Water Quality Assessment Research Articles

Ion-chromatographic determination of common anions in drinking water in some regions of the Republic of Armenia

The present study investigated the anion composition of drinking water from various cities and villages in Armenia, including spring water and tap water samples. The simultaneous separation was achieved for fluoride, chloride, bromide, nitrite, nitrate, sulfate, and phosphate anions with the correlation coefficients >0.998. Validation of the method was carried out in accordance with the requirements of guidelines in terms of selectivity, specificity, system suitability, linearity, accuracy, precision, lower limits of detection and quantitation, robustness, and stability. The method was suitable for routine drinking water quality assessment in line with the national or World Health Organization (WHO) regulations. The validated method was further used to analyze drinking water samples from different regions of Armenia. The observed concentrations for the anions in drinking water complied with the WHO limits for drinking water with some exceptions. The test method can be submitted to quality control laboratories as a convenient and practical tool for routine analysis and monitoring studies.

Assessment of water quality in the piped water supply system by using Water Quality index method

Background Drinking water of the right and approved quality is a basic requirement for the development of any civilization. According to SDG 6 it is crucial to provide every citizen with equitable water quality and quantity. Methods The study area is the smart city of Bhubaneswar with 67 wards and three zones: the north zone, south-west zone, and south-east zone with around 12.4 lakh population. To maintain the water quality in the supply pipeline in this city, which is always assumed to be safe, it needs to be examined within certain time intervals to check the contamination. In this context, studies on water quality parameters in the supply pipeline network from different anticipated vulnerable areas have been collected for testing. In this context, water samples were collected from areas near industries, market complexes, educational institutions, and construction sites of each ward of Bhubaneswar. Nearly 10 water quality parameters were tested and analyzed using the weighted arithmetic water quality index method. This method takes input of all the parameters and provides the overall water quality index value, which classifies the water in different grades like excellent, good, poor, and very poor quality. Results According to the WHO (2011) guidelines, the study found that there is a deviation in the parameters like 10.78% in pH, 19.48% in dissolved oxygen, 43.88% in conductivity, and 22.95% in hardness from the standard limit, but the overall water quality index indicates the water is not in the poor and very poor range. Also, water quality index values identify that the water in the south-east zone is excellent compared to the north and south-west zones. The reason for the slight deterioration is due to the underground cable works, road works and also some areas where the old pipeline system is still existing due to personal encroachment of people as per Public health Engineering department.

Exploring riverine aquatic animal diversity and establishing aquatic ecological monitoring approaches tailored to the Qinling region via eDNA technology.

This study thoroughly examines biodiversity and aquatic ecosystems across 14 sampled sites within the Shitou River basin by utilizing environmental DNA technology. Through integrated analysis and high-throughput sequencing, the study elucidates a diverse array of biodiversity, encompassing 27 fish species and 341 freshwater benthic macroinvertebrates (FBM) species. Using various biodiversity indices, we found significant differences in diversity and stability across different environments. Regions with more complex habitats had higher species richness and evenness. Further analyses showed complex relationships between diversity metrics for FBM and fish, indicating potential interactions between these groups. The standardized mean score (SMS) was developed to aid in the assessment of water quality. Specifically, SMS scoring revealed that sites STH3, STH4, and STH14 excelled across multiple dimensions, earning an "Excellent" rating, while site STH12 was rated as "Poor" due to subpar performance across several metrics. This project not only enhances current understanding regarding aquatic ecological dynamics but also establishes a strong foundation for detailed environmental evaluation and monitoring, aligned with the priorities of contemporary ecological management and caution.

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.

Assessment of exurban expansion on water quality in Rock Creek of the Yellowstone River in southcentral Montana, USA

Exurban expansion can degrade significantly stream water quality. This study was conducted on Rock Creek of the Clark’s Fork of the Yellowstone River in southcentral Montana, USA. The impact from recent exurban expansion was evaluated by a baseline study measuring total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN) and nitrites (NO2) and nitrates (NO3) together. This was part of a larger study by Montana Department of Environmental Quality to assess water quality, identify sources of pollution and develop total maximum daily loads (TMDL). Water samples were measured for these analytes at nine (2022) and 11 (2023) locations with different land-uses over seven periods (both years). Seasonal spikes, corresponding to runoff (2022, late May), occurred in TSS, TP and TN. Data from 2023 showed similar but reduced levels occurring in June for TSS and TP. Total suspended solids, TP, NO2 and NO3 concentrations increased by location year over year, 2022 to 2023. The high relative concentrations of NO2 and NO3 in Rock Creek are primarily associated with exurban expansion, a significant threat to stream quality and wildlife habitat in Montana. The results will assist county planners in land use decisions.

Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics. Data synchronization and fusion are achieved through recursive KF methods, enabling real-time adaptive management in response to environmental fluctuations such as seasonal changes, precipitation (6–18%), and evaporation rates (1.2–11.9 mm/day). Over a 30-day evaluation period, the model accurately predicted chlorophyll concentrations, reaching 128 mg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mg}/\\hbox {m}^{3}$$\\end{document} in mid-level inflow regions (3.6 m water elevation) compared to 86 mg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mg}/\\hbox {m}^{3}$$\\end{document} in extreme inflow areas (5.5 m). The integration of Markov feature extraction and eigenvalue estimation enhanced prediction stability and sensitivity, with the KF maintaining computational efficiency at 7.8 ms per computation cycle. The model’s accuracy was validated by achieving a residual error of less than 0.05 with minimal noise interference. Overall, the system provides a resilient and precise framework for real-time lake water quality assessment, capable of handling multi-parameter uncertainties and dynamic environmental changes, thereby supporting informed decision-making for aquatic ecosystem management.

Python-Based Water Quality Evaluation of Tranza and Albarika Wells

Introduction: The increase in waterborne diseases in certain remote areas of Benin led the study to conduct surveys on the different waters used by these inhabitants. Aims: This study is conducted to assess the probable risks related to the physicochemical and bacteriological quality of well water consumed by the population of the Tranza and Albarika neighborhoods. Place and Duration of Study: Water samples were collected from 20 wells (open and closed in Albarika and Tranza districts) in the Parakou commune and analyzed at the LaKReCA laboratory between April and July 2024. Methodology: A two-stage methodology was used to approach this study. Thus, a survey was conducted with the aim of determining the actual fraction of the population using well water as drinking water. The physicochemical and bacteriological parameters were determined according to standard normalization in order to assess water quality using Python software for the statistical study. Results: The results show that 26.66% of the inhabitants of the Tranza and Albarika districts are subscribers to SONEB against 20.01% who use well water as drinking water. In addition, the waters are colored (16.16±5.86 Pt/Co), cloudy (69±3.22 NTU), loaded (678.58±6.07 µS/cm), rich in magnesium (132.42±7.87mg/L), calcium (52.73±1.68mg/L), chlorine (88.23±1.96mg/L) and have high alkalinity contents (194.49±16.57mg/L). They are hard (185.09±5.09 mg/L) and can cause waterborne diseases depending on their total coliform (23 CFU) and E. coli (5 CFU) contents. The correlation between the parameters shows that a magnesium content equal to 67 mg/L leads to a high value of total coliform and calcium in well water. Conclusion: Ultimately, the origin of the pollution can be attributed to sanitation, household waste, the transfer of pollutants from surface layers, the conditions of drawing water and the structure of the wells. In the long term, the use of well water could constitute a significant health risk for the majority of the inhabitants of Tranza and Albarika in the city of Parakou.

Application of time series and multivariate statistical models for water quality assessment and pollution source apportionment in an Urban River, New Jersey, USA.

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23-30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources.

Application of Heavy Metal Pollution Index (HPI) of Water Quality Assessment in Tengi River

Application of Heavy Metal Pollution Index (HPI) of Water Quality Assessment in Tengi River

An Assessment of Water Quality and Pollution Sources in a Source Region of Northwest China

China prioritizes ensuring drinking water safety, particularly in the water-scarce northwest region. This study, utilizing water quality data from 52 village and town water sources since August 2022, assesses water quality, with a specific focus on key indicators related to organic pollution sources. This study provides a scientific foundation for enhancing water quality in these sources. Employing category factor analysis for classification and grading, principal component analysis for qualitative analysis of key evaluation indicators, and the absolute principal component linear regression equation for quantitative calculation of pollution sources, this study reveals that all 52 water sources meet quality standards. Principal component analysis categorizes pollution sources as diverse types of organic compounds in surface water. Source analysis calculations highlight decay-type organic substances as major contributors to increased water color and permanganate index, with pollution contribution rates of 54.78% and 31.31%, respectively. Fecal-type organic substances dominate the increase in dissolved total solids and total coliforms, with pollution contribution rates of 56.65% and 40.16%, respectively. Additionally, high-molecular-weight organic substances exhibit lower concentrations in the water. This article presents a systematic water quality assessment methodology, which is used for the first time to qualitatively assess the types of water sources and to quantitatively trace specific sources of organic pollution in source water in northwest China. This systematic study’s results, involving initial assessment followed by traceability, recommend the adoption of a simple contact filtration and disinfection process to enhance water quality in the region.

