Risk Prioritization Research Articles

Failure mode and Effect Analysis of personal fall arrest system under the intuitionistic fuzzy environment

Background and aimsIntuitionistic fuzzy sets (IFS) theory is more powerful than classic fuzzy sets theory in handling uncertainty. A new approach for Failure Mode and Effect Analysis (FMEA) was developed based on IFS and group decision-making (known as IF-FMEA) for investigating Personal Fall Arrest System (PFAS). MethodFMEA parameters, including occurrence, consequence, and detection, were re-defined based on a seven-point linguistic scale. Each linguistic term was associated with an intuitionistic triangular fuzzy set. Opinions on the parameters were gathered from a panel of experts, integrated using the similarity aggregation method, and defuzzified utilizing the center of gravity approach. ResultsNine failure modes were identified and analyzed using both FMEA and IF-FMEA. The risk priority numbers (RPNs) and prioritization obtained from the two approaches were different, highlighting the importance of using IFS. The highest RPN was associated with the lanyard web failure, while the failure of the anchor D-ring had the least RPN. Detection score was higher for metal parts of the PFAS, suggesting that failures in these parts are harder to detect. ConclusionIn addition to being economical in terms of calculations, the proposed method was efficient in handling uncertainty. Different parts of a PFAS create different levels of risk.

Occurrence and multi-pathway health risk assessment of trihalomethanes in drinking water of Wuxi, China

Chlorination is widely used to disinfect drinking water to keep humans safe from microorganisms. During chlorination, chlorine and its compounds react with contaminants to form disinfection by-products (DBPs). Toxicological and epidemiological studies have demonstrated that trihalomethanes (THMs) are the most widely investigated DBPs in drinking water, and their exposure has been associated with some adverse health effects. However, studies about risk characteristics in this field are limited. We estimated the health risks of THMs exposure in drinking water through multi-pathways, and systematically analyzed the factors influencing health risks of THMs in Wuxi, China. A total of 488 drinking water samples were collected and analyzed for THMs from four water treatment utilities from 2008 to 2016 in Wuxi. And water exposure parameters were obtained from 602 participants by structured questionnaires. The median concentration of THMs ranged from 6.71 μg/L to 9.18 μg/L. The cumulative cancer risk of THMs exposure through multi-pathways was 1.26 × 10−4, and CHBr2Cl made the largest contribution to the total cancer risk (48.25%). The non-cancer risk of THMs exposure was 2.02 × 10−1. Health risks of the exposure to THMs in drinking water in summer were significantly higher than that in winter (P = 0.0003 for cancer risk, and P = 5.95 × 10−7 for non-cancer risk). In our study, the average individual disability-adjusted life years (DALYs) lost was 1.27 × 10−4 per person-year (ppy). This study attempted to use DALYs for risk assessment of THMs, which will provide useful information for risk comparison and prioritization of hazards in drinking water. This suggested that potential higher risk might exist, and possible measures could be considered to decrease the health risks.

Risk Prioritizing Method at the Stage of Qualitative Risk Analysis in the Process of Project Implementation

Current trends in project management include risk management in the process of project implementation. After identifying the risks of the project, the task arises to identify the most dangerous risks for the project out of all numerous risks, in order to subsequently take planned actions to avoid them or reduce their impact. This objective – the task of prioritizing (ranking) risks – is reached at the stage of qualitative risk analysis. As a rule, the danger (importance) of risks is determined by the risk magnitude, which depends on the occurrence probability of a risky event and the impact of risk on the main target parameters of the project. However, due to the peculiarities of the methods of determining the amount of risk at the stage of qualitative analysis, the problems of prioritizing several risks having an equal amount of risk often arise. Moreover, there are no rules or recommendations on how to practically act in such cases and prioritization is carried out with a high degree of subjectivity. Therefore, the purpose of this study is to propose a new risk prioritization method that project teams could practically use at the stage of qualitative risk analysis in these cases. This improvement in qualitative risk analysis methods will expand the ability of project participants to manage risks, reduce subjectivity in decision-making and, accordingly, improve the quality of project implementation. To do this a risk prioritization method based on the use of the analytical hierarchy procedure proposed by T. Saaty is suggested. This method consists in decomposition of the problem to be solved – obtaining hierarchies, and synthesis based on either quantitative estimates or relative judgments. Additional parameters (criteria) are considered for implementation of this method that characterize the compared risks, as well as the value coefficients of these criteria in the overall assessment. Based on the estimates obtained for each criterion and each risk, an overall score is calculated taking into account certain weighting factors of the criteria for each risk. As a result, according to the estimates obtained, risks are prioritized. The use of such a procedure enables reaching a reasonable decision, obtained not by means of simple conclusions, but on the basis of comparative assessments of each risk. This, accordingly, reduces subjectivity of risk prioritization. The proposed method is described in detail in the article and can be used in real projects.

