There is a pressing need for tools that can rapidly predict ${\mathrm{PM}}_{2.5}$ concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting practical scenario analysis. To address this, we propose a novel method integrating a conditional U-Net surrogate model with health impact assessments, enabling swift estimation of ${\mathrm{PM}}_{2.5}$ concentrations and related health effects. The U-Net model was trained with 2019 South Korean ${\mathrm{PM}}_{2.5}$ data, including precursor emissions and boundary conditions. Our model showed high accuracy and significant efficiency, reducing processing times while maintaining reliability. By combining this surrogate model with the EPA’s BenMAP-CE tool, we estimated potential premature deaths under various emission reduction scenarios in South Korea, extending projections to 2050 to account for demographic changes. Additionally, we assessed the required ${\mathrm{PM}}_{2.5}$ emission reductions needed to counteract the increase in premature deaths due to an aging population. This integrated framework offers an efficient, user-friendly tool that bridges complex air quality modeling with practical policy evaluation, supporting the development of effective strategies to reduce ${\mathrm{PM}}_{2.5}$-related health risks and estimate economic benefits.

There is a pressing need for tools that can rapidly predict PM2.5 concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting practical scenario analysis. To address this, we propose a novel method integrating a conditional U-Net surrogate model with health impact assessments, enabling swift estimation of PM2.5 concentrations and related health effects. The U-Net model was trained with 2019 South Korean PM2.5 data, including precursor emissions and boundary conditions. Our model showed high accuracy and significant efficiency, reducing processing times while maintaining reliability. By combining this surrogate model with the EPA’s BenMAP-CE tool, we estimated potential premature deaths under various emission reduction scenarios in South Korea, extending projections to 2050 to account for demographic changes. Additionally, we assessed the required PM2.5 emission reductions needed to counteract the increase in premature deaths due to an aging population. This integrated framework offers an efficient, user-friendly tool that bridges complex air quality modeling with practical policy evaluation, supporting the development of effective strategies to reduce PM2.5-related health risks and estimate economic benefits.

The evaporation of chemicals in applied chemical products such as cleaning products and paint has been evaluated using the evaporation mode of the ConsExpo model. However, it remains controversial whether the ConsExpo model can be used for non-applied chemical products such as air fresheners, because the ConsExpo model assumes that the mass of the non-applied chemical products does not change during the time of use. If most of the materials in the product are volatile, the product mass can decrease. To explain the effect of a change in product mass, the ‘Chemical Product Evaporation Model (CPEM)’ was developed. This study demonstrated that the product mass decreases linearly when the surface area of the product is invariant, theoretically and experimentally. It was found that the ConsExpo evaporation model can be applied to products in which the other materials do not evaporate, and the CPEM can be applied to products in which the other materials are volatile. If the target substance in a product evaporates completely before the exhaustion time, the average concentration of a target substance in the air can be estimated simply from its initial concentration in the product and the product mass reduction rate. Otherwise, we recommend using the CPEM.

Junghyun Park, Yong-Chul Jang, and Jong-Hyeon Lee. J Environ Health Sci. 2023;49:362-71. https://doi.org/10.5668/JEHS.2023.49.6.362