Search Results (151)

Water scarcity is driving increasing interest in potable water reuse, particularly in arid regions. However, inadequately treated reclaimed water may pose health risks from waterborne pathogens, including gastrointestinal infections. This study applied quantitative microbial risk assessment (QMRA) to estimate infection risks associated with Escherichia coli (E. coli), Cryptosporidium, rotavirus, and adenovirus using pilot-scale data from El Paso Water’s Pure Water Center advanced purification facility. Pathogen concentrations in source and treated water were fitted to probability distributions and incorporated into a probabilistic Monte Carlo QMRA framework to estimate exposure doses, daily and cumulative infection risks, and treatment performance (expressed as log reduction values (LRVs)) and were compared to U.S. Environmental Protection Agency (EPA) drinking water risk thresholds. E. coli demonstrated the greatest treatment effectiveness, with a median daily treated water infection risk of 1.45 × 10⁻⁶ and a median LRV of 3.32. Cryptosporidium demonstrated limited reduction in infection risk (median LRV = 0.976), while rotavirus demonstrated minimal reduction following treatment. Although adenovirus infection risk decreased after treatment, the residual risk remained high. Viral risk estimates were strongly influenced by non-detects and limited observations. These findings highlight the importance of robust monitoring and probabilistic QMRA approaches for evaluating uncertainty and treatment performance in advanced potable reuse systems. Full article

The transfer of heavy metals (HMs) from industrial park soils to edible crops is strongly crop-specific; however, these differences have not been considered in probabilistic dietary health risk assessments. This study examined agricultural fields surrounding a high-altitude industrial park in southwestern China, where paired samples of surface soil, Chinese cabbage, and maize were collected and analyzed for Cd, Pb, Cr, Ni, Cu, Zn, Hg, and As. Spearman rank correlation and principal component analysis (PCA) were applied to identify elemental associations and probable sources; bioaccumulation factors (BAFs) were calculated to compare HM transfer and accumulation across the two crops; and Monte Carlo simulation was used to estimate probabilistic dietary health risks across age groups. The results showed that soil HMs in the study area displayed mixed-source characteristics, with marked enrichment of Pb, Cd, and Hg. After multiple comparison correction, only soil Ni showed a significant positive correlation with Ni in maize. Chinese cabbage had a relatively high accumulation capacity for Cd, whereas maize grains showed relatively limited transfer of most HMs, indicating pronounced crop-specific differences. Cd, Pb, Cr, Hg, and As concentrations in Chinese cabbage remained within permissible limits. For health risk, P95 hazard index (HI) values stayed below 1 across all age groups, indicating that non-carcinogenic risk was generally acceptable under the exposure scenarios evaluated; adults, however, carried higher estimated lifetime carcinogenic risk than other age groups. Sensitivity analysis revealed that As and Cd in Chinese cabbage were the dominant contributors to non-carcinogenic and carcinogenic risks, respectively, while the contribution of Cr to risk estimates was highly dependent on its speciation and toxicological parameter assumptions. By combining soil–crop monitoring, bioaccumulation analysis, and probabilistic risk assessment, this study characterizes crop-specific HM transfer patterns and identifies the principal drivers of health risk in industrially influenced agricultural systems, providing a scientific basis for risk-oriented monitoring, food safety management, and the long-term sustainable use of agricultural land in industrial–agricultural interface regions. Full article

Background/Objectives: Burnout and eating-disorder screening risk are relevant concerns among health sciences students, but evidence from northern Peru remains limited. The primary objective was to examine whether an archived study-specific burnout classification (independent/explanatory variable) was associated with probable eating-disorder symptomatology (dependent/outcome variable) among students enrolled in Human Medicine, Nursing, and Dentistry. Methods: A cross-sectional study was conducted at a private university in the Lambayeque Region, Peru, during the 2023-II academic semester. The analytical sample comprised 200 students. Eating-disorder screening was assessed with the Eating Attitudes Test-26 (EAT-26), and burnout was assessed with a 22-item Maslach Burnout Inventory (MBI) version. The revision uses retained aggregate outputs because individual-level item data are no longer available. Results: Seventy-four students (37.0%) were in the archived probable eating-disorder symptomatology category. The corresponding proportions were 0/53 (0.0%) in the study-specific low MBI category, 21/80 (26.3%) in the medium category, and 53/67 (79.1%) in the high category (chi-square = 86.05, p < 0.001; Cramer’s V = 0.656). Exploratory differences were also observed by sex, living arrangement, academic program, employment status, study time, and sleep duration. Conclusions: The archived categorical results show a strong bivariate association between the two study-specific screening classifications. Because the design was cross-sectional and non-probabilistic and the item-level data needed to verify instrument scoring and reliability are unavailable, the findings should not be interpreted as causal or diagnostic. Full article

