Risk Management Decision Support Research Articles

Research on Enterprise Financial Information Risk Identification and Dynamic Control Management Model Based on Genetic Algorithm

In the contemporary global economic landscape, the imperative for enterprise financial informatization as a catalyst for efficiency, cost reduction, and innovation is evident. However, the deepening integration of informatization introduces corresponding risks, necessitating the establishment of effective risk management systems. This paper proposes a genetic algorithm-based model for enterprise financial informatization risk management, aiming to offer precise and actionable solutions. The study integrates genetic algorithms to enhance the adaptability and flexibility of the risk management model in the dynamic information environment. Through in-depth research on corporate financial informatization risks, a multi-dimensional risk management control model is constructed, considering technical, organizational, and environmental factors. The genetic algorithm introduces a new perspective for model optimization, enabling efficient search and optimization capabilities. The model not only identifies but also controls and governs risks, providing timely intervention and effective risk management. Moreover, the genetic algorithm facilitates intelligent decision support for risk management by adapting to changing environments. Empirical analysis validates the model’s feasibility and effectiveness in real enterprise cases, emphasizing its practicality.

The risk of acquiring avian influenza from commercial poultry products and hen eggs: A qualitative assessment

High pathogenicity and low pathogenicity avian influenza (HPAI and LPAI) viruses primarily infect birds, but they can also cause illness in other species, including humans. Some avian influenza (AI) strains can cause fatality rates of over 50 % in human infections. In October 2021, there was a substantial increase in the number of AI infections reported in birds in the UK. Given concerns that more infected and/or contaminated poultry products might reach retail, a risk assessment was performed to ensure that advice relating to the handling and consumption of these products remained appropriate.The products considered in this risk assessment were commercial chicken and turkey products, farmed duck and geese products, and table eggs. The risk pathway included the likelihood animals or eggs from an infected flock would be sent for further processing, whether the resulting products would be released to retail after inspection, viral persistence during distribution and storage, and the ability of AI viruses to infect humans via the gastrointestinal route. The risk from any AI virus, not just the A(H5N1) strain that began circulating in 2021, was considered. Data was obtained from literature searches and FSA surveys.The risk assessment determined that the likelihood of human infection with AI from poultry products for the UK population from handling and consuming commercial chicken or turkey products was negligible with low uncertainty, and for farmed duck and geese products was very low with medium uncertainty. The likelihood of infection for people in the UK from handling and consuming hen table eggs was very low with low uncertainty. The uncertainty rankings relate to the differing amounts of data available for each group of poultry products. The severity of illness in humans from AI infection was considered high with medium uncertainty. The conclusions of this risk assessment for UK consumers largely reflected advice and assessments from other countries and previous UK assessments. Given this, current guidance for handling and consuming poultry products was considered appropriate despite the increase in infections in birds during the 2021/22 and 2022/23 avian flu seasons. Since AI viruses were considered generally, these risk characterisations may need to be revisited based on evidence specific to a circulating virus to support risk management decisions.

A risk decision support model for hydrogen refueling stations based on cloud-analytic hierarchy process

With the rapid development of the hydrogen energy industry and the vigorous promotion of the application of hydrogen fuel cell vehicles, hydrogen refueling station (HRS) has gradually penetrated into cities, mostly built in densely populated areas. Once leakage, fire, and/or explosion accident occurs, it is highly likely to cause casualties, property damage, and environmental pollution. In order to avoid HRS risks, this paper establishes a risk decision support model based on Cloud-Analytic Hierarchy Process for HRS. Firstly, the static and dynamic features of HRS emergency plan selection are analyzed, and the weight coefficient of each feature is determined by using the cloud analysis hierarchy process. Then, the case-based reasoning theory and the weighted Euclidean distance based on the cloud model are used to establish a decision support model based on emergency plan optimization. Finally, taking the HRS hydrogen storage tank fire as an example, the risk decision support model based on the cloud analysis hierarchical process is applied to form the optimal scheme of the HRS hydrogen storage tank fire emergency plan. The results show that the weighted Euclidean distance scheme selection method based on cloud model can be effectively combined with the weight acquisition method based on cloud analysis hierarchy process to provide decision support for HRS emergency risk management and control.

