Development of a comprehensive air risk index and its application to high spatial-temporal health risk assessment in a large industrial city.

Environ Pollut

Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea; Research and Management Center for Particulate Matter in the Southeast Region of Korea, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea. Electronic address:

Published: December 2024

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 PM 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 national-scale real-time CARI system by enhancing the spatio-temporal resolution of HAP data through the use of machine learning.

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Source
http://dx.doi.org/10.1016/j.envpol.2024.125545DOI Listing

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