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

The objective of this study was to establish a test method for assessing radon exhalation rates from building materials considering radon related environmental policy and research in Korea. This method was established in consideration of cost-effectiveness based on the International Standards Organization (ISO) method and the closed chamber method, which is an evaluation method for the emission of hazardous chemical substances from building materials in Korea. The assessment of radon exhalation rates from five types each of granite and marble used in the construction industry in Korea gave mean radon exhalation rates of 0.497 ± 0.467 Bq/m²∙h from granite and 0.193 ± 0.113 Bq/m²∙h from marble, indicating higher radon exhalation rates from granite. These results are consistent with those of a previous study, indicating that granites are more likely to show higher radon exhalation rates than marbles.

Livestock farms are recognized sources of ammonia emissions, impacting nearby regions’ fine dust particle concentrations, though the full extent of this impact remains uncertain. Air dispersion models, commonly employed to estimate particulate matter (PM) levels, are heavily reliant on data quality, resulting in varying levels of accuracy. This study compares the performance of both air dispersion models and spatiotemporal deep learning models in estimating PM concentrations in Republic of Korea’s livestock-farming areas. Hourly PM concentration data, alongside temperature, humidity, and air pressure, were collected from seven monitoring stations across the study area. Using a 200 m × 200 m prediction grid, forecasts were generated for both 1 h and 24 h intervals using the Graz Lagrangian model (GRAL) and a one-dimensional convolutional neural network combined with the long short-term memory algorithm (1DCNN-LSTM). Results highlight the potential of the deep learning model to enhance PM prediction, indicating its promise as an effective alternative or supplement to conventional air dispersion models, particularly in data-scarce areas such as those surrounding livestock farms. Gaining a comprehensive understanding and evaluating the advantages and disadvantages of each approach would offer valuable scientific insights for monitoring atmospheric pollution levels within a specific area.

Hyungjin Hong, Jiwon Choi, Sungwon Yoon, Heechun Kim, and Cheolmin Lee. J Environ Health Sci. 2021;47:425-31. https://doi.org/10.5668/JEHS.2021.47.5.425

Shin-Young Park, Dann-Ki Yoon, Hyeok Jang, Sung Won Yoon, and Cheol-Min Lee. J Environ Health Sci. 2023;49:218-27. https://doi.org/10.5668/JEHS.2023.49.4.218

Conventional health risk assessments do not adequately reflect short-term exposure characteristics following chemical accidents. We aimed to evaluate the efficacy of existing assessment methods and propose a more suitable risk assessment approach for short-term exposure to hazardous chemicals. We analyzed foundational studies used to derive reference concentration (RfC), reference dose (RfD), and minimal risk level (MRL) values and applied these health guidance values (HGVs) to a hypothetical chemical accident scenario. An analysis of the studies underlying each HGV revealed that, except for the RfC for formaldehyde and the RfD for toluene, all values were derived under research conditions comparable to their respective exposure durations. Given the differing toxicity mechanisms between acute and chronic exposures, MRLs that were aligned with the corresponding exposure durations supported more appropriate risk management decisions. The health risk assessment results showed that RfC/RfD-based hazard quotients (HQs) were consistently higher than MRL-based HQs across all age groups and both substances, indicating that RfC/RfD values tend to yield more conservative risk estimates. For formaldehyde, the use of RfC instead of MRL resulted in an additional 208 tiles (2.08 km²) being classified as areas of potential concern (HQ > 1) relative to the MRL-based evaluation. These findings highlighted that the selection of HGVs can significantly influence the spatial extent of areas of potential concern, potentially altering health risk determinations for large population groups. This study provides a scientific basis for improving exposure and risk assessment frameworks under short-term exposure conditions. It also serves as valuable foundational data for developing effective and rational risk management strategies during actual chemical accidents. To the best of our knowledge, this is the first study to apply MRLs to a short-term chemical accident scenario and directly compare them with traditional reference values.