Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China

Critical review on toxic contaminants in surface water ecosystem: sources, monitoring, and its impact on human health

Surface water pollution is a critical and urgent global issue that demands immediate attention. Surface water plays a crucial role in supporting and sustaining life on the earth, but unfortunately, till now, we have less understanding of its spatial and temporal dynamics of discharge and storage variations at a global level. The contamination of surface water arises from various sources, classified into point and non-point sources. Point sources are specific, identifiable origins of pollution that release pollutants directly into water bodies through pipes or channels, allowing for easier identification and management, e.g., industrial discharges, sewage treatment plants, and landfills. However, non-point sources originate from widespread activities across expansive areas and present challenges due to its diffuse nature and multiple pathways of contamination, e.g., agricultural runoff, urban storm water runoff, and atmospheric deposition. Excessive accumulation of heavy metals, persistent organic pollutants, pesticides, chlorination by-products, pharmaceutical products in surface water through different pathways threatens food quality and safety. As a result, there is an urgent need for developing and designing new tools for identifying and quantifying various environmental contaminants. In this context, chemical and biological sensors emerge as fascinating devices well-suited for various environmental applications. Numerous chemical and biological sensors, encompassing electrochemical, magnetic, microfluidic, and biosensors, have recently been invented by hydrological scientists for the detection of water pollutants. Furthermore, surface water contaminants are monitored through different sensors, proving their harmful effects on human health.

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.

Identification of water pollution sources and analysis of pollution trigger conditions in Jiuqu River, Luxian County, China

Against the backdrop of ecological conservation and high-quality development in the Yangtze River Basin, there is an increasing demand for enhanced water pollution prevention and control in small watersheds. To delve deeper into the intricate relationship between pollutants and environmental features, as well as explore the key factors triggering pollution and their corresponding warning thresholds, this study was conducted along the Jiuqu River, a strategically managed unit in the upstream region of the Yangtze River, between 2022 and 2023. A total of seven monitoring sites were established, from which 161 valid water samples were collected. The k-nearest neighbors mutual information (KNN-MI) technique indicated that water temperature (WT) and relative humidity (RH) were the main environmental factors. The principal component analysis (PCA) of ten water quality parameters and three environmental factors unveiled the distinguishing characteristics of the primary pollution sources. Consequently, the pollution sources were categorized as treated wastewater > groundwater runoff > phytoplankton growth > abstersion wastewater > agricultural drainage. Furthermore, the regression decision tree (RDT) algorithm was used to explore the combined effects between pollutants and environmental factors, and to provide visual decision-making process and quantitative results for understanding the triggering mechanism of organic pollution in Jiuqu River. It conclusively identifies total phosphorus (TP) as the predominant triggering parameter with the threshold of 0.138 mg/L. The study is helpful to deal with potential water pollution problems preventatively and shows the interpretability and predictive performance of the RDT algorithm in water pollution prevention.

Spatial dynamics and risk assessment of phosphorus in the river sediment continuum (Qinhuai River basin, China)

This study investigated the concentration and fractionation of phosphorus (P) using sequential P extraction and their influencing factors by introducing the PLS-SEM model (partial least squares structural equation model) along this continuum from the Qinhuai River. The results showed that the average concentrations of inorganic P (IP) occurred in the following order: urban sediment (1499.1 mg/kg) > suburban sediment (846.1-911.9 mg/kg) > rural sediment (661.1 mg/kg) > natural sediment (179.9 mg/kg), and makes up to 53.9-87.1% of total P (TP). The same as the pattern of IP, OP nearly increased dramatically with increasing the urbanization gradient. This spatial heterogenicity of P along a river was attributed mainly to land use patterns and environmental factors (relative contribution affecting the P fractions: sediment nutrients > metals > grain size). In addition, the highest values of TP (2876.5 mg/kg), BAP (biologically active P, avg, 675.7 mg/kg), and PPI (P pollution index, ≥ 2.0) were found in urban sediments among four regions, indicating a higher environmental risk of P release, which may increase the risk of eutrophication in overlying water bodies. Collectively, this work improves the understanding of the spatial dynamics of P in the natural-rural-urban river sediment continuum, highlights the need to control P pollution in urban sediments, and provides a scientific basis for the future usage and disposal of P in sediments.