Deep Learning Empowered Water Quality Assessment: Leveraging IoT Sensor Data with LSTM Models and Interpretability Techniques

Addressing the imperative demand for accurate water quality assessment, this paper delves into the application of deep learning techniques, specifically leveraging IoT sensor datasets for the classification and prediction of water quality parameters. The utilization of LSTM (Long Short-Term Memory) models navigates the intricacies inherent in environmental data, emphasizing the balance between model accuracy and interpretability. This equilibrium is achieved through the deployment of interpretability methods such as LIME, SHAP, Anchor, and LORE. Additionally, the incorporation of advanced parameter optimization techniques focuses on fine-tuning essential parameters like learning rates, batch sizes, and epochs to optimize model performance. This comprehensive approach ensures not only precise predictions but also enhances the transparency and interpretability of the model, addressing the critical need for actionable information in water quality management. The research significantly contributes to the convergence of deep learning, IoT, and environmental science, offering valuable tools for informed decision-making while highlighting the importance of fine-tuning parameters for optimal model performance

An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the “11th Five-Year Plan” and “12th Five-Year Plan” periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin’s overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the “11th Five-Year Plan” and “12th Five-Year Plan” periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin.

Water quality assessment using A Novel Iraqi water quality index for Euphrates River between Karbala and Babylon provinces as a case study

تواجه أجزاء كثيرة من العالم مشاكل ندرة المياه بسبب محدودية الموارد المائية تزامنا مع تزايد عدد السكان. الأنهار هي أنظمة حيوية للمياه العذبة والتي تعتبر بالغة الأهمية لاستمرار الحياة. يتناول هذا العمل مراقبة وتقييم نوعية مياه نهر الفرات بين محافظتي كربلاء وبابل لعشرة مواقع بالقرب من تصريف المنشآت الصناعية وتصريف الأراضي الزراعية ومياه الصرف الصحي ومرافق اخرى ، وتم أخذ العينات شهرياً ومن ثم تحويل النتائج إلى موسمين (جاف – رطب). تم إجراء العديد من التحاليل الفيزيائية والكيميائية والميكروبية بناءً على الطرق القياسية. تم قياس المعلمات التالية (الدالة الحامضية, المتطلب البايلوجي للاوكسجين, الأوكسجين المذاب, درجة حرارة الماء,القولونيات الكلية, العسرة الكلية, الكبريتات, النترات, الكلورايد, الصوديوم, الفوسفات, كاربونات الصوديوم المتبقية, نسبة امتزاز الصوديوم, نسبة الصوديوم المذابة, خطورة المغنيسيوم, البورون, النتريت والمغنيسيوم). وباستخدام مؤشر جودة المياه العراقي الجديد الذي يتناسب مع النظم المائية العراقية ويعكس الواقع البيئي للمياه العراقية، يتضمن مؤشر IWQI الجديد المكونات المذكورة أعلاه، وأظهرت النتائج أن نوعية مياه نهر الفرات في المواقع المدروسة تراوحت بين (رديئة - سيئة للغاية) بالنسبة للمياه العذبة أما بالنسبة لملائمة المياه للزراعة والحياة المائية والشرب فقد تراوحت بين (جيد – غير صالح) خلال المواسم الرطبة والجافة. وبناء على هذه النتائج نستنتج أن المنشآت الصناعية ومياه الصرف الصحي في المدن والمجتمعات الكثيفة التي تعيش على طول ضفة النهر تؤثر سلبا على نوعية مياه النهر.

Surface water quality assessment and probable health threats of metal exposure in the Tano South Municipality, Ahafo, Ghana

The prime goal of this study was to assess the surface water quality and the associated health risks for residents in the Tano South Municipality, Ghana. The WHO drinking water criteria were compared with the findings of an analysis of eight surface waters for parameters such as physiochemical parameters, nutrient levels, and concentrations of selected metals. An evaluation was performed regarding the potential non-carcinogenic and carcinogenic risks associated with metal exposure by oral and dermal absorption. The estimated water quality index indicated that seven water samples were deemed unsuitable for human consumption, while one sample indicated very low water quality. The fluoride levels in all water samples were below the limit of detection, although it guards against dental caries. All of the water samples had mean concentrations of Cd and Fe above WHO guideline values, while one water sample had a Pb concentration that was higher than recommended. Principal component analysis showed that aside from the natural source, human-induced sources such as runoff of excess chemicals and soil erosion from adjacent farm soils were responsible for the substantial levels of contaminants in surface water samples. There was a possibility of non-carcinogenic consequences for children in seven out of eight water samples. However, cancer risk for Cd and Pb was not likely for adults and children in the study area. Findings serve as a representative case study for other districts and call on water managers to treat surface waters to guard against harmful health consequences and safeguard the designated buffer zones.

