Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection and Processing
2.1.1. Hourly Ground-Level PM2.5 Measurements
2.1.2. Satellite-Derived AOD
2.1.3. Meteorological Factors
2.1.4. Data Integration
2.2. Methodology
2.2.1. Physics-Based Corrections
2.2.2. Model Structure and Validation
3. Results and Discussion
3.1. Descriptive Statistics
3.2. Model Fitting and Validation
3.3. Seasonal Estimation of PM2.5 Mass Concentrations
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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SDS Title | Explanations |
---|---|
Image_Optical_Depth_Land_And_Ocean (3k) | AOD at 550 nm for both ocean and land with all quality data using the DT algorithm. |
Land_Ocean_Quality_Flag (3k) | Quality flag for land and ocean aerosol retrievals (0 = bad, 1 = marginal, 2 = good, 3 = very good) |
Deep_Blue_Aerosol_Optical_Depth_550_Land (10k) | AOD at 550 nm for land with all quality data using the DB algorithm |
Deep_Blue_Aerosol_Optical_Depth_550_Land_QA_Flag (10k) | Deep Blue aerosol confidence flag (0 = no confidence, 1 = marginal, 2 = good, 3 = very good) |
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Zhang, T.; Liu, G.; Zhu, Z.; Gong, W.; Ji, Y.; Huang, Y. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model. Int. J. Environ. Res. Public Health 2016, 13, 974. https://doi.org/10.3390/ijerph13100974
Zhang T, Liu G, Zhu Z, Gong W, Ji Y, Huang Y. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model. International Journal of Environmental Research and Public Health. 2016; 13(10):974. https://doi.org/10.3390/ijerph13100974
Chicago/Turabian StyleZhang, Tianhao, Gang Liu, Zhongmin Zhu, Wei Gong, Yuxi Ji, and Yusi Huang. 2016. "Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model" International Journal of Environmental Research and Public Health 13, no. 10: 974. https://doi.org/10.3390/ijerph13100974