A health risk model for rural households based on the distribution of multi pollutants. (21st March 2023)

• Record Type: Journal Article
• Authors: Rathnamala, G. V.; Shivashankara, G. P.; Ashwini, R. M.; Rashmi, H. R.; Bhowmik, Basuraj
• Is Part Of: Water science and technology. Volume 87:Number 7(2023)
• Page Start: 1686
• Page End: 1702
• Publication Date: 2023-03-21
• Subjects: environment -- health risks -- pollutants -- rural households; Water -- Pollution; Sewage -- Purification; Water quality management; Periodicals; 628.168
• DOI: 10.2166/wst.2023.084
• Languages: English
• ISSNs: 0273-1223

Abstract

Abstract: Rapid consumerism and improper waste disposal create widespread environmental degradation through the air, water sources and landfills in India's rural areas. This work develops a health risk prediction model to score villages based on quantitative and qualitative factors. Quantitative observations regarding pollutant levels and qualitative responses are collected from various households. that are risk labelled against WHO standards. The health risk model is designed to correlate the qualitative factors. A total of 2, 370 rural households spread across three districts of Karnataka were selected. The study found that the health risk score predicted by the model has a higher significant correlation (0.8) to various existing pollutant factors. The study found that source of drinking water (0.87), quality of drinking water (0.81), drainage canal availability (0.72), type of drainage (0.73), stagnant water (0.71), toilet availability (0.83), maintenance frequency (0.83), cooking fuel type (0.77), cigarette use (0.71), garbage piles up (0.73) and the percentage composition of wastes (0.74) was found to have a higher positive correlation to the health of rural households. The villages with higher health risks can be identified, and suitable mitigation plans can be designed to mitigate the health risk by state authorities. HIGHLIGHTS: This work develops a health risk prediction model to score the villages based on quantitative and qualitative factors. The study found that the health risk score predicted by the model has a higher significant correlation (>0.8) to various existing pollutant factors. Graphical Abstract