Probabilistic Risk Model Research Articles

A New Approach Refined Probabilistic Health Risk Assessment of Shaoguan Smelter Based on Microenvironment - Guangdong Province, China, 2021.

This study introduces a novel method for developing an advanced exposure conceptual model tailored for health risk assessment, focusing on microenvironments. The research was conducted at a major smelter in China to assess the health risks associated with trace metals (TMs) pollutants in the facility and the surrounding soil. Deterministic risk assessment indicated that cobalt, cadmium, antimony, manganese, arsenic, plumbum, and mercury (Co, Cd, Sb, Mn, As, Pb, and Hg) necessitated further evaluation through probabilistic risk assessment to assess potential health risks to residents. The 95% quantile concentrations of other TMs were found to be within acceptable health risk limits. For the probabilistic risk assessment, exposure parameters such as body weight, respiration rate, and exposure duration were collected using a questionnaire. This targeted assessment of the residential microenvironment revealed it as the site of the highest carcinogenic (CR) and non-carcinogenic risks (NCR), with values ranging from 2.84×10-5 to 6.7×10-5 and 1.59 to 5.57, respectively. The primary contaminants posing the greatest health risks in residential and industrial areas have been identified as As, Pb, and Mn. The probabilistic health risk model, which focuses on microenvironmental factors, yields more precise results and offers a valuable tool for managing soil health risks.

Comparing components for seismic risk modelling using data from the 2019 Le Teil (France) earthquake

Abstract. Probabilistic seismic hazard and risk models are essential to improving our awareness of seismic risk, to its management, and to increasing our resilience against earthquake disasters. These models consist of a series of components, which may be evaluated and validated individually, although evaluating and validating these types of models as a whole is challenging due to the lack of recognized procedures. Estimations made with other models, as well as observations of damage from past earthquakes, lend themselves to evaluating the components used to estimate the severity of damage to buildings. Here, we are using a dataset based on emergency post-seismic assessments made after the Le Teil 2019 earthquake, third-party estimations of macroseismic intensity for this seismic event, shake maps, and scenario damage calculations to compare estimations under different modelling assumptions. First we select a rupture model using estimations of ground motion intensity measures and macroseismic intensity. Subsequently, we use scenario damage calculations based on different exposure models, including the aggregated exposure model in the 2020 European Seismic Risk Model (ESRM20), as well as different site models. Moreover, a building-by-building exposure model is used in scenario calculations, which individually models the buildings in the dataset. Lastly, we compare the results of a semi-empirical approach to the estimations made with the scenario calculations. The post-seismic assessments are converted to EMS-98 (Grünthal, 1998) damage grades and then used to estimate the damage for the entirety of the building stock in Le Teil. In general, the scenario calculations estimate lower probabilities for damage grades 3–4 than the estimations made using the emergency post-seismic assessments. An exposure and fragility model assembled herein leads to probabilities for damage grades 3–5 with small differences from the probabilities based on the ESRM20 exposure and fragility model, while the semi-empirical approach leads to lower probabilities. The comparisons in this paper also help us learn lessons on how to improve future testing. An improvement would be the use of damage observations collected directly on the EMS-98 scale or on the damage scale in ESRM20. Advances in testing may also be made by employing methods that inform us about the damage at the scale of a city, such as remote sensing or data-driven learning methods fed by a large number of low-cost seismological instruments spread over the building stock.

Cost-Effective Whole Transcriptome Sequencing Landscape and Diagnostic Potential Biomarkers in Active Tuberculosis.

