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.

Impact of industrial hotspots on Tamla nala and Nunia nala confluence - a tributary of the Damodar river.

Clean water is essential for drinking, household use, and agriculture. Researchers studied 39 sites near Tamla nala and Nunia nala channels in Durgapur and Asansol City (West Bengal) to assess the deterioration level of water due to industrial discharge. During the first phase out of three, the researchers conducted a spatial representation of various physicochemical parameters, such as temperature, pH, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total Hardness (TH), Electrical Conductivity (EC), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), significant anions such as chloride (Cl-), nitrate (NO3-), phosphate (PO4-3), sulfate (SO42-), cyanide (CN-1) and fluoride (F-), as well as heavy metals/metalloids such as lead (Pb), cadmium (Cd), chromium (Cr), iron (Fe), copper (Cu), nickel (Ni), mercury (Hg) and arsenic (As). As observed the parameters at various sites along the stream exceeded threshold limits majorly due to industrial discharge: highest pH, TDS, TH, EC, Cl-, SO42- at site 26; Fe at site 1, TSS, COD, CN- at site 33, 31, 2 respectively; Cd, Ni, Cu at site 19; Hg and Pb at site 3 and As at site 20. Contaminated areas were marked in red and secure areas in green. Additionally, the HMPI (Heavy metal pollution index) was estimated for eight locations to understand the impact of heavy metal pollution in the second phase of the study. An extremely high HMPI indicates heightened toxicity and health risks for both residents and outsiders. The Canadian Water Quality Index (1.0) was calculated for eight sites in the third phase based on seventeen parameters. The resulting WQI value was below 44, indicating poor water quality at the sites. Due to the poor quality and critical heavy metal toxicity, the authors recommended continuous monitoring, strict regulation enforcement, increased treatment capacity, Zero Liquid Discharge implementation, and raising awareness among residents.

Age-dependent potential health risk assessment due to radioactive radon-222 in the environs of highly populated Durgapur industrial zone and nearby Bakreswar hot spring, India.

It is well known that exposure to a high concentration of radon-222 causes severe health effects, including cancer. The present article includes a survey on radon-222 in the water bodies of the city Durgapur [non-geothermal area] and nearby Bakreswar hot spring [geothermal province], India. The possible sources of radon from natural radionuclides and industries have been discussed in the article. Durgapur is a densely populated [~ 3680 persons/km2] industrial city with a population of 0.57 million. On the other hand, many tourists and pilgrims usually visit Bakreswar throughout the year. Age-dependent potential health risk assessments of the dwellers at Durgapur and Bakreswar due to radon exposure were performed for the first time. The present work is the first attempt to estimate the mean ingestion /and inhalation dose per annum, total effective dose [TED] per annum and the health risk assessment for cancer in adults, children and infants due to radon exposure at Durgapur and Bakreswar. In some cases, the values of TED exceed the permissible limit of 100 micro Sievert per year [µSv/y] as recommended by EUC and WHO. The radiation profile maps relating to radon concentration and associated contour maps of health risk factors [HRF] for the adults, children and infants were also prepared for the first time. Some areas were identified as high-risk zones, and the dwellers are prone to a high risk of cancer. The article also proposed several techniques to reduce radon in water and buildings. The authors also recommended banning some water sources to protect people from radon risk. This study will help scientists, policymakers, industrialists, farmers, government agencies and public health departments.

A study on water pollution scenario of the Damodar river basin, India: assessment of potential health risk using long term database (1980-2019) and statistical analysis.

The present work deals with a detailed study of India's Damodar river basin's pollution profile (groundwater, surface water, industrial water, and mine water). The present paper aims to create a large data bank comprising the latest (2019; through water sampling and analysis) and historical (1980-2018; through literature survey) data on heavy metal loads (HML) and other hydrochemical parameters in water bodies of the Damodar basin (at 99 sampling locations). Bivariate and multivariate statistical analyses were applied to determine the possible sources of the HML in the groundwater and surface water. Anthropogenic inputs from industrial effluents and mining activities were identified as the sources of the HML. The degree of HML exposure (Fe, Cu, Zn, Mn, As, Co, Cd, Hg, Cr, Ni, and Cu) was considered for computing the heavy metal pollution indices (HMPI). Associated potential health risk to the local population was also studied. Most HMPIs (vary within 50-9000) appeared to cross the critical value (~ 100). According to the results of noncarcinogenic risk, most hazard indices (varied within 0.01-116.34) surpassed the allowable limit (~ 1), demonstrating detrimental health effects on adults and children. Ni, Cd, As, and Cr showed very high cancer indices (varied within 9.5 × 10-5-1.76 × 10-1) that could be considered as high risk (≫ 1 × 10-4, allowable limit) for cancer via ingestion and dermal pathways. A carcinogenic risk assessment map of the basin was also prepared for the first time. Durgapur and Burnpur-Asansol cities have been identified as the most vulnerable areas. The authors also compared the water quality parameters of the Damodar river with other highly polluted and major rivers of India. The authors recommended (i) strict regulation and efficient management of HML monitoring, (ii) initiating public awareness programme about Damodar's pollution, and (iii) a detailed medical survey to understand the impact of water pollution on the population.