Quantitative Risk Assessment Approach Research Articles

Salmonella Prevalence in Raw Cocoa Beans and a Microbiological Risk Assessment to Evaluate the Impact of Cocoa Liquor Processing on the Reduction of Salmonella

Salmonella in raw cocoa beans (n = 870) from main sourcing areas over nine months was analyzed. It was detected in 71 (ca. 8.2%) samples, with a contamination level of 0.3–46 MPN/g except for one sample (4.1 × 104 CFU/g). Using prevalence and concentration data as input, the impact of thermal treatment in cocoa processing on the risk estimate of acquiring salmonellosis by a random Belgian chocolate consumer was calculated by a quantitative microbiological risk assessment (QMRA) approach. A modular process risk model from raw cocoa beans to cocoa liquor up to a hypothetical final product (70–90% dark chocolate tablet) was set up to understand changes in Salmonella concentrations following the production process. Different thermal treatments during bean or nib steam, nib roasting, or liquor sterilization (achieving a 0–6 log reduction of Salmonella) were simulated. Based on the generic FAO/WHO Salmonella dose–response model and the chocolate consumption data in Belgium, salmonellosis risk per serving and cases per year at population level were estimated. When a 5 log reduction of Salmonella was achieved, the estimated mean risk per serving was 3.35 × 10−8 (95% CI: 3.27 × 10−10–1.59 × 10−7), and estimated salmonellosis cases per year (11.7 million population) was 88 (95% CI: <1–418). The estimated mean risk per serving was 3.35 × 10−9 (95% CI: 3.27 × 10−11–1.59 × 10−8), and the estimated salmonellosis cases per year was 9 (95% CI: <1–42), for a 6 log reduction. The current QMRA model solely considered Salmonella reduction in a single-step thermal treatment in the cocoa process. Inactivation obtained during other process steps (e.g. grinding) might occur but was not considered. As the purpose was to use QMRA as a tool to evaluate the log reduction in the cocoa processing, no postcontamination from the processing environment and ingredients was included. A minimum of 5 log reduction of Salmonella in the single-step thermal treatment of cocoa process was considered to be adequate.

Salmonella prevalence in raw cocoa beans and a microbiological risk assessment to evaluate the impact of cocoa liquor processing on the reduction of Salmonella

Salmonella in raw cocoa beans (n= 870) from main sourcing areas over nine months was analyzed. It was detected in 71 (ca. 8.2%) samples, with a contamination level of 0.3-46 MPN/g except for one sample (4.1×104 CFU/g). Using prevalence and concentration data as input, the impact of thermal treatment in cocoa processing on the risk estimate of acquiring salmonellosis by a random Belgian chocolate consumer was calculated by a quantitative microbiological risk assessment (QMRA) approach. A modular process risk model from raw cocoa beans to cocoa liquor up to a hypothetical final product (70-90% dark chocolate tablet), was set up to understand changes of Salmonella concentrations following the production process. Different thermal treatments during bean or nib steam, nib roasting or liquor sterilization (achieving a 0-6 log reduction of Salmonella) were simulated. Based on the generic FAO/WHO Salmonella dose-response model and the chocolate consumption data in Belgium, salmonellosis risk per serving and cases per year at population level were estimated. When a 5 log reduction of Salmonella was achieved, the estimated mean risk per serving was 3.35×10-8 (95% CI: 3.27×10-10-1.59×10-7), and estimated salmonellosis cases per year (11.7 million population) was 88 (95% CI: <1-418). The estimated mean risk per serving was 3.35×10-9 (95% CI: 3.27×10-11-1.59×10-8), and the estimated salmonellosis cases per year was 9 (95% CI: <1-42), for a 6 log reduction. The current QMRA model solely considered Salmonella reduction in a single-step thermal treatment in the cocoa process. Inactivation obtained during other process steps (e.g. grinding) might occur but was not considered. As the purpose was to use QMRA as a tool to evaluate the log reduction in the cocoa processing, no post-contamination from the processing environment and ingredients was included. A minimum of 5 log reduction of Salmonella in the single-step thermal treatment of cocoa process, was considered to be adequate.

