Agreement of Land Use Regression Models with Personal Exposure Measurements of Particulate Matter and Nitrogen Oxides Air Pollution

2013 ◽  
pp. 130712144458004 ◽  
Author(s):  
Denise Montagne ◽  
Gerard Hoek ◽  
Mark Nieuwenhuijsen ◽  
Timo Lanki ◽  
Arto Pennanen ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Nitrogen Oxides ◽  
Regression Models ◽  
Personal Exposure ◽  
Land Use Regression ◽  
Exposure Measurements

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 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 Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

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 to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran

2014 ◽  
Vol 488-489 ◽  
pp. 343-353 ◽  
Author(s):  
Hassan Amini ◽  
Seyed Mahmood Taghavi-Shahri ◽  
Sarah B. Henderson ◽  
Kazem Naddafi ◽  
Ramin Nabizadeh ◽  
...  
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Sulfur Dioxide ◽  
Spatial Variability ◽  
Regression Models ◽  
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

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
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