Evaluating surface water quality using water quality index in Beiyun River, China

Multi-scale response relationship between water quality of rivers entering lakes from different pollution source areas and land use intensity: a case study of the three lakes in central Yunnan

As the main supply source of lakes, the water quality of the rivers entering the lakes directly determines the water safety and sustainable development of the lakes. Human activities are the direct cause of changes in the water quality of rivers entering lakes, and land use intensity is the direct manifestation of human activities on the land surface. Although significant progress has been made in studying the relationship between land use changes and water quality in lakes, there is still a lack of research on exploring the relationship between land use intensity and water quality at multiple scales, especially in comparative studies of different pollution source areas. To address this problem, this study used Pearson's correlation analysis and land use intensity index method to explore the response relationship between river water quality and land use intensity at different spatial and temporal scales and different pollution source areas using three lakes in central Yunnan as examples. The results showed that land use intensity was generally positively correlated with water quality, but the response relationship between land use intensity and different water quality indicators was significantly different at different scales and for different pollution source areas. Compared to non-urban areas, the impact of land use intensity on water quality is more significant in urban areas. Compared to the rainy season, the correlation between CODNa, TP, and NH3-N values and land use intensity is stronger during the dry season, while the correlation between COD, TN, and land use intensity is weaker during the dry season. When viewed at different scales, different water quality indicators have different scale effects, but overall, the larger the scale, the stronger the correlation. Therefore, in the work of lake water environmental governance, it is necessary to consider comprehensively from multiple scales and perspectives and adopt measures that are more suitable for regional water pollution prevention and control.

Assessment of seasonal water quality and land use land cover change in Subarnarekha watershed of Ranchi stretch in Jharkhand

In this paper, the assessment of seasonal water quality and land use land cover change in the Subarnarekha watershed in Ranchi stretch was analysed. Agricultural runoff along with climate change adds to the pollution risk to the Subarnarekha River of Ranchi stretch in Jharkhand. Water quality indicators, like acidity alkalinity (ALK), total dissolved solids (TDS), hardness (H), dissolved oxygen (DO), biochemical oxygen demand (BOD), chlorides (CL-), electrical conductivity (EC), salinity (SAL), resistivity (RES) and pH, were assessed as per the standard method. During monsoon season, acidity, alkalinity, hardness, chlorides, salinity, pH and DO decreased, whereas EC, TDS, BOD and resistivity increased in comparison to pre-monsoon season. In post-monsoon, chloride problem was observed very high. Hardness was least in monsoon and maximum in post-monsoon season. EC and BOD increased in monsoon season in comparison to other seasons. Statistical analysis like HCA (hierarchical cluster analysis) and PCA (principal component analysis) also confirmed the problem of TDS, EC, chloride and hardness in the area. WQI (water quality index) analysis showed that the water quality was poor to unsuitable on all the sampling points throughout the study area in all seasons. LULC (land use land cover) and NDWI (normalized difference water index) analysis had also concluded that due to high rate of urbanization, the area has undergone a massive change in terms of forest cover and water bodies. The need for afforestation, forest protection and wetland protection can be clearly seen from the result of this study.

Sampling frequency optimization of the water quality monitoring network in São Paulo State (Brazil) towards adaptive monitoring in a developing country

Water quality monitoring networks (WQMNs) that capture both the temporal and spatial dimensions are essential to provide reliable data for assessing water quality trends in surface waters, as well as for supporting initiatives to control anthropogenic activities. Meeting these monitoring goals as efficiently as possible is crucial, especially in developing countries where the financial resources are limited and the water quality degradation is accelerating. Here, we asked if sampling frequency could be reduced while maintaining the same degree of information as with bimonthly sampling in the São Paulo State (Brazil) WQMN. For this purpose, we considered data from 2004 to 2018 for 56 monitoring sites distributed into four out of 22 of the state's water resources management units (UGRHIs, "Unidades de Gerenciamento de Recursos Hídricos"). We ran statistical tests for identifying data redundancy among two-month periods in the dry and wet seasons, followed by objective criteria to develop a sampling frequency recommendation. Our results showed that the reduction would be feasible in three UGRHIs, with the number of annual samplings ranging from two to four (instead of the original six). In both seasons, dissolved oxygen and Escherichia coli required more frequent sampling than the other analyzed parameters to adequately capture variability. The recommendation was compatible with flexible monitoring strategies observed in well-structured WQMNs worldwide, since the suggested sampling frequencies were not the same for all UGRHIs. Our approach can contribute to establishing a methodology to reevaluate WQMNs, potentially resulting in less costly and more adaptive strategies in São Paulo State and other developing areas with similar challenges.