FAIR: Cyber Security Risk Quantification In Logistics Sector

For many industries, the fourth industrial revolution has brought technological advancements in the form of advances in the integration of physical and digital technologies, particularly for cyber-physical systems, that go far beyond speed, scalability, storage, and cost-effectiveness. Smart manufacturing, smart factories, smart warehousing, and smart logistics are every aspect of the enabling Fourth Industrial Revolution. As a result of these improvements and advantages, new attack surfaces have been created that benefit malicious actors. For effective cyber security risk management in the face of converging cyber attacks, it is essential to quantify cyber risks across or within organisations that estimate cyber security risks in monetary value. This study explored and integrated the FAIR methodology (a well-recognized approach for quantitative cyber security risk assessment) to quantify cyber risk. A case study was conducted with one of the largest logistics companies in Scandinavia.

A Comprehensive Approach to Quantitative Risk Assessment of Rockfalls on Buildings Using 3D Model of Rockfall Runout

Rockfalls are incidents of nature that take place when rocks or boulders break from a steep slope and fall to the ground. They can pose considerable threats to buildings placed in high-risk zones. Despite the fact that the impact of a rockfall on a building can cause structural and non-structural damage, few studies have been undertaken to investigate the danger associated with this event. Most of these studies indicated that the risk resulting from rockfall hazards is hard to forecast and assess. A comprehensive quantitative risk assessment approach for rockfalls on buildings is developed and described in this paper and applied for the Mtein village in Mount Lebanon. This method employs a 3D model to simulate the rockfall trajectories using a combination of digital elevation data, field surveys, and orthorectified aerial photographs. The spatial and temporal probability of rockfalls were evaluated using the analysis of historical data in two triggering-factor scenarios: earthquake and precipitation. The findings show that, during the period of 1472 years between the years 551 (the first observed large earthquake in Lebanon) and the current year of the study (2023), the temporal probability will potentially be equal to 0.002 and 0.105 in the cases of earthquake- and rainfall-triggered rockfalls, respectively, while the maximal damage values are expected to be 232 USD and 10,511 USD per year, respectively. The end result is a final map presenting the risk values assigned to each building that could be damaged by rockfalls.

Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment

Abstract Identifying, analyzing, and evaluating cybersecurity risks are essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach is lacking to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors’ techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates the systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.

CYBERSECURITY RISK ASSESSMENT IN BANKING: METHODOLOGIES AND BEST PRACTICES

Cybersecurity risk assessment in banking is the process of identifying, analyzing, and evaluating the cyber threats and vulnerabilities that may affect the confidentiality, integrity, and availability of the information systems and data of banks and their customers. Cybersecurity risk assessment in banking helps banks to prioritize and implement appropriate controls and measures to mitigate the cyber risks and to comply with the relevant regulations and standards. This study focusses on identifying effective risk assessment strategies, highlighting how they can be adapted and applied in various banking environments, especially in developing economies like Nigeria. As the banking industry continues to evolve in the digital era, the significance of robust cybersecurity measures cannot be overstated. This paper delves into the critical domain of Cybersecurity Risk Assessment in Banking, exploring various methodologies and best practices employed to safeguard financial institutions against evolving cyber threats. The dynamic landscape of cyber risks faced by banks, ranging from sophisticated malware and phishing attacks to insider threats and system vulnerabilities are examined. The paper provides an in-depth analysis of established and emerging methodologies for conducting effective cybersecurity risk assessments in the banking sector. It explores quantitative and qualitative risk assessment approaches, threat modeling, and scenario analysis, shedding light on their respective strengths and limitations. Moreover, the document highlights the importance of aligning risk assessment methodologies with industry regulations and compliance standards to ensure a comprehensive and regulatory-compliant cybersecurity framework. Best practices for cybersecurity risk management in banking are scrutinized, emphasizing the integration of proactive threat intelligence, continuous monitoring, and incident response planning. The role of advanced technologies, including artificial intelligence and machine learning, in enhancing the efficiency of risk assessment processes is also discussed. Furthermore, the paper addresses the human element in cybersecurity, emphasizing the significance of training and awareness programs to mitigate risks associated with human error and social engineering attacks. By synthesizing insights from industry practices, regulatory guidelines, and technological advancements, this paper offers a comprehensive guide for banking professionals, cybersecurity practitioners, and policymakers involved in fortifying the resilience of financial institutions against cyber threats. Ultimately, the research aims to contribute to the ongoing discourse on cybersecurity risk assessment in banking, providing actionable insights to navigate the complex landscape of digital risks and ensuring the continued trust and security of the financial ecosystem.&#x0D; Keywords: Cybersecurity; Risk Assessment; Banking; Methodologies; Cyber Threat; Artificial Intelligence; Best Practices

