Understanding the Rapid Change of PM2.5 Using Low-cost Air Quality IoT Sensors
Abstract
This paper presents an analysis of PM2.5 monitoring using low-cost IoT sensors to understand the concentration change in relation to the meteorological data and demonstrate the benefit of the sensors toward air quality management. A case of rapid change of PM2.5 from a high concentration (150 µg/m3) in the morning to a low concentration (15 µg/m3) in the noon was depicted in Bangkok, Thailand on 15 December 2020. This was an unusual phenomenon. PM2.5 data were collected from SEA-HAZEMON platform during 8-16 December 2020 while vertical profiles of temperature and wind speed were collected from Bangna Agromet, Bangkok, Thailand from the on-line platform for the same period. Mixing layer height (MH) was estimated using temperature profiles for morning and noon periods. Correlation (R) analysis was used to explore relationships of estimated MH and PM2.5, and wind speed (WS) and PM2.5. Both MH and WS showed the inverse relationship on PM2.5 with R for MH and PM2.5 was -0.5495 and for WS and PM2.5 was -0.4682. For 15 December 2020, rapid change of PM2.5 in the noon was well explained by the strong wind above 500 m. height that help enhance PM2.5 dispersion along with the expansion of MH. With a real-time data record and open data for download, IoT sensors enrich the understanding of air pollution behaviors for further improvement and enable identification of relevant air pollution events timely. IoT sensors could bring more benefits to the areas, particularly for low- and middle-income countries, where standard monitoring stations are unfeasible.
