Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China
Abstract
:1. Introduction
2. Data and Methods
2.1. Data
2.1.1. Himawari-8 Satellite Products
2.1.2. Ground-Level PM2.5 Concentrations
2.1.3. Meteorological Variables
2.2. Methods
2.2.1. Model Development and Validation
2.2.2. Spatial Pattern Analysis of PM2.5 Concentrations
3. Results
3.1. Descriptive Statistics
3.2. Model Fitting and Validation
3.3. Variable Importance Assessment
3.4. Spatiotemporal Distribution of Model-Estimated PM2.5 Concentrations over Central East China
3.5. Spatial Agglomeration Pattern of Model-Estimated PM2.5 Concentrations over Central East China
4. Discussion
4.1. Comparison with Previous Studies
4.2. Potential Limitations and Room for Model Improvement
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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N | R2 | RMSE (µg m−3) | MPE (µg m−3) | RPE (%) | Slope | |
---|---|---|---|---|---|---|
MAM | 145,310 | 0.82 | 15.9 | 10.0 | 20.2 | 0.80 |
JJA | 90,530 | 0.72 | 11.8 | 7.5 | 20.4 | 0.78 |
SON | 109,793 | 0.86 | 16.3 | 10.2 | 18.4 | 0.82 |
DJF | 144,020 | 0.87 | 21.8 | 12.4 | 17.6 | 0.83 |
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Liu, J.; Weng, F.; Li, Z.; Cribb, M.C. Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China. Remote Sens. 2019, 11, 2120. https://doi.org/10.3390/rs11182120
Liu J, Weng F, Li Z, Cribb MC. Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China. Remote Sensing. 2019; 11(18):2120. https://doi.org/10.3390/rs11182120
Chicago/Turabian StyleLiu, Jianjun, Fuzhong Weng, Zhanqing Li, and Maureen C. Cribb. 2019. "Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China" Remote Sensing 11, no. 18: 2120. https://doi.org/10.3390/rs11182120