Atmospheric Concentrations of Inorganic Pollutants (No2, So2 and Ozone) in Eski̇şehi̇r: Spatial and Vertical Variations, Weekday-Weekend Differences

Üzmez, Özlem Özden

doi:10.18038/aubtda.376520

Cited by 2 publications

(1 citation statement)

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“…Particulate matter pollution is known to be the single largest environmental health risk in Europe [1] and various adverse health effects are linked to the exposure to particulate matter (PM) [2,3]. With the continuous rapid growth of urban areas, dense air quality monitoring within these environments becomes increasingly important since significant contrasts in ambient air quality and level of PM2.5 can exist within different city microenvironments and consequently manifest different degree of health risks [4,5].…”

Section: Introductionmentioning

confidence: 99%

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

Dmitrašinović,

Radonić,

Živković

et al. 2024

Sustainability

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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 R2 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 R2 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/m3 and 2.80/3.17 μg/m3, 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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Section: Introductionmentioning

confidence: 99%