs and alleviate river water quality degradation. Here, we coupled five water quality parameters (TOC, TN, NO3--N, NH4+-N, TP), collected from October 2020 to September 2021, to construct the river eutrophication index (EI) to assess river water quality. Meanwhile, based on redundancy analysis, patch-generating land use simulation model, and stepwise multiple linear regression model comprehensively analyze the Fengyu River watershed landscape patterns evolution and their impact on river eutrophication. Results indicated that current rivers reach eutrophic levels, and EI reaches 40.7. The landscape patterns explain 88.2 % of river eutrophication variation, while the LPI_Con metric is critical and individually explained 21.5 %. Furthermore, eutrophication in the watershed will increase in 2040 under the natural development (ND) scenario, and the EI will reach 44.4. In contrast, farmland protection (FP) scenarios and environmental protection (EP) scenarios contribute to mitigating eutrophication, the EI values are 38.2 and 38.1, respectively. The results provide a potential mechanistic explanation that river eutrophication is a consequence of unreasonable landscape pattern evolution. Guiding the landscape patterns evolution based on critical driver factors from a planning perspective is conducive to mitigating river water quality degradation.

Spatiotemporal response of water quality in fragmented mangroves to anthropogenic activities and recommendations for restoration

Mangroves have received substantial attention for their pivotal role as ecological barriers between land and sea, owing to their capacity to effectively capture considerable quantities of terrestrial pollutants. Mangrove fragmentation has been a widespread global trend. There is limited information on the water quality status of these small scattered mangrove patches in coastal sub-developed areas, coupled with a paucity of efficient and intuitive assessment methodologies. To address this gap, the Water Quality Index (WQI) was introduced to evaluate the spatiotemporal characteristics of mangrove water quality. The major sources of pollution and anthropogenic activities that affect mangrove water quality were identified. The results revealed an average WQI value of 44.1 ± 13.3 for mangrove patches, consistently indicating a "low" water quality classification throughout all seasons. Both the size and natural conditions impact the water quality of mangroves. The large artificial patch (WQI: 56.4 ± 7.61) and the natural patch (WQI: 46.6 ± 13.6) exhibited relatively superior water quality, while the WQI value of a size-equivalent artificial patch compared with the natural patch is 38.6 ± 11.8. Aquaculture was the primary human activity that adversely affected the water quality of mangroves, and the potential sources of pollution were rainfall runoff and river discharge. These findings elucidate the unfavorable water quality characteristics and dominant pollution of fragmented mangroves, and validate the applicability of the WQI method for long-term evaluation of the water quality in mangrove patches. This study provides a basis for decision-making in water quality assessment and management of coastal wetlands and marine ecosystems. Scientific guidance to the management for mangrove protection and restoration was offered, such as regulating aquaculture activities, controlling non-point source pollution, implementing mangrove reforestation by using native species in historical mangrove sites.

Comprehensive spatio-temporal benchmarking of surface water quality of Hindon River, a tributary of river Yamuna, India: Adopting multivariate statistical approach

The Hindon River is the main tributary of river Yamuna and it is a significant source of surface water, which flows through the major cities of western Uttar Pradesh, India. The indiscriminate development of industries and urbanization along river basin coupled with rapid population growths contribute various amounts of pollutant in the river. Therefore, the present study was conducted to assess the spatial-temporal variability of river water quality (seventeen physicochemical parameters and eight heavy metals) during pre- and post-monsoon seasons for 5 years data at 19 sampling sites along the river stretch. Indices associated with water quality and heavy metals were computed to scale the accurate state of risk associated to its use for drinking and irrigation. During the pre- and post-monsoon seasons, only four sites were found having safe water quality index (WQI) values. The mean heavy metal concentrations are found in order of Zn > Fe > Pb > Cu > Cr > Cd > Ni > Mn. Considering the spatial and temporal distribution, the study benchmarked the water quality of Hindon River for priority attention.

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model's water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a "medium" level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

Water quality variation and driving factors quantitatively evaluation of urban lakes during quick socioeconomic development

Rapid urbanisation has caused a significant impact on the ecological environment of urban lakes in the world. To maintain the harmonious development of urban progress and water quality, it is essential to evaluate water quality variation and explore the driving factors quantitatively. A comprehensive evaluation method with cluster analysis and Kriging interpolation was used to explore the spatiotemporal variation in a typical urban lake in China, Chaohu Lake, from 2011 to 2020. The correlation between water quality and socioeconomic factors was evaluated by Pearson correlation analysis. Results indicated that: total phosphorus (TP) and total nitrogen (TN) were the key pollution parameters of Chaohu Lake. The pollution situation was gradually improving, however, and the improvement in chemical oxygen demand (COD) is more evident due to anthropogenic control. The spatial heterogeneity of water quality in Chaohu Lake is remarkable, and the water quality is poor in the west but better in the east. Natural attributes of lakes and external load were the main reasons for the spatial heterogeneity. The western residential areas of Chaohu Lake Basin (CLB) are concentrated, and a large amount of industrial and domestic sewage exacerbates water pollution in the west of tributaries. In contrast, the implementation of water environmental governance policies in recent years has alleviated water pollution. From 2011 to 2020, water quality has improved by 23%-35% in the west and 7%-14% in the east. This study provided a framework for quantitatively assessing water quality variation and its driving forces in urban lakes.

