The methodology for modeling the distribution of certain air pollutant for the city of Belgrade in winter 2015 is presented in the paper. Land Use Regression (LUR) was used for modeling and visualization of spatial distribution of air pollution concentration in the city. NO2 concentrations were sampled at 46 locations, and predictive variables were calculated based on the road category, traffic intensity, demographic data, altitude, household furnaces and land use. These variables were calculated using buffers of different sizes. Linear regressions between NO2 and predictive spatial variables were calculated. Afterwards, the most significant predictors were used for multivariate regression model. Quality of the final model was checked using measurement available at certain locations. The RMSE of 9.8 μg/m³ and the coefficient of determination (R2) of 0.6 were obtained. These results indicate that traffic has the largest impact on air pollution concentration especially near the major roa...ds. Prediction should help in deciding which air protection measures are to be taken in order to preserve and improve the city environment. The lack of data that are collected by using quite a few sensor stations is still rather limiting for the successful monitoring of air pollution in the city of Belgrade.
Keywords:
Air pollution / Nitrogen Dioxide / Land-use regression / GIS
Source:
4th BAB International Conference Decoding Balkan: Architecture, Urbanism, Planning, Belgrade, 2019, 2019, 18-25
Publisher:
University of Belgrade - Faculty of Civil Engineering
TY - CONF
AU - Brodić, Nenad
AU - Milić, Stevan
AU - Mitrović, Momir
AU - Cvijetinović, Željko
AU - Mihajlović, Dragan
AU - Kovačević, Jovan
AU - Stančić, Nikola
PY - 2019
UR - http://grafar.grf.bg.ac.rs/handle/123456789/1966
AB - The methodology for modeling the distribution of certain air pollutant for the city of Belgrade in winter 2015 is presented in the paper. Land Use Regression (LUR) was used for modeling and visualization of spatial distribution of air pollution concentration in the city. NO2 concentrations were sampled at 46 locations, and predictive variables were calculated based on the road category, traffic intensity, demographic data, altitude, household furnaces and land use. These variables were calculated using buffers of different sizes. Linear regressions between NO2 and predictive spatial variables were calculated. Afterwards, the most significant predictors were used for multivariate regression model. Quality of the final model was checked using measurement available at certain locations. The RMSE of 9.8 μg/m³ and the coefficient of determination (R2) of 0.6 were obtained. These results indicate that traffic has the largest impact on air pollution concentration especially near the major roads. Prediction should help in deciding which air protection measures are to be taken in order to preserve and improve the city environment. The lack of data that are collected by using quite a few sensor stations is still rather limiting for the successful monitoring of air pollution in the city of Belgrade.
PB - University of Belgrade - Faculty of Civil Engineering
C3 - 4th BAB International Conference Decoding Balkan: Architecture, Urbanism, Planning, Belgrade, 2019
T1 - Monitoring Air Pollution Using GIS: Case Study for the City of Belgrade
EP - 25
SP - 18
ER -
@conference{
author = "Brodić, Nenad and Milić, Stevan and Mitrović, Momir and Cvijetinović, Željko and Mihajlović, Dragan and Kovačević, Jovan and Stančić, Nikola",
year = "2019",
abstract = "The methodology for modeling the distribution of certain air pollutant for the city of Belgrade in winter 2015 is presented in the paper. Land Use Regression (LUR) was used for modeling and visualization of spatial distribution of air pollution concentration in the city. NO2 concentrations were sampled at 46 locations, and predictive variables were calculated based on the road category, traffic intensity, demographic data, altitude, household furnaces and land use. These variables were calculated using buffers of different sizes. Linear regressions between NO2 and predictive spatial variables were calculated. Afterwards, the most significant predictors were used for multivariate regression model. Quality of the final model was checked using measurement available at certain locations. The RMSE of 9.8 μg/m³ and the coefficient of determination (R2) of 0.6 were obtained. These results indicate that traffic has the largest impact on air pollution concentration especially near the major roads. Prediction should help in deciding which air protection measures are to be taken in order to preserve and improve the city environment. The lack of data that are collected by using quite a few sensor stations is still rather limiting for the successful monitoring of air pollution in the city of Belgrade.",
publisher = "University of Belgrade - Faculty of Civil Engineering",
journal = "4th BAB International Conference Decoding Balkan: Architecture, Urbanism, Planning, Belgrade, 2019",
title = "Monitoring Air Pollution Using GIS: Case Study for the City of Belgrade",
pages = "25-18"
}
Brodić, N., Milić, S., Mitrović, M., Cvijetinović, Ž., Mihajlović, D., Kovačević, J.,& Stančić, N.. (2019). Monitoring Air Pollution Using GIS: Case Study for the City of Belgrade. in 4th BAB International Conference Decoding Balkan: Architecture, Urbanism, Planning, Belgrade, 2019
University of Belgrade - Faculty of Civil Engineering., 18-25.
Brodić N, Milić S, Mitrović M, Cvijetinović Ž, Mihajlović D, Kovačević J, Stančić N. Monitoring Air Pollution Using GIS: Case Study for the City of Belgrade. in 4th BAB International Conference Decoding Balkan: Architecture, Urbanism, Planning, Belgrade, 2019. 2019;:18-25..
Brodić, Nenad, Milić, Stevan, Mitrović, Momir, Cvijetinović, Željko, Mihajlović, Dragan, Kovačević, Jovan, Stančić, Nikola, "Monitoring Air Pollution Using GIS: Case Study for the City of Belgrade" in 4th BAB International Conference Decoding Balkan: Architecture, Urbanism, Planning, Belgrade, 2019 (2019):18-25.
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