Quantitative health risk assessment of microbial hazards from water sources for community and self-supply drinking water systems

In low and medium income countries (LMIC) drinking water sources (wells and boreholes) often contain a high number of pathogenic microorganisms, that can pose significant human and environmental health risks. In this study, a quantitative microbial risk assessment approach based on existing literature was conducted to evaluate and compare the quantitative health risks associated with different age groups using various drinking water supply systems. Results showed that both community-supply and self-supply modes exhibit similar levels of risk. However, the self-supply water source consistently showed higher risks compared to the community-supply one. Borehole water was found to be a more suitable option than well water, consistently showing between 5 and 8 lower health risks for E. coli and fecal coliform levels, respectively. The sensitivity analysis further showed the importance of prioritizing the reduction of E. coli concentration in well water and fecal coliform concentration in borehole water. This study offers a fresh perception on quantifying the impact of exposure concentration and age groups, shedding light on how they affect environmental health risks. These findings provide valuable insights for stakeholders involved in the management and protection of water sources.

Failure probability analysis of hydrogen doped pipelines based on the Bayesian network

Damage from hydrogen, such as hydrogen embrittlement in natural gas pipelines, is a possibility. There are currently lacking ways for calculating the failure of hydrogen doped pipelines quantitatively; instead, research on the safety of them is mostly concentrated on hydrogen compatibility tests and consequence analysis. A method for quantifying hydrogen doped pipelines losses using Bayesian network-based failure probability analysis is presented. The influence of hydrogen on pipeline failure is introduced using the expert scoring method based on the analysis of historical failure data for gas pipelines. The failure probabilities of various components of hydrogen doped pipelines are then quantified by combining fuzzy mathematical theory and hierarchical analysis, which is subsequently utilized as prior probabilities to input into the Bayesian network for quantitative calculation. The results show that the Bayesian network can more accurately quantify the failure probability of hydrogen doped pipelines. They also show that the Bayesian network can provide a practical approach for quantitative risk assessment and safety evaluation, as well as a decision basis for the prevention of hydrogen doping pipeline failure accidents.

APPLICATION OF QMRA TO EVALUATE THE POTENTIAL HEALTH RISKS ASSOCIATED WITH BACTERIAL CONTAMINATION OF WATERS IN ROYAL BELUM STATE PARK, MALAYSIA

Temenggor Lake located in Royal Belum State Park is an important water resource for indigenous people, an ecosystem for aqua organisms and a place for water recreational activities for visitors. The aim of this research was to use a quantitative microbial risk assessment (QMRA) approach to calculate the probability of potential pathogen infection risk to the visitors and the indigenous community. Thirty-six water samples were collected from the surface waters of Temenggor Lake from recreational water activity and indigenous people activity sites from November 2020 till December 2021. Membrane filtration technique was used for bacterial quantification. The risk was estimated using the calculation of the daily risk of infection, annual risk of infection, and risk of illness per annum and per event. The water quality during sampling periods at year-end showed higher bacterial concentrations related to rainfall events. Bacterial concentrations at visitor water activity areas were within Category A allowing for recreational activities with full body contact while water quality at indigenous people water activity areas were classified as Category B to be used mainly for secondary body contact and hence not allowed for swimming and bathing. Notably, indigenous people's settlement areas recorded a higher risk of illness for both E. coli and Salmonella spp., mainly at station A03 (Kg. Sungai Tiang). It has been revealed that water quality in indigenous people and visitor water activity areas were significantly different due to being affected by different types of human activity and other factors such as geohydrological conditions, topography, land use, environmental conditions and events. This study provides baseline data to help in future management strategies to ensure the conservation of the natural landscape and economic viability, in addition to the protection of the local community.

Effects of adsorption and filtration processes on greywater microbiological contamination and the potential human health risk reduction

Abstract Recycling treated greywater (GW) for onsite, non-potable applications can reduce the potable water demand typically used for non-potable purposes. The conventional methods for GW treatment are limited in their ability to remove wide-ranging pollutants in ways that are inexpensive and use low energy. For this reason, effective and low-cost onsite treatment options are in demand. This study examines the effectiveness of sand filtration (SF), granulated blast furnace slag (GBFS), and activated carbon (AC) in the treatment of GW from a residential apartment building in Sharjah, United Arab Emirates. The study relies on four different pilot-scale experimental setups to investigate the effectiveness of SF, AC, and GBFS in treating microorganisms from GW and evaluate the microbial risk reduction using these treatment processes. A quantitative microbial risk assessment (QMRA) approach is used for risk assessment. Results show that GBFS achieves a higher reduction of total coliform (TC) (0.54–2.05 log removal) and fecal coliform (FC) (1.96–2.30 log removal) than AC. SF improves reduction by 0.13–3.39 log removal and 1.11–3.68 log removal for TC and FC, respectively. The study also reveals substantial FC and Escherichia coli risk reduction by SF, AC and GBFS.

