Evaluation Of Water Quality Research Articles

Application of multivariate statistical techniques in the assessment of long-term surface water quality in Dong Thap Province, Vietnam.

This study assessed the surface water quality in Dong Thap province during 2013-2023 using multivariate statistical techniques. Water quality index (WQI), Pearson correlation, principal component analysis (PCA), and cluster analysis (CA) were determined based on monitoring data for parameters. The findings showed that only some water samples had pH values below the permissible standard, while 95.5% of samples exceeded the standard of Escherichia coli in surface water. In some years, 100% samples having BOD5, TSS, NH4+, and E. coli exceeded the permissible limits. The PCA identified four, five, and seven major potential sources during 2013-2015, 2016-2020, and 2021-2023, respectively, explaining 75.9%, 63.0%, and 63.0% of variation in the corresponding duration times. For heavy metals, the number of water samples containing As, Cu, and Pb exceeding the water permissible limits were 15.5%, 21.4%, and 2.8% samples in 2021, respectively, but no heavy metal was above standard in 2022 and 2023. The evaluation of average water quality based on WQI showed that 79.31% and 76.19% of the sample sites were classified to be average during 2016-2020 and 2021-2023, respectively, and others were bad water quality. However, the water quality showed an improving trend with 70% of monitoring positions considered as good water quality in 2023. The results showed that anthropogenic activities and hydrological regime were the main source of pollution. This study provides guidance to policymakers in developing water management strategies to have proper actions to prevent water pollution.

Water Quality Assessment using Selected Macroinvertebrate Based Indices and Water Quality Index of Sungai Air Hitam Selangor

A study was conducted from July to December 2022 at Sungai Air Hitam, a small tributary of the Selangor River located within the Tanjung Karang Sub-basin in Malaysia (coordinates: 3° 24' 27" N, 101° 25' 54" E to 3° 28' 14" N, 101° 26' 59" E). This confluence is situated near three major downstream water treatment plants. The study assessed six water quality parameters—pH, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammonia (NH3), and suspended solids (SS)—to calculate the Water Quality Index (WQI). Macroinvertebrates were sampled simultaneously using the dipping net method to obtain biotic indices for further evaluation of water quality. The results indicated that the WQI classified Sungai Air Hitam as Class III, with scores ranging from 56.9 to 64.6, suggesting the river is suitable for water supply and fisheries. However, the Biological Monitoring Working Party (BMWP) index categorized the water quality as poor, with scores between 30 and 42. Similarly, the Average Score Per Taxon (ASPT) ranged from 3.25 to 5.25, indicating pollution or environmental impact, while the Family Biotic Index (FBI) further classified the river as having poor to very poor water quality, with scores between 6.57 and 8.11. Overall, the study suggests that Sungai Air Hitam has experienced some degree of ecological degradation. These findings emphasize the need for continuous monitoring and remediation efforts to preserve and restore water quality.

Integrating Water Quality Index (WQI) and Multivariate Statistics for Regional Surface Water Quality Evaluation: Key Parameter Identification and Human Health Risk Assessment

Assessing regional water quality and evaluating the associated risks to human health posed by aquatic contaminants are paramount for conserving and managing surface water resources and formulating effective local policy decisions. This study utilizes 17 water quality parameters collected monthly from nine national monitoring stations in Nanning City, China, from January 2021 to March 2023, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), copper (Cu), zinc (Zn), fluoride (F-), selenium (Se), arsenic (As), mercury (Hg), cadmium (Cd), chromium (Cr), and lead (Pb). Analyses were performed utilizing the Water Quality Index (WQI) and multiple stepwise regression to ascertain seasonal and spatial variations in water quality and to identify key water quality parameters. Human health risks were evaluated, focusing on eight heavy metals. The results indicated that the average WQI for the designated area was 94.1, with individual monitoring stations displaying WQIs ranging from 93.22 to 96.44, categorizing the water quality as “excellent”. The WQI exhibited seasonal fluctuations, peaking in spring and winter while decreasing in summer and autumn. All measured parameters met national standards for Class I and II surface waters. The stepwise regression analysis facilitated the construction of minimized WQI models (WQImin) derived from three different training and testing datasets, with a WQImin model incorporating six key parameters, i.e., DO, CODMn, NH3-N, Hg, WT, and Se. The concentration of heavy metals in the water exhibited a sequence of Zn (3.35 µg/L) > Cr (2.00 µg/L) > Cu (1.36 µg/L) > As (1.29 µg/L) > Se (0.32 µg/L) > Pb (0.17 µg/L) > Cd (0.03 µg/L) > Hg (0.01 µg/L), with all metals adhering to the Class I standard. Children are more susceptible to the adverse effects of heavy metal contamination than adults, exhibiting a total environmental health risk of 1.28 times greater. Cr was identified as the predominant contributor to the aggregate health risk, accounting for over 80% of the risk in adults and children, followed by As (19.1%). Future protection efforts are recommended to prioritize the control of Cr and As concentrations in Nanning City.

