Frequency Analysis of Particulate Matter in Urban Environments under Low-cost Sensors

Air pollution comes to be a research relevance due to the over pollution generated by urban environments and their human health impact. The growing interest has impulsed the development of low-cost ultra-fine particulate matter (PM) sensors, and numerous projects involving WSN devices. However, monitoring ultra-fine particles present different challenges to define an optimal estimation rate. There is no defined estimation rate to different PM events of interest, or involving frequency behavior of PM signals sensed by light-scattering based sensors at higher rates than professional PM metering stations. This study presents a meticulous analysis of PM signals' frequency behavior from an open-source dataset to correctly determine the maximum frequency needed to digitalize air pollution signals. The results obtained from the analysis allow setting a period rate for estimating the concentration of particulate material with less resolution affectation and reducing the generation of data and energy consumption.