Water Quality Index Scores Research Articles

Assessing microbiological and heavy metal pollution in surface waters associated with potential human health risk assessment at fish ingestion exposure

Fish represent a significant source of nutrients but also cause negative health effects due to their bioaccumulation capacity for pollutants. The aim of this study was to examine the transfer of metals from the water of several rivers (Somes, Tisa, Sasar, Lapus, Lăpusel) to fish (Caras sp) tissue (subcutaneous fat, muscles, liver, intestines, kidneys, gills, brain, and eyes) and to identify and assess the carcinogenic and non-carcinogenic health risks of Arsenic (As), Cadmium (Cd), Nickel (Ni), Manganese (Mn), Cooper (Cu), Lead (Pb), Chromium (Cr) and Zinc (Zn) through the ingestion of fish (muscles and subcutaneous fat tissues). The obtained results indicated that a diet consisting of fish is particularly vulnerable, particularly in children compared to adults. The risk assessment results were below the threshold limit, although the fish samples contained heavy metals, with values exceeding the permitted limits of Fe (4.41–1604 mg/kg), Cr (727–4155 µg/kg), Zn (4.72–147 mg/kg), and Ni (333–2194 µg/kg). The studied surface waters are characterized by low and high degrees of pollution with heavy metals, as indicated by the heavy metal pollution index scores (HPI: 12.4–86.4) and the heavy metal evaluation index scores (HEI: 1.06–17.6). The considerable pollution levels are attributed to the high Mn content (0.61–49.7 µg/kg), which exceeded the limit up to fifty times. A consistent set of physico-chemical analysis (pH, electrical conductivity, total hardness, turbidity, chloride, sulphate, nitrate, nitrite, ammonium, Ca, Mg, Na, K) was analysed in water samples as well. Considering the water quality index scores (WQI: 16.0–25.2), the surface waters exhibited good quality. Microbiological results indicated the presence of Listeria monocytogenes and atypical colonies of coagulase-positive staphylococcus in fish. In contrast, the surface waters from which fish samples were collected were positive for Escherichia coli, and coliform bacteria intestinal Enterococci. Based on the study’s results, it is recommended to exercise caution in the case of children related to the consumption of fish and using the waters for drinking purposes. This study provides important data of considerable novelty to the riparian population, researchers, and even policy makers on the quality status and potential levels of contamination of river waters, fish and the bioaccumulation of heavy metals in fish that may cause adverse effects on human health if consumed, and similarly the heavy metal pollution degree of waters and the non-carcinogenic risk of heavy metals through ingestion and skin absorption of water in children and adults (the study area is a significant source of fisheries).

Indigenous people doing citizen science to assess water quality using the BMWP in rivers of an arid semi-arid biosphere reserve in Mexico

Arid and semi-arid areas are among the most threatened ecosystems on the planet. The Tehuacán-Cuicatlán Biosphere Reserve (TCBR), in southeastern Mexico, is an arid and semi-arid area with high biological diversity and human settlements of eight ethnic groups. Two rivers drain the reserve, Río Grande (RG) and Río Salado (RS), which are not subject to water quality monitoring by government agencies; however, measures of water quality of these rivers are needed to focus conservation actions on this resource. This work aimed to test the effectiveness of participatory water quality monitoring with the participation of three actors: Reserve management leaders, local communities, and academics, to monitoring water quality in the rivers of the TCBR. Ninety-two residents were trained to carry out water quality biomonitoring using the Biological Monitoring Working Party (BMWP) index calibrated for the reserve. The BMWP uses macroinvertebrate families to display numerical and categorical water quality scores. Additionally, the Water Quality Index (WQI) was assessed and the Normalized Difference Vegetation Index (NDVI) of the riparian zones was estimated in each study site. The mean WQI scores were 69.24 for RS (no treatment necessary for most crops and necessary treatment for public water supply) and 75.16 for RG (minor purification for crops requiring high-quality water and necessary treatment for public water supply). The BMWP showed five water quality categories (Excellent, Very Good, Good, Fair, and Poor), showing higher water quality scores in the upper portion of the basins and capable of discriminating study sites with lower scores close to human settlements. At one study site, data from participatory monitoring impelled actions taken to address a pollution source and influenced policy focus, reaching the maximum level of participatory-based monitoring. This led to avoid the discharge of wastewater into the river to conserve and protect the water resource. WQI is closely related to BMWP; however, the latter was far more sensitive to detecting areas affected by domestic water discharges. The NDVI presented low values for the TCBR, being lower in RS (the driest area). Although the NDVI showed a weak relationship with BMWP values, areas with higher NDVI values generally achieved higher BMWP values. The results of this study highlight the high sensitivity of the BMWP to detect several water quality conditions in the rivers running through the TCBR when compared to WQI. In addition, the usefulness of biomonitoring using the BMWP index was evident, as well as the importance of the participation of local inhabitants contributing to the knowledge of water quality in biosphere reserves and carrying out timely measures that allow the rivers in these reserves to be maintained in good condition.

Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach

Monitoring of groundwater (GW) resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization of water quality index (WQI) models has proven effective in monitoring GW resources, it has faced substantial criticism due to its inconsistent outcomes, prompting the need for more reliable assessment methods. Therefore, this study addressed this concern by employing the data-driven root mean squared (RMS) models to evaluate groundwater quality (GWQ) in the coastal Bhola district near the Bay of Bengal, Bangladesh. To enhance the reliability of the RMS-WQI model, the research incorporated the extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For the assessment of GWQ, the study utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3−), ammonium (NH4+), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and iron (Fe). In terms of the GW indicators, concentration of K, Ca and Mg exceeded the guideline limit in the collected GW samples. The computed RMS-WQI scores ranged from 54.3 to 72.1, with an average of 65.2, categorizing all sampling sites' GWQ as “fair.” In terms of model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) in predicting GWQ accurately. Furthermore, the RMS-WQI model exhibited minimal uncertainty (<1 %) in predicting WQI scores. These findings implied the efficacy of the RMS-WQI model in accurately assessing GWQ in coastal areas, that would ultimately assist regional environmental managers and strategic planners for effective monitoring and sustainable management of coastal GW resources.

Effect of activated carbon compaction on water filtration efficiency

Water contamination in rural Malaysian areas, mainly caused by logging activities leading to soil erosion and river pollution, presents a significant threat to water supplies. In response, a specialized activated carbon water filtering device was developed to target the absorption of organic molecules. The impact of compaction of activated carbon on water filtering efficiency was evaluated. Testing both compacted and uncompacted activated carbon filters with contaminated river water, the study utilized the Malaysia Department of Environment’s (DOE) water quality index (WQI) to assess filter effectiveness. The results revealed that water filtered through compacted activated carbon was clearer and less yellowish compared to the uncompacted counterpart. Moreover, the compacted filter showed higher dissolved oxygen levels, lower ammoniacal nitrogen levels, and a lower pH, resulting in a significantly higher WQI score of 80.4 compared to 78.8 for the uncompacted filter. Further analysis via an adsorption isotherm test demonstrated the superior ability of compacted activated carbon to absorb acetic acid, as evidenced by higher lines in the Freundlich isotherm model graphs. These findings emphasize the efficacy of compacted activated carbon in water filtration, advocating for its integration into filter construction to enhance water quality in rural regions.

