The explainable potential of coupling hybridized metaheuristics, XGBoost, and SHAP in revealing toluene behavior in the atmosphere.

Toluene is a neurotoxic aromatic hydrocarbon and one of the major representatives of volatile organic compounds, known for its abundance, adverse health effects, and role in the formation of other atmospheric pollutants like ozone. This research introduces the enhanced version of the reptile search metaheuristics algorithm which has been utilized to tune the extreme gradient boosting hyperparameters, to investigate toluene atmospheric behavior patterns and interactions with other polluting species within defined environmental conditions. The study is based on a two-year database encompassing concentrations of inorganic gaseous contaminants every hour (NO, NO2, NOx, and O3), particulate matter fractions (PM1, PM2.5, and PM10), m,p-xylene, toluene, benzene, total non-methane hydrocarbons, and meteorological data. The experimental outcomes were validated against the results of extreme gradient boosting models optimized by seven other recent powerful metaheuristics algorithms. The best-performing model has been interpreted by employing Shapley additive explanations method. In the study, we have focused on the relationship between toluene and benzene, as its most important predictor, and provided a detailed description of environmental conditions which directed their interactions.

Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate.

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 ngm-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, PM10, and total nitrogen oxide (NOx) concentrations appear to be the major factors affecting benzo(a)pyrene concentrations and its environmental fate.

The PM2.5-bound polycyclic aromatic hydrocarbon behavior in indoor and outdoor environments, part II: Explainable prediction of benzo[a]pyrene levels.

Among the polycyclic aromatic hydrocarbons (PAH), benzo[a]pyrene (B[a]P) has been considered more relevant than other species when estimating the potential exposure-related health effects and has been recognized as a marker of carcinogenic potency of air pollutant mixture. The current understanding of the factors which govern non-linear behavior of B[a]P and associated pollutants and environmental processes is insufficient and further research has to rely on the advanced analytical approach which averts the assumptions and avoids simplifications required by linear modeling methods. For the purpose of this study, we employed eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP) attribution method, and SHAP value fuzzy clustering to investigate the concentrations of inorganic gaseous pollutants, radon, PM2.5 and particle constituents including trace metals, ions, 16 US EPA priority PM2.5-bound PAHs and 31 meteorological variables, as key factors which shape indoor and outdoor PM2.5-bound B[a]P distribution in a university building located in the urban area of Belgrade (Serbia). According to the results, the indoor and outdoor B[a]P levels were shown to be highly correlated and mostly influenced by the concentrations of Chry, B[b]F, CO, B[a]A, I[cd]P, B[k]F, Flt, D[ah]A, Pyr, B[ghi]P, Cr, As, and PM2.5 in both indoor and outdoor environments. Besides, high B[a]P concentration events were recorded during the periods of low ambient temperature (<12 °C), unstable weather conditions with precipitation and increased soil humidity.

Fatty acids, persistent organic pollutants, and trace elements in small pelagic fish from the eastern Mediterranean Sea.

Fatty acids (FAs) composition, 24 persistent organic pollutants (POPs), and 16 trace elements were examined in small pelagic fish (sardine, anchovy, round sardinella, chub and horse mackerels) caught by a fishing fleet for more than three years in the eastern Mediterranean Sea. Five Unmix source profiles associated with both sources, such as overlapping diet, including low-niche marine organisms and inputs from the surrounding environmental compartments were resolved. Inorganic compounds were notably more abundant in fish tissue than organochlorine xenobiotics. Comparison with the values of toxicological parameters revealed that the examined fish species are safe for human consumption, while the content of FAs emphasized the studied species as a valuable source of nutrients. A significant linear correlation was not observed between the 18 FAs and lipophilic organochlorines. Based on the obtained database, future assessments of the quality of edible fish species and the aquatic environment of the eastern Mediterranean Sea, which is known as an important fishing ground, could be significantly improved.

The PM2.5-bound polycyclic aromatic hydrocarbon behavior in indoor and outdoor environments, part I: Emission sources.

The previous research, aimed at exploring the relationships between the indoor and outdoor air quality, has evidenced that outdoor PM2.5-bound polycyclic aromatic hydrocarbons (PAH) levels exhibit significant daily and seasonal variations which does not necessary corresponds with PAH indoor dynamics. For the purpose of this study, a three-month measurement campaign was performed simultaneously at indoor and outdoor sampling sites of a university building in an urban area of Belgrade (Serbia), during which the concentrations of O3, CO, SO2, NOx, radon, PM2.5 and particle constituents including trace metals (As, Cd, Cr, Mn, Ni and Pb), ions (Cl-, Na+, Mg2+, Ca2+, K+, NO3-, SO42- and NH4+) and 16 US EPA priority PAHs were determined. Additionally, the analysis included 31 meteorological parameters, out of which 24 were obtained from Global Data Assimilation System (GDAS1) database. The Unmix and PAH diagnostic ratios analysis resolved the source profiles for both indoor and outdoor environment, which are comparable in terms of their apportionments and pollutant shares, although it should be emphasized that ratio-implied solutions should be taken with caution since these values do not reflect emission sources only. The highest contributions to air quality were attributed to sources identified as coal combustion and related pyrogenic processes. Noticeable correlations were observed between 5- and 6-ring high molecular weight PAHs, but, except for CO, no significant linear dependencies with other investigated variables were identified. The PAH level predictions in the indoor and outdoor environment was performed by using machine learning XGBoost method.

Introducing of modeling techniques in the research of POPs in breast milk - A pilot study.