Remote Sensing Inversion of Water Quality Grades Using a Stacked Generalization Approach

Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution.

Performance of Machine Learning, Artificial Neural Network (ANN), and stacked ensemble models in predicting Water Quality Index (WQI) from surface water quality parameters, climatic and land use data

Assessing water quality is essential for managing freshwater resources, safeguarding ecosystems, and guaranteeing public health. Traditional water quality assessment methods suffer from seasonal sampling, multi-parameter requirements, and labor-intensive sampling processes, which are major constraints for the frequent monitoring of vast river basins. To overcome this issue, the study modeled the remote sensing-based climatic and land use parameters with Principal Component Analysis (PCA) to leverage Artificial Neural Networks (ANN) and machine learning (ML) algorithms to predict the Water Quality Index (WQI). The Weighted Arithmetic Water Quality Index (WAWQI) method was used to calculate the WQI of the Godavari River Basin for the available 19 stream water quality parameters (SWQPs). Further, PCA was applied to reduce the dimensionality of the parameters from 19 to 6. These results led to the development of two modeling methods to predict the WQI. In the first method, the correlation-based model was developed to predict WQI by evaluating six SWQPs. The second method, the causal-effect model, uses land use and meteorological factors to determine WQI using causality. Using advanced AutoML techniques, the initial pool of 40 ML models was meticulously evaluated and refined, culminating in the selection of the top three exemplary models such as Extreme Gradient Boosting (XGB), Extra Trees (ET), and Random Forest (RF). In both methods, XGB models show better prediction, with the coefficient of determination (R2) value of 0.95 during training and 0.83 during testing in method one. Whereas in the second method, R2 of 0.93 in training and 0.80 in testing are obtained. Further, XGB, ET, and ANN outputs were stacked with each model to enhance these results in both methods. Among these three stacked models, the stacked ANN_ML model performed better compared to stacked XGB_ML and stacked ET_ML. In the first method, the stacked ANN_ML model predicts R2 values of 0.95 and 0.91 for training and testing. In the second method, 0.95 and 0.90 for training and testing are obtained using stacked ANN_ML model. These findings emphasize the stacked model prediction ability to capture nonlinear relationships in the parameters and the novel approach of land use and climate parameters based WQI prediction, which replace the laborious, time-consuming SWQP measurements.

Viability and Profitability of Cage Fish Farming on Lake Victoria: A Case of Bugiri-Kiwuulwe Cage Fish Farms in Wakiso District, Uganda

Fisheries and aquaculture have crucial contributions to world’s wellbeing and prosperity. Among aquaculture practices, cage fish farming ranks highest in fish production. This study was set out to assess viability and profitability of cage fish farming on Lake Victoria, a case of Bugiri-Kiwuulwe cage fish farms in Katabi Town Council, Wakiso District, Uganda. The study sought to assess water quality physico-chemical parameters within and around fish cages, investigates costs of materials used in fish cage fabrications and production, and to find profitability of cage fish farm units. A case study design with quantitative and qualitative approaches were employed to obtain data for the stated objectives. A total of 66 respondents from a target population of 79 study participants were used to generate data, which were entered into SPSS version 23 for analysis. Both male and females, including a few youths participated in the study. Their education levels ranged from primary to university. Majority were single, while others were married and a few were widows and widowers. Most of the water parameters tested were in the recommended ranges by WHO and supported aquatic life, hence promoted cage fish farming apart from total hardness, calcium and magnesium hardness, calcium, chemical oxygen demand and potassium which were slightly below the recommended ranges. Majority of the farmers had participated in the business for less than 5 years and the activity brought profits to the farmers an equivalent of UGX 8,943,200 ($ 2,354) to the farmers per season, an indicator that the business venture is very profitable and viable. Hence, it can easily be adopted by members of the community for improvement of their livelihoods. It is recommended that more research be undertaken such that there is formulation of the fish feeds at the local level aimed at the reduction of the higher costs incurred on the imported fish feeds. The government is also encouraged to subsidize on the taxes imposed on implements used in cage fish farming for encouraging more and more farmers to join in the business venture.