Artificial Neural Network (ANN)-Based Water Quality Index (WQI) for Assessing Spatiotemporal Trends in Surface Water Quality—A Case Study of South African River Basins

Artificial neural networks (ANNs) are powerful data-oriented “black-box” algorithms capable of assessing and delineating linear and multifaceted non-linear correlations between the dependent and explanatory variables. Through the years, neural networks have proven to be effective and robust analytical techniques for establishing artificial intelligence-based tools for modelling, estimating, and projecting spatial and temporal variations in water bodies. Accordingly, ANN-based algorithms gained increased attention and have emerged as practical alternatives to traditional approaches for hydro-chemical analysis. ANNs are among the widely used computer systems for modelling surface water quality. Considering their wide recognition, resilience, flexibility, and accuracy, the current study employs a neural network-based methodology to construct a novel water quality index (WQI) model suitable for analysing South African rivers. The feed-forward, back-propagated multilayered perceptron model has three parallel-distributed neuron layers interconnected with seventy weighted links orientated laterally from left to right. First, the input layer includes thirteen neuro-nodes symbolising thirteen explanatory variables, including NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4, and turbidity (NTU). Second, the hidden layer consists of eleven neuro-nodes accountable for computational tasks. Lastly, the output layer features one neuron responsible for conveying network outcomes using a single-digit WQI rating extending from zero to one hundred, where zero represents substandard water quality and one hundred denotes exceptional water quality. The AI-based model was developed using water quality data obtained from six monitoring locations within four drainage basins under the management of the Umgeni Water Board in the KwaZulu-Natal Province of South Africa. The dataset comprises 416 samples randomly divided into training, testing, and validation sets using a proportional split of 70:15:15%. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) technique was utilised to conduct backpropagation training and adjust synapse weights. The dependent variables are the WQI scores from the universal water quality index (UWQI) model developed specifically for South African river basins. The ANN demonstrated enhanced efficiency through an overall correlation coefficient (R) of 0.985. Furthermore, the neural network attained R-values of 0.987, 0.992, and 0.977 for the training, testing, and validation intervals. The ANN model achieved a Nash–Sutcliffe efficiency (NSE) value of 0.974 and coefficient of determination (R2) of 0.970. Sensitivity analysis provided additional validation of the preparedness and computational competence of the ANN model. The typical target-to-output error tolerance for the ANN model is 0.242, demonstrating an adequate predictive ability to deliver results comparable with the target UWQI, having the lowest and highest index ratings of 75.995 and 94.420, respectively. Accordingly, the three-layer neural network is scientifically sound, with index values and water quality evaluations corresponding to the UWQI results. The current research project seeks to document the processes used and the outcomes obtained.

Water physicochemical quality as driver of spatial and temporal patterns of microbial community composition in lake ecosystems

This study aimed at monitoring and management of the surface water and potentially pathogenic microbes of Lake Tonga (Algeria) with respect to. It characterized the main bacterial diversity patterns of Lake Tonga and predicted from water physicochemical parameters and water quality index (WQI) the distribution of bacterial species and the main indicator groups of faecal water contamination. Water samples were taken monthly at three sampling sites of different water depths. Several physicochemical parameters were measured; of which some were included in computing WQI to characterize the water quality of the lake. Counting, isolation and bacterial identification methods were used to characterize the existing aerobic heterotrophic bacteria. The composition of the microbial community of the waterbody of Lake Tonga included an abundant culturable bacterial flora belonging to several bacterial families and whose specific richness varied between water depths of the sites sampled. Species richness of the bacteria identified phenotypically varied between 7 and 11 per sample. The site with shallow water was the richest in bacterial species, compared to moderate and deep waters. The redundancy analysis showed the main physicochemical drivers of the microbial community composition. Our findings showed that high WQI scores indicated the water quality deterioration which triggered the increase in total load of faecal indicator bacterial groups. This study identified in Lake Tonga an important culturable aerobic bacterial flora whose specific richness and distribution varied spatially following the effects of water physicochemical parameters. Lake Tonga needs an integrated management plan to mitigate human disturbances declining water quality.

