Information Risk Analysis Research Articles

Project Risk Analysis and Early Warning of Culture and Tourism Group Financial Leasing Company

Under the background of strict regulation, due to the cyclical adjustment of the financial leasing industry, some top companies have maintained a growth trend in asset size and net profit. Internal differentiation has emerged in the financial leasing industry, increasing operational pressure, and a slowing trend in asset size growth. Although the overall pressure on credit quality has eased, downward pressure on asset quality is widespread, and the recovery period for narrowing profit space is still long. With the continuous upgrading and iteration of digitization, intelligence, and specialization, Culture and Tourism Group Financial Leasing Company should strengthen its professional ethics, improve its risk analysis and prediction capabilities, design integrated business guidelines for due diligence information collection and risk analysis, rely on big data to build a leasing asset identification, evaluation, monitoring, and early warning system, and establish a comprehensive, multi angle, and lean risk analysis and early warning mechanism. Through case analysis, problems are identified in a timely manner, and continuous reflection and improvement are made. Business process diagrams are drawn according to different scenarios, crisis warning indicators are set up, and a lean risk management team is formed. Crisis warning signals are continuously tracked and released, and processes and asset quality management are continuously improved. A lean risk control system is established to reduce risk losses and create benefits for the enterprise.

Trends in Burdens of Disease by Transmission Source (USA, 2005–2020) and Hazard Identification for Foods: Focus on Milkborne Disease

BackgroundRobust solutions to global, national, and regional burdens of communicable and non-communicable diseases, particularly related to diet, demand interdisciplinary or transdisciplinary collaborations to effectively inform risk analysis and policy decisions.ObjectiveU.S. outbreak data for 2005–2020 from all transmission sources were analyzed for trends in the burden of infectious disease and foodborne outbreaks.MethodsOutbreak data from 58 Microsoft Access® data tables were structured using systematic queries and pivot tables for analysis by transmission source, pathogen, and date. Trends were examined using graphical representations, smoothing splines, Spearman’s rho rank correlations, and non-parametric testing for trend. Hazard Identification was conducted based on the number and severity of illnesses.ResultsThe evidence does not support increasing trends in the burden of infectious foodborne disease, though strongly increasing trends were observed for other transmission sources. Morbidity and mortality were dominated by person-to-person transmission; foodborne and other transmission sources accounted for small portions of the disease burden. Foods representing the greatest hazards associated with the four major foodborne bacterial diseases were identified. Fatal foodborne disease was dominated by fruits, vegetables, peanut butter, and pasteurized dairy.ConclusionThe available evidence conflicts with assumptions of zero risk for pasteurized milk and increasing trends in the burden of illness for raw milk. For future evidence-based risk management, transdisciplinary risk analysis methodologies are essential to balance both communicable and non-communicable diseases and both food safety and food security, considering scientific, sustainable, economic, cultural, social, and political factors to support health and wellness for humans and ecosystems.

Genome-wide association reveals host-specific genomic traits in Escherichia coli

BackgroundEscherichia coli is an opportunistic pathogen which colonizes various host species. However, to what extent genetic lineages of E. coli are adapted or restricted to specific hosts and the genomic determinants of such adaptation or restriction is poorly understood.ResultsWe randomly sampled E. coli isolates from four countries (Germany, UK, Spain, and Vietnam), obtained from five host species (human, pig, cattle, chicken, and wild boar) over 16 years, from both healthy and diseased hosts, to construct a collection of 1198 whole-genome sequenced E. coli isolates. We identified associations between specific E. coli lineages and the host from which they were isolated. A genome-wide association study (GWAS) identified several E. coli genes that were associated with human, cattle, or chicken hosts, whereas no genes associated with the pig host could be found. In silico characterization of nine contiguous genes (collectively designated as nan-9) associated with the human host indicated that these genes are involved in the metabolism of sialic acids (Sia). In contrast, the previously described sialic acid regulon known as sialoregulon (i.e. nanRATEK-yhcH, nanXY, and nanCMS) was not associated with any host species. In vitro growth experiments with a Δnan-9 E. coli mutant strain, using the sialic acids 5-N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) as sole carbon source, showed impaired growth behaviour compared to the wild-type.ConclusionsThis study provides an extensive analysis of genetic determinants which may contribute to host specificity in E. coli. Our findings should inform risk analysis and epidemiological monitoring of (antimicrobial resistant) E. coli.

