A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States

Environmental Science and Technology

Volumn 47, Issue 13, 2013, Pages 7233-7241

  Beckerman, Bernardo S ^a   Jerrett, Michael ^a   Serre, Marc ^b   Martin, Randall V ^c   Lee, Seung Jae ^d   Van Donkelaar, Aaron ^c   Ross, Zev ^e   Su, Jason ^a   Burnett, Richard T ^f  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b University of North Carolina at Chapel Hill   (United States)

^c DALHOUSIE UNIVERSITY   (Canada)

^d Risk Management Solutions Inc   (United States)

^e ZEVROSS SPATIAL ANALYSIS   (United States)

^f HEALTH CANADA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AERODYNAMIC DIAMETERS; AMBIENT PARTICULATE MATTER; BAYESIAN MAXIMUM ENTROPIES; FINE PARTICULATE MATTER; GROUND LEVEL CONCENTRATIONS; LAND-USE REGRESSION MODELS; MACHINE LEARNING METHODS; SPATIOTEMPORAL VARIABILITY;

ESTIMATION; INTERPOLATION; LAND USE; LEARNING SYSTEMS; MAXIMUM ENTROPY METHODS; REGRESSION ANALYSIS;

REMOTE SENSING;

ATMOSPHERIC POLLUTION; CONCENTRATION (COMPOSITION); ESTIMATION METHOD; LAND USE CHANGE; MONITORING; NUMERICAL MODEL; PARTICULATE MATTER; POLLUTION EXPOSURE; REMOTE SENSING; SPATIOTEMPORAL ANALYSIS;

ARTICLE; LAND USE; MACHINE LEARNING; PARTICULATE MATTER; REMOTE SENSING; SPATIOTEMPORAL ANALYSIS; TRAFFIC; UNITED STATES;

AIR POLLUTANTS; BAYES THEOREM; ENVIRONMENTAL MONITORING; MODELS, THEORETICAL; MOTOR VEHICLES; PARTICLE SIZE; PARTICULATE MATTER; REGRESSION ANALYSIS; REMOTE SENSING TECHNOLOGY; SPATIO-TEMPORAL ANALYSIS; UNITED STATES;

UNITED STATES;

EID: 84880112020     PISSN: 0013936X     EISSN: 15205851     Source Type: Journal    
DOI: 10.1021/es400039u     Document Type: Article

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