Tuberculosis (TB) is a prevalent and severe infectious disease that poses a significant threat to human health. However, it is frequently disregarded as there are not enough quick and accurate ways to diagnose tuberculosis. Here, we develop a strategy for tuberculosis detection to address the challenges, including an experimental strategy, namely, Double Adapter Directional Capture sequencing (DADCSeq), an easily operated and low-cost whole transcriptome sequencing method, and a computational method to identify hub differentially expressed genes as well as the diagnosis of TB based on whole transcriptome data using DADCSeq on peripheral blood mononuclear cells (PBMCs) from active TB and latent TB or healthy control. Applying our approach to create a robust and stable TB multi-mRNA risk probability model (TBMMRP) that can accurately distinguish active and latent TB patients, including active TB and healthy controls in clinical cohorts, this diagnostic biomarker was successfully validated by several independent cross-platform cohorts with favorable performance in differentiating active TB from latent TB or active TB from healthy controls and further demonstrated superior or similar diagnostic accuracy compared to previous diagnostic markers. Overall, we develop a low-cost and effective strategy for tuberculosis diagnosis; as the clinical cohort increases, we can expand to different disease kinds and learn new features through our disease diagnosis strategy.

Seismic Risk Model for the Beijing–Tianjin–Hebei Region, China: Considering Epistemic Uncertainty from the Seismic Hazard Models

This study presents a probabilistic seismic risk model for the Beijing–Tianjin–Hebei region in China. The model comprises a township-level residential building exposure model, a vulnerability model derived from the Chinese building taxonomy, and a regional probabilistic seismic hazard model. The three components are integrated by a stochastic event-based method of the OpenQuake engine to assess the regional seismic risk in terms of average annual loss and exceedance probability curve at the city, province, and regional levels. The novelty and uniqueness of this study are that a probabilistic seismic risk model for the Beijing–Tianjin–Hebei region in China is developed by considering the impact of site conditions and epistemic uncertainty from the seismic hazard model.

Development of a probabilistic risk model for pharmaceuticals in the environment under population and wastewater treatment scenarios.

Preparing for future environmental pressures requires projections of how relevant risks will change over time. Current regulatory models of environmental risk assessment (ERA) of pollutants such as pharmaceuticals could be improved by considering the influence of global change factors (e.g., population growth) and by presenting uncertainty more transparently. In this article, we present the development of a prototype object-oriented Bayesian network (BN) for the prediction of environmental risk for six high-priority pharmaceuticals across 36 scenarios: current and three future population scenarios, combined with infrastructure scenarios, in three Norwegian counties. We compare the risk, characterized by probability distributions of risk quotients (RQs), across scenarios and pharmaceuticals. Our results suggest that RQs would be greatest in rural counties, due to the lower development of current wastewater treatment facilities, but that these areas consequently have the most potential for risk mitigation. This pattern intensifies under higher population growth scenarios. With this prototype, we developed a hierarchical probabilistic model and demonstrated its potential in forecasting the environmental risk of chemical stressors under plausible demographic and management scenarios, contributing to the further development of BNs for ERA. Integr Environ Assess Manag 2024;20:1715-1735. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Time-dependent probabilistic tsunami risk assessment: application to Tofino, British Columbia, Canada, subjected to Cascadia subduction earthquakes

A new time-dependent probabilistic tsunami risk model is developed to facilitate the long-term risk management strategies for coastal communities. The model incorporates the time-dependency of earthquake occurrence and considers numerous heterogeneous slip distributions via a stochastic source modeling approach. Tidal level effects are examined by considering different baseline sea levels. The model is applied to Tofino, British Columbia, Canada within the Cascadia subduction zone. High-resolution topography and high-quality exposure data are utilized to accurately evaluate tsunami damage and economic loss to buildings. The results are tsunami loss curves accounting for different elapsed times since the last major event. The evolutionary aspects of Tofino’s time-dependent tsunami risk profiles show that the current tsunami risk is lower than the tsunami risk based on the conventional time-independent Poisson occurrence model. In contrast, the future tsunami risk in 2100 will exceed the time-independent tsunami risk estimate.

