The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning
Abstract
Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.
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The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Building Predictive Models in R Using the caret Package
Kuhn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 8,863
Brook, R. D., Rajagopalan, S., Pope, C. A., Brook, J. R., Bhatnagar, A., Diez-Roux, A. V., Holguin, F., Hong, Y., Luepker, R. V., Mittleman, M. A., et al. (2010) · Circulation · doi:10.1161/cir.0b013e3181dbece1 · Cited by 6,548
GeoDa: An Introduction to Spatial Data Analysis
Anselin, L., Syabri, I., & Kho, Y. (2006) · Geographical Analysis · doi:10.1111/j.0016-7363.2005.00671.x · Cited by 2,555
Completion of the 2006 National Land Cover Database for the conterminous United States.
Fry, J. A., Xian, G., Jin, S., Dewitz, J. A., Homer, C. G., Yang, L., Barnes, C. A., Herold, N. D., & Wickham, J. (2011) · Photogrammetric Engineering & Remote Sensing · Cited by 2,448
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., & Soja, A. J. (2011) · Geoscientific Model Development · doi:10.5194/gmd-4-625-2011 · Cited by 1,850
caret: Classification and Regression Training
Kuhn, M. (2007) · CRAN: Contributed Packages · doi:10.32614/cran.package.caret · Cited by 1,786
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
Woodsmoke Health Effects: A Review
Naeher, L. P., Brauer, M., Lipsett, M., Zelikoff, J. T., Simpson, C. D., Koenig, J. Q., & Smith, K. R. (2007) · Inhalation Toxicology · doi:10.1080/08958370600985875 · Cited by 1,530
Cited by
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
Tehrany, M. S., Jones, S., & Shabani, F. (2019) · CATENA · doi:10.1016/j.catena.2018.12.011 · Cited by 396
Global biogeography and projection of soil antibiotic resistance genes
Zheng, D., Yin, G., Liu, M., Hou, L., Yang, Y., Van Boeckel, T. P., Zheng, Y., & Li, Y. (2022) · Science Advances · doi:10.1126/sciadv.abq8015 · Cited by 266
Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., & Zhang, M. (2017) · Atmospheric Environment · doi:10.1016/j.atmosenv.2017.02.023 · Cited by 258
Qiu, Y., Niu, J., Zhang, C., Chen, L., Su, B., & Zhou, S. (2025) · Water Research · doi:10.1016/j.watres.2025.123129 · Cited by 54
Metadata
- Authors
- Reid, C. E., Jerrett, M., Petersen, M. L., Pfister, G. G., Morefield, P. E., Tager, I. B., Raffuse, S. M., & Balmes, J. R.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-02-27
- Volume
- 49
- Issue
- 6
- Pages
- 3887-3896
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W2316167246
- Cited by
- 256
- References
- 63
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Reid, C. E., Jerrett, M., Petersen, M. L., Pfister, G. G., Morefield, P. E., Tager, I. B., Raffuse, S. M., & Balmes, J. R. (2015). Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning. Environmental Science & Technology. https://doi.org/10.1021/es505846r
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