Rong Li1,2, Jianhua Gong 1,3, Liangfu Chen1, Zifeng Wang1

  • 1 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100101, China
  • 2 University of Chinese Academy of Sciences, Beijing 100049, China
  • 3 Zhejiang-CAS Application Center for Geoinformatics, Jiaxing 314100, China

Received: January 6, 2015
Revised: March 2, 2015
Accepted: April 3, 2015
Download Citation: ||https://doi.org/10.4209/aaqr.2015.01.0009  

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Cite this article:
Li, R., Gong, J., Chen, L. and Wang, Z. (2015). Estimating Ground-Level PM2.5 Using Fine-Resolution Satellite Data in the Megacity of Beijing, China. Aerosol Air Qual. Res. 15: 1347-1356. https://doi.org/10.4209/aaqr.2015.01.0009


HIGHLIGHTS

  • It used MODIS 3 km AOD data to predict PM2.5 in Beijing China.
  • Mixed effects model was used to improve the correlation between PM2.5 and AOD.
  • Predicting the spatial and temporal distribution of PM2.5 in Beijing in 2013.

 

ABSTRACT


Estimating ground-level PM2.5 in urban areas from satellite-retrieved AOD data is limited because of the coarse resolution of the data. The spatial resolution of recent MODIS Collection 6 aerosol data has increased from 10 km to 3 km. Taking advantage of this new AOD dataset, we used a mixed effects model to calibrate the day-to-day relationship between satellite AOD and ground-level PM2.5 concentrations. Regional daily PM2.5 concentrations were estimated by the AOD from March 1, 2013, to February 28, 2014, in the megacity of Beijing. Compared with the simple linear regression model, the accuracy of the PM2.5 prediction improved significantly, with an R2 of 0.796 and a root mean squared error of 16.04 µg/m3. The results showed high PM2.5 concentrations in the intra-urban region of Beijing because of local emissions. The PM2.5 concentrations were relatively low in the northern rural area but high in the southern rural area, which was close to the industrial sector in Hebei Province. We found that the 3 km AOD produces detailed spatial variability in the Beijing area but introduces somewhat large biases due to missing AOD pixels.


Keywords: Particulate matter; Air quality; Satellite remote sensing; Statistical models


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