The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing
Abstract
Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R(2) is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m(3). The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m(3). Our predicted annual PM2.5 concentrations indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level 2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.
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Cites
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
Rodriguez, J. D., Perez, A., & Lozano, J. A. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.187 · Cited by 1,890
Cited by
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. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b05833 · Cited by 1,206
Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., et al. (2019) · Environment International · doi:10.1016/j.envint.2019.104909 · Cited by 733
Qi, Y., Li, Q., Karimian, H., & Liu, D. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.01.333 · Cited by 601
Zamani Joharestani, M., Cao, C., Ni, X., Bashir, B., & Talebiesfandarani, S. (2019) · Atmosphere · doi:10.3390/atmos10070373 · Cited by 538
West, J. J., Cohen, A., Dentener, F., Brunekreef, B., Zhu, T., Armstrong, B., Bell, M. L., Brauer, M., Carmichael, G., Costa, D. L., et al. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b03827 · Cited by 390
Metadata
- Authors
- Ma, Z., Hu, X., Huang, L., Bi, J., & Liu, Y.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-06-13
- Volume
- 48
- Issue
- 13
- Pages
- 7436-7444
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W2312602772
- Cited by
- 568
- References
- 1
- FWCI
- 27.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ma, Z., Hu, X., Huang, L., Bi, J., & Liu, Y. (2014). Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing. Environmental Science & Technology. https://doi.org/10.1021/es5009399
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