Applications of digital twin technology in construction safety risk management: a literature review

PurposeWith increasing complexity of construction projects and new construction processes and methods are adopted, more safety hazards are emerging at construction sites, requiring the application of the modern risk management methods. As an emerging technology, digital twin has already made valuable contributions to safety risk management in many fields. Therefore, exploring the application of digital twin technology in construction safety risk management is of great significance. The purpose of this study is to explore the current research status and application potential of digital twin technology in construction safety risk management.Design/methodology/approachThis study followed a four-stage literature processing approach as outlined in the systematic literature review procedure guidelines. It then combined the quantitative analysis tools and qualitative analysis methods to organize and summarize the current research status of digital twin technology in the field of construction safety risk management, analyze the application of digital twin technology in construction safety risk management and identify future research trends.FindingsThe research findings indicate that the application of digital twin technology in the field of construction safety risk management is still in its early stages. Based on the results of the literature analysis, this paper summarizes five aspects of digital twin technology's application in construction safety risk management: real-time monitoring and early warning, safety risk prediction and assessment, accident simulation and emergency response, safety risk management decision support and safety training and education. It also proposes future research trends based on the current research challenges.Originality/valueThis study provides valuable references for the extended application of digital twin technology and offers a new perspective and approach for modern construction safety risk management. It contributes to the enhancement of the theoretical framework for construction safety risk management and the improvement of on-site construction safety.

Analysis and Prediction of the Trend of Chinese and American Stock Market Indexes Based on ARIMA Model

The comparison and analysis of Chinese and American stock market indexes are important in the context of global financial markets. As the global economy becomes more interconnected, the trend of stock market indexes in China and the US has important implications for global investors and policymakers. Through in-depth study of the trend, influencing factors and forecasts of the Chinese and American stock market indexes, we can better provide decision support for international investment and risk management. This study adopts ARIMA model as the main analysis tool to fit and forecast the historical data of important indexes of Chinese and American stock markets. Additionally, this study discusses the factors affecting the Chinese and American stock market indexes, so as to better explain the volatility and periodicity of the stock market. The results of the study show that the predictions of the ARIMA model are close to the actual data, which has important implications for investors, financial analysts and policymakers. This study is significant for understanding the trend of Chinese and American stock market indexes and their influencing factors, and provides valuable information for investors, analysts and policymakers to help them formulate effective investment strategies and evaluate portfolio performance.

Landscape Pattern Identification and Ecological Risk Assessment Employing Land Use Dynamics on the Loess Plateau

The Loess Plateau region is characterized by fragmented habitats and ecological vulnerability. Analyzing the changes in land use and ecological risk within the region is of great significance for promoting high-quality development of the Loess Plateau. The study utilized land use data from 2000, 2010, and 2020 in the Loess Plateau region to assess the spatio-temporal variation in land use patterns and landscape ecological risks, aiming to provide valuable references and decision support for ecological risk management and sustainable development in the area. The results indicated that the main land use types in the region are grassland and cropland. From 2000 to 2020, forest, grassland, and water areas increased by 1.39 × 106, 6.25 × 105, and 7.09 × 104 ha, respectively. The impervious area increased rapidly, growing from 9.77 × 104 ha in 2000 to 1.85 × 106 ha in 2020. The cropland decreased by 1.82 × 106 ha from 2000 to 2020, with 4.61 × 105, 4.95 × 106, and 8.91 × 105 ha of cropland converted to forest, grassland, and impervious area, respectively. The fragmentation of the ecological landscape in the region has decreased, and the diversity and richness of landscape types have increased. The fragmentation of cropland, forest, and grassland has decreased, and landscape patches have become more concentrated. High-value areas of landscape ecological risk in the region show a trend of continuous aggregation, altering the dispersion pattern of high-risk areas. Currently, high-risk areas of landscape ecology in the Loess Plateau region are mainly concentrated in northern Shaanxi and some areas along the Yellow River, such as Yulin, Yan’an, Ordos, and others. Currently, the ecological environment remains a bottleneck constraining the high-quality development of the Loess Plateau. It is necessary to persist in coordinated governance and ecological engineering construction, and improving the quality of ecological environment is a prerequisite for consolidating the social foundation and leading the high-quality development of the ecological industry on the Loess Plateau.

