Surface Water Quality Index Research Articles

Validation of physicochemical water quality results through cross-checking with biological indices

ABSTRACT Biological assessment of rivers is critical for evaluating ecosystem health, detecting ecological changes, validating physicochemical monitoring data, assessing the effectiveness of water treatment systems, conserving biodiversity, and guiding water resource management. In this study, the surface water quality of the Karkheh River basin was evaluated using the National Sanitation Foundation Water Quality Index (NSFWQI) and the Iranian Surface Water Quality Index (IRWQIsc) for qualitative analysis. For biological monitoring, the Biological Monitoring Working Party (BMWP) index, along with the Average Score per Taxon (ASPT) and the Hilsenhoff Biotic Index (HFBI), was employed to provide a comprehensive assessment. Water quality was analyzed across 18 sampling stations, revealing significant spatial variation. Upstream areas exhibited relatively good conditions, with NSFWQI and IRWQIsc values of 59 and 68, respectively. In contrast, downstream locations near urban and agricultural areas demonstrated poor water quality, with values of 27 and 33, respectively. Overall, approximately 70% of the river's length has been severely impacted, highlighting the need for targeted planning and management to mitigate pollution. The biological indices corroborated the physicochemical results, revealing severe ecological stress in polluted regions. The close alignment between the NSFWQI and biological indicators emphasizes the importance of a multifaceted approach in assessing aquatic health. Key factors contributing to the river's degradation include excessive water extraction, untreated wastewater discharge, and diminished self-purification capacity, particularly in downstream areas.

Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model.

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.

Water quality forecasting using a Novel Hybrid DNN-MBGD optimization and WAWQI technique for assessment of surface water quality index in 10 districts of Uttar Pradesh

Water quality forecasting using a Novel Hybrid DNN-MBGD optimization and WAWQI technique for assessment of surface water quality index in 10 districts of Uttar Pradesh

Application of artificial intelligence for forecasting surface quality index of irrigation systems in the Red River Delta, Vietnam

Water sources for irrigation systems in the Red River Delta are crucial to the socioeconomic growth of the region's communities. Human activities (discharge) have polluted the water source in recent years, and the water source from upstream is limited. Currently, the surface water quality index (WQI), which is calculated from numerous surface water quality parameters (physical, chemical, microbiological, heavy metals, etc.) is frequently used to evaluate the surface water quality of irrigation systems. However, the calculation of the WQI from water quality monitoring parameters remains constrained due to the need for a large number of monitoring parameters and the relative complexity of the calculation. To better serve the assessment of surface water quality in the study area, it is crucial and essential to conduct research to identify an efficient and accurate method of calculating the WQI. This study used machine learning and deep learning algorithms to calculate the WQI with minimal input data (water quality parameters) to reduce the cost of monitoring surface water quality. The study used the Bayes method (BMA) to select important parameters (BOD5, NH4+, PO43−, turbidity, TSS, coliform, and DO). The results indicate that the machine learning model is more effective than the deep learning model, with the gradient boosting model having the most accurate prediction results because it has the highest coefficient of determination R2 (0.96). This is a solid scientific basis and an important result for the application of machine learning and deep learning algorithms to calculate WQI for the research area. The study also demonstrated the potential of artificial intelligence algorithms to improve water quality forecasting compared to traditional methods with minimal cost and time.

Spatial interpolation of water quality index based on Ordinary kriging and Universal kriging

Water is a very vital needed substance in order to maintain the important activities of humans. However, contaminated water can transmit diseases such as typhoid, dysentery, diarrhea, cholera and polio. Pakistan is a highly affected country due to the scarcity of safe and healthy water sources. The current study mainly focuses to determine water quality in the selected area. For this purpose, an integrated surface water quality index (SWQI) based on 18 hydrochemical parameters is employed. SWQI substantially correlates with pH, EC, SO4, HCO3 and heavy metals. Therefore, these parameters are utilized in the calculation of SWQI. The results of SWQI show that about 69.23% of samples are ‘very good quality’. Moreover, 30.77% of the total samples are of poor quality and are identified as unsuitable for drinking. Further, Ordinary kriging (OK) and Universal kriging (UK) are used to predict the SWQI at unobserved locations and map the SWQI in the study area to explore the spatial distribution. The prediction performance of the two kriging methods is assessed through cross-validation in terms of root mean square prediction error (RMSPE). It is concluded that the prediction performance of the UK is better than OK in terms of RMSPE.

