Comparison of MISR aerosol optical thickness with AERONET measurements in Beijing metropolitan area
Introduction
Atmospheric aerosols affect our environment from global to regional to local scale. On global and regional scales, their impacts on Earth radiation budget and cloud microphysics are considered a major uncertainty in climate change. Ground level aerosols, also known as particulate matter (PM), have been associated with multiple adverse health effects (Pope et al., 1995). Many countries in the world have designated PM as a criteria air pollutant. Therefore, long term PM monitoring has been of importance, especially for those heavily polluted locations.
The Multi-angle Imaging SpectroRadiometer (MISR), aboard the NASA's Earth Observing System (EOS) Terra satellite, provides global information on tropospheric aerosol properties. Viewing the sunlit Earth almost simultaneously at nine angles along its track, MISR obtains 4-spectral (446, 558, 672 and 866 nm) imagery at 1.1 km spatial resolution in the non-red bands and 275 m resolution in the red band. It has a periodic coverage between two and nine days depending on the latitude (Diner et al., 1998, Martonchik et al., 2002). MISR's unique combination of multiple bands and angles enables it to retrieve aerosol optical thickness (AOT) and additional particle properties at a resolution of 17.6 km over both land and ocean, with no assumption about the absolute land surface reflectance or its spectral characteristics in the aerosol retrieval algorithm (Martonchik et al., 2002, Martonchik et al., 1998).
Generally, MISR AOT retrieval is validated by comparing with ground-based sun photometer measurements. The Aerosol Robotic Network (AERONET) is a worldwide network of automatic sun photometers and data archive, providing spectral aerosol optical thickness as well as aerosol microphysical properties (Holben et al., 1998). Due to their relatively high accuracy (AOT uncertainty < ±0.01 at wavelengths > 440 nm), AERONET data have been widely used as a standard for validating satellite aerosol retrievals (Dubovik et al., 2000, Holben et al., 1998).
Early MISR AOT data (prior to version 15) have been validated under various scenarios. Diner et al. (2001) for the first time compared the MISR AOT with AERONET over southern Africa from the August to September 2000, showing that MISR AOT compare favorably with AERONET with a positive bias of 0.02 and an overestimation of 10%. Liu et al. (2004b) conducted a validation based on 16 AERONET sites over the United States, and found a good agreement between the MISR and AERONET AOTs after two outliers were excluded (linear regression analysis using MISR AOT as the response variable yielded an R2 of 0.80, a slope of 0.88 and an intercept of 0.04). Good agreement was also obtained in the desert regions, where the surface reflectance is high (Christopher and Wang, 2004, Martonchik et al., 2004). In Abdou et al. (2005), AOT retrieved by both the Moderate Resolution Imaging SpectroRadiometer (MODIS) and MISR were both compared with AERONET to explore the similarities and differences between them, and result showed MISR has a lower bias than MODIS over land (regression result: MISR = 0.83 × AERONET + 0.03, r = 0.86). Kahn et al. (2005) conducted a comprehensive global validation of MISR AOT using two years of MISR and AERONET AOT data, stratified by season and expected aerosol type. Detailed analyses were made on the likely causes for the trends and outliers to improve the MISR aerosol retrieval algorithm. It should be noted that validation of the MISR aerosol product is still underway, and the retrieval algorithm is still being refined.
Although Kahn et al. (2005) covered three polluted urban sites, i.e., Mexico City, Kanpur in northern India, and Shirahama in southern Japan, the AOT values at these sites are substantially lower than those found in Beijing as shown in the current analysis. Beijing is one of the largest metropolitan areas in the world. Studies have demonstrated that the aerosol loading is extremely high in the urban areas of Beijing (Eck et al., 2005). Traditionally, the major particle emission sources consist of industrial emissions, coal burning for winter heating and power supply, and long-range transported dust. In recent years, traffic emission has become a major contributor to the severe air pollution in Beijing, making the particle composition more complex and variable (He et al., 2001, Sun et al., 2004). This study is to assess MISR AOT quality in Beijing using information from AERONET, and analyze the likely causes of the MISR–AERONET discrepancies. In addition, because of the rapid economic growth and urbanization, air pollution has become a serious problem for Beijing. Several pollution-control measures have been deployed since 1990 s, such as natural gas substitution to coal and use of low-sulfur coal. However, inhalable particles (PM10, particles smaller than 10 μm in aerodynamic diameter) pollution is still at a level higher than the Chinese national ambient air quality standard. It has been demonstrated that satellite remote sensing aerosol products, such as MISR AOT, combined with the surface monitoring networks, can provide a cost-effective way to monitor and forecast air quality (Chu et al., 2003, Liu et al., 2004a, Liu et al., 2005). Therefore, it is also a motivation of this study to explore the application of satellite remote sensing in monitoring pollution in China.
The rest of the paper is organized as follows. In Section 2, we describe the data used in the current study. In Section 3, we first summarize the matched MISR and AERONET AOT data, and then we use various statistical tools to study the impacts of the averaging time window of AERONET AOT and the spatial averaging of MISR AOT on the agreement between AERONET and MISR AOT values. In addition to the analysis of MISR and AERONET AOT data, we also examine the spatial variability of ground-level PM mass concentrations as an indicator of the particle loading in the air column. The final section summarizes the results and draws the conclusions.
Section snippets
Data
We downloaded the AERONET level 2 (quality assured) data of the AERONET Beijing site from April 2002 to October 2004 from the AERONET data archive (http://aeronet.gsfc.nasa.gov). This site is at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, which is located in densely populated urban area of Beijing. A sun photometer (Cimel Electronique, France) at this site was installed on the roof of the IAP building (39.98°N, 116.38°E, and 30 m above the ground, shown in Fig. 1).
Results and discussion
This section contains our analysis of the data described in Section 2. We start with exploratory data analysis through summary statistics, data stratification into years and seasons, and data visualization of time series plots. We then study the impact of aerosol temporal and spatial variability on the agreement between MISR and AERONET AOT values using scatterplots, simple linear regression, weighted linear regression, and difference analysis. Finally, we analyze the heterogeneity of aerosol
Conclusions
MISR retrieved AOTs are compared with AERONET AOT measurements in Beijing metropolitan area with extremely high aerosol loadings. Data are collected with methods used in previous validation studies by using two-hour temporal averaging for AERONET AOT and both central and 3 × 3 surrounding averaged retrievals for MISR AOT. When all the matched MISR–AERONET AOT data are included in the analysis, our results show that MISR AOT is strongly correlated with AERONET AOT, but on average is 29% lower. A
Acknowledgements
This study is supported in part by Microsoft Research Asia, through a research grant to the Microsoft Statistics and Information Technology Laboratory of Peking University. The international collaboration is also supported in part by National Science of Foundation of China (60325101,60628102) and Ministry of Education of China (306017). The work of Bin Yu is partially supported by U.S. NSF Grant DMS-0306508 and U.S. ARO Grant W911NF-05–1-0104. The authors would like to thank Dr. Ralph Kahn and
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