Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model

Tianhao Zhang, Wei Gong, Zhongmin Zhu, Kun Sun, Yusi Huang, Yuxi Ji
2016 Atmosphere  
The estimation of ambient particulate matter with diameter less than 10 µm (PM 10 ) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM 10 more accessible to relevant departments and scientific research institutions, a semi-physical geographically weighted regression (GWR) model was established in this study to estimate nationwide mass concentrations of PM 10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters. The
more » ... sults demonstrated that applying physics-based corrections could remarkably improve the quality of the dataset for better model performance with the adjusted R 2 between PM 10 and AOD increasing from 0.08 to 0.43, and the fitted results explained approximately 81% of the variability in the corresponding PM 10 mass concentrations. Annual average PM 10 concentrations estimated by the semi-physical GWR model indicated that many residential regions suffer from severe particle pollution. Moreover, the deviation in estimation, which primarily results from the frequent changes in elevation, the spatially heterogeneous distribution of monitoring sites, and the limitations of AOD retrieval algorithm, was acceptable. Therefore, the semi-physical GWR model provides us with an effective and efficient method to estimate PM 10 at large scale. The results could offer reasonable estimations of health impacts and provide guidance on emission control strategies in China. Atmosphere 2016, 7, 88 2 of 13 As satellite remote sensing has been generally employed to make up for the limitation in spatial coverage of ground measurements, a potentially effective method has been put forward to predict PM 10 concentrations using satellite-derived aerosol optical depth (AOD) [8] [9] [10] [11] [12] . Satellite-derived AOD, which measures light extinction in one atmospheric column, is directly related to the quantity of particles in this column and can be converted into mass concentration of particulate matter using empirical methods [9, 10] . Up until now, previous studies developed quantitative relationships between satellite-derived AOD and ground-measured PM for different parts of the world, using empirical models, such as linear regression [13] [14] [15] and several types of non-linear regression [13] . In the past few years, related parameters, such as meteorological factors and geographical data, were used in establishing more advanced models for improving the accuracy of PM estimation, such as the alternating conditional expectation model (ACE) [16] , generalized additive model [17] , mixed effects model [18, 19] , and artificial neural network model (ANN) [13, 20] . As the correlation between AOD and PM is presumed to change along with spatial context [6], the geographically weighted regression model (GWR) based on a regional regression technique was adopted to constrain the spatial nonstationarity and variability in large-scale regressions [21, 22] . Furthermore, relative humidity and vertical distribution of aerosol extinction coefficients have been taken into consideration when estimating ground-level PM 10 using satellite-derived AOD from the perspective of physics, which significantly improve the correlation between PM 10 and AOD [23] . The combination of physical correction and the GWR model significantly increases the accuracy of fine particle concentration estimation compared to two conventional statistical models [24] . Nevertheless, no national-scale study on GWR modeling of PM 10 and satellite-derived AOD using physical revision have been reported, and whether the physical correction by vertical distribution and relative humidity effectively embed in the GWR model at the national-scale remains to be examined. In this study, we first adopted a physics-based correction method on dealing with nationwide AOD and PM 10 for validating the effectiveness of physical correction at a large-scale. Reanalysis meteorological parameters were then introduced into the semi-physical GWR model to estimate national-scale spatial distribution of PM 10 . In order to quantitatively evaluate the performance of the semi-physical GWR model, the original GWR model was established under similar conditions for comparing the fitted PM 10 with ground measurements. Moreover, the seasonal modeling by semi-physical GWR model was conducted to reveal the seasonal variation in accuracy of model performance. Finally, the nationwide spatial distribution of the satellite-retrieved PM 10 using the semi-physical GWR model was demonstrated to evaluate pollution level and existed limitations.
doi:10.3390/atmos7070088 fatcat:gtezsaqe7zdcngnmk7oitgmx5q