Evaluation of surface water quality in Brahmani River Basin, Odisha (India), for drinking purposes using GIS-based WQIs, multivariate statistical techniques and semi-variogram models

Evaluation of surface water quality in Brahmani River Basin, Odisha (India), for drinking purposes using GIS-based WQIs, multivariate statistical techniques and semi-variogram models

Evaluating the water quality of the rice–fish co-culture pattern based on the modified NSF water quality index model

The rice–fish co-culture pattern, a sustainable agricultural production method, plays a significant role in enhancing farmland ecological and economic benefits. However, few studies have evaluated the water quality of this system using the water quality index (WQI). In this study, improvements were made to the United States National Sanitation Foundation WQI (NSFWQI), including indicator selection, weight calculation, and sub-indexing, using principal component analysis (PCA), a minimum data set (MDS), linear function equations, and a comprehensive water quality assessment of the double-cropping rice–fish (DRF) co-culture and (DR) rice patterns. The results showed that using PCA; pH, dissolved oxygen (DO), ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), and the Shannon–Wiener index were selected from 11 water quality indicators to construct the MDS for WQI calculation. Based on the WQI results, the DRF had a negative impact on water quality, with the Shannon–Wiener index the greatest contributor to the WQI (22.9 %), consistently ranging between 1 and 3. Although total phosphorus (TP) and chemical oxygen demand (COD) were not included in the MDS for the WQI calculation, their significant increase during the late rice season in the DRF indicated water quality deterioration, further validating the accuracy of the improved NSFWQI. The improved NSFWQI model provides a more objective analysis of water quality under the rice–fish co-culture pattern. Compared with existing water quality evaluation methods, this approach offers advantages such as comprehensiveness, low cost, and standardization, and presents an effective alternative for water quality assessment.

Hydrochemical characteristics, cross-layer pollution and environmental health risk of groundwater system in coal mine area: a case study of Jiangzhuang coal mine.

Long-term coal mining activities have significantly disturbed the groundwater system, resulting in aquifer water characterized by high levels of Na+, SO42-, and total dissolved solid (TDS), posing environmental health risks. To investigate the disturbance effects of coal mining activities on the groundwater system and ascertain the goaf water (OGW) environmental impacts, this study focuses on the surface water (SW), major aquifers, and OGW of Jiaozhuang Coal Mine. Through ion analysis and self-organizing map (SOM) clustering, the study analyzes the hydrochemical characteristics of the aquifer water, summarizes the accumulation patterns of OGW, and evaluates water quality of irrigation and drinking using sodium adsorption ratio (SAR), sodium percentage (SSP), and comprehensive pollution index (F). The results show that the hydrochemical characteristics of the groundwater system are influenced by a combination of cation exchange, dissolution, and mixing processes, with deep aquifers exhibiting high Na+ and SO42- levels. The OGW mainly originates from the coal roof sandstone aquifers water (RSW) and 3rd limestone aquifer water (3LW). Additionally, the groundwater shows high alkalinity and salinity hazards, with irrigation water quality assessments falling into general and unsuitable water quality area. Moreover, the groundwater quality is below Class III standards, with the worst being Class V, rendering it unsuitable as a drinking water source. Untreated discharge of OGW to the surface can easily threaten human drinking water health. The study results are helpful in identifying and controlling groundwater pollution caused by coal mining, ensuring the safety and sustainable utilization of water resources in mining areas and surrounding regions.