A multi-step water quality prediction model based on the Savitzky-Golay filter and Transformer optimized network

Effective water quality prediction techniques are essential for the sustainable development of water resources and implementation of emergency response mechanisms. However, the water environment conditions are complex, and the presence of a large amount of noise in the water quality data makes it difficult to reveal the long-term trends or cycles of the data, affecting the acquisition of serial correlation in the data. In addition, the loss function based on the vertical Euclidean distance will produce a prediction lag problem, and it is difficult to make an accurate multi-step prediction of water quality series. This paper presents a multi-step water quality prediction model for watersheds that combines Savitzky-Golay (SG) filter with Transformer optimized networks. Among them, the SG filter highlights data trend change and improves sequence correlation by smoothing the potential noise of original data. The transformer network adopts a sequence-to-sequence framework, which contains a position encoding module and a self-attentive mechanism to perform multi-step prediction while effectively obtaining the sequence correlation. Moreover, the DIstortion Loss including shApe and TimE (DILATE) loss function is introduced into the model to solve the problem of prediction lag from two aspects of shape error and time error to improve the model's generalization ability. An example validates the model with the benchmark model at four monitoring stations in the Lanzhou section of the Yellow River basin in China. The results show that the predictions of the proposed model have the correct shape, temporal positioning, and the best accuracy in a multi-step prediction task for four sites. It can provide a decision-making basis for comprehensive water quality control and pollutant control in the basin.

Prioritizing pharmaceutically active compounds (PhACs) based on occurrence-persistency-mobility-toxicity (OPMT) criteria: an application to the Brazilian scenario

A study of Quantitative Structure Activity Relationship (QSAR) was performed to assess the possible adverse effects of 25 pharmaceuticals commonly found in the Brazilian water compartments and to establish a ranking of environmental concern. The occurrence (O), the persistence (P), the mobility (M), and the toxicity (T) of these compounds in the Brazilian drinking water reservoirs were evaluated. Moreover, to verify the predicted OPMT dataset outcomes, a quality index (QI) was also developed and applied. The main results showed that: (i) after in silico predictions through VEGA QSAR, 19 from 25 pharmaceuticals consumed in Brazil were classified as persistent; (ii) moreover, after in silico predictions through OPERA QSAR, 15 among those 19 compounds considered persistent, were also classified as mobile or very mobile. On the other hand, the results of toxicity indicate that only 9 pharmaceuticals were classified with the highest toxicity level. Ultimately, the QI of 7 from 25 pharmaceuticals were categorized as 'optimal'; 15 pharmaceuticals were categorized as 'good'; and only 3 pharmaceuticals were categorized as 'regular'. Therefore, based on the QI criteria used, it is possible to assume that this OPMT prediction dataset had a good reliability. Efforts to reduce emissions of OPMT-pharmaceuticals in Brazilian drinking water reservoirs are encouraged.

Linking multivariate statistical methods and water quality indices to evaluate the natural and anthropogenic geochemical processes controlling the water quality of a tropical watershed

The improvement of water management requires monitoring techniques that accurately evaluate water quality status and detect the effects of land use changes on water chemistry. This study aimed to evaluate how multivariate statistical methods and water quality indices can be applied together to evaluate the processes controlling water chemical composition and the overall water quality status of a tropical watershed. Thirty-four water samples were collected in the Formoso River basin, located on the border of the Amazon Forest. Water parameters were measured in situ using a multiparameter and in the lab using spectroscopic and volumetric techniques. The water quality dataset was interpreted through principal component analysis, multivariate linear regression, and water quality indices. Statistical methods allowed us to identify the sources and geochemical processes controlling water quality chemistry, which were carbonate dissolution, runoff/erosion, nutrient input due to anthropogenic activities, and redox reactions in flooded zones. They were also used to create linear functions to evaluate the effects of land use changes on the geochemical processes controlling water chemistry. Conversely, the water quality indices provide information about the overall condition of the water. The Weight-Arithmetic Quality Index correctly evaluates water suitability for its multiple uses, according to the Brazilian guidelines. Conversely, the Ontario Water Quality Index is not suitable to evaluate the water quality of tropical rivers, since the usual higher water temperature and the low oxygen contents associated with tropical environments result in biased water quality evaluations by this index.