Risk Assessment Framework Research Articles

A Regional-Scale Assessment-Based SARS-CoV-2 Variants Control Modeling with Implications for Infection Risk Characterization.

The emergence and progression of highly divergent SARS-CoV-2 variants have posed increased risks to global public health, triggering the significant impacts on countermeasures since 2020. However, in addition to vaccination, the effectiveness of non-pharmaceutical interventions, such as social distancing, masking, or hand washing, on different variants of concern (VOC) remains largely unknown. This study provides a mechanistic approach by implementing a control measure model and a risk assessment framework to quantify the impacts of control measure combinations on the transmissions of five VOC (Alpha, Beta, Delta, Gamma, and Omicron), along with a different perspective of risk assessment application. We applied uncontrollable ratios as an indicator by adopting basic reproduction number (R 0) data collected from a regional-scale survey. A risk assessment strategy was established by constructing VOC-specific dose-response profiles to implicate practical uses in risk characterization when exposure data are available. We found that social distancing alone was ineffective without vaccination in almost all countries and VOC when the median R 0 was greater than two. Our results indicated that Omicron could not be contained, even when all control measure combinations were applied, due to its low threshold of infectivity (~3×10-4 plague-forming unit (PFU) mL-1). To facilitate better decision-making in future interventions, we provide a comprehensive evaluation of how combined control measures impact on different countries and various VOC. Our findings indicate the potential application of threshold estimates of infectivity in the context of risk communication and policymaking for controlling future emerging SARS-CoV-2 variant infections.

Cyber Security Assessment of An Interoperable Port Call and Voyage Optimization tool

Abstract The MISSION project aims to revolutionize maritime transport by developing a digital tool for real-time optimization of port calls and voyages, thereby reducing fuel consumption, cutting greenhouse gas emissions, and decreasing waiting times through enhanced coordination and information sharing among stakeholders. However, the security of the involved IT systems is critical to ensure safe and reliable operations. This paper introduces harborLang, a novel threat modeling language tailored for the maritime sector, built on the Meta Attack Language (MAL) framework. harborLang addresses the unique security challenges in maritime transport by enabling the modeling and mitigation of potential threats through detailed attack simulations. By integrating harborLang with the Yet Another Cybersecurity Risk Assessment Framework (YACRAF), the project enhances its risk analysis capabilities, allowing for precise threat scenarios that reflect the maritime environment’s complexities. The combined use of harborLang and YACRAF facilitates comprehensive cybersecurity risk assessments, significantly improving decision-making, operational safety, and the overall cybersecurity posture of maritime and port operations.

A Comprehensive Security Risk Assesment Framework SRAF_IoV for Internet of Vehicles.

The advancement in digital world have led to the development of smart vehicles that connect to the Internet of Vehicles (IoV) Infrastructure. These smart vehicle also connects and communicate with other vehicles, and their connected components. Once connected, they can share information such as speed, direction, and distance between vehicles.IoV enhances traffic infrastructure by reducing congestion and incidents, ultimately improving road safety within highways and metropolitan cities. However, when smart vehicles use IoV to share data through vehicle-to-vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to everything (V2X) they also introduce security risks and vulnerabilities to the IoV Infrastructure. Malicious actors can exploit these vulnerabilities to alter data within the connected units of IoV infrastructure, such as electronic control units, onboard units, control networks, and sensors. Securing data within the IoV ecosystem is crucial to prevent malicious activity that could compromise confidentiality, integrity, and availability. In this study, a security risk assessment framework for IoV (SRAF-IoV) has been developed to identify vulnerabilities within the IoV network. SRAF-IoV that assesses security risks in connected vehicles. It evaluates vulnerabilities and assigns a risk level based on different communication channels (V2V, V2I, and V2X). The proposed framework was tested against various cyber-attacks to determine risk levels associated with different attack types (low, medium, high, and critical). The performance of SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV can push the smart vehicle industry to make critical security decisions during the development of smart vehicles.

