In this paper, we explore the computational capabilities of advanced modeling tools to reveal the factors that shape the observed benzene levels and behavior under different environmental conditions. The research was based on two-year hourly data concentrations of inorganic gaseous pollutants, particulate matter, benzene, toluene, m, p-xylenes, total nonmethane hydrocarbons, and meteorological parameters obtained from the Global Data Assimilation System. In order to determine the model that will be capable of achieving a superior level of performance, eight metaheuristics algorithms were tested for eXtreme Gradient Boosting optimization, while the relative SHapley Additive exPlanations values were used to estimate the relative importance of each pollutant level and meteorological parameter for the prediction of benzene concentrations. According to the results, benzene levels are mostly shaped by toluene and the finest aerosol fraction concentrations, in the environment governed by temperature, volumetric soil moisture content, and momentum flux direction, as well as by levels of total nonmethane hydrocarbons and total nitrogen oxide. The types of conditions which provided the environment for the impact of toluene, the finest aerosol, and temperature on benzene dynamics are distinguished and described.

Polycyclic aromatic hydrocarbons (PAHs) refer to a group of several hundred compounds, among which 16 are identified as priority pollutants, due to their adverse health effects, frequency of occurrence, and potential for human exposure. This study is focused on benzo(a)pyrene, being considered an indicator of exposure to a PAH carcinogenic mixture. For this purpose, we have applied the XGBoost model to a two-year database of pollutant concentrations and meteorological parameters, with the aim to identify the factors which were mostly associated with the observed benzo(a)pyrene concentrations and to describe types of environments that supported the interactions between benzo(a)pyrene and other polluting species. The pollutant data were collected at the energy industry center in Serbia, in the vicinity of coal mining areas and power stations, where the observed benzo(a)pyrene maximum concentration for a study period reached 43.7 $\mathrm{ng}{\mathrm{m}}^{-3}$. The metaheuristics algorithm has been used to optimize the XGBoost hyperparameters, and the results have been compared to the results of XGBoost models tuned by eight other cutting-edge metaheuristics algorithms. The best-produced model was later on interpreted by applying Shapley Additive exPlanations (SHAP). As indicated by mean absolute SHAP values, the temperature at the surface, arsenic, PM${}_{10}$, and total nitrogen oxide (NOx) concentrations appear to be the major factors affecting benzo(a)pyrene concentrations and its environmental fate.

The aim of this study was to investigate the association between short- and long-term exposure to particulate matter (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and soot and mortality attributed to circulatory and respiratory diseases in Belgrade area (Serbia). The analyzed data set comprised results of regular pollutant monitoring and corresponding administrative records on frequency of daily mortality in the period 2009–2014. Nonlinear exposure–response dependencies and delayed effects of temperature were examined by means of distributed lag nonlinear models. The air pollutant loadings and circulatory system-related death rates in Belgrade area are among the highest in Europe. Data demonstrated that excess risk of death with short-term exposure to elevated concentrations of PM₁₀, SO₂, and soot was not significant, whereas marked effect size estimates for exposure over 90 d preceding mortality were found. The influence of chronic exposure was shown to be greater for respiratory than circulatory system-related mortality. When stratified by age and gender, higher risk was noted for male individuals below the age of 65 years.