International Journal of Environmental Science and Development

Abstract—Greenhouse gas emissions are found in the atmosphere that absorbs energy within a given thermal range. These gases are also often found in solid waste management facilities, which are caused by massive piles of garbage. This study was conducted to develop a system that can monitor greenhouse gas emissions in a Material Recovery Facility. The study was also directed to further help in complying with the environmental laws in the Philippines. The developed monitoring system is a low-cost, portable device capable of detecting gases concentrations which are commonly found in solid waste management facilities. The system is integrated with a web application that can be used by solid waste engineers, and even citizens in monitoring greenhouse gas levels and potential actions. Self-Calibration was performed to develop an algorithm that can be integrated using Linear Regression. Isolation Testing and Third Party Testing were performed to ensure the device’s readings are accurate and reliable prior to Usability Testing. Tests results indicate that emission readings from the prototype are within the acceptable and expected range base on standards. These results allowed collection of data that were used in employing Clustering Technique to understand the emission patterns, and provide descriptive analysis of Green House Gas emissions in two zones of a Material Recovery Facility.

Index Terms—Greenhouse gas, internet of things, zone-based sensory, solid waste management, self-calibration, clustering.

Dennis A. Martillano, John Noel Corpuz, John Marfil Disonglo, and Jeffrey B. France are with Malayan Colleges Laguna, Philippines (e-mail: dennis.martillano@upou.edu.ph, noeljcorpuz@gmail.com, john.disonglo@gmail.com, jbfrance1626@gmail.com@gmail.com).

[PDF]

Cite: Dennis A. Martillano, John Noel Corpuz, John Marfil Disonglo, and Jeffrey B. France, "Borehole and Near-Surface Greenhouse Gas Emission Monitoring System with Self-calibrating Algorithm and Zone-Based Data Analysis via Clustering Technique," International Journal of Environmental Science and Development vol. 11, no. 4, pp. 186-193, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).