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Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east
Wei Wang1, Feiyue Mao2, Bin Zou1
1School of Geosciences and Info-Physics, Central South University, China.
Abstract:
Widespread and severe PM1.0 (particulate matter ≤1.0 μm) pollution in China has a significant negative influence on human health. However, knowledge of the regional spatiotemporal distribution of PM1.0 has been hindered by sparsely distributed PM1.0 concentration data. In this work, a two-stage model (linear mixed effect-bagged tree model) was proposed for estimating hourly PM1.0 pollution levels from July 2015 to June 2017 over central and east China by using Himawari-8 aerosol products and coincident geographic data, meteorology, and site-based PM1.0 concentrations from ground monitoring network. The cross-validation for the developed model displayed R2 and mean absolute error value of 0.80 and 9.3 μg/m3, respectively. Validation demonstrated that the model accurately estimated hourly PM1.0 concentrations with high R2 of 0.63-0.85 and low bias of 8.7-10.1 μg/m3. The estimated PM1.0 concentrations on daily scale showed peaks with PM1.0 of 36.9 ± 8.4 μg/m3 at rush hours during daytime. Seasonal distribution displayed that summer was cleanest with an average PM1.0 of 20.9 ± 6.8 μg/m3 and winter was the most polluted season with an average PM1.0 of 45.6 ± 16.8 μg/m3. These results indicated that the proposed satellite-based model can estimate reliable spatial distribution of PM1.0 concentrations over a large-scale region.
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What are Estimates?
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...

