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

Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir

Danjiangkou Reservoir has been widely concerned as the water source of the world's longest cross basin water transfer project. Biogenic elements are the foundation of material circulation and key factors affecting water quality. However, there is no comprehensive study on the biogenic elements in tributaries of Danjiangkou Reservoir, hindering a detailed understanding of geochemical cycling characteristics of biogenic elements in this region. Guanshan River, one of the main tributaries that directly enter the Danjiangkou Reservoir, was token as the research object. Spatiotemporal distribution characteristics of basic water quality parameters and biogenic elements were studied. Water quality was comprehensively evaluated through water quality index (WQI). Absolute principal component score-multiple linear regression (APCS-MLR) model was adopted to explore the main sources of biogenic elements. Results showed that, in terms of season, the concentrations of total nitrogen (TN), total phosphorus (TP), and dissolved organic carbon (DOC) were significantly higher in wet season than in dry season, while no significant differences were found for dissolved inorganic carbon (DIC) and dissolved silica (DSi). Spatially, the concentrations of dissolved carbon, DIC, TN, and TP in the middle and lower reaches were higher than that in the upstream. DOC concentration peaked in the middle reaches, while DSi showed higher concentrations in the upstream. WQI values indicated that the river water quality was between good and excellent, although the water quality in wet season was slightly worse than that in the dry season. PCA extracted five potential sources, which accounting for 84.12% of the total variance, including rock weathering, mixed source of sewage discharge and agricultural non-point source pollution, dissolved soil CO2, seasonal factor, and agricultural non-point source pollution. These sources contributed 38.96%, 12.33%, 13.54%, 23.95%, and 11.21% to river water quality parameters, respectively. Strengthening the monitoring of biogenic elements, controlling pollutant discharge, and exploring the relationship between biogenic elements and other pollutants are important for the water environment management in this basin.

Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector

In line with sustainable development goals (SDGs), precise quantification of water pollution and analysis of environmental interactions are crucial for effectively safeguarding water resources. In this study, Nemerow's pollution index was used to evaluate water quality, three receptor models were used to identify pollution sources, and Geodetector analysis was applied to explore environmental interactions in the North Shangyu Plain, Southeast China. Using 5207 surface water samples from September 2023 with 11 physicochemical parameters, the results showed that surface rivers in the North Shangyu Plain exhibited varying degrees of pollution: slight pollution upstream, moderate pollution in midstream and downstream, and concentrated high pollution in certain areas, with TN, CODCr, and TP as the primary pollutants. Multimethod source apportionment significantly improved the accuracy of pollution source attribution and identified five main sources: domestic sewage (1.42%-3.54%) characterized by NO3-N, phytoplankton source (38.43%-50.05%) indicated by chl and PC, agricultural cultivation (16.1%-17.63%) marked by TP and CODMn, industrial wastewater (17.64%-25.1%) primarily associated with TN, and natural source (10.32%-13.26%) characterized by DO, NH3-N, and CODCr. Influencing factor analysis validated the source identification. Natural factors had minor impacts on water parameters, while pollution control from agricultural activities was suggested to diversify fertilizer types rather than merely reduce quantities. The combined effects of industrial and aquaculture activities intensified pollution from TN, chl, and PC, underscoring the need for targeted management practices. This study showed the objectivity and reliability of using a combined approach of multiple receptor models and Geodetector to evaluate the river water quality status, which helps assist decision-makers in formulating more effective water resource protection strategies.

Seasonal responses of microbial communities to water quality variations and interaction of eutrophication risk in Gehu Lake