The DECIDE project: from surveillance data to decision-support for farmers and veterinarians.

Farmers, veterinarians and other animal health managers in the livestock sector are currently missing sufficient information on prevalence and burden of contagious endemic animal diseases. They need adequate tools for risk assessment and prioritization of control measures for these diseases. The DECIDE project develops data-driven decision-support tools, which present (i) robust and early signals of disease emergence and options for diagnostic confirmation; and (ii) options for controlling the disease along with their implications in terms of disease spread, economic burden and animal welfare. DECIDE focuses on respiratory and gastro-intestinal syndromes in the three most important terrestrial livestock species (pigs, poultry, cattle) and on reduced growth and mortality in two of the most important aquaculture species (salmon and trout). For each of these, we (i) identify the stakeholder needs; (ii) determine the burden of disease and costs of control measures; (iii) develop data sharing frameworks based on federated data access and meta-information sharing; (iv) build multivariate and multi-level models for creating early warning systems; and (v) rank interventions based on multiple criteria. Together, all of this forms decision-support tools to be integrated in existing farm management systems wherever possible and to be evaluated in several pilot implementations in farms across Europe. The results of DECIDE lead to improved use of surveillance data and evidence-based decisions on disease control. Improved disease control is essential for a sustainable food chain in Europe with increased animal health and welfare and that protects human health.

A hybrid generalized TODIM based risk prioritization method for failure mode and effect analysis with linguistic Z-numbers

The traditional failure mode and effect analysis (FMEA), as an effective risk analysis technique, has several limitations in the uncertainty modeling and the weights determination of the risk indicators. This paper aims to propose a hybrid risk prioritization method simultaneously considering the characteristics of the reliability associated with the FMEA team members’ evaluation information and their psychological behavior to enhance the performance of the traditional FMEA model. The hybrid risk prioritization method is developed based on the generalized TODIM method and the weighted entropy measure with the linguistic Z-numbers (LZNs). First, the LZNs are adopted to depict the FMEA team members’ cognition information and the reliability of these information. Second, a weighted entropy measure based on the fuzzy entropy and the LZNs is developed to obtain the risk indicators’ weights. Finally, the generalized TODIM method with the LZNs is constructed to obtain the risk priority orders of failure modes, which can effectively simulate the FMEA team members’ psychological character. The applicability and effectiveness of the proposed risk prioritization method is validated through an illustrative example of an integrated steel plant. The results of sensitivity analysis and comparative analysis indicate that the proposed hybrid risk prioritization method is effective and valid, and can get more accurate and practical risk ranking results to help enterprises formulate accurate risk prevention and control plans.

An interdisciplinary approach to the identification and prioritization of environmental risks in northern industrial coastal areas of the Persian Gulf

An interdisciplinary approach to the identification and prioritization of environmental risks in northern industrial coastal areas of the Persian Gulf

Comparison of prioritisation algorithms for the selection of patients for medication reviews in the emergency department: a cross-sectional study.