Future invasion risk assessment of the peacock bass in Neotropical ecoregions: A conceptual and testable model

Protecting freshwater biodiversity from non-native invasive species and their impacts is crucial for minimising both regional and global deterioration of ecosystem services. The yellow peacock bass Cichla ocellaris, a predatory freshwater fish endemic to the Amazon region, has been translocated within several Neotropical ecoregions, exhibiting harmful effects on biodiversity. In order to facilitate the prioritization of management efforts, which are essential for defining conservation actions in extensive and diverse areas, we propose a risk assessment index that integrates multiple ecological and environmental variables within a comprehensive framework, here applied for C. ocellaris. By considering fish species richness, occurrence frequency, and climate suitability, our index provides a more nuanced understanding of invasion risks. Within regions categorized as high and very high risk, where ecological impacts of C. ocellaris have been extensively documented (e.g., the Southern and North-East Brazilian regions, as well as Central American and Caribbean ecoregions), numerous environments are increasingly conducive to the establishment of this invasive species, exacerbated by a surge in dam constructions. Our analysis projects a significant increase in suitable habitats by 5% to 6.5%, with Southern and Northeastern Brazil and Central America identified as critical zones. Thus, our reproducible risk assessment framework, which considers freshwater ecoregions as conservation units, not only directs effective resource allocation for control measures but also enhances our ability to predict and mitigate the ecological impacts of non-native species introductions, providing a valuable tool for both policymakers and conservationists.

Source-risk and uncertainty assessment of trace metals in surface sediments of a human-dominated seaward catchment in eastern China

Current total concentration-based methods for source attribution and risk assessment often overestimate metal risks, thereby impeding the formulation of effective risk management strategies. This study aims to develop a framework for source-specific risk assessment based on metal bioavailability in surface river sediments from a human-dominated seaward catchment in eastern China. Metal bioavailability was quantified using chemical fractionation results, and source apportionment was conducted using the positive matrix factorization (PMF) model. Risk assessment integrated these findings using two indices: the Potential Ecological Risk Index (PERI) and the Mean Probable Effect Concentration Quotient (mPEC-Q), with uncertainty addressed via Monte Carlo simulations. Results indicated that average total concentrations of Cu, Pb, Zn, Cr, Hg, Cd, and As exceeded their respective background levels by 1.63 to 15.00 times. The residual fraction constituted the majority, accounting for 53.84 % to 77.79 % of total concentrations, suggesting significant natural origins. However, source apportionment revealed a predominant contribution from anthropogenic activities, including industrial smelting, agricultural practices, and atmospheric deposition. The contributions were found to vary between 5.35 % and 40.03 % when the total concentration was adjusted to bioavailable content. Total concentration-based PERI/mPEC-Q assessments indicated high/moderate risk levels, decreasing to considerable/low risk levels with bioavailability adjustment. Hg and Cd were identified as priority metals. Further incorporating source appointment parameters into the risk assessment, industrial smelting was identified as the primary contributor, accounting for 66.06 % of total risk by total concentration and 65.63 % by bioavailability. This underscores the role of bioavailability in mitigating risk overestimation. Monte Carlo simulations validated industrial smelting as a major risk contributor. This study emphasizes the importance of considering bioavailability in the source-risk assessment of sediment-metals, crucial for targeted risk management in urbanized catchment areas.

Dynamic risk assessment framework for fire of power critical infrastructure: The case study of UHV converter transformer

Dynamic risk assessment framework for fire of power critical infrastructure: The case study of UHV converter transformer

Analysis of Short-Term Heavy Rainfall-Based Urban Flood Disaster Risk Assessment Using Integrated Learning Approach

Accurate and timely risk assessment of short-term rainstorm-type flood disasters is very important for ecological environment protection and sustainable socio-economic development. Given the complexity and variability of different geographical environments and climate conditions, a single machine learning model may lead to overfitting issues in flood disaster assessment, limiting the generalization ability of such models. In order to overcome this challenge, this study proposed a short-term rainstorm flood disaster risk assessment framework under the integrated learning model, which is divided into two stages: The first stage uses microwave remote sensing images to extract flood coverage and establish disaster samples, and integrates multi-source heterogeneous data to build a flood disaster risk assessment index system. The second stage, under the constraints of Whale Optimization Algorithm (WOA), optimizes the integration of random forest (RF), support vector machine (SVM), and logistic regression (LR) base models, and then the WRSL-Short-Term Flood Risk Assessment Model is established. The experimental results show that the Area Under Curve (AUC) accuracy of the WRSL-Short-Term Flood Risk Assessment Model is 89.27%, which is 0.95%, 1.77%, 2.07%, 1.86%, and 0.47% higher than RF, SVM, LR, XGBoost, and average weight RF-SVM-LR, respectively. The accuracy evaluation metrics for accuracy, Recall, and F1 Score have improved by 5.84%, 21.50%, and 11.06%, respectively. In this paper, WRSL-Short-Term Flood Risk Assessment Model is used to carry out the risk assessment of flood and waterlogging disasters in Henan Province, and ArcGIS is used to complete the short-term rainstorm city flood and waterlogging risk map. The research results will provide a scientific assessment basis for short-term rainstorm city flood disaster risk assessment and provide technical support for regional flood control and risk management.