Modelling language for cyber security incident handling for critical infrastructures

Cyber security incident handling is a consistent methodology with which to ensure overall business continuity. However, specifically handling incidents for critical information infrastructures is challenging owing to the inherent complexity and evolving nature of the threat. Despite the number of contributions made to cyber incident handling, there is little evidence of literature that focuses on modelling activities that will enhance developers’ abilities to model incident handling processes and activities according to different views. Modelling languages of this nature should integrate essential concepts and a descriptive implementation process in order to enable developers to analyse, represent and reason about the crucial incident handling efforts required to support critical information infrastructures. The aim of this paper is, as part of the CyberSANE EU project, to develop a Cyber Incident Handling Modelling Language (CIHML) that focuses explicitly on modelling incident handling in the context of a critical information infrastructure. The work is innovative in its approach because it consolidates concepts from various domains such as security requirements, forensics, threat intelligence, critical infrastructures and cyber incident handling. The approach will allow the phases of the incident handling lifecycle to be modelled from three different views (critical information infrastructures, threat and risk analysis, and incident response). An implementation process is also proposed, which will serve as a comprehensive guide for developers in order to create these modelling views. Finally, CIHML is evaluated using a real-life scenario from the CyberSANE project to demonstrate its applicability. The incident observed had a severe impact on the overall business continuity of the context studied. The results obtained from the study show that CIHML can help critical information infrastructure operators to identify, evaluate, represent and model cyber incidents in critical information systems, in addition to providing the support required to determine the response strategies needed in order to mitigate these cyber-attacks.

Arctic Heritage at Risk: Insights into How Remote Sensing, Robotics and Simulation Can Improve Risk Analysis and Enhance Safety

Increased and enhanced utilisation of remote sensing and robotics in the Arctic can further enhance cultural safety and well-being and reduce the risks posed to archaeologists, heritage workers and others in the field. In this preliminary scoping survey, the authors review the current use of these technologies and consider a range of related issues, from cultural safety to nefarious use by criminals. Initial discussions with experts have informed areas of concern; and the potential for further integration. In the future, the University of Tromsø’s new Tromsø Arctic Simulation Integration Centre (TASIC) will be utilised to evaluate a range of scenarios to inform risk analysis and contribute towards safety enhancement in the Arctic Heritage at Risk Project (Arctic-HARP). The following is an overview of the significant state-of-the-art technologies and related matters.

Risky Business: Critical Reflections on the Sources of Information for Risk Analysis Under the German Supply Chain Law and Some Ramifications for Global South Stakeholders

Like other mandatory Human Rights Due Diligence (mHRDD) laws, the German Supply Chain Law, the Lieferkettensorgfaltsplichtengesetz (LkSG), imposes a number of due diligence obligations on covered companies geared towards protection of human rights and the environment in the value chains of Transnational Corporations (TNCs). More specifically, as per Section 4(1) in-scope companies are expected to establish an effective and appropriate risk management system that will enable them to comply with their due diligence obligations. As part of establishing such a risk management system companies must “conduct an appropriate risk analysis…to identify the human rights and environment-related risks in its own business area and at its direct suppliers” (Sec 5. (1) LkSG). This contribution argues that a proper risk analysis is the cornerstone of an effective risk management system, and ultimately of compliance with due diligence obligations. It is therefore necessary to interrogate how covered companies carry out such risk analysis, and crucially, what sources of information they use for this purpose, as well as how they secure such information. Using the CSR Risk Check tool as an exemplar this contribution will critically analyze the pitfalls of carrying out risk analysis in ways that continue to entrench asymmetrical relations between Global North and Global South actors.

Takeoff runway overrun risk assessment in aviation safety based on human pilot behavioral characteristics from real flight data

Aviation safety is an important part of safety science and pilot behavior is a major factor in flight safety analysis. However, when analyzing unsafe events, such as runway overrun, majority of studies do not quantify the impact of pilot behavior on flight safety. The aim of this paper is to discover the pilot behavioral characteristics from actual flight data and apply them to construct a takeoff runway overrun risk assessment model. Concretely, the time series clustering is used to mine the pilot behavioral characteristics based on pilot operating data. Then, the similarity theory is introduced to construct a data-based man–machine-environment system expression including pilot behavioral characteristics to get the takeoff distance for the runway overrun risk assessment. The results of case study showed that 3 types of pilot behavioral characteristics were found in selected fleet, which mainly differ in the pilot reaction time after VR and operation input speed of the control column, and RO is more likely to happen under the combined influence of longer reaction time and slower operation input speed. The proposed method makes full use of real flight data from airlines under different conditions incorporating pilot behavior characteristics and can inform risk analysis of other unsafe events such as hard landing, tail strike, etc.

