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

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

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

scholarly journals Air Pollution Measurements and Land-Use Regression in Urban Sub-Saharan Africa Using Low-Cost Sensors—Possibilities and Pitfalls

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1357
Author(s):  
Asmamaw Abera ◽  
Kristoffer Mattisson ◽  
Axel Eriksson ◽  
Erik Ahlberg ◽  
Geremew Sahilu ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Rapid Urbanization ◽  
Final Model ◽  
Sub Saharan

Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables were included in the final model, of which both were related to emissions from traffic sources. Some concern regarding influential observations remained in the final model. Long-term PM2.5 and wind direction data were obtained from the city’s meteorological station, which should be used to validate the representativeness of our sensor measurements. The PM2.5 long-term data were however not reliable. Means of obtaining good reference data combined with longer sensor measurements would be a good way forward to develop a stronger LUR model which, together with improved knowledge, can be applied towards improving the quality of health. A health impact assessment, based on the mean level of PM2.5 (23 µg/m3), presented the attributable burden of disease and showed the importance of addressing causes of these high ambient levels in the area.

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

Estimating Long-term Exposure to Air Pollution in 38 Study Areas in Europe in a Harmonized Way Using Land Use Regression Modeling (ESCAPE Project)

Epidemiology ◽  
2011 ◽  
Vol 22 ◽  
pp. S82
Author(s):  
Rob Beelen ◽  
Kees de Hoogh ◽  
Marloes Eeftens ◽  
Kees Meliefste ◽  
Marta Cirach ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Modeling ◽  
Land Use Regression

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

2018 ◽  
Vol 163 ◽  
pp. 16-25 ◽  
Author(s):  
Luke D. Knibbs ◽  
Craig.P. Coorey ◽  
Matthew J. Bechle ◽  
Julian D. Marshall ◽  
Michael G. Hewson ◽  
...  
Keyword(s):  
Land Use ◽  
Nitrogen Dioxide ◽  
Exposure Assessment ◽  
Regression Models ◽  
Land Use Regression

scholarly journals Land Use Regression Model for Exposure Assessment to PM2.5 and PM10 in Rio de Janeiro, Brazil

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Oliveira ◽  
M Santana ◽  
M Marques ◽  
R Griep ◽  
M Fonseca ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Exposure Assessment ◽  
Rio De Janeiro ◽  
Regression Models ◽  
Predictor Variables ◽  
P Value ◽  
Final Model ◽  
Pm2.5 And Pm10

Abstract Background Air pollution is a major public health problem. The latest data from World Health Organization show that 9 out of 10 people breathe air containing high levels of pollutants and that about 4.2 million deaths were caused by exposure to fine particles in 2016. Therefore, the aim of our study was to elaborate a model for long-term exposure assessment to air pollution. Methods This study was developed in Rio de Janeiro city, it has 1,200.255 km² large, about 6.7 million residents and located in the southeastern region of Brazil. The information of PM2.5, PM10 and predictor variables were obtained from government agencies. The potential predictor variables have been used: temperature, relative humidity, vehicular traffic base, Census, altitude databases, vegetation cover, land use, rock masses, hydrographic and hydrographic sub-basins, urban zoning and road network. For the development of Land Use Regression models, linear regression models were specified using the supervised stepwise procedure. Cook D statistics were used to detect influential observations. The overall model performance was evaluated by leave-one-out cross validation (LOOCV). Results The annual mean of PM2.5 and PM10 was 11.73 (SD = ± 4.84) and 35.57 (SD = ± 8.91) μg·m−3, respectively. The R2 value in the final model for PM2.5 was 0.3812 and p-value: 0.0907. The performance evaluated by LOOCV was not also good, the RMSE was 0.2920, with R2 value of 0.1820. The R2 value in the final model for PM10 was 0.73, p-value: 0.001. The performance evaluated by LOOCV was also good, the RMSE was 0.1386, with R2 value of 0.5832. Conclusions The model could be applied in areas where there is no monitoring of air quality, thus, enabling the evaluation of the health impact of exposed populations, providing support for decision-making and development of public and investments policies, medium impact and long-term, more targeted in the following areas: health, environment, transportation and urban planning. Key messages Oswaldo Cruz Foundation. Universidade do Estado do Rio de Janeiro.

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