Advancing Sustainable Healthcare Technology Management: Developing a Comprehensive Risk Assessment Framework with a Fuzzy Analytical Hierarchy Process, Integrating External and Internal Factors in the Gulf Region

Advancing Sustainable Healthcare Technology Management: Developing a Comprehensive Risk Assessment Framework with a Fuzzy Analytical Hierarchy Process, Integrating External and Internal Factors in the Gulf Region

Diversification strategies for indirect real estate. Intersection of business, economics, and society in shanghai mixed-use developments

This study examines the impact of large mixed-use developments on the financial performance of publicly listed real estate firms in Shanghai from 2009 to 2019. The research uses a Multi-Criteria Decision Making (MCDM) approach, considering 14 key performance indicators (KPIs) across three categories: Corporate and Financial Information, Strategy Indicators, and Specifics of Mixed-Use Development. The article analyses the performance of large, listed investors on the Hong Kong, Shanghai, and Singapore Stock Exchanges, providing insights into their evolution over the past decade. The literature review highlights the relationship between companies’ performance and corporate finance, capital structure, and financial ratios. It identifies an existing gap in research related to mixed-use assets and indirect real estate. By using the MCDM framework, the study evaluates these assets and provides actionable insights for global investors, asset managers, and developers, helping them make informed decisions in indirect real estate investment. The findings contribute to understanding investment strategy formation in dynamic real estate markets and support informed diversification and urban policy development. This paper offers a useful appraisal framework for future MCDM risk assessments, assisting stakeholders in navigating the complexities of the real estate landscape in one of the world’s foremost metropolitan hubs. It also offers recommendations and insights on best practices for global listed firms while providing a framework for future research in the field of property investment.

The Role of Scenario-Building in Risk Assessment and Decision-Making on Urban Water Reuse

Urban resilience and water resilience are both increasingly relying on urban non-potable water reuse under the context of the Climate Emergency, but sound risk assessment is lacking. Compared to the state of art, the proposed framework for health risk assessment and management of urban non-potable water reuse includes (i) an additional step for establishing the context and (ii) the risk identification step being extended to introduce a description of the activities from which the hazard exposure scenarios may be built. This novel scenario-building process allows for a clear and comprehensive risk description, assessment, and treatment. The model of risk management is structured around three primary components: the decision-makers, i.e., the municipal services and the population at risk (users and workers); data elements relevant for the risk management process (reclaimed water quality, hazards, hazardous events, sites where exposure can happen, exposure routes, and activities developed by the population at risk and their vulnerabilities); and the links between the decision-makers and these elements and between the elements themselves. Its application in a representative case study shows that the framework comprehensively guides decision-making and communication to relevant stakeholders. From this practical exercise, the main recommendations were derived for risk mitigation by the municipal risk manager and the park users.

Risk Assessment of Community-Scale High-Temperature and Rainstorm Waterlogging Disasters: A Case Study of the Dongsi Community in Beijing

