National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging

Abstract

Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has the largest number of air pollution deaths per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively low. We address this knowledge gap by developing and evaluating national empirical models for China incorporating land-use regression (LUR), satellite measurements, and universal kriging (UK). We test the resulting models in several ways, including (1) comparing models developed using forward stepwise regression vs. partial least squares (PLS) regression, (2) comparing models developed with and without satellite measurements, and with and without UK, and (3) 10-fold cross-validation (CV), leave-one-province-out(LOPO) CV, and leave-one-city-out(LOCO) CV. Satellite data and kriging are complementary in making predictions more accurate: kriging improved the models in well-sampled areas; satellite data substantially improved performance at locations far away from monitors. Stepwise forward selection performs similarly to PLS in 10-fold CV, but better than PLS in LOPO-CV. Our best models employ forward selection and UK, with 10-fold CV R2 of 0.89 (for both 2014 and 2015) for PM2.5 and of 0.73 (year-2014) and 0.78 (year-2015) for NO2. Population-weighted concentrations during 2014-2015 decreased for PM2.5 (58.7 {\mu}g/m3 to 52.3 {\mu}g/m3) and NO2 (29.6 {\mu}g/m3 to 26.8 {\mu}g/m3). We produced the first high resolution national LUR models for annual-average concentrations in China. Models were applied on 1 km grid to support future research. In 2015, more than 80% of the Chinese population lived in areas that exceed the Chinese national PM2.5 standard, 35 {\mu}g/m3. Results here will be publicly available and may be useful for environmental health research.