Evaluation of Surface Water Quality Indices in Mthatha River Using Multivariate Statistical Techniques

Mthatha town of Eastern Cape Province, South Africa has been challenged to address the pollutant issues that are coming from rampant densification and effluent concentration discharge from the Mthatha Correctional Services Centre and the Efata School for the Blind and Deaf which have caused ineffable impaired damage to the Mthatha River Catchment (MRC). This paper is aimed at identifying drivers of poor water quality in the catchment and classified the River’s water quality into different cluster groups for proper pollutant source control measures. Water quality parameters data comprising of pH; conductivity; Phosphorus; Ammonia (NH4-N); Feacals; and E-coli covering 95 percent and 105 percent of the upstream and downstream sections of the River were available at ten monitored sites of the river catchment. These datasets covering eight years 2012-2020 were analysed in this study. Factor analysis as a choice of principal component analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) was used to deduce inferences for the pollutants’ subsequent classification. The results classified the catchment into three different clusters of lower pollutant (LP), medium pollutant (MP), and high pollutant (HP) areas, with PC1 accounting for 84.54% of the total variance from the three components classification. Adaptive catchment managers would find usefulness in the employed statistical tools in ensuring real-time measures for river non-point pollutants sources control that could offer additional benefits in maintaining a safe life above and below water in the preservation of their public values benefit. The study recommends the issuance of compliance notices and non-point pollutant source control measures to improve the water quality (WQ) parameters.

Application of multivariate statistical methods to enhance the water quality monitoring system of Kashmir Valley with special emphasis on side-stream pollution

Abstract Surface waterbodies, on which the growing population of Kashmir Valley is reliant in a variety of ways, are increasingly deteriorated due to anthropogenic pollution from rapid economic development. This research aims to assess the quality of the surface waterbodies in the north-eastern region of Kashmir Valley. Standard analytical procedures were used to analyze the water samples taken from 11 distinct sampling stations for 14 physicochemical parameters. The results were compared with the standard permissible levels which showed that the water quality of rivers and lakes in the north-east Himalayan region has steadily declined. Furthermore, multivariate statistical techniques were used with the goal of identifying key variables that influence seasonal and sectional water quality variations. The analysis of variance (ANOVA) analysis revealed that there is substantial spatio-temporal variability in the water quality parameters. According to principal component analysis (PCA) results, four primary components, which together accounted for 79.23% of the total variance, could be used to evaluate all data. Chemical, organic, and conventional pollutants were found to be significant latent factors influencing the water quality of rivers in the study region. The results indicate that PCA and ANOVA may be used as vital tools to identify crucial surface water quality indices and the most contaminated river sections.

Influence of Urban and Residential Areas on Surface Water Quality in the Vietnamese Mekong Delta Coastal Province

This study was conducted to assess the impact of residential and urban activities on surface water quality in Ben Tre province in 2021. Water quality data were collected in the dry and rainy seasons at 15 locations for evaluating the parameters of temperature, pH, dissolved oxygen (DO), salinity, turbidity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), ammonium (N-NH4+), nitrate (N-NO3−), orthophosphate (P-PO43−), iron (Fe), coliform using national technical regulation on surface water quality (QCVN 08-MT: 2015/BTNMT, A1) and surface water quality index (VN_WQI). Principal component analysis (PCA) and cluster analysis (CA) methods were used to evaluate sampling sites and identify key parameters and potential pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, Fe and coliform are pollutants significantly affect surface water quality in residential and urban areas. Salinity, nutrients, heavy metals and microorganisms had high values in the dry season, while turbidity, suspended solids, dissolved oxygen, and organic matter were higher in the rainy season. Water quality was assessed from moderate to very good indicated by VN_WQI index. PCA results revealed that five key factors explaining 87.2% of the variation and 12 main parameters (temperature, salinity, turbidity, TSS, DO, BOD, COD, N-NH4+, N-NO3−, P-PO43−, Fe and coliform) affecting water quality. CA results showed that the current monitoring locations can be reduced from 15 locations to 12 locations, which could save 20% of monitoring costs. Future study should focus on investigating concrete sources of water pollution for appropriate management measures.

