Time-weighted assessment of personal PM2.5 exposure of patients with allergies using portable monitors in Seoul Metropolitan Area, Korea.

Patients with allergies are more sensitive to fine particulate matter (PM2.5) than the general population; however, since PM2.5 exposure levels are influenced by microenvironments, time, and activity patterns, epidemiological studies using conventional stationary monitors face challenges in accurately estimating personal exposure levels. Therefore, this study analyzed the personal PM2.5 exposure characteristics of 86 individuals with allergies living in Seoul using portable monitors and GPS units from February to April 2024. The Kolmogorov-Smirnov test confirmed that the measured PM2.5 concentrations did not follow a normal distribution. Therefore, non-parametric statistical methods such as the Kruskal-Wallis and Wilcoxon rank-sum tests were used to assess statistically significant differences in PM2.5 concentrations. Over 90% of their time was spent indoors, with outdoor environments and schools (weekdays) and transportation (weekends) having the highest average PM2.5 concentrations. The lowest PM2.5 concentrations were consistently observed at home on both weekdays (12.76 μg/m) and weekends (13.46 μg/m). Despite this, the time spent at home resulted in the highest integrated exposure levels (weekdays: 58.25%; weekends: 71.14%). The highest levels of time spent and integrated exposure at home were similarly observed across all five subpopulations (child, student, employed, unemployed, and housewife). The average PM2.5 exposure concentrations did not exceed the WHO 24-h PM2.5 exposure guideline of 15 μg/m. However, analysis of the 5-min interval personal PM2.5 exposure concentrations revealed that participants exceeded this threshold 5.9% and 31.25% of the time on weekdays and weekends, respectively, indicating a higher frequency of high-concentration exposure on weekends. These findings quantitatively identify the primary microenvironments where patients with allergies are exposed to PM2.5 during the day and demonstrate that personalized air quality information provides better insights into personal PM2.5 exposure sources. These results should serve as foundational data for technology development aimed at elucidating the correlation between PM2.5 exposure and allergic diseases and for providing personalized air quality management guidelines.