Large Scale Air Pollution Estimation Method Combining Land Use Regression and Chemical Transport Modeling in a Geostatistical Framework

2014 ◽  
Vol 48 (8) ◽  
pp. 4452-4459 ◽  
Author(s):  
Yasuyuki Akita ◽  
Jose M. Baldasano ◽  
Rob Beelen ◽  
Marta Cirach ◽  
Kees de Hoogh ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Large Scale ◽  
Estimation Method ◽  
Chemical Transport ◽  
Transport Modeling ◽  
Land Use Regression ◽  
Chemical Transport Modeling

scholarly journals Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5

2016 ◽  
Vol 50 (10) ◽  
pp. 5111-5118 ◽  
Author(s):  
Meng Wang ◽  
Paul D. Sampson ◽  
Jianlin Hu ◽  
Michael Kleeman ◽  
Joshua P. Keller ◽  
...  
Keyword(s):  
Land Use ◽  
Chemical Transport ◽  
Transport Modeling ◽  
Geostatistical Model ◽  
Land Use Regression ◽  
Chemical Transport Modeling

scholarly journals Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area

Atmosphere ◽  
2016 ◽  
Vol 7 (3) ◽  
pp. 48 ◽  
Author(s):  
Frauke Hennig ◽  
Dorothea Sugiri ◽  
Lilian Tzivian ◽  
Kateryna Fuks ◽  
Susanne Moebus ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Modeling ◽  
Transport Modeling ◽  
Land Use Regression ◽  
Ruhr Area ◽  
Pollution Concentrations

scholarly journals Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

2021 ◽  
Vol 13 (9) ◽  
pp. 4933
Author(s):  
Saimar Pervez ◽  
Ryuta Maruyama ◽  
Ayesha Riaz ◽  
Satoshi Nakai
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Urban Areas ◽  
Industrial Development ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Land Use Classification ◽  
Land Use Regression ◽  
Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

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 Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

2011 ◽  
Vol 45 (26) ◽  
pp. 4412-4420 ◽  
Author(s):  
Laina D. Mercer ◽  
Adam A. Szpiro ◽  
Lianne Sheppard ◽  
Johan Lindström ◽  
Sara D. Adar ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Universal Kriging ◽  
Oxides Of Nitrogen ◽  
Land Use Regression ◽  
Ethnic Study

Using spatio-temporal land use regression models to address spatial variation in air pollution concentrations in time series studies

2017 ◽  
Vol 10 (9) ◽  
pp. 1139-1149 ◽  
Author(s):  
Konstantina Dimakopoulou ◽  
Alexandros Gryparis ◽  
Klea Katsouyanni
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Time Series ◽  
Spatial Variation ◽  
Regression Models ◽  
Land Use Regression ◽  
Time Series Studies ◽  
Spatio Temporal ◽  
Pollution Concentrations

scholarly journals Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

2018 ◽  
Vol 52 (21) ◽  
pp. 12563-12572 ◽  
Author(s):  
Kyle P. Messier ◽  
Sarah E. Chambliss ◽  
Shahzad Gani ◽  
Ramon Alvarez ◽  
Michael Brauer ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Mobile Monitoring ◽  
Land Use Regression ◽  
Google Street View ◽  
Street View

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.

Improved Estimation of Background Ozone and Emission Impacts Using Chemical Transport Modeling and Data Fusion

2021 ◽  
pp. 89-94
Author(s):  
T. Nash Skipper ◽  
M. Talat Odman ◽  
Yongtao Hu ◽  
Petros Vasilakos ◽  
Armistead G. Russell
Keyword(s):  
Data Fusion ◽  
Chemical Transport ◽  
Transport Modeling ◽  
Chemical Transport Modeling ◽  
Background Ozone
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