A multivariate approach to assessing the water quality of the Bamako reach of the Niger River in Mali as irrigation water

Agricultural production in the Bamako region has been raised, and its output quality has been questionable due to the discharge of wastewater into the Niger River. This study assessed the Niger River water body variations for irrigation application temporally and spatially. Thirteen parameters, potential of hydrogen, electrical conductivity, nitrate, total dissolved solids, phosphate, sulfate, chloride, ammonium, calcium, magnesium, potassium, sodium, and bicarbonate, were analyzed at the 15 sampling locations. Parameters examination indicated that most pollutants had higher concentrations over the high-flow phase than in the low-flow period. All parameters were within the Food and Agriculture Organization's recommended values levels. Irrigation variables, sodium adsorption ratio, sodium percentage, soluble sodium percentage, residual sodium bicarbonate, Kelly's ration, permeability index, total hardness, and potential salinity showed the water samples' convenience for irrigation. However, the magnesium hazard concentration exceeded the recommended values levels. Besides, the chloroalkaline indices indicated a trend of degradation that should be addressed. Therefore, a river management plan and regular irrigation water quality monitoring are needed to reduce water hardness in Bamako. The Niger River's sustainable management process must be thrived on all actors' participation. A scientific assessment will be conducted using appropriate methods to identify pollution sources in Bamako. The results of this study will serve as a cornerstone for future investigations concerning the quality of surface water, which is essential for irrigation purposes. PRACTITIONER POINTS: Human activities affected the Niger River water bodies in Bamako city. Quantitative and qualitative assessments reveal the pollution status and trend of the Niger River. The water quality trend is better in the low-flow season, which is an ideal period for vegetable production in Bamako. Most multivariate approaches indicated that the Niger River water is healthy for irrigation purposes. Magnesium hazard exceeded the standard levels, and the chloroalkaline indices indicated a trend of the Niger River water quality deterioration.

Optimization of water quality index models using machine learning approaches

To optimize the water quality index (WQI) assessment model, this study upgraded the parameter weight values and aggregation functions. We determined the combined weights based on machine learning and game theory to improve the accuracy of the models, and proposed new aggregation functions to reduce the uncertainty of the model. A new water quality assessment system was established, and took the Chaobai River Basin as a case study. To optimize the weight, two combined weights were established based on game theory. The weight CWAE was combined by the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). The weight CWAL was combined by AHP and machine learning (LightGBM). CWAL was judged to be an optimal composite weight by comparing the coefficient of variation (CV) values and the Kaiser-Meyer-Olkin (KMO) extracted values. To reduce the uncertainty of the model, we proposed two aggregation functions, the Sinusoidal Weighted Mean (SWM) and the Log-weighted Quadratic Mean (LQM). The three water quality assessment models (WQIS, WQIL and WQIW) were established based on the optimal weights besides. All three models had good reliability. Both WQIS and WQIW models had low eclipsing problems (25.49% and 18.63%). The accuracy of the models was ranked as WQIS > WQIW > WQIL. The uncertainty of WQIs (0.000) in assessing poor water quality was low, and so was WQIW (0.259) in assessing good water quality. Overall, the WQIS model was recommended for assessing poor water quality and the WQIW model was recommended for assessing good water quality. The assessment results of WQIS showed that the Chaobai River Basin was "slightly polluted", and the water quality upstream was better than that downstream. TN was the main pollutant in the basin, and there was slight pollution with CODMn, CODCr, BOD5, etc. There was little metal contamination, only a few months exceeded Class I. The model established in this study can provide a reference for the same type work of water quality assessment. The assessment results can provide a scientific basis for the protection of the regional water environment.

Portable Multi-Channel Electrochemical Device with Good Interaction and Wireless Connection for On-Site Testing

It is very important to rapidly test the key indicators of water in the field to fully evaluate the quality of the regional water environment. However, a high-resolution measuring device that can generate small currents for low-concentration analytes in water samples is often bulky, complex to operate, and difficult for data sharing. This work introduces a portable multi-channel electrochemical device with a small volume, good interaction, and data-sharing capabilities called PMCED. The PMCED provides an easy-to-operate graphical interactive interface to conveniently set the parameters for cyclic voltammetry or a differential pulse method performed by the four electrode channels. At the same time, the device, with a current sensitivity of 100 nA V^-1, was applied to the detection of water samples with high background current and achieved a high-resolution measurement at low current levels. The PMCED uses the Narrow Band Internet of Things (NB-IoT) to meet the needs for uploading data to the cloud in remote areas. The electrochemical signal preprocessing and chemometrics models run in the cloud, and the final results are visualized on a web page, providing a remote access channel for on-site testing results.

