Characterizing Monsoonal Variation on Water Quality Index of River Mahi in India using Geographical Information System

Spatiotemporal variations, sources, pollution status and health risk assessment of dissolved trace elements in a major Arabian Sea draining river: insights from multivariate statistical and machine learning approaches

River Mahi drains through semi-arid regions (Western India) and is a major Arabian Sea draining river. As the principal surface water source, its water quality is important to the regional population. Therefore, the river water was sampled extensively (n = 64, 16 locations, 4 seasons and 2 years) and analyzed for 11 trace elements (TEs; Sr, V, Cu, Ni, Zn, Cd, Ba, Cr, Mn, Fe, and Co). Machine learning (ML) and multivariate statistical analysis (MVSA) were applied to investigate their possible sources, spatial-temporal-annual variations, evaluate multiple water quality parameters [heavy metal pollution index (HPI), heavy metal evaluation index (HEI)], and health indices [hazard quotient (HQ), and hazard index (THI)] associated with TEs. TE levels were higher than their corresponding world average values in 100% (Sr, V and Zn), 78%(Cu), 41%(Ni), 27%(Cr), 9%(Cd), 8%(Ba), 8%(Co), 6%(Fe), and 0%(Mn), of the samples. Three principal components (PCs) accounted for 74.5% of the TE variance: PC-1 (Fe, Co, Mn and Cu) and PC-2 (Sr and Ba) are contributed from geogenic sources, while PC-3 (Cr, Ni and Zn) are derived from geogenic and anthropogenic sources. HPI, HEI, HQ and THI all indicate that water quality is good for domestic purposes and poses little hazard. ML identified Random forest as the most suitable model for predicting HEI class (accuracy: 92%, recall: 92% and precision: 94%). Even with a limited dataset, the study underscores the potential application of ML to predictive classification modeling.

Evaluation of Surface Water Quality in the Betwa River Basin through the Water Quality Index Model and Multivariate Statistical Techniques

The Betwa River Basin (BRB), a sub-basin of the River Yamuna, is the oldest flowing water system in Central India. The water quality of the rivers are under stress, hence regular monitoring and appraisal is required to know the health of the rivers. Factor analysis and principal component analysis (FA/PCA) multivariate statistical techniques were used to extract three and four varimax factors that explained 96.408 and 100.00 percent of the total variance in water quality, respectively. Cluster analysis (CA) categorizes observed items into distinct quality categories based on correlations between stations and years. Point industrial/sewage effluents, diffuse pollution as runoff from arable land, erosion, and natural source pollution contribute to the pollution of the BRB. As a result, water quality is threatened or impaired, and conditions often departed from natural or desirable levels at Rajghat, Garrauli, Mohana, and Shahijina stations. According to the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), the surface water quality at the Rajghat and Mohana stations corresponds to fair ecological status. However, the surface water quality of the Garrauli and Shahijina stations has a marginal water quality as per CCME-WQI. From 1985 to 2018, the Shahijina had the most considerable load of nutrients and organic matter, as determined by the CCME-WQI and by comparing the water quality data. A thorough examination had revealed a fluctuating trend in the BRB pollution, particularly at all stations. Results indicate that between 1985 and 2018, the only defense mechanism of the river was the auto purification mechanism, which is strongly influenced by the drought, point pollution source, and extreme meteorological events that probably cause these fluctuations.

Landfill leachate a new threat to water quality: a case study from the Temperate Himalayas

Landfills are commonly seen as the most cost-efficient and practical approach to waste management in various regions around the world. Nonetheless, the infiltration of hazardous materials from poorly managed dumping sites remains a significant environmental issue in most developing countries such as India. Leachate serves as a prominent point source of contamination in many environmental media like soil, groundwater, and surface water around the world. So the prime issues humans are experiencing are associated with water quality. Thus, the investigation was undertaken to assess the impact of leachate from the Achan landfill on surface water quality in the Temperate Himalayas. Monitoring was done during in all four seasons, viz., spring, summer, autumn, and winter. Among the sites, the leachate outflow site was found to have the highest mean value of pH (7.95), EC (2.16 dS/m), total nitrogen (2.64 mg/l), P (4.75 mg/l), K (1.41 mg/l), Ca (107.45 mg/l), Mg (54.93 mg/l), Zn (0.8 mg/l), Fe (1.78 mg/l), Cu (0.66 mg/l), Mn (0.81 mg/l), BOD (21.47 mg/l), COD (66.24 mg/l), temperature (14.22 °C), turbidity (14.29 NTU), while lowest mean values of all parameters were recorded at control site. Among the seasons, summer season was found to have maximum value of pH (7.9), EC (2.36 dS/m), total nitrogen (2.54 mg/l), P (4.0 mg/l), K (0.89 mg/l), Ca (85.94 mg/l), Mg (43.91 mg/l), Fe (1.4 mg/l), Cu (0.52 mg/l), Mn (0.64 mg/l), BOD (22.82 mg/l), COD (65.87 mg/l), temperature (18.99 °C), and turbidity (8.49 NTU). The maximum mean value of Zn (0.66 mg/l) was recorded during winter season, while other parameters were found to be minimum during winter season. From this study, we concluded that a decreasing trend was observed during all the seasons in the concentration of all physico-chemical parameters with an increase in distance from the landfill. So it is recommended that the leachate should be treated at the source before disposing into the water body and the landfill should be lined properly to prevent the entry of leachate into water sources.