Application of copula-based Bayesian network method to water leakage risk analysis in cross river tunnel of Wuhan Rail Transit Line 3

Water leakage typically infiltrates through bolt holes and segment joints, posing a threat to the stability of tunnel structures. In this paper, we develop a copula-based Bayesian network (CBN) method for dependency modeling of complex systems to conduct risk analysis and management of water leakage in operational tunnels. Thirteen potential factors are used to construct the water leakage risk system, and the permeate water volume is selected as the observation index of water leakage risk. An application to a realistic cross-river tunnel of Wuhan Rail Transit Line 3 is implemented to verify the effectiveness of the developed model. Through correlation analysis, the bolt failure rate and seal breakage and aging rate are diagnosed as critical factors with the greatest impact on the water leakage risk. Forward and backward reasoning enables the model to be dynamically updated to provide decision support for water leakage risk management. This research contributes to the following: (a) expressing the dependence of the factors affecting water leakage; (b) solving the problem of modeling uncertainty and information fusion; and (c) updating the model to realize the safety control of tunnel water leakage.

Research on Risk Evaluation of Hydropower Engineering EPC Project Based on Improved Fuzzy Evidence Reasoning Model

As clean renewable energy with strong advantages, hydropower plays an extremely important role in promoting green development and energy allocation patterns. Hydropower project construction is characterized by long duration, large scale, high cost, many participants, and complex construction conditions, and is closely related to the economy, society, and ecological environment, and its construction management mode and construction risk management have become the focus of extensive attention from all walks of life. In this paper, the risk evaluation index system of hydropower engineering EPC project is constructed, and the linear weighted combination method is introduced to determine the comprehensive weights based on the calculation of weights by sequential relationship method and entropy weight method, and the improved fuzzy normal distribution is introduced as the subordinate function distribution of fuzzy evaluation level based on DS evidence theory and fuzzy theory. The risk evaluation model of a hydropower engineering EPC project is also established. Meanwhile, the model was analyzed with hydropower project examples to verify the accuracy and practicality of the model, which can guide hydropower project stakeholders to manage hydropower project risks comprehensively, collaboratively, and efficiently, and provide decision support for hydropower project construction risk management.

Asset Portfolio Risk Management Study-Empirical Analysis Based on VaR Model

This research paper is based on an empirical analysis of portfolio risk management using the value-at-risk (VaR) model. First, the theoretical foundations of portfolio management are introduced, including portfolio theory, asset pricing models, risk management theory and methods, and VaR models. Then the research methodology is described, including data sources and processing, VaR model construction, and risk metric calculation and interpretation. The empirical results section shows the risk analysis and source identification of asset portfolios, the estimation of VaR model parameters and confidence intervals, and the results of VaR calculations and risk measures at different confidence levels. Finally, the results are discussed and analyzed, including the interpretation and comparison of the risk measure results and the advantages and disadvantages of the VaR model. The paper also provides practical risk management recommendations and decision support for investors based on the results of the study. Limitations of the study and future research directions are also discussed. Overall, insights into portfolio risk management using VaR models are provided and a useful reference for researchers and investors interested in this area.

Spatial Modeling of Flood-Vulnerability as Basic Data for Flood Mitigation

Identifying risks in flood-prone areas is necessary to support risk management decisions. This research was conducted to establish a vulnerability model of flood hazards in the city of Pontianak. The model was based on the scoring and weighting of biophysical factors. The AHP method and logical formulations were used to establish the model. The result showed that the accuracy of the model used by AHP to determine the vulnerability of floods was 80% in Pontianak City. The accuracy of the model using logical formulations to determine the vulnerability level of a flood was 84%. The Kappa accuracy value in model 1 is 76.7%. The model of flood vulnerability explains that most of Pontianak City has a very high level of flood vulnerability, which is 31,440,568.8 m2 or 29.11% of the total research area of 108,003,319.8 m2. The vulnerable area is 29,945,485.7 m2 or 27.73%, and the less safe area is 22,126,936.3 m2 or 20.49%, with the safe area being 24,490,328.7 m2or 22.67% of the total area. This research contributes to the government to establish policies regarding flood management and urban development in the future, and as an effort to mitigate against flooding. Doi: 10.28991/CEJ-2023-09-04-02 Full Text: PDF