Characteristics analysis of water pollutants in Cihu Lake, China, based on a multivariate statistical analysis method

In order to understand the sources of pollutants and the temporal and spatial distribution characteristics of the water quality in Cihu Lake, China, the monitoring data of seven water quality indicators from 12 sampling sites from 2015 to 2019 were selected, and the temporal and spatial variation laws of the water quality and pollution sources were analyzed by the use of the multivariate statistical analysis method. The results show that nitrogen and phosphorus pollution in the lake is dominant. The average concentrations of total nitrogen (TN) and total phosphorus (TP) exceed the surface water quality Class III standards by 1.6 and 2.2 times, respectively. Spatially, the results of the cluster analysis showed that the water quality in Cihu Lake can be categorized into three regions: the northern half of the lake, the southern half of the lake, and the canal entering the lake. Temporally, the water quality in these three regions can be classified into three categories: March to May (the northern half of Cihu Lake), September to November (the southern half of Cihu Lake), and September (the canal entering Cihu Lake). The discriminant analysis results showed that NH₃-N, TN, COD_Cr, and BOD₅ are the main factors that affect the uneven spatial distribution of the water quality of Cihu Lake, while TN, DO, and COD_Mn are the main factors that affect the temporal difference in the northern half of Cihu Lake, and NH₃-N, TP, COD_Cr, DO, COD_Mn, TN, and TP are the main factors affecting the temporal difference in the southern half of Cihu Lake and the canal entering Cihu Lake. It was found that the water pollution in the study area can be mainly attributed to the incoming water and urban domestic pollution. The main pollution sources for the canal entering Cihu Lake and the southern half of Cihu Lake are the water from the sewage treatment plant and the domestic sewage that has not been intercepted, while the northern half of Cihu Lake is mainly affected by surface runoff, mixed rainwater and sewage, and internal pollution.

Aziz NA, Alelyani S, Mohana M, Hosain M. Improving Water Quality Index Prediction Using Regression Learning Models

Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.

Development of water quality management strategies based on multi-scale field investigation of nitrogen distribution: a case study of Beiyun River, China

Accurately quantifying the distribution of nitrogen (N) contaminants in a river ecosystem is an essential prerequisite for developing scientific water quality management strategy. In this study, we have conducted a series of field investigations along the Beiyun River to collect samples from multiple scales, including surface water, riverbed sediments, vadose zone, and aquifer, for evaluating the spatial distribution of N; besides, column simulation experiments were carried out to characterize the transport behavior of N in riverbed sediments. The surface water of the Beiyun River was detected to be eutrophic because of its elevated total N concentration, which is 33 times of the threshold value causing the potential eutrophication. The hydrodynamic dispersion coefficient (D) of riverbed sediments was estimated by CXTFIT 2.1, demonstrating that the D of upstream section was lower than that of midstream and downstream sections (D_upstream < D_midstream < D_downstream), with the estimated annual N leaching volume of 130,524, 241,776, and 269,808 L/(m²·a), respectively. The average total N concentration in vadose zone and aquifer of upstream Sect. (297.88 mg/kg) was obviously lower than that of midstream Sect. (402.62 mg/kg) and downstream Sect. (447.02 mg/kg). Based on multi-scale investigation data, subsequently, water quality management strategies have been achieved, that is, limiting the discharge of N from the midstream and downstream banks to the river and setting up the impermeable layer in the downstream reaches to reduce infiltration. The findings of this study are of great significance for the improvement of river environmental quality and river management.

Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization

Undeniably, there is a link between water resources and people's lives and, consequently, economic development, which makes them vital in health and the environment. Proper water quality forecasting time series has a crucial role in giving on-time warnings for water pollution and supporting the decision-making of water resource management. The principal aim of this study is to develop a novel and cutting-edge ensemble data intelligence model named the weighted exponential regression and hybridized by gradient-based optimization (WER-GBO). Indeed, this is to reach more meticulous sodium (Na⁺) prediction monthly at Maroon River in the southwest of Iran. This developed model has advantages over other previous methodologies thanks to the following merits: (i) it can improve the performance and ability by mixing the outputs of four distinct data intelligence (DI) models, i.e., adaptive neuro-fuzzy inference system (ANFIS), least square support vector regression (LSSVM), Bayesian linear regression (BLR), and response surface regression (RSR); (ii) the proposed model can employ a Cauchy weighted function combined with an exponential-based regression model being optimized by GBO algorithm. To evaluate the performance of these models, diverse statistical indices and graphical assessment including error distributions, box plots, scatter-plots with confidence bounds and Taylor diagrams were conducted. According to obtained statistical metrics and verified validation procedures, the proposed WER-GBO resulted in promising accuracy compared to other models. Furthermore, the outcomes revealed the WER-GBO (R = 0.9712, RMSE = 0.639, and KGE = 0.948) reached more accurate and reliable results than other methods such as the ANFIS, LSSVM, BLR, and RSR for Na prediction in this study. Hence, the WER-GBO model can be considered a constructive technique to forecast the water quality parameters.

Ecosystem health of the Beiyun River basin (Beijing, China) as evaluated by the method of combination of AHP and PCA

Ecosystem services provided by river ecosystems rely on healthy ecosystem structure and ecological processes. The Beijing-Tianjin-Hebei urban is a typical water-deficient area. As an important part of the urban-rural integration construction, evaluating the health status of the Beiyun River Basin and discovering the weak links in the water environment are the basis for improving the health of the basin. In this study, analytic hierarchy process (AHP) was used to establish an evaluation index system for the Beiyun River Basin from 5 aspects including water quality, biology, ecology, hydrology, and social functions, and the principal component analysis (PCA) was then used to assign weights to the index layer. The evaluation results showed that the health evaluation results of the Beiyun River Basin in 2019 are "sub-healthy," and the overall health status is getting worse from northwest to southeast. In the middle reaches of the region, the evaluation result is "healthy," followed by the upstream, and the downstream is the worst. The results showed that areas with less human interference or orderly intervention are in better health. High eutrophication level, low bio-diversity, and low vegetation coverage are the main indicators that leads to poor ecosystem health in the Beiyun River Basin. For the comprehensive management of the Beiyun River, the improvement of water quality and habitat ecological restoration are key actions to the health of the upstream ecosystem. The improvement of the health status of the downstream should focus on equal emphasis on water quality and quantity, restoration of biodiversity, and improvement of the quality of the riparian ecological environment.

Spatial Distribution and Source Identification of Water Quality Parameters of an Industrial Seaport Riverbank Area in Bangladesh

The Pasur River is a vital reservoir of surface water in the Sundarbon area in Bangladesh. Mongla seaport is located on the bank of this river. Many industries and other commercial sectors situated in this port area are discharging waste into the river without proper treatment. For this reason, geospatial analysis and mapping of water pollutant distribution were performed to assess the physicochemical and toxicological situation in the study area. We used different water quality indices such as Metal Index (MI), Comprehensive Pollution Index (CPI), and Weighted Arithmetic Water Quality Index Method (WQI) to improve the understanding of pollution distribution and processes determining the quality of river water. Multivariate statistical methods were used to evaluate loads and sources of pollutants in the Pasur River system. The results indicate that the sources of contaminants are both geogenic and anthropogenic, including untreated or poorly treated wastewater from industries and urban domestic waste discharge. The concentration range of total suspended solid (TSS), chloride, iron (Fe), and manganese (Mn) were from 363.2 to 1482.7, 108.2 to 708.93, 1.13 to 2.75, and 0.19 to 1.41 mg/L, respectively, significantly exceeding the health-based guideline of WHO and Bangladeshi standards. The high Fe and Mn contents are contributions from geogenic and anthropogenic sources such as industrial waste and construction activities. The average pH value was 8.73, higher than the WHO and Bangladeshi standard limit. WQI (ranging from 391 to 1336), CPI (6.71 to 23.1), and MI (7.23 to 23.3) were very high and greatly exceeded standard limits indicating that the Pasur River water is highly polluted. The results of this study can be used as a first reference work for developing a surface water quality monitoring system and guide decisionmakers for priorities regarding wastewater treatment.

