Snježana Herceg Romanić

Benzene is a carcinogenic urban volatile organic compound whose variability reflects interactions among emissions, atmospheric chemistry, and meteorology. We quantified relationships between hourly benzene concentrations and O₃, CO, SO₂, NO₂, and more than twenty meteorological predictors using regulatory air-quality observations from the Ksaverska cesta urban background station in Zagreb, Croatia, complemented by NOAA GDAS meteorological data (2017–2023; n = 39,124). Metaheuristically optimized tree-ensemble models were interpreted using iterative Shapley Additive Global Importance (iSAGE), SHapley Additive exPlanations (SHAP), and an environmental settings framework. The novelty of the study lies in integrating global predictor importance, signed local SHAP attributions, and environmental-setting classification to interpret benzene variability within recurring emission–chemistry–meteorology regimes. The best-performing Extra Trees model achieved R² = 0.87, RMSE = 0.46 µg m⁻³, and MAE = 0.24 µg m⁻³. iSAGE identified CO (1.47), O₃ (0.55), and near-surface air temperature (0.35) as the leading predictors of modeled benzene concentrations, whereas SO₂ (0.18) and NO₂ (0.07) had substantially lower global importance. SHAP showed a positive CO-benzene association, with relative contributions shifting from negative to positive at approximately 0.3–0.6 mg m⁻³; O₃ contributions became predominantly negative above 30–40 µg m⁻³; and temperature contributions shifted from positive below 7 °C to negative across 7–12 °C. Environmental setting analysis associated the strongest benzene accumulation with stable winter conditions and the weakest accumulation with well-ventilated, photochemically active regimes. These site- and model-specific results reveal nonlinear co-pollutant and meteorological influences on urban benzene variability. Theoretically, the study advances the interpretation of urban VOC variability by linking model-based predictor attributions with physically interpretable atmospheric regimes; practically, it supports the identification of conditions favoring benzene accumulation and can inform regime-sensitive air-quality assessment and management.

Polychlorinated biphenyls (PCBs) remain a major concern in marine ecosystems, where bioaccumulation in fish occurs as complex congener mixtures whose dynamics challenge conventional indicator approaches. This study develops and evaluates a data-driven framework for refining mixture-based indicators of PCB contamination by integrating ensemble machine learning with explainable artificial intelligence. Focusing on PCB-138 as a target indicator of cumulative PCB burden, we analyse concentrations of 24 organochlorines together with biological covariates in four Mediterranean edible pelagic fish species (sardine, anchovy, horse mackerel, and chub mackerel). Comparative evaluation of indicator performance shows that alternative congener combinations, including i4 PCBs (-138, -153, -170, -180), i6 PCBs (-138, -153, -170, -180, -118, -123), and mixtures incorporating DDD and DDE, more effectively represent total PCB burden than traditional indicator groups. Clustering identifies two distinct bioaccumulation settings, characterized by high-concentration coherent congener effects and low-concentration heterogeneous responses, demonstrating that indicator performance depends on concentration range and mixture context. The study illustrates how interpretable machine learning approaches can serve as formal tools for indicator evaluation and optimisation, strengthening long-term monitoring and management of legacy contaminants in marine ecosystems, particularly under conditions of persistent exposure and renewed inputs from sediment remobilization and riverine transport.

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine–metabolic disorder characterized by endocrine disruption, insulin resistance, hyperandrogenism, and chronic low-grade inflammation, in which oxidative stress has been proposed as a mechanistic link between metabolic and reproductive dysfunction. This narrative review summarizes current evidence on redox-related mechanisms and evaluates dietary and supplemental antioxidants in PCOS. Clinical trials, systematic reviews, and mechanistic studies were examined to assess antioxidant classification, signaling pathways, and outcomes related to metabolic, endocrine, reproductive, and oxidative stress parameters. Antioxidant interventions frequently modify circulating redox biomarkers and may improve selected metabolic indices; however, consistent effects on hormonal regulation, ovulation, and long-term clinical outcomes remain limited and heterogeneous. Differences in study design, antioxidant formulation and dosage, baseline metabolic status, and outcome selection complicate interpretation, while emerging evidence suggests modulation by lifestyle factors and gut microbiota-related mechanisms. Overall, antioxidants appear to act primarily through modulation of endogenous redox regulation rather than direct reactive oxygen species scavenging and are best considered adjuncts to lifestyle-based management. Further phenotype-informed and longitudinal studies using clinically relevant endpoints are required to clarify therapeutic relevance in PCOS.

The Kupa River (Croatia), a tributary of the Danube basin forming part of the Slovenian border, was heavily contaminated with polychlorinated biphenyls (PCBs) between 1962 and 1985 due to improper handling and downstream transport via the Krupa and Lahinja rivers. This study evaluated the occurrence, interspecific distribution, and human health implications of PCBs and polycyclic aromatic hydrocarbons (PAHs) in fish (Northern pike, Common carp, Grass carp, Pike-perch, Wels catfish, Bream, and Chub) from the Croatian Kupa River. PCB concentrations were consistently higher than PAH levels across all species. In 30% of samples, Σ6 non-dioxin-like PCBs exceeded the European Commission maximum permissible level for freshwater fish (125 ng⋅g⁻¹ wet weight). Of the 11 PAHs analyzed, only fluoranthene and pyrene were detected. Self-Organizing Map identified distinct pollutant patterns, with chub showing the highest variability and accumulation. PCB concentrations position the Kupa River among moderately to highly impacted European freshwater systems affected by legacy industrial contamination. Health risk assessment, incorporating updated national consumption data, indicates that long-term, uncontrolled consumption of Kupa River fish may pose risks due to PCB exposure, while PAH-related risks appear negligible. These findings highlight the persistence of legacy PCB pollution and the need for integrated sediment–biota monitoring.

