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.

Frequency Analysis of Particulate Matter in Urban Environments under Low-cost Sensors / RAMIREZ ESPINOSA, GUSTAVO ADOLFO; Montrucchio, Bartolomeo; Gandino, Filippo; Rebaudengo, Maurizio. - ELETTRONICO. - (2021), pp. 97-105. ((Intervento presentato al convegno 2021 International Conference on Computer Communication and Artificial Intelligence ({CCAI}) tenutosi a Guangzhou, China nel 7-9 May 2021 [10.1109/ccai50917.2021.9447517].

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

Gustavo Ramirez Espinosa;Bartolomeo Montrucchio;Filippo Gandino;Maurizio Rebaudengo
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2914317