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A COMPARATIVE ANALYSIS OF PMs CONCENTRATION MEASURED

Gabriel Suciu

Abstract

More than thirty percent of deaths from stroke, lung cancer, and heart disease are due to air pollution. Due to the current economic global situation where half of the world has no access to clean energy, furthermore, it seems highly unlikely that IC engines will go out of business anytime soon, we face the reality that our engines will continue to pump out harmful emissions. The main pollutants are particulate matter, a mix of solid and liquid droplets arising mainly from fuel combustion and road traffic. Thus, when facing the issue of air pollution management, the current initiatives are focused on traditional air pollution monitoring networks that imply the use of expensive instruments, require specialized training, a large physical footprint, and massive power draw. Although these conventional air monitoring networks provide the basis of understanding of pollution trends and their associated health effects, it's reasonable to understand that we face a lack of knowledge (data, higher spatial resolution) in order to satisfy the increased public demand for more personalized information. But, the measuring of air pollution is an evolving technology landscape; the immediate solution seems to be the Internet of Things. This technology can revolutionize the way we monitor the environment and how we adapt to risks and challenges. The latest developments of remote sensing technologies promise a higher density of data and allow to capture environmental data near real-time. Although the current market presents various solutions for air quality monitoring, there is an issue of data accuracy, how reliable are this sensing equipment? This paper presents a case study to identify the confidence level of data captured by WSN sensor networks (based on laser-based monitoring equipment) while compared with data from gravimetric stations and laser-based monitoring equipment. Also, we analyzed the measurements captured by different types of equipment with the same technology as well as data monitored by the same equipment in order to assess the level of data variability.

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DOI resolver: doi.org/10.5593/sgem2019/4.1/s19.102

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