Machine learning, Water Quality Index, and GIS-based analysis of groundwater quality

Abstract Water is essential for life, as it supports bodily functions, nourishes crops, and maintains ecosystems. Drinking water is crucial for maintaining good health and can also contribute to economic development by reducing healthcare costs and improving productivity. In this study, we employed five different machine learning algorithms – logistic regression (LR), decision tree classifier (DTC), extreme gradient boosting (XGB), random forest (RF), and K-nearest neighbors (KNN) – to analyze the dataset, and their prediction performance were evaluated using four metrics: accuracy, precision, recall, and F1 score. Physiochemical parameters of 30 groundwater samples were analyzed to determine the Water Quality Index (WQI) of Pano Aqil city, Pakistan. The samples were categorized into the following four classes based on their WQI values: excellent water, good water, poor water, and unfit for drinking. The WQI scores showed that only 43.33% of the samples were deemed acceptable for drinking, indicating that the majority (56.67%) were unsuitable. The findings suggest that the DTC and XGB algorithms outperform all other algorithms, achieving overall accuracies of 100% each. In contrast, RF, KNN, and LR exhibit overall accuracies of 88, 75, and 50%, respectively. Researchers seeking to enhance water quality using machine learning can benefit from the models described in this study for water quality prediction.

Assessment of Water Quality of East Hammar Marsh Using Water Quality Index (WQI) Following the Cessation of Saline Tide in 2018

The water quality index (WQI) was studied monthly, from November 2018 to October 2019, at three stations (Al-Saddah, Al-Burgah, &amp; Al-Marsa) in the Basrah Governorate's East Hammar Marsh. This study measured various environmental factors, including water temperature, pH, dissolved oxygen, biological oxygen demand, light penetration, salinity, total dissolved salts, total hardness, nitrates, nitrites, phosphate, sulfate, calcium, and magnesium. After the end of the salty tide in 2018, the first station showed poor seasonal evidence 43.7 (low WQI score) in the winter, fair 67.6 and 64.9 (third category) in the spring and summer respectively, and marginal 55.9 (fourth category) in the summer and fall. The second and third stations had poor WQI score in the Winter and marginal in the rest of the seasons. The results indicate that the water quality is polluted and deviates from its optimal state. The WQI values varied significantly across all sites. The lack of freshwater drainage and ongoing marine water impacts are the reasons for the deterioration of water quality. Treatment is recommended to address this issue.

Water Quality Index Assessment of River Ganga at Haridwar Stretch Using Multivariate Statistical Technique.

The Ganges (Ganga) river contributes significant water resources for the ecology and economy, but it frequently encounters severe deterioration due to cumulative impact from upstream natural and anthropogenic variables. Knowledge and understanding ofthe dynamic behavior of such networks remain a significant challenge, particularly in the context of rising environmental pressures, such as climate change and industrialization, as well as constraints in both process and data understanding across geographies. An interdisciplinary approach is required to be developed to investigate the hydrogeochemical dynamics and anthropogenic sources influencing water quality in major river systems. The present study has been carried out to evaluate the characterization of river water quality in terms of the physico-chemical & bacteriological parameters. Also, the development of a water quality index (WQI) for Domestic (drinking) and Spiritual (bathing) usage is a part of the study. The water quality index has been developed using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI). The river's water quality index score in the present study lies in the range of 38.32 to 79.82, indicating the quality of water from fair to poor for drinking purposes. The highest water quality index value of 79.82 has been observed at Guru Kashnik Ghat, while the lowest WQI value of 38.32 has been observed at Har ki Pauri for drinking purposes. However, the water quality score for bathing purposes ranged from 71.04 to 91.22 thus signifying the quality of the water from fair to good for bathing purposes. The highest water quality index value of 91.22 has been assessed at Guru Kashnik Ghat, while the lowest WQI value of 71.04 has been assessed at Bhimgoda Barrage. The developed water indices assessment in the present study will be beneficial for society to provide a benchmark for the control of water pollution in River Ganga. These findings will support policymakers and stakeholders in addressing water quality issues in a more efficient and effective manner. The study also emphasizesthe requirement for ongoing water quality monitoring and evaluation in order to guarantee the long-term well-being of the river and its ecosystems.