Quantification of Campylobacter contamination on chicken carcasses sold in retail markets in the United Arab Emirates

BackgroundCampylobacter is among the leading causes of foodborne zoonotic disease worldwide, with chicken meat accounting for the majority of human illnesses. This baseline study generates the first quantitative data for Campylobacter contamination in the United Arab Emirates chicken meat. Such data will help inform risk analysis and develop evidence-based food safety management.MethodsFor a year, chilled whole chicken carcasses (n = 315) belonging to seven different companies were collected from retail supermarkets. According to standard methods, Campylobacter enumeration was achieved by a direct plating in all chicken samples, and isolates were confirmed using multiplex PCR.ResultsCampylobacter spp. were recovered from 28.6% (90/315) of the samples. Campylobacter enumeration results indicated that 71.4% of the tested samples were contaminated with < 1 log10 CFU (colony-forming units)/g, and 7% were contaminated with ≥3 log10 CFU/g. The mean Campylobacter concentration was 2.70 log10 CFU/g, with a standard deviation of 0.41 log10 CFU/g. Campylobacter counts varied significantly in relation to the sourcing chicken processing companies. Six out of the seven surveyed companies provided Campylobacter positive samples. Moreover, significantly higher (p-value< 0.0001) counts were found to be associated with smaller size chicken carcasses (weighted 600–700 g; compared to the other categories, 800 g and 900–1000 g). Interestingly, C. coli was present in 83% of the positive samples, while C. jejuni was only detected in 6.4% of the samples. Compared with studies from other countries utilizing the same enumeration method, the UAE chicken appears to have a lower prevalence but a higher Campylobacter count per gram of carcasses. Higher Campylobacter counts were significantly associated with smaller carcasses, and C. coli was the dominant species detected in this study’s samples.ConclusionThese results add to our understanding of the local, regional and global epidemiology of Campylobacter in chicken meat. Outputs of the current study may aid in developing a risk assessment of Campylobacter in the UAE, a country among the biggest per capita consumption markets for chicken meat worldwide.

A data-driven Bayesian network model integrating physical knowledge for prioritization of risk influencing factors

The coupling of multiple factors stemming from propagation effects and interdependency relationships among risks is prone to generate major accidents. It is of necessity to develop a feasible model with limited cases, which can generate reliable causal relationship evolution. To prioritize risk-influencing factors (RIFs) and investigate their relationships, we proposed a data-driven Bayesian Network (BN) model integrating physical information for risk analysis. Based on collected data, we combined prior knowledge with structure learning and parameter learning to obtain a BN model. In structure learning, we compared three structure learning algorithms including Bayesian search (BS), greedy thick thinning (GTT), and PC algorithm to obtain a robust directed acyclic graph (DAG). In parameter learning, we selected the expectation maximization (EM) algorithm to quantify the dependence and determine the probability distribution of node variables. This study provides a method to capture crucial factors and their interdependent relationships. To illustrate the applicability of the model, we developed a data-driven BN by taking the blowout accident as the case study. Eventually, we introduced vulnerability and resilience metrics for prioritizing risks through network propagation to conduct emergency plans and mitigation strategies.

Review of Ship Behavior Characteristics in Mixed Waterborne Traffic

Through the continuous development of intellectualization, considering the lifecycle of ships, the future of a waterborne traffic system is bound to be a mixed scenario where intelligent ships of different autonomy levels co-exist, i.e., mixed waterborne traffic. According to the three modules of ships’ perception, decision-making, and execution, the roles of humans and machines under different autonomy levels are analyzed. This paper analyzes and summarizes the intelligent algorithms related to the three modules proposed in the last five years. Starting from the characteristics of the algorithms, the behavior characteristics of ships with different autonomous levels are analyzed. The results show that in terms of information perception, relying on the information perception techniques and risk analysis methods, the ship situation can be judged, and the collision risk is evaluated. The risk can be expressed in two forms, being graphical and numerical. The graphical images intuitively present the risk level, while the numerical results are easier to apply into the control link of ships. In the future, it could be considered to establish a risk perception system with digital and visual integration, which will be more efficient and accurate in risk identification. With respect to intelligent decision-making, currently, unmanned ships mostly use intelligent algorithms to make decisions and tend to achieve both safe and efficient collision avoidance goals in a high-complexity manner. Finally, regarding execution, the advanced power control devices could improve the ship’s maneuverability, and the motion control algorithms help to achieve the real-time control of the ship’s motion state, so as to further improve the speed and accuracy of ship motion control. With the upgrading of the autonomy level, the ship’s behavior develops in a safer, more efficient, and more environment-friendly manner.

