A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US
Abstract
:1. Introduction
2. Data and Methods
2.1. Data Collection
2.2. Climate Regions and Temporal Domains
2.3. National Bayesian Downscaling Model
2.4. Model Fitting and Prediction
3. Results
3.1. Data Description and Summary
3.2. Regional and Temporal Varying Geographical Associations
3.3. Model Cross-Validation
3.4. Model Prediction
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Regions | PM2.5 (SD) | AOD (SD) |
---|---|---|
West | 10.72 (7.17) | 0.10 (0.12) |
Northwest | 6.23 (4.05) | 0.12 (0.11) |
Southwest | 7.40 (4.75) | 0.10 (0.11) |
Northern Rockies and Plains | 7.40 (4.11) | 0.12 (0.13) |
Upper Midwest | 10.33 (5.87) | 0.18 (0.17) |
South | 10.17 (5.09) | 0.13 (0.15) |
Southeast | 10.83 (5.34) | 0.15 (0.17) |
Ohio Valley | 11.29 (5.79) | 0.17 (0.17) |
Northeast | 10.68 (6.10) | 0.19 (0.19) |
Regions | Number of Records | Number of Days | Number of Monitors | Coverage |
---|---|---|---|---|
West | 17,096 | 356 | 159 | 30% |
Northwest | 9486 | 295 | 170 | 19% |
Southwest | 9567 | 363 | 138 | 19% |
Northern Rockies and Plains | 7463 | 328 | 150 | 15% |
Upper Midwest | 6208 | 304 | 145 | 14% |
South | 15,899 | 364 | 189 | 23% |
Southeast | 17,525 | 361 | 257 | 19% |
Ohio Valley | 18,642 | 354 | 361 | 15% |
Northeast | 8913 | 302 | 238 | 12% |
Region | Temporal | AOD | Fire | Forest | Emission | RH | TMP | Vgrd | Ugrd | Hpbl | Road | AOD * TMP | R2 | Slope |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
West | 1 | 21.2 (5.9) | −0.7 (0.3) | 0.6 (0.2) | 2.5 (0.2) | 2.4 (0.4) | 1.2 (0.2) | −0.2 (0.1) | 9.8 (3.4) | 0.65 | 0.88 | |||
2 | 4.1 (1) | −0.8 (0.3) | 0.4 (0.1) | 0.7 (0.1) | 2.7 (0.2) | −0.1 (0.1) | 0.77 | 0.94 | ||||||
3 | 31.2 (5.2) | 0.2 (0.1) | −1.9 (0.3) | 0.8 (0.3) | 0.6 (0.1) | 1.4 (0.1) | −0.4 (0.1) | −0.7 (0.1) | −8.5 (2.5) | 0.72 | 0.91 | |||
Northwest | 1 | −1.5 (0.5) | −0.7 (0.3) | −1.9 (0.5) | 0.57 | 0.84 | ||||||||
2 | 5.4 (1.1) | 0.1 (0) | −0.2 (0.1) | 0.4 (0.1) | 1.5 (0.2) | 4.4 (1) | 0.62 | 0.92 | ||||||
3 | 25.4 (3.8) | 0.4 (0.1) | −0.4 (0.2) | 1.2 (0.4) | −0.4 (0.1) | 14.7 (2.1) | 0.69 | 0.9 | ||||||
Southwest | 1 | 10.6 (5) | 0.3 (0.1) | −0.7 (0.2) | 0.5 (0.2) | 2.4 (0.3) | 0.5 (0.1) | −11 (2.8) | 0.69 | 0.89 | ||||
2 | 5.5 (1.8) | −0.3 (0.1) | 0.4 (0.2) | 3.5 (0.3) | 0.3 (0.1) | 0.6 (0.1) | 0.6 | 0.88 | ||||||
3 | 18.8 (4.5) | −0.5 (0.2) | 0.7 (0.2) | −0.2 (0.1) | −0.3 (0.1) | 0.68 | 0.9 | |||||||
Northern Rockies and Plains | 1 | 0.4 (0.1) | 1.2 (0.3) | 0.7 (0.2) | 0.82 | 0.95 | ||||||||
2 | 4.4 (1.5) | 0.3 (0.1) | 0.3 (0.1) | 2.4 (0.2) | 0.4 (0.1) | 3.1 (1) | 0.67 | 0.92 | ||||||
3 | 11.1 (2.1) | 0.3 (0.1) | 2.1 (0.2) | −0.6 (0.1) | −0.4 (0.1) | 0.73 | 0.92 | |||||||
Upper Midwest | 1 | 0.5 (0.3) | 1.3 (0.2) | −0.4 (0.2) | 0.79 | 0.95 | ||||||||
2 | 4.4 (1.7) | 0.3 (0.1) | −0.6 (0.2) | 0.9 (0.1) | 2.7 (0.3) | 1 (0.1) | −0.3 (0.1) | 3.7 (1) | 0.82 | 0.95 | ||||