Forestry impacts on stream flows and temperatures: A quantitative synthesis of paired catchment studies across the Pacific salmon range

Abstract Forestry is pervasive across temperate North America and may influence aquatic environmental conditions such as flows and temperatures, as well as important species such as Pacific salmon (Oncorhynchus spp.). While there have been many large‐scale forestry experiments using paired catchment designs, these studies have yet to be quantitatively synthesized. Thus, it remains unclear whether forestry impacts are consistent, context‐dependent or unpredictable. This study aims to quantitatively synthesize forestry impacts on streamflow and temperature, through a systematic review and synthesis of paired catchment studies across the range of Pacific salmon. Specifically, we investigated whether generalizable relationships exist between forestry intensity (percent watershed harvested) and impacts to streamflow and temperature. We also examined whether watershed features (climate, hydrology and lithology) and harvest method mediated forestry impacts. We extracted information from 35 unique paired‐catchments from California to Alaska. Forestry had strong impacts on peak and low flows and maximum summer water temperatures, but responses were quite variable. Across all catchments, forestry elevated peak flows ~20% (n = 31 catchments), reduced low flows ~25% (n = 13 catchments) and increased maximum summer temperatures ~15% (n = 35 catchments) on average. However, these impacts were variable and were not predictable based on forestry intensity, thus broader stressor–response relationships were not supported. Forestry impacts on peak flows and maximum summer temperatures varied spatially. Peak flow impacts increased with northward latitude and temperature impacts decreased with eastward longitude. However, the magnitude of impacts were unrelated to other watershed attributes, which included climate (precipitation and aridity), rain versus snow hydrology, elevation and bedrock lithology. Harvest method and riparian buffer presence also had no detected effects on forestry impacts across studies and statistical models explained a low proportion of variation overall. Collectively, our results indicate that forestry can have substantial impacts on key environmental conditions; however, the magnitude of impact was variable and could not be clearly linked to easily measured watershed characteristics. This implies that forestry impacts may not be broadly predictable. Probabilistic risk models based on distributions of potential impacts may therefore be more useful for watershed management in data‐poor situations.

Machine learning for the prediction of local asteroid damages

Risk assessment studies of local asteroid hazards traditionally simulate the physics of meteors with engineering models tailored to analyze tens-of-millions of scenarios. However, these simplified approaches still need to solve time-dependent ODEs to model the entry process and the resulting ground damage. With a computational cost of O(0.01 CPU.s) per scenario, simulating these large numbers of potential entry conditions can take several days on local computers. To improve computational efficiency, we propose in this paper an orthogonal approach based on machine learning models to predict the size of damaged areas given a list of entry parameters. We train 5 machine learning methods and compare the predictions to the outputs of NASA’s state-of-the-art Probabilistic Asteroid Impact Risk (PAIR) model. PAIR’s remarkable capability to handle a broad spectrum of entry conditions and a high volume of scenarios establishes it as our reference model, with PAIR estimates assumed ground truth and used to train, test, and validate the machine learning models. The ML models, initially trained with PAIR’s primitive input variables, are then further enhanced with engineered features to improve the performance of interpretable models. We find that complex models like neural networks are well-suited to estimate blast hazards, while simpler, interpretable linear models can accurately assess thermal damage. For both types of hazards, the radii of damaged areas can be predicted with less than 10% average relative errors for radii greater than 100 km. The CPU time is decreased by a factor O(103) compared to the PAIR model, enabling the simulation of millions of scenarios within minutes on a local computer. Beyond serving as surrogate models, we incorporate the machine learning models into a comprehensive Shapley sensitivity analysis, yielding a ranking of entry parameters based on their contributions to ground damages. These results offer valuable insights for prioritizing observation missions.