Evaluating infection risks and importance of hand hygiene during the household laundry process using a quantitative microbial risk assessment approach

Contaminated laundry contributes to infectious disease spread in residential and home health care settings. The objectives were to (1) evaluate pathogen transmission risks for individuals doing laundry, and (2) compare hand hygiene timing to reduce risks. A quantitative microbial risk assessment using experimental data from a laundry washing effectiveness study was applied to estimate infection risks from SARS-CoV-2, rotavirus, norovirus, nontyphoidal Salmonella, and Escherichia coli in 4 laundry scenarios: 1 baseline scenario (no hand hygiene event) and 3 hand hygiene scenarios (scenario 1: after moving dirty clothes to the washing machine, scenario 2: after moving washed clothes to the dryer, and scenario 3: hand hygiene events following scenario 1 and 2). The average infection risks for the baseline scenario were all greater than 2 common risk thresholds (1.0×10-6and 1.0×10-4). For all organisms, scenario 1 yielded greater risk reductions (39.95%-99.86%) than scenario 2 (1.35%-55.25%). Scenario 3 further reduced risk, achieving 1.0×10-6(SARS-CoV-2) and 1.0×10-4risk thresholds (norovirus and E. coli). The modeled results suggest individuals should reduce hand-to-facial orifice (eyes, nose, and mouth) contacts and conduct proper hand hygiene when handling contaminated garments. More empirical data are needed to confirm the estimated risks. The data and code that support the findings of this study can be retrieved via a Creative Commons Zero v1.0 Universal license in GitHub at https://github.com/yhjung1231/Laundry-QMRAproject-2022.git DOI: http://doi.org/10.5281/zenodo.7122065.

A quantitative risk assessment approach for developing contingency plans at a geologic carbon storage site

AbstractGeologic carbon storage (GCS) is an increasingly important technology for reducing carbon dioxide (CO2) emissions to the atmosphere. The leakage risks associated with GCS are an environmental and human health concern, however, and site operators must develop contingency plans that thoroughly consider leakage risks and identify potential mitigation strategies. Here, we use a GCS system model (the National Risk Assessment Partnership's Open‐Source Integrated Assessment Model, NRAP‐Open‐IAM) to evaluate different contingency plans for a hypothetical GCS site. In the scenario considered, an unplugged legacy well is discovered near the site after 5 years of CO2 injection. Our simulations show that the planned operation has a relatively high chance of causing brine leakage through the legacy well and into the two overlying aquifers, the shallower of which has potable water—an unacceptable outcome. To reduce this risk, we consider five remedial response scenarios that manipulate reservoir pressures through brine extraction, injection rate reduction, and early injection stopping. NRAP‐Open‐IAM is used to quantify the degree to which each scenario reduces the probability of brine leakage at the site amidst reservoir uncertainty. Evaluation of the different scenarios suggests that reduction of injection rates effectively reduces leakage risks while maintaining a substantial fraction of the initially intended cumulative CO2 storage. In the event of an emergency, the reservoir pressure management strategies considered here can provide operators more time while they pursue a more permanent solution. The analyses demonstrated here fit into a larger workflow we propose for evaluating the contingency plans of GCS sites. © 2023 Society of Chemical Industry and John Wiley &amp; Sons, Ltd.

Evaluation of Seasonal Variation on the Health Risks Using the Quantitative Microbial Risk Assessment Approach in a Wastewater Treatment Plant in Hamadan, Iran.