A comprehensive review of water quality indices for lotic and lentic ecosystems

Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

Water quality assessment and pollution source apportionment using multivariate statistical techniques: a case study of the Laixi River Basin, China

Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3 years (2018-2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12 months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans.

Performance assessment of data driven water models using water quality parameters of Wangchu river, Bhutan

Abstract

Multifarious anthropogenic activities triggered by rapid urbanization has led to contamination of water sources at unprecedented rate, with less surveillance, investigation and mitigation. The use of artificial intelligence (AI) in tracking and predicting water quality parameters has surpassed the use of other conventional methods. This study presents the assessment of three main models: adaptive neuro fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) on water quality parameters of Wangchu river located at capital city of Bhutan. The performance and predictive ability of these models are compared and the optimal model for predicting the parameters are recommended based on the coefficient correlation (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE) evaluation criteria. Overall NSE and RMSE, the ANN model predicted parameters with maximum efficiency of 97.3 percent and minimum error of 8.57. The efficiency of MLR and ANFIS model are 95.9 percent and 94.1 percent respectively. The overall error generated by MLR and ANFIS are 10.64 and 12.693 respectively. From the analysis made, the ANN is recommended as the most suitable model in predicting the water quality parameters of Wangchu river. From the six-training function of ANN, trainBR (Bayesian Regularization) achieved the CC of 99.8%, NSE of 99.3% and RMSE of 9.822 for next year data prediction. For next location prediction, trainBR achieved CC of 99.2%, NSE of 98.4% and RMSE of 6.485, which is the higher correlation and maximum efficiency with less error compared to rest of the training functions. The study represents first attempt in assessing water quality using AI technology in Bhutan and the results showed a positive conclusion that the traditional means of experiments to check the quality of river water can be substituted with this reliable and realistic data driven water models.

Article highlights

Analysis of Surface Water Quality in Upstream Province of Vietnamese Mekong Delta Using Multivariate Statistics

The study employed different statistical approaches to assess surface water quality in the upstream region of the Vietnamese Mekong Delta. The dataset included seven parameters (i.e., temperature, pH, total suspended solids (TSS), five-day biological oxygen demand (BOD5), chemical oxygen demand (COD), ammonium nitrogen (NH4+-N) and coliform) at seventy-three locations. Cluster analysis (CA) and principal component analysis (PCA) were applied to analyze spatial variations in surface water quality and recognize the important parameters. The findings revealed that surface water quality was deteriorated by organic matters (high BOD5 and COD), nutrients and microorganisms. Particularly, urban areas were found to be more polluted than the other areas. The PCA results indicated that three potential water pollution sources, including industry, urban and tourism, could explain 87.03% of the total variance. Coliform was identified as the leading latent factor that controls surface water quality in the study area. CA grouped the sampling locations into 11 groups, in which the groups of the baseline monitoring sites and large rivers had better water quality. The results indicated a significant impact of anthropogenic activities (especially, urban and tourism practices) in surface water quality degradation. Moreover, CA suggested that the numbers of the sampling sites could be reduced from 73 to 58 locations, lowering 20.54% of the monitoring cost. Thus, the study recommends scrutinizing the current surface water quality monitoring system to be more economic and urgently implementing appropriate solutions to mitigate coliform pollution in the smaller water bodies.

The study of biological activity of mandelic acid and its alkali metal salts in wastewaters

In the present work we compared the biological activity of mandelic acid (MA) and its Li, Na, K, Rb and Cs salts. The study also investigated the effect of raw wastewaters (RW) and treated wastewaters (TW), comparable to microbial medium (MM) on the biological activity of the tested chemical compounds used in concentrations of 5; 2.5; 1.25; 0.625; 0.3125 mg/ml. In the present experiment the evaluation of the following parameters was performed: E. coli (ATCC 25922) cells viability, growth inhibition of E. coli (ATCC 25922), the inhibition of GFP protein, genotoxicity and ROS generation. Our results showed that three main factors differentiated the antibacterial activity of MA and its Li, Na, K, Rb and Cs salts: study environment (MM, RW, TW), metal forming salt of mandelic acid and concentration of tested compounds. Additionally, raw and treated wastewater, compared to microbial medium, changes the antimicrobial activity of MA and its salts in relation to the E. coli strain. We also detected that both MA and its salts affect the GFP protein and the induction of the recA promoter (genotoxicity test). The activity of the tested salts in relation to these two parameters is strictly dependent on the type of salt-forming metal and the concentration used. The analysis of ROS synthesis suggests that in the majority of the studied mandelic acid salts, oxidative stress is the dominant mechanism of cytotoxicity and genotoxicity. We also showed that both raw wastewaters (RW) and treated wastewaters (TW), compared to microbial medium (MM), change significantly the activity of MA and its salts.