This study used advanced statistical and machine learning methods to investigate organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in breast milk, assuming that in a complex biological mixture, the pollutants emitted from the same source or with similar properties are statistically interrelated and possibly exhibit non-linear dynamics. The elaborated analyses such as Unmix source apportionment characterized individual source groups, while guided regularized random forest indicated the pollutant dependence on the ortho-chlorine atom attached to the congener's phenyl ring and mother's age. Mutual associations among PCBs were further discussed, but the results implied they were mostly not related to child delivery. PCB congeners -153, -180, -170, -118, -156, -105, and -138 appeared to be compounds of the outmost importance for mutual prediction with reference to their interrelations regarding chemical structure and metabolic processes in the mother's body. Finally, machine learning methods, which provided prediction relative errors lower than 30% and correlation coefficients higher than 0.90, suggested a possible strong non-linear relationship among the pollutants and consequently, the complexity of their pathways in the breast milk.

Explainable extreme gradient boosting tree-based prediction of toluene, ethylbenzene and xylene wet deposition.

Current research suggests that, apart from photochemical reactions, toluene, ethylbenzene and xylene (TEX) removal from ambient air might be affected by atmospheric precipitation, depending on the concentrations and water solubility of the compounds, Henry's law, physico-chemical properties of the water, as well as the frequency and intensity of precipitation events. Nevertheless, existing knowledge of the role that wet deposition plays in biogeochemical cycles of volatile species remains insufficient, and this topic requires more scientific effort to be explored and understood. In this study, we employed the eXtreme Gradient Boosting tree ensemble for revealing TEX transfer from ambient air to rainwater, and applied a novel SHapley Additive exPlanations feature attribution framework to examine the relevance of the monitored parameters and identify key factors that govern wet deposition of TEX. According to the results, main impacts, including ambient air TEX concentrations, and rainwater and air temperatures, and occasional, but less important impacts, including wind speed, air pressure, turbidity, and total organic carbon, NO3-, Cl- and K+ rainwater concentration, shaped TEX partition between gaseous and aqueous phases during rain events.

Temperature-related mortality estimates after accounting for the cumulative effects of air pollution in an urban area.

BACKGROUND: To propose a new method for including the cumulative mid-term effects of air pollution in the traditional Poisson regression model and compare the temperature-related mortality risk estimates, before and after including air pollution data. RESULTS: The analysis comprised a total of 56,920 residents aged 65 years or older who died from circulatory and respiratory diseases in Belgrade, Serbia, and daily mean PM10, NO2, SO2 and soot concentrations obtained for the period 2009-2014. After accounting for the cumulative effects of air pollutants, the risk associated with cold temperatures was significantly lower and the overall temperature-attributable risk decreased from 8.80 to 3.00 %. Furthermore, the optimum range of temperature, within which no excess temperature-related mortality is expected to occur, was very broad, between -5 and 21 °C, which differs from the previous findings that most of the attributable deaths were associated with mild temperatures. CONCLUSIONS: These results suggest that, in polluted areas of developing countries, most of the mortality risk, previously attributed to cold temperatures, can be explained by the mid-term effects of air pollution. The results also showed that the estimated relative importance of PM10 was the smallest of four examined pollutant species, and thus, including PM10 data only is clearly not the most effective way to control for the effects of air pollution.

Single and combined effects of air pollutants on circulatory and respiratory system-related mortality in Belgrade, Serbia.

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.

Assessment of PM10 pollution level and required source emission reduction in Belgrade area.

The aim of this study was to assess PM10 pollution level and estimate required source emission reduction in Belgrade area, the second largest urban center in the Balkans. Daily mass concentrations and trace metal content (As, Cd, Cr, Mn, Ni, Pb) of PM10 were evaluated for three air quality monitoring sites of different types: urban-traffic (Slavija), suburban (Lazarevac) and rural (Grabovac) under the industrial influence, during the period of 2012-13. Noncompliance with current Air Quality Standards (AQS) was noticeable: annual means were higher than AQS at Slavija and Lazarevac, and daily frequency threshold was exceeded at all three locations. Annual means of As at Lazarevac were about four times higher than the target concentration, which could be attributed to the proximity of coal-fired power plants, and dust resuspension from coal basin and nearby ash landfills. Additionally, levels of Ni and Cr were significantly higher than in other European cities. Carcinogenic health risk of inhabitants' exposure to trace metals was assessed as well. Cumulative cancer risk exceeded the upper limit of acceptable US EPA range at two sites, with Cr and As as the major contributors. To estimate source emission reduction, required to meet AQS, lognormal, Weibull and Pearson 5 probability distribution, functions (PDF) were used to fit daily PM10 concentrations. Based on the rollback equation and best fitting PDF, estimated reduction was within the range of 28-98%. Finally, the required reduction obtained using two-parameter exponential distribution suggested that risks associated to accidental releases of pollutants should be of greater concern.

Forecasting of VOC emissions from traffic and industry using classification and regression multivariate methods.

In this study, advanced multivariate methods were applied for VOC source apportionment and subsequent short-term forecast of industrial- and vehicle exhaust-related contributions in Belgrade urban area (Serbia). The VOC concentrations were measured using PTR-MS, together with inorganic gaseous pollutants (NOx, NO, NO2, SO2, and CO), PM10, and meteorological parameters. US EPA Positive Matrix Factorization and Unmix receptor models were applied to the obtained dataset both resolving six source profiles. For the purpose of forecasting industrial- and vehicle exhaust-related source contributions, different multivariate methods were employed in two separate cases, relying on meteorological data, and on meteorological data and concentrations of inorganic gaseous pollutants, respectively. The results indicate that Boosted Decision Trees and Multi-Layer Perceptrons were the best performing methods. According to the results, forecasting accuracy was high (lowest relative error of only 6%), in particular when the forecast was based on both meteorological parameters and concentrations of inorganic gaseous pollutants.