EXPOSURE ASSESSMENT TO AIR POLLUTION IN FOUR SPANISH AREAS. LAND USE REGRESSION APPLICATION

2011 ◽  
Vol 2011 (1) ◽  
Author(s):  
Marisa Estarlich ◽  
Ferran Ballester ◽  
Ana Fernandez-Somoano ◽  
Inmaculada Aguilera ◽  
Aitana Lertxundi ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Exposure Assessment ◽  
Land Use Regression

scholarly journals A national satellite-based land-use regression model for air pollution exposure assessment in Australia

2014 ◽  
Vol 135 ◽  
pp. 204-211 ◽  
Author(s):  
Luke D. Knibbs ◽  
Michael G. Hewson ◽  
Matthew J. Bechle ◽  
Julian D. Marshall ◽  
Adrian G. Barnett
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Exposure Assessment ◽  
Land Use Regression ◽  
Air Pollution Exposure ◽  
Pollution Exposure

scholarly journals Impact of Residential Mobility on Exposure Assessment in Longitudinal Air Pollution Studies: A Sensitivity Analysis within the ESCAPE Project

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Anna Oudin ◽  
Bertil Forsberg ◽  
Magnus Strömgren ◽  
Rob Beelen ◽  
Lars Modig
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Exposure Assessment ◽  
Residential Mobility ◽  
Large Population ◽  
Average Concentration ◽  
Exposure Misclassification ◽  
Land Use Regression

Exposure misclassification in longitudinal studies of air pollution exposure and health effects can occur due to residential mobility in a study population over followup. The aim of this study was to investigate to what extent residential mobility during followup can be expected to cause exposure misclassification in such studies, where exposure at the baseline address is used as the main exposure assessment. The addresses for each participant in a large population-based study (N>25,000) were obtained via national registers. We used a Land Use Regression model to estimate theNOxconcentration for each participant's all addresses during the entire follow-up period (in average 14.6 years) and calculated an average concentration during followup. The Land Use Regression model explained 83% of the variation in measured levels. In summary, theNOxconcentration at the inclusion address was similar to the average concentration over followup with a correlation coefficient of 0.80, indicating that air pollution concentration at study inclusion address could be used as indicator of average air pollution concentrations over followup. The differences between an individual's inclusion and average follow-up mean concentration were small and seemed to be nondifferential with respect to a large range of factors and disease statuses, implying that bias due to residential mobility was small.

Individual-Based Exposure Assessment to Traffic-Related Air Pollution: Comparing Land Use Regression and Dispersion Modelling in Multiple European Cities

2014 ◽  
Vol 2014 (1) ◽  
pp. 1985
Author(s):  
Kees de Hoogh* ◽  
Michal Korek ◽  
Jaakko Kukkonen ◽  
Menno Keuken ◽  
Gerard Hoek ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Exposure Assessment ◽  
Dispersion Modelling ◽  
Land Use Regression ◽  
European Cities

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.

scholarly journals Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing

2021 ◽  
Vol 18 (11) ◽  
pp. 5884
Author(s):  
Junli Liu ◽  
Panli Cai ◽  
Jin Dong ◽  
Junshun Wang ◽  
Runkui Li ◽  
...  
Keyword(s):  
Land Use ◽  
Exposure Assessment ◽  
Activity Pattern ◽  
Cell Phone ◽  
Total Population ◽  
Cell Phones ◽  
Land Use Regression ◽  
Pm2.5 Exposure ◽  
Dynamic Exposure ◽  
Pm2.5 Concentration

The spatiotemporal locations of large populations are difficult to clearly characterize using traditional exposure assessment, mainly due to their complicated daily intraurban activities. This study aimed to extract hourly locations for the total population of Beijing based on cell phone data and assess their dynamic exposure to ambient PM2.5. The locations of residents were located by the cellular base stations that were keeping in contact with their cell phones. The diurnal activity pattern of the total population was investigated through the dynamic spatial distribution of all of the cell phones. The outdoor PM2.5 concentration was predicted in detail using a land use regression (LUR) model. The hourly PM2.5 map was overlapped with the hourly distribution of people for dynamic PM2.5 exposure estimation. For the mobile-derived total population, the mean level of PM2.5 exposure was 89.5 μg/m3 during the period from 2013 to 2015, which was higher than that reported for the census population (87.9 μg/m3). The hourly activity pattern showed that more than 10% of the total population commuted into the center of Beijing (e.g., the 5th ring road) during the daytime. On average, the PM2.5 concentration at workplaces was generally higher than in residential areas. The dynamic PM2.5 exposure pattern also varied with seasons. This study exhibited the strengths of mobile location in deriving the daily spatiotemporal activity patterns of the population in a megacity. This technology would refine future exposure assessment, including either small group cohort studies or city-level large population assessments.

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 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
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