With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread application of the Internet of Things and novel sensing technologies has given rise to structural health monitoring data increasingly characterized by massive volume, high dimensionality, multi-source heterogeneity, and strong spatiotemporal coupling. Traditional data processing methods based on mechanical analysis, empirical formulas, or numerical simulation have increasingly exposed limitations of insufficient accuracy, lengthy computation times, and weak generalization capability when confronted with such engineering big data. Machine learning and deep learning technologies, by virtue of their superior nonlinear mapping capability, advantages in feature extraction from massive data, and flexible architectural design, provide solutions for efficient knowledge extraction and intelligent assessment of underground engineering monitoring data. This paper reviews the current application status and frontier advances of machine learning technologies in the field of underground engineering monitoring data processing in recent years. First, the development trajectory of analytical algorithms evolving from classical shallow machine learning, through temporal and spatial deep learning, to physics-data dual-driven approaches is delineated. Second, targeting the critical challenges of missing field data and sparse sensor deployment, spatiotemporal fusion imputation techniques and spatial reconstruction methods incorporating mechanical prior knowledge are thoroughly evaluated, elucidating the paradigm shift in monitoring philosophy from discrete point-based alarming to inference-augmented sparse sensing that approximates full-field state awareness through model-dependent estimation rather than direct measurement. Third, the applications of machine learning in underground structural deformation mechanism interpretation, key influencing factor identification based on explainable artificial intelligence (AI), and rapid back-analysis of geomechanical parameters are summarized. Finally, composite network architectures and physics-constrained guidance strategies for non-stationary deformation time series prediction under complex and variable working conditions are discussed. A methodological audit of the 73 included studies—of which 33 enter the quantitative comparison tables—reveals that 26 of the 33 audited studies (78.8%) validate exclusively on single-project data, only 1 study conducts rigorous out-of-distribution generalization testing, and none of the 33 studies (0%) provides uncertainty quantification. These findings highlight cross-project generalization and probabilistic prediction as important methodological challenges. This paper aims to provide theoretical references and methodological guidance for safety early warning, intelligent construction, and full life-cycle health management of underground engineering. Full article

Heavy metal contamination in urban topsoil is one of the most serious environmental threats to children’s health, particularly through ingestion, dermal contact, and inhalation exposure routes. The objectives of this study were: (1) to assess the probabilistic exceedance-based priority of eight heavy metals (As, Cd, Co, Cr, Cu, Ni, Pb, and Zn) with respect to regulatory threshold exceedance in Debrecen, Hungary; (2) to map the spatial distribution of exceedance probabilities using sequential indicator simulation (SISIM) with 100 equiprobable realizations per element (1000 for Cr) on a 50 m grid; and (3) to develop a toxicologically weighted composite exceedance index based on the Hungarian regulatory action thresholds and classify the results into priority categories. For Cd, the exceedance probability exceeded p > 0.50 in approximately 98% of the study area, and for Cr, in approximately 82% of the study area (regenerated at $N = 1000$; the Cr threshold lies near the sample median, so the p > 0.50 area is ensemble-size sensitive and was under-converged at $N = 100$). Approximately 86% of the study area fell into the Very Low Priority class, approximately 14% into the Low Priority class, and less than 0.1% of the area exceeded the Moderate Priority threshold. Monte Carlo perturbation of the child exposure relevance factors confirmed strong spatial rank stability of H(x) (median Spearman $\rho = 0.989$), indicating that the priority pattern is robust even though areas close to the Very Low Priority/Low Priority boundary may change class. This paper contributes single-threshold exceedance-probability maps at regulatory limits and a toxicity-weighted exceedance-priority index $H\left(x\right)$—a methodological and interpretive advance over our previous concentration mapping, using the same measurements with no new sampling. By constructing the composite index is toxicity-weighted: arsenic and cadmium carry ≈88% of the child weight, so $H\left(x\right)$ chiefly resolves As- and Cd-driven priority, with the remaining metals refining local class boundaries. Receptor prioritization is a screening output to guide confirmatory sampling, not a definitive risk classification. Full article