Sensitivity study of impact risk model results to thermal radiation damage model for large objects

NASA's Probabilistic Asteroid Impact Risk (PAIR) model assesses the likelihood of potential damage for asteroid impact scenarios. Fast-running models are used to capture the effects of different hazards. This paper looks specifically at local ground damage hazards, including blast overpressure and thermal radiation damage, for large object impact scenarios. A sensitivity study is conducted to determine which parameters, and over what ranges, cause impact risks to become sensitive to thermal damage. Two additional thermal models with different approaches are used for comparison. The study determined the current thermal model is most sensitive to the luminous efficiency parameter that reflects the model's uncertainty in the amount of energy contributing to the thermal radiation damage. This sensitivity was most apparent for the highest severity damage levels. Comparisons of the three models showed that, in addition to sensitivities within the models, the impact risks are also sensitive to the choice of thermal model. The study results were applied to the 2023 Planetary Defense Conference hypothetical asteroid impact scenario (800 m diameter, 10.29 Gt energy), and parameter ranges of interest were determined. At the serious damage level, luminous efficiencies above 0.006 (0.6%) showed a small chance of thermal playing an important role, while luminous efficiencies above 0.0008 (0.08%) led to thermal playing a significant role at the unsurvivable damage severity level. Study results are used to identify key areas where additional model refinement and better knowledge of asteroid properties may be important for improving damage estimates.

Model parameter influence on probabilistic flood risk analysis

Flood risk models are critical for effective evidence-based flood management interventions. Risk models typically analyse flood hazards, elements (e.g., buildings) exposed, and their susceptibility to monetary losses. These model parameters are inherently uncertain and their interactions within a risk model create an uncertain loss outcome. This study constructed an object-level probabilistic flood risk model for Westport (New Zealand) to analyse parameter uncertainty and sensitivities for residential building monetary loss prediction. Several model parameter combinations were assessed: 1) water depth spatial sampling method, 2) replacement valuation method, and 3) univariable and multivariable vulnerability model. Monetary loss uncertainty was analysed using test models that varied parameter combinations. Sensitivity to parameter uncertainty distributions were assessed using a global sensitivity analysis based on Sobol's method. Monetary loss estimates ranged between ± NZD 1 million to ± NZD 6 million (90 % confidence interval) for test models by varying parameter combinations. Loss estimates ranged from 2 % to 100 % across the analysed test models. Replacement valuation and vulnerability models were the primary drivers of loss uncertainty. Sobol first-order indices confirmed vulnerability models had the largest parameter influence on uncertainty while total-order indices showed replacement value has a larger overall contribution. The study outcomes emphasise flood risk modellers' need to investigate model uncertainties to inform accuracy improvement solutions.

Impact of the Uncertainty in the Parameters of the Earthquake Occurrence Model on Loss Estimates of Urban Building Portfolios

Abstract Understanding the potential socioeconomic losses due to natural hazards, such as earthquakes, is of foremost importance in the field of catastrophe risk management. The construction of a probabilistic seismic risk model is complex and requires the tuning of several parameters essential to represent the seismic hazard of the region, the definition of the exposed inventory characteristics, and its vulnerability to ground motion. Because significant uncertainties could be associated with each model component, the loss estimates are often highly volatile. Nevertheless, to reduce the conceptual complexity and the computational burden, in many real-life applications these uncertainties are either not adequately treated or neglected altogether. The false high fidelity of the ensuing loss estimates can mislead decision-making strategies. Hence, it is useful to assess the influence that the variability in the estimated values of the model input parameters may exert on the final risk results and their relevant contributions. To this purpose, we have performed a sensitivity analysis of the results of an urban seismic risk assessment for Isfahan (Iran). Systematic variations have been applied to the values of the parameters that control the earthquake occurrence in the probabilistic seismic hazard model. Curves of input–output relative variations were built for different risk metrics with the goal of identifying the parameters most sensitive to input uncertainty. Our findings can be useful to support risk managers and practitioners in the process of building seismic hazard and risk models. We found that the Gutenberg–Richter a and b values, the maximum magnitude, and the threshold magnitude are large contributors to the variability of important risk measures, such as the 475 yr and the average annual loss, with the more frequent losses being, in general, most sensitive.

