References

ACP

Atmospheric Chemistry and Physics

ACP

Atmos. Chem. Phys.

1680-7324

Copernicus Publications

Göttingen, Germany

10.5194/acp-13-10907-2013

A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions

Chudnovsky

¹ ² Tang

¹ Lyapustin

https://orcid.org/0000-0003-1105-5739

³ Wang

⁴ Schwartz

¹ Koutrakis

Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA

Department of Geography and Human Environment, Tel Aviv University, Tel Aviv, Israel

NASA Goddard Space Flight Center, Greenbelt, MD, USA

Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD 21228, USA

07 11 2013

13 21 10907 10917

2013

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://acp.copernicus.org/articles/13/10907/2013/acp-13-10907-2013.html

The full text article is available as a PDF file from https://acp.copernicus.org/articles/13/10907/2013/acp-13-10907-2013.pdf

Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides aerosol optical depth (AOD) at 1 km resolution. The relationship between MAIAC AOD and PM2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard super site during 2002–2008 was investigated and also compared to the AOD–PM2.5 relationship using conventional MODIS 10 km AOD retrieval from Aqua platform (MYD04) for the same days and locations. The correlations for MYD04 and for MAIAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM2.5 ground concentrations. The slightly higher correlation coefficient (r) for MAIAC can be related to its finer resolution resulting in better correspondence between AOD and EPA monitoring sites. Regardless of resolution, AOD–PM2.5 relationship varies daily, and under certain conditions it can be negative (due to several factors such as an EPA site location (proximity to road) and the lack of information about the aerosol vertical profile). By investigating MAIAC AOD data, we found a substantial increase, by 50–70% in the number of collocated AOD–PM2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data are more capable in capturing spatial patterns of PM2.5. Importantly, the performance of MAIAC AOD retrievals is slightly degraded but remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to increase significantly the number of days for PM2.5 spatial pattern prediction based on satellite observations.

References 1

Ackerman, S., Strabala, K., Menzel, W., Frey, R., Moeller, C., and Gumley, L.: Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103, 32141–32157, 1998.

Barnaba, F., Putaud, J. P., Gruening, C., dell'Acqua, A., and Dos Santos, S.: Annual cycle in co-located in situ, total-column, and height-resolved aerosol observations in the Po Valley (Italy): Implications for ground-level particulate matter mass concentration estimation from remote sensing, J. Geophys. Res., 115, D19209, <a href="http://dx.doi.org/10.1029/2009JD013002">https://doi.org/10.1029/2009JD013002</a>, 2010.

Bell, M., Ebisu, K., and Peng, R.: Community-level spatial heterogeneity of chemical constituent levels of fine particulates and implications for epidemiological research, J. Expo. Sci. Env. Epid., 21, 372–384, <a href="http://dx.doi.org/10.1038/jes.2010.24">https://doi.org/10.1038/jes.2010.24</a>, 2011.

Christopher, S. and Gupta, P.: Satellite Remote Sensing of Particulate Matter Air Quality: The Cloud-Cover Problem, J. Air Waste Manage., 60, 596–602, <a href="http://dx.doi.org/10.3155/1047-3289.60.5.596">https://doi.org/10.3155/1047-3289.60.5.596</a>, 2010.

Chudnovsky, A., Lee, H.-J., Kostinski, A., Kotlov, T., and Koutrakis, P.: Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite, J. Air Waste Manage., 62, 1022–1031, <a href="http://dx.doi.org/10.1080/10962247.2012.695321">https://doi.org/10.1080/10962247.2012.695321</a>, 2012.

Chudnovsky, A., Kostinski, A., Lyapustin, A., and Koutrakis, P.: Spatial scales of pollution from variable resolution satellite imaging, Environ. Pollut., 172, 131–138, 2013.

Emili, E., Popp, C., Pettita, M., Riffler, M., Wunderle, S., and Zebisch, M. PM10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region, Remote Sens. Environ., 114, 11, <a href="http://dx.doi.org/10.1016/j.rse.2010.05.024">https://doi.org/10.1016/j.rse.2010.05.024</a>, 2010.