Assessment of Surface Water Quality Index of Gwalior-Chambal Region Using Fuzzy-Based Approach

Assessment of Surface Water Quality Index of Gwalior-Chambal Region Using Fuzzy-Based Approach

Biophysical and Socioeconomic Characterization of the Timari River Micro-basin in the Central Tropics of Peru

The study was carried out in the Timari river micro-basin belonging to the Río Negro district of the Satipo province, with the objective of evaluating the influence of the vulnerability to contamination of surface water resources in the Timari-Río Negro river micro-basin. The results in the physical aspect were: area 1061.16 ha with length of channel 7.36 km and 44 tributaries, slope of the channel 12%, mean elevation of 1176.47 meters above sea level; the biophysical aspect presents: temperature from 19.87°C to 24.16°C, precipitation from 1693 mm to 2063 mm, evapotranspiration from 1083.31 mm to 1519.81 mm and slopes ranging from flat to steep. The population in the socioeconomic aspect presents 40% illiteracy and 60% some degree of education, productive activities are based 83% on agriculture and 17% trade, monoculture predominates with 60% and diversified crops at 40%. The flow rate was 0.248 m3 /s; while the raw surface water quality index presents 81.28 characterized as good quality and ends with 41.87 as poor quality.

Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

The aim of this paper is to present the potential of using neural network modelling for the prediction of the surface water quality index (WQI). An artificial neural network modelling has been performed using the physicochemical parameters (TDS, chloride, TH, nitrate, and manganese) as an input layer to the model, and the WQI as an output layer. The physicochemical parameters have been taken from five measuring stations of the river Warta in the years 2014-2018 via the Chief Inspectorate of Environmental Protection (GIOŚ). The best results of modelling were obtained for networks with 5 neurons in the hidden layer. A high correlation coefficient (general and within subsets) 0.9792, low level of MSE in each subset (training, test, validation), as well as RMSE at a level of 0.624507639 serve as a confirmation. Additionally, the maximum percentage of an error for WQI value did not exceed 4%, which confirms a high level of conformity of real data in comparison to those obtained during prediction. The aforementioned results clearly present that the ANN models are effective for the prediction of the value of the Surface water quality index and may be regarded as adequate for application in simulation by units monitoring condition of the environment.

Determination of the water quality index in two rural properties in the Municipality of Guatambu-SC/ Determinação do índice de qualidade da água em duas propriedades rurais no Município de Guatambu-SC

Knowing the quality of water resources is fundamental to guarantee its different functions in the environment. In this sense, this study highlighted the relationship between land use and occupation with water quality, since the objective was to assess water quality in two rural properties in the municipality of Guatambu in the state of Santa Catarina, Brazil. Five points for analysis and their geographic coordinates were demarcated in each property using the Global Positioning System (GPS) and inserted into the Quantum GIS (QGIS) software for spatial assessment. For the water quality analysis of each point, the following parameters were determined: pH, electrical conductivity, dissolved oxygen, color, turbidity, alkalinity, chlorides and biochemical oxygen demand. According to these results, the Bascarán surface water quality index (SWQIB) was estimated. As demonstrated by the results, both properties presented an SWQIB ranging from “medium” to “good”. However, when comparing the points intended for human consumption with the Brazilian Decree 2,914 of December 2011, it was found that the color and turbidity parameters were in disagreement in some cases. However, these two parameters can easily vary. In addition, it was noted that points classified with SWQIB "good" have a better protection area when compared to points classified with SWQIB "medium".

Mathematical computation of water quality index to determine surface water quality of manyad storage tank, Aurangabad district, Maharashtra, India

The present work aims to assess the water quality index (WQI) of Manyad Storage tank, of Aurangabad district, Maharashtra, India. The surface water sample were assessed for physico-chemical parameters namely pH, Electrical Conductivity, Total Dissolved Solid, Total Hardness, Bicarbonate, Chlorides, Sulphates, Nitrates, Calcium, Magnesium, Sodium, Potassium and Fluoride in post-monsoon season of 2020. For calculating the water quality index (WQI), thirteen parameters namely, pH, electrical conductivity, total dissolved solids, total hardness, bicarbonate, calcium, magnesium, sodium, potassium, chloride, sulphate, nitrate and fluorides were considered. Weighted arithmetic water quality index method was used to find surface water quality index (WQI). The water quality index of surface water found to be 96.24 indicating poor water category, which shows that whole water from the Manyad Storage tank is of very poor quality and treatment must be given before use for drinking purpose. Â