Source apportionment and source specific health risk assessment of HMs and PAHs in soils with an integrated framework in a typical cold agricultural region in China

A new innovative methodology system framework for source apportionment and source-specific risk assessment has been proposed and actively applied to identify the contamination characteristics, oriented sources and health risks associated with contamination levels of Heavy metals (HMs) and Polycyclic Aromatic Hydrocarbons (PAHs) in soils, a typical cold agricultural region in Northeastern China. To achieve this meaningful goal, a large-scale dataset including 1780 top soil samples, 10 HMs and 16 priority PAHs has been organized and collected from a typical study area in China. The total concentrations of the 10 selected HMs in study area range from 0.05 to 2147.40 mg/kg, with an average of 549.25 ± 541.37 mg/kg. The average concentrations of PAHs for (3–6)-rings are 16.60 ± 18.90, 26.40 ± 28.20, 9.51 ± 13.00 and 1.99 ± 5.30 ng/g, respectively. On the base of optimized literature source fingerprints for HM and PAH, a widely used receptor model, positive matrix factorization (PMF) has been applied to apportion the contamination sources HMs and PAHs in soils. Then source-specific health risk of soil HMs and PAHs have been assessed using the probabilistic incremental lifetime cancer risk model incorporated with source apportionment results data. Fertilizer residues/coke oven comprise the primary contamination source contributors of HMs and PAHs with corresponding contributions of 32.23 % and 27.93 % for HMs and 37.94 % for PAHs. Fertilizer/pesticide residues contributes most to the risks of soil HMs (28.8 %), followed by fossil fuel combustion (24.6 %), mining activities (20.2 %), traffic and vehicle emission (16.3 %) and electroplating/dyeing (14.1 %). Meanwhile, the ranking of health risks from the five identified contamination sources of soil PAHs are resident discharge, coal-fired boilers, coke oven emission, gasoline combustion and power plant, with the contribution of 27.1 %, 25.3 %, 17.3 %, 15.5 % and 14.8 %.And relatively, source-specific risk assessment demonstrates fossil fuel and coal combustion contribute the greatest impact to the total risk of HMs and PAHs (61.7 % and 56.1 %), respectively. This study provides a good example of how the source specific health risk assessment can be utilized to reduce the contamination in soils.

Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation

Integration of Uncrewed Aircraft into unsegregated airspace requires robust and objective risk assessment in order to prevent exposure of existing airspace users to additional risk. A probabilistic Mid-Air Collision risk model is developed based on surveillance traffic data for the intended operational area. Simulated probable traffic scenarios are superimposed on a desired Uncrewed Aircraft operation and then sampled using Monte Carlo methods. The results are used to estimate the operation-specific collision probability with known uncertainty in the output. The methodology is demonstrated for an example medical logistics operation in the United Kingdom, and a Target Level of Safety is used as a benchmark to decide whether the operation should be permitted.

The transcriptional landscape and diagnostic potential of long non-coding RNAs in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically relevant biomarkers for early detection. Here, we comprehensively characterized the transcriptional landscape of long non-coding RNAs (lncRNAs) in paired tumor and normal tissue specimens from 93 ESCC patients, and identified six key malignancy-specific lncRNAs that were integrated into a Multi-LncRNA Malignancy Risk Probability model (MLMRPscore). The MLMRPscore performed robustly in distinguishing ESCC from normal controls in multiple in-house and external multicenter validation cohorts, including early-stage I/II cancer. In addition, five candidate lncRNAs were confirmed to have non-invasive diagnostic potential in our institute plasma cohort, showing superior or comparable diagnostic accuracy to current clinical serological markers. Overall, this study highlights the profound and robust dysregulation of lncRNAs in ESCC and demonstrates the potential of lncRNAs as non-invasive biomarkers for the early detection of ESCC.

