Winter and Summer PM 2.5 Land Use Regression Models for the City of Novi Sad, Serbia

Sonja Dmitrašinović, Jelena Radonić, Marija Živković, Željko Ćirović, Milena Jovašević-Stojanović and Miloš Davidović ()
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Sonja Dmitrašinović: Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Jelena Radonić: Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Marija Živković: VIDIS Centre, “Vinča” Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovića Alasa 12-14, 11000 Belgrade, Serbia
Željko Ćirović: VIDIS Centre, “Vinča” Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovića Alasa 12-14, 11000 Belgrade, Serbia
Milena Jovašević-Stojanović: VIDIS Centre, “Vinča” Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovića Alasa 12-14, 11000 Belgrade, Serbia
Miloš Davidović: VIDIS Centre, “Vinča” Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovića Alasa 12-14, 11000 Belgrade, Serbia

Sustainability, 2024, vol. 16, issue 13, 1-27

Abstract: In this study, we describe the development of seasonal winter and summer (heating and non-heating season) land use regression (LUR) models for PM 2.5 mass concentration for the city of Novi Sad, Serbia. The PM 2.5 data were obtained through an extensive seasonal measurement campaign conducted at 21 locations in urban, urban/industrial, industrial and background areas in the period from February 2020–July 2021. At each location, PM 2.5 samples were collected on quartz fibre filters for 10 days per season using a reference gravimetric pump. The developed heating season model had two predictors, the first can be associated with domestic heating over a larger area and the second with local traffic. These predictors contributed to the adjusted R 2 of 0.33 and 0.55, respectively. The developed non-heating season model had one predictor which can be associated with local traffic, which contributed to the adjusted R 2 of 0.40. Leave-one-out cross-validation determined RMSE /mean absolute error for the heating and non-heating season model were 4.04/4.80 μg/m 3 and 2.80/3.17 μg/m 3 , respectively. For purposes of completeness, developed LUR models were also compared to a simple linear model which utilizes satellite aerosol optical depth data for PM 2.5 estimation, and showed superior performance. The developed LUR models can help with quantification of differences between seasonal levels of air pollution, and, consequently, air pollution exposure and association between seasonal long-term exposure and possible health risk implications.

Keywords: fine particulate matter; land use regression (LUR); heating/non-heating seasonal PM 2.5 model; digital twin; aerosol optical depth (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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