Site selection of check dams using geospatial techniques in Debre Berhan region, Ethiopia - water management perspective

Remote sensing and GIS technology were very helpful to determine an appropriate location of freshwater storage in Amhara, Ethiopia. The techniques were used to investigate the impact of lithology, surface geomorphology, slope parameters, drainage flow, drainage density, lineament density, land cover parameters on relief, and aerial and linear features and to understand their interrelationships. Morphometric parameters such as mean stream length (Lsm), stream length ratio (RL), bifurcation ratio (Rb), mean bifurcation ratio (Rbm), relief ratio (Rh), drainage density (Dd), stream frequency (Fs), drainage texture (Rt), form factor (Rf), circularity ratio (Rc), and elongation ratio (Re) were calculated. Spatial maps of morphometric parameters were produced by using AHP (analytical hierarchy process) of ArcGIS 10.3. Final priority map was generated by the overlay of those parameters with five categories of poor (16.6%), low (41.63%), moderate (29.61%), high (8.88%), and very high (3.28%) storage locations. The map showed that this study area belonged to the low to moderate storage location. The results exhibit precision-based assessment of the suitability for the dam construction sites of 6, 7, and 9 sub-basin zones. The outcome of this study strengthens the knowledge of geospatial analysis for water resources vulnerability and also allows policymakers in this drought-prone area to sustainably manage water supplies.

Development of fuzzy analytic hierarchy process based water quality model of Upper Ganga river basin, India

The ecological sustainability of rivers is in question due to severe pollution and lack of stringent regulations. Long term (1990-2016) water quality data of five stations namely Haridwar, Bareilly, Kanpur, Prayagraj and Varanasi of Upper Ganga river, India was considered for analysis using fuzzy analytical process (FAHP) based water quality index (WQI) to assess surface water quality. The value of water physical, biological and chemical parameters of temporal resolution (monthly, seasonal and yearly) indicate that value of electrical conductivity (EC), total dissolved solids (TDS), biological oxygen demand (BOD), chemical oxygen demand (COD), total alkalinity (Mg CaCO₃), total hardness (Mg CaCO₃), calcium (Ca), magnesium (Mg), sodium (Na), chlorine (Cl) and bicarbonate (HCO₃) were observed very high compared to recommended value of Bureau of Indian Standards (BIS) and World Health Organization (WHO) at Kanpur, Prayagraj and Varanasi stations. However, low value of parameters is observed at Haridwar and Bareilly stations. Also, the high deviation was observed in water quality parameters during 1990-2010 whereas the deviation of parameters is decreased in 2011-2016. It is observed from the piper diagram that magnesium and bicarbonate at Haridwar, sodium, potassium and bicarbonate in Bareilly, Kanpur, Prayagraj and Varanasi stations are dominant during monthly and seasonal periods. The fuzzy based WQI value indicate that water quality is excellent to poor at Haridwar, while poor to unsuitable in Bareilly, Kanpur, Prayagraj and Varanasi during monthly and seasonal periods. The water quality ranges from poor to unsuitable during the 1990-2010 period and good to very poor during the 2011-2016 period at Bareilly, Kanpur, Prayagraj and Varanasi stations. Whereas very good to good during 1990-2010 and excellent to good during 2011-2016 at Haridwar. It was also determined that water quality parameters (Ca, Na+K, SO₄, Hardness, Cl and Mg) and WQI values were increased with length of the stream. It indicates that drain discharge, urban growth, urban functions, ecological footprints and crop area increment were key sources of pollution.