Five years after the Brumadinho dam collapse: Evaluation of water quality based on combined analysis of land use and environmental data

The collapse of the dam in the Paraopeba River watershed in 2019 triggered significant concerns regarding water quality in the region. This study aimed to assess, five years after the disaster, the effects on water quality and understand the underlying factors of environmental pressure contributing to the observed changes. To perform the evaluation, the study utilized surface water quality data pre-disaster (2012–2018) and post-disaster (2019–2023), environmental data regarding the identification of high-polluting potential industries operating in the region of interest, and land use for the watershed as a combined evaluation. Nonparametric statistical tests Kruskal-Wallis, complemented by Dunn's, were employed to assess the significance of changes in water quality parameters post-collapse. The results indicate a relatively stable baseline scenario of land use dominated by agriculture and pasture, with minor changes observed in forest cover and urban development. However, post-collapse assessments showed significant variations in water quality parameters, with turbidity exceeding conformity levels by up to 68 % (over 100 NTU), dissolved iron (Fe) by up to 70 % (over 0.3 mg.L–1), manganese (Mn) by up to 91 % (over 0.1 mg.L–1), dissolved aluminum (Al) by up to 83 % (over 0.01 mg.L–1), and lead (Pb) by up to 26 % (over 0.01 mg.L–1). Statistical tests suggested possible effects of the dam collapse on turbidity, pH, dissolved Fe, Mn, and dissolved Al. Temporal analysis showed constant effects on water quality, with notable increases in dissolved Fe and Mn concentrations observed upstream post-disaster and persistent impacts downstream. New mining activities licensed after 2019 may have contributed to the deterioration of water quality, highlighting the relevant relationship of anthropogenic activities and the environmental disaster in the Paraopeba River watershed.

Water Quality Evaluation and Monitoring Model (WQEM) Using Machine Learning Techniques with IoT

Water Quality Evaluation and Monitoring Model (WQEM) Using Machine Learning Techniques with IoT

Effects of environmental and biological characteristics on leaf decomposition under wetland restoration conditions

The implementation of ecological restoration projects, such as wetland rehabilitation, is essential for achieving sustainable development. However, current research on monitoring recovery status primarily focuses on evaluating water quality and aquatic community structure, with limited understanding of macroinvertebrate and microbial networks, as well as the functions of restored ecosystems. To address this gap, we compared the leaf decomposition rates of reed (Phragmites australis) and sedge (Carex appendiculata) in short-term (1, 2, and 3 years) and long-term (17 years) restoration wetlands, using natural wetlands as controls to assess ecosystem function. Our results indicate that the leaf decomposition rate in restored wetlands is significantly lower than in natural wetlands. No significant difference in decomposition rates was detected between the two types of leaves (P>0.05). All evaluation categories, including biological network structure, influenced leaf decomposition in different wetlands, with distinct effects observed on reed and sedge leaves. Compared to sedges, the decomposition of reed leaves exhibited a stronger correlation with macroinvertebrates while showing a weaker association with microorganisms and co-occurring network topological parameters. Overall, the execution of wetland restoration projects positively influences the recovery of biodiversity co-occurrence networks and ecosystem functions. These findings provide valuable insights for environmental and sustainability assessments.

Water quality assessment for aquacultivation in Quang Ngai city, Quang Ngai province

Aquatic product development and growth are significantly influenced by the water supply used for aquaculture. Evaluating the water quality available for this activity is vital since Quang Ngai city has been a major hub for the growth of aquaculture in Quang Ngai province. Twelve surface water quality parameters were monitored and analyzed at eight sampling points in the river basins of An Nghia and Tinh Khe communes from May to September 2023. The Water Quality Index (WQI) was calculated, and methods including Principal Component Analysis (PCA), calculation of environmental variable weights based on principal components (PC), and Pearson correlation were used to assess the correlation of the investigated factors. In the river basin of An Nghia and Tinh Khe communes, 12 criteria for the assessment of surface water quality were tracked and examined using 8 sampling locations between May and September in 2023. The degree of linkage was assessed using principal component analysis (PCA), weighting environmental factors based on principal components (PC), and Pearson correlation. The water quality index (WQI) was calculated between the factors of the research. The findings show that: (1) The parameters exceeded the permitted limit: DO, Alkalinity, NH4+, TP, Vibrio SPP, Coliform, TOC, and TN. The two metrics with the greatest levels of pollution among them are Vibrio SPP and NH4+. The WQI index ranges from 52-74 and is within the average water quality range (51-75); (2) PCA analysis reveals that only the first two principal components, PC1 and PC2 can account for 74.39% of the variance, with NH4+ (0.089) having the highest weight; (3) NH4+ and Vibrio SPP content have a strong correlation (r = 0.95), indicating that both of these parameters can be used to evaluate water quality in the study area in general.