Domestic and irrigation water quality on the southern slopes of Mount Kilimanjaro

This study assessed the water quality for drinking and irrigation purposes on the southern slopes of Mt. Kilimanjaro during the dry season under low flow conditions. Fifty-one samples covering eight different water sources (i.e., stream water from natural and anthropogenic impacted streams, domestic water, spring water, rainfall, groundwater, lake water and water from irrigation canals) were collected in a snapshot sampling campaign over 10 days in February 2023. First, physical, chemical and biological parameters were analysed and compared with Tanzanian and international drinking and irrigation water quality requirements. The samples were then ranked according to their suitability for drinking and/or irrigation using water quality indices (WQI). All drinking water quality parameters except for E. coli and turbidity were within the permissible limits. A generalised problem of faecal contamination was found in the study area, including in domestic water, highlighting the need to identify sources of contamination and remediate them before distribution. The drinking water quality index (DWQI) classified 89% of the samples as unsuitable and 11% as excellent for drinking. Irrigation water quality parameters were within the guidelines of restriction of use except for pH in 5 samples. In contrast to the DWQI, the vast majority of the water samples (88%) can be used for irrigation without restrictions according to the irrigation water quality index (IWQI). The suitability of water for irrigation was also assessed using three indices, i.e., Kelley’s Index, Soluble Sodium Percentage and Magnesium Ratio, which indicated potential problems with excess of sodium (about 30% of the samples) and magnesium (about 20%). Further studies combining suitability indices, soil characteristics and crop types are recommended to assess water quality for irrigation use.

Modelling water quality parameters using model tree, random forest, and non-linear regression for Mula-Mutha River, Pune, India.

Evaluation of vital water-quality indicators, especially biological and chemical demand of oxygen (BOD and COD), is important for environmental factors, human health, and agricultural output. In the recent past, data-driven techniques (DDT) offer the ability to automate water quality assessment with more reliable and rapid evaluation. The present study thus aims to utilize various DDTs: random forest (RF), model tree (MT), and non-linear-regression (NLR) to predict vital water quality indicators such as BOD and COD for the three stretches of Mula-Mutha River, Pune, India. Since the river has three stretches: Mutha, Mula, and Mula-Mutha respectively, BOD-COD models have been developed separately for each using MT, RF, and NLR. Data analysis using a violin diagram is done to understand the data characteristics. Further, the models developed were developed using the appropriate input parameters for predicting BOD and COD. Error measures including coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the constructed models. The Taylor diagram, scatter plot, and hydrograph were also used for visual performance analysis. The findings suggest that the MT and RF techniques exhibit a stronger connection between the actual and anticipated levels of BOD and COD, with NLR following closely behind. Practical acceptance of these approaches is increased by RF in the form of trees, MT with an output in the form of a sequence of equations, and NLR with a single equation. These findings help us gain insight into DDT's water quality assessment model, which will also help future researchers and water quality professionals make decisions.

Identifying groundwater characteristics and controlling factors in Jiaozhou Bay’s northern coastal region, China: a combined approach of multivariate statistics, isotope analysis, and field empirical investigations

Explicit identification of hydrochemical processes and their controlling factors within groundwater systems is critical for the sustainable utilization of water resources in coastal urban areas. This study was undertaken in the North Coastal Region of Jiaozhou Bay (NCRJB), located in the eastern part of Shandong Province, China, an area grappling with significant issues of groundwater quality degradation and water scarcity. A total of 105 groundwater samples and 34 surface water samples, collected from 2020 to 2024, were analyzed and studied using various hydrogeological tools, multivariate statistical analyses, and water quality assessment methods. These include the Piper diagram, hydrochemical facies evolution diagram (HFE-D), Principal Components Analysis (PCA), correlation analysis, stable isotope analysis, Water Quality Index (WQI), and USSL diagrams. The results indicated that all surface water and pore groundwater samples were categorized as Na-Cl type, exhibiting high Total Dissolved Solids (TDS) and Electrical Conductivity (EC) values, characteristics that render them poor to unsuitable for drinking and irrigation purposes. The fracture groundwater is predominantly of the Ca-Na-Cl mixed type, with average suitability for irrigation and a limited proportion (22.5%) deemed suitable for drinking. Seawater intrusion, primarily through the surface water system, and the impact of human activities were identified as the predominant controlling factors con-tributing to the degradation of the local groundwater environment. Field empirical investigations further validated the results derived from hydrogeological assessments, multivariate statistical analyses, and isotopic approaches. The long-term shifts in hydrochemical properties, along with the latent threat of seawater intrusion, exhibit an upward trend during the dry season and show a certain degree of mitigation during the wet season. This study highlights that field investigations, in conjunction with hydrochemical tools, multivariate statistical analyses, and stable isotope analysis, can successfully furnish reliable insights into the predominant mechanisms governing regional groundwater evolution within the context of long-term and intricate envi-ronmental settings.