Analysis of spatio-temporal variations of river water quality and construction of a novel cost-effective assessment model: a case study in Hong Kong

Assessment of river water quality has been attracting a great deal of attention because of its important implications for the living environment of human beings and aquatic organisms. River water quality is commonly assessed using dozens of different water quality parameters. However, different parameters may contain redundant information, which could lead to the waste of monitoring efforts. Thus, this study constructed a novel cost-effective assessment model of river water quality using the 1-year monitoring data collected from 23 sampling stations in the water control zone of Tolo Harbour and Channel in Hong Kong. First, the spatio-temporal variations of water quality parameters and the overall status of river water quality were analyzed based on all 19 parameters using Kruskal-Wallis test, hierarchical cluster analysis, and the water quality index (WQI). The results indicated that most water quality parameters and overall water quality status varied significantly over space, but did not exhibit obvious seasonal differences; and 99.27% of water samples were identified to be in good or excellent status of overall WQI. Then, using principal component analysis (PCA)/factor analysis (FA) and Pearson's correlation analysis, eight parameters, including 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonia-nitrogen (NH₃-N), nitrate-nitrogen (NO₃-N), chlorophyll-a (Chl-a), fluoride (F^-), total suspended solids (TSS), and arsenic (As), were verified to be responsible for the greatest contributions to water quality, the assessment of overall water quality status. These eight crucial parameters were further employed to establish six cost-effective water quality assessment models. Using the overall WQI as the benchmark, the results of linear regression analysis demonstrated that the cost-effective model constructed based on BOD₅, COD, NH₃-N, NO₃-N, F^-, TSS, and As were the optimal water quality assessment model, which can achieve the most reliable results with reduced parameters.

Surface Water Quality Assessment and Contamination Source Identification Using Multivariate Statistical Techniques: A Case Study of the Nanxi River in the Taihu Watershed, China

Understanding the spatiotemporal patterns of water quality is crucial because it provides essential information for water pollution control. The spatiotemporal variations in water quality for the Nanxi River in the Taihu watershed of China were evaluated by a water quality index (WQI) and multivariate statistical techniques; additionally, the potential sources of contamination were identified. The data set included 22 water quality parameters collected during the monitoring period from 2015 to 2020 for 14 monitoring stations. WQI assessment revealed that approximately 85% of monitoring stations were classified as “medium-low” water quality, and most showed continuous improvement in water quality. Cluster analysis divided the 14 monitoring stations into three clusters (low contamination, medium contamination and high contamination). Discriminant analysis identified pH, petroleum, volatile phenol, chemical oxygen demand, total phosphorus, F, S, fecal coliform, SO4, Cl, NO3-N, total hardness, NO2-N and NH3 as important parameters affecting spatial variations. Factor analysis identified four potential contamination source types: nutrient, organics, feces and oil. This study demonstrated the usefulness of multivariate statistical techniques in assessing large data sets, identifying contamination source types, and better understanding spatiotemporal variations in water quality to restore and protect water resources.

Evaluating the risks of spatial and temporal changes in nonpoint source pollution in a Chinese river basin

In watershed management, it is of great importance to evaluate the risks of nonpoint source (NPS) pollution. In this study, the Nonpoint Source Pollution Risk Index (NSPRI), a multi-factor NPS risk assessment model that was based on the source-sink landscape theory, was proposed and applied in Muzhuhe River Basin, Shandong, China to (1) highlight spatial and temporal variations in the risks from nitrogen and phosphorus losses, and (2) identify how the basin characteristics influenced the risk of nutrient loss. According to the analysis on land use change, the study area is featured with high proportions of forest and agricultural land uses; the area of urban and industrial land had increased considerably from 2000 and 2018. Based on the division of the calculated risk indices on subbasin scale, the area with extremely high risks has decreased from 56,442 ha to 43,922 ha. The average and coefficient of variation (CV) values of NSPRI in the river basin have dropped from 1.3 to 1.1, and from 78.2% to 48.9%, respectively. The distribution of NSPRI suggested an increase in spatial clustering and improvements in the ecological balance. Correlation analysis of the Soil and Water Assessment Tool (SWAT) model (R² > 0.68, ENS > 0.59) and NSPRI indicated the applicability of the method used (r > 0.84, p < 0.01). Analysis on the impact of metrics of land use composition, landscape, and environmental settings on NSPRI indicated that the water quality was more significantly correlated with land use composition, landscape pattern and vegetation cover than with flow path distance, soil erodibility, and rainfall erosivity. Moreover, results of redundancy analysis revealed that nutrient loss risk was better explained by land use compositions than by landscape configuration. The assessment method provided scientific support for NPS pollution control from the perspective of source-sink landscape theory.