Risk prioritisation algorithms provide patients with a risk category that guides pharmacists to choose those needing medication reviews (MRs) the most. For this study the Medicine Risk Score (MERIS) and a modified Assessment of Risk Tool (ART) were used. To examine how the selection of patients by the clinical pharmacists in an emergency department for MRs compared with the categorisation provided by MERIS and a modified version of ART (mART). Furthermore, examine the agreement between MERIS and mART. A cross-sectional study was conducted using data on all admitted patients during a two-month period. Data were entered into the prioritisation algorithms and independently ranked by the six pharmacists who were observed as they selected patients for MR. Risk scores and categorisations were compared between the algorithms and the pharmacists' ranking using t-test, Z-test, Chisquare, Kruskal Wallis H-test, or Kappa statistics. The study included 1133 patients. Significant differences were found between the pharmacists and the algorithms. The sensitivity and specificity of MERIS were 37.8% and 73.6%, for mART, 33.0% and 75.9%. Kappa was 0.58, showing moderate agreement. No significant differences were observed between the individual pharmacists' selection, but differences were significant between how pharmacists ranked the importance of the provided MRs. Pharmacists disagreed with the risk categorisation provided by MERIS and mART. However, MERIS and mART had similar sensitivity, specificity, and moderate agreement.Further research should focus on how clinical algorithms affect the selection of patients and on the importance of the MRs carried out by pharmacists.

Comparative analysis between different risk score calculation approaches

PurposeTotal risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.Design/methodology/approachThirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.FindingsThe results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.Originality/valueThe originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

Achieving textile supply chain reliability through risk mitigation: a stakeholders perspective

The textile supply chains (TSCs) are exposed to unprecedented risks which can disrupt the sustainable process of the manufacturing industries. It is necessary to analyze the impact of risks and to create reliability in the end-to-end supply chain. The aim of this research is to identify the supply chain reliability (SCR)-related risks and proposed a risk mitigation model based on a systematic toolkit of strategies for handling the risks. For this purpose, a total of 10 key SCR-related risks have been recognized and prioritized using a detailed review of the literature and industrial expert’s opinions. A risk mitigation methodology is presented for improving SCR through its relevant criteria, i.e. risks factors, based on the intensity of importance. This study focuses in determining and evaluating the key potential risks through the blend of mathematical multi-criteria decision-making (MCDM) methods namely fuzzy FMEA, fuzzy AHP, fuzzy TOPSIS, and sensitivity analysis. This study develops a novel approach for objectively projecting future supply chain risks that can be managed timely in achieving SCR. This study also contributed to fill the existing literature gap for prioritization of key risks in the TSCs. The results highlight how postponement in supply chain risks is identified as highly prioritized whereas, manufacturing risks are ranked as second highly prioritized, and disruption risk is ranked as the last one. The concerned stakeholders can adopt the proposed structured methodology as a way forward in formulating risk mitigation strategies and tackling the SC risks in achieving the optimal level of supply reliability.

Optimization of sensor deployment sequences for hazardous gas leakage monitoring and source term estimation

Nowadays, chemical safety has attracted considerable attention, and chemical gas leakage monitoring and source term estimation (STE) have become hot spots. However, few studies have focused on sensor layouts in scenarios with multiple potential leakage sources and wind conditions, and studies on the risk information (RI) detection and prioritization order of sensors have not been performed. In this work, the monitoring area of a chemical factory is divided into multiple rectangles with a uniform mesh. The RI value of each grid node is calculated on the basis of the occurrence probability and normalized concentrations of each leakage scenario. A high RI value indicates that a sensor at a grid node has a high chance of detecting gas concentrations in different leakage scenarios. This situation is beneficial for leakage monitoring and STE. The methods of similarity redundancy detection and the maximization of sensor RI detection are applied to determine the sequence of sensor locations. This study reveals that the RI detection of the optimal sensor layout with eight sensors exceeds that of the typical layout with 12 sensors. In addition, STE with the optimized placement sequence of the sensor layout is numerically simulated. The statistical results of each scenario with various numbers of sensors reveal that STE is affected by sensor number and scenarios (leakage locations and winds). In most scenarios, appropriate STE results can be retained under the optimal sensor layout even with four sensors. Eight or more sensors are advised to improve the performance of STE in all scenarios. Moreover, the reliability of the STE results in each scenario can be known in advance with a specific number of sensors. Such information thus provides a reference for emergency rescue.

