A holistic framework of water quality evaluation using water quality index (WQI) in the Yihe River (China)

Identification of foam susceptible locations in the Delhi Reach of the Yamuna River

Recently, the occurrence of foam formation across many rivers has been a cause of concern for the global scientific community. The primary reasons behind foam formation include anionic surfactants, nutrients, organic and inorganic substances, and pathogens, which have been widely studied in the past. However, the issue of foam formation on water surfaces and identifying foam-susceptible locations has not been addressed comprehensively in the past literature. To address this, the present study, for the first time in river management literature, proposes a unified framework to investigate the foam formation issue and identify foam-susceptible locations over Delhi's reach of the Yamuna River, a stretch known for witnessing extensive pollution and excessive foam formation. The foam-related parameters were initially identified, and efficiency scores for four locations-Wazirabad Barrage (u/s), ITO Bridge, Nizamuddin Bridge, and Okhla Barrage (d/s)-were evaluated using the data envelopment analysis model. It was observed that three locations demonstrated low-efficiency scores in comparison to Wazirabad (u/s), indicating a high susceptibility to foam formation, which is critical from an environmental perspective, characterized by elevated levels of nutrients, surfactants, and organic pollutants. The reduced freshwater availability, lack of dissolved oxygen, discharge of untreated or partially treated effluents from multiple drains, and high concentrations of surfactants were noticed, which necessitate focused interventions in this area. In response, the study recommends remedial measures, including ensuring adequate environmental flow, pollutant oxidation, phytoremediation, stringent regulations, and public awareness to address foam formation issues in the Yamuna River.

A novel water quality risk assessment framework for reservoir water bodies coupling key parameter selection and dynamic warning threshold determination

Water quality early warning is crucial for protecting ecological security and controlling pollution in lakes and reservoirs. However, the traditional warning level may not provide accurate data for a specific area. Therefore, it is necessary to design an adaptive early warning threshold and identification system that conforms to the actual operating environment. This study monitored nine water quality parameters-water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH3-N)-monthly from 11 sampling sites in the Danjiangkou Reservoir, i.e., the largest artificial lake in Asia, from 2017 to 2022. The reservoir was divided into three sub-areas by cluster analysis: Danku, Hanku, and Water intake. The Water Quality Index (WQI) was used for comprehensive spatiotemporal water quality evaluation, and a minimum WQI (WQImin) model was developed using multiple linear regression. Finally, a water quality risk early-warning model was proposed based on frequency analysis, categorizing water quality into six levels. The findings reveal that the water quality in each area maintains at "good" or "excellent" levels during the study period. The average WQI values, from lowest to highest, are Hanku (75.44), Danku (78.78), and Water intake (79.07). This result shows that the water quality of Danjiangkou Reservoir has been maintained at a good level due to the pollution control and management of Chinese government after the operation of the Middle Route of the South-to-North Water Diversion Project of China. The WQImin models for each area have different key parameters: WT, DO, TN, TP, and COD are common in all areas, whereas NH3-N is included in both Hanku and Danku models. BOD5 and pH were unique to the Danku and Water intake models, respectively. TN and TP are identified as the key parameters affecting water quality safety in Danjiangkou Reservoir. The risk thresholds for TN and TP in Hanku are significantly higher than those in Danku and Water intake, indicating that the water quality in Hanku is the worst. These thresholds are dynamically revised through the early warning model as new data became available. The proposed risk assessment framework provides a robust tool for water quality risk early warning and offers a scientific and reliable reference for administrative departments to implement effective water environment risk prevention and management strategies.

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.

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.

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.

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 Environment Characteristics and Water Quality Assessment of Water Source of Diversion System of Project from Hanjiang to Weihe River

The water source of the water diversion project from the Hanjiang River to the Weihe River is one of the most important drinking water sources in China. Its water quality is related to the water safety of the long-distance water diversion system from the Hanjiang to Weihe Rivers. In order to explore the spatiotemporal change trend of the water environment characteristics of the water source area and analyze the key factors that have a greater impact on it, this study collected 9 types of water environment physical and chemical parameters from 10 water quality monitoring sections from 2017 to 2019; the water environment characteristics of the water source area of the water diversion system from the Hanjiang River to the Weihe River were analyzed and evaluated by using the variance analysis method, the hierarchical cluster analysis method and the water quality identification index evaluation method. The results were as follows. (1) There was spatiotemporal heterogeneity in a number of physical and chemical parameters in the water body of the water source. In terms of time, the concentrations of CODMn, COD, BOD5 and F- were higher in the flood season (July-October) than in the non-flood season (November-June). The concentrations of DO, TP and TN in the non-flood season were higher than those in the flood season. Spatially, the concentration of physical and chemical parameters of the water body in the Huangjinxia Reservoir area was higher than that in the Sanhekou Reservoir area. (2) The water quality of the water source area was good. The comprehensive water quality reached the Class II water quality standard of surface water environmental quality. Time showed that the comprehensive water quality in the non-flood season was better than that in the flood season. Spatially, the overall water quality of the tributaries was better than that of the mainstream. TN is a key indicator that affects water quality. (3) The spatial and temporal differences in water quality in water source areas are mainly affected by factors such as rainfall, temperature and human activities. This study can provide a scientific and data basis for related research on maintaining and improving the quality of the ecological environment of the water source areas of the Hanjiang to Weihe River Water Diversion System.