Abstract
To investigate the spatial and temporal variability of air quality (CO, NO2, O3, and PM2.5) with a high spatial resolution in various adjacent micro-environments, 30 sets of sensor-nodes were deployed within an 800 × 800 m monitoring domain in the center of the largest megacity (Seoul) in South Korea. The sensor network was operated in summer and winter. The daily variation in air pollutant concentrations revealed a similar trend, with discernible concentration differences among monitoring sub-sites and a government-operated air quality monitoring station. These differences in pollutant levels (except PM2.5) among the sub-sites were pronounced in the daytime with high volumes of traffic. The coefficient of divergence and Pearson correlation coefficient showed that spatial and temporal variability was more significant in summer than winter. Ozone displayed the greatest spatial variability, with little temporal variability among the sub-sites and a negative correlation with NO2, implying that ozone concentrations were primarily determined by vehicular NOX emissions due to NO titration effects under the urban canopy. The PM2.5 concentration displayed homogeneous spatial and temporal distributions over the entire monitoring period, implying that PM2.5 monitoring with at least a 1 × 1 km resolution is sufficient to examine the spatial and temporal heterogeneity in urban areas.
Highlights
We considered the relative difference (RD) in air pollutant concentrations between the monitoring sub-sites and air quality monitoring station (AQMS)
RD of PM2.5 in winter was insignificant at all sub-sites for both the cold wave and polluted periods. These results suggest that the spatial distributions of PM2.5 were homogeneous regardless of the season, at least within the monitoring spatial scale (~1 × 1 km), even in urban areas with a dense traffic network and various different micro-environments
A number of studies have suggested the potential for high-density air quality monitoring with cost-effective sensors
Summary
Traffic emissions are the major source of air pollution in urban areas [1], accounting for 90% of primary pollutant emissions (e.g., NOX and CO) in Korea [2]. Populations living near major roadways have a high risk of acute and chronic health problems due to air pollution exposure [3,4,5]. The population living in urban areas accounted for 56% of the global population in 2020, which is expected to increase to up to 68% in 2050 worldwide It is important to assess the health risks associated with being exposed to air pollutants emitted from dense urban road-networks by monitoring the highly spatiotemporally resolved air quality in various urban micro-environments
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