Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment

• Published in: Atmospheric environment (1994) Vol. 152; pp. 477 - 489
• Main Authors: Li, Tongwen, Shen, Huanfeng, Zeng, Chao, Yuan, Qiangqiang, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-03-2017
• Subjects: AOD; Assessment; GRNN; PM2.5; Point-surface fusion; Satellite remote sensing; Satellite remote sensing; Point-surface fusion; GRNN; AOD; PM2.5; Assessment
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2017.01.004

Fine particulate matter (PM2.5, particulate matters with aerodynamic diameters less than 2.5μm) is associated with adverse human health effects, and China is currently suffering from serious PM2.5 pollution. To obtain spatially continuous ground-level PM2.5 concentrations, several models established by the point-surface fusion of station measurements and satellite observations have been developed. However, how well do these models perform at national scale in China? Is there space to improve the estimation accuracy of PM2.5 concentration? The contribution of this study is threefold. Firstly, taking advantage of the newly established national monitoring network, we develop a national-scale generalized regression neural network (GRNN) model to estimate PM2.5 concentrations. Secondly, different assessment experiments are undertaken in time and space, to comprehensively evaluate and compare the performance of the widely used models. Finally, to map the yearly and seasonal mean distribution of PM2.5 concentrations in China, a pixel-based merging strategy is proposed. The results indicate that the conventional models (linear regression, multiple linear regression, and semi-empirical model) do not obtain the expected results at national scale, with cross-validation R values of 0.49–0.55 and RMSEs of 30.80–31.51μg/m3, respectively. In contrast, the more advanced models (geographically weighted regression, back-propagation neural network, and GRNN) have great advantages in PM2.5 estimation, with R values ranging from 0.61 to 0.82 and RMSEs from 20.93 to 28.68μg/m3, respectively. In particular, the proposed GRNN model obtains the best performance. Furthermore, the mapped PM2.5 distribution retrieved from 3-km MODIS aerosol optical depth (AOD) products agrees quite well with the station measurements. The results also show that the approach used in this study has the capacity to provide reasonable information for the global monitoring of PM2.5 pollution in China.
•A national-scale GRNN model is developed to estimate PM2.5 concentration in China.•The performance of the widely used models is comprehensively evaluated and compared.•A pixel-based merging strategy is proposed to map the mean PM2.5 distribution.