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Abstract <p>PM<sub>2.5</sub>, a fine particle, is sufficiently dangerous for classification as a Class-1 carcinogen, and it remarkably contributes to premature death. This study aimed to assess population exposure to PM<sub>2.5</sub> in Seoul, South Korea, using data from air quality monitoring stations (AQMSs) and sensor-based monitoring instruments (SAMIs). Five AQMSs were verified using co-location tests, and population distribution data were segmented by sex and age in hourly grids. The population-weighted average concentration (PWAC) was calculated using occupancy rates and indoor-to-outdoor PM<sub>2.5</sub> concentration ratios. Premature death was estimated using a concentration–risk function and compared to those of the WHO (World Health Organization) guidelines. In addition, machine-learning models, including random forest (RF) and a support vector machine (SVM), were used for PM<sub>2.5</sub> estimation, achieving high accuracy (R² =0.83, RMSE = 10.1&#xa0;µg/m³). The PWAC was found to be approximately 8% higher in spring than in fall, with the average seasonal PM<sub>2.5</sub> levels exceeding the WHO annual standard of 15&#xa0;µg/m³. Reducing PM<sub>2.5</sub> concentrations to the WHO recommended level of 5&#xa0;µg/m³ could lower premature deaths by over 70%. A SAMI, when integrated with an AQMS, could enhance real-time PM<sub>2.5</sub> monitoring and forecasting, thereby reducing public-health risks by enabling timely interventions.</p> Graphical abstract <p></p>

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Spatiotemporal assessment of population exposure and premature mortality using models for estimation of PM2.5 concentrations

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Jihun Shin,
  • Jaemin Woo,
  • Youngtae Choe,
  • Gihong Min,
  • Wonho Yang

摘要

Abstract

PM2.5, a fine particle, is sufficiently dangerous for classification as a Class-1 carcinogen, and it remarkably contributes to premature death. This study aimed to assess population exposure to PM2.5 in Seoul, South Korea, using data from air quality monitoring stations (AQMSs) and sensor-based monitoring instruments (SAMIs). Five AQMSs were verified using co-location tests, and population distribution data were segmented by sex and age in hourly grids. The population-weighted average concentration (PWAC) was calculated using occupancy rates and indoor-to-outdoor PM2.5 concentration ratios. Premature death was estimated using a concentration–risk function and compared to those of the WHO (World Health Organization) guidelines. In addition, machine-learning models, including random forest (RF) and a support vector machine (SVM), were used for PM2.5 estimation, achieving high accuracy (R² =0.83, RMSE = 10.1 µg/m³). The PWAC was found to be approximately 8% higher in spring than in fall, with the average seasonal PM2.5 levels exceeding the WHO annual standard of 15 µg/m³. Reducing PM2.5 concentrations to the WHO recommended level of 5 µg/m³ could lower premature deaths by over 70%. A SAMI, when integrated with an AQMS, could enhance real-time PM2.5 monitoring and forecasting, thereby reducing public-health risks by enabling timely interventions.

Graphical abstract