Emili, E., Lyapustin, A., Wang, Y., Popp, C., Korkin, S., and Zebisch, M.: High spatial resolution aerosol retrieval with MAIAC: Application to mountain regions, J. Geophys. Res., 116, D23211, <a href="http://dx.doi.org/10.1029/2011JD016297">https://doi.org/10.1029/2011JD016297</a>, 2011.

Engel-Cox, J., Hoff, R., Rogers, R., Dimmick, F., Rush, A., and Szykman, J.: Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization, Atmos. Environ., 40, 8056–8067, 2006.

Estellés, V., Martínez-Lozano, J. A., Pey, J., Sicard, M., Querol, X., Esteve, A. R., Utrillas, M. P., Sorribas, M., Gangoiti, G., Alastuey, A., and Rocadenbosch, F.: Study of the correlation between columnar aerosol burden, suspended matter at ground and chemical components in a background European environment, J. Geophys. Res.-Atmos., 117, D04201, <a href="http://dx.doi.org/10.1029/2011JD016356">https://doi.org/10.1029/2011JD016356</a>, 2012.

Gao, B.-C., Yang, P., Han, W., Li, R., and Wiscombe, W.: An algorithm using visible and 1.375 mm channels to retrieve cirrus cloud reflectances from aircraft and satellite data, IEEE Trans. Geosci. Remote, 40, 1659–1688, 2002.

Gupta, P. and Christopher, S. A.: Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach, J. Geophys. Res., 114, D14205, <a href="http://dx.doi.org/10.1029/2008JD011496">https://doi.org/10.1029/2008JD011496</a>, 2009a.

Gupta, P. and Christopher, S. A.: Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach, J. Geophys. Res., 114, D20205, <a href="http://dx.doi.org/10.1029/2008JD011497">https://doi.org/10.1029/2008JD011497</a>, 2009b.

Gupta, P., Christopher, S., Wang, J., Gehrig, R., Lee, Y., and Kumar, N.: Satellite remote sensing of particulate matter and air quality assessment over global cities, Atmos. Environ., 40, 5880–5892, 2006.

Hilker, T., Lyapustin, A., Tucker, C., Sellers, P., Hall, F., and Wang, Y.: Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter, Rem. Sens. Environ., 127, 370–384, 2012.

Hoff, R. and Christopher, S.: Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?, J. Air Waste Manage., 59, 645–675, 2009.

Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – Afederated instrument network and data archive for aerosol characterization, Rem. Sens. Environ., 66, 1–16, 1998.

Kanabkaew, T.: Prediction of hourly particulate matter concentrations in Chiangmai, Thailand using MODIS aerosol optical depth and ground-based meteorological data, Environment Asia, 6, 65–70, 2013.

Kaufman, Y. J., Tanre, D., Remer, L. A., Vermote, E. F., Chu, A., and Holben, B. N.: Operational remote sensing of tropospheric aerosol over land from EOS MODerate resolution imaging spectroradiometer, J. Geophys. Res., 102, 17051–17067, 1997.

Kloog, I., Koutrakis, P., Coull, B., Lee, H., and Schwartz, J.: Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements, Atmos. Environ., 45, 6267–6275, 2011.

Kloog, I., Nordio, F., Coull, B. A., and Schwartz, J. D.: Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states, Environ. Sci. Technol., 46, 11913e11921, <a href="http://dx.doi.org/10.1021/es302673e">https://doi.org/10.1021/es302673e</a>, 2012.

Kumar, N., Foster, A., Chu, A., Peters, T., and Willis, R.: Satellite Remote Sensing for Developing Time-Space Resolved Estimates of Ambient Particulate in Cleveland, OH, Aerosol. Sci. Tech., 45, 1090–1108, <a href="http://dx.doi.org/10.1080/02786826.2011.581256">https://doi.org/10.1080/02786826.2011.581256</a>, 2011.

Lee, H. J., Liu, Y., Coull, B. A., Schwartz, J., and Koutrakis, P.: A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations, Atmos. Chem. Phys., 11, 7991–8002, <a href="http://dx.doi.org/10.5194/acp-11-7991-2011">https://doi.org/10.5194/acp-11-7991-2011</a>, 2011.

Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and 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., 112, D13211, <a href="http://dx.doi.org/10.1029/2006JD007811">https://doi.org/10.1029/2006JD007811</a>, 2007.

Levy, R. C., Remer, L. A., Kleidman, R. G., Mattoo, S., Ichoku, C., Kahn, R., and Eck, T. F.: Global evaluation of the Collection 5 MODIS dark-target aerosol products over land, Atmos. Chem. Phys., 10, 10399–10420, <a href="http://dx.doi.org/10.5194/acp-10-10399-2010">https://doi.org/10.5194/acp-10-10399-2010</a>, 2010.

Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech. Discuss., 6, 159–259, <a href="http://dx.doi.org/10.5194/amtd-6-159-2013">https://doi.org/10.5194/amtd-6-159-2013</a>, 2013.

Li, R.-R., Kaufman, Y. J., Gao, B.-C., and Davis, C. O.: Remote sensing of suspended sediments and shallow coastal waters, IEEE Trans. Geosci. Remote, 41, 559–566, 2003.

Liu, Y., Kahn, R. A., Turquety, S., Yantosca, R. M., and Koutrakis, P.: Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: Part 2 – A case study, J. Air Waste Manage., 57, 1360–1369, <a href="http://dx.doi.org/10.3155/1047-3289.57.11.1360">https://doi.org/10.3155/1047-3289.57.11.1360</a>, 2007.

Liu, Y., Paciorek, C. J., and Koutrakis, P.: Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information, Environ. Health Persp., 117, 886–892, 2009.

Logue, J., Small, M., Stern, D., Maranche, J., and Robinson, A.: Spatial variation in ambient air toxics concentrations and health risks between industrial-influenced, urban, and rural sites, J. Air Waste Manage., 60, 271–286, 2010.

Lyapustin, A., Wang, Y., and Frey, R.: An Automatic Cloud Mask Algorithm Based on Time Series of MODIS Measurements, J. Geophys. Res., 113, D16207, <a href="http://dx.doi.org/10.1029/2007JD009641">https://doi.org/10.1029/2007JD009641</a>, 2008.

Lyapustin, A., Martonchik, J., Wang, Y., Laszlo, I., and Korkin, S.: Multi-Angle Implementation of Atmospheric Correction (MAIAC): Part 1. Radiative Transfer Basis and Look-Up Tables, J. Geophys. Res., 116, D03210, <a href="http://dx.doi.org/10.1029/2010JD014985">https://doi.org/10.1029/2010JD014985</a>, 2011a.

Lyapustin, A., Wang, Y., Laszlo, I., Kahn, R., Korkin, S., Remer, L., Levy, R., and Reid, J. S.: Multi-Angle Implementation of Atmospheric Correction (MAIAC): Part 2. Aerosol Algorithm, J. Geophys. Res., 116, D03211, <a href="http://dx.doi.org/10.1029/2010JD014986">https://doi.org/10.1029/2010JD014986</a>, 2011b.

Lyapustin, A., Wang, Y., Laszlo, I., Hilker, T., Hall, F., Sellers, P., Tucker, J., and Korkin, S.: Multi-Angle Implementation of Atmospheric Correction for MODIS (MAIAC). 3: Atmospheric Correction, J. Rem. Sens. Environ., 127, 385–393, <a href="http://dx.doi.org/10.1016/j.rse.2012.09.002">https://doi.org/10.1016/j.rse.2012.09.002</a>, 2012a.

Lyapustin, A., Korkin, S., Wang, Y., Quayle, B., and Laszlo, I.: Discrimination of biomass burning smoke and clouds in MAIAC algorithm, Atmos. Chem. Phys., 12, 9679–9686, <a href="http://dx.doi.org/10.5194/acp-12-9679-2012">https://doi.org/10.5194/acp-12-9679-2012</a>, 2012b.

Martins, J. V., Tanre, D., Remer, L. A., Kaufman, Y. J., Mattoo, S., and Levy, R.: MODIS cloud screening for remote sensing of aerosol over oceans using spatial variability, Geophys. Res. Lett., 29, 1619, <a href="http://dx.doi.org/10.1029/2001GL013252">https://doi.org/10.1029/2001GL013252</a>, 2002.

