Spatiotemporal prediction of fine particulate matter during the 2008 Northern California wildfires using machine learning

Environmental Science and Technology

Volumn 49, Issue 6, 2015, Pages 3887-3896

  Reid, Colleen E ^a,g   Jerrett, Michael ^a,f   Petersen, Maya L ^a   Pfister, Gabriele G ^b   Morefield, Philip E ^c   Tager, Ira B ^a   Raffuse, Sean M ^d   Balmes, John R ^a,e  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^c U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^d SONOMA TECHNOLOGY INC   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF CALIFORNIA   (United States)

^g Wood Johnson Health and Society Scholar   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AEROSOLS; ARTIFICIAL INTELLIGENCE; ATMOSPHERIC AEROSOLS; FIRES; FORECASTING; GEOSTATIONARY SATELLITES; LAND USE; LEARNING ALGORITHMS; MEAN SQUARE ERROR; METEOROLOGY; OPTICAL PROPERTIES; POPULATION STATISTICS; SATELLITE IMAGERY; SATELLITES;

10-FOLD CROSS-VALIDATION; CHEMICAL TRANSPORT MODELS; FINE PARTICULATE MATTER; GEOSTATIONARY OPERATIONAL ENVIRONMENTAL SATELLITES; ROOT MEAN SQUARED ERRORS; SPATIO-TEMPORAL PREDICTION; SPATIOTEMPORAL VARIABILITY; TEMPORAL CHARACTERISTICS;

LEARNING SYSTEMS;

AEROSOL COMPOSITION; ATMOSPHERIC PLUME; ATMOSPHERIC POLLUTION; LEARNING; OPTICAL DEPTH; PARTICULATE MATTER; POLLUTION EXPOSURE; PREDICTION; SATELLITE DATA; SIZE DISTRIBUTION; SPATIOTEMPORAL ANALYSIS; WILDFIRE;

AEROSOL; ARTICLE; CHEMICAL TRANSPORT MODEL; CONCENTRATION (PARAMETERS); EXHAUST GAS; FIRE; GENERALIZED BOOSTING MODEL; LAND USE; MACHINE LEARNING; MATHEMATICAL MODEL; MEASUREMENT ERROR; METEOROLOGY; OPTICAL DEPTH; PARTICULATE MATTER; POLLUTION MONITORING; POLLUTION TRANSPORT; PREDICTION; PREDICTOR VARIABLE; SATELLITE IMAGERY; SPATIOTEMPORAL ANALYSIS; UNITED STATES; WILDFIRE; AIR POLLUTANT; ALGORITHM; ANALYSIS; ARTIFICIAL INTELLIGENCE; PREDICTIVE VALUE; SMOKE; THEORETICAL MODEL;

CALIFORNIA; UNITED STATES;

AEROSOL; AIR POLLUTANT; PARTICULATE MATTER; SMOKE;

AEROSOLS; AIR POLLUTANTS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CALIFORNIA; FIRES; MODELS, THEORETICAL; PARTICULATE MATTER; PREDICTIVE VALUE OF TESTS; SMOKE;

EID: 84924973031     PISSN: 0013936X     EISSN: 15205851     Source Type: Journal    
DOI: 10.1021/es505846r     Document Type: Article

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