Background and Objectives: Decision-making capacity is essential for informed consent, yet its assessment in emergency departments (EDs) is often subjective and inconsistently documented. Older adults are particularly vulnerable to impaired capacity during acute illness. We aimed to develop a scalable, electronic health record (EHR)-based approach to support early identification of older ED patients at risk of decisional vulnerability using the Medical Information Mart for Intensive Care (MIMIC)-IV database. Methods: We conducted a retrospective cohort study of 51,195 ED patients aged ≥65 years. Clinicians manually reviewed 2000 patients using a conservative consensus protocol to establish a consensus-derived proxy for decisional vulnerability. Such an approach yielded a definitive reference subset (capacity vs. no capacity) and an “uncertain” category when consensus was not achieved. We developed a weak-supervision expectation–maximization (EM) label model that combined multiple noisy labeling functions derived from triage vital signs, acuity measures, arrival mode, and large language model (LLM)-classified chief complaints to estimate the probabilistic risk of impaired capacity. Model discrimination and calibration were assessed on an independent holdout subset of definitive reference labels using receiver operating characteristic area under the curve (ROC-AUC), precision–recall area under the curve (PR-AUC), calibration plots, and Brier score. To support clinically conservative use, operating thresholds were a priori constrained to limit automated flagging to ≤15% of patients, with the remaining ones deferred for clinician review. Results: On the definitive holdout set, the weak-supervision model achieved an ROC-AUC of approximately 0.855 and a PR-AUC of approximately 0.837. Calibration assessment demonstrated residual miscalibration in the generative posterior, which improved after a lightweight discriminative refinement step (logistic regression trained on EM-derived probabilistic labels), reducing the Brier score to approximately 0.224 on holdout evaluation. Under the prespecified operational constraint (≤15% auto-flagged), the model functioned as a conservative, selective alerting strategy, achieving high specificity and positive predictive value while identifying only a minority of patients with decisional vulnerability. Conclusions: This study demonstrates a methodological proof of concept for using weak supervision to model a retrospectively defined proxy for decisional vulnerability from routinely collected ED EHR data. The framework is intended to support conservative, triage-oriented prioritization. Further prospective validation, external testing, and workflow governance are needed before clinical implementation. Full article

Battery Energy Storage Systems (BESS), utilising chemistries based on Nickel Manganese Cobalt (NMC) containing lithium-ion devices, often present fire safety hazards that existing qualitative risk frameworks, including NFPA 855’s 5 × 5 consequence-likelihood matrix, are insufficiently granular to quantify. This paper presents an original probabilistic risk assessment (PRA) of fire hazards associated with BESS for a 485.52 kWh NMC installation at the Equinix SG4-4A data centre in Singapore, using Monte Carlo simulation (N = 10,000 iterations) to characterise uncertainty in hydrogen fluoride (HF) gas dose, time to Immediately Dangerous to Life or Health (IDLH) concentration, cabinet-to-cabinet propagation probability, and suppression effectiveness. The HF yield is modelled as a triangular distribution (0.3–0.8 g/kWh, mode 0.5 g/kWh), ventilation activation delay as log-normal (median 90 s), and suppression effectiveness as a piecewise function of water application delay. The results demonstrated that HF dose exceeded the National Institute for Occupational Safety and Health (NIOSH) IDLH of 25 mg/m³ in 100% of simulated scenarios for both single- and two-compartment designs, thus confirming that threshold HF toxicity was essentially unavoidable for any occupant present during a full thermal runaway event, and that ventilation alone cannot achieve adequate risk reduction. The single-stage suppression effectiveness was found to be only 37.9% (mean), providing quantitative confirmation that two-stage (clean agent + water) suppression is warranted for NMC chemistry. The two-compartment design was found to reduce the peak HF dose by 50%, and also reduced the mean IDLH clearance time from 599 to 301 min, thus shifting residual risk from As Low As Reasonably Practicable (ALARP)-tolerable to broadly acceptable under UK Health and Safety Executive (HSE) criteria. The paper proposes a quantitative PRA framework as a complement to NFPA 855 Chapter 5’s qualitative Hazard Mitigation Analysis, enabling more informed engineering decisions for BESS fire safety. To the best of our knowledge, this is the first study to apply Monte Carlo simulation to HF dose modelling in a tropical data-centre BESS context and thereby address a documented gap in the literature. Full article