Munchak, L. A., Levy, R. C., Mattoo, S., Remer, L. A., Holben, B. N., Schafer, J. S., Hostetler, C. A., and Ferrare, R. A.: MODIS 3 km aerosol product: applications over land in an urban/suburban region, Atmos. Meas. Tech., 6, 1747–1759, <a href="http://dx.doi.org/10.5194/amt-6-1747-2013">https://doi.org/10.5194/amt-6-1747-2013</a>, 2013.

Paciorek, C. J. and Liu, Y.: HEI Health Review Committee Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States, Research report (Health Effects Institute), 5–83, discussion 85–8391, 2012.

Paciorek, C. J., Liu, Y., Moreno-Macias, H., and Kondragunta, S.: Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5, Environ. Sci. Technol., 42, 5800–5806, 2008.

Pelletier, B., Santer, R., and Vidot, J.: Retrieving of particulate matter from optical measurements: a semiparametric approach, J. Geophys. Res.-Atmos., 112, D06208, <a href="http://dx.doi.org/10.1029/2005JD006737">https://doi.org/10.1029/2005JD006737</a>, 2007.

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., and Holben, B. N.: The MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62, 947–973, 2005.

Remer, L. A., Mattoo, S., Levy, R. C., and Munchak, L. A.: MODIS 3 km aerosol product: algorithm and global perspective, Atmos. Meas. Tech., 6, 1829–1844, <a href="http://dx.doi.org/10.5194/amt-6-1829-2013">https://doi.org/10.5194/amt-6-1829-2013</a>, 2013.

Schaap, M., Apituley, A., Timmermans, R. M. A., Koelemeijer, R. B. A., and de Leeuw, G.: Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands, Atmos. Chem. Phys., 9, 909–925, <a href="http://dx.doi.org/10.5194/acp-9-909-2009">https://doi.org/10.5194/acp-9-909-2009</a>, 2009.

Tian, J. and Chen, D.: A Semi-Empirical Model for Predicting Hourly Ground-Level Fine Particulate Matter (PM2.5) Concentration in Southern Ontario from Satellite Remote Sensing and Ground-Based Meteorological Measurements, Remote Sens. Environ., 114, 221–229, 2010.

Tsai, T.-C., Jeng, Y.-J., Chu, D. A., Chen, J.-P., and Chang, S.-C.: Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008, Atmos. Environ., 45, 4777–4788, 2011.

Van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., and Villeneuve, P. J.: Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application, Environ. Health Persp., 118, 847–855, 2010.

Van Donkelaar, A., Martin, R. V., Spurr, R. J. D., Drury, E., Remer, L. A., Levy, R. C., and Wang, J.: Optimal estimation for global ground-level fine particulate matter concentrations, J. Geophys. Res.-Atmos., 118, 5621–5636, 2013.

Wang, J. and Christopher, S. A.: Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies, Geophys. Res. Lett., 30, 2095, <a href="http://dx.doi.org/10.1029/2003GL018174">https://doi.org/10.1029/2003GL018174</a>, 2003.

Wen, G., Marshak, A., Cahalan, R. F., Remer, L. A., and Kleidman, R. G.: 3-D aerosol-cloud radiative interaction observed in collocated MODIS and ASTER images of cumulus cloud fields, J. Geophys. Res., 112, D13204, <a href="http://dx.doi.org/10.1029/2006JD008267">https://doi.org/10.1029/2006JD008267</a>, 2007.

Wu, D., Lau, A. K.-H., Leung, Y., Bi, X., Li, F., Tan, H., Liao, B., and Chen, H.: Hazy weather formation and visibility deterioration resulted from fine particulate (PM2.5) pollutions in Guangdong and Hong Kong, Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae, 32, 2660–2669, 2012.

Zhang, H., Hoff, R. M., and 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., 59, 1358–1369, 2009.

Zhu, Y., Skuhn, M., and Hinds, W.: Comparison of Daytime and Nighttime Concentration Profiles and Size Distributions of Ultrafine Particles near a Major Highway, Environ. Sci. Technol., 40, 2531–2536, 2006.