دورية أكاديمية
Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.
| العنوان: | Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning. |
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| المؤلفون: | Reid CE; †Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California 94720, United States., Jerrett M; †Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California 94720, United States.; ¶Environmental Health Sciences Department, Fielding School of Public Health, University of California, Los Angeles, California 90095, United States., Petersen ML; ‡Epidemiology Division, School of Public Health, University of California, Berkeley, California 94720, United States.; §Biostatistics Division, School of Public Health, University of California, Berkeley, California 94720, United States., Pfister GG; ∥Atmospheric Chemistry Division, National Center for Atmospheric Research, Boulder, Colorado 80301, United States., Morefield PE; ⊥National Center for Environmental Assessment, U.S. Environmental Protection Agency, Washington, D.C. 20460, United States., Tager IB; ‡Epidemiology Division, School of Public Health, University of California, Berkeley, California 94720, United States., Raffuse SM; #Sonoma Technology, Inc., Petaluma, California 94954, United States., Balmes JR; †Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California 94720, United States.; ∇Department of Medicine, University of California, San Francisco, California 94143, United States. |
| المصدر: | Environmental science & technology [Environ Sci Technol] 2015 Mar 17; Vol. 49 (6), pp. 3887-96. Date of Electronic Publication: 2015 Feb 27. |
| نوع المنشور: | Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S. |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 0213155 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1520-5851 (Electronic) Linking ISSN: 0013936X NLM ISO Abbreviation: Environ Sci Technol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Washington DC : American Chemical Society Original Publication: Easton, Pa. : American Chemical Society, c1967- |
| مواضيع طبية MeSH: | Algorithms* , Fires* , Models, Theoretical*, Particulate Matter/*analysis, Aerosols/analysis ; Air Pollutants/analysis ; Artificial Intelligence ; California ; Predictive Value of Tests ; Smoke/analysis |
| مستخلص: | 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. |
| معلومات مُعتمدة: | 1R21ES016986 United States ES NIEHS NIH HHS; CD300430 United States CD ODCDC CDC HHS |
| المشرفين على المادة: | 0 (Aerosols) 0 (Air Pollutants) 0 (Particulate Matter) 0 (Smoke) |
| تواريخ الأحداث: | Date Created: 20150205 Date Completed: 20151203 Latest Revision: 20150317 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1021/es505846r |
| PMID: | 25648639 |
| قاعدة البيانات: | MEDLINE |
Find this issue from the American Chemical Society | تدمد: | 1520-5851 |
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| DOI: | 10.1021/es505846r |