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.

Fine particulate matter (PM2.5) pollution poses significant health risks, necessitating accurate predictions for effective management measures for the mitigator of PM2.5. While the Community Multiscale Air Quality (CMAQ) model is widely utilized to simulate PM2.5 concentrations, its computational demands often limit its application in testing various emission reduction scenarios. This study introduces a data-driven approach to emulate CMAQ simulations through AI surrogate model with a conditional U-Net architecture. The model, trained on a dataset of CMAQ-simulated PM2.5 concentrations from across South Korea in 2013, incorporates region-specific emission source activity data as inputs. It accurately predicts hourly changes in PM2.5 concentrations, facilitating the estimation of monthly and annual average concentrations under diverse emission reduction scenarios. Demonstrating robust performance in replicating the spatial and temporal patterns of CMAQ simulations, this surrogate model significantly cuts computational costs and times. Our findings offer policymakers a practical and efficient tool for evaluating the effectiveness of emission control strategies and enhancing decision-making processes in air quality management.

When predicting the health impacts of PM_2.5 from future air quality changes, it is crucial to consider both air quality improvements and population aging. This study divided future emission scenarios into a base and control scenario to project air quality from 2015 to 2030 and assess health outcomes. The GUIDE model, an Integrated Assessment Model (IAM), was used to estimate future emissions, while the CMAQ (Chemical Transport Model) and BenMAP (Health Impact Model) evaluated health impacts resulting from changes in air quality in Korea. The study focused on the impact of population aging on future health outcomes. Both scenarios showed improved PM_2.5 concentrations, with the control scenario showing more substantial improvements due to stronger policy measures. When applying current age patterns, health impacts decreased as PM_2.5 concentrations decreased. However, when considering future population aging, health impacts increased despite improved air quality. The results excluding aging show that the number of premature deaths due to cardiovascular disease and all other causes caused by PM_2.5 is 18,413 in the base year, while in the future control scenario, the number decreases to 11,729. In contrast, when aging is taken into account, the number of premature deaths increases to 23,037. This finding suggests that, although PM_2.5 concentrations are expected to decline, the increasing proportion of elderly individuals will exacerbate health risks. Therefore, accounting for aging population trends is essential when studying the health impacts of future air quality changes.