Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China
Abstract
:1. Introduction
2. Methodology
2.1. PM and Meteorological Data
2.2. MODIS AOT Data
2.3. Data Analysis
3. Results
3.1. Relationship between PM10 and AOT and Its Seasonal Variability
Season (coefficient) | Statistical parameter | P (hPa) | T (0C) | RH (%) | WV (m s-1) |
---|---|---|---|---|---|
Spring (0.52) | AVE | 1,015.47 | 16.27 | 53.24 | 2.23 |
SD | 7.41 | 6.18 | 9.78 | 0.77 | |
Summer (0.87) | AVE | 1,004.81 | 29.49 | 59.17 | 2.55 |
SD | 3.74 | 2.36 | 9.09 | 1.04 | |
Autumn (0.80) | AVE | 1,020.24 | 16.76 | 67.49 | 1.43 |
SD | 4.58 | 5.23 | 6.84 | 0.76 | |
Winter (0.47) | AVE | 1,027.15 | 3.03 | 52.31 | 2.07 |
SD | 5.46 | 4.01 | 14.53 | 1.07 |
3.2. Meteorological Influence on Residuals of PM10 Estimates
4. Discussion
4.1. Selection of Meteorological Parameters
4.2. Differential Scales of Measurement of Air Pollutants
5. Conclusions
Acknowledgments
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Gao, J.; Zha, Y. Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China. Remote Sens. 2010, 2, 2136-2147. https://doi.org/10.3390/rs2092136
Gao J, Zha Y. Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China. Remote Sensing. 2010; 2(9):2136-2147. https://doi.org/10.3390/rs2092136
Chicago/Turabian StyleGao, Jay, and Yong Zha. 2010. "Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China" Remote Sensing 2, no. 9: 2136-2147. https://doi.org/10.3390/rs2092136