Risk Assessment Approach Research Articles

Exploring practices, challenges, and priorities for human health and ecological risk assessments in Indigenous communities in Canada.

Indigenous peoples in Canada are disproportionately exposed to environmental contaminants and may face elevated health risks related to their unique cultural, spiritual, and economic relationships with the land, including the use of traditional food systems. However, to date, institutionalized approaches to assess risks to human and ecological health from contaminants have not been well developed or implemented with Indigenous community contexts in mind. There is regulatory interest in developing new approach methods for risk assessment, and thus an opportunity to increase their relevance to Indigenous communities in which they will be ultimately applied. Therefore, we conducted an anonymous mixed-methods survey of those involved with risk assessment in Indigenous communities in Canada to: (1) understand risk assessment practice in Indigenous communities, (2) explore challenges with conventional assessment methods and compare these across sectors, and (3) gather perspectives on the development of new approaches. In all, 38 completed survey responses were received (14% response rate). Respondents were from Indigenous community environment and health offices (21% of respondents), Indigenous governments (8%), federal and provincial governments (21%), and academia (45%). Risk communication was seen as the most challenging part of risk assessment (71% responded "difficult"), and nearly all respondents agreed that time (86%), cost (76%), and resource availability (86%) were "moderate" to "serious" problems. Few respondents (16%) had heard of "new approach methods" for risk assessment, and 76% of respondents (and 100% of community-based respondents) agreed on the need to develop improved risk assessment approaches. To modernize risk assessment, respondents recommended advancing cumulative risk assessment methods, improving risk communication, and promoting Indigenous leadership and Traditional Knowledge in assessment activities. Integr Environ Assess Manag 2024;20:1677-1692. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19

Individual testing of samples is time- and cost-intensive, particularly during an ongoing pandemic. Better practical alternatives to individual testing can significantly decrease the burden of disease on the healthcare system. Herein, we presented the clinical validation of Segtnan™ on 3929 patients. Segtnan™ is available as a mobile application entailing an AI-integrated personalized risk assessment approach with a novel data-driven equation for pooling of biological samples. The AI was selected from a comparison between 15 machine learning classifiers (highest accuracy = 80.14%) and a feed-forward neural network with an accuracy of 81.38% in predicting the rRT-PCR test results based on a designed survey with minimal clinical questions. Furthermore, we derived a novel pool-size equation from the pooling data of 54 published original studies. The results demonstrated testing capacity increase of 750%, 60%, and 5% at prevalence rates of 0.05%, 22%, and 50%, respectively. Compared to Dorfman’s method, our novel equation saved more tests significantly at high prevalence, i.e., 28% (p = 0.006), 40% (p = 0.00001), and 66% (p = 0.02). Lastly, we illustrated the feasibility of the Segtnan™ usage in clinically complex settings like emergency and psychiatric departments.

Rockburst early warning based on microseism and critical point theory

During the tunnel construction, rockbursts occurred, posing a risk to safety. To mitigate this risk, a microseismic monitoring system was installed in the tunnel to provide early warning of rockbursts. After conducting research on the evolution of microseismic events in terms of number, moment magnitude, and energy within a specific time frame, an approach for rockbursts risk assessment based on microseismic moment magnitude was proposed. The critical point theory was utilized to quantitatively analyze the energy release process of the surrounding rock before intense rockbursts. This analysis revealed that the energy release of the surrounding rock before rockbursts can be divided into three stages: stable energy release stage, transitional energy release stage and accelerated energy release stage. The obtained process, derived from the critical point theory, was compared and analyzed alongside the evolution process of value of b, cumulative apparent volume, and energy index.

Probabilistic risk assessment of residential exposure to electric arc furnace steel slag using Bayesian model of relative bioavailability and PBPK modeling of manganese.

Electric arc furnace (EAF) slag is a coproduct of steel production used primarily for construction purposes. Some applications of EAF slag result in residential exposures by incidental ingestion and inhalation of airborne dust. To evaluate potential health risks, an EAF slag characterization program was conducted to measure concentrations of metals and leaching potential (including oral bioaccessibility) in 38 EAF slag samples. Arsenic, hexavalent chromium, iron, vanadium, and manganese (Mn) were identified as constituents of interest (COIs). Using a probabilistic risk assessment (PRA) approach, estimated distributions of dose for COIs were assessed, and increased cancer risks and noncancer hazard quotients (HQs) at the 50th and 95th percentiles were calculated. For the residents near slag-covered roads, cancer risk and noncancer HQs were <1E - 6 and 1, respectively. For residential driveway or landscape exposure, at the 95th percentile, cancer risks were 1E - 6 and 7E - 07 based on oral exposure to arsenic and hexavalent chromium, respectively. HQs ranged from 0.07 to 2 with the upper bound due to ingestion of Mn among children. To expand the analysis, a previously published physiologically based pharmacokinetic (PBPK) model was used to estimate Mn levels in the globus pallidus for both exposure scenarios and further evaluate the potential for Mn neurotoxicity. The PBPK model estimated slightly increased Mn in the globus pallidus at the 95th percentile of exposure, but concentrations did not exceed no-observed-adverse-effect levels for neurological effects. Overall, the assessment found that the application of EAF slag in residential areas is unlikely to pose a health hazard or increased cancer risk.

A risk assessment approach for road collapse along tunnels based on an improved entropy weight method and K-means cluster algorithm

Ground collapse often occurs when strata are disturbed, particularly during tunnel construction, threatening the safety of people and underground facilities. Therefore, evaluating the risk of road collapse during and after tunnel construction is essential. Considering this, an improved entropy weight method was adopted to establish an evaluation system for assessing the collapse risk level of the Qianjin West Road, through which the Kunshan metro line S1 passes. The main implementation steps of the evaluation system include ten assessment indices, data visualization of indices, weight determination for indices, a cluster algorithm for the classification of results, and analysis of the evaluation results. In contrast to the traditional single method, and to improve the reliability of the risk assessment results, the method proposed in this paper considers two aspects. First, to eliminate subjective or objective biases generated by a single evaluation method, the entropy weight method (EM) and analytic hierarchy process (AHP) were integrated to formulate the improved entropy weight method. Second, to divide the evaluation results more reasonably, the k-means cluster algorithm using the Calinski Harabasz method was applied. The reliability and accuracy of the proposed improved entropy weight method are verified based on the case of Line S1. Furthermore, a method comparison and the concept of the coefficient of variation method were adopted to demonstrate the reliability of the risk assessment results. The improved entropy weight method can be used to evaluate ground collapse risk levels during and after tunnel construction.

Variation of total alpha and beta activities and Rn-222 concentrations in the water supply system of an Italian volcanic region: How safe is tap water for human consumption?

Total alpha and beta activities and Rn-222 concentrations were determined in water from different sections of seven aqueducts belonging to the water supply system of Campania region (Italy), known worldwide for its volcanism. Statistical analysis was performed on data to account for their variability across the aqueduct sections, and results were discussed considering the geology of reservoirs, the potential mixing processes occurring along the pipe network, the building/constituting materials of the aqueduct sections, and the integrity of the infrastructure. Guidelines proposed by Italian and international regulation entities were considered to determine if total alpha and beta activities and Rn-222 concentrations found at the taps of the different aqueducts should be considered detrimental to public health. Based on a deterministic and a stochastic approach, a health risk assessment was also tested for Rn-222, assuming direct ingestion and showering as potential exposure pathways. Results showed that applying guidelines returned an absence of hazard, whereas risk assessment returned a high probability of exposure to unacceptable Rn-222 doses for some aqueducts. Beyond the usefulness of obtained results to plan actions to improve the safety of drinking water in Campania, our outcomes represent a warning for bodies dealing with public health at any level: the use of guidelines can bring an underestimation of the risks exerted by the exposure to Rn-222 on human health. Further, using a probabilistic approach in risk assessment accounting for uncertainty can favor risk forecasts based on more “realistic” scenarios.

Predictions of tissue concentrations of myclobutanil, oxyfluorfen, and pronamide in rat and human after oral exposures via GastroPlusTM physiologically based pharmacokinetic modelling

ABSTRACT Heritage agrochemicals like myclobutanil, oxyfluorfen, and pronamide, are extensively used in agriculture, with well-established studies on their animal toxicity. Yet, human toxicity assessment relies on conventional human risk assessment approaches including the utilization of animal-based ADME (Absorption, Distribution, Metabolism, and Excretion) data. In recent years, Physiologically Based Pharmacokinetic (PBPK) modelling approaches have played an increasing role in human risk assessment of many chemicals including agrochemicals. This study addresses the absence of PBPK-type data for myclobutanil, oxyfluorfen, and pronamide by generating in vitro data for key input PBPK parameters (Caco-2 permeability, rat plasma binding, rat blood to plasma ratio, and rat liver microsomal half-life), followed by generation of PBPK models for these three chemicals via the GastroPlusTM software. Incorporating these experimental input parameters into PBPK models, the prediction accuracy of plasma AUC (area under curve) was significantly improved. Validation against rat oral administration data demonstrated substantial enhancement. Steady-state plasma concentrations (Css) of pronamide aligned well with published data using measured PBPK parameters. Following validation, parent-based tissue concentrations for these agrochemicals were predicted in humans and rats after single or 30-day repeat exposure of 10 mg/kg/day. These predicted concentrations contribute valuable information for future human toxicity risk assessments of these agrochemicals.

