Spatiotemporal Exposure Assessment of PM2.5 Concentration Using a Sensor-Based Air Monitoring System

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 m...

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Bibliographic Details
Published in:Atmosphere 2024-06, Vol.15 (6), p.664
Main Authors: Shin, Jihun, Woo, Jaemin, Choe, Youngtae, Min, Gihong, Kim, Dongjun, Kim, Daehwan, Lee, Sanghoon, Yang, Wonho
Format: Article
Language:English
Subjects:
Accuracy
Air
Air monitoring
Air pollution
Air quality
Air quality measurements
Asthma
Distance
Estimation
Geographic information systems
Interpolation
Interpolation techniques
Monitoring instruments
Monitoring systems
Outdoor air quality
Particulate matter
PM2.5
Pollutants
population exposure
Risk assessment
sensor-based instruments
Sensors
Spatial distribution
spatiotemporal resolution
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Summary: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.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos15060664