Assessment of coastal surface water quality for irrigation purpose

Abstract The study aimed to assess the coastal surface water quality for irrigation purposes through the analysis of the water samples of some selected estuaries, rivers, and ponds. The analysis results showed that the mean value of typical water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium (Na+), and chloride (Cl−) ions exceeded the permissible limit of the Department of Environment (DoE), Bangladesh 2010, and FAO, 1985 for the pre- and post-monsoon seasons. The Piper diagram indicated a Na-Cl water type, especially during the pre- and post-monsoon seasons. The water quality parameters in the areas showed a higher amount than the standard permissible limits, indicating that the quality is deteriorating. The water quality index values for domestic uses showed very poorly to unsuitable in most of the surface waters except pond water, especially during the pre- and post-monsoon periods. The surface water quality index for irrigation purpose usages was found to be high and/ or severely restricted (score: 0–55) during the pre- and post-monsoon seasons. The study observed that due to saline water intrusion, the water quality deterioration started from post-monsoon and reached its highest level during the pre-monsoon season, which gradually depreciates the water quality in coastal watersheds of Bangladesh.

A Study on the Relationship between Land Use Change and Water Quality of the Mitidja Watershed in Algeria Based on GIS and RS

Many catchments in northern Algeria, including the coastal Mitidja Basin in the north central part of the country have been negatively affected by the deterioration of water quality in recent years. This study aims to discover the relationship between land use change and its impact on water quality in the coastal Mitidja river basin. Based on the data of land use and water quality in 2000, 2010 and 2017, the relationship between land use change and surface water quality index in the Mitidja Watershed was discussed through GIS and statistical analysis. The results show that the physical and chemical properties of the Mitidja river basin have obvious spatial heterogeneity. The water quality of upstream was better than that of downstream. There was a significant spatial relationship between the eight water quality indicators and three land use types, including urban residential land, agricultural land and vegetation. In most cases, settlements and agricultural land are the dominant factors leading to river pollution, and higher vegetation coverage helps to improve water quality. The regression model revealed that percentage of urban settlement area was a predictor for NH4-N, BOD5, COD, SS, PO4-P, DO and pH, while vegetation was a predictor for NO3-N. The analysis also showed that during this period, urban settlement areas increased sharply, which has a significant impact on water quality variables. Agricultural land only had a significant positive correlation with PO4-P. The results provide an effective way to evaluate river water quality, control water pollution and land use management by landscape pattern.

Evaluating the surface Water quality index fuzzy and its influence on water treatment

The RWQI (Raw Water Quality Index) was developed in compliance with deterministic models, in order to assess the treatability of natural waters for human consumption by means of conventional process, basing on the similar procedure of the well-known Water Quality Index developed by the National Sanitation Foundation (WQINSF). The RWQI, however, seemed to require further adjustments regarding mainly rating criteria. In order to find a non-deterministic model that would allow handling uncertainties, the non-linearity of the parameters and the knowledge of the specialists, the Fuzzy Logic was used in new approach to the RWQI. In this context, the new index (named Raw Water Quality Index Fuzzy - RWQIF) was applied to data set of 24 water sources in Brazil evaluating the correlations with the RWQI, the WQICETESB, treated water turbidity and coagulant dose. The inverse correlation between the RWQIF with coagulant dose and treated water turbidity was obtained. However, the low intensity of these correlations indicated that there are other intervening factors in this relation. Finally, this research points out the RWQIF as a suitable support decision-making tool that allows managers to prioritize watershed protection actions in short, medium and long-term, aiming at minimizing the water treatment costs and improving the treated water quality.

Assessment of surface water quality around opencast coal mines for sustainable utilization potentials: a case study in Jharkhand, India

Opencast coal mining has caused severe environmental degradation globally. In India, Jharkhand is infamous for the environmental damage already caused by unregulated coal mining practices so far. In the present investigation, surface water samples from 31 locations in Charhi and Kuju coal mining areas were analysed for physical and chemical parameters of water. Statistical analysis was performed to understand the magnitude of contamination and suitability of the water samples for agricultural as well as domestic uses. Analysis of variance indicated significant site-wise (6 out of 15 parameters) and seasonal variations (13 out of 15 parameters), while Pearson correlation indicated significant positive correlations between TDS-EC (r2 = 0.74–0.99) and total hardness—HCO3− (r2 = 0.95–0.98). Scatter plot between Ca, Mg and HCO3 revealed a positive interrelationship in all the three seasons. Surface water quality indices exhibited variable results of sustainable utilization potential of the water for various uses. Though water quality index advocated the quality of water to be good in the majority of the samples, health water quality index based on selected parameters hinted, the majority of the samples to be of poor category. Observations collated together suggest that though the area is in the initial phases of mining, contamination of the surface water bodies is increasing gradually and higher concentrations of metals (As, Fe, Mn, Se) are the most significant problem now. This study can serve as a baseline data and an early sentinel towards the increasing pollution and its effective environmental management in similar mining regions of the world.