Evaluating Critical Safety and Health Risks by Job Safety Analysis and Analytic Hierarchy Process in Industrial Printing

Background: Critical risks are one of the most important problems in industries, which have high costs for the industry. This study aimed to assess critical safety and health risks through job safety analysis and analytic hierarchy process in industrial printing. Materials and Methods: The present study was conducted as a descriptive and analytical in one of the printing industries in Iran, in 2020. Critical risks were collected by visiting and observing the job safety analysis checklist. Then, critical risk prioritization was performed by an analytic hierarchy process using Expert Choice software, version 11. Results: A total of 23 hazards were identified. Mechanical hazards (61%) and chemical hazards (9%) had the highest and lowest frequencies, respectively. The identified safety and health hazards included musculoskeletal disorders (MSDs) in the upper and lower back, respiratory and skin exposure to chemicals, the risk of electric shock, and mechanical hazards (hand entrapment, hand amputation, and falling objects). Conclusion: The highest priority of critical risk was related to MSDs of the upper and lower back. The next priority of critical risk was related to chemical agents, respiratory exposure to organic solvents, and dermal exposure to organic solvents.

Productivity Analysis and Associated Risks in Steel Structures

Construction can be analyzed at industry, firm, project, and activity/task levels. Given that there are differences between the concepts of productivity and uses of productivity data, depending on the level of analysis, there is no single meaning of construction productivity, except of an output/input ratio. Furthermore, there is little knowledge in the extant literature about steel structure productivity, sustainability, and risks. Moreover, through the investigation of the grey literature, i.e., the national or European reports on construction productivity, the indexes given are at aggregate levels. This paper aims to fill this gap and provide a holistic approach to the levels of productivity, sustainability, and the risks involved in the construction process in several steel structure types from similar projects constructed by a company that has operated within the field of steel structures for several decades. From a homogeneous database of 71 steel structure projects constructed in the last decade, several curves are derived concerning productivity per work phase. For this research, productivity is construed as a ratio of output/(cycle time). Through a literature review and interviews with experienced site engineers, a risk registry was compiled by the authors concerning sixteen (16) risks encountered in the construction process. The TOPSIS multi-criteria analysis program is used for the prioritization of risks and the @RISK program for the probabilistic cost analysis of the identified risks.

Improving the effectiveness of multidisciplinary team meetings on skin cancer: Analysis of the National Cancer Research UK survey responses

IntroductionSkin cancer is the most common form of cancer in the UK, comprising at least 25% of all new cancer diagnoses. Many patients require referral to the local or specialist skin cancer multidisciplinary team (MDT) for ongoing management. However, national data have shown that Specialist Skin Cancer MDTs are costly and do not currently meet NICE standards for composition and quoracy. Innovative solutions to these problems are therefore warranted. MethodsWe performed a secondary comparative analysis of 3563 quantitative responses to two Cancer Research UK commissioned surveys along with subanalysis of 282 skin cancer MDT respondents. ResultsGood uniformity was observed amongst skin respondents in the belief that risk stratification and prioritization of complex cases were the most important factors compared to other cancer MDT members. The most important priorities for areas requiring change to MDT working deemed by the skin MDT were 1) imaging and pathology results ready for the meeting, 2) time to discuss patients in detail, 3) clear meeting owner in charge, and 4) clear agenda, in advance of the meeting. There was agreement (median Likert score 4) amongst skin MDT respondents that patients should be placed on protocolized treatment pathways. ConclusionThe responses of skin MDT respondents analyzed in the current study support changes to meeting attendance, preparation, and protocolized streaming. In line with other studies, we support tumor-specific guidance for streamlining MDT discussions. We also encourage stakeholders to adopt an evidence-based approach to test, develop, and reassess changes in this herculean task.

Risk evaluation and prioritization of contaminants of emerging concern and other organic micropollutants in two river basins of central Argentina