Retrieve water quality parameters of urban rivers from hyperspectral images through feature interaction based two-stage method fused with spectral unmixing and spatial relatedness

Monitoring water quality through efficient quantification of water quality parameters (WQPs) is of paramount importance to environmental management of urban rivers. Unmanned aerial vehicle (UAV) remote sensing techniques posed a great opportunity for visualizing spatial distributions of WQPs concentrations with higher flexibility and monitoring frequency compared to satellite remote sensing techniques, assisting to trace potential contamination sources and prevent water quality from degradation. However, current methods of water quality monitoring usually involved large masses of water samples as training data to keep calculation accuracy every time their study area changed, increasing financial cost and incurring time delay in monitoring and evaluating water quality. This study proposed a UAV-based two-stage multidirectional fusion with probabilistic matrix factorization (TSMF-PMF) method in unified framework, to effectively quantify WQPs including chemical oxygen demand (COD), biochemical oxygen demand (BOD), and turbidity from UAV hyperspectral images. TSMF-PMF established feature interaction and outlier feedback module, ensuring relatively high calculation stability with less training samples. The experimental results showed that TSMF-PMF achieved good performance on predicting concentrations of WQPs with coefficient of determination (R2) and mean absolute percentage error (MAPE) ranging from 0.82 to 0.91 and from 5.35 % to 10.23 %. This study established theoretical and technical foundation to optimize environmental management scheme of urban rivers and provided an application demonstration.

Automated Water Quality Assessment Using Big Data Analytics

Water is one of the world's most precious resources, essential to life. Industrial waste, agricultural runoff, and urban discharge degrade water, rendering it unfit for consumption. Water quality monitoring and evaluation are more important than ever. Big Data analytics is used to examine water quality utilizing enormous datasets of pH, hardness, solids concentration, chloramine, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. This work classifies water potability, which is vital for human consumption, using strong machine learning on massive datasets. Classifiers were Random Forest, Gradient Boosting, and Support Vector Machine on 3,276 water bodies. The Random Forest classifier obtained the highest accuracy at 66.77% after significant data preparation and training, followed by Gradient Boosting at 66.01% and SVM at 62.80%. This shows that Big Data analytics and machine learning algorithms can interpret complex water quality data for public health and natural resource management. The Random Forest classifier and SVM in this study accurately calculate water potability. Prediction algorithms consider water cleanliness data and may aid public safety and water resource monitoring.

Water quality evaluation by WQI and ICOs for the rivers of Joya de los Sachas and Francisco de Orellana

A monitoring program enabled the evaluation of water quality and the simulation of the pollution dynamics of the rivers in the Joya de los Sachas and Francisco de Orellana cantons in Ecuador. For this assessment one used the WQI (water quality index) and the pollution indices ICOMO (organics contamination), ICOSUS (suspended solids), ICOMI (mineralization), ICOTRO (trophy system), and ICOpH (pH Index). The mathematical model operates with several input parameters, including dissolved oxygen, fecal coliforms, the biochemical oxygen demand (BOD5), turbidity, total solids, total phosphorus, pH, total nitrogen, suspended solids, alkalinity, conductivity, temperature, and the concentrations of nitrates, nitrites, and phosphates. WQI values were obtained from ICO pollution indices. The pollution of the Sacha area is significant, WQI = 0.292, which represents an advanced degree of pollution, since increased BOD values correspond to a high load of organic pollutants. They can originate from household, agricultural, industrial, and soil erosion waste. Likewise, the Sacha River has an ICOSUS = 0.16, which renders water treatment difficult, without impacting human health. Calculated WQI values reveal that the water quality of the Sacha, Basura and Coca rivers is low, being highly contaminated. In contrast, the Huamayacu, Jivino Rojo, Jivino Negro, and Jivino Blanco rivers are commonly polluted.