Marine waters assessment using improved water quality model incorporating machine learning approaches

In marine ecosystems, both living and non-living organisms depend on “good” water quality. It depends on a number of factors, and one of the most important is the quality of the water. The water quality index (WQI) model is widely used to assess water quality, but existing models have uncertainty issues. To address this, the authors introduced two new WQI models: the weight based weighted quadratic mean (WQM) and unweighted based root mean squared (RMS) models. These models were used to assess water quality in the Bay of Bengal, using seven water quality indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), and molybdate reactive phosphorus (MRP). Both models ranked water quality between “good” and “fair” categories, with no significant difference between the weighted and unweighted models’ results. The models showed considerable variation in the computed WQI scores, ranging from 68 to 88 with an average of 75 for WQM and 70 to 76 with an average of 72 for RMS. The models did not have any issues with sub-index or aggregation functions, and both had a high level of sensitivity (R2 = 1) in terms of the spatio-temporal resolution of waterbodies. The study demonstrated that both WQI approaches effectively assessed marine waters, reducing uncertainty and improving the accuracy of the WQI score.

Repas lama dam water quality and heavy metal contamination using Qual2k

Repas Lama Dam in Bentong Pahang has been experiencing pollution by Non-Point Sources (NPS) that can be found upstream of the Sungai Repas, the pollution being so evident that discoloration of the water is observable at the location. The area is littered with mines active and inactive, with many of the inactive mines being improperly shut down. When there are mining activities, some major waste or tailings are produced as the by-products of the process which drastically increases the amount of pollution that a water body that runs through these areas will contain. The Water Quality Parameters at a location, whether biological, chemical, or physical, can give a lot of insight into the level of pollution found in a water body. Through collecting these parameters and running them through a water quality model, it can give a lot of information on the present state of a river and help in attaining predictions of the health of a river. QUAL2K is the water quality model that was used to analyse the water at Old Repas Dam and further make predictions on the state of the river after 10 years. After the modelling process, the water quality parameters and the heavy metals present were analyzed using the Malaysian National Water Quality Standard (NWQS), which also assisted in classifying the water at Old Repas Dam based off individual parameters and its overall Water Quality Index score. The major water quality parameters that were used for this study are Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Ammonia Nitrogen, pH and Total Suspended Solids (TSS). In terms of heavy metal analysis, the heavy metals that were looked at are Arsenic (As), Lead (Pb), Zinc (Zn), Copper (Cu), Tin (Sn) and Aluminium (Al). The data for these parameters being collected from studies that were conducted on the site in 2021 and information given by the Department of Statistics Malaysia.

Modelling transformation and its impact on water quality of the wetlands of lower Gangetic plain: a geospatial approach for sustainable resource management

ABSTRACT Over the past few decades, water quality in aquatic ecosystems has been deteriorating steadily due to population growth, urbanization and industry expansion, discharge of municipal wastewater, and agricultural runoff into surface water sources. Wetlands of the Indo-Gangetic plain are disappearing continuously by several geo-genic and anthropogenic factors. The study focuses on the most important wetland ecosystems in the Malda district of the lower Gangetic plain in terms of area and socioeconomic value. The study aims to emphasize the land transformation around the selected wetlands, and to determine the current status of the water quality of the wetlands. From 2003 to 2018, the land use land cover (LULC) and land transformation of wetland areas were computed using geospatial data. The water quality of the wetlands has been measured at different sampling sites in pre- and post-monsoon seasons. The focus of the study is to identify the causal factors of the degradation and disappearance of wetlands. For the water quality index (WQI), on-site and laboratory analyses of the chosen parameters were conducted. Over the past 15 years, the built-up area increased by+98.08%, +90.32%, and +67.17%, while waterbody experienced a considerable negative change, with percentages of −21.30%, −26.83%, and −29.63% for the Chakla Beel, Chatra wetland, and Nayabandh wetland complex, respectively. The WQI revealed poor and very poor water categories at various wetland sites, under the WQI scores with 100–200 indicating “poor water” and 200–300 “very poor water.” The study analyzed the causes of the change in land use and alteration in water quality of selected wetland ecosystems.