A research gap exists in baseline concentrations of organic micropollutants in South American rivers. Identification of areas with different degrees of contamination and risk to the inhabitant biota is needed to improve management of freshwater resources. Here we inform the incidence and ecological risk assessment (ERA) of current used pesticides (CUPs), pharmaceutical and personal care products (PPCPs) and cyanotoxins (CTX) measured in two river basins from central Argentina (South America). Risk Quotients approach was used for ERA differentiating wet and dry seasons. High risk was associated to CUPs in both basins (45 % and 30 % of sites from Suquía and Ctalamochita rivers, respectively), mostly in the basins extremes. Main contributors to risk in water were insecticides and herbicides in Suquía river and insecticides and fungicides in Ctalamochita river. In Suquía river sediments, a very high risk was observed in the lower basin, mainly from AMPA contribution. Additionally, 36 % of the sites showed very high risk of PCPPs in Suquía river water, with the highest risk downstream the wastewater treatment plant of Córdoba city. Main contribution was from a psychiatric drug and analgesics. In sediments medium risk was observed at the same places with antibiotics and psychiatrics as main contributors. Few data of PPCPs are available in the Ctalamochita river. The risk in water was low, with one site (downstream Santa Rosa de Calamuchita town) presenting moderated risk caused by an antibiotic. CTX represented in general medium risk in San Roque reservoir, with San Antonio river mouth and the dam exit showing high risk during the wet season. The main contributor was microcystin-LR. Priority chemicals for monitoring or further management include two CUPs, two PPCPs, and one CTX, demonstrating a significant input of pollutants to water ecosystems from different sources and the need to include organic micropollutants in current and future monitoring.

Evaluation of risks impeding sustainable mining using Fermatean fuzzy score function based SWARA method

Sustainability in the mining and raw materials sector is a key target in the EU Green deal agenda. The aim of this work is to determine the degree of importance of risks that may impede sustainable mining, considering UN Sustainable Development Goals (SDGs) indicators and EU initiatives, taking as a case study the mining sector in Greece. A total of 49 risks for sustainable mining, under six categories, were identified by means of expert consultation and review of the literature. The identification and prioritization of potential risks can provide a pathway towards sustainable mining operations. The risks factors weighting is enhanced using a new Fermatean fuzzy score function with Stepwise Weight Assessment Ratio Analysis (SWARA). The proposed model is a powerful tool to handle the uncertainties and inaccuracies in the information regarding the weights of the risks. The main research findings indicate that the most important risks for sustainable mining in Greece are irresponsible mining, the lack of license to operate, and poor environmental monitoring, which are directly connected to the aim and scope of SDG12: responsible consumption and production. In addition, according to the results the category with the highest risk for sustainable mining is the one of “Risk to Environment”. A complete list of risks and risk categories, and their ranking is presented and discussed creating a priority of actions in the framework of European and international initiatives to set a road map to sustainable mining. This work provides a benchmark for future studies, with the aim of providing a tool for evaluating and ranking global risk factors that may affect sustainable mining development.

Species-specific bioaccumulation and risk prioritization of psychoactive substances in cultured fish

Psychoactive substances are becoming new concern in environmental research with their increasing applications and the potential threats to fishery production as these substances could alter the behavior of fish and consequently affect the yield and quality of cultured fish. In this study, the accumulation and risk of twenty psychoactive substances were investigated in five species of cultured fish in Eastern China. The results showed that the total concentrations of these twenty psychoactive substances ranged from 0.15 to 0.92 ng mL−1 in the plasma among the five species of cultured fish with an order of perch > crucian carp > bighead carp > grass carp > silver carp. Diazepam (DIAZ) and methamphetamine (MAMP) were identified as the most frequently detected compounds, which were found in 100% and 93% of the samples with a median concentration of 0.15 and 0.12 ng mL−1 in the plasma, respectively. Although all of the psychoactive substances posed low or negligible risk, species-specific analysis of risk prioritization revealed that alprazolam, MAMP, temazepam and DIAZ exhibited the greatest potentials of hazard to all species of the cultured fish but with a species-dependent variation. These findings suggest that the adverse effects of psychoactive substances on fishery production, especially on different species, should be considered.

