The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors
Open accessAbstract
We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R(2) = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m(3) WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.
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Cites
Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., AlMazroa, M. A., Amann, M., Anderson, H. R., Andrews, K. G., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61766-8 · Cited by 11,805
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Cited by
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Self-citationImpacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets
Anenberg, S. C., Miller, J., Minjares, R., Du, L., Henze, D. K., Lacey, F., Malley, C. S., Emberson, L., Franco, V., Klimont, Z., et al. (2017) · Nature · doi:10.1038/nature22086 · Cited by 750
Regional and global contributions of air pollution to risk of death from COVID-19
Pozzer, A., Dominici, F., Haines, A., Witt, C., Münzel, T., & Lelieveld, J. (2020) · Cardiovascular Research · doi:10.1093/cvr/cvaa288 · Cited by 463
“5 concentrations were taken from model-integrated satellite data, for the year 2019.(8,27) The horizontal resolution is 0.”
MethodHalf the world’s population are exposed to increasing air pollution
Shaddick, G., Thomas, M. L., Mudu, P., Ruggeri, G., & Gumy, S. (2020) · npj Climate and Atmospheric Science · doi:10.1038/s41612-020-0124-2 · Cited by 428
Anenberg, S. C., Henze, D. K., Tinney, V., Kinney, P. L., Raich, W., Fann, N., Malley, C. S., Roman, H., Lamsal, L., Duncan, B., et al. (2018) · Environmental Health Perspectives · doi:10.1289/ehp3766 · Cited by 337
Self-citationYu, W., Ye, T., Zhang, Y., Xu, R., Lei, Y., Chen, Z., Yang, Z., Zhang, Y., Song, J., Yue, X., et al. (2023) · The Lancet Planetary Health · doi:10.1016/s2542-5196(23)00008-6 · Cited by 215
Green growth efficiency of Chinese cities and its spatio-temporal pattern
Ma, L., Long, H., Chen, K., Tu, S., Zhang, Y., & Liao, L. (2019) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2019.03.049 · Cited by 138
Global mapping of eco-environmental vulnerability from human and nature disturbances
Nguyen, K.-A., & Liou, Y.-A. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.01.407 · Cited by 126
Kleinschmitt, C., Boucher, O., & Platt, U. (2018) · Atmospheric Chemistry and Physics · doi:10.5194/acp-18-2769-2018 · Cited by 84
Air pollution-related health and climate benefits of clean cookstove programs in Mozambique
Anenberg, S. C., Henze, D. K., Lacey, F., Irfan, A., Kinney, P., Kleiman, G., & Pillarisetti, A. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa5557 · Cited by 50
Globally regionalized monthly life cycle impact assessment of particulate matter
Oberschelp, C., Pfister, S., & Hellweg, S. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c05691 · Cited by 41
Amegbor, P. M. (2022) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154957 · Cited by 28
Shim, S., Sung, H., Kwon, S., Kim, J., Lee, J., Sun, M., Song, J., Ha, J., Byun, Y., Kim, Y., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18136817 · Cited by 24
Borzino, N., Otto, M., Kjellstrom, T., & Kai Wei Lee, J. (2026) · Environmental Research: Climate · doi:10.1088/2752-5295/ae8320
Metadata
- Authors
- van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., Lyapustin, A., Sayer, A. M., & Winker, D. M.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-03-24
- Volume
- 50
- Issue
- 7
- Pages
- 3762-3772
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W2310114729
- Cited by
- 1,206
- References
- 1
- FWCI
- 73.57
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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