Background/Objectives: Adolescent pregnancy remains a global public health challenge associated with adverse outcomes for both mothers and newborns. This study aimed to use two instruments and several multivariate techniques to identify a minimal set of variables that reproduces the instrument-based classification of adolescent pregnancy risk, while remaining parsimonious with the original instruments. Methods: A cross-sectional, quantitative methodological study was conducted among Colombian schoolgirls, comprising 160 adolescents in the face-validity phase and 319 in the risk-estimation and modeling phase. The IRENE and REND instruments, originally developed to assess the risk of adolescent pregnancy, underwent cultural adaptation, face validity and content validity. Subsequently, the instruments were administered to estimate the risk. Finally, Factor Analysis and Categorical Principal Component Analysis were applied as exploratory dimensionality reduction techniques to identify the most relevant variables for retention, thereby preserving the parsimony of the original versions. Results: Overall, 80.3% of participants were classified as not at risk, while the remainder were classified as at risk by one or both instruments. The results suggest that variables such as who the adolescent lives with, the age at which the adolescent had their first complete sexual intercourse, and how the adolescent would respond to an unplanned pregnancy are factors associated with the instrument-based risk classification. Subsequently, dimensionality reduction and logistic regression analyses identified a small subset of variables that can be used to reproduce the instrument-based classification of adolescent pregnancy risk. Conclusions: The reduced set of variables reproduced the instrument-based risk classification with high internal accuracy. Because the predictors and the outcome derive from the same instruments, these findings reflect internal reproduction of the instrument classification rather than prediction of an observed pregnancy, and they require external validation. Overall, the reduced and optimized set of variables from the IRENE and REND instruments offers a parsimonious approach that reproduces the instrument-based risk classification and that may support rapid screening once validated in independent adolescent samples. The main limitations are the use of a single institution, the non-probabilistic convenience sample, and the absence of an observed pregnancy outcome. Full article

Soil heavy metal pollution poses a threat to agricultural sustainability, food safety, and human health. The ecologically fragile Yellow River Basin is a critical hub for agriculture, energy, and mining; however, soil heavy metal studies remain fragmented, and basin-wide syntheses are limited almost entirely to agricultural soils. This study presents a basin-wide analysis of As, Cd, Cr, Cu, Ni, Pb, and Zn in topsoil, based on 2498 sampling locations compiled from 347 publications, using an integrated framework of receptor modeling, multivariate spatial statistics, self-organizing maps, and probabilistic human health and ecological risk assessment. Four pollution sources, namely agricultural–industrial, emissions, mining–smelting, and geogenic/lithogenic, were resolved. Agriculture–industry and emissions posed considerable ecological risks (mean PER = 367.9 and 353.4), with Cd and Pb accounting for 95.7% of the risk. The non-carcinogenic hazard was negligible for adults, but 8.6% of sites exceeded the safe threshold for children, and the carcinogenic risk surpassed 10⁻⁶ for all groups, with 2.6–9.6% of sites exceeding 10⁻⁴. Spatially, the strongest multimetal contamination corridors are the Baiyin–Lanzhou corridor (upper–middle reaches) for Cu-Pb-Zn (mining–smelting) and the Xi’an–Weinan belt (middle reaches) for Cd-Pb (agricultural–industrial and emissions). Multivariate clustering was more extensive (56.1% of sites) than single-metal clustering (13.1–26.2%), confirming coherent source-linked zones. Ecological risks were driven by Cd and Pb, whereas human health risks were driven by As, Cr, and Ni. This divergence and the strong spatial organization of the risk clusters highlight the need for source-specific, spatially targeted mitigation, which requires monitoring across all land use types. The compiled dataset, although extensive, is constrained by heterogeneity in sampling periods and analytical methods and by sparse coverage in some grassland, desert, and plateau regions. Full article

Reuse of human excreta and derivatives is becoming a common practice in areas with agricultural predominance. While in situ treated faeces through ecological sanitation (Ecosan), known as “faecal by-products” are being used to sustain soil nutrients and improve on-site sanitation, the concern remains about the health risks related to the survival of pathogens in these by-products in the community of farmers. This study assessed the survival of faecal pathogens and estimated microbial risks associated with the use of Ecosan faecal by-products in agriculture. The quantitative microbial risks assessment (QMRA) framework was used to estimate the risks posed by each faecal pathogen in solid and semi-solid faecal by-products under the probabilistic model of Monte Carlo simulation. Ascaris lumbricoides (6.5 eggs/gr), Taenia species (0.3 egg/gr), Schistosoma species (9.3 cercariae/gr), Entamoeba species (4.4 cysts/gr), and Escherichia coli (451 Cfu/gr) were detected in semi-solid faecal products. Exposure scenarios were observed throughout four critical points: vault faecal by-products removal/unloading, transport, collection, and application of faecal by-products in the gardens. Due to the presence of eggs and cysts, an estimated annual risk of infections was found in semi-solid faecal by-products with Schistosoma species (88%) and Ascaris lumbricoides (90%). Both concentrations were above World Health organisation (WHO) standards of associated infective risks of 0–10% of helminths in faecal sludge applied in the gardens. The users of faecal by-products, particularly farmers are exposed not only to high concentrations of helminth eggs but also to protozoa and bacteria with infective risks of Entamoeba species (99%) and E. coli species (62%). A stepwise implementation of faecal pathogens die-off during treatment of faecal by-products in compliance with the WHO’s 2018 guidelines can prevent the use of unsanitary faecal by-products. According to these findings, the proper control of intestinal protozoa and soil-transmitted helminths (STHs) should be enforced through personal protective measures in Burera district, Rwanda. Full article

Acrylamide (AA) is a contaminant with carcinogenic and genotoxic properties that occur in heat-produced food products. This study aimed to evaluate the occurrence of AA in different coffee products commercially sold in retail markets of Costa Rica and to develop a probabilistic exposure assessment model to assess the potential human health risk due to its consumption. The average AA concentration in the coffee samples analyzed (n = 110) was 110.29 ± 151.61 µg kg⁻¹. The mean dietary exposure (DE) values, for the middle-bound (MB) approach, varied from 0.025 to 0.083 µg kg⁻¹ BW per day. The margin of exposure (MOE) was calculated with a BDML₁₀: 430 μg kg⁻¹ BW day⁻¹ for neurotoxicity and 170 μg kg⁻¹ BW day⁻¹ for cancer effect, according to EFSA (2015). No neurotoxicity risk was identified as MOE values ranged from 4291 to 467,984 for the adult male population, from 4566 to 477,203 for the adult females, from 4265 to 506,062 for the male minors and from 2512 to 495,151 for the female minors. On the other hand, MOE values for the carcinogenic risk were below 10,000 for the mean and P95th coffee consumers, denoting a possible health concern. The values ranged from 1696 to 6717 for the adult male population, from 1805 to 7201 for the female adults, from 1686 to 6304 for the male minors and from 993 to 2155 for the female minors. The mean incremental lifetime cancer risk (ILCR) values for male adult, female adult, male minor, and female minor were 1.7 × 10⁻⁵, 1.6 × 10⁻⁵, 1.9 × 10⁻⁵, and 3.9 × 10⁻⁵, respectively, for the MB approach. These results denote a potential or considerable risk in consumption of coffee due to AA intake. Thus, no neurotoxicity risk was identified; however, a potential carcinogenic risk was observed based on MOE and ILCR results. Full article

The occurrence of pesticide residues in pome fruits and their implications for consumer health remain critical concerns in food safety. In this study, 222 pesticide residues were analysed in 155 samples of apples, pears, and quinces collected from Türkiye between October 2025 and March 2026 using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Residues were detected in 76.4% of apples, 86% of pears, and 30% of quinces, with frequent multi-residue patterns and notable occurrences of non-approved compounds. Pear samples exhibited the highest contamination levels, with maximum residue level (MRL) exceedance rates reaching 30%, compared to 14.5% in apples and 2% in quinces. Quality assessment based on the index of quality for residues (IqR) indicated that 96% of quince samples were classified as excellent or good, demonstrating the most favourable profile among the evaluated commodities. Risk ranking analysis further indicated that acetamiprid was the only high-risk pesticide in apples, whereas residues in pears were predominantly medium risk, and all detected compounds in quinces fell within the low-risk category. Deterministic risk assessment indicated that chronic exposure remained well below levels of concern for both adults and children. Under combined pome fruit consumption, acetamiprid and spirodiclofen were identified as the main contributors to chronic hazard index (HIc), accounting for 33% and 13% of HIc, respectively. However, acute exposure exceeded the safety threshold (HQa > 1) in children for acetamiprid in both apples and pears. Probabilistic modelling confirmed right-skewed exposure distributions and highlighted increased risk under cumulative consumption scenarios. Full article

