Winter and Summer PM2.5 Land Use Regression Models for the City of Novi Sad, Serbia

被引:0
|
作者
Dmitrasinovic, Sonja [1 ]
Radonic, Jelena [1 ]
Zivkovic, Marija [2 ]
Cirovic, Zeljko [2 ]
Jovasevic-Stojanovic, Milena [2 ]
Davidovic, Milos [2 ]
机构
[1] Univ Novi Sad, Fac Tech Sci, Trg Dositeja Obradovica 6, Novi Sad 21000, Serbia
[2] Univ Belgrade, Vinca Inst Nucl Sci, Natl Inst Republ Serbia, VIDIS Ctr, Mike Petrovica Alasa 12-14, Belgrade 11000, Serbia
关键词
fine particulate matter; land use regression (LUR); heating/non-heating seasonal PM2.5 model; digital twin; aerosol optical depth; ATMOSPHERIC PARTICULATE MATTER; AIR-POLLUTION EXPOSURE; SPATIAL VARIATION; NO2; CONCENTRATIONS; NITROGEN-DIOXIDE; AREAS; PM10; PERFORMANCE; O-3;
D O I
10.3390/su16135314
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this study, we describe the development of seasonal winter and summer (heating and non-heating season) land use regression (LUR) models for PM2.5 mass concentration for the city of Novi Sad, Serbia. The PM2.5 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, PM2.5 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 mu g/m(3) and 2.80/3.17 mu g/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 PM2.5 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.
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页数:27
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