Air quality, especially particulate matter, has recently attracted a lot of attention from governments, industry, and academia, motivating the use of denser air quality monitoring networks based on low-cost sensing strategies. However, low-cost sensors are frequently sensitive to aging, environmental conditions, and pollutant cross-sensitivities. These issues have been only partially addressed, limiting their usage. In this study, we develop a low-cost particulate matter monitoring system based on special-purpose acquisition boards, deployed for monitoring air quality on both stationary and mobile sensor platforms. We explore the inﬂuence of all model variables, the quality of different calibration strategies, the accuracy across different concentration ranges, and the usefulness of redundant sensors placed in each station. The collected sensor data amounts to about 50GB of data, gathered in six months during the winter season. Tests of statically immovable stations include an analysis of accuracy and sensors’ reliability made by comparing our results with more accurate and expensive standard β radiation sensors. Tests on mobile stations have been designed to analyze the reactivity of our system to unexpected and abrupt events. These experiments embrace traffic analysis, pollution investigation using different means of transport and pollution analysis during peculiar events. With respect to other approaches, our methodology has been proved to be extremely easy to calibrate, to offer a very high sample rate (one sample per second), and to be based on an open-source software architecture. Database and software are available as open source in .
A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN / Montrucchio, Bartolomeo; Giusto, Edoardo; Ghazi Vakili, Mohammad; Quer, Stefano; Ferrero, Renato; Fornaro, Claudio. - In: IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY. - ISSN 0018-9545. - ELETTRONICO. - (2020).
|Titolo:||A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/TVT.2020.3035554|
|Appare nelle tipologie:||1.1 Articolo in rivista|