A land use regression model incorporating data on industrial point source pollution

Journal of Environmental Sciences (China)

Volumn 24, Issue 7, 2012, Pages 1251-1258

  Chen, Li ^a   Wang, Yuming ^a   Li, Peiwu ^a   Ji, Yaqin ^b,c   Kong, Shaofei ^b,c   Li, Zhiyong ^b,c   Bai, Zhipeng ^d  

^a TIANJIN NORMAL UNIVERSITY   (China)

^b NANKAI UNIVERSITY   (China)

^c State Environmental Protection Key Lab of Urban Ambient Air Particulate Matter Pollution Control   (China)

^d CHINESE RESEARCH ACADEMY OF ENVIRONMENTAL SCIENCES   (China)

Author keywords

Air pollution; GIS; Land use regression; Point source; Tianjin

Indexed keywords

AGRICULTURAL LAND; DEPENDENT VARIABLES; DIFFERENT LAND USE TYPES; EXPOSURE ASSESSMENT; GASEOUS POLLUTANTS; GREEN SPACES; HEALTH EFFECTS; INDUSTRIAL LAND; LAND USE REGRESSION; LAND-USE REGRESSION MODELS; LINEAR RELATIONSHIPS; METEOROLOGICAL CONDITION; MODEL PERFORMANCE; MODEL PREDICTION; MONITORING SITES; MULTIPLE LINEAR REGRESSION EQUATIONS; OVERALL-MODEL; PHYSICAL CONDITIONS; POINT SOURCE POLLUTION; POINT SOURCES; POLICY SUPPORT; POPULATION DENSITIES; REGIONAL ENVIRONMENTS; ROAD NETWORK; SPATIAL ASPECT; TIANJIN; TIANJIN , CHINA; TRAFFIC VARIABLES; TRAFFIC VOLUMES; WATER AREAS;

AIR POLLUTION; FOG; FORECASTING; GEOGRAPHIC INFORMATION SYSTEMS; INDUSTRY; LINEAR REGRESSION; NITROGEN OXIDES; POPULATION STATISTICS; REMOTE SENSING; SULFUR DIOXIDE;

LAND USE;

NITROGEN DIOXIDE; SULFUR DIOXIDE;

ATMOSPHERIC POLLUTION; INDUSTRIAL EMISSION; LAND USE; MODEL TEST; MODEL VALIDATION; NITROGEN DIOXIDE; NUMERICAL MODEL; PARTICULATE MATTER; POINT SOURCE POLLUTION; PREDICTION; REGRESSION ANALYSIS; SULFUR DIOXIDE; TRAFFIC EMISSION; URBAN ATMOSPHERE; URBAN POLLUTION;

AIR POLLUTANT; AIR POLLUTION; ARTICLE; CHINA; ENVIRONMENTAL MONITORING; GEOGRAPHIC MAPPING; PARTICULATE MATTER; STATISTICAL MODEL; STATISTICS;

AIR POLLUTANTS; AIR POLLUTION; CHINA; ENVIRONMENTAL MONITORING; GEOGRAPHIC MAPPING; LINEAR MODELS; NITROGEN DIOXIDE; PARTICULATE MATTER; SULFUR DIOXIDE;

CHINA; TIANJIN;

EID: 84863863376     PISSN: 10010742     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1001-0742(11)60902-9     Document Type: Article

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