River Diatoms Reflect Better Past than Current Environmental Conditions

The time required for diatom assemblages to integrate the abiotic variables of the aquatic environment has been subject of discussion for many years, with contrasting opinions as shown by field observations and experiments. Numerous diatom-based water quality indices are being used in the bioassessment of freshwaters; one of the most widely used (Specific Polluosensitivity Index or SPI) is compared in this study with water quality status as measured by the Water Quality Index (WQI) obtained from limnological parameters measured at 37 fluvial stations belonging to the Duero Basin (NW Spain). The correlation existing between both metrics was determined using different time lags between water and diatom sample collection. Results show that the correlation coefficient increases as the time gap decreases, but is maximal when comparing WQI scores measured one month before diatom sampling. We thus confirm the initial hypothesis that diatom indices actually indicate past (1 month ago) and not current water conditions in rivers. The practical consequences of this finding are briefly discussed.

Inconsistency of PCA-based water quality index – Does it reflect the quality?

The formalization of a stable water quality index (WQI) from measured hydrogeochemical parameters is essential for the identification and classification of water resources. In the principal component analysis (PCA) based WQI approach, the parameter weight is derived using either PC loading or rotated factor loading from a large number of samples pooled for WQI measurement. The PCA-based approach is paradoxical, as the calculated WQI rating of a sample would rather be dependent on the size, and composition of the population. Though this issue is well anticipated, no attempt has been made to regularize or measure the extent of WQI disagreement. In the present study, the WQI of 106 groundwater samples analyzed for 12 different hydrochemical parameters were modelled using PC loading or rotated factor loading (referred to as PCQ-1, PCQ-2, respectively) approach. Analysis reveals PCQ-1 to be positively biased in 78 % of samples and rating disagreements were evident in 9.43 % of samples. WQI of the data set was estimated using repeated (1000) random non-overlapping 2 to 5-fold data partitioning (containing 21 to 83 samples in each fold) adopting either an in-sample (test set) or out-sample (train set) modelling approach. The mean of WQI deviations in repeated resampling from the reference (i.e., using the entire dataset) has been positive in most of the samples using the PCQ-1 model, irrespective of the fold partition size. The median root mean square deviation values of the data set increased with the number of fold partitioning for in-sample calibration for both PCQ-1 and PCQ-2 approaches. The exclusion of a single water quality parameter from the PCA model can cause up to a 60 % deviation of the WQI score in some water samples. The cross-validation and Monte Carlo resampling approach can serve as a framework to test the stability of PCA-based WQI.

Urban stream rehabilitation in a densely populated Brazilian metropolis

Environmental rehabilitation of urban streams has been widely applied in Global North countries, at least since the 1970s, but it is a recent approach in Global South countries. The objective of this paper is to evaluate whether the rehabilitation experience carried out since 2006 in three urban stream sites in the third-largest Brazilian metropolis (c. 5.5 million inhabitants) was and continues to be effective in terms of socio-environmental improvement after 10 years of interventions. These interventions included the rehabilitation of watercourses (e.g., improvement of water quality through the management of sewage and garbage, stabilization of riverbanks, revegetation of riparian zones, riverbed naturalization, removal of riverbank housing). We evaluated water quality, physical habitat structure, and benthic macroinvertebrate assemblages in three test sites in three sampling periods: pre-intervention (2004–2005), early post-intervention (2008–2011) and late post-intervention (2018–2019). Additionally, three reference-stream sites (2018–2019) were assessed to compare the conditions of the three tested sites versus the reference sites. We also assessed citizen perceptions concerning the interventions through questionnaires given to urban stream residents at the three tested sites in early and late post-rehabilitation (215 in 2008, 180 in 2019). The results of water quality monitoring showed a significant improvement in most parameters used to calculate the Water Quality Index (WQI) in the early intervention phase, and WQI scores have improved since. The physical habitat and macroinvertebrate indicators indicated moderate improvements. The residents indicated increased appreciation of the environmental improvements over 10 years. Given the results in Belo Horizonte, we believe that implementation and evaluation of similar projects and programs aimed at rehabilitating urban streams are technically viable using our approaches throughout the Global South.

