Efficient Real Time Particulate Matter Emission Monitoring System Using Serverless Computing

Abstract

The Coal-ablaze Bull Trench Kiln (BTK) is a traditional and widely used newlinebrick-making method, representing a unique blend of cultural heritage and newlinefunctional design for the construction industry. However, BTK-based brick kilns newlineare significant contributors to air pollution, emitting high levels of PM pollutants newline(Particulate Matter2.5, Particulate Matter10), that are recognized for having newlineextended periods of atmospheric suspension. These emissions pose considerable newlineenvironmental and health risks, affecting air quality and endangering all forms of newlinelife in the vicinity. Monitoring these emissions through traditional, static air newlinequality stations presents challenges due to the high costs and infrastructure newlinedemands of these stations, which are typically sparsely located. This scenario newlineemphasizes the urgent need for alternative, cost-effective approaches to newlinemonitoring, such as leveraging IoT, deep learning, and serverless computing newlinetechnologies. While IoT devices are ideal due to their portability and lower costs, newlinetheir readings are sensitive to ambient air fluctuations in temperature and newlinehumidity, necessitating regular calibration to ensure data accuracy newline