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Titolo:
Modeling spatial variation in area source emissions
Autore:
Dai, J; Rocke, DM;
Indirizzi:
Univ Calif Davis, Ctr Image Proc & Integrated Computing, Davis, CA 95616 USA Univ Calif Davis Davis CA USA 95616 grated Computing, Davis, CA 95616 USA
Titolo Testata:
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
fascicolo: 1, volume: 5, anno: 2000,
pagine: 7 - 21
SICI:
1085-7117(200003)5:1<7:MSVIAS>2.0.ZU;2-T
Fonte:
ISI
Lingua:
ENG
Soggetto:
MAXIMUM-LIKELIHOOD METHODS; POISSON; RATES;
Keywords:
air pollution; grid effects; overdispersion; Poisson regression;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Citazioni:
21
Recensione:
Indirizzi per estratti:
Indirizzo: Dai, J Univ Calif Davis, Ctr Image Proc & Integrated Computing, Davis, CA 95616 USA Univ Calif Davis Davis CA USA 95616 Computing, Davis, CA 95616 USA
Citazione:
J. Dai e D.M. Rocke, "Modeling spatial variation in area source emissions", J AGRIC BIO, 5(1), 2000, pp. 7-21

Abstract

Urban and regional air quality needs to be analyzed at various geographical scales. Area source emission inventories usually estimate total emissionsfor various industrial and commercial activities at the county or larger scales. Consequently, information on spatial variation of emissions within acounty, a critical requirement for urban airshed modeling, is largely unavailable. This paper proposes a Poisson regression approach that enables us to model small-area variation in source activities and to allocate county or regionwide emission estimates to subcounty units. The new approach is used to model the spatial distribution of automobile refinishing activities inthe Sacramento modeling region in California. The paper addresses the problem of overdispersion of variance in Poisson regression and evaluates the effect of grid location on modeling results. The usefulness of geographical information systems in spatial statistical analysis is demonstrated.

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Documento generato il 03/07/20 alle ore 01:00:31