Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin, northeastern China, from 2011 to 2015

Copula-based analysis of socio-economic impact on water quantity and quality: A case study of Yitong River, China

Socio-economic development has a significant impact on both water quantity and quality. However, few studies have considered the complex relationship between water quantity and quality when evaluating such impact. In this study, three indicators based on copula model were proposed, namely, water quantity improvement degree (WQID_w), water quality improvement degree (WQID_q) and water quantity and quality joint improvement degree (WQJID). These indicators were used to assess the impact of social economy on water quantity and quality, and applied to a case study in Yitong River in Northeast China from 2021 to 2025. Four scenarios were set to explore the impact of socio-economic development and water resources protection on WQID_w, WQID_q and WQJID. The maximum WQID_w, WQID_q and WQJID were <1 under the business-as-usual scenario, which showed that the present socio-economic pattern caused great damage to river water quantity and quality. The combined effect of socio-economic development and water resources protection increased the WQJID of COD and NH₃-N by 1.67 and 1.30. This showed that attention should be paid to water resources protection while developing social economy. Compared with comprehensive evaluation, separate evaluation of water quality will underestimate the impact of social economy on rivers, while separate evaluation of water quantity will overestimate the impact. The relationships between WQID_w, WQID_q and WQJID were quantified. Meanwhile, the uncertainty of the evaluation was controlled by the selection of water quality indicators. The WQID_q, WQID_w and WQJID proposed in this study provide a comprehensive assessment tool for guiding water resources management.

Groundwater quality and human health risk assessment in selected coastal and floodplain areas of Bangladesh

Groundwater aquifers are a common source of drinking water in Bangladesh. However, groundwater contamination is a major public health concern across the country. This research aims to examine the groundwater quality and health concerns using a random sampling process. Multivariate statistical and health risk analyses of elements were performed to determine the source of contaminants and their effects on human health. A total of 24 parameters were analyzed, where Na⁺, NH₄⁺, K⁺, Mg²⁺, F^-, NO₃^-, Mn, Fe, Se, U, and As concentrations were found to be high in different sampling points compared to the Department of Environment of Bangladesh (DoE), and the World Health Organization (WHO) groundwater quality standards. Principal Component Analysis (PCA) and Cluster Analysis (CA) identified the dominant and potential sources of contaminants in the groundwater aquifer, including geogenic, salinity intrusion, industrial, and agricultural. The results of the degree of contamination level (C_d) and the heavy metal pollution index (HPI) showed that 28% and 12% of the sampling points had high levels of heavy metal contamination, indicating a high risk for human health issues. Cr concentrations were found to have a higher carcinogenic (cancer) risk than As and Cd concentrations. Hazard quotient (HQ) and hazard index (HI) scores expressed the hazardous status and possible chronic effects in the context of individual sampling points. For both child and adults, 44% and 36% of the sampling points had a high HI score, indicating the possibility of long-term health risks for local populations.

Spatial variation in ciliate communities with respect to water quality in the Delhi NCR stretch of River Yamuna, India

The River Yamuna emerges from Saptarishi Kund, Yamunotri and merge with River Ganges at Allahabad, India. Anthropogenic stress has affected the water quality of the river Yamuna drastically in the stretch traversing Delhi and its satellite towns (National Capital Region, NCR). In the present study, effect of water quality on the microbial life in the River Yamuna was analyzed using ciliate communities (Protista, Ciliophora) as bio-indicators. Water samples were collected from six sampling sites chosen according to the levels of pollution along the river and water quality was analysed using standard physicochemical factors. As the river traverses Delhi NCR, water quality deteriorates considerably as indicated by the Water Quality Index at the selected sampling sites. Seventy-four ciliate species representing nine classes were recorded. Based on the Shannon diversity index, maximum species diversity was found at the point where the river enters Delhi. The saprobity index showed the river water was beta-mesosaprobic when the river enters Delhi and alpha-mesosaprobic at downstream sites after the first major drain outfall. Significant relationship between the spatial variation in ciliate communities and abiotic parameters indicate that ciliates can be used as effective bioindicators of pollution in the River Yamuna.

A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

Urban stormwater runoff represents a significant challenge for the practical assessment of diffuse pollution sources on receiving water bodies. Given the high dimensionality of the problem, the main goal of this study was the comparison of linear and non-linear machine learning (ML) methods to characterize urban nutrient runoff from impervious surfaces. In particular, the principal component analysis (PCA) for the linear technique and the self-organizing map (SOM) for the non-linear technique were chosen and compared considering the high number of successful applications in the water quality field. To strengthen this comparison, these techniques were supported by well-known linear and non-linear methods. Those techniques were applied to a complete dataset with precipitation, flow rate, and water quality (sediments and nutrients) records of 577 events gathered for a watershed located in Southern Italy. According to the results, both linear and non-linear techniques can represent build-up and wash-off, the two main processes that characterize urban nutrient runoff. In particular, non-linear methods are able to capture and represent better the rainfall-runoff process and the transport of dissolved nutrients in urban runoff (dilution process). However, their computational time is higher than the linear technique (0.0054 s vs. 15.24 s, for linear and non-linear, respectively, in our study). The outcomes of this study provide significant insights into the application of ML methods for the water quality field.

Biological consequences of environmental pollution in running water ecosystems: A case study in zooplankton

Biodiversity in running water ecosystems such as streams and rivers is threatened by chemical pollution derived from anthropogenic activities. Zooplankton are ecologically indicative in aquatic ecosystems, owing to their position of linking the top-down and bottom-up regulators in aquatic food webs, and thus of great potential to assess ecological effects of human-induced pollution. Here we investigated the influence of water pollution on zooplankton communities characterized by metabarcoding in Songhua River Basin in northeast China. Our results clearly showed that varied levels of anthropogenic disturbance significantly influenced water quality, leading to distinct environmental pollution gradients (p < 0.001), particularly derived from total nitrogen, nitrate nitrogen and pH. Redundancy analysis showed that such environmental gradients significantly influenced the geographical distribution of zooplankton biodiversity (R = 0.283, p = 0.001). In addition, along with the trend of increasing environmental pollution, habitat-related indicator taxa were shifted in constituents, altering from large-sized species (e.g. arthropods) in lightly disturbed areas to small-sized organisms (e.g. rotifers and ciliates) in highly disturbed areas. All these findings clearly showed that anthropogenic activity-derived water pollution significantly influenced biological communities. Thus, biotic consequences of human-induced environmental pollution in running water ecosystems should be deeply investigated. More importantly, the findings of biotic consequences should be well integrated into existing monitoring programs to further assess impacts of anthropogenic disturbance, as well as to advance the management of running water ecosystems for conservation and ecological restoration.