A machine learning approach to investigate the impact of land use land cover (LULC) changes on groundwater quality, health risks and ecological risks through GIS and response surface methodology (RSM).

Groundwater resources are enormously affected by land use land cover (LULC) dynamics caused by increasing urbanisation, agricultural and household discharge as a result of global population growth. This study investigates the impact of decadal LULC changes in groundwater quality, human and ecological health from 2009 to 2021 in a diverse landscape, West Bengal, India. Using groundwater quality data from 479 wells in 2009 and 734 well in 2021, a recently proposed Water Pollution Index (WPI) was computed, and its geospatial distribution by a machine learning-based 'Empirical Bayesian Kriging' (EBK) tool manifested a decline in water quality since the number of excellent water category decreased from 30.5% to 28% and polluted water increased from 44% to 45%. ANOVA and Friedman tests revealed statistically significant differences (p < 0.0001) in year-wise water quality parameters as well as group comparisons for both years. Landsat 7 and 8 satellite images were used to classify the LULC types applying machine learning tools for both years, and were coupled with response surface methodology (RSM) for the first time, which revealed that the alteration of groundwater quality were attributed to LULC changes, e.g. WPI showed a positive correlation with built-up areas, village-vegetation cover, agricultural lands, and a negative correlation with surface water, barren lands, and forest cover. Expansion in built-up areas by 0.7%, and village-vegetation orchards by 2.3%, accompanied by a reduction in surface water coverage by 0.6%, and 2.4% in croplands caused a 1.5% drop in excellent water and 1% increase in polluted water category. However, ecological risks through the ecological risk index (ERI) exhibited a lower risk in 2021 attributed to reduced high-risk potential zones. This study highlights the potentiality in linking LULC and water quality changes using some advanced statistical tools like GIS and RSM for better management of water quality and landscape ecology.

Assessment of potential health risks from heavy metal pollution of surface water for drinking in a multi-industry area in Mali using a multi-indices approach.

The Niger River, Bamako's population's primary drinking water source, is threatened by human activities. This study examines the Niger River pollution trend using heavy metals pollution indexes and Bamako's population's non-carcinogenic and carcinogenic related health risks. Parameters were monitored at fifteen sampling locations in low and high flow seasons. pH (7.30-7.50) and fluoride (0.15-0.26 mg/L) were within the normal drinking water range. Among seven heavy metals (copper, zinc, cadmium, nickel, iron, manganese, and lead), the latter three were above the drinking water standard. The degree of contamination was negative, pointing to better water quality. However, the heavy metal evaluation index (HEI) was below the mean (5.88), between the mean and twice the mean, indicating a low and medium degree of pollution. Besides, heavy metal pollution indexes (HPI) were above the standard value (100), explaining a low-medium pollution level. High values of HPI could be explained by the industrial units' intensive activities coupled with the runoff effect. The hazard index (HI) indicated a low and medium non-carcinogenic health risk for adults and children. The probability of cancer risk (PCR) of nickel showed a cancer risk. Therefore, the river was polluted with trace elements and could not be used for drinking water without any treatment.

Spatiotemporal assessment of groundwater quality in the Central Ganga Plain, India, using multivariate statistical tools.

In the present study, the water quality status of the Sadar block in Pratapgarh district of Uttar Pradesh, India, was calculated by Water Pollution Index (WPI) using 15 groundwater quality parameters (pH, turbidity, EC (electric conductivity), TDS (total dissolved solids), salinity, total hardness, Ca2+, K+, Mg2+, Na+, Cl-, F-, SO42-, HCO3-, and NO3-) at 40 different study locations. It is found that 10%, 25%, and 65%, of samples, fall under the good, moderately polluted, and highly polluted category, respectively. Irrigation suitability of groundwater has also been evaluated by using irrigation suitability parameters, i.e., Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Kelly Ratio (KR), Percentage Sodium (% Na), and Magnesium Hazards (MH). Based on the analysis, it was determined that most of the samples were suitable for irrigation in the area. Irrigation practices are found to be unsuitable for Kelly's ratio of 37.5%, MH 60%, and RSC 55% samples. WPI and Pearson's correlation matrix (PCA) have suggested a significant linear relationship among the water quality parameters based on the test results. The Gibbs plot indicates that the majority of samples fall under the "evaporation dominance" category while the rest fall under the "rock weathering dominance" suggesting that the complex geochemical mechanisms are active in the study area. This study would be very helpful to government authorities and it would aid them to suggest alternate drinking water resources in affected areas.

