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Water pollution and carbon dioxide emissions from solid waste landfills: Probabilistic monitoring and evaluation

Paper Topic: 
Water and Wastewater Treatment

Pages :
78 - 90

Corresponing Author: 
S. VijayaShanthy, K. Saravanan, E.B. Priyanka, V. Sampathkumar
Paper ID: 
Paper Status: 
Date Paper Accepted: 
Paper online: 
Visual abstract: 

Owing to increased population, the ground water pollution and disposal of solid waste from the domestic, commercial and the industrial sources has become higher. Mainly, leachate from sanitary landfills increase the ground water pollution and disposal of solid wastes may produce the emission of carcinogenic greenhouse gases. The gases include Carbon-dioxide (CO2), Nitrogen-dioxide (NO2), Methane (CH4) and Hydrogen Sulphide (H2S) which will contributes to the major and detrimental in nature. To know about the extremity of the gases, implementation of integrated sensors networks can transmit and store data in the cloud using the Internet of Things (IoT) to perform future prediction using Machine Learning Algorithm. To estimate ground water pollution, sample locations have been chosen using random sampling method which holds 89% efficiency in data sampling. Due to the increased proportion of liquid and solid waste at 17:93 ratio existence in the city, the study elaborates the health impacts of pathogens by pointing as a cancer capital of around 34% influence rates. Based on the CO2 emission and water pollution analysis, the modelling is done using Linear Regression Machine Learning algorithm which flags up the emission rate that could occur for the next three months with 86% accuracy. Also, appropriate mitigation measures can be suggested to the local government in the view of reducing both ground water pollution and the gaseous emissions. Finally, the identified pollution potential in the Vendipalayam site is compared with other landfill sites level of pollution for providing the adaptive measures further enhances environmental sustainability.

IoT. Emission gas monitoring, Linear Regression, Future Prediction, Environment Sustainability