Spatiotemporal Exposure Assessment of PM_2.5 Concentration Using a Sensor-Based Air Monitoring System

Abstract

Sensor-based air monitoring instruments (SAMIs) can provide high-resolution air quality data by offering a detailed mapping of areas that air quality monitoring stations (AQMSs) cannot reach. This enhances the precision of estimating PM2.5 concentration levels for areas that have not been directly measured, thereby enabling an accurate assessment of exposure. The study period was from 30 September to 2 October 2019 in the Guro-gu district, Seoul, Republic of Korea. Four models were applied to assess the suitability of the SAMIs and visualize the temporal and spatial distribution of PM2.5. Assuming that the PM2.5 concentrations measured at a SAMI located in the center of the Guro-gu district represent the true values, the PM2.5 concentrations estimated using QGIS spatial interpolation techniques were compared. The SAMIs were used at seven points (S1–S7) according to the distance. Models 3 and 4 accurately estimated the unmeasured points with higher coefficients of determination (R2) than the other models. As the distance from the AQMS increased from S1 to S7, the R2 between the observed and estimated values decreased from 0.89 to 0.29, respectively. The auxiliary installation of SAMIs could resolve regional concentration imbalances, allowing for the accurate estimation of pollutant concentrations and improved risk assessment for the population.

Keywords

• PM_2.5
• sensor-based instruments
• interpolation
• spatiotemporal resolution
• population exposure