Assessing Land-Cover Effects on Stream Water Quality in Metropolitan Areas Using the Water Quality Index

This study evaluated the influence of different land-cover types on the overall water quality of streams in urban areas. To ensure national applicability of the results, this study encompassed ten major metropolitan areas in South Korea. Using cluster analysis, watersheds were classified into three land-cover types: Urban-dominated (URB), agriculture-dominated (AGR), and forest-dominated (FOR). For each land-cover type, factor analysis (FA) was used to ensure simple and feasible parameter selection for developing the minimum water quality index (WQImin). The chemical oxygen demand, fecal coliform (total coliform for FOR), and total nitrogen (nitrate-nitrogen for URB) were selected as key parameters for all land-cover types. Our results suggest that WQImin can minimize bias in water quality assessment by reducing redundancy among correlated parameters, resulting in better differentiation of pollution levels. Furthermore, the dominant land-cover type of watersheds, not only affects the level and causes of pollution, but also influences temporal patterns, including the long-term trends and seasonality, of stream water quality in urban areas in South Korea.

Seasonal assessment of drinking water sources in Rwanda using GIS, contamination degree (Cd), and metal index (MI)

The quality of drinking water source remains as a major concern in areas of developing and underdeveloped countries worldwide. The treatment and supply of drinking water in Rwanda are carried out by Water and Sanitation Corporation, a state-owned public company. However, it is not able to supply water to all households. Consequently, the non-serviced households depend on natural water sources, like springs, to meet their water requirements. Nevertheless, the water quality in these springs is scarcely known. Therefore, this study assessed and compared metal elements in drinking water sources in the dry and rainy seasons in 2017 using the contamination degree, metal index, and geographic information systems to reveal the spatial distribution of water quality within the considered water sources of springs in Rwanda. The samples were collected monthly from nine water sources of springs and the measured elements are aluminium, calcium, copper, iron, manganese, and zinc. The metal index indicated that during the dry season and rainy season, the sites of Kibungo (1.10 and 1.26) and Kinigi (1.01 and 1.54) have assessed a metal index which is higher than 1. Thus, the water quality of those sites was getting the threshold of warning. The analysis indicated that pollutants are easily transported into water bodies during the rainy season in urban and rural areas to a greater extent than during the dry season .

Hydrogeochemistry and water quality of the Kordkandi-Duzduzan plain, NW Iran: application of multivariate statistical analysis and PoS index

Kordkandi-Duzduzan plain is one of the fertile plains of East Azarbaijan Province, NW of Iran. Groundwater is an important resource for drinking and agricultural purposes due to the lack of surface water resources in the region. The main objectives of the present study are to identify the hydrogeochemical processes and the potential sources of major, minor, and trace metals and metalloids such as Cr, Mn, Cd, Fe, Al, and As by using joint hydrogeochemical techniques and multivariate statistical analysis and to evaluate groundwater quality deterioration with the use of PoS environmental index. To achieve these objectives, 23 groundwater samples were collected in September 2015. Piper diagram shows that the mixed Ca-Mg-Cl is the dominant groundwater type, and some of the samples have Ca-HCO₃, Ca-Cl, and Na-Cl types. Multivariate statistical analyses indicate that weathering and dissolution of different rocks and minerals, e.g., silicates, gypsum, and halite, ion exchange, and agricultural activities influence the hydrogeochemistry of the study area. The cluster analysis divides the samples into two distinct clusters which are completely different in EC (and its dependent variables such as Na⁺, K⁺, Ca²⁺, Mg²⁺, SO₄^2-, and Cl^-), Cd, and Cr variables according to the ANOVA statistical test. Based on the median values, the concentrations of pH, NO₃^-, SiO₂, and As in cluster 1 are elevated compared with those of cluster 2, while their maximum values occur in cluster 2. According to the PoS index, the dominant parameter that controls quality deterioration is As, with 60% of contribution. Samples of lowest PoS values are located in the southern and northern parts (recharge area) while samples of the highest values are located in the discharge area and the eastern part.