Identifying waterborne disease prone areas using geospatial approach along the right bank of Yamuna River in Delhi

ABSTRACTThis paper presents a spatial risk analysis of waterborne diseases using analytical hierarchy process that rely on geographical information system (GIS)-automated techniques. We selected nine parameters for assessing waterborne diseases prone areas (WBDP). All the weighted parameter layers were integrated to obtain WBDP map. Of the total WBDP area, 42% area was found under medium category, 34% and 24% area under high and low category, respectively. We have also empirically analysed the factors influencing WBDP areas using regression model. Results revealed that explanatory variables like purification of water, washing hand before drinking and eating, and Surface Water Quality Index (SWQI) are the most influential and positively associated while dipping hand in the vessel is negatively associated with the WBD. Further, WBDP areas were validated by analysing their relationship with the actual incidences of WBD(s). Our study may prove to be beneficial for managing and formulating guidelines for the WBDP areas in similar geographical regions.Abbreviations: GIS: Geographical information system; WBDP: Water borne diseases prone areas; SWQI: Surface water quality index; WBD: Water borne Disease; MDG: Millennium Development Goal; AHP: Analytical hierarchical process; GPS: Global Positioning System; NCR: National Capital Region; IDW: Inverse distance weighted; pH: Potential of Hydrogen; TDS: Total dissolved solid; COD: Chemical Oxygen Demand; BOD: Biochemical Oxygen Demand; TA: Total alkalinity; TH: Total Hardness; Cl: Chlorination; SO4: Sulphate, NO3: Nitrate; FL: Floride; ICMR: Indian Council of Medical Research; ISB: Indian Standards Bureau; RW: Relative weights; WQI: Water Quality Index; DJB: Delhi Jal Board; MDG: Millennium Development Goals; GWQI: Ground Water Quality Index; MCD: Municipality Corporation of Delhi; CPCB: Central Pollution Control Board

Evaluation of surface water quality for drinking purposes using fuzzy inference system

The aim of this research is to propose a surface water quality index using fuzzy inference system. Three stations and ten parameters (pH, TDS, Ca, Mg, Na, K, Cl, SO4, SO4 and HCO3) are selected from Oued El Hai Basin of Algeria to develop the approach. The results show that calcium and sulfate are the dominate ions in the three stations. TDS is strongly and positively correlated with SO4 and HCO3. From ANOVA test, there are no significant differences for all parameters except Ca and K in terms of their temporal variation. Water-rock interactions and anthropogenic process are the main factors that are controlling the surface water quality. The water quality index is assessed with the FWQI index, and the results show that the values of WQI and FWQI have similar characteristics regarding the water quality index.

Evaluation and estimation of surface water quality in an arid region based on EEM-PARAFAC and 3D fluorescence spectral index: A case study of the Ebinur Lake Watershed, China

Development of tools for real-time monitoring of water quality is crucial for management of stream water pollution. In this study, fluorescence excitation-emission matrix (EEM) with parallel factor analysis (PARAFAC) were used to estimate five-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), suspended sediment (SS), total nitrogen (TN), pH, total phosphorus (TP), ammonia nitrogen (NH3+-N), turbidity (NTU), and dissolved oxygen (DO) concentrations in inland freshwater streams. The study area was located in Ebinur Lake Watershed, Xinjiang Uygur Autonomous Region, China. Our results showed that the four fluorescence components were successfully extrapolated by the PARAFAC factor analysis modeling from the fluorescence EEM data including microbial humic-like (C1), terrestrial humic-like organic substances (C2, C4), and protein-like organic substances (C3). The four PARAFAC components were selected as the water quality monitoring index of river water samples. Three-dimensional fluorescence index analysis showed that terrigenous organic pollution was the main organic pollution type in the watershed. This indicates that the watershed is subject to human disturbance, given the large variation of organic pollution in the water body. There was a significant correlation between the three-dimensional fluorescence index, the fluorescence components, and surface water quality index. In addition, the application of three-dimensional fluorescence index and fluorescence component to establish water quality index produced a model fitting coefficient greater than 0.5, which demonstrated that the accuracy of the model was in line with monitoring requirements in practice. This implies that the use of three-dimensional fluorescence index and three-dimensional fluorescence component to monitor Ebinur Lake Watershed water quality has a great potential.