With the advancement of urbanization and acceleration of global warming, extreme weather and climate events are becoming increasingly frequent and severe, and climate risk continues to rise. Each community is irreplaceable and important in coping with extreme climate risk and improving urban resilience. In this study, the Dongsi Community in the functional core area of Beijing was explored, and the risk assessment of high temperatures and rainstorm waterlogging was implemented at the community scale. Local navigation observations were integrated into a theoretical framework for traditional disaster risk assessment. The risk assessment indicator system for community-scale high-temperature and rainstorm waterlogging disasters was established and improved from a microscopic perspective (a total of 22 indicators were selected from the three dimensions of hazard, exposure, and vulnerability). Geographic Information Systems (GIS) technology was used to integrate geographic information, meteorological, planning, municipal, socioeconomic and other multisource information layers, thus enabling more detailed spatial distribution characteristics of the hazard, exposure, vulnerability, and risk levels of community-scale high temperatures and rainstorm waterlogging to be obtained. The results revealed that the high-risk area and slightly high-risk area of high-temperature disasters accounted for 13.5% and 15.1%, respectively. The high-risk area and slightly high-risk area of rainstorm waterlogging disasters accounted for 9.8% and 31.6%, respectively. The high-risk areas common to high temperatures and waterlogging accounted for 3.9%. In general, the risk of high-temperature and rainstorm waterlogging disasters at the community scale showed obvious spatial imbalances; that is, the risk in the area around the middle section of Dongsi Santiao was the lowest, while a degree of high temperatures or rainstorm waterlogging was found in other areas. In particular, the risk of high-temperature and rainstorm waterlogging disasters along Dongsi North Street, the surrounding areas of Dongsi Liutiao, and some areas along the Dongsi Jiutiao route was relatively high. These spatial differences were affected to a greater extent by land cover (buildings, vegetation, etc.) and population density within the community. This study is a useful exploration of climate risk research for resilient community construction, and provides scientific support for the planning of climate-adaptive communities, as well as the proposal of overall adaptation goals, action frameworks, and specific planning strategies at the community level.

Selective enrichment of high-risk antibiotic resistance genes and priority pathogens in freshwater plastisphere: Unique role of biodegradable microplastics

Microplastics (MPs) has been concerned as emerging vectors for spreading antibiotic resistance and pathogenicity in aquatic environments, but the role of biodegradable MPs remains largely unknown. Herein, field in-situ incubation method combined with metagenomic sequencing were employed to reveal the dispersal characteristics of microbial community, antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and virulence factors (VFs) enriched by MPs biofilms. Results showed that planktonic microbes were more prone to enrich on biodegradable MPs (i.e., polyhydroxyalkanoate and polylactic acid) than non-biodegradable MPs (i.e., polystyrene, polypropylene and polyethylene). Distinctive microbial communities were assembled on biodegradable MPs, and the abundances of ARGs, MGEs, and VFs on biofilms of biodegradable MPs were much higher than that of non-biodegradable MPs. Notably, network analysis showed that the biodegradable MPs selectively enriched pathogens carrying ARGs, VFs and MGEs concurrently, suggesting a strong potential risks of co-spreading antibiotic resistance and pathogenicity through horizontal gene transfer. According to WHO priority list of Antibiotic Resistant Pathogens (ARPs) and ARGs health risk assessment framework, the highest abundances of Priority 1 ARPs and Rank I risk ARGs were found on polylactic acid and polyhydroxyalkanoate, respectively. These findings elucidate the unique and critical role of biodegradable MPs for selective enrichment of high-risk ARGs and priority pathogens in freshwater environments.

Indicator-based risk assessments for urban hazard resilience: an application for flash floods

ABSTRACT Urban resilience to environmental hazards can be substantially improved if proactive planning is mainstreamed as a risk mitigation strategy. To enable such transition, a resource-efficient and transparent framework is urgently needed for undertaking vulnerability and risk assessments, to consequently inform risk-aware proactive planning on spatially variable assets with very diverse characteristics. This study proposes an indicator-based risk-assessment framework, for efficiently treating the spatially distributed diverse physical and social components comprising a city network. The proposed framework decomposes the risk to its three main dimensions, i.e. hazard, exposure and vulnerability; with the latter accounting for both the susceptibility of the city network to the considered hazard and its capacity to cope. The inclusion of the capacity to cope as a distinct element, that accounts for both city and building-block level preparatory actions, is a unique feature, much sought in Decision Support Systems, that allows to directly quantify their effectiveness towards risk mitigation. The outcome of spatially variable risk estimates, could be exploited by urban planners and city authorities for undertaking risk-aware urban design, development and resilience enhancement decisions. The framework is demonstrated herein for the case of flash floods but could be tailored to accommodate other types of environmental hazards.

