Rapid PM2.5-Induced Health Impact Assessment: A Novel Approach Using Conditional U-Net CMAQ Surrogate Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Preparing Datasets
2.1.1. Community Multiscale Air Quality
2.1.2. Control Matrix
2.2. Emulating CMAQ Simulation
2.2.1. Conditional U-Net Architecture
2.2.2. Gridding Strategy
2.3. Evaluation Methods
2.3.1. Performance Metrics
2.3.2. Contribution Analysis Using SHAP Value
2.4. Health Impact Assessment
2.4.1. BenMAP-CE Methodology
- Air quality data (monitored or modeled)
- Detailed population demographics
- Baseline health incidence rates
- Concentration-response functions from the epidemiological literature
- Economic valuation methods
2.4.2. Input Parameters of Health Impact Assessment
3. Results
3.1. Emulation Performance Evaluation
3.1.1. Emulation Results
3.1.2. Input Contribution Analysis
3.1.3. Computational Performance
3.2. Health Impact Assessment
4. Discussion and Conclusions
4.1. Development of a CMAQ Emulator Using a Conditional U-Net
4.2. Health Impact Assessment and Policy Implications
4.3. Limitations and Future Research
4.4. Overall Summary and Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BenMAP | Environmental Benefits Mapping and Analysis Program |
CMAQ | Community Multiscale Air Quality Modeling System |
CNN | Convolutional Neural Networks |
CPU | Central Processing Unit |
CVD | Cardiovascular diseases |
EPA | Environmental Protection Agency |
GPU | Graphics Processing Unit |
KOSIS | Korean Statistical Information Service |
LHS | Latin Hypercube Sampling |
MAE | Mean Absolute Error |
Ammonia | |
NMAE | Normalized Mean Absolute Error |
Nitrogen oxides | |
Particulate matter with diameter less than 2.5 μm | |
RD | Respiratory diseases |
SHAP | SHapley Additive exPlanations |
Sulfur dioxide | |
VOC | Volatile Organic Compounds |
WHO | World Health Organization |
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Region Name | VOC | Activity | ||||
---|---|---|---|---|---|---|
Seoul | 0.514 | 0.927 | 0.945 | 0.692 | 1.109 | - |
Incheon | 0.611 | 1.087 | 0.949 | 0.546 | 0.778 | - |
Busan | 0.504 | 1.364 | 1.046 | 1.192 | 1.249 | - |
Daegu | 1.274 | 0.951 | 0.708 | 1.247 | 0.786 | - |
Gwangju | 0.872 | 1.069 | 0.621 | 0.840 | 1.497 | - |
Gyeonggi-do | 0.574 | 1.379 | 1.436 | 0.842 | 1.177 | - |
Gangwon-do | 1.479 | 1.167 | 0.540 | 1.098 | 1.173 | - |
Chungbuk-do | 1.134 | 0.710 | 0.725 | 1.410 | 0.503 | - |
Chungnam-do | 0.520 | 0.562 | 0.812 | 1.021 | 0.994 | - |
Gyeongbuk-do | 1.063 | 1.073 | 1.192 | 1.343 | 1.045 | - |
Gyeongnam-do | 0.581 | 1.337 | 1.057 | 0.811 | 0.671 | - |
Jeonbuk-do | 0.702 | 1.286 | 0.580 | 1.150 | 1.063 | - |
Jeonnam-do | 1.311 | 1.187 | 1.001 | 1.281 | 0.805 | - |
Jeju-do | 1.034 | 1.224 | 1.395 | 0.520 | 0.752 | - |
Daejeon | 1.173 | 0.718 | 1.386 | 0.762 | 0.864 | - |
Ulsan | 1.197 | 0.919 | 0.528 | 1.284 | 0.534 | - |
Sejong | 0.996 | 1.394 | 0.549 | 0.787 | 1.343 | - |
Boundary | - | - | - | - | - | 1.000 |
Target Year | Target Region | Metrics [Units] | Data Category | Score |
---|---|---|---|---|
2019 | South Korea | MAE [μ] | Training set | 0.222 |
Test set | 0.221 | |||
NMAE [%] | Training set | 1.788 | ||
Test set | 1.762 | |||
[-] | Training set | 0.996 | ||
Test set | 0.996 |
Method | Processor | Batch Size | Time Consumed |
---|---|---|---|
CMAQ v4.7 | CPU (simulation) | - | ∼24 h/scenario |
Conditional U-Net | CPU (training) | 256 | ∼10 s/epoch |
GPU (training) | 256 | ∼1 s/epoch | |
CPU (prediction) | 32 | ∼10 ms/scenario | |
GPU (prediction) | 32 | ∼1 ms/scenario |
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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. Atmosphere 2024, 15, 1186. https://doi.org/10.3390/atmos15101186
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. Atmosphere. 2024; 15(10):1186. https://doi.org/10.3390/atmos15101186
Chicago/Turabian StyleLee, Yohan, Junghyun Park, Jinseok Kim, 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" Atmosphere 15, no. 10: 1186. https://doi.org/10.3390/atmos15101186