A hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regression

Atmospheric Environment

Volumn 131, Issue , 2016, Pages 390-399

  Di, Qian ^a   Koutrakis, Petros ^a   Schwartz, Joel ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Chemical transport model; Epidemiology; Land use regression; Neural network; PM2.5; PM2.5 components

Indexed keywords

EPIDEMIOLOGY; FORECASTING; IMAGE RESOLUTION; MONITORING; NEURAL NETWORKS; ORGANIC CARBON; POLLUTION;

BACKWARD PROPAGATION NEURAL NETWORKS; CHEMICAL TRANSPORT MODELS; CROSS-VALIDATION TECHNIQUE; EPIDEMIOLOGICAL STUDIES; HYBRID PREDICTION MODELS; LAND USE REGRESSION; METEOROLOGICAL VARIABLES; POLLUTANT CONCENTRATION;

LAND USE;

AMMONIA; CARBON; NITRATE; ORGANIC CARBON; SODIUM CHLORIDE; SULFATE;

AMMONIUM; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC POLLUTION; DUST; EPIDEMIOLOGY; LAND USE; NUMERICAL MODEL; ORGANIC CARBON; SEA SALT; SPATIAL RESOLUTION;

AIR POLLUTANT; AIR POLLUTION; ARTICLE; DUST; LAND USE; LONG TERM EXPOSURE; PARTICULATE MATTER; PRIORITY JOURNAL; SIMULATION; UNITED STATES; URBAN AREA;

UNITED STATES;

EID: 84959010964     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2016.02.002     Document Type: Article

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