The assessment of the amount of water pollution and its suitability for drinking of the Tysmienica River Basin, Poland.

The quality and potability of waters of the Tysmienica River Basin were determined in 2017 at eight measuring stations. The paper presents analyses of the physical and chemical parameters of surface waters of the basin. With regard to suspended solids, pH value, electric conductivity, sulphates, ammonia, chlorides and phosphate, the water was classified as having a very good ecological status. In the case of Kjeldahl nitrogen, the waters were classified as having a good ecological status. In the case of the remaining parameters, i.e. BOD, DO, TOC and COD, the status varied among stations. The values of all the physical and chemical parameters complied with the values for undisturbed conditions. Only ammonia and COD showed significant differences among stations. The WPI values for each measuring point ranged from 0.90 to 1.56, what corresponds to the descriptive indicators of moderately polluted water. The high concentrations of COD and TOC indicate that high-performance treatment processes (category A3) must be used to ensure that the water is suitable for drinking.

Cleaning the river Damodar (India): impact of COVID-19 lockdown on water quality and future rejuvenation strategies.

Globally, it is established that the partial lockdown system assists to improve the health of the total environment due to inadequate anthropogenic actions in different economic sectors. The ample research on fitness of environment has been proved that the strict imposition of lockdown was the blessings of environment. The river Damodar has historical significance and lifeline for huge population of Jharkhand and West Bengal state of India but in the recent years the water quality has been deteriorated due to untreated industrial effluents and urban sewage. The main objective of this study is to examine the water quality of river Damodar during and prelockdown phase for domestic use and restoration of river ecosystem. A total of eleven (11) effluent discharge sites were selected in prelockdown and during lockdown phase. A new approach of water quality assessment, i.e., water pollution index (WPI) has been applied in this study. WPI is weightage free, unbiased method to analysis of water quality. The result shows that the physical, chemical and heavy elements were found beyond the standard limit in prelockdown period. The cation and anion were arranged in an order of Na2+ > K+ > Ca2+ > Mg2+ and Cl- > So4 - > No3 - > F- in both the sessions. WPI of prelockdown showed that about 100% water samples are of highly polluted. WPI of lockdown period showed that around 90.90% samples improved to 'good quality' and 9.10% of samples are of 'moderately polluted.' Hypothesis testing by 't' test proved that there was a significant difference (ρ = 0.05%) in values of each parameter between two periods. Null hypothesis was rejected and indicated the improvement of river water quality statistically. Spatial mapping using Arc GIS 10.4 interpolation (IDW) helps to understand spatial intensity of pollution load in two periods. This research study should be helpful for further management and spatial diagnosis of water resource of river Damodar.

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.

Water quality assessment in Qu River based on fuzzy water pollution index method.

A fuzzy improved water pollution index was proposed based on fuzzy inference system and water pollution index. This method can not only give a comprehensive water quality rank, but also describe the water quality situation with a quantitative value, which is convenient for the water quality comparison between the same ranks. This proposed method is used to assess water quality of Qu River in Sichuan, China. Data used in the assessment were collected from four monitoring stations from 2006 to 2010. The assessment results show that Qu River water quality presents a downward trend and the overall water quality in 2010 is the worst. The spatial variation indicates that water quality of Nanbashequ section is the pessimal. For the sake of comparison, fuzzy comprehensive evaluation and grey relational method were also employed to assess water quality of Qu River. The comparisons of these three approaches' assessment results show that the proposed method is reliable.

Hemato-biochemical alteration in the bronze featherback Notopterus notopterus (Pallas, 1769) as a biomonitoring tool to assess riverine pollution and ecology: a case study from the middle and lower stretch of river Ganga.