A national seismic risk model for Canada: Methodology and scientific basis

Canada is exposed to rare but potentially destructive earthquakes that threaten densely settled metropolitan centers in many parts of the country. To assess the impacts and consequences of future natural-hazard events and help advance policy goals and objectives of the Sendai Framework for Disaster Risk Reduction, Natural Resources Canada, through a collaborative partnership with the Global Earthquake Model Foundation, produced a national seismic risk model. Developing this model has required the creation of a national exposure inventory, Canadian-specific fragility and vulnerability curves, and significant simplification of the Canadian Seismic Hazard Model which forms the basis for the design seismic hazard values of the National Building Code of Canada. Using the Global Earthquake Model Foundation’s OpenQuake Engine, probabilistic stochastic risk modeling is completed under baseline and simulated retrofit conditions to assess seismic risk at the neighborhood level for all settled areas in Canada. Output risk metrics include the expected immediate physical impacts of earthquake events such as building damage, casualties, and direct economic losses. This article documents the technical details of the modeling approach including a description of novel data sets in use, a summary of the extensive sensitivity testing undertaken, and characterization of quality control implemented in the absence of usable validating earthquake loss data. The results from this model, such as loss exceedance curves and annual average losses, provide an open, accessible and quantitative base of evidence for decision-making at local, regional, and national levels. As a large country with a complex seismic hazard model and dispersed populations, this Canadian study is unique. However, the challenges faced and solutions offered are likely to be of interest to other nations pursuing similar programs.

Crown snow load outage risk model for overhead lines

In the northern hemisphere, snow accumulating on trees and overhead lines causes widespread outages in the electricity distribution networks. Accurate outage risk models are an essential element in improving the resilience of modern distribution networks. In this paper, a Random Forest-based model for estimating the susceptibility of overhead lines to outages caused by tree crown snow loads is proposed. The model uses a novel combination of an aerial inspection outage risk dataset, an advanced forest crown snow load risk map, a canopy height model, and forest characteristics data. All predictor variables used in the study are available as open data. As a result, outage risk probability in 50 m overhead line sections for a distribution network was generated. Cross-validation of the model showed a good predictive performance with a receiver operating characteristic area under curve (ROC AUC) of 0.75 and an accuracy of 0.74. The impact of the predictor variables was investigated by using Shapley additive explanations (SHAP) values. The most impactful variables were the forest crown snow load risk, the number of nearby canopy height model pixels, and the birch tree volume. The outage risk probability model developed in this paper could be similarly applied to assess the crown snow load risk in other distribution networks or even in other types of networks, such as roads and railways.

Non-negligible health risks caused by inhalation exposure to aldehydes and ketones during food waste treatments in megacity Shanghai

Aldehydes and ketones in urban air continue to receive regulatory and scientific attention for their environmental prevalence and potential health hazard. However, current knowledge of the health risks and losses caused by these pollutants in food waste (FW) treatment processes is still limited, especially under long-term exposure. Here, we presented the first comprehensive assessment of chronic exposure to 21 aldehydes and ketones in urban FW-air environments (e.g., storage site, mechanical dewatering, and composting) by coupling substantial measured data (383 samples) with Monte Carlo-based probabilistic health risk and impact assessment models. The results showed that acetaldehyde, acetone, 2-butanone and cyclohexanone were consistently the predominant pollutants, although the significant differences in pollution profiles across treatment sites and seasons (Adonis test, P < 0.001). According to the risk assessment results, the estimated cancer risk (CR; mean range: 1.6 × 10−5-1.12 × 10−4) and non-cancer risk (NCR; mean range: 2.98–22.7) triggered by aldehydes and ketones were both unacceptable in most cases (CR: 37.8%–99.3%; NCR: 54.2%–99.8%), and even reached the limit of concern to CR (1 × 10−4) in some exposure scenarios (6.18%–16.9%). Application of DALYs (disability adjusted life years) as a metric for predicting the damage suggested that exposure of workers to aldehydes and ketones over 20 years of working in FW-air environments could result in 0.02–0.14 DALYs per person. Acetaldehyde was the most harmful constituent of all targeted pollutants, which contributed to the vast majority of health risks (>88%) and losses (>90%). This study highlights aldehydes and ketones in FW treatments may be the critical pollutants to pose inhalation risks.

