Incorporating satellite-derived data with annual and monthly land use regression models for estimating spatial distribution of air pollution

2019 ◽  
Vol 114 ◽  
pp. 181-187 ◽  
Author(s):  
Chun-Sheng Huang ◽  
Tang-Huang Lin ◽  
Hung Hung ◽  
Cheng-Pin Kuo ◽  
Chi-Chang Ho ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Spatial Distribution ◽  
Regression Models ◽  
Land Use Regression ◽  
Derived Data

Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California

2013 ◽  
Vol 77 ◽  
pp. 172-177 ◽  
Author(s):  
Bernardo S. Beckerman ◽  
Michael Jerrett ◽  
Randall V. Martin ◽  
Aaron van Donkelaar ◽  
Zev Ross ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Models ◽  
Land Use Regression

Temporal stability of land use regression models for traffic-related air pollution

2013 ◽  
Vol 64 ◽  
pp. 312-319 ◽  
Author(s):  
Rongrong Wang ◽  
Sarah B. Henderson ◽  
Hind Sbihi ◽  
Ryan W. Allen ◽  
Michael Brauer
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Models ◽  
Temporal Stability ◽  
Land Use Regression

Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution

2014 ◽  
Vol 476-477 ◽  
pp. 378-386 ◽  
Author(s):  
Evi Dons ◽  
Martine Van Poppel ◽  
Luc Int Panis ◽  
Sofie De Prins ◽  
Patrick Berghmans ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Models ◽  
Land Use Regression

scholarly journals Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model

2019 ◽  
Vol 9 (14) ◽  
pp. 2936 ◽  
Author(s):  
Israel Hinojosa-Baliño ◽  
Oscar Infante-Vázquez ◽  
Maite Vallejo
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Spatial Distribution ◽  
Spatial Analysis ◽  
Mexico City ◽  
Land Use Regression ◽  
Personal Exposures ◽  
High Concentrations ◽  
Spatial Distribution Map ◽  
Very High

In this study, the spatial distribution of PM2.5 air pollution in Mexico City from 37 personal exposures was modeled. Meteorological, demographic, geographic, and social data were also included. Geographic information systems (GIS), spatial analysis, and Land-Use Regression (LUR) were used to generate the final predictive model and the spatial distribution map which revealed two areas with very high concentrations (up to 109.3 µg/m3) and two more with lower concentrations (between 72 to 86.5 µg/m3) (p < 0.05). These results illustrate an overview trend of PM2.5 in relation to human activity during the studied periods in Mexico City and show a general approach to understanding the spatial variability of PM2.5.

Land Use Regression Models For PM2.5 Concentration In An Area With Point, Line, Area Sources Of Air Pollution

2015 ◽  
Vol 2015 (1) ◽  
pp. 1024
Author(s):  
Chia-Pin Chio ◽  
Ruei-Hao Shie ◽  
Jui-Huan Lee ◽  
Chang-Chuan Chan
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Models ◽  
Land Use Regression ◽  
Point Line ◽  
Pm2.5 Concentration

scholarly journals Erratum: Comparison of regression models with land-use and emission data to predict the spatial distribution of traffic-related air pollution in Rome

2008 ◽  
Vol 18 (3) ◽  
pp. 339-339 ◽  
Author(s):  
Mats Rosenlund ◽  
Francesco Forastiere ◽  
Massimo Stafoggia ◽  
Daniela Porta ◽  
Mara Perucci ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Spatial Distribution ◽  
Regression Models ◽  
Emission Data

scholarly journals Comparison of regression models with land-use and emissions data to predict the spatial distribution of traffic-related air pollution in Rome

2007 ◽  
Vol 18 (2) ◽  
pp. 192-199 ◽  
Author(s):  
Mats Rosenlund ◽  
Francesco Forastiere ◽  
Massimo Stafoggia ◽  
Daniela Porta ◽  
Mara Perucci ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Spatial Distribution ◽  
Regression Models
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