Identifying influencing groundwater parameter on human health associate with irrigation indices using the Automatic Linear Model (ALM) in a semi-arid region in India.

Quality of water for the purposes of irrigation is a serious threat to the sustainable development of the agriculture sector. The main objective of this study is to evaluate the suitability of groundwater for irrigation purposes using various irrigation indices such as: Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Percentage Sodium (%Na), Magnesium Hazards (MH), Permeability Index (PI), Potential Salinity (PS), Residual Sodium Bicarbonate (RBSC), Kelly's Ratio (KR), Synthetic Harmful Coefficient (K), and Exchangeable Sodium Percentage (ESP). A total of 30 samples were collected from the bore well of agricultural farmland and analysed for cations and anions. MH reveal that 53.33 % of samples exceed the permissible level. PS shows that 43.33 % of samples are marginally affected and 33.33 % of samples are unsuitable for use in irrigation. About 76 % of the groundwater samples were suitable for irrigation and the remainder require treatment before use. Automatic Linear Modelling (ALM) is used to predict the major influence parameter for MH and PS are RBSC, RSC and K value of groundwater. ALM shows that excess magnesium concentration and salinity are the primary factors that affect the suitability of groundwater for irrigation use. This integrated technique showed that water from approximately 25 % of the sample locations would require treatment before use. This study will improve the pattern of irrigation, identify sources of contamination and highlight the importance of organic fertilizers to develop and enhance the sustainable practices in the study region.

Machine learning approach to evaluate the groundwater quality and human health risk for sustainable drinking and irrigation purposes in South India.

The main purpose of this study was to evaluate the suitability of groundwater for sustainable drinking and irrigation purposes using various indices, such as the nitrate pollution index, agriculture suitability index (ASI), non-carcinogenic human risk assessment (NCHRA), and radial basic function (RBF) model. The novelty of the present study is to develop the ASI model and integrate with RBF model to identify the highly dominating parameter in chemical equilibrium of groundwater. Results showed that >85% of sample locations were suitable for drinking purposes, and the nitrate concentration in groundwater had a negative impact on the overall quality of water. Approximately 12 and 19 sample locations were contaminated owing to the high nitrate concentrations in the study region. The NCHRA study identified that approximately 8.5%, 27.28%, 29.54%, 40.40%, and 28.20% of area was excessively affected during the winter compared to summer season for people 6 to 12 y, 13 to 19 y, 20 to 29 y, 30 to 65 y, and >65 y of age. The RBF model shows that the R2 values for each season were 0.84 and 0.85 during summer and winter, respectively. The north-east and central parts of the study region were found to be more contaminated. The present study identified that, pathway of nitrate contaminant from the agriculture field towards to the sample locations. Overall, parent rock weathering, carbonate ion dissolution, and infiltration of rainwater and leachate from municipal waste dumping yards were the dominant factors influencing the chemical composition of groundwater. The present study achieved the vibrant knowledge about source of contamination, health effect on human body and impact on agriculture uses to develop the cleaner water supply system. The study results will be helpful in enhancing the sustainable action plan for water management in the study area.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning.

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 µg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

Geochemical evaluation and human health risk assessment of nitrate-contaminated groundwater in an industrial area of South India.

The primary goal of this study is to evaluate the groundwater quality and conduct a non-carcinogenic risk assessment of nitrate contamination in an industrialized and high-density region of South India. A total of 40 sampling sites were identified in and around the industrial area, and samples were collected during the pre-monsoon and post-monsoon seasons. Piper and Gibbs' diagram shows that rock-water interaction, lithological characteristics and ion-exchange processes are the primary factors determining groundwater quality. The novel entropy water quality index (EWQI) indicated that 32 and 37.5% of the water in the study area were unsuitable for drinking purposes during both the pre-monsoon and post-monsoon seasons, respectively. Due to landfill leachate and modern agricultural activity, the nitrate concentration in groundwater post-monsoon had increased by 17.11%. The nitrate pollution index (NPI) value of groundwater exceeded the contaminated level by 22.77%. The non-carcinogenic human health risk assessment revealed that 35 and 40% of adult males, 37.5 and 52.5% of adult females and 42.5 and 55% of children during the pre-monsoon and post-monsoon periods were exposed to an increased concentration of nitrate in groundwater. The non-carcinogenic risk level to the exposed population in the study region descends in the following order: children > > females > males. The study suggests that low body weight in children is a direct result of consumption of low-quality water and that adult men and women suffer less severe consequences.

Integration of multi criteria decision analysis and GIS for evaluating the site suitability for aquaculture in southern coastal region, India.

Deterioration of water and soil quality, poor infrastructure facilities and improper maintenance are the major factors that govern aquaculture growth and production in major part of India. In the present study aims to identify the suitable land for aquaculture growth and suggest the sustainable practice to improvise the growth of aquaculture in study region. With use of analytical hierarchy process (AHP) the various significant parameters such as geology, pH, salinity, soil media, slope, geomorphology, land use land cover, distance to water, settlement and road networks were analyzed and based on these characteristics, thematic maps were prepared. The results are revealed that, that 882.13 km2 area was most suitable, 1264.88 km2 area was suitable and 14.00 km2 area was unsuitable for aquaculture in the study region. The study results will helpful to decision makers and to make a design plan for aquaculture growth in the study region.