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_2.5 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 national-scale real-time CARI system by enhancing the spatio-temporal resolution of HAP data through the use of machine learning.

中文翻译：

---

某大型工业城市空气综合风险指数的制定及其在高时空健康风险评估中的应用


颗粒物 （PM） 包含各种有害空气污染物 （HAP），这些污染物会对人类健康产生不利影响，因此需要一个综合指数来代表相关的健康风险。作为回应，本研究为韩国最大的工业城市蔚山开发了一个新的指数，即综合空气风险指数 （CARI）。该指数利用其吸入单元风险整合了多环芳烃 （PAH） 和重金属的毒性加权浓度。根据概率健康风险将 CARI 分为四个风险级别。在蔚山的八年（2013-2020 年）中，多环芳烃暴露的风险呈下降趋势，而重金属的风险保持稳定，反映了不同的排放模式和主要来源类型。PAH 和重金属对 CARI 的贡献分别为 38.1% 和 61.9%，凸显了重金属对人类健康的影响更大。与 PM_2.5 浓度的月度变化不同，CARI 值在夏季趋于增加，在春季和秋季趋于下降，这表明了当地排放的影响，尤其是石化和有色金属工业设施的影响。此外，机器学习模型增强了 CARI 的时空分辨率，表明工业区的“不健康”日的频率是城市地区的 2.4 倍。总之，CARI 是评估工业城市健康风险和制定基于风险的管理计划的有前途的工具。此外，我们建议通过使用机器学习来提高 HAP 数据的时空分辨率，从而开发一个国家规模的实时 CARI 系统。