High-resolution analysis of seasonal spatial variation for urban air pollution: Implications for exposure assessment

Exposure to air pollutants is associated with significant health effects. Accurate exposure assessment remains a critical yet challenging aspect of environmental health research. Although air quality monitoring station (AQMS) data are commonly used as a surrogate for personal exposure, diverse spatial-temporal variations can lead to significant exposure misclassification. This study aimed to identify the seasonal spatial variation of five air pollutants (PM_2.5, PM₁₀, NO₂, CO, and O₃) at city and small spatial scales (1 km²) in Seoul, Korea, using data from the AQMSs and in-situ monitoring sites (IMSs) for high-resolution measurements. Measurements were conducted across four seasons over one year, covering city-, district-, and small-scale spatial units, with a detailed focus on a 1 km² area within one administrative district. The air pollutant concentrations were obtained from the 25 AQMSs in each district. Fine-scale measurements were carried out at eight IMSs within a 1 km² area surrounding a single AQMS in Guro-gu, Seoul. To enable direct comparison, measurements from the AQMS and IMS were simultaneously collected following standard monitoring protocols. Moran's index was used as an indicator of spatial autocorrelation to identify the homogeneity and heterogeneity of air pollutants by spatial units. Concentrations of pollutants at IMSs were overall higher than those at nearby the AQMS, except for O₃ concentrations in the spring and summer. Seasonal spatial autocorrelation patterns in city-scale areas did not reflect variations in small-scale areas. These findings highlight the limitations of relying solely on AQMS data for exposure assessment and underscore the value of integrating high-resolution data to reduce estimation errors. This study provides a framework for enhancing air quality management and exposure assessment strategies by accounting for spatial-temporal variations, especially in areas lacking dense monitoring networks.Implications: This study integrates city-scale air quality monitoring station (AQMS) data with high-resolution in-situ measurements to reveal discrepancies in seasonal spatial patterns of air pollutants across different spatial scales in Seoul, Korea. By directly comparing AQMS and IMS data using standard monitoring methods, it demonstrates that commonly used AQMS-based exposure estimates may significantly underestimate pollution levels in small-scale urban environments. This high-resolution approach highlights the critical need for incorporating fine-scale monitoring to improve personal exposure assessment in areas with sparse monitoring coverage.