Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

Journal of the Air and Waste Management Association

Volumn 65, Issue 5, 2015, Pages 628-640

  Alam, Md Saniul ^a   McNabola, Aonghus ^a  

^a TRINITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

AIR QUALITY; ATMOSPHERIC MOVEMENTS; LINEAR REGRESSION; MOTOR TRANSPORTATION; NEURAL NETWORKS; POPULATION STATISTICS;

AIR POLLUTION MODELING; BACK TRAJECTORY ANALYSIS; MULTIPLE LINEAR REGRESSION MODELS; NON-PARAMETRIC REGRESSION; PREDICTIVE PERFORMANCE; SPATIO-TEMPORAL VARIATION; STATISTICAL TECHNIQUES; TRANS-BOUNDARY AIR POLLUTIONS;

LAND USE;

AIR POLLUTION; AMBIENT AIR; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONCENTRATION (PARAMETERS); CONCEPTUAL FRAMEWORK; ENVIRONMENTAL EXPOSURE; EXHAUST GAS; LAND USE; MULTIPLE LINEAR REGRESSION ANALYSIS; NONLINEAR SYSTEM; NONPARAMETRIC TEST; PARTICULATE MATTER; POPULATION DENSITY; PREDICTION; PRIORITY JOURNAL; SPATIOTEMPORAL ANALYSIS; AIR POLLUTANT; ANALYSIS; AUSTRIA; CITY; ENVIRONMENTAL MONITORING; HUMAN; IRELAND; PARTICLE SIZE; PROCEDURES; REGRESSION ANALYSIS; SEASON; THEORETICAL MODEL; TIME;

AIR POLLUTANT; PARTICULATE MATTER;

AIR POLLUTANTS; AUSTRIA; CITIES; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL MONITORING; HUMANS; IRELAND; MODELS, THEORETICAL; PARTICLE SIZE; PARTICULATE MATTER; REGRESSION ANALYSIS; SEASONS; TIME FACTORS;

EID: 84942597126     PISSN: 10962247     EISSN: 21622906     Source Type: Journal    
DOI: 10.1080/10962247.2015.1006377     Document Type: Article

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