Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Data Collection and Preprocessing
2.2.1. In Situ PM2.5 Data
2.2.2. Satellite-Retrieved AOD Data
2.2.3. PM2.5 Emissions Related Data
2.2.4. Dispersion Conditions Data
2.2.5. Data Integration
2.3. GAM Modeling
2.3.1. GAM Structure
2.3.2. Thin Plate Regression Splines
2.3.3. Model Development and Validation
3. Results
3.1. Model Fitting and Validation
3.2. Contributing Factors of GAM Models
3.3. PM2.5 Concentration Surfacesacross Time Scales
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
BTH | Beijing-Tianjin-Hebei region |
GAM | Generalized Additive Model |
RMSE | Root Mean Square Error |
LUR | land Use Regression |
AOD | Aerosol Optical Depth |
CTM | Chemical Transport Model |
GIS | Geographic Information Systems |
NCP | North China Plain |
CEMC | China Environmental Monitoring Center |
TEOM | Tapered Element Oscillating Microbalance |
CNAAQS | Chinese National Ambient Air Quality Standards |
GTS | Global Telecommunication System |
CNMC | Chinese National Meteorological Information Center |
RESDC | Data Center for Resources and Environmental Sciences |
NASA | National Aeronautics and Space Administration |
USGS | United States Geological Survey |
GLM | Generalized Linear Model |
AIC | Akaike Information Criterion |
CV | Cross Validation |
WHO IT-1 | World Health Organization Air Quality Interim Target II-1 |
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GIS Dataset | Predictor Variables | Unit | Buffer Size (m) | |
---|---|---|---|---|
AOD | AOD | unitless | NA | |
Meteorological parameters | Temperature | K | NA | |
Wind speed | m/s | NA | ||
Relative humidity | % | NA | ||
Atmospheric pressure | hPa | NA | ||
Precipitation | mm | NA | ||
Pollution sources | Percentage of ground dust area in a buffer | Open pit field | % | 500, 1000, 2000, 3000 |
Stacked substance | % | 500, 1000, 2000, 3000 | ||
Construction site | % | 500, 1000, 2000, 3000 | ||
Natural bare surface | % | 500, 1000, 2000, 3000 | ||
Crush stampede yard | % | 500, 1000, 2000, 3000 | ||
Else | % | 500, 1000, 2000, 3000 | ||
Number of industrial plants in a buffer | Iron and steel smelting and rolling processing plants | count | 400, 600, 800, 1000, 2000, 30,000 | |
Thermal power plants | count | 400, 600, 800, 1000, 2000, 30,000 | ||
Heat production and supply plants | count | 400, 600, 800, 1000, 2000, 30,000 | ||
Cement building materials plants | count | 400, 600, 800, 1000, 2000, 30,000 | ||
Petrochemical plants | count | 400, 600, 800, 1000, 2000, 30,000 | ||
Non-ferrous metal smelting and processing plants | count | 400, 600, 800, 1000, 2000, 30,000 | ||
Coal mining plants | count | 400, 600, 800, 1000, 2000, 30,000 | ||
Paper and paper products plants | count | 400, 600, 800, 1000, 2000, 30,000 | ||
Pharmaceutical manufacturing plants | count | 400, 600, 800, 1000, 2000, 30,000 | ||
Distance to the nearest industrial plants | Iron and steel smelting and rolling processing plants | m | NA | |
Thermal power plants | m | NA | ||
Heat production and supply plants | m | NA | ||
Cement building materials plants | m | NA | ||
Petrochemical plants | m | NA | ||
Non-ferrous metal smelting and processing plants | m | NA | ||
Coal mining plants | m | NA | ||
Paper and paper products plants | m | NA | ||
Pharmaceutical manufacturing plants | m | NA | ||
Road network | Length of all roads in a buffer | m | 50, 60, 70, 80, 90, 100, 150, 250, 300, 350, 400, 450, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000 | |
Land use/cover | Percentage of built-up area in a buffer | % | 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000 | |
Percentage of forests area in a buffer | % | 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000 | ||
Percentage of grasslands area in a buffer | % | 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000 | ||
Percentage of water area in a buffer | % | 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000 | ||
Terrain | DEM elevation | m | NA | |
Population | Population density | count | NA |
GAM Models Established | LUR Models Established | |
---|---|---|
Spring (N = 78) | PM2.5 ≈ s(AOD) + s(WS) + s(TEMP) + s(RH) | PM2.5 ≈ AOD + WS + TEMP + RH |
Summer (N = 78) | PM2.5 ≈ AOD + s(FOR(5000 m)) + s(BUI(5000 m)) + s(TEMP) | PM2.5 ≈ AOD + TEMP + PRE |
Autumn (N = 78) | PM2.5 ≈ s(AOD) + s(WS) + s(ELEV) + s(GD(3000 m)) | PM2.5 ≈ AOD + PREC + WS + GD(3000 m) |
Winter (N = 78) | PM2.5 ≈ s(AOD) + s(CS(2000 m)) + s(TEMP) + WS | PM2.5 ≈ AOD + TEMP + WS + CS(2000 m) |
Annual (N = 78) | PM2.5 ≈ s(AOD) + s(TEMP) + s(GRA(5000 m)) + s(SS(2000 m)) + WS + PRE | PM2.5 ≈ AOD + WS + GD(3000 m) + PM(600 m) |
Season | Model Fitting | Cross-Validation | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Adj R2 | RMSE (μg/m3) | Adj R2 | RMSE (μg/m3) | |||||||
GAM | LUR | GAM | LUR | Bias (%) * | GAM | LUR | GAM | LUR | Bias (%) * | |
Spring (N = 78) | 0.96 | 0.87 | 4.43 | 7.96 | 44.4 | 0.92 | 0.87 | 6.23 | 8.31 | 25.1 |
Summer (N = 78) | 0.88 | 0.72 | 6.52 | 9.68 | 32.6 | 0.78 | 0.71 | 8.91 | 9.85 | 9.5 |
Autumn (N = 78) | 0.94 | 0.83 | 6.88 | 11.60 | 40.7 | 0.87 | 0.84 | 10.49 | 12.68 | 17.3 |
Winter (N = 78) | 0.90 | 0.84 | 11.33 | 12.20 | 7.1 | 0.85 | 0.84 | 14.05 | 15.13 | 7.1 |
Annual (N = 78) | 0.96 | 0.83 | 4.82 | 9.32 | 48.3 | 0.90 | 0.85 | 7.52 | 9.78 | 23.1 |
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Zou, B.; Chen, J.; Zhai, L.; Fang, X.; Zheng, Z. Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling. Remote Sens. 2017, 9, 1. https://doi.org/10.3390/rs9010001
Zou B, Chen J, Zhai L, Fang X, Zheng Z. Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling. Remote Sensing. 2017; 9(1):1. https://doi.org/10.3390/rs9010001
Chicago/Turabian StyleZou, Bin, Jingwen Chen, Liang Zhai, Xin Fang, and Zhong Zheng. 2017. "Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling" Remote Sensing 9, no. 1: 1. https://doi.org/10.3390/rs9010001