Exploring the relationship between urbanization and water environment based on coupling analysis in Nanjing, East China

The degradation of water environment (WE) has constrained the sustainable development of cities, while the rapid urbanization also exacerbates water environment change. However, the complicated relationship between urbanization and WE is far from clearly understood. In this study, a comprehensive index system for urbanization and WE was applied along with the System Index Evaluation Model (SIEM) and a Coupling Coordination Degree Model (CCDM) to analyze the coupling between urbanization and WE in Nanjing, East China, from 1990 to 2018. The comprehensive index of urbanization increased from 0.0392 in 1990 to 0.9890 in 2018, showing a clear increasing trend. Demographic and spatial urbanization made the largest contribution to urbanization development from 1990 to 2010, while economic urbanization became the largest contributor to urbanization development between 2011 and 2018. Under the combined effects of pressure, state, and response subsystems, the comprehensive WE index showed an upward trend with some fluctuations from 1990 to 2018. The degree of coupling coordination between urbanization and WE displayed an overall upward tendency, growing from 0.18 in 1990 to 0.95 in 2018. The coupling coordination state transitioned from a serious imbalance during the low coupling period (1990-1992) into the superior coordination of the highly coupled period (2011-2018). With the continuous urbanization in the future, in addition to ensuring the optimal management of surface water, protection of groundwater should be reinforced. The results advance our understanding of the dynamic relationship between urbanization and WE and provide important implications for urban planning and water resource protection.

Evaluation of Seasonal and Spatial Variations in Water Quality and Identification of Potential Sources of Pollution Using Multivariate Statistical Techniques for Lake Hawassa Watershed, Ethiopia

The magnitude of pollution in Lake Hawassa has been exacerbated by population growth and economic development in the city of Hawassa, which is hydrologically closed and retains pollutants entering it. This study was therefore aimed at examining seasonal and spatial variations in the water quality of Lake Hawassa Watershed (LHW) and identifying possible sources of pollution using multivariate statistical techniques. Water and effluent samples from LHW were collected monthly for analysis of 19 physicochemical parameters during dry and wet seasons at 19 monitoring stations. Multivariate statistical techniques (MVST) were used to investigate the influences of an anthropogenic intervention on the physicochemical characteristics of water quality at monitoring stations. Through cluster analysis (CA), all 19 monitoring stations were spatially grouped into two statistically significant clusters for the dry and wet seasons based on pollution index, which were designated as moderately polluted (MP) and highly polluted (HP). According to the study results, rivers and Lake Hawassa were moderately polluted (MP), while point sources (industry, hospitals and hotels) were found to be highly polluted (HP). Discriminant analysis (DA) was used to identify the most critical parameters to study the spatial variations, and seven significant parameters were extracted (electrical conductivity (EC), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), sodium ion (Na+), and potassium ion (K+) with the spatial variance to distinguish the pollution condition of the groups obtained using CA. Principal component analysis (PCA) was used to qualitatively determine the potential sources contributing to LHW pollution. In addition, three factors determining pollution levels during the dry and wet season were identified to explain 70.5% and 72.5% of the total variance, respectively. Various sources of pollution are prevalent in the LHW, including urban runoff, industrial discharges, diffused sources from agricultural land use, and livestock. A correlation matrix with seasonal variations was prepared for both seasons using physicochemical parameters. In conclusion, effective management of point and non-point source pollution is imperative to improve domestic, industrial, livestock, and agricultural runoff to reduce pollutants entering the Lake. In this regard, proper municipal and industrial wastewater treatment should be complemented, especially, by stringent management that requires a comprehensive application of technologies such as fertilizer management, ecological ditches, constructed wetlands, and buffer strips. Furthermore, application of indigenous aeration practices such as the use of drop structures at critical locations would help improve water quality in the lake watershed.

Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. Thus, quantitative analysis of the relationship between different land cover types and river water quality contributes to a better understanding of the effects of land cover on water quality, the landscape planning of water quality protection, and integrated water resources management. Based on water quality data of 2006–2018 at 18 typical water quality stations in the Yangtze River basin, this study analyzed the spatial and temporal variation characteristics of water quality by using the single-factor water quality identification index through statistical analysis. Furthermore, the Spearman correlation analysis method was adopted to quantify the spatial-scale and temporal-scale effects of various land uses, including agricultural land (AL), forest land (FL), grassland (GL), water area (WA), and construction land (CL), on the stream water quality of dissolved oxygen (DO), chemical oxygen demand (CODMn), and ammonia (NH3-N). The results showed that (1) in terms of temporal variation, the water quality of the river has improved significantly and the tributaries have improved more than the main rivers; (2) in the spatial variation respect, the water quality pollutants in the tributaries are significantly higher than those in the main stream, and the concentration of pollutants increases with the decrease of the distance from the estuary; and (3) the correlation between DO and land use is low, while that between NH3-N, CODMn, and land use is high. CL and AL have a negative effect on water quality, while FL and GL have a purifying effect on water quality. In particular, AL and CL have a significant positive correlation with pollutants in water. Compared with NH3-N, CODMn has a higher correlation with land use at a larger scale. The results highlight the spatial scale and seasonal dependence of land use on water quality, which can provide a scientific basis for land management and seasonal pollution control.

Nitrogen Loss in Vegetable Field under the Simulated Rainfall Experiments in Hebei, China

Agricultural non-point source pollution is one of the main factors contaminating the environment. However, the impact of rainfall on loss of non-point nitrogen is far from well understood. Based on the artificial rainfall simulation experiments to monitor the loss of dissolved nitrogen (DN) in surface runoff and interflow of vegetable field, this study analyzed the effects of rainfall intensity and fertilization scheme on nitrogen (N) loss. The results indicated that fertilizer usage is the main factor affecting the nitrogen loss in surface runoff, while runoff and rainfall intensity play important roles in interflow nitrogen loss. The proportion of DN lost through the surface runoff was more than 91%, and it decreased with increasing rainfall intensity. There was a clear linear trend (r2 > 0.96) between the amount of DN loss and runoff. Over 95% of DN was lost as nitrate nitrogen (NN), which was the major component of nitrogen loss. Compared with the conventional fertilization treatment (CF), the amount of nitrogen fertilizer applied in the optimized fertilization treatment (OF) decreased by 38.9%, and the loss of DN decreased by 28.4%, but root length, plant height and yield of pak choi increased by 6.3%, 2.7% and 5.6%, respectively. Our findings suggest that properly reducing the amount of nitrogen fertilizer can improve the utilization rate of nitrogen fertilizer but will not reduce the yield of pak choi. Controlling fertilizer usage and reducing runoff generation are important methods to reduce the DN loss in vegetable fields.

The Use of Constructed Wetland for Mitigating Nitrogen and Phosphorus from Agricultural Runoff: A Review

The loss of nitrogen and phosphate fertilizers in agricultural runoff is a global environmental problem, attracting worldwide attention. In the last decades, the constructed wetland has been increasingly used for mitigating the loss of nitrogen and phosphate from agricultural runoff, while the substrate, plants, and wetland structure design remain far from clearly understood. In this paper, the optimum substrates and plant species were identified by reviewing their treatment capacity from the related studies. Specifically, the top three suitable substrates are gravel, zeolite, and slag. In terms of the plant species, emergent plants are the most widely used in the constructed wetlands. Eleocharis dulcis, Typha orientalis, and Scirpus validus are the top three optimum emergent plant species. Submerged plants (Hydrilla verticillata, Ceratophyllum demersum, and Vallisneria natans), free-floating plants (Eichhornia crassipes and Lemna minor), and floating-leaved plants (Nymphaea tetragona and Trapa bispinosa) are also promoted. Moreover, the site selection methods for constructed wetland were put forward. Because the existing research results have not reached an agreement on the controversial issue, more studies are still needed to draw a clear conclusion of effective structure design of constructed wetlands. This review has provided some recommendations for substrate, plant species, and site selections for the constructed wetlands to reduce nutrients from agricultural runoff.