Security-Aware and Time-Guaranteed Service Placement in Edge Clouds

Most of the emerging applications such as the Deep Neural Networks (DNN) based smart Internet of Things (IoT) systems need intensive and high-performance computing, which is contradictory to the limited resources of IoT/terminal devices. It is a big challenge to offload all tasks to the cloud due to the bandwidth limitation, processing overhead, and transmission costs. Edge computing as an extension of cloud computing that can provide abundant computing resources near the edge of the network and thereby can potentially improve the QoS of applications. However, offloading tasks to the edge servers is liable to external security threats. How to balance the response time and the security of application services is a big challenge for realizing good application service placement. This paper proposes a time and security efficient task scheduling framework in the edge-cloud environment. The corresponding computing models are established, such as the security-related model, time model, and the risk probability model. Then, a time-guaranteed and security-aware task scheduling algorithm is proposed including the domain construction, the security-aware task ranking, and the task dispatching. Extensive simulation experiments have been conducted. Results show that the proposed method has better performance than the other four compared methods in general.

Status assessment and probabilistic health risk modeling of polycyclic aromatic hydrocarbons (PAHs) in surface soil across China

Polycyclic aromatic hydrocarbon (PAH) accumulation in topsoil is getting particular concern with the rapid development of urbanization and industrialization, while the overall pollution status and related risk posed by PAHs received limited attentions at the national scale. This study conducted an overview of published data on 16 priority control PAHs by USEPA on the related peer-reviewed 207 research papers between 2000 and 2020 in 30 provinces of China. Based on that, the pollution levels, composition status, spatial distribution pattern, ecological risk, and human health risk posed by soil PAHs were evaluated. Monte Carlo simulation was adopted to model the probabilistic health risk and identify the contributor of such risks. Results demonstrated the concentrations of ∑16PAHs in soil varied from “undetected” to 261 μg g−1 with a mean value of 0.63 μg g−1, indicating the obvious accumulation of PAHs in topsoil of most provinces in China compared with the guideline value (0.2 μg g−1). The concentrations of ∑16PAHs in surface soil of China has obvious regional characteristics. Higher concentrations of soil PAHs are mainly distributed in north, northwest, and eastern regions, especially in Xinjiang, Shandong, Jiangsu, Sichuan, and Guangxi Province. Risk assessment indicates potential ecological and human health risk were posed by soil PAHs, therefore, reducing soil PAHs concentration and exposure frequency are the most effective pathways to protect human health. Despite the fact that risks posed by soil PAHs are generally low, concentrations of PAHs in some sites are relatively high. It is necessary to take effective measures to remediate soil PAHs pollution in certain areas to reduce concentration and associated risks.

Probabilistic Seismic Multi-hazard Risk and Restoration Modeling for Resilience-informed Decision Making in Railway Networks

ABSTRACT Transportation systems, such as railways, are considered critical infrastructure. It is essential to identify potential hazards that can affect the functionality of these systems and quantify metrics that can be used for resilience-informed decision-making. This paper develops an integrated probabilistic model for seismic multi-hazard risk and restoration assessment of railway systems by accounting for the effects of seismic waves propagation, liquefaction and landslide as main phenomena affecting the integrity of distributed networked infrastructure; this is done via a GIS-based user interface. An illustrative case study is then presented to assess the seismic risk and restoration of the Tehran-Sari railway in Iran. The implementation results demonstrate the capability of the presented methodology to quantify physical metrics (including combined damage state of network components, component- and system-level functionality and restoration) and economic loss. These metrics can assist officials with implementing retrofit plans to reduce loss and improve the resilience of railway system segments.