Machine Learning Based Inversion of Water Quality Parameters in Typical Reach of Rural Wetland by Unmanned Aerial Vehicle Images

Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R2 of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R2 and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas.

A Localized Evaluation of Surface Water Quality Using GIS-Based Water Quality Index along Satpara Watershed Skardu Baltistan, Pakistan

Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (K+, Na+, Mg2+, Ca2+), major anions (Cl−, SO42−, NO3−, HCO3−), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions.

Inisiasi Pengolahan Air Embung dengan Teknologi Multimedia Filter (MMF) sebagai Solusi Air Bersih di Kabupaten Bojonegoro

Clean water and proper sanitation are fundamental human needs. Nganti Village, located in Ngraho Subdistrict, Bojonegoro Regency, faces significant drought and clean water shortages, particularly during the dry season. The water quality remains subpar despite the presence of springs and water reservoirs. Consequently, processing raw water from the village's reservoir can offer a viable solution. This service activity aims to harness water reservoirs as a clean water source through multimedia filter (MMF) technology. The activities, conducted from 2022 to the end of 2023, encompass four stages: socializing the program plans, constructing multimedia filters (MMF), installing the water treatment unit on-site, and evaluating water quality, followed by necessary follow-ups. The primary activities involve installing water treatment units with MMF technology in collaboration with village officials and the community. The treated water is then tested for quality at the IPB University laboratory. Test results indicate that MMF technology significantly improves water quality, reducing parameters such as Magnesium, Manganese, Calcium, Iron, and Phosphate by 25.8‒97.8%, thus meeting quality standards. However, some parameters, such as TSS, COD, and Zinc, still exceed quality standards, necessitating further processing. This advanced water treatment requires involvement from the local government, particularly in funding through community empowerment programs, to install advanced treatment units and provide operational support for these units.

Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

To evaluate the pollution sources and dynamics of the upper reaches of the Tarim River, 10 typical sampling points were selected, and 23 water quality parameters from 2020 to 2022 were analyzed using one-way analysis of variance, the comprehensive Water Quality Identification Index (WQI), and Principal Component Analysis (PCA). The pollution status, sources, and contribution rates of water quality were investigated using the Absolute Principal Component-Multiple Linear Regression Model (APCS-MLR) and Positive Matrix Factorization (PMF). The results indicated that the water quality parameters of dissolved oxygen (DO), chemical oxygen demand (CODMn), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP), fluoride ions (F−), and ammonia-nitrogen (NH3-N) in the upper reaches of the Tarim River exceed standards, with noticeable spatial variations observed for each parameter. The water quality evaluation grades in the upper reaches of Tarim River primarily indicate “moderate” and “good” levels, with DO, TN, NH3-N, and electrical conductivity (EC) being the key parameters influencing variations in water quality. The source analysis results from APCS-MLR and PMF yielded similar outcomes, identifying six potential pollution sources. Among these, soil weathering, livestock and poultry breeding, and agricultural activities exhibited higher contribution rates. Specifically, the contribution rates for these sources according to APCS-MLR were 44.11%, 19.63%, and 11.67%, respectively; while according to PMF they are 24.08%, 17.88%, and 27.54%, respectively. Furthermore, industrial pollution sources contributed at a rate of 6.01% according to APCS-MLR, while urban living sources contributed at a rate of 2.13%. However, based on PMF analysis, the contribution rates for industrial pollution sources increased significantly to 16.71%. Additionally, APCS-MLR identified natural sources as contributing at a rate of 16.45%, whereas PMF suggested that a combination of agricultural activities and natural sources contributed at a lower rate of only 9.52%. In conclusion, the water quality within the upper reaches of the Tarim River is predominantly satisfactory. Nonetheless, localized pollution, primarily attributable to human activities, presents a substantial challenge. These observations provide critical insights into improving and protecting the fragile water quality of the Tarim River.

