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
Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.