Version 1
: Received: 2 September 2024 / Approved: 2 September 2024 / Online: 3 September 2024 (10:34:49 CEST)
How to cite:
Lee, Y.; Park, J.; Kim, J.; Woo, J.-H.; Lee, J.-H. Rapid PM2.5-Induced Health Impact Assessment: A Novel Approach Using Conditional U-Net CMAQ Surrogate Model. Preprints2024, 2024090151. https://doi.org/10.20944/preprints202409.0151.v1
Lee, Y.; Park, J.; Kim, J.; Woo, J.-H.; Lee, J.-H. Rapid PM2.5-Induced Health Impact Assessment: A Novel Approach Using Conditional U-Net CMAQ Surrogate Model. Preprints 2024, 2024090151. https://doi.org/10.20944/preprints202409.0151.v1
Lee, Y.; Park, J.; Kim, J.; Woo, J.-H.; Lee, J.-H. Rapid PM2.5-Induced Health Impact Assessment: A Novel Approach Using Conditional U-Net CMAQ Surrogate Model. Preprints2024, 2024090151. https://doi.org/10.20944/preprints202409.0151.v1
APA Style
Lee, Y., Park, J., Kim, J., Woo, J. H., & Lee, J. H. (2024). Rapid PM2.5-Induced Health Impact Assessment: A Novel Approach Using Conditional U-Net CMAQ Surrogate Model. Preprints. https://doi.org/10.20944/preprints202409.0151.v1
Chicago/Turabian Style
Lee, Y., Jung-Hun Woo and Jong-Hyeon Lee. 2024 "Rapid PM2.5-Induced Health Impact Assessment: A Novel Approach Using Conditional U-Net CMAQ Surrogate Model" Preprints. https://doi.org/10.20944/preprints202409.0151.v1
Abstract
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.
Keywords
Air pollutant prediction; CMAQ surrogate model; Health impact assessment
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