Evaluating water quality of rock glacier outflows in the Western Alps, Italy: a regional perspective.

Intact rock glaciers (RG) are considered valuable water storage because containing permafrost ice volumes. The hydrological relevance of RG is forecasted to increase with respect to glaciers under climate change scenarios, as well as RG's role as water resources in alpine basins for multiple uses. Besides the assessment of water amount stored in intact rock glaciers, the evaluation of water quality is of primary importance. Here, we present the results of a chemical survey performed on five outflows from intact RG in 2020-2023 in the Piedmont region, Western Alps, Italy, along a latitudinal gradient and in different geological settings. The survey aimed to assess the water quality of RG outflows based on chemical indicators (major ions, nutrients, trace metals). Sampling and analyses were performed according to standard methods for freshwater samples, paying specific attention to the analytical quality and consistency of the data. We considered seasonal and interannual variability of the main chemical variables and the possible effects of RG outflows on the chemistry of lakes and ponds located in proximity to the RG. All the investigated sites were characterized by low to moderate ion content, low nutrients, and trace metals close to or below the detection limit, indicating a good water quality status. Results suggested lithology as the main factor affecting the chemical composition of RG outflows. The results of this study indicate it is advisable to develop shared protocols and joint monitoring programs for data collecting at RG outflow sites all over the Alps, possibly integrating chemical and biological indicators, with the final aim of monitoring the water quality of these valuable resources and its temporal evolution under climate change.

Assessment of Climate Change Impacts on Hydrology Using an Integrated Water Quality Index

Traditional Water Quality Indices (WQIs) often fail to capture the significant impact of flow velocity on water quality, especially under varying hydrological conditions. In this study, an Integrated Water Quality Index (IWQI) was developed by combining water quality parameters and flow rate, providing a more comprehensive assessment under various flow conditions. Compared to traditional indices, the IWQI showed slightly lower correlations in individual parameter performance, but it performed well in evaluating water quality changes associated with flow variations. Parameters such as Total Phosphorus (TP), Total Coliforms (TC), and Fecal Coliforms (FC), which are prevalent pollutants in the Cheongmi River, significantly influenced IWQI scores. River water quality was evaluated using input data simulated under a climate change scenario. When precipitation was abundant, the IWQI score remained relatively stable even with reduced flow rates. However, during periods of insufficient rainfall, water quality deteriorated sharply. While general water quality parameters exhibited approximately a 10% change as flow decreased, TC and FC showed rapid deterioration, with change rates ranging from 20% to 60%. These findings underscore the importance of managing TC and FC, particularly when insufficient rainfall is predicted, as they are major sources of pollution.

Irrigation suitability assessment of urban wastewater for sustainable agriculture: a case study of Musi river, India

In urban areas with dense populations and high water demands, effective management of water resources is crucial for sustainable development. It includes optimizing water usage and recycling wastewater to balance supply and demand. Urban rivers, which now carry sewage year-round due to increased urban water consumption, are increasingly used for irrigation in peri-urban regions. This study aims to assess the suitability of an urban stream for irrigation compared to catchment groundwater, using hydrochemical parameters to evaluate water quality indices. Analysis of major ion distribution from upstream to downstream reveals a significant increase of TDS (r2 = 0.65; p < 0.001) in the surface water and a decrease (r2 = 0.93; p < 0.001) in groundwater samples. The calculated Weighted Arithmetic-Water Quality Index (WA-WQI) indicates that around 85% and 75% of surface water and groundwater samples from the Musi river are classified as very poor to unsuitable for any use, highlighting critical pollution levels. Based on irrigation suitability indices, only 16% of the surface water samples and 60% of the groundwater samples are suitable for irrigation. The nitrate concentration increased from less than 10 mg l−1 in the upstream region to over 100 mg l−1 in the urban stretch. Nitrogen-Phosphate-Potassium (NPK) ratios across the basin are exceptionally higher, representing crops irrigated using the Musi river water might not require fertilizer applications. The adoption of fertilizer application concerning supplied water hydrochemistry, particularly the NPK ratio, might limit the excess usage of fertilizer and benefit farmers. Additionally, this practice may also limits the excess input of nitrate to the groundwater in the Musi river basin.