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Central European Journal of Geosciences

, Volume 6, Issue 1, pp 17–26 | Cite as

High resolution aerosol data from MODIS satellite for urban air quality studies

  • A. ChudnovskyEmail author
  • A. Lyapustin
  • Y. Wang
  • C. Tang
  • J. Schwartz
  • P. Koutrakis
Research Article

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM2.5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R2 =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM2.5 ground concentrations. Finally, we studied the relationship between PM2.5 and AOD at the intra-urban scale (⩽10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM2.5 relationship does not depend on relative humidity and air temperatures below ∼7 °C. The correlation improves for temperatures above 7–16 °C. We found no dependence on the boundary layer height except when the former was in the range 250–500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM2.5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM2.5 mass concentrations are highly correlated with the actual observations (out-of-sample R2 of 0.86). Therefore, adjustment for the daily variability in the AOD-PM2.5 relationship provides a means for obtaining spatially-resolved PM2.5 concentrations.

Keywords

Remote Sensing PM2.5 exposure assessment urban air quality Aerosol Optical Depth MODIS MAIAC mixed effects model 

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References

  1. [1]
    Monks P., A. Baklanov, Simpson D., Fuzzi S., Stohl A., Williams M.L., Akimoto H., M. Amann et al., Atmospheric composition change — global and regional air quality, Atmos. Environ. 2009, 43, 5268–5350CrossRefGoogle Scholar
  2. [2]
    Miller K., Siscovick D., Sheppard L., Shepherd K., Sullivan J., Anderson G., Kaufman J., Long-term exposure to air pollution and incidence of cardiovascular events in women. New England Journal of Medicine 356, 447–458. (2007).CrossRefGoogle Scholar
  3. [3]
    Lepeule J., Laden F, Dockery D, Schwartz J. Chronic exposure to fine particles and mortality: an extended follow-up of the Harvard six cities study from 1974 to 2009. Environmental Health Perspectives 2012, 120, 965–970CrossRefGoogle Scholar
  4. [4]
    Pope III C.A., Burnett R.T., Thun M.J., Calle E.E., Krewski D., Ito K., Thurston G.D., Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate pollution. JAMA: the Journal of the American Medical Association, 2002, 287, 1132–41CrossRefGoogle Scholar
  5. [5]
    Zhu Y, Skuhn, Mayo P, Hinds W., Comparison of Daytime and Nighttime Concentration Profiles and Size Distributions of Ultrafine Particles near a Major Highway. Environmental Science and Technology, 2006, 40, 2531–2536CrossRefGoogle Scholar
  6. [6]
    Bell M., Ebisu K., Peng R., Community-level spatial heterogeneity of chemical constituent levels of fine particulates and implications for epidemiological research. J. Exposure Science and Environ. Epidemiol., 2010, 24, 1–13, doi:10.1038/jes.2010.24Google Scholar
  7. [7]
    Hoff R., Christopher S., Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? J. Air Waste Manage Assoc., 2009, 59, 645–675CrossRefGoogle Scholar
  8. [8]
    Lyapustin A., Wang Y., Laszlo I., Kahn R., Korkin S., Remer L., Levy R., Reid J.S., Multi-Angle Implementation of Atmospheric Correction (MAIAC): Part 2. Aerosol Algorithm, J. Geophys. Res., 2011, 116, D03211, doi:10.1029/2010JD014986Google Scholar
  9. [9]
    Levy R.C., Remer L.A., Mattoo S., Vermote E.F., Kaufman Y.J., Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. J. Geophys. Res., 2007, 112, D13Google Scholar
  10. [10]
    Holben B.N., Coauthors, 1998: AERONET-A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ., 1998, 66, 1–16CrossRefGoogle Scholar
  11. [11]
    Lyapustin A, Wang Y, Frey R. An Automatic Cloud Mask Algorithm Based on Time Series of MODIS Measurements. J. Geophys. Res., 2008, 113, D16207, doi:10.1029/2007JD009641CrossRefGoogle Scholar
  12. [12]
    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.: The MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 2005, 62, 947–973CrossRefGoogle Scholar
  13. [13]
    Lee H.J., Liu Y., Coull B.A., Schwartz J., Koutrakis P., A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmos. Chem. Phys., 2011, 11, 7991–8002CrossRefGoogle Scholar
  14. [14]
    Chudnovsky A., Hyung J.L., Kostinski A., Kotlov T., Koutrakis P., Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite. Journal of the Air & Waste Management Association, 2012, 62, 1022–1031, DOI: 10.1080/10962247.2012.695321CrossRefGoogle Scholar
  15. [15]
    Lindstrom M.L., Bates D.M., Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. JASA Journal of the Acoustical Society of America 1998, 83, 1014–1021Google Scholar
  16. [16]
    Zhang H., Hoff R.M., Engel-Cox J.A., The Relation between MODIS Aerosol Optical Depth and PM2.5 over the United States: a Geographical Comparison by EPA Regions. J. Air & Waste Manage. Assoc., 2009, 59, 1358–1369CrossRefGoogle Scholar
  17. [17]
    Gupta P., Christopher S.A., Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. J. Geophys. Res., 2009, 114, D20205, doi:10.1029/2008JD011497CrossRefGoogle Scholar
  18. [18]
    Kumar N., Foster A., Chu A., Peters T., Willis R., Satellite Remote Sensing for Developing Time-Space Resolved Estimates of Ambient Particulate in Cleveland, OH. Aeros. Sci. Technol., 2011, 45, 1090–1108, DOI: 10.1080/02786826.2011.581256CrossRefGoogle Scholar
  19. [19]
    Chudnovsky A., Kostinski A., Lyapustin A., Koutrakis P., Spatial scales of pollution from variable resolution satellite imaging. Environ. Pollut., 2013, 172, 131–138CrossRefGoogle Scholar
  20. [20]
    Emili E., Lyapustin A., Wang Y., Popp C., Korkin S., Zebisch M., High spatial resolution aerosol retrieval with MAIAC: Application to mountain regions. J. Geophys. Res., 2011, 116, D23211Google Scholar

Copyright information

© Versita Warsaw and Springer-Verlag Wien 2014

Authors and Affiliations

  • A. Chudnovsky
    • 1
    • 2
    Email author
  • A. Lyapustin
    • 3
  • Y. Wang
    • 4
  • C. Tang
    • 1
  • J. Schwartz
    • 1
  • P. Koutrakis
    • 1
  1. 1.Department of Environmental HealthHarvard School of Public HealthBostonUSA
  2. 2.Department of Geography and Human EnvironmentTel-Aviv UniversityTel-AvivIsrael
  3. 3.GEST / UMBC, NASA Goddard Space Flight CenterBaltimoreUSA
  4. 4.Baltimore CountyUniversity of MarylandBaltimoreUSA

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