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

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

References

[1] Chan C K, Yao X (2008). Air pollution in mega cities in China. Atmos Environ, 42(1): 1–42

[2] Chen B, Hong C, Kan H (2004). Exposures and health outcomes from outdoor air pollutants in China. Toxicology, 198(1–3): 291–300

[3] Chen C H, Wang B Y, Fu Q Y, Green C, Streets D G (2006). Reductions in emissions of local air pollutants and co-benefits of Chinese energy policy: a Shanghai case study. Energy Policy, 34(6): 754–762

[4] Hao N, Valks P, Loyola D, Cheng Y F, Zimmer W (2011). Space-based measurements of air quality during the World Expo 2010 in Shanghai. Environ Res Lett, 6(4): 1–9

[5] He Q, Li C, Tang X, Li H, Geng F, Wu Y (2010). Validation of MODIS derived aerosol optical depth over the Yangtze River Delta in China. Remote Sens Environ, 114(8): 1649–1661

[6] Hinds W C (1999). Aerosol Technology Properties, Behavior, and Measurement of Airborne Particles. 2nd ed. New York: Wiley-Interscience, 504

[7] Huang N E, Shen Z, Long S R, Wu M C, Shih E H, Zheng Q, Tung C C, Liu H H (1998). The Empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis. Proceedings of Royal Society London, 454(1971): 903–995

[8] Huang W, Tan J, Kan H, Zhao N, Song W, Song G, Chen G, Jiang L, Jiang C, Chen R, Chen B (2009). Visibility, air quality and daily mortality in Shanghai, China. Sci Total Environ, 407(10): 3295–3300

[15] Hutchison K D, Smith S, Faruqui S (2004). The use of MODIS data and aerosol products for air quality prediction. Atmos Environ, 38(30): 5057–5070

[16] Hutchison K D, Smith S, Faruqui S (2005). Correlation MODIS aerosol optical thickness data with ground-base PM2.5 observations across Texas for use in a real-time air quality prediction system. Atmos Environ, 39(37): 7190–7203

[9] Jiang D, Zhang Y, Hu X, Zeng Y, Tan J, Shao D (2004). Progress in developing an ANN model for air pollution index forecast. Atmos Environ, 38(40): 7055–7064

[10] Kan H, Chen B (2003a). A case-crossover analysis of air pollution and daily mortality in Shanghai. J Occup Health, 45(2): 119–124

[11] Kan H, Chen B (2003b). Air pollution and daily mortality in Shanghai: a time-series study. Arch Environ Health, 58(6): 360–367

[12] Kan H, Chen B (2004). Particulate air pollution in urban areas of Shanghai, China: health-based economic assessment. Sci Total Environ, 322(1–3): 71–79

[13] Kan H, London S J, Chen G, Zhang Y, Song G, Zhao N, Jiang L, Chen B (2007). Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China. Environ Int, 33(3): 376–384

[14] Kaufman Y J, Tanré D, Boucher O (2002). A satellite view of aerosols in the climate system. Nature, 419(6903): 215–223

[17] Levy R C, Leptoukh G G, Kahn R, Zubko V, Gopalan A, Remer L A (2009). A critical look at deriving monthly aerosol optical depth from satellite data. IEEE Trans Geosci Rem Sens, 47(8): 2942–2956

[18] Levy R C, Remer L A, Mattoo S, Vermote E F, Kaufman Y J (2007). Second-generation operation algorithm: retrieval of aerosol properties over land from inversion of moderate resolution imaging spectroradiometer spectral reflectance. J Geophys Res, 112(D13): 1–21

[19] Mage D, Ozolins G, Peterson P, Webster A, Orthofer R, Vandeweerd V, Gwynne M (1996). Urban air pollution in megacities of the world. Atmos Environ, 30(5): 681–686

[20] Remer L A, Kaufman Y J, Tanre D, Mattoo S, Chu D A, Martins J V, Li R R, Ichoku C, Levy R C, Kleidman R G, Eck T F, Vermote E, Holben B N (2005). The MODIS aerosol algorithm products and validation. J Atmos Sci, 62(4): 947–973

[21] UNEP (United Nations Environment Programme) (2010). UNEP Environmental Assessment, EXPO 2010, Shanghai, China, 1–147

[22] Wang J, Christopher S A (2003). Intercomparison between satellite-derived aerosol optical thickness and PM_2.5 mass: implications for air quality studies. Geophys Res Lett, 30(2095): 1–4

[23] Wang J, Xu X, Spurr R, Wang Y, Drury E (2010). Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: implications for air quality monitoring in China. Remote Sens Environ, 114(11): 2575–2583

[24] Wang Y, Zhuang G, Zhang X, Huang K, Xu C, Tang A, Cheng J, An Z (2006). The ion chemistry, seasonal cycle, and sources of PM_2.5 and TSP aerosol in Shanghai. Atmos Environ, 40(16): 2935–2952

[25] World Health Organization (WHO) (1987). Air Quality Guidelines for Europe. WHO Regional Publications, European Series No. 23, WHO Regional Office for Europe, Copenhagen

[26] World Health Organization/United Nations Environment Programme (WHO/UNEP) (1992). Urban Air Pollution in Megacities of the World. Oxford: Blackwell

[27] World Health Organization/United Nations Environment Programme (WHO/UNEP) (1994). Air Pollution in the World’s Megacities Environment, 36: 4–37

[28] Wu Z, Huang N E (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1): 1–41

[29] Zhang Y H, Huang W, London S J, Song G X, Chen G H, Jiang L L, Zhao N Q, Chen B H, Kan H D (2006). Ozone and daily mortality in Shanghai, China. Environ Health Perspect, 114(8): 1227–1232

Acknowledgements

The research was partially supported by Shanghai Science and Technology Support Program—Special for EXPO (Grant No. 10DZ0581600), a grant from Shanghai Institute of Urban Ecology and Sustainability, and Dragon 3 project (ID:10644) organized by the European Space Agency and National Remote Sensing Center of China. The ENVISAT ASAR image is provided by the European Space Agency. We thank two anonymous reviewers for their valuable comments and suggestions, which are helpful for us to improve the paper.

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg