Search Results (59)

Coastal lagoon Salt Ponds are highly valuable wetland systems where traditional salt production coexists with ecosystems of significant ecological importance, often characterized by high environmental sensitivity. In data-scarce coastal settings, particularly those located near river channels and drainage networks, assessing multiple environmental hazards remains a major challenge. This study proposes a simplified and transferable methodological framework for multi-hazard assessment in coastal Salt Pond environments (DAFFLE; Data Acquisition Fluvial Flooding and Liquefaction Evaluation), with particular emphasis on areas where field data are limited and fluvial processes and seismic effects may interact. The approach integrates three main components: first, improved terrain modelling using global elevation datasets and ICESat-2 laser altimetry data to better represent very flat coastal areas; second, flood hazard simulation by modelling water depths under different flood scenarios to map potential inundation; third, liquefaction susceptibility is assessed using surficial geological data and key geomorphological parameters, producing simplified probabilistic hazard maps informed by existing seismic hazard datasets or scenario-based assumptions. The proposed framework provides a scalable and practical tool for first-order multi-hazard assessment in vulnerable coastal Salt Pond environments. It supports comparative hazard analyses and decision-making in regions where detailed site-specific data and extensive field investigations are not available, offering a consistent baseline for coastal lagoon Salt Pond risk evaluation and management. Full article

The Anning River, traversing the mineral-rich Panxi region, is highly susceptible to contamination by potentially toxic elements (PTEs). This study systematically investigated the contamination profiles, source apportionment, and probabilistic human health risks of eight PTEs in the surface sediments of the basin. Index-based evaluations revealed that Cd acts as the dominant ecological threat, exhibiting extreme enrichment, whereas V, Cr, and Ni reflect natural background signatures. Receptor modeling via Positive Matrix Factorization (PMF) successfully decoupled four distinct sources: mining and smelting emissions (Cd, Zn), natural lithogenic weathering (V, Cr, Ni), mixed traffic/urban inputs (Pb, Cu), and a Tl-specific mixed source. Crucially, while deterministic approaches suggested safe exposure levels, probabilistic Monte Carlo simulations uncovered hidden vulnerabilities: children face a striking 60.51% probability of exceeding the acceptable total carcinogenic risk (TCR) threshold of 1.0 × 10⁻⁴, primarily governed by Cr and Ni. These findings underscore the urgent need for differentiated environmental management in similar mining-impacted basins. Specifically, stringent source controls for Cd must be implemented alongside exposure pathway interruptions to safeguard vulnerable demographics from Cr and Ni. Full article

Environmental site-specific risk assessment (SSRA) has developed into a complex, multi-tiered discipline that integrates contaminated land characterization, ecological endpoint selection, exposure pathway analysis, and uncertainty quantification to facilitate remediation decision-making. This literature review integrates findings from 33 highly relevant peer-reviewed publications over three decades (1994–2024). Overall, our narrative synthesis indicated that while SSRA methodologies share a common conceptual foundation, the implementation varies notably across regions, contaminants, and ecological contexts. This review reveals a gradual move away from deterministic screening-level assessments and toward probabilistic, spatially explicit frameworks that incorporate multiple lines of evidence, advanced uncertainty analysis, and decision support systems. The key findings show that tiered assessment methods that combine screening and definitive evaluations are still the most common and that Bayesian networks, Monte Carlo simulations, and weight-of-evidence methods are being used more to deal with uncertainty in parameters and models. Full article

Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual synthesis of foundational fire-science literature. The evidence base contains 179 unique references, including an AI-focused corpus, classical deterministic and probabilistic fire-danger and spread models, global ignition and lightning studies, remote-sensing and fuel-moisture foundations, decision-support tools, and governance literature. We define prevention-facing AI as systems that support pre-ignition or pre-escalation decisions and compare studies by data source, model design, validation protocol, forecast horizon, transferability, interpretability, and management action. The synthesis shows that AI is most mature for multimodal sensing, smoke/fire detection, susceptibility mapping, and short-horizon forecasting, but less mature for prospective decision-support validation, cross-ecosystem transfer, and operational accountability. AI is therefore most useful when it is hybrid, interpretable, and deployment-aware: it should complement established fire-weather and spread-model baselines while converting ecological observations into timely and actionable prevention judgments. Full article

Marine ecosystem degradation may reduce state revenues, increase recovery spending, and weaken fiscal sustainability, yet Indonesia does not yet have a routine governance mechanism that links marine natural capital valuation to fiscal-risk assessment in the State Budget Financial Note. This article develops a governance design, Blue Natural Capital Value at Risk (BNC-VaR), to translate changes in marine ecosystem conditions into fiscal-exposure signals for Indonesian public finance. Ecological condition indicators, such as fish-stock status, coral-reef condition, and mangrove extent, are converted into traceable valuation parameters and then into structured outputs, including fiscal-exposure scenarios, budget-relevance notes, and medium-term fiscal-sustainability readings across revenue, expenditure, deficit, and financing channels. The design treats ecological change as affecting the fiscal position through mediated and disclosable pathways rather than automatic causal effects. It adapts Value at Risk as a risk logic for public fiscal governance rather than as a conventional market-based probabilistic measure. Using theory synthesis and a model-paper approach across six analytical stages, the study produces five design principles, four formal propositions, and a five-component institutional architecture, with the Directorate General of State Assets Management positioned as a valuation custodian. As a conceptual contribution, BNC-VaR offers an operational architecture and implementation roadmap for future empirical testing in Indonesia and other archipelagic or marine-resource-dependent fiscal systems. Full article

Wildfire regimes in mountain forest landscapes are shaped by complex interactions among topography, climate, vegetation, and human activity. However, predicting and interpreting fire occurrence in topographically heterogeneous regions remains challenging because fire–environment relationships vary strongly across elevation gradients and temporal scales. This study developed a hybrid machine-learning framework integrating an Information Value Model (IVM), Random Forest (RF), and Convolutional Neural Network (CNN) to decode elevation-mediated wildfire regimes in western Sichuan, China, a mountainous forest region characterized by strong vertical environmental gradients and high ecological conservation value. Multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) burned-area records, topographic variables, monthly meteorological data, vegetation indices, land-cover information, and human-accessibility proxies, were integrated at a 500 m spatial resolution. Environmentally comparable non-fire samples were generated from unburned vegetated pixels, and model training, RF-based feature selection, hyperparameter tuning using Particle Swarm Optimization (PSO), and performance evaluation were conducted within a nested spatial block cross-validation framework. The model produced continuous wildfire occurrence probabilities and showed strong discriminatory performance under the adopted validation protocol, with AUC values exceeding 0.95 across temporal datasets and low probability-error metrics. RF importance and correlation analyses identified mean temperature, elevation, and precipitation as the dominant predictors of wildfire probability. Spatial analyses revealed pronounced elevation-mediated differentiation in wildfire regimes: low-elevation valleys showed higher fire probability and stronger associations with human-accessibility proxies, whereas high-elevation plateau areas exhibited lower and more scattered fire patterns associated with climatic constraints. Seasonal and monthly analyses further showed that winter and spring fires dominated the regional fire regime, with risk intensifying during the pre-monsoon dry period. By combining probabilistic fire-risk mapping, spatial-context learning, and elevation-gradient interpretation, this study provides a transferable framework for understanding wildfire regimes in complex mountain forest landscapes. The findings support adaptive forest fire management, targeted monitoring, and risk zoning in mountainous regions where forest ecosystems, human activities, and conservation values intersect. 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

Background: The increasing consumption of psychoactive pharmaceuticals has led to their continuous release into aquatic environments. Methods: This study assessed the ecotoxicological responses, phytoremediation capacity, and ecological risk of seven psychoactive pharmaceuticals—citalopram (CIT), sertraline (SER), fluoxetine (FLU), alprazolam (ALP), clonazepam (CLO), risperidone (RIS), and topiramate (TOP)—using Lemna minor under controlled exposure conditions. Plants were exposed to a concentration gradient, and physiological endpoints, including relative growth rate, chlorophyll content, and maximum photosystem II efficiency (Fv/Fm), were evaluated alongside compound removal and abiotic degradation. Results: Dose–response modeling revealed substantial variability in toxicity, with TOP (EC₅₀ = 74.11 ng L⁻¹), CLO (104.8 ng L⁻¹), and RIS (138.5 ng L⁻¹) exhibiting the highest potency, whereas FLU (1751 ng L⁻¹), CIT (89,941 ng L⁻¹), and ALP (465,351 ng L⁻¹) were less toxic. Relative growth rate was the most sensitive endpoint. Mixture exposure did not result in additional toxicity compared to the most responsive individual compounds. Abiotic degradation was negligible for most compounds (<3%), except for SER (~42%) and FLU (~22%). In contrast, L. minor achieved net removal efficiencies of up to 81%, although reductions occurred under mixed conditions. Probabilistic risk assessment indicated a high ecological risk (msPAFtotal = 1.0), with RIS as the dominant contributor. Full article

Balancing ecological protection, carbon sinks, and development is a practical challenge in mountainous regions. Using Yunnan Province, China, as a case study, this paper develops a knowledge-guided probabilistic framework for carbon-oriented territorial zoning. The framework combines an indicator system, corridor analysis of pattern, risk and potential, knowledge-graph rule encoding, Bayesian mechanism calibration, and constrained posterior decoding on 11,853 effective planning cells. The results show a clear conservation–development gradient in the carbon sink priority surface: high-priority areas are concentrated in western and southwestern Yunnan, whereas low-priority areas cluster around major urban centers. Corridor analysis identifies a central resistance belt and several urban–rural bottlenecks, indicating that connectivity constraints are concentrated in a limited number of critical links. The final zoning assigns 35.4% of grids to integrated development, 25.9% to emergency intervention, 14.5% to long-term conservation, 13.8% to priority restoration, and 10.4% to risk control. Zone separability is generally strong, with one-versus-rest AUC values ranging from 0.777 to 0.995. Land use enrichment further supports the zoning results: integrated development contains 78.85% of built-up land and 45.93% of cropland, whereas Emergency intervention, priority restoration, and long-term conservation together contain 70.01% of forest area. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

Colibactin, a genotoxin produced by pks⁺ E. coli, imprints highly specific mutational signatures SBS88 and ID18 in colorectal cancer (CRC) and even in normal colonic crypts. Population-scale analyses show these signatures are enriched in early-onset CRC, vary geographically, and are imprinted early during tumor evolution, where probabilistic attribution indicates that colibactin contributes to a measurable fraction of APC driver mutations in colibactin-positive cancers. Beyond colibactin, Fusobacterium nucleatum exerts clade-specific effects on tumor ecology and therapy response, with data supporting both chemoresistance and sensitization to anti-PD-1 in microsatellite stable (MSS) CRC. This article covers mechanistic, genomic, and molecular epidemiology evidence, outlines analytic standards for signature detection (whole-genome sequencing (WGS)/whole-exome sequencing (WES), single-sample fitting, and limits at low mutation counts), and charts translational paths spanning noninvasive screening (stool metagenomics + mutational signatures in tissue/circulating tumor DNA (ctDNA)), risk stratification, and microbial-targeted interventions (antibiotics, phages, ClbP inhibitors). Framing microbiome–genome crosstalk as a tractable axis enables testable clinical hypotheses for precision oncology. Full article

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures. Full article

The Z.D. granite weathering crust rare earth deposit in Ganzhou, China is a world-class resource. In situ leaching extraction may mobilize potentially toxic elements (PTEs) into surrounding soils. This study analyzed nine PTEs (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn) in top soils within and around the mining area. A multi-method approach was employed, integrating geochemical assessment, pollution and ecological risk indices, and probabilistic health risk evaluation via Monte Carlo simulation and source apportionment using Positive Matrix Factorization (PMF) combined with Geographic Information System (GIS) analysis. Results indicated generally low background levels, though with localized Pb enrichment, and an overall low level of pollution and ecological risk. However, for children in nearby areas with prolonged exposure, there was a 9.11% probability of non-carcinogenic risk and a 13.82% probability of carcinogenic risk. PMF-GIS analysis traced PTEs to four sources: natural parent material, industrial emissions, mining/riverine transport, and agriculture. In conclusion, while current soil environmental risks in the Z.D. mining area remain manageable, the study underscores the need to monitor progressive PTE accumulation and children’s health risks. This work provides a scientific basis for targeted soil management and ecological restoration in rare earth mining regions. Full article

This study evaluated the ecological and health risks associated with metals in Peruvian avocado cultivation from a One Health perspective. Between January and September 2025, a total of 190 soil and fruit samples were collected from major producing regions (Amazonas, Áncash, Ayacucho, Cusco, Huancavelica, Ica, La Libertad, and Lima) to quantify arsenic (As), cadmium (Cd), chromium (Cr), mercury (Hg), nickel (Ni), and lead (Pb) using microwave plasma atomic emission spectrometry (MP-AES). Results showed regional variability in soil metal concentrations, with higher As (76.17 ± 17.35 mg/kg), Cd (0.55 ± 1.04 mg/kg), and Pb (25.35 ± 6.02 mg/kg). Cr concentrations in avocados were below the detection limit (<0.003 mg/kg), while As (<0.003–0.192 mg/kg), Cd (<0.005–0.130 mg/kg), Hg (<0.005–0.428 mg/kg), Ni (<0.005–0.172 mg/kg), and Pb (<0.005–0.396 mg/kg) exhibited broader concentration ranges. Bioaccumulation (BAF) values < 1 confirmed low translocation. The geo-accumulation index (Igeo) and ecological risk (ER) indicated uncontaminated or moderately contaminated soils with low ecological risk. In terms of health risk, the hazard quotient (HQ) and hazard index (HI) were <1, representing a low level of concern for non-genotoxic effects. The cancer risk (CR) values for both metals ranged from 10⁻⁸ to 10⁻⁵, indicating a non-significant carcinogenic risk for Pb (<10⁻⁶) and an acceptable risk for Cd (10⁻⁴). Full article