Quantitative microbial spoilage risk assessment of plant-based milk alternatives by Geobacillus stearothermophilus in Europe

Geobacillus stearothermophilus is one of the predominant spoilers of UHT-treated food products, due to its extremely heat-resistant spores. However, the surviving spores should be exposed to temperature higher than their minimum growth temperature for a certain time to germinate and grow to spoilage levels. Considering the projected temperature increase due to climate change, the events of non-sterility during distribution and transportation are expected to escalate. Hence, the aim of this study was to build a quantitative microbial spoilage risk assessment (QMRSA) model to quantify the risk of spoilage of plant-based milk alternatives within Europe. The model consists of four main steps: 1. Initial contamination of raw materials 2. Heat inactivation of spores during UHT treatment 3. Partitioning 4. Germination and outgrowth of spores during distribution and storage. The risk of spoilage was defined as the probability of G. stearothermophilus to reach its maximum concentration (Nmax = 107.5 CFU/mL) at the time of consumption. The assessment was performed for North (Poland) and South (Greece) Europe, and the risk of spoilage was estimated for the current climatic conditions and a climate change scenario. Based on the results, the risk of spoilage was negligible for the North European region, while the risk of spoilage in South Europe was 6.2 × 10−3 95% CI (2.3 × 10−3;1.1 × 10−2) under the current climatic conditions. The risk of spoilage was increased for both tested countries under climate change scenario; from zero to 1.0 × 10−4 in North Europe, risk multiplied 2 or 3 in South Europe depending on air conditioning implementation at consumer’s place. Therefore, the heat treatment intensity and the use of insulated trucks during distribution were investigated as mitigation strategies and led to significant reduction of the risk. Overall, the QMRSA model developed in this study can support risk management decisions of these products by quantify the potential risk under current climatic conditions and climate change scenarios.

Learning-assisted intelligent risk assessment of highway project investment

Highway project has the characteristics of large investment scale and high investment risk. Aiming at the problem of investment risk management, this paper takes 15 highway investment projects in recent ten years as the research object, and establishes an investment risk index system including 12 first-class indexes and 30 second-class indexes. The hierarchical weight model of highway engineering investment risk assessment is proposed. The intelligent evaluation of highway engineering investment risk by extreme learning machine and broad learning system algorithm is discussed. The comparative experimental results show that the improved intelligent evaluation model can evaluate and predict the investment risk of highway engineering projects more effectively. The R-square value of the improved intelligent evaluation model is increased by 0.35, and the accuracy is greatly improved. It can provide decision support for highway engineering project investment risk management.

Effectiveness of Secondary Risk–Reducing Strategies in Patients With Unilateral Breast Cancer With Pathogenic Variants of BRCA1 and BRCA2 Subjected to Breast-Conserving Surgery: Evidence-Based Simulation Study