Evaluation of water quality using water quality index, synthetic pollution index, and GIS technique: a case study of the river Gomti, Lucknow, India.

The river Gomti, one of India's most polluted rivers, passing through Lucknow, Uttar Pradesh, India, has been selected for this study. An attempt has been made to assess its water quality status by combining the water quality index (WQI) and synthetic pollution index (SPI). Further, the data integration with the geographic information system (GIS) along with twelve water quality parameters for the seven sampling stations (S1 to S7) over 5years (2013-2017) has been performed. The study area showed a variation of WQI from 78.993 to 249.388 and SPI from 0.868 to 2.096 in 5years. The map interpolated through GIS revealed that the WQI falls into the category of severely polluted (76-100) and unsuitable for human consumption (> 100), while SPI lies in the category of moderately polluted (0.5-1.0) and severally polluted (1.0-3.0). The BOD and COD were found to significantly influence the WQI and SPI scores. With the constant release of waste effluents into the river, all selected parameters increased from S1 to S7. Based on the study, effective wastewater management is immediately required to improve water quality and support any sustainable river restoration plan.

Assessment of the effects of COVID-19 lockdown period on groundwater quality of a significant rice land in an urban area of Türkiye.

In the current research, the impact of the COVID-19 lockdown period on groundwater quality of Lower Meriç Plain (Thrace Region of Türkiye) was evaluated. Some significant nutrient characteristics (NO3−, NO2−, and PO43−), salinity characteristics (EC, TDS, and salinity), and physical characteristics (temperature, DO, pH, and turbidity) were investigated in groundwater samples collected from 45 sampling points in pre-lockdown and lockdown periods. Water quality index (WQI) and nutrient pollution index (NPI), Pearson correlation index (PCI), cluster analysis (CA), one-way ANOVA test (OWAT), and factor analysis (FA) were applied to assess ecological risk. Excluding recorded statistical differences in temperature and DO due to climatic conditions (p < 0.05), levels of all the investigated water quality parameters show no statistically significant differences and no significant reduction in pollutants measured in the lockdown period. On the contrary, the WQI and NPI scores have increased between the rates of 4.76–27.10% during the lockdown period. In the lockdown period, although the reduction of industry or limited production of many industrial facilities reduced the inorganic contaminant releases to the environment, ongoing agricultural activities and domestic wastes caused to prevent the reduction of organic pollutants in groundwater of the region during the lockdown period.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-20959-8.

Geospatial assessment of water quality using principal components analysis (PCA) and water quality index (WQI) in Basho Valley, Gilgit Baltistan (Northern Areas of Pakistan).

