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National predictions of NO2 and PM2.5 based on Land Use Regression, satellite data and Universal Kriging--version2.0
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Pred_no2_2013_to_2017.7z.001
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Pred_pm25_2013_2017.7z.001
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README.md

README.md

ChinaLUR-V2.0

National predictions of NO2 and PM2.5 based on Land Use Regression, satellite data and Universal Kriging--version2.0
Format:Geotiff
Spatial Resolution: 1km×1km
Temporal Resolution: Annual average
Time coverage: 2013~2017
Pollutants: NO2,PM2.5
Units: Concentrations for both pollutants (NO2, PM2.5) are in units of μg/m3      
Map size: 4835×4044    
Projection: China_Albers_Equal_Area_Conic(datum:WGS84)
proj4 code: +proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
HOW TO OPEN/READ THE FILE: Using ArcGIS or any programming language with GDAL package (e.g. python+gdal/R+rgdal)
Data creator: Hao Xu
Author: Hao Xu, Matthew Bechle and Julian Marshall
Contact: Hao Xu (xuhao13@mails.tsinghua.edu.cn / xuhao13@uw.edu)
PLEASE email Hao Xu to ask for the passcode of these files
**These data must not be used for publications before authorized. !!All the 7z files should be downloaded before decompress them!!
If you use these data, please cite them as: Xu, H., Bechle, M.J., Wang, M., Szpiro, A.A., Vedal, S., Bai, Y., Marshall, J.D., 2019. National PM2. 5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Sci. Total Environ. 655, 423-433. https://doi.org/10.1016/j.scitotenv.2018.11.125

Performance of PM2.5 models(10-fold CV)
year R2 RMSE RPE
2013 0.92 6.58 9.65%
2014 0.90 6.46 10.5%
2015 0.89 6.06 11.6%
2016 0.88 5.91 12.3%
2017 0.87 5.60 12.1%
Performance of NO2 models(10-fold CV)
year R2 RMSE RPE
2013 0.69 7.08 16.9%
2014 0.73 6.66 17.6%
2015 0.77 5.97 18.8%
2016 0.78 5.72 17.9%
2017 0.80 5.52 16.6%

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