Application of Stationary IoT Sensor Networks to Reduce Exposure to Particulate Matter During Local Hotspot Events in
Sangwon Bang1,2, Kyung Hwan Kim1, Kwang-Rae Kim3
1Center for Climate and Carbon Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
Abstract:
Growing public concern over the health impacts of particulate matter (PM2.5 and PM10) has highlighted the need for air quality monitoring networks that can raise an alert when high PM2.5 and PM10 concentrations are detected. However, existing networks often have insufficient spatiotemporal resolution to detect localized events with high PM2.5 and PM10 concentrations. In this study, the existing air quality monitoring network in Seoul was complemented by Smart Seoul Data of Things (S-Dot), which is an Internet of Things (IoT)-based sensor network that collects various types of urban data. S-Dot sensors were used to track the spatial distribution and temporal evolution of high-concentration plumes generated by a fireworks festival, and the PM2.5 and PM10 concentrations measured by the S-Dot sensors were corrected with data measured by precision instruments at the closest air quality monitoring station. While the existing high-concentration alert system is designed to issue warnings when averaged PM2.5 concentrations measured at twenty-five air quality monitoring stations across the entire urban area exceed a threshold for a specified duration, the high spatial resolution of S-Dot was leveraged to provide localized alerts in near real time on plume location and movement. A new alert protocol is suggested to help reduce public exposure to pollutants, mitigate associated health risks, and encourage behavioral changes to improve air quality.
