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Cited by 256

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

Reid, Colleen E., Jerrett, Michael, Petersen, Maya L., Pfister, Gabriele G., Morefield, Philip E., Tager, Ira B., Raffuse, Sean M., Balmes, John R.·2015·Environmental Science & Technology
DOI 10.1021/es505846r
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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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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
DOI
10.1021/es505846r
OpenAlex
W2316167246
Cited by
256
References
63
FWCI
7.28
Type
journal article
Language
en
Open access
closed

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