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

Abstract

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.