Intensifying heatwaves threaten the reliability of electric distribution systems, yet the quantitative relationship between heatwave characteristics and observed power outage behavior remains poorly understood at multi-year, statewide scales. This study develops an event-based, spatiotemporal framework to quantify heatwave-induced outage risk across 254 Texas counties from 2014–2021 by integrating county-level EAGLE-I outage records with reanalysis-derived heat index measurements. An adaptive percentile-based threshold identifies 3048 heatwave events; logistic regression quantifies the probabilistic relationship between heat intensity and major-outage occurrence under three severity definitions. Across 3048 identified heatwave events, 51% involved at least one outage, a rate significantly above the non-heatwave warm-season baseline and revealing widespread heat-related reliability challenges. Outage severity and duration exhibit heavy-tailed distributions, with a small number of extreme events disproportionately affecting customers. Logistic regression models under three severity definitions (P90, P95, and ≥500 customers) demonstrate that heat intensity is a statistically robust probabilistic predictor of major outages, with each +1 °F increase in mean event heat index raising the odds by approximately 43–52%. The predicted probability of a P90-severity major outage approximately doubles across the interquartile range of event heat intensity (~7% to ~14%), providing actionable guidance for utility pre-staging decisions during forecast heatwave episodes. These findings offer a scalable methodology for climate-related reliability assessment, supporting grid hardening, resource planning, and public health preparedness. Full article

OnVeMCS 1.1 is a standalone software for probabilistic human health risk assessment of pollutants in soil, sediment, water, air, and food, enabling Monte Carlo simulation (MCS) of risks across multiple exposure pathways. The hazard index (HI) and cancer risk metrics (TCR/ILCR) for ingestion, inhalation, and dermal contact are quantified using the standard dose/concentration approach. Users can manually enter analyte concentrations with various probability distributions or import them from Excel templates, and select scenario-specific exposure factor sets for residents (children and adults), outdoor and indoor workers, and food consumers. The software supports both one-dimensional (1D) and two-dimensional Monte Carlo simulation (2D MCS) modes. The results are presented through a variety of plots, including histograms and cumulative distribution functions (CDFs), pathway/analyte contribution charts, sensitivity analysis plots, nested CDFs, and uncertainty ribbons. The software also allows the overlay of two or more outputs and the inclusion of regulatory thresholds (HI = 1; TCR/ILCR = 10⁻⁶–10⁻⁴). The results are exported to a multi-sheet Excel workbook containing raw arrays, summary tables, exceedance probabilities, and sensitivity data. OnVeMCS operates quickly, with even 2D MCSs being completed in several seconds. OnVeMCS is distributed as a single Windows installer file with data examples and is free for the academic community. Full article

Polycyclic aromatic hydrocarbons (PAHs) are major carcinogenic pollutants in urban air, and inhalation exposure poses health risks, particularly for primary school children aged 6–14 years in school environments. Traditional deterministic models for incremental lifetime cancer risk (ILCR) assessment often fail to adequately quantify variability and epistemic uncertainty in exposure parameters. This study develops a multi-layered probabilistic framework that progresses from deterministic calculations through one-dimensional Monte Carlo and sensitivity-guided two-dimensional Monte Carlo to a hierarchical (second-order) two-dimensional Monte Carlo simulation. The hierarchical approach samples hyper-parameters of the input distributions (means, standard deviations, and modes) in the outer loop, while exposure variables are sampled in the inner loop using Latin hypercube sampling. Applied to PAH and BaP_eq concentrations measured indoors and outdoors during heating and non-heating seasons, the framework yielded mean total ILCR values of 1.42 × 10⁻⁶ for children and 1.18 × 10⁻⁶ for adults. The hierarchical 2D MC produced 95% confidence intervals on the 95th percentiles of [9.17 × 10⁻⁷, 5.67 × 10⁻⁶] for children and [6.48 × 10⁻⁷, 5.57 × 10⁻⁶] for adults, with outdoor heating identified as the dominant exposure pathway. Although the air sampling campaign was conducted in 2011–2012, the data remain representative for evaluating seasonal and microenvironmental variability of PAHs in urban school settings in the region, as PAH levels are predominantly driven by persistent combustion sources. This framework provides more comprehensive uncertainty quantification for complex environmental exposure scenarios. Full article