Risk assessment for gas transmission station based on cloud model based multilevel Bayesian network from the perspective of multi-flow intersecting theory

Accidents occurred in the natural gas transmission stations may result in casualties, equipment damage, property loss and even political and ecological impact. It is necessary to propose an approach for the gas transmission stations risk assessment so as to identify risk factors and prevent accidents. However, insufficient attention has been paid to the complex interactions and coupling patterns of these factors with current risk assessment methods. Hence, a novel risk analysis strategy is needed for industrial site risk assessment. Given industrial site risk factors, the multi-flow intersecting theory (MIT) is first defined to consider material, information and behavior risk analysis. Then the multilevel Bayesian network is constructed to represent these factors from different flows. To handle issues of insufficient statistical data and subjectivity associated with expert judgement, cloud model and fuzzy Bayesian are incorporated. For the defuzzification, existing cloud model is improved by introducing the similarity degree of standard cloud and evaluation cloud. Then the prior and conditional probabilities obtained from cloud model are input into Bayesian network for further reasoning and accident probability prediction. To validate the utility of the proposed method, the gas transmission startup process was chosen for risk assessment.

A digital twin framework for product to-be-designed analysis based on operation data

Product design needs to take full account of requirements from the lifecycle of product. However, researcher considered more requirements from product manufacturing/assemble stage, and less requirements from product operation stage. Identifying subsystems to be improved and risk analysis is two methods applied to product design considering the effect of failure at product operation stage. But these methods are labor-intensive and time-consuming tasks and need a lot of expert knowledge. There are huge amounts of data including inspection reports and maintenance records in product operation stage. These data can offer meaningful feedback on next-generation product design. The past few years, digital twin has gained considerable attention because it is characterized by two-way interactions between the digital and physical worlds. This paper proposes a digital twin (DT) framework for product to-be-designed analysis based on operation data. DT data is collected and store from the operation stage of current product. DT data is processed and analyzed to acquire the operation knowledge by Clustering algorithm, Frequent Pattern-growth algorithm, Multi-attribute decision-making, etc. The failure knowledge is used for the design of next-generation product. It can help designer to identify subsystems to be improved and offer the failure information for risk analysis. The failure knowledge also predicts the easily failure subsystems at operation stage of current product. A case of Tunnel Boring Machine is given to illustrate the implementation process of the framework and realize the part function of the framework.

A novel risk assessment approach for strait/canal security evaluation along the 21st Century Maritime Silk Road

The 21st Century Maritime Silk Road (MSR) designed by China is of great importance to maritime transportation, economic development, and environmental protection. However, the straits/canals along the MSR have been struggling with pirate attacks, terrorism, and accidents which pose challenges for the security of MSR. There is a strong need for further investigation in strait/canal security evaluation. However, the traditional risk modelling approach used in risk assessment indicates challenges due to its incapability of dealing with incomplete data, uncertainties, and subjective judgment. Thus, we propose a novel strait/canal security assessment framework to evaluate the security of the strait/canal along the MSR on the basis of a fuzzy evidential reasoning approach. The subjective risk analysis information collection and processing process from multiple experts can be embedded in the framework in a systematic way to provide maritime stakeholders to evaluate maritime security along the MSR. The results provide decision makers with useful insights and standard tools on enhancing strait/canal security, effective routes planning as well as operational efficiency.

Including Host Availability and Climate Change Impacts on the Global Risk Area of Carpomya pardalina (Diptera: Tephritidae)

Fruit flies are a well-known invasive species, and climate-based risk modeling is used to inform risk analysis of these pests. However, such research tends to focus on already well-known invasive species. This paper illustrates that appropriate risk modeling can also provide valuable insights for flies which are not yet “on the radar.”Carpomya pardalinais a locally important cucurbit-infesting fruit fly of western and central Asia, but it may present a risk to other temperate countries where melons are grown. MaxEnt models were used to map the risk area for this species under historical and future climate conditions averaged from three global climate models under two shared socio-economic pathways in 2030 and 2070 from higher climate sensitivity models based on the upcoming 2021 IPCC sixth assessment report. The results showed that a total of 47.64% of the world’s land mass is climatically suitable for the fly; it could establish widely around the globe both under current and future climates with host availability. Our MaxEnt modeling highlights particularly that Western China, Russia, and other European countries should pay attention to this currently lesser-known melon fly and the melons exported from the present countries. The current and expanding melon trade could offer direct invasion pathways to those regions. While this study offers specific risk information onC. pardalina, it also illustrates the value of applying climate-based distribution modeling to species with limited geographic distributions.

