Robustness of Land-Use Regression Models Developed from Mobile Air Pollutant Measurements

2017 ◽  
Vol 51 (7) ◽  
pp. 3938-3947 ◽  
Author(s):  
Marianne Hatzopoulou ◽  
Marie France Valois ◽  
Ilan Levy ◽  
Cristian Mihele ◽  
Gang Lu ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Models ◽  
Air Pollutant ◽  
Land Use Regression

scholarly journals Development of Land Use Regression models for particulate matter and associated components in a low air pollutant concentration airshed

2016 ◽  
Vol 144 ◽  
pp. 69-78 ◽  
Author(s):  
Mila Dirgawati ◽  
Jane S. Heyworth ◽  
Amanda J. Wheeler ◽  
Kieran A. McCaul ◽  
David Blake ◽  
...  
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Regression Models ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Land Use Regression

Development of land-use regression models to estimate particle mass and number concentrations in Taichung, Taiwan

2021 ◽  
pp. 118303
Author(s):  
Ta-Yuan Chang ◽  
Ching-Chih Tsai ◽  
Chang-Fu Wu ◽  
Li-Te Chang ◽  
Kai-Jen Chuang ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Models ◽  
Particle Mass ◽  
Land Use Regression

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

scholarly journals A New Technique for Evaluating Land-use Regression Models and Their Impact on Health Effect Estimates

Epidemiology ◽  
2016 ◽  
Vol 27 (1) ◽  
pp. 51-56 ◽  
Author(s):  
Meng Wang ◽  
Bert Brunekreef ◽  
Ulrike Gehring ◽  
Adam Szpiro ◽  
Gerard Hoek ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Models ◽  
Health Effect ◽  
New Technique ◽  
Land Use Regression ◽  
A New Technique

scholarly journals Performance of Multi-City Land Use Regression Models for Nitrogen Dioxide and Fine Particles

2014 ◽  
Vol 122 (8) ◽  
pp. 843-849 ◽  
Author(s):  
Meng Wang ◽  
Rob Beelen ◽  
Tom Bellander ◽  
Matthias Birk ◽  
Giulia Cesaroni ◽  
...  
Keyword(s):  
Land Use ◽  
Nitrogen Dioxide ◽  
Regression Models ◽  
Fine Particles ◽  
Land Use Regression

scholarly journals Evaluation of Land Use Regression Models for NO2 and Particulate Matter in 20 European Study Areas: The ESCAPE Project

2013 ◽  
Vol 47 (9) ◽  
pp. 4357-4364 ◽  
Author(s):  
Meng Wang ◽  
Rob Beelen ◽  
Xavier Basagana ◽  
Thomas Becker ◽  
Giulia Cesaroni ◽  
...  
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Regression Models ◽  
European Study ◽  
Land Use Regression

scholarly journals Evaluation of Land Use Regression Models for Nitrogen Dioxide and Benzene in Four US Cities

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Shaibal Mukerjee ◽  
Luther Smith ◽  
Lucas Neas ◽  
Gary Norris
Keyword(s):  
Land Use ◽  
Nitrogen Dioxide ◽  
Regression Models ◽  
El Paso ◽  
Passive Samplers ◽  
Data Availability ◽  
Border Crossings ◽  
Monitoring Networks ◽  
Land Use Regression ◽  
Pollutant Concentrations

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.

Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation

2015 ◽  
Vol 49 (14) ◽  
pp. 8712-8720 ◽  
Author(s):  
Denise R. Montagne ◽  
Gerard Hoek ◽  
Jochem O. Klompmaker ◽  
Meng Wang ◽  
Kees Meliefste ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Variation ◽  
Black Carbon ◽  
Ultrafine Particles ◽  
Regression Models ◽  
Short Term ◽  
Land Use Regression ◽  
Term Monitoring ◽  
Carbon Based
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