Pollution risk evaluation of groundwater wells based on stochastic and deterministic simulation of aquifer lithology

Groundwater pollution risk evaluation is an important basis for developing groundwater protection measures and management strategies, and its accuracy directly affects the effectiveness of protection measures. The heterogeneity of the aquifer significantly affects the transport process of pollutants, increasing the uncertainty of pollutant risk assessment. However, in the actual site, borehole data that reveal aquifer heterogeneity are costly, and only a limited number of borehole data are available, which cannot accurately describe the heterogeneity of the aquifer, thus limiting the accuracy of groundwater pollution risk assessment. In order to overcome the above problems, this paper proposes a groundwater pollution risk assessment framework based on the stochastic and deterministic simulation of aquifer lithology. Based on the statistical characteristics of the change of lithology type in the actual borehole, the framework uses Markov chain to generate some sets of random lithology field and transforms them into heterogeneity parameter field, so as to realize the stochastic assessment of the pollution risk of groundwater resource wells. Furthermore, combined with the pumping test data, the parameter field that is most suitable for the actual situation is selected to evaluate the pollution risk deterministically. Finally, the stochastic and deterministic results are combined to comprehensively evaluate the pollution risk of groundwater resource wells. Through a case study in a river valley plain, the feasibility of the above framework is verified, and good application effects are achieved. This study provides a feasible method for accurately assessing groundwater pollution risk, which is helpful to reduce the impact of uncertain factors on pollution risk assessment, and thus provides a more reliable basis for groundwater management and decision-making.

A novel framework for multiple thermokarst hazards risk assessment and controlling environmental factors analysis on the Qinghai-Tibet Plateau

Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the degradation of ice-rich permafrost has a significant impact on infrastructure construction and local ecology; therefore, it is necessary to assess its risk. Currently, high-precision and harmonized assessment tools for multiple thermokarst hazards risk assessment are lacking, and the mechanisms governing the environmental interactions of thermokarst hazards have not been fully clarified. In this study, a novel multiple thermokarst hazards risk assessment framework was proposed by combining stacking machine learning and potential environmental factors to assess the risk of thermokarst hazards in the Yangtze River source region (YRSR). In addition, model performance was improved by model optimization. Finally, structural equation modeling (SEM) was used to assess the controlling environmental factors for the thermokarst hazards in the YRSR. The results show that slope and precipitation contribute the most to the modeling accuracy of thermokarst lakes and thaw slumps, respectively. Model optimization improved the base model modeling accuracy by approximately 2 % ∼ 7 %, with XGBoost having the highest sensitivity to model optimization and the highest modeling accuracy. In terms of the ensemble strategy, the stacking model and ensemble model significantly improved the risk mapping accuracy, and the stacking model was better than the ensemble model, with accuracies of 92.39 % and 93.36 % for thermokarst lakes and thaw slumps, respectively. Compared with previous results, the results of this study are more representative of the YRSR. Finally, via SEM, terrain factors and soil factors were identified as controlling environmental factors for the risk of thaw slumps and thermokarst lakes, respectively. This study proposes a high-precision risk assessment method for thermokarst hazards in permafrost regions, and contributes to a deeper understanding of the interaction mechanisms between thermokarst hazards and environmental factors.

Interpretable physics-aware alkali-silica reaction expansion prediction

The alkali-silica reaction (ASR) is a major contributor to the aging and degradation of infrastructure. Understanding ASR-induced expansion in concrete structures and accurately predicting its future progression are critical components of effective risk assessment frameworks. This paper presents a study focused on developing and interpreting an advanced machine learning model specifically designed to predict ASR expansion. The model strategically integrates two powerful algorithms to achieve this goal. A comprehensive database comprising 2000 samples of ASR expansion data with various attributes was used to train the model. The first algorithm, eXtreme Gradient Boosting (XGBoost), was employed to establish a predictive model for ASR expansion, achieving approximately 90% validation accuracy. The second algorithm, SHapley Additive exPlanations (SHAP), was applied to assess the relative importance of the factors influencing the XGBoost model’s predictions. This approach provided valuable physical and quantitative insights into the input–output relationships, which are often obscured in conventional machine learning methods. The study revealed that higher silica content, elevated alkali levels, and longer reaction times are strongly correlated with increased ASR expansion. In contrast, larger aggregate sizes and higher water-to-cement ratios were associated with reduced expansion. Additionally, the analysis identified alkali content as the most influential factor in determining the ultimate ASR expansion, while silica content emerged as the key parameter affecting the characteristic time of the ASR curve.