Multicenter Analysis of Cardiometabolic-Related Diagnosesin Transgender Adolescents

Background: Studies of transgender adults on gender affirming hormone therapy have shown increased rates of cardiovascular events (myocardial infarction and stroke) and features of metabolic syndrome including increased body mass index (BMI, transgender males) and dyslipidemia (transgender males and females). Studies in youth are limited by mostly small, single-center studies. With 1.8% of adolescents identifying as transgender, and increasing referral rates to gender clinics for care, more information is needed to better inform clinicians, patients and families about potential health risks in this cohort. PEDSnet, a pediatric Learning Health System, captures data from some of the largest pediatric care institutions in the U.S. We aimed to evaluate differences in diagnoses related to metabolic syndrome or cardiometabolic risk among youth with a diagnosis of gender dysphoria compared to matched cisgender controls. Methods: All youth with a diagnosis of gender dysphoria (n=4,177) and at least one outpatient encounter were extracted from the PEDSnet database (6 sites, years 2009-2019) and propensity-score matched on 7 variables (year of birth, age at last visit, site, race, ethnicity, insurance status, duration in database) to 4 controls without gender dysphoria (n=16,664). The odds of having a diagnosis of overweight/obesity, hypertension, dyslipidemia, dysglycemia, liver dysfunction, polycystic ovary syndrome (PCOS) or metabolic syndrome, was examined using generalized estimating equations with an interaction term for sex (as listed in the chart). Results: Of the transgender youth, 66% were female sex, 73% were white race, 9% were of Hispanic ethnicity, and 61% had private insurance. The control group was similar: 67% female sex, 72% white, 9% Hispanic, and 61% had private insurance. Transgender youth and controls both had on average 7 years duration in the database and were 16.2 years old at last visit. Transgender youth had higher odds of dyslipidemia (OR: 1.6 [95% CI: 1.3, 1.8], p<0.0001) and metabolic syndrome diagnoses (1.9 [95% CI: 1.2, 3.0], p=0.0086), with no significant difference based on sex, as compared to controls. Transgender youth with a female sex (though not male sex) had higher odds of overweight/obesity (1.7 [95% CI: 1.5, 1.9], p<0.0001) and PCOS diagnoses (1.9 [95% CI: 1.3, 2.8], p=0.0006), as compared to controls. There was no significant difference in the odds of having a diagnosis of hypertension, dysglycemia or liver dysfunction in transgender youth compared to controls. Conclusions: This large, geographically diverse cohort of transgender youth had a higher odds of being diagnosed with dyslipidemia and metabolic syndrome (both sexes), as well as elevated BMI or PCOS among those with a female sex listed. Future investigations will include analyses based on GAHT prescriptions and will further inform risk analysis and monitoring guidelines in this population.

Delay Identification of the HVAC Project on West Madura Offshore Oil Company Using the House of Risk Method

This research is focused on the analysis of delays or risks to the implementation of the "Procurement and Installation HVAC Installation" contract at West Madura Offshore Oil Company, which has a working area in the Offshore area. This study uses the HOR (House of Risk) method as an alternative to the ISO31000: 2009 risk management method that has been used internally by West Madura Offshore Oil Company The basis for selecting the HOR (House of Risk) method is the existence of a tool to see the impact (severity) and relations (relations) between the event of a delay and the factoring agent. In identification of the cause of the delay the method used are collecting data primary through Focus Group Discussion between Internal Expert and Secondary data which use project data for Offshore HVAC Project in the period of the year 2016-2018. The HOR (House of Risk) method in conducting this process is presented as follow review historical data and related document, Focus Group Discussion as information gathering techniques, Risk analysis and Risk Register. There are five significant risks from the results of research using the HOR (House of Risk) method, namely Poor-quality shop and coordination drawings, weather constraints, limited quantity and quality of HR (contractors and subcontractors), Absence of duct fabrication workshops at the site and lack of communication between contractor and subcontractor.

Assessing the use of animal health platforms: User's needs, preferences and constraints.

