Analysis of air quality variability in Shanghai using AOD and API data in the recent decade

doi:10.1007/s11707-013-0357-z

Front Earth Sci

2013, Vol. 7

Issue (2) : 159-168 https://doi.org/10.1007/s11707-013-0357-z

RESEARCH ARTICLE

Analysis of air quality variability in Shanghai using AOD and API data in the recent decade

Qing ZHAO^1,2(

), Wei GAO^1,2,3, Weining XIANG⁴, Runhe SHI^1,2, Chaoshun LIU^1,2, Tianyong ZHAI^1,2, Hung-lung Allen HUANG⁵, Liam E. GUMLEY⁵, Kathleen STRABALA⁵

1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200062, China; 2. Joint Laboratory for Environmental Remote Sensing and Data Assimilation, ECNU&CEODE, Colorado State University, Shanghai 200062, China; 3. Colorado State University, Natural Resource Ecology Laboratory, Fort Collins, Colorado 80521, USA; 4. Shanghai Key Laboratory for Urban Ecology and Sustainability, East China Normal University, Shanghai 200062, China; 5. University of Wisconsin–Madison, Cooperative Institute for Meteorological Satellite Studies (CIMSS), Madison, Wisconsin 53706, USA

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Abstract

We use the aerosol optical depth (AOD) measured by the moderate resolution imaging spectrometer (MODIS) onboard the Terra satellite, air pollution index (API) daily data measured by the Shanghai Environmental Monitoring Center (SEMC), and the ensemble empirical mode decomposition (EEMD) method to analyze the air quality variability in Shanghai in the recent decade. The results indicate that a trend with amplitude of 1.0 is a dominant component for the AOD variability in the recent decade. During the World Expo 2010, the average AOD level reduced 30% in comparison to the long-term trend. Two dominant annual components decreased 80% and 100%. This implies that the air quality in Shanghai was remarkably improved, and environmental initiatives and comprehensive actions for reducing air pollution are effective. AOD and API variability analysis results indicate that semi-annual and annual signals are dominant components implying that the monsoon weather is a dominant factor in modulating the AOD and API variability. The variability of AOD and API in selected districts located in both downtown and suburban areas shows similar trends; i.e., in 2000 the AOD began a monotonic increase, reached the maxima around 2006, then monotonically decreased to 2011 and from around 2006 the API started to decrease till 2011. This indicates that the air quality in the entire Shanghai area, whether urban or suburban areas, has remarkably been improved. The AOD improved degrees (IDS) in all the selected districts are (8.6±1.9)%, and API IDS are (9.2±7.1)%, ranging from a minimum value of 1.5% for Putuo District to a maximum value of 22% for Xuhui District.

Keywords air quality of Shanghai MODIS AOD API EEMD method World Expo 2010

Corresponding Author(s): ZHAO Qing,Email:jennifer.zhao0510@gmail.com

Issue Date: 05 June 2013

Cite this article:

Qing ZHAO,Wei GAO,Weining XIANG, et al. Analysis of air quality variability in Shanghai using AOD and API data in the recent decade[J]. Front Earth Sci, 2013, 7(2): 159-168.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-013-0357-z
https://academic.hep.com.cn/fesci/EN/Y2013/V7/I2/159

Fig.1 An ENVISAT ASAR image of Shanghai taken on May 31, 2010. The urban area is in a red rectangle. (ENVISAT ASAR data is provided by the European Space Agency.)

Fig.2 Terra MODIS aerosol optical depth image at 02:38UTC on August 13, 2010, acquired at East China Normal University Satellite Ground Receiving Station. Shanghai City, the study area, is marked as a bold circle. The AOD color codes are shown on the right. The black area shows no data.

Tab.1 Breakpoints of APIs and Health Implications (partially referred to Jiang et al., (2004))

Fig.3 API monitoring stations used in this study. Black triangle represents the center of downtown of Shanghai that is used as a reference point to measure the distance to each API monitoring station.

Fig.4 Monthly AOD data in Shanghai from March 2000 to January 2011 derived from Terra MODIS observations (solid line). Dash-dot line represents a long-term variability trend of the data set derived from the EEMD analysis.

Fig.5 IMFs C1-C5 derived from 11 years of monthly AOD data of Shanghai from March 2000 to January 2011. The black dashed curve is the last ten-year average value.

Fig.6 Monthly AOD data of Xuhui District of Shanghai from March 2000 to October 2011 observed by Terra MODIS (top) and decomposed IMFs C1-C6. Dash-dot line represents a long-term variability trend.

Fig.7 Daily API data of Xuhui District of Shanghai observed by Shanghai Environmental Monitoring Center (SEMC) from January 2006 to October 2011 (top) and decomposed IMFs C1-C5. Dash-dot line represents a long-term variability trend.

Fig.8 Variability trends of AOD in selected districts of Shanghai from 2000 to 2011. Curves in different colors represent the cases of different districts listed on top right. Note that Districts Hongkou and Yangpu share the pink curve, and Xuhui, Jing’an and Putuo the light blue one. Red curve represents the average of top three curves.

Fig.9 Variability trends of API in selected districts of Shanghai from 2006 to 2011

Fig.10 Improved degrees (IDS) of AOD and API in the selected districts of Shanghai from 2006 to 2011

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