2026-05-31T02:20:02.334Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:11931 2026-05-27T10:24:15Z 2

Urban Benzene Pollution During the COVID-19 State of Emergency: Insights from an Interpretable Artificial Intelligence Approach to Multi-Scale Urban Environmental Data 2026 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/11931 https://www.mdpi.com/2413-8851/10/6/298 G. Joseph Isibor T. Bezdan G. Jovanović N. Radić S. Stanišić N. Stanić A. Stojić M. Perišić Benzene is a major carcinogenic urban pollutant whose variability reflects interactions between emission sources, human activity, and atmospheric conditions. Although COVID-19 restrictions generally reduced traffic-related emissions, the combined effects of mobility changes, residential activity, and policy interventions on urban benzene dynamics remain insufficiently understood. This study investigated benzene variability in Belgrade, Serbia, during the COVID-19 state of emergency, from 15 March to 6 May 2020. A multi-source dataset was used, integrating high-resolution VOC measurements by PTR-quad-MS, meteorological variables, regulatory air-quality indicators, epidemiological data, mobility proxies, and quantified government-response measures. Tree-based ensemble machine-learning models, metaheuristic hyperparameter optimization, and explainable artificial intelligence methods, including SAGE and SHAP, were applied to examine non-linear and time-lagged relationships within the urban atmospheric system. The results showed that benzene variability was primarily associated with co-measured non-target VOCs, reflecting shared urban emission-source structures. Mobility and policy-related predictors contributed through short delayed responses, with an estimated response window of approximately 48–72 h. Sustained mobility reductions were associated with lower benzene concentrations, whereas increased residential activity partially offset traffic-related reductions. Within the Belgrade case study, these findings demonstrate the potential of interpretable machine learning to extract robust patterns from heterogeneous urban environmental datasets, while emphasizing the need for validation across additional cities and non-pandemic conditions before broader generalization. article https://doi.org/10.3390/urbansci10060298 10 6 2413-8851 Urban Science