Public health quality in Gilgit Baltistan (GB) is at threat due to multiple water-borne diseases. Anthropogenic activities are accelerating the burden of pollution load on the glacio-fluvial streams and surface water resources of Basho Valley in Skardu district of GB. The present research has investigated the drinking water quality of the Basho Valley that is being used for domestic purposes. The study also comprehends public health status by addressing the basis drinking water quality parameters. A total of 23 water samples were collected and then analyzed to elucidate the current status of physico-chemical, metals, and microbial parameters. Principal component analysis (PCA) was applied and three principal components were obtained accounting 53.04% of the total variance, altogether. PCA identified that metallic and microbial parameters are the major factor to influence the water quality of the valley. Meanwhile, water quality index (WQI) was also computed and it was observed that WQI of the valley is characterized as excellent in terms of physico-chemical characteristics; however, metals and microbial WQI shows most of the samples are unfit for drinking purpose. Spatial distribution is also interpolated using the Inverse distance weight (IDW) to anticipate the results of mean values of parameters and WQI scores. The study concludes that water quality is satisfactory in terms of physico-chemical characteristics; however, analysis of metals shows that the concentrations of copper (Cu) (0.40 ± 0.16mg/L), lead (Pb) (0.24 ± 0.10mg/L), zinc (Zn) (6.77 ± 27.1mg/L), manganese (Mn) (0.19 ± 0.05), and molybdenum (Mo) (0.07 ± 0.02mg/L) are exceeding the maximum permissible limit as set in the WHO guidelines for drinking water. Similarly, the results of the microbial analysis indicate that the water samples are heavily contaminated with fecal pollution (TCC, TFC, and TFS > 3 MPN/100mL). On the basis of PCA, WQI, and IDW, the main sources of pollution are most likely to be concluded as the anthropogenic activities including incoming pollution load from upstream channels. A few underlying sources by natural process of weathering and erosion may also cause release of metals in surface and groundwater. This study recommends ensuring public health with regular monitoring and assessment of water resources in the valley.

A coupling methodology of the analytic hierarchy process and entropy weight theory for assessing coastal water quality.

Rapid economic development in coastal areas has gradually increased the risk of coastal water quality deterioration. The assessment methods of coastal water quality are multifarious, but many depend on either subjective judgment or objective calculation. We proposed a weighted sum methodology by integrating the subjective analytic hierarchy process and objective entropy theory (AHP-entropy weight methodology) to obtain an overall evaluation of coastal water quality. The mathematical models to transform the biochemical and physical parameter values and soluble substance concentrations into index scores have been formulated in comparison to the national water quality classification scheme. The application of the AHP-entropy weight methodology was demonstrated in the nearshore area of Yangjiang city, China, based on 23 seawater sampling stations in autumn 2017 and spring 2018. Datasets including biochemical and physical parameters, nutrients, and heavy metals have been converted into water quality index scores based on the proposed mathematical model. Results revealed that the overall water quality fell into the "good" class in both sampling seasons. The spatial distribution of the water quality index scores demonstrated that the relatively worse water quality occurred in estuarine and nearshore areas, signifying the negative effect of coastal anthropogenic activities. The statistical analyses like the hierarchical cluster analysis interpreted that the river input acted as a main source of pollutants in the study area. The AHP-entropy weight methodology could be a preferred way to assist decision-makers in properly evaluating the current state of coastal water quality in an unbiased, objective manner.

Using different multivariate approaches to assess water quality of qanats in arid zones of Southern Central Mexico.

Qanats in the aquifer of the Tehuacán Valley (Mexico) represent an ancient way of using groundwater that is still practiced today. They are used mainly for agricultural irrigation. However, anthropogenic activities have jeopardized the use of these aquifers. We analyzed 24 qanats in the Tehuacán Valley to assess water quality. Based on 24 physicochemical variables, a water quality index (WQI) was constructed on a zero-to-100 scale, divided into five water quality classes. A decision-tree analysis was applied to identify the parameters with the highest influence on the WQI, considering the water quality classes as categorical responses and the values of physicochemical variables as drivers of these categories. We produced interpolation maps to identify trends. The relationship between the WQI and the normalized difference indices of vegetation and salinity (NDVI and NDSI, respectively) was analyzed using a ternary diagram. WQI scores showed that 12.5% of the qanats have very good quality; 25%, good quality; and the remaining (62.5%) range from moderate to unacceptable quality. The CHAID classification-tree method correctly explained 83.3% of the categories, with sulfates, alkalinity, conductivity, and nitrates as the main parameters that explain water quality. WQI was inversely related to NDVI and NDSI, showing seasonal differences. Interpolation maps suggest a better water quality in the northern zone of the aquifer.