The study investigated PM_2.5 and heavy metal pollutant concentrations in Seoul and Wonju, South Korea, emphasizing the importance of considering PM_2.5 chemical constituents for health impact assessments. While PM_2.5 concentrations were similar between the two cities with slight variations, heavy metal concentrations differed significantly. Regional sources, PM_2.5 composition, and meteorological conditions influenced these variations. Exposure to Fe was highest in all areas, with some heavy metals exceeding permissible levels, stressing the need to consider regional characteristics in assessments. Different heavy metals influence health risks differently in each city. When examining the HI (the summation of the hazard quotient for heavy metals), Seoul remained below an HI value of 1, while Wonju exceeded this threshold when exposed to CTE concentrations. In Seoul, As had a significant health impact of PM_2.5, while in Wonju, Mn was more influential. Source apportionment confirmed different pollution sources in Seoul and Wonju, affecting the distribution of PM_2.5 constituents in the atmosphere. Effective PM_2.5 management requires a comprehensive approach considering chemical constituents and health impacts not just mass-based PM_2.5 management, emphasizing regional-specific assessments and policy considerations to mitigate health risks. Therefore, tailored management strategies based on regional characteristics are necessary. Graphical Abstract

This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM_2.5 concentration through a multiple linear regression model. The atmospheric conditions and air pollution detected in one-minute intervals using sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside and outside houses from May 2019 to April 2021 were used to develop the prediction model. By dividing the multiple linear regression model into one-hour increments, we attempted to overcome the limitation of not representing the multiple linear regression model’s characteristics over time and limited input variables. The multiple linear regression (MLR) model classified by time unit showed an improvement in explanatory power by up to 9% compared to the existing model, and some hourly models had an explanatory power of 0.30. These results indicated that the model needs to be subdivided by time period to more accurately predict indoor PM_2.5 concentrations.

Particulate matter (PM) can cause illness, including respiratory diseases, and PM_2.5 compositions are likely to vary according to the emission profiles of industrial complexes. This study analyzed and compared the concentrations and distributions of PM_2.5 and heavy metals in two regions of Republic of Korea: Yeosu·Gwangyang, which houses a massive national industrial complex, and Dangjin, which houses power plants. Further, we conducted a health risk assessment on the residents of the areas near these industrial complexes. Measurements were taken at five different points in each setting over a two-year period from August 2020 to August 2022. We found differences in PM_2.5 concentrations and heavy metal composition ratios across the sites. Specifically, PM_2.5 concentrations exceeded the standard of 1 at all measurement sites, while the specific heavy metals exceeding the standard varied across the sites. Ultimately, we observed regional differences in PM_2.5 composition across measurement sites across and within the two regions and variations in health risks and according health effects due to the absence of PM_2.5 toxicity values, and compared the health risks of two industrial complexes with different characteristics. These findings underscore the importance of considering not only PM_2.5 but also its composition in exposure and health risk assessments.

This study evaluates the chemical components of an aerodynamic diameter less than 2.5 μm (PM_2.5) and its health risks in Seoul, Incheon, and Wonju, South Korea. The results revealed significant regional variations, particularly under the reasonable maximum exposure scenario, with Seoul’s average daily dose (6.4 × 10⁻¹ µg/kg/day) approximately 2 times higher than Incheon (5.8 × 10⁻¹ µg/kg/day) and Wonju (3.2 × 10⁻¹ µg/kg/day) under the central tendency exposure scenario. Furthermore, exposure to the chemical components comprising PM_2.5 can surpass risk thresholds when PM_2.5 concentrations exceed the national standard levels. These findings suggest the potential benefits of preventive measures, such as minimizing outdoor exposure, especially for individuals over 60 years of age, to help reduce health risks. However, further research is needed to confirm the effectiveness of these measures in different regions. The study also highlighted the variation in the health impacts of PM_2.5 concentrations and its chemical components across the different regions. The results suggest that relying solely on PM_2.5 concentrations for health risk assessments may underestimate the risks associated with carcinogenic components such as chromium (Cr, VI). However, under the reasonable maximum exposure (RME) scenario, the excess cancer risk (ECR) for Cr (VI) exceeds the acceptable threshold in all three regions, suggesting a high carcinogenic risk under the RME scenario. For example, the ECR for Cr(VI) in Seoul was calculated as 1.4 × 10⁻⁴, Incheon as 2.0 × 10⁻⁴, and Wonju as 1.2 × 10⁻⁴. Therefore, we emphasize the importance of incorporating both the mass concentration of PM_2.5 and its chemical constituents when conducting health risk assessments to inform region-specific health policies to mitigate health risks, particularly for vulnerable populations.

Particulate matter (PM) composition and distribution heavily rely on the spatiotemporal characteristics of its emission sources. This study analyzed the distribution characteristics and sources of fine PM (PM_2.5) and its chemical components at five locations near the Yeosu and Gwangyang industrial complexes. Samples were chemically analyzed, considering their ionic, carbonaceous, and trace elements, from August 2020 to July 2023. Spatial characteristics were examined using the coefficient of divergence and Pearson correlation coefficient, and temporal characteristics were investigated using monthly, seasonal, and annual distribution patterns. The range of average PM_2.5 concentrations at the five measurement sites was 17.2–20.3 μg/m³. The time-series analysis indicated that ionic species consistently comprised the largest proportion of PM_2.5 across all seasons and years (51.4–60.3%), with the highest proportion observed in winter. Trace elements and carbonaceous species were present in higher proportions in spring (19.4%) and autumn (35.7%), respectively. High concentrations of PM_2.5 and its chemical components were identified by temporal variations and emission sources. Principal component analysis showed that the chemical components of PM_2.5 were mainly from soil dust, vehicular emissions, the steel industry, and other pollution sources. This study provides foundational data for future research aimed at air pollutant emissions management and PM_2.5 source apportionment.