Gehu Lake, as a key upstream reservoir of Taihu Lake, China, plays a crucial role in improving the water quality, and eutrophication control of the Taihu Lake Basin. Although the microbial communities are significantly important in maintaining the ecological health of lake, the microbial response to water quality, especially for eutrophication has been rarely reported in Gehu Lake. In this study, the water quality parameters and the corresponding effects on the structure and function of microbial communities were determined seasonally. It was found that the poorest water quality in summer (Water Quality Index = 116.52) with severe eutrophication (Trophic Level Index >70), was primarily driven by agricultural non-point sources (33.4%) and seasonal pollution (23.8%). The chemical oxygen demand (COD) was the most important indicator of water quality that affected the concentration of Chlorophyll-a (Chla) according to Pearson correlation analysis (p < 0.001), random forest modeling (p < 0.01), and structural equation modeling (path coefficient = 0.926). Redundancy analysis revealed that total nitrogen, total phosphorus, Chla, and COD significantly influenced the microbial community (p < 0.05). Microbial co-occurrence networks demonstrated significantly seasonal variations, and winter exhibited a more complex structure under lower temperature and limited nutrients compared to the other seasons. In addition, the Chla-sensitive microbial species that involved in nitrogen and phosphorus metabolism were identified as the biological indicators of eutrophication in response to the changes of seasonal water quality. These findings have taken insights into the interactions between water quality and microbial communities, and might provide the basis for improvement of the ecological and environmental management of Gehu Lake, as well as the control of eutrophication in Taihu Lake.

A Groundwater Quality Assessment Model for Water Quality Index: Combining Principal Component Analysis, Entropy Weight Method, and Coefficient of Variation Method for Dimensionality Reduction and Weight Optimization, and Its Application

Groundwater underpins water supply for most of the world's regions, yet its sustainable utilization has been markedly compromised by inappropriate exploitation and a multitude of pollution sources. Water quality evaluation has emerged as an essential strategy to guarantee the optimized utilization and vigilant conservation of water resources. In this study, principal component analysis (PCA), entropy weight method (EWM), coefficient of variation method (CVM), and Water Quality Index (WQI) were used to construct an integrated WQI groundwater quality assessment model that integrates PCA-CVM-EWM for dimensionality reduction and weight optimization. Taking a village in Shandong Province, China, as an example, PCA identified seven evaluation indicators. The CVM-EWM were coupled to calculate comprehensive weights through the principle of minimum information entropy, followed by a comprehensive assessment of groundwater quality based on WQI values. The results indicated that Class III groundwater predominated in the study area, accounting for 74%, with localized pollution present. The hydrochemical type of the groundwater was primarily SO4·HCO3-Ca, significantly influenced by human activities. The coefficients of variation for Fe, Mn, and NH4-N all exceeded 1. Compared to other methods, the optimized WQI model demonstrated superior performance in the selection of evaluative indicators, weight distribution, and comprehensive water quality assessment, showing a distinct advantage for water quality data with numerous hydrochemical indicators and substantial coefficients of variation. The findings provided a scientific reference for diagnosing groundwater quality issues and formulating preventive and control measures. PRACTITIONER POINTS: A comprehensive water quality index evaluation model was constructed. Optimized steps for selecting indicators and assigning weights for the water quality index model. Selection of evaluation indicators based on indicator correlation analysis. The variability of hydrochemical data is considered.

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.

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.

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

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.

Understanding phosphorus fractions and influential factors on urban road deposited sediments

Phosphorus (P) is a primary pollutant that builds-up on urban road surfaces. Understanding the fraction and load characteristics of P, as well as their relationship with urban factors, is helpful for assessing the ecological risk of urban receiving water bodies. This study presents the characteristics of build-up loads of P fractions in road-deposited sediments (RDS) in Guangzhou, China, analyzes their correlation with three urban factors (road, traffic, and land-use area), and then estimates the exceedance probability of P in stormwater runoff over the past 10 years. The results showed that detrital apatite phosphorus (De-P) performed the highest build-up load on urban road surfaces, followed by apatite phosphorus (Ca-P), iron-bound phosphorus (Fe-P), exchangeable phosphorus (Ex-P), aluminum-bound phosphorus (Al-P), organophosphorus (POP), dissolved inorganic phosphorus (DIP), occluded phosphorus (Oc-P), and dissolved organic phosphorus (DOP). Depression depth, road materials, and land-use fractions affected the P fractions. The P in the RDS may have originated from three distinct sources: road background, domestic waste, and untreated wastewater discharge. In the most recent 10 years, the event mean concentrations of total P in the RDS have had a 30 % probability of exceeding 0.4 mg L-1, which indicates a serious threat of P to receiving water bodies. The outcomes of this study are expected to provide valuable guidance for elucidating the principal categories of urban non-point source P pollution and enhancing the ecological health of urban water environments.

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.