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최성득

Choi, Sung-Deuk
Environmental Analytical Chemistry Lab.
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Development of a comprehensive air risk index and its application to high spatial-temporal health risk assessment in a large industrial city

Author(s)
Lee, Sang-JinCho, In-GyuLee, Ho-YoungJu, Jeong-TaeShin, Hye-JungChoi, Sung-Deuk
Issued Date
2025-02
DOI
10.1016/j.envpol.2024.125545
URI
https://scholarworks.unist.ac.kr/handle/201301/86139
Citation
ENVIRONMENTAL POLLUTION, v.367, pp.125545
Abstract
Particulate matter (PM) contains various hazardous air pollutants (HAPs) that can adversely affect human health, highlighting the need for an integrated index to represent the associated health risks. In response, this study developed a novel index, the comprehensive air-risk index (CARI), for Ulsan, the largest industrial city in South Korea. This index integrates toxicity-weighted concentrations of polycyclic aromatic hydrocarbons (PAHs) and heavy metals using their inhalation unit risks. CARI was categorized into four risk levels based on probabilistic health risks. Over eight years (2013-2020) in Ulsan, the risk from PAH exposure showed a decreasing trend, whereas the risk from heavy metals remained stable, reflecting different emission patterns and major source types. PAHs and heavy metals contributed 38.1% and 61.9% to CARI, respectively, highlighting the greater impact of heavy metals on human health. Unlike the monthly variations in PM2.5 concentrations, CARI values tended to increase in the summer and decrease in the spring and fall, indicating the impact of local emissions, particularly from petrochemical and non-ferrous industrial facilities. Moreover, a machine learning model enhanced the spatio-temporal resolution of CARI, showing that 'unhealthy' days were 2.4 times more frequent in industrial areas than in urban areas. In conclusion, CARI is a promising tool for assessing health risks in industrial cities and for developing risk-based management plans. Furthermore, we propose the development of a nationalscale real-time CARI system by enhancing the spatio-temporal resolution of HAP data through the use of machine learning.
Publisher
ELSEVIER SCI LTD
ISSN
0269-7491
Keyword (Author)
Heavy metalsMachine learningRisk indexRisk assessmentPAHs
Keyword
OUTDOOR AIRAMBIENT AIRPOLLUTIONEXPOSUREAPPORTIONMENTRANDOM FORESTHEAVY-METALSPOLYCYCLIC AROMATIC-HYDROCARBONSPARTICULATE MATTERRESIDENTIAL AREA

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