Assessment of water quality condition and spatiotemporal patterns in selected wetlands of Punjab, India

Wetlands are one of the most productive aquatic ecosystems on earth, and their water quality is an indicative of their suitability for maintaining various ecosystem services. In this study, different statistical techniques and water quality index (WQI) were employed to access the status and spatiotemporal patterns in water quality of seven selected (two natural and five manmade) wetlands of Punjab. The results revealed that the status of water quality in the selected wetlands was between good and poor during studied seasons (summer, monsoon, and winter) of year 2019. The principal component analysis identified three groups of wetlands with distinct water quality characteristics with spatial patterns: Kahnuwan Chhamb and Keshopur Miani having nearly similar values of pH, total dissolve salts, electrical conductivity, chemical oxygen demand, total alkalinity, bicarbonate and ammonium content; Ropar, Kanjli, and Harike having higher value of nutrients than the other wetlands; and Ranjit Sagar and Nangal with low value of measured water quality characteristics. Further, analysis of variance revealed that all analyzed water quality parameters showed temporal patterns in water quality except water pH, electrical conductivity, dissolved oxygen, biological oxygen demand, and phosphate content. This comparative study enhanced our knowledge about the spatiotemporal patterns in water quality and in the future will be helpful to the policymakers and concerned authorities for developing better water quality management strategies for these wetlands.

Assessing the fluvial system resilience of the river Bacchiglione to point sources of pollution in Northeast Italy: a novel Water Resilience Index (WRI) approach

Modelling and evaluating the resilience of environmental systems has recently raised significant interest among both practitioners and researchers. However, it has not yet been used to measure the absorption and recovery capacities of a river subject to varying levels of pollution due to natural and anthropic sources of contamination within the basin. Fast worldwide population growth and climate change are contributing to an increased degradation status in surface water bodies and to a decreased efficiency of their natural self-purification processes. Decision-makers are, therefore, more and more encouraged to implement alternative management strategies focussed on improving the system resilience to current and future perturbations. To this end, a novel Water Resilience Index (WRI), based on different quality parameters, was developed, and it is here proposed to estimate the ability of the river Bacchiglione, located in Northeast Italy, absorb continuous and unpredictable changes due to potential effects of point sources of pollution, that is, urban and industrial wastewater, and still maintain its vital functions. This new index is integrated in a mathematical model, which represents the river as an influence diagram where the nodes are the gauged stations and the arcs are the fluvial reaches among the stations, to identify the river reaches in need of resilience improvement. In addition, in order to simplify the analytical procedure and lower the costs and times of the monitoring activities, a principal component analysis is also used, as it is able to reduce the number of the water quality parameters to be collected from the sampling stations, distributed along the main river, and thus to calculate a minimum WRI. The good agreement between the results obtained by both the original and minimum WRI shows the effectiveness of the proposed methodology. This approach could be applied to all basins with the same issues, and not just in the Italian case study here analysed, as it might be a valid tool to plan interventions and mitigation actions, protecting the resource from pollution risks and achieving environmental quality and Sustainable Development Goals both in the water bodies and their surrounding territories. In addition, this strategy could be integrated in the existing models supporting local decision-makers and administrators, aiming at increasing the resilience of urban and rural areas to pollution phenomena and facilitating the development of effective policies to reduce the impacts of global change on water quality.

Health risk assessment based on source identification of heavy metals: A case study of Beiyun River, China

Long-term retention and accumulation of heavy metals in rivers pose a great threat to the stability of ecosystems and human health. In this study, Beiyun River was taken as the example to quantitatively identify pollution sources and assess the pollution source-oriented health risk. A total of 8 heavy metals (Mn, Ni, Pb, Zn, As, Cr, Cd, and Cu) in Beiyun River were measured. Ordinary kriging (OK) and inverse distance weight (IDW) methods were used to predict the distribution of heavy metals. The results showed that the OK method is more accurate, and heavy metal pollution in the midstream and downstream is much more serious than that in the upstream. Principal component analysis-multiple linear regressions (PCA-MLR) and positive matrix factorization (PMF) methods were used to quantitatively identify pollution sources. The coefficient of determination (R²) of PMF is closer to 1, and the analyzed pollution source is more refined. Furthermore, the result of source identification was imported into the health risk assessment to calculate the hazard index (HI) and carcinogenic risk (CR) of various pollution sources. The results showed that the HI and CR of As and Ni to local residents were serious in the Beiyun River. Industrial activities (23.0%) are considered to be the largest contribution of heavy metals in Beiyun River, followed by traffic source (17%), agricultural source (16%), and atmospheric deposition (16%). The source-oriented risk assessment indicated that the largest contribution of HI and CR is agricultural source in the Beiyun River, followed by industrial activities. This study provides a "target" for the precise control of pollution sources, which is of great significance for improving the fine management of the water environment in the basin.