A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China

2016 ◽  
Vol 565 ◽  
pp. 607-615 ◽  
Author(s):  
Chao Liu ◽  
Barron H. Henderson ◽  
Dongfang Wang ◽  
Xinyuan Yang ◽  
Zhong-ren Peng
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Nitrogen Dioxide ◽  
Spatial Variation ◽  
Fine Particulate Matter ◽  
Land Use Regression ◽  
Fine Particulate

A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China

2019 ◽  
Vol 177 ◽  
pp. 108597 ◽  
Author(s):  
Lan Jin ◽  
Jesse D. Berman ◽  
Joshua L. Warren ◽  
Jonathan I. Levy ◽  
George Thurston ◽  
...  
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Nitrogen Dioxide ◽  
Regression Model ◽  
Fine Particulate Matter ◽  
Land Use Regression ◽  
Urban Core ◽  
Fine Particulate

scholarly journals Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1018
Author(s):  
Chun-Sheng Huang ◽  
Ho-Tang Liao ◽  
Tang-Huang Lin ◽  
Jung-Chi Chang ◽  
Chien-Lin Lee ◽  
...  
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Elemental Composition ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Prediction Models ◽  
Fine Particulate Matter ◽  
Sensitivity Analyses ◽  
Land Use Regression ◽  
Fine Particulate

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling

2019 ◽  
Vol 53 (15) ◽  
pp. 8925-8937 ◽  
Author(s):  
Ellis Shipley Robinson ◽  
Rishabh Urvesh Shah ◽  
Kyle Messier ◽  
Peishi Gu ◽  
Hugh Z. Li ◽  
...  
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Regression Modeling ◽  
Land Use Regression ◽  
Fine Particulate ◽  
Particulate Matter Components
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