3 | 9.5 (3) | 0.4 (0.1) | −0.6 (0.2) | 0.3 (0.1) | 2.5 (0.2) | 0.4 (0.1) | −0.3 (0.1) | −0.2 (0.1) | 0.85 | 0.96 | ||||
South | 1 | 13.1 (2.2) | 0.5 (0) | −0.3 (0.1) | 1.4 (0.2) | 0.3 (0.1) | −0.2 (0.1) | 0.59 | 0.91 | |||||
2 | 0.2 (0.1) | 0.5 (0.1) | 4.2 (0.3) | −0.2 (0.1) | 0.2 (0.1) | 0.3 (0.1) | 4.5 (1.1) | 0.67 | 0.94 | |||||
3 | 14.7 (1.9) | 0.3 (0) | −0.7 (0.1) | −0.2 (0.1) | 1 (0.2) | 0.3 (0.1) | −0.4 (0.1) | −0.4 (0.1) | 0.3 (0.1) | 0.65 | 0.93 | |||
Southeast | 1 | 15.1 (1.9) | 0.3 (0) | −0.3 (0.1) | −0.3 (0.1) | 0.8 (0.2) | 0.5 (0.1) | 4.6 (0.9) | 0.68 | 0.94 | ||||
2 | 4.6 (1.6) | 0.1 (0) | 1.7 (0.2) | 7 (0.4) | −0.6 (0.1) | 0.2 (0.1) | 6.1 (1.1) | 0.74 | 0.95 | |||||
3 | 11.1 (1.4) | 0.3 (0) | −0.6 (0.1) | −0.7 (0.1) | 0.8 (0.2) | 0.7 (0.1) | −0.3 (0.1) | 6.9 (1.1) | 0.69 | 0.94 | ||||
Ohio Valley | 1 | 21.2 (2.9) | 0.7 (0) | −0.5 (0.1) | 0.4 (0.1) | 0.7 (0.2) | 0.7 (0.1) | −0.3 (0.1) | 5.5 (1) | 0.68 | 0.94 | |||
2 | 5.7 (1.3) | 2.2 (0.1) | 5.5 (0.3) | 0.3 (0.1) | 0.2 (0.1) | 2.9 (0.7) | 0.74 | 0.95 | ||||||
3 | 14.4 (5.2) | 0.3 (0.1) | −0.8 (0.1) | 1.7(0.2) | 0.5 (0) | −0.3 (0) | −0.3 (0.1) | 3.3(1.3) | 0.77 | 0.95 | ||||
Northeast | 1 | 10.6 (2.8) | 0.4 (0.1) | 0.9 (0.1) | −0.4 (0.1) | 0.8 | 0.95 | |||||||
2 | −0.2 (0.1) | 1.2 (0.2) | 6.4 (0.4) | −0.4 (0.1) | 8.5 (1.1) | 0.84 | 0.96 | |||||||
3 | 31 (2.5) | 1.5 (0.2) | −0.8 (0.2) | 1.8 (0.2) | 1.4 (0.4) | 27.5 (2) | 0.8 | 0.95 |
Regions | R2 | Intercept | Slope |
---|---|---|---|
West | 0.69 | 0.04 | 0.99 |
Northwest | 0.60 | 0.35 | 0.95 |
Southwest | 0.54 | 0.40 | 0.94 |
Northern Rockies and Plains | 0.60 | 0.29 | 0.95 |
Upper Midwest | 0.76 | −0.04 | 0.99 |
South | 0.59 | 0.27 | 0.97 |
Southeast | 0.69 | 0.19 | 0.98 |
Ohio Valley | 0.71 | 0.07 | 0.99 |
Northeast | 0.78 | 0.07 | 0.99 |
Regions | R2 | Intercept | Slope |
---|---|---|---|
West | 0.46 | 0.36 | 1.02 |
Northwest | 0.39 | 1.01 | 0.83 |
Southwest | 0.40 | 0.96 | 0.87 |
Northern Rockies and Plains | 0.37 | 0.94 | 0.90 |
Upper Midwest | 0.69 | −0.01 | 0.99 |
South | 0.50 | 0.38 | 0.96 |
Southeast | 0.58 | 0.77 | 0.92 |
Ohio Valley | 0.65 | 0.18 | 0.97 |
Northeast | 0.70 | 0.33 | 0.97 |
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Share and Cite
Wang, Y.; Hu, X.; Chang, H.H.; Waller, L.A.; Belle, J.H.; Liu, Y. A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US. Int. J. Environ. Res. Public Health 2018, 15, 1999. https://doi.org/10.3390/ijerph15091999
Wang Y, Hu X, Chang HH, Waller LA, Belle JH, Liu Y. A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US. International Journal of Environmental Research and Public Health. 2018; 15(9):1999. https://doi.org/10.3390/ijerph15091999
Chicago/Turabian StyleWang, Yikai, Xuefei Hu, Howard H. Chang, Lance A. Waller, Jessica H. Belle, and Yang Liu. 2018. "A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US" International Journal of Environmental Research and Public Health 15, no. 9: 1999. https://doi.org/10.3390/ijerph15091999