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Abstract Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.

Systemic risk capability assessment methodology: A new approach for evaluating inter-connected risks in seaport ecosystems

The ever-increasing systemic risks posed by disasters and the impacts of climate change have emerged as formidable challenges that demand comprehensive assessment and understanding. This study endeavours to address this critical need by introducing the innovative Systemic Risk Capability Assessment (SRCA) methodology. Unlike traditional risk assessment approaches, SRCA is uniquely designed to not only assess systemic risk but also operationalize its management, making it particularly suited for safeguarding critical infrastructure, with a specific focus on seaports. The SRCA methodology, offers a quasi-quantitative framework that goes beyond conventional risk assessment, enabling a deeper understanding of the dynamics and interdependencies inherent to seaport ecosystems. In the pursuit of demonstrating the practicality and versatility of the SRCA methodology, this study applies the model to two hypothetical ports, accompanied by the utilization of synthetic data. By doing so, it elucidates the step-by-step analysis facilitated by SRCA, highlighting its potential for enhancing the resilience of seaports to systemic risks. The results underscore the limitations of traditional risk assessment methodologies when confronted with the complexity of systemic risks in seaports, thereby emphasizing the significance of the proposed SRCA methodology. In conclusion, this research contributes significantly to the field of systemic risk management, particularly within the critical infrastructure domain.

Financial Performance and Bankruptcy Risk Analysis: An Application on Private Health Insurance Companies in Turkey

This research evaluates the correlation between the financial performances of seven insurance companies operating in Turkey from 2018 to 2022 and their risk of bankruptcy. In the study, data obtained from the year-end financial statements of the companies were used, weights were assigned to the criteria determined by the ENTROPY methodology, and the performance rankings of the companies were obtained using the WASPAS method. The Altman-Z model was applied to determine the risk of bankruptcy. The decisive criteria in the financial performance ranking are profitability ratios such as Asset Profitability, Equity Profitability, and Net Profit Margin. While Turkey Insurance and Anadolu Insurance's leadership positions in the sector were identified, Ray Insurance and Mapfre Insurance were determined as the companies showing the weakest performance. Although the research shows that financial performance affects the risk of bankruptcy, it also reveals that it is not the sole factor in determining the likelihood of bankruptcy. In this context, the evaluation of financial performance in the insurance industry should be considered as part of a more holistic risk assessment approach.

New Approach Methodologies (NAMs) for ad hoc human health risk assessment of food and non-food products - Proceedings of a workshop

RIVM convened a workshop on the use of New Approach Methodologies (NAMs) for the ad hoc human health risk assessment of food and non-food products. Central to the workshop were two case studies of marketed products with a potential health concern: the botanical Tabernanthe iboga which is used to facilitate mental or spiritual insight or to (illegally) treat drug addiction and is associated with cardiotoxicity, and dermal creams containing female sex hormones, intended for use by perimenopausal women to reduce menopause symptoms without medical supervision. The workshop participants recognized that data from NAM approaches added valuable information for the ad hoc risk assessment of these products, although the available approaches were inadequate to derive health-based guidance values. Recommendations were provided on how to further enhance and implement NAM approaches in regulatory risk assessment, specifying both scientific and technical aspects as well as stakeholder engagement aspects.

Risk Prioritization in A Manufacturing Project with Fuzzy SWARA and Fuzzy MOORA Methods

Businesses must constantly change and develop to keep up with customer needs and changes in the market. Not being able to adapt to change or not being ready enough can bring risks. These changes and risks can affect both the process and the outputs of the process. It is essential to be prepared for the possible risks experienced in the operations, especially in project-based, production-to-order enterprises, to meet the customer's demands and to comply with the delivery date. Businesses must implement an effective risk assessment process to achieve this. This study implemented a risk prioritization for a new business project that produces energy storage systems by having to order. Changes must be made in the production process for an additional feature to be added to the product in line with the customer's request. The risks posed by the change and the short deadline were analyzed and prioritized. A new model was developed by developing the classical Decision Matrix Risk Assessment (DMRA) method, one of the risk assessment methods to evaluate the ten identified risks. The "severity" component of the classical DMRA method is divided into three sub-components (the severity of delivery time, the severity of cost, and the severity of quality) to be evaluated more specifically. Moreover, since the weights of these sub-components on the risks are different, the components are weighted with the Fuzzy SWARA method. The weights obtained from the Fuzzy SWARA method are used in the Fuzzy MOORA method, and the risks are prioritized. Thus, the effectiveness of the classical risk assessment approach has increased by detailing the "severity" component of the risk assessment components and weighting and prioritizing the components using multi-criteria decision-making (MCDM) techniques.

Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies

Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks.

Clinical application value of Ultrafast Pulse Wave Velocity in early cardiovascular injury of Immunoglobulin A nephropathy.

To investigate the application and the related influencing factors of ultrafast pulse wave velocity (ufPWV) in theevaluation of carotid artery elasticity in patients with Immunoglobulin A nephropathy (IgAN). A totalof 156 IgAN patients and 50 healthy individuals were selected as the control group. The ufPWV technique was employed tomeasure carotid arterial elasticity parameters, including carotid intima-media thickness (cIMT), pulse wave velocity at thebeginning of systole (BS-PWV) and pulse wave velocity at the end of systole (ES-PWV). Across the three groups(control group, IgAN patients with normal renal function, and IgAN patients with renal dysfunction) there was an increasingtrend observed in cIMT, BS-PWV, and ES-PWV. Additionally, ES-PWV exhibited higher sensitivity than BS-PWV. Correlationanalysis revealed that BS-PWV and ES-PWV were positively correlated with age, body mass index (BMI), systolic bloodpressure (SBP), high-sensitivity C-reactive protein (hs-CRP), creatinine (Cr), blood urea nitrogen (BUN), and uric acid (UA)and negatively correlated with estimated glomerular filtration rate (eGFR). The ufPWV technique allows for therapid and direct measurement of arterial elasticity parameters in the neck and represents a novel approach for the early diagnosisand quantitative assessment of arterial stiffness risk in IgAN patients.

Metallomic mapping of gut and brain in heavy metal exposed earthworms: A novel paradigm in ecotoxicology

This study explored the uptake of lead in the epigeic earthworm Dendrobaena veneta exposed to 0, 1000, and 2500 μg Pb/g soil. The soil metal content was extracted using strong acid digestion and water leaching, and analysed by means of Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to estimate absolute and bioavailable concentrations of metals in the soil. The guts and heads of lead-exposed earthworms were processed into formalin-fixed and paraffin embedded sections for high-resolution multi-element metallomic imaging via Laser Ablation ICP-MS (LA-ICP-MS). Metallomic maps of phosphorus, zinc, and lead were produced at 15-μm resolution in the head and gut of D. veneta. Additional 4-μm resolution metallomic maps of the earthworm brains were taken, revealing the detailed localisation of metals in the brain. The Pb bioaccumulated in the chloragogenous tissues of the earthworm in a dose-dependent manner, making it possible to track the extent of soil contamination. The bioaccumulation of P and Zn in earthworm tissues was independent of Pb exposure concentration. This approach demonstrates the utility of LA-ICP-MS as a powerful approach for ecotoxicology and environmental risk assessments.

ATT&CK-based Advanced Persistent Threat attacks risk propagation assessment model for zero trust networks

In recent years, the growing frequency and intensity of Advanced Persistent Threats (APTs) have significantly undermined the legitimacy and financial stability of government agencies, enterprises, and other entities. Moreover, these attacks have shown the inherent vulnerabilities in conventional border defense strategies. The emergence of the zero trust network architecture can be attributed to the increasing complexity of the cyber threat landscape. With the application of risk assessment, this paper effectively tackles the challenges posed by conventional network defense limitations and enhances the efficiency of the access control decision-making process. Nevertheless, the existing risk assessment approaches primarily focus on conventional security assessment objectives, which exhibits a deficiency in the ability to dynamically assess APT attacks. The Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) Framework introduced in this paper is a novel approach to mitigating APT attacks. This paper aims to mine and analyze the frequent item set and correlation of cyber threat penetration attack techniques. The paper also intends to construct an attack technique relationship diagram and develop a tactical prediction model for cyber threat penetration attacks using the Markov chain model. Finally, our study aims to establish a risk propagation model for APT threats based on the aforementioned model. The approach presented in this paper significantly enhances the capacity of zero trust networks in addressing sophisticated cyber threats.

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.

Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable. To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility. Risk prediction study. Outpatients potentially eligible for primary cardiovascular prevention. The CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated. 10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score. Among 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]). Retrospective study design using electronic medical records. On the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data. None.

Antibiotic resistome in landfill leachate and impact on groundwater

Landfill leachate is a hotspot in antibiotic resistance development. However, little is known about antibiotic resistome and host pathogens in leachate and their effects on surrounding groundwater. Here, metagenomic sequencing was used to explore profiles, host bacteria, environmental risks and influencing factors of antibiotic resistome in raw and treated leachate and surrounding groundwater of three landfills. Results showed detection of a total of 324 antibiotic resistance genes (ARGs). The ARGs conferring resistance to multidrug (8.8 %–25.7 %), aminoglycoside (13.1 %–39.2 %), sulfonamide (10.0 %–20.9 %), tetracycline (5.7 %–34.4 %) and macrolide-lincosamide-streptogramin (MLS, 5.3 %–29.5 %) were dominant in raw leachate, while multidrug resistance genes were the major ARGs in treated leachate (64.1 %–83.0 %) and groundwater (28.7 %–76.6 %). Source tracking analysis suggests non-negligible influence of leachate on the ARGs in groundwater. The pathogens including Acinetobacter pittii, Pseudomonas stutzeri and P. alcaligenes were the major ARG-carrying hosts. Variance partitioning analysis indicates that the microbial community, abiotic variables and their interaction contributed most to the antibiotic resistance development. Our results shed light on the dissemination and driving mechanisms of ARGs from leachate to the groundwater, indicating that a comprehensive risk assessment and efficient treatment approaches are needed to deal with ARGs in landfill leachate and nearby groundwater. Environmental implicationsAntibiotic resistance genes are found abundant in the landfill sites, and these genes could be disseminated into groundwater via leaching of wastewater and infiltration of leachate. This results in deterioration of groundwater quality and human health risks posed by these ARGs and related pathogens. Thus measures should be taken to minimize potential negative impacts of landfills on the surrounding environment.

Critical loads for alkalization in terrestrial ecosystems

Critical loads are a risk assessment approach that has supported large decreases in atmospheric acidic deposition globally. In Canada, SOx emissions fell by approximately 70 % between 1990 and 2021, whereas total particulate matter (TPM) emissions increased by about 40 %, mostly after 2010. Base cations are a major component of TPM, and critical load models consider base cation deposition as beneficial to ecosystems insomuch as it reduces the risk of acidification. However, close to point sources, high levels of alkaline dust deposition have altered soil chemistry and caused an undesirable shift in ecosystem state; something that critical loads are designed to prevent. In this study, the simple mass balance model (SMB) was modified with the objective of preventing base cation accumulation in soil above an acceptable threshold. The concept was applied to a forested site close to large emission sources of sulphur, nitrogen, and base cations in the Oil Sands region of Alberta, Canada. At this site, base cation leaching measured at 25 cm was approximately three times higher than estimated background leaching and exceeded combined SO4 + NO3 leaching. The critical load for alkalization was exceeded under each scenario considered in this study, although the exceedance was marginal if all N in current deposition was assumed to leach from soil. While this framework can easily be applied to regional and national critical load efforts, the main uncertainties of the proposed approach include base cation deposition estimates, assumptions regarding the behavior of N in soil, the selection of an appropriate Alkle(crit) and the long-term immobilization of deposited base cations in soil.

Agent-based risk modeling of domino effects in urban LNG stations

As the transfer hub between natural gas and users, urban LNG stations (ULSs) are the important link of the whole LNG industry chain. Due to special geographical location and various process equipment, potential risks in ULSs, especially domino effect accidents, usually lead to catastrophic consequences and social impacts. In this study, a dynamic risk assessment model for analyzing domino effects in urban LNG stations (DRADL) based on agent modelling and simulation is proposed. Tanks and tank cars as well as vaporizers and boilers in ULSs are modeled. A set of physical process and effect models are employed as the simulation process in this paper. Considering the lightweight nature and narrow flammability limit of LNG vapor cloud, a cyclic process is applied to judge the consequences of vapor cloud dispersion, which could avoid overestimating the results. A real urban LNG station is taken as a case study for modeling its potential domino effect risk. The results demonstrate the effectiveness and the advantages of the proposed ABMS approach for LNG dynamic risk assessment. The proposed DRADL model takes into account the combined effects of explosion and fire and LNG characteristics, which can provide guidance for risk management and rescue operation of domino effect accidents in urban LNG stations.