Mapping seasonal PM2.5 distribution in Novi Sad: From sensor-based observations to land use regression modelling

Abstract

Air pollution in Serbia is characterized by pronounced seasonal variability, with the heating period representing the most critical phase in terms of fine particulate matter (PM2.5). Understanding both short-term dynamics and the spatial distribution of PM2.5 in urban environments remains a key challenge. This study focuses on the assessment of PM2.5 variability in Novi Sad, combining high-resolution sensor measurements with spatial modeling perspectives. A field campaign was conducted at 21 locations across urban, industrial, mixed-use, and background environments during heating and non-heating seasons. The use of low-cost sensors enabled detailed insight into short-term fluctuations and daily patterns of PM2.5 concentrations. Wind and pollution roses were employed to explore dominant dispersion patterns and to better understand the influence of prevailing meteorological conditions on pollutant transport. To support data reliability, sensor measurements were compared with reference measurements, demonstrating a satisfactory level of agreement and confirming their suitability for further analysis and spatial interpretation. As a key outcome, high-resolution seasonal prediction maps of PM2.5 are presented as an extension of previously developed Land Use Regression (LUR) models for Novi Sad, representing the first implementation of this approach in Serbia. These maps reveal spatial distribution patterns, pollution hotspots, and potential exposure gradients across the urban area. In addition, a preliminary assessment of chronic health impacts based on PM2.5 exposure at selected monitoring locations will be discussed. Particular attention will be given to the interpretation of prediction maps and their role in understanding spatial exposure patterns in urban environments.