The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
A novel calibration approach of MODIS AOD data to predict PM 2.5 concentrations
Open accessAbstract
Abstract. Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM2.5, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concentrations measured at 26 US Environmental Protection Agency (EPA) PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM2.5-AOD relationships was used to predict location-specific PM2.5 levels. PM2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concentrations. Furthermore, the estimated PM2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM2.5 concentrations within the study domain.
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AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., et al. (1998) · Remote Sensing of Environment · doi:10.1016/s0034-4257(98)00031-5 · Cited by 7,743
Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2011) · Wiley Series in Probability and Statistics · doi:10.1002/9781119513469 · Cited by 4,639
The MODIS Aerosol Algorithm, Products, and Validation
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R.-R., Ichoku, C., Levy, R. C., Kleidman, R. G., et al. (2005) · Journal of the Atmospheric Sciences · doi:10.1175/jas3385.1 · Cited by 3,122
Fine Particulate Air Pollution and Hospital Admission for Cardiovascular and Respiratory Diseases
Dominici, F., Peng, R. D., Bell, M. L., Pham, L., McDermott, A., Zeger, S. L., & Samet, J. M. (2006) · JAMA · doi:10.1001/jama.295.10.1127 · Cited by 2,634
van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., & Villeneuve, P. J. (2010) · Environmental Health Perspectives · doi:10.1289/ehp.0901623 · Cited by 1,623
Is Daily Mortality Associated Specifically with Fine Particles?
Schwartz, J., Dockery, D. W., & Neas, L. M. (1996) · Journal of the Air & Waste Management Association · doi:10.1080/10473289.1996.10467528 · Cited by 1,561
Self-citationLevy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., & Kaufman, Y. J. (2007) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2006jd007811 · Cited by 1,278
Global evaluation of the Collection 5 MODIS dark-target aerosol products over land
Levy, R. C., Remer, L. A., Kleidman, R. G., Mattoo, S., Ichoku, C., Kahn, R., & Eck, T. F. (2010) · Atmospheric Chemistry and Physics · doi:10.5194/acp-10-10399-2010 · Cited by 1,206
Wang, J., & Christopher, S. A. (2003) · Geophysical Research Letters · doi:10.1029/2003gl018174 · Cited by 838
Satellite remote sensing of particulate matter and air quality assessment over global cities
Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y., & Kumar, N. (2006) · Atmospheric Environment · doi:10.1016/j.atmosenv.2006.03.016 · Cited by 627
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Machine learning in geosciences and remote sensing
Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016) · Geoscience Frontiers · doi:10.1016/j.gsf.2015.07.003 · Cited by 1,107
A systematic review of the physical health impacts from non-occupational exposure to wildfire smoke
Liu, J. C., Pereira, G., Uhl, S. A., Bravo, M. A., & Bell, M. L. (2015) · Environmental Research · doi:10.1016/j.envres.2014.10.015 · Cited by 685
Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach
Hu, X., Belle, J. H., Meng, X., Wildani, A., Waller, L. A., Strickland, M. J., & Liu, Y. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.7b01210 · Cited by 629
Self-citationAssessing PM 2.5 Exposures with High Spatiotemporal Resolution across the Continental United States
Di, Q., Kloog, I., Koutrakis, P., Lyapustin, A., Wang, Y., & Schwartz, J. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b06121 · Cited by 510
Self-citationEmissions estimation from satellite retrievals: A review of current capability
Streets, D. G., Canty, T., Carmichael, G. R., de Foy, B., Dickerson, R. R., Duncan, B. N., Edwards, D. P., Haynes, J. A., Henze, D. K., Houyoux, M. R., et al. (2013) · Atmospheric Environment · doi:10.1016/j.atmosenv.2013.05.051 · Cited by 419
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Metadata
- Authors
- Lee, H. J., Liu, Y., Coull, B. A., Schwartz, J., & Koutrakis, P.
- Venue
- Atmospheric Chemistry and Physics
- Publisher
- Copernicus GmbH
- Published
- 2011-08-05
- Volume
- 11
- Issue
- 15
- Pages
- 7991-8002
- ISSN
- 1680-7316 (ISSN-L)
- 1680-7324
- OpenAlex
- W2083944525
- Cited by
- 408
- References
- 27
- FWCI
- 10.63
- Type
- journal article
- Language
- en
- Open access
- gold
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