Fishes are poikilothermic animals and are rapid responders to any sort of ecological alteration. The responses in the fish can be easily assessed from their hematological and biochemical responses. To study the variation in the hemato-biochemical parameters in retort to ecological alteration and ecological regime, a study was conducted at six different sampling stations of the middle and lower stretches of river Ganga. Various hematological and biochemical responses of fishes were also monitored in response to multiple ecological alterations. For the assessment of ecological alteration, various indices were calculated such as the water pollution index (WPI), National Sanitation Foundation-water quality index (NSF-WQI), and Nemerow's pollution index (NPI) has been calculated based on various water quality parameters such as dissolved oxygen (DO), pH, total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), electrical conductivity (EC), biochemical oxygen demand (BOD), chlorinity (CL), total nitrogen (TN), and total phosphorus (TP). The hematological parameters such as WBC, RBC, platelet, hemoglobin, and hematocrit were monitored. The serum biochemical parameters such as SGPT, SGOT, ALP, amylase, bilirubin, glucose, triglyceride (TRIG), and cholesterol (CHOL) were investigated. The study revealed that NSF-WQI varied from 45.08 at Buxar to 110.63 at Rejinagar and showed a significantly positive correlation with SGPT, SGOT, ALP, TRIG, CHOL, and WBC, whereas a significantly negative correlation was observed between TRIG and RBC. WPI varied from 19 to 23 and showed a significant positive correlation with SGOT and a negative correlation was observed with total nitrogen. The PCA analysis illustrated the significance of both natural as well as anthropogenic factors on riverine ecology. Strong positive loading was observed with SGPT, SGOT, ALP, and platelet. The study signified the need for monitoring the hemato-biochemical responses of fishes in response to alterations in the ecological regime.

The Use of the Contamination Index and the LWPI Index to Assess the Quality of Groundwater in the Area of a Municipal Waste Landfill.

Environmental degradation caused by the migration of pollutants from landfills is one of the biggest problems for urban areas. Systematic monitoring of groundwater in the vicinity of waste dumps allows for an assessment of the degree of risk to the soil and water environment. In this paper, spatiotemporal variation of groundwater pollution near a municipal landfill in Sosnowiec (southern Poland) was investigated. For this purpose, the monitoring results of five physicochemical indicators from 2014-2019 were used. This study presents an example of the application of the Landfill Water Pollution Index (LWPI) and the Cd Contamination Index. The obtained results indicated that the tested waters were negatively influenced by municipal landfills, especially in the southern part (piezometers P8 and P10). The values of the Contamination Index even reached a value equal of about 1400, while the values of the LWPI index reached 305. Significantly lower values of both indicators were obtained using the results of monitoring studies for other piezometers located upstream from the landfill but belonging to the observation network of a neighboring facility. The indices used permit a determination of the level of groundwater contamination from the described landfill and can be used in similar research areas.

Assessing pollution profiles along Little Akaki River receiving municipal and industrial wastewaters, Central Ethiopia: implications for environmental and public health safety.

Little Akaki River drains residential, industrial, and agricultural irrigation areas of Addis Ababa City Administration and is exposed to point and non-point sources of pollution. The purpose of this study was to identify sources, evaluate the levels of river water pollution, and its implications for environmental and public health. Pollution indices and multivariate statistical analyses were used to determine sources and levels of the river water pollution. Trace metals concentration was determined using inductive coupled plasma optical emission and spectrophotometer (ICP-OES). The average concentrations of COD, BOD, TDS, NO3-N, NH3-N, SO4 -2, and PO4 -3 ranged from 40. 33 ± 5. 13 to 425 ± 8. 00 mg/L; 12.34 ± 0.11 to 188 ± 7.07 mg/L; 48.00 ± 0.83 to 915. 57 ± 1. 27 mg/L; 1.56 ± 1.01 to 66.50 ± 6.36 mg/L; 0.15 ± 0. 08 to 42.83 ± 11.43 mg/L; 20.50 ± 10.61 to 77.50 ± 17.68 mg/L; and 0.35 ± 0.33 to 37.95 ± 0.92 mg/L, respectively. The average concentrations of Zn ranged (0.048 ± 0.037 to 0.318 ± 0.158 mg/L), Cr (0.012 ± 0.007 to 0.203 ± 0.199 mg/L), Cd (<0.014 ± 0.0007 to 0.02 ± 0.001 mg/L) and Pb (0.031 ± 0.008 to 0.124 ± 0.034 mg/L). The comprehensive water pollution index values varied from 0.84-13.32, indicating that at all sampling sites (except for sampling site S1), the river water was heavily polluted (CPI >2.01). Heavy metal pollution index values further demonstrated potential environmental and public health implications. The principal component analysis revealed a total of 88.99% variation in the dataset, mainly contributed by organic matter, nutrients, dissolved salts, and trace metals that originated from anthropogenic sources. Contamination of the river water has impaired its suitability for urban agriculture, aquaculture, livestock drinking, and recreational purposes. Thus, improving the river water quality is recommended to mitigate potential adverse effects and promote sustainable use of water resources.