A GIS policy approach for assessing the effect of fertilizers on the quality of drinking and irrigation water and wellhead protection zones (Crete, Greece)

Fertilizers have undoubtedly contributed to the significant increase in yields worldwide and therefore to the considerable improvement of quality of life of man and animals. Today, attention is focussed on the risks imposed by agricultural fertilizers. These effects include the dissolution and transport of excess quantities of fertilizer major- and trace-elements to the groundwater that deteriorate the quality of drinking and irrigation water. In this study, a map for the Fertilizer Water Pollution Index (FWPI) was generated for assessing the impact of agricultural fertilizers on drinking and irrigation water quality. The proposed methodology was applied to one of the most intensively cultivated with tree crops area in Crete (Greece) where potential pollutant loads are derived exclusively from agricultural activities and groundwater is the main water source. In this region of 215 km², groundwater sampling data from 235 wells were collected over a 15-year time period and analyzed for the presence of anionic (ΝΟ^-3, PO^-3₄) and cationic (K⁺¹, Fe⁺², Mn⁺², Zn⁺², Cu⁺², B⁺³) fertilizer trace elements. These chemicals are the components of the primary fertilizers used in local tree crop production. Eight factors/maps were considered in order to estimate the spatial distribution of groundwater contamination for each fertilizer element. The eight factors combined were used to generate the Fertilizer Water Pollution Index (FWPI) map indicating the areas with drinking/irrigation water pollution due to the high groundwater contamination caused by excessive fertilizer use. Moreover, by taking into consideration the groundwater flow direction and seepage velocity, the pathway through which groundwater supply become polluted can be predicted. The groundwater quality results show that a small part of the study area, about 8 km² (3.72%), is polluted or moderately polluted by the excessive use of fertilizers. Considering that in this area drinking water sources (wells) are located, this study highlights an analytic method for delineation wellhead protection zones. All these approaches were incorporated in a useful GIS decision support system that aids decision makers in the difficult task of protection groundwater resources.

A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices

Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy indices developed in this research are reliable and flexible when used in groundwater quality assessment for drinking purposes.

Evaluation of Surface Water Quality by Using GIS and a Heavy Metal Pollution Index (HPI) Model in a Coal Mining Area, India

Twenty eight surface water samples were collected from fourteen sites of the West Bokaro coalfield, India. The concentration of Mn, Cu, Zn, Ni, As, Se, Al, Cr, Ba, and Fe were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) for determination of seasonal fluctuations and a heavy metal pollution index (HPI). The HPI values were below the critical pollution index value of 100. Metal concentrations were higher in the pre-monsoon season as compared to the post-monsoon season. The Zn, Ni, Mn, As, Se, Al, Ba, Cu, and Cr concentrations did not exceed the desirable limits for drinking water in either season. However, at many sites, concentrations of Fe were above the desirable limit of the WHO (2006) and Indian drinking water standard (BIS 2003) in both seasons. The water that contained higher concentrations of Fe would require treatment before domestic use.

Modeling groundwater quality over a humid subtropical region using numerical indices, earth observation datasets, and X-ray diffraction technique: a case study of Allahabad district, India

Water is undoubtedly the vital commodity for all living creatures and required for well-being of the human society. The present work is based on the surveys and chemical analyses performed on the collected groundwater samples in a part of the Ganga basin in order to understand the sources and evolution of the water quality in the region. The two standard indices such as water quality index and synthetic pollution index for the classification of water in the region are computed. The soil and sediment analysis are carried out with the help of X-ray diffractometer (XRD) for the identification of possible source of ions in water from rock and soil weathering. The dominant minerals which include quartz, muscovite, plagioclase, and orthoclase are reported in the area. The study further utilizes the multivariate statistical techniques for handling large and complex datasets in order to get better information about the groundwater quality. The following statistical methods such as cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA) are applied to handle the large datasets and to understand the latent structure of the data. Through FA/PCAs, we have identified a total of 3 factors in pre-monsoon and 4 factors in post-monsoon season, which are responsible for the whole data structure. These factors explain 77.62 and 82.39% of the total variance of the pre- and post-monsoon datasets. On the other hand, CA depicted the regions that have similar pollutants origin. The average value of synthetic pollution index of groundwater during pre-monsoon is 9.27, while during post-monsoon, it has been recorded as 8.74. On the other hand, the average values of water quality index of groundwater during pre-monsoon and post-monsoon seasons are found as 217.59 and 233.02, respectively. The study indicates that there occurs an extensive urbanization with gradual vast development of various small- and large-scale industries, which is responsible for degradation in water quality. The overall analysis reveals that the agricultural runoff, waste disposal, leaching, and irrigation with wastewater are the main causes of groundwater pollution followed by some degree of pollution from geogenic sources such as rock and soil weathering, confirmed through XRD analysis.