Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model

2016 ◽  
Vol 50 (23) ◽  
pp. 12894-12902 ◽  
Author(s):  
Jules Kerckhoffs ◽  
Gerard Hoek ◽  
Kyle P. Messier ◽  
Bert Brunekreef ◽  
Kees Meliefste ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Black Carbon ◽  
Carbon Concentration ◽  
Ultrafine Particle ◽  
Short Term ◽  
Land Use Regression ◽  
Black Carbon Concentration

Comparison of Ultrafine Particles and Black Carbon Concentrations from a Mobile and Short-Term Land-Use Regression Model

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Jules Kerckhoffs* ◽  
Gerard Hoek ◽  
Kees Meliefste ◽  
Bert Brunekreef ◽  
Roel Vermeulen
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Black Carbon ◽  
Ultrafine Particles ◽  
Short Term ◽  
Land Use Regression ◽  
Carbon Concentrations

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

A spatio-temporal land use regression model to assess street-level exposure to black carbon

2020 ◽  
Vol 133 ◽  
pp. 104837
Author(s):  
Joris Van den Bossche ◽  
Bernard De Baets ◽  
Dick Botteldooren ◽  
Jan Theunis
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Black Carbon ◽  
Land Use Regression ◽  
Street Level ◽  
Spatio Temporal

scholarly journals Developing a black carbon land use regression model for the Denver, CO metropolitan area

2019 ◽  
Vol 3 ◽  
pp. 261-262
Author(s):  
Martenies S ◽  
WeMott S ◽  
Kuiper G ◽  
Lorber K ◽  
Dawson C ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Black Carbon ◽  
Metropolitan Area ◽  
Land Use Regression

scholarly journals The association between short‐term residential black carbon concentration on blood pressure in a general population sample

Indoor Air ◽  
2020 ◽  
Vol 30 (4) ◽  
pp. 767-775 ◽  
Author(s):  
Felicia A. Rabito ◽  
Qiang Yang ◽  
Hao Zhang ◽  
Derek Werthmann ◽  
Arti Shankar ◽  
...  
Keyword(s):  
Blood Pressure ◽  
General Population ◽  
Black Carbon ◽  
Carbon Concentration ◽  
Population Sample ◽  
General Population Sample ◽  
Short Term ◽  
Black Carbon Concentration

Development of a land use regression model for black carbon using mobile monitoring data and its application to pollution-avoiding routing

2020 ◽  
Vol 183 ◽  
pp. 108619 ◽  
Author(s):  
Annelies Van den Hove ◽  
Jan Verwaeren ◽  
Joris Van den Bossche ◽  
Jan Theunis ◽  
Bernard De Baets
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Black Carbon ◽  
Monitoring Data ◽  
Mobile Monitoring ◽  
Land Use Regression

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.

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