Wastewater treatment plants (WWTPs) are a source of airborne bacterial contamination that can pose health risks to staff. The aim of this study was to evaluate seasonal variations in the health risks of exposure to Staphylococcus aureus bioaerosols using the quantitative microbial risk assessment (QMRA) approach in a WWTP in Hamadan, Iran. This was a descriptive cross-sectional study. This study determined the emission concentrations of S. aureus bioaerosols in summer and winter. Then, the health risks of three exposure scenarios (the worker, field engineer, and laboratory technician) were evaluated using the QMRA approach. The bioaerosol samples were collected every 12 days in both summer and winter of 2021 with a nutrient agar using a single-stage cascade impactor (Quick Take 30, SKC Inc.) in both outdoor and indoor environments. The results demonstrated that in both seasons, S. aureus bioaerosol concentrations in outdoor and indoor environments were below the standard established by the American Conference of Governmental Industrial Hygienists (500 CFU/m3 ). While in summer, the annual infection risks and the disease burden for the three exposure scenarios in both outdoor and indoor environments were higher than the United States Environmental Protection Agency (≤10-4 pppy) and the World Health Organization (WHO) (≤10-6 DALYs pppy-1) benchmarks, respectively. The findings provided high health risks for staff in the three exposure scenarios of an indoor environment, which should not be ignored, as well as emphasizing the use of the QMRA approach to estimate health risks caused by occupational exposure to bioaerosols and taking executive measures to protect staff working in the WWTPs.

Quantitative Risk Assessment Approach to a Dynamic Safety Evaluation: Skikda’s Coastal City Liquefied Gas Plant

Quantitative Risk Assessment Approach to a Dynamic Safety Evaluation: Skikda’s Coastal City Liquefied Gas Plant

Transport and removal of spores of Bacillus subtilis in an alluvial gravel aquifer at varying flow rates and implications for setback distances

To guarantee proper protection from fecally transmitted pathogen infections, drinking water wells should have a sufficiently large setback distance from potential sources of contamination, e.g. a nearby river. The aim of this study was to provide insight in regards to microbial contamination of groundwater under different flow velocities, which can vary over time due to changes in river stage, season or pumping rate. The effects of these changes, and how they affect removal parameters, are not completely understood. In this study, field tracer tests were carried out in a sandy gravel aquifer near Vienna, Austria to evaluate the ability of subsurface media to attenuate Bacillus subtilis spores, used as a surrogate for Cryptosporidium and Campylobacter. The hydraulic gradient between injection and extraction was controlled by changing the pumping rate (1, 10 l/s) of a pumping well at the test site, building upon previously published work in which tracer tests with a 5 l/s pumping rate were carried out. Attachment and detachment rate coefficients were determined using a HYDRUS-3D model and ranged from 0.12 to 0.76 and 0–0.0013 h−1, respectively. Setback distances were calculated based on the 60-day travel time, as well as a quantitative microbial risk assessment (QMRA) approach, which showed similar results at this site; around 700 m at the highest pumping rate. Removal rates (λ) in the field tests ranged from 0.2 to 0.3 log/m, with lower pumping rates leading to higher removal. It was shown that scale must be taken into consideration when determining λ for the calculation of safe setback distances.

Assessment microbial risks for the presence of Cryptosporidium spp. and Giardia spp. based on the surveillance of the water supply systems in Colombia, 2014–2018

AbstractCryptosporidium spp. and Giardia spp. cause gastrointestinal diseases of zoonotic origin as well transmitted from person to person, being various reported outbreaks associated with water. The infecting (oo)cyst forms of these parasites are highly resistant to water treatments such as chlorine disinfection and fast filtration. The objective of this study was to assess the microbial risk of infection and symptomatic illness by the ingestion of Giardia spp. and Cryptosporidium spp. in water for human consumption in Colombia, based on the results of water quality surveillance. The detection method was according to the USEPA method 1623. Concentration data of the different points of distribution were grouped according to the pathogen and type of treatment (no treatment; chlorine treatment; chlorine treatment + coagulant). Annual microbial risks of infection and symptomatic diseases were estimated using the quantitative microbial risk assessment approach that included parasite concentrations, the dose–response model, the ingestion rates of water by children and adults, and the morbidity rate of the diseases. The mean annual microbial risk of infection for Giardia spp. was 29.8% for treated water and 50.4% for untreated water, while being 6.0% and 17.7%, respectively, for Cryptosporidium spp. Microbial risk of symptomatic illness for Giardia spp, was 8.2% for treated water and 13.9% for untreated water, while being 3.6% and 10.6%, respectively, for Cryptosporidium spp. The estimated annual microbial risks of infection exceeded the acceptable value of 10−4 (0.01%) recommended by USEPA. Results obtained in this study suggest the need to reduce the microbial risk of infection to protozoan parasites by improving the water treatment, by adopting better handling practices for livestock manure and treatment processes of human feces.Practitioner Points The presence of Cryptosporidium spp was identified in 28 (6.2%) samples and Giardia spp in 29 (6.4%) in water for human consumption in Colombia. The mean annual risk of symptomatic illness due to infection by Giardia spp or Cryptosporidium spp ranges from 33.6%, for treated water, to 58.1%, for untreated water. Annual risks ingestion of protozoa studying in water for human exceed of 10‐4 (0.01%) recommended by USEPA.