CRM-SYSTEMS IN MARKETING RISKS MANAGEMENT OF BANK`S RISK PROTECTION PROJECTS

The article examines the theoretical, methodological and practical aspects of the formation of information and analytical support for marketing risk management of risk protection projects of a commercial bank in the conditions of modern challenges of the uncertainty of the marketing environment and global digital transformations. Requirements for an effective bank risk management system based on modern technological tools and innovative methodological approaches are formulated. The directions of the transformation of risk management paradigms were analyzed, the expediency of a systemic multidimensional approach to the implementation of integrated risk management based on modern international regulatory standards was clarified. The need for the convergence of project management methods in risk management for better prioritization of risks, consistency with the strategic goal of activity, and planning of steps to optimally achieve the goal is substantiated. The marketing orientation of bank risk protection projects is revealed. It is proposed to expand the list of banks' risks and to transform the weighting coefficients of the existing ones in the conditions of innovative technological transformations in order to strengthen socio-ecological responsibility to society. Particular attention is paid to determining the place of marketing risks in the management system of banks, as a critical element of an effective risk management model. The direction of improving the information and analytical support of the bank's risk protection projects through the implementation of CRM systems as an information communication tool and an information source on marketing risk management is proposed.

Seismic risk prioritisation schemes for reinforced concrete bridge portfolios

A significant portion of the existing bridge inventory in Italy is decades old, requiring continuous maintenance and safety assessment approaches. Recent collapses of existing reinforced concrete bridges have piqued public interest, placing pressure on management agencies to define methodologies with which to prioritise asset maintenance and to effectively utilise their limited resources. When looking for decision variables to perform this prioritisation, seismic risk assessment metrics, such as average annual losses (AAL), are an appealing choice. However, obtaining this metric for a large bridge inventory is technically challenging and requires large amounts of information that are seldom available, promoting the development of practical approaches that can predict the relative priority of assets within a portfolio, based on processing simple indicators with acceptable accuracy. In this research, a case study of 617 bridges from the Italian road network was assessed considering state-of-the-art approaches to calculate total losses. The results were explored with data science techniques, identifying the main features that drive the relative importance of bridges in terms of AAL and using them as guidance to calibrate a simplified methodology, based on the recent Italian Guidelines for Bridge Safety Assessment. The proposed AAL-based modifications demonstrate a notable improvement in the definition of bridge assessment priorities, as well as providing further resolution in the classification for more efficient decision making.

HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization.

Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration () of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans. Our objective was to develop a machine learning (ML) model to predict blood concentrations () of chemicals and prioritize chemicals of health concern. We curated the of compounds mostly measured at population levels and developed an ML model for chemical predictions by considering chemical daily exposure (DE) and exposure pathway indicators (), half-lives (), and volume of distribution (). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances. We curated the of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and , the mean absolute error (MAE) values of 1.28 and , the mean absolute percentage error (MAPE) of 0.29 and 0.23, and of 0.80 and 0.72 across test and testing sets. Subsequently, the human of 7,858 ToxCast chemicals were successfully predicted, ranging from to . The predicted were then combined with ToxCast in vitro bioassays to prioritize the ToxCast chemicals across 12 in vitro assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants. We have shown that the accurate prediction of "internal exposure" from "external exposure" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.

Integrating social and ecological information to identify high-risk areas of human-crocodile conflict in the Indonesian Archipelago

Crocodile attacks on humans and subsequent retaliations are a pressing issue for saltwater crocodile conservation. As human-crocodile conflict is complex, integrating social and ecological information better explains the drivers and patterns of these interactions. Our study aims to incorporate ecological factors associated with the intensity of crocodile attacks together with social factors of mass media reports to identify high-risk areas of human-crocodile conflict in Indonesia. We compiled reports of crocodile attacks in the 2010–2019 period from media reports, field surveys, and local informants. The presence of attack was estimated by evaluating the influence of habitat, climate, human, and reporting effort. As tone of media coverage can reflect and shape reader's tolerance about a certain issue, we assessed the headline's tone from each media article that reported crocodile attacks from 2017 to 2019. A total of 665 crocodile attacks were recorded and mainly distributed in western and central Indonesia. The estimated number of crocodile attacks was higher in areas with lower forest biomass and human density, and wider cellular network coverage. Negative media coverages were frequently reported in western Indonesia. By combining social information of negative media reporting and the ecological information of crocodile attacks hotspots, we identified 170,500 km2 priority risk areas in the western part of Indonesia, a notable 65.8 % reduction in area size compared to the attack hotspots. We highlight the application of socio-ecological information in risk prioritization to address the rising trends of negative human-wildlife interactions.