We investigated benzene variability in an urban environment using an interpretable, setting-based artificial intelligence framework. A seven-year dataset (2017–2023) of hourly pollutant concentrations (benzene, NO₂, SO₂, CO, O₃) measured in Zagreb (Croatia) was analyzed, as were meteorological variables. Multiple-ensemble decision tree models were developed, with hyperparameters optimized using metaheuristic algorithms. The best-performing model, Extra Trees optimized by the Sine Cosine Algorithm, achieved an R² of 0.87. Model interpretation employed Shapley additive explanations (SHAP), followed by PaCMAP embedding and HDBSCAN clustering to identify coherent environmental settings. Seven settings (C0–C6) and one residual group were identified, representing pollution-enhancing, suppressing, and transitional regimes. Two settings dominated benzene extremes. C6 reflected winter stagnation, characterized by strong combustion influence (CO contribution of 11.9%), shallow boundary layers (~290 m), weak winds, and high humidity. C4 represented a synoptic stability regime with enhanced heat fluxes and diminished after the COVID-19 period, consistent with altered anthropogenic activity. Low-benzene settings (C0, C1, C3) were associated with stronger mixing and higher oxidizing capacity, while transitional settings (C2, C5) reflected moderate conditions. Overall, the results show that a small number of environmental settings governed the benzene extremes, providing a transferable and interpretable framework for air quality assessment and policy support.

Human milk has been used for over 70 years to monitor pollutants such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). Despite the growing body of data, our understanding of the pollutant exposome, particularly co-exposure patterns and their interactions, remains limited. Artificial intelligence (AI) offers considerable potential to enhance biomonitoring efforts through advanced data modelling, yet its application to pollutant dynamics in complex biological matrices such as human milk remains underutilized. This study applied an AI-based framework, integrating machine learning, metaheuristic hyperparameter optimization, explainable AI, and postprocessing, to analyze PCB-170 levels in breast milk samples from 186 mothers in Zadar, Croatia. Among 24 analyzed POPs, the most influential predictors of PCB-170 concentrations were hexa- and hepta-chlorinated PCBs (PCB-180, -153, and -138), alongside p,p’-DDE. Maternal age and other POPs exhibited negligible global influence. SHAP-based interaction analysis revealed pronounced co-behavior among highly chlorinated congeners, especially PCB-138–PCB-153, PCB-138–PCB-180, and PCB-180–PCB-153. These findings highlight the importance of examining pollutant interactions rather than individual contributions alone. They also advocate for the revision of current monitoring strategies to prioritize multi-pollutant assessment and focus on toxicologically relevant PCB groups, improving risk evaluation in real-world exposure scenarios.

The Adriatic Sea plays a crucial role as both a significant fishing ground and a thriving trading market for small pelagic edible fish. Recognized for their nutritional value, these fish are esteemed for their high protein content and abundance of polyunsaturated omega-3 and omega-6 fatty acids, making them a sought-after and healthful food choice. Nevertheless, pelagic species can also serve as a reservoir for lipophilic organochlorine pollutants, posing potential risks to human health. In this study, we compared traditional classification methods traditional principal component analysis (PCA) and Ward’s clustering with an advanced self-organizing map (SOM) algorithm in determining distribution patterns of 24 organochlorines and 19 fatty acids in sardine and anchovy samples taken from the eastern Adriatic. The outcomes reveal the strengths and weaknesses of the three approaches (PCA, Ward’s clustering, and SOM). However, it is evident that SOM has proven to be the most effective in offering detailed information and data visualization. Although sardines and anchovies exhibit similar distribution patterns for p,p′-DDE, PCB-28, PCB-138, PCB-153, PCB-118, and PCB-170, they differ in the concentrations of fatty acids such as stearic, palmitic, myristic, oleic, docosapentaenoic, and docosahexaenoic acid. Our findings supply valuable insights for environmental authorities and fish consumers concerning the potential risks associated with organochlorines in these two types of fish.

The recognition of certain herbicides as endocrine disrupting compounds has raised concerns due to their ability to interfere with the normal functioning of the endocrine system, which regulates various physiological processes in organisms. The objective of this study was to assess the possible human health risks associated with terbuthylazine and endocrine-disrupting herbicides atrazine, acetochlor, and metolachlor in the drinking, surface, and groundwater of the Zagreb city region, Croatia. We relied on advanced statistical methods and principal component analysis (PCA), which revealed higher levels of atrazine and acetochlor in drinking and groundwater samples and higher presence of metolachlor and terbuthylazine in surface waters. To evaluate the danger to human health, various exposure scenarios have been assessed. The risk of direct human exposure to analyzed herbicides through drinking or bathing with drinking (tap) or groundwater, as well as from recreational activities like swimming in rivers, streams, and lakes, has been quantified. In addition to these direct exposure scenarios, indirect ones based on consumer goods, fruits, and vegetables, treated with surface and groundwater for irrigation, were assessed to investigate the danger to human health. Judging by the reported herbicide levels there was no significant risk of carcinogenic (CR ≤ 1 × 10⁻⁶) or non-carcinogenic (HI < 1) diseases, not even when we assessed the so-called “cocktail effect” of combined the herbicide exposure in different waters.