Cyber-XAI-Block: an end-to-end cyber threat detection & fl-based risk assessment framework for iot enabled smart organization using xai and blockchain technologies

Cyber-XAI-Block: an end-to-end cyber threat detection & fl-based risk assessment framework for iot enabled smart organization using xai and blockchain technologies

Risk assessment of patients with underlying health conditions who present for IVF treatment.

The increasing number of women with underlying health conditions seeking in vitro fertilization (IVF) necessitates a comprehensive risk assessment framework tailored to their needs. This review aims to provide a practical framework for IVF specialists to systematically assess and manage the risks associated with medically complex patients. The GRASP mnemonic (Genetics, Retrieval, Anaesthetics, Stimulation, Pregnancy) is introduced to facilitate a thorough risk assessment process. The review emphasizes the importance of obtaining detailed medical histories, corroborating information from other healthcare providers, and involving maternal medicine specialists in pre-pregnancy counselling. Furthermore, it highlights the ethical considerations of informed consent, advocating for fertility specialists to take a central role in coordinating care and ensuring patient understanding of risks. Continuous health evaluation throughout the IVF process and clear communication of risks are crucial for improving patient outcomes.

A sidewalk-level urban heat risk assessment framework using pedestrian mobility and urban microclimate modeling

Climate change and the associated increase in heat-related hazards pose a pressing threat to urban residents’ health and well-being. People, when walking in particular, are at risk of experiencing heat stress as they navigate urban environments. This study proposes a novel heat risk assessment framework combining pedestrian mobility modeling with urban microclimate modeling. Using this framework, we assessed pedestrian heat-related exposure and risk in urban areas by integrating the Universal Thermal Climate Index (UTCI) as the hazard and pedestrian trips to critical destinations as exposure. We considered temporal variation, in both hazard and exposure, by examining different time periods during the day—morning peak, midday, and evening peak. In addition to hazard and exposure, we also considered vulnerability by focusing on young children and older adults. We contribute to improving the spatial resolution of heat risk assessment by analyzing the hazard for pedestrian trips between home locations and five critical destinations—bus stops, rail stations, parks, schools, and commercial amenities—at the address-point level and using a pedestrian network comprising sidewalks and crosswalks. Our framework helps identify sidewalks with high heat exposure levels as well as home locations with high cumulative heat risk, accounting for walking trips to critical destinations along feasible routes. We demonstrated the effectiveness of this framework by applying it to a 36-square-kilometer area of central Los Angeles, CA. Our findings offer valuable information to urban planners and policy-makers, supporting evidence-based prioritization of intervention sites, climate adaptation strategies, and policy decisions essential for climate-proof planning. By implementing targeted interventions in areas where heat hazard is expected to affect the most vulnerable pedestrians, planners can create heat-resilient, pedestrian-friendly environments while prioritizing the health and well-being of vulnerable groups. This study contributes to the growing knowledge of robust risk assessment methodologies for climate-proof planning, specifically with regard to addressing outdoor heat-related risks during extreme heat events.

Which organic contaminants should be paid more attention: Based on an improved health risk assessment framework

The increasing chemical pollution of the drinking water is widely concerned. Large number of organic contaminants cannot be removed by conventional water treatment technology due to their low concentration, and long-term exposure may pose significant risks to human health. Which organic contaminants in drinking water should be given more attention has been a topic of great concern in recent years. To identify the organic contaminants that need attention, this research proposes an improved health risk screening method to quantitatively analyze the risks of accumulation, persistence, toxicity, and antibiotic resistance. Compared with conventional method, 26 compounds were added to the improved screening list, including 9 DBPs (e.g., NDMA), 3 antibiotics (e.g., oxytetracycline), PFNA and other compounds. Overall, antibiotics and plasticizers rose in the risk rankings. From the perspective of the proportion of total risk value, a single risk plays a decisive role (more than 99%) in the ranking. This change suggests that antibiotic resistance and the accumulation of organic matter are as important as their toxic risks to humans. 58 compounds were recommended for the priority control organic contaminants list in drinking water. This list provides the necessary information for authoritative regulations to monitor, control, assess, and manage the risks of environmentally relevant compounds in drinking water in China.