A new approach for quantitative risk assessment of gas explosions on FPSO

Gas explosions on floating, production, storage and off-loading units (FPSO) cause catastrophic consequences. It is vital to perform an accurate risk assessment for preventing these hazards. This paper presents a comprehensive quantitative risk assessment approach for evaluating the whole gas explosion process from initial leakage to final explosion. A stochastic sampling method is proposed to determine the explosion scenarios by using historical statistics data. Secondly three operational barriers: gas detection, emergency shutdown and ignition prevention are arranged to prevent the development from leakage to explosion. The sequential Bayesian Network (BN) predictive model is constructed to describe the hazard development by considering Human and organizational factors (HOFs). As low as reasonably practicable (ALARP) principle is performed to assess the explosion risks. The proposed approach has been applied to a case study focusing on estimating the risk levels for humans and constructions. It turns out that HOFs have significant effect on the normal functions for operational barriers, if ignoring the HOFs effect the accident risks will be underestimated. This approach is especially good at updating the results whenever new data becomes available. Several safety measures can be recommended based on the diagnostic analysis of BN.

Human health risk assessment for (re)emerging protozoan parasites in surface water used for public supply and recreational activities

Gastrointestinal diseases caused by protozoan parasites remain a major challenge in developing countries and ingestion of contaminated surface water represents one of the main sources by which these diseases are contracted. This study assessed the risk of infection and diseases caused by Cryptosporidium spp. and Giardia sp. due to ingestion of surface water used for public supply and recreational activities, focusing on the southeastern Brazilian Pardo River and applying the USEPA 1623 method to quantify (oo)cyst concentrations. Infection and disease probabilities due to ingestion of drinking water or during recreational activities were estimated using the Quantitative Microbial Risk Assessment (QMRA) approach. Mean concentrations of Cryptosporidium spp. and Giardia sp. in surface water ranged from 0.2 to 0.4 oocysts L-1 and 0.2 to 4.4 cysts L-1, respectively. Considering public water supply, annual infection probabilities were higher for adults than children and exceeded the USEPA limit; also, disease probabilities were higher for adults than children. For recreational activities, annual infection and disease probabilities were higher for children, followed by men and women. The occurrence of both parasites likely reflects raw sewage discharge, effluent from sewage treatment plants, and diffuse sources of pollution, such as runoff from pasture lands and deforested riparian forest corridors. Our results highlight substantial infection risks by both parasite types after conventional treatment of water used for public supply and also call for careful monitoring of water bodies used for recreational purposes.

A methodological approach of QRA for slow-moving landslides at a regional scale

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km2, Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results.

Quantitative Risk Assessment of Population Affected by Tropical Cyclones Through Joint Consideration of Extreme Precipitation and Strong Wind—A Case Study of Hainan Province

AbstractTropical cyclone disasters frequently occur in the southeastern coastal areas of China, and the concentration of population and economy has further increased the risk of tropical cyclone disasters in this region. The effects of tropical cyclones on populations involve a complex interaction between hazard intensity, environmental factors, exposure level, and fortification capacity. Therefore, quantifying the relationship among these factors is imperative for early warnings, risk assessments, and mitigation strategies for tropical cyclone disasters. In this study, a comprehensive tropical cyclone quantitative risk assessment approach considering the joint relation between strong wind and extreme precipitation and environmental, exposure and fortification factors was developed using the generalized additive model (GAM) and copula function for the case of Hainan province, China. The 10 min maximum wind speed (MW), maximum daily rainfall (MR), elevation (ELE), total population (POP), and time (t) were ultimately selected by the GAM as explanatory variables for the affected population rate (APR). A 50% increase in theMWproduced a 48.779%–82.657% increase in theAPR, a 50% increase in theMRproduced a 19.534%–25.631% increase in theAPR, and a 50% increase in the POP produced a 0.796%–15.193% decrease in theAPR. TheAPRobviously increased with the return period increase in Hainan province. TheAPRof cites (counties) ranged from 0.443 to 0.632 for a once‐in‐10‐year return period and ranged from 0.518 to 0.829 for a once‐in‐50‐year return period.