Animal health information systems or risk analysis tools are indispensable not only for animal health surveillance, but also to observe the evolution and risk of disease incursion into a disease-free area. Given the various information that can be derived from these both animal information systems and risk analysis tools, different international and national organizations have customized or created their own systems/tools to provide specific information for use by the respective countries. Moreover, with the increase of technology and data storage, they have become more accessible and widely used by professionals in animal and human health sciences. This study aimed to establish user's preferences, needs and constraints in respect of these animal information systems and risk analysis tools. An online survey was conducted and answered by 213 respondents from 132 countries. The respondents were animal health or public health professionals in different employment sectors (mostly in government, research and university institutions) and various fields of competency (highest for animal and public health). The majority of respondents used the animal health information systems frequently and on a weekly basis, with prevention measures of diseases being regarded as the most useful information. Descriptive epidemiology was more used/needed than analytical epidemiology. Risk analysis was performed by the majority of the respondents (70%), using a qualitative approach more than a quantitative or semi-qualitative. The primary objectives were to produce risk assessment and preparedness in areas involving origin and spread of animal diseases. The features most sought after in risk analysis tools were pathways of introduction and spread assessment. The level of satisfaction was higher for the platform which is most used by the respondents. Overall, these results could be taken into consideration when improving an already available platform, or when creating a new efficient tool.

ACTUARY METHODS FOR ANALYSIS OF INFORMATION RISKS

The article discusses actuarial methods of information risk analysis. The concept of information risk is given, and strategies and methods of risk management are described. The fund estimates for losses in the individual risk model, in the collective risk model and in the Shuette Nesbitt model are considered. Methods for assessing the required size of the fund and insurance premium are considered. The estimates obtained can be used both by the risk holder to form the fund necessary to cover losses (risk financing method), and by insurance organizations to calculate the insurance premium.

A Risk Analysis Framework for Prioritizing and Managing Biosecurity Threats.

The increasing need to manage biosecurity threats, such as diseases, zoonoses, and biological weapons, poses serious challenges for risk analysts and policymakers. These threats are large in number, can occur concurrently, and may cause multiple tangible and intangible impacts. They often have an emerging nature, exacerbated by incomplete evidence about their probability of occurrence and potential impacts. There is also a limited amount of time and resources available to evaluate the risks posed by each threat, and it is difficult to learn from past projects. On the other hand, there is also a need to provide policymakers with transparent and consistent threat prioritizations, together with evidence-based recommendations. In response to these challenges, we propose a risk analysis framework for the prioritization and management of biosecurity threats. The framework encompasses key design choices that analysts may use in risk analysis projects along three dimensions: risk support, risk group, and risk organization. The framework has prescriptive value, as a design tool to inform risk analysis projects in this context, along with descriptive value, as a learning tool to understand past projects. We applied the framework prescriptively in two biosecurity threat prioritization projects for the UK Department for Environment, Food and Rural Affairs, and illustrate its descriptive value by reporting our experience of these projects as in-depth case studies. Overall, the proposed framework provides important insights into the impact of different design choices on the success of risk analysis projects for biosecurity threat prioritizations.

Role of water chemistry on stability, aggregation, and dissolution of uncoated and carbon-coated copper nanoparticles

Intentional or accidental release of copper nanoparticles (Cu-NPs) from consumer products during manufacturing, use, and end-of-life management could pose health and ecological risks. This paper presents a detailed study on the role of water chemistry on the fate of uncoated and carbon-coated Cu-NPs dispersed in aqueous cetyltrimethylammonium bromide (CTAB) surfactant in the presence and absence of humic acids (HAs). A range of water chemistry and HAs had minimum impact on hydrodynamic diameter and zeta-potential values of uncoated and carbon-coated Cu-NPs. The water pH significantly (p < 0.001) affected the aggregation of uncoated Cu-NPs unlike that of carbon-coated Cu-NPs; however, the presence of HAs increased the stability of uncoated Cu-NPs. Although CTAB is considered as an efficient dispersant to stabilize Cu-NPs, the effect descended with time for uncoated Cu-NPs. The dissolution of Cu over time decreased with increasing pH for both uncoated (0.5–50% weight) and carbon-coated (0.5–40% weight) Cu-NPs. However, carbon-coated Cu-NPs exhibited significant dissolution (p < 0.001) at neutral pH than uncoated Cu-NPs may be due to the additional carbon it acquired during coating. Increasing HAs concentration from 0 to 15 mg L−1 at pH 5.5 inhibited aggregations but enhanced dissolution of the uncoated and carbon-coated Cu-NPs. These findings inform risk analysis of Cu-NPs including how Cu-NPs fate, mobility and bioavailability are modulated by particles coating and dispersant, HAs presence, water chemistry and exposure time in dispersion media.