Approximately 62% of patients with breast cancer with a pathogenic variant (BRCA1 or BRCA2) undergo primary breast-conserving therapy. The study aims to develop a personalized risk management decision support tool for carriers of a pathogenic variant (BRCA1 or BRCA2) who underwent breast-conserving therapy for unilateral early-stage breast cancer. We developed a Bayesian network model of a hypothetical cohort of carriers of BRCA1 or BRCA2 diagnosed with stage I/II unilateral breast cancer and treated with breast-conserving treatment who underwent subsequent second primary cancer risk-reducing strategies. Using event dependencies structured according to expert knowledge and conditional probabilities obtained from published evidence, we predicted the 40-year overall survival rate of different risk-reducing strategies for 144 cohorts of women defined by the type of pathogenic variants (BRCA1 or BRCA2), age at primary breast cancer diagnosis, breast cancer subtype, stage of primary breast cancer, and presence or absence of adjuvant chemotherapy. Absence of adjuvant chemotherapy was the most powerful factor that was linked to a dramatic decline in survival. There was a negligible decline in the mortality in patients with triple-negative breast cancer, who received no chemotherapy and underwent any secondary risk-reducing strategy, compared with surveillance. The potential survival benefit from any risk-reducing strategy was more modest in patients with triple-negative breast cancer who received chemotherapy compared with patients with luminal breast cancer. However, most patients with triple-negative breast cancer in stage I benefited from bilateral risk-reducing mastectomy and risk-reducing salpingo-oophorectomy or just risk-reducing salpingo-oophorectomy. Most patients with luminal stage I/II unilateral breast cancer benefited from bilateral risk-reducing mastectomy and risk-reducing salpingo-oophorectomy. The impact of risk-reducing salpingo-oophorectomy in patients with luminal breast cancer in stage I/II increased with age. Most older patients with the BRCA1 and BRCA2 pathogenic variants in exons 12-24/25 with luminal breast cancer may gain a similar survival benefit from other risk-reducing strategies or surveillance. Our study showed that it is mandatory to consider the complex interplay between the types of BRCA1 and BRCA2 pathogenic variants, age at primary breast cancer diagnosis, breast cancer subtype and stage, and received systemic treatment. As no prospective study results are available at the moment, our simulation model, which will integrate a decision support system in the near future, could facilitate the conversation between the health care provider and patient and help to weigh all the options for risk-reducing strategies leading to a more balanced decision.

An Integrated Risk Management Framework for Global Supply Chains

In this study, a risk management framework is developed to support risk management decisions in global supply chains. The proposed framework covers all phases of risk management, namely, risk identification, risk miti-gation and control. In the risk identification phase of the framework, the supply chain is decomposed into either material-level or product-level sub-networks according to the decision maker’s preference. Afterwards, the most crit-ical sub-network is modelled to evaluate different risk mitigation strategies. In particular, a combination of redun-dancy and flexibility strategies is considered for risk mitigation. These strategies are evaluated by simulation models in terms of their effectiveness and efficiency. While inventory holding cost is used as efficiency measure, effective-ness of the strategies is measured by premium freight ratio. The proposed framework provides a comprehensive and reliable decision support since it covers all phases of risk management and relies on quantitative data, and statistical analysis in risk modelling. Moreover, it is flexible as it can be easily adapted to any change in supply chain environ-ment and strategy. In order to show the applicability of the framework, a practical demonstration is presented for a European automotive company. The results indicate that the proposed framework improves the supply chain perfor-mance in terms of efficiency and effectiveness.

Accident analysis and risk prediction of tank farm based on Bayesian network method

In recent decades, many attempts have been made to establish the cause-effect relationship model of accidents, while little work has been carried out to comprehensively consider the interdependence between the causal factors and their complex interactions with the accident outcomes. In this study, a novel accident analysis approach based on Bayesian networks (BNs) was proposed to achieve quantitative accident analysis and dynamic risk prediction of accident types and consequences. To develop the BN-based accident analysis model, a total of 1144 accident cases occurred in tank farm of China from 1960 to 2018 were collected. The BN model that can comprehensively characterize the dependencies among accident elements was established through structural learning based on accident case analysis and parameter learning based on EM algorithm. The reliability and validity of the BN model were verified by k-fold cross-validation method and comparison of predicted data with real data, and the results showed that the BN model had good classification and prediction performance. Furthermore, the established BN model was applied to the accident occurred in Huangdao, China. The analysis results show that not only the accident outcome can be accurately predicted, but also the hidden correlation can be deeply explored through the established BN model. The proposed method and findings can provide technical reference for accident investigation and analysis, and provide decision support for accident prevention and risk management.

An investigation of a risk management decision support system for Iraqi construction projects

Risk management (RM) is one of the critical success factors for the construction of projects, project participants generally do not have sufficient knowledge pertinent to risk management concept and the number of risk management support tools which facilitate the process are rather low. Research problem can be identify by the process of risk management, applying risk management in construction projects, types and identified, assessing and controlling of risk in construction projects. In order to facilitate risk management activities, decision support tools that will enable risk identification, analysis and response strategy formulation should be developed. Decision support tools are necessary for systematic identification of risks, scenario generation, proactive management of risk and integration of risk management activities with other project management functions such as planning, cost estimating and monitoring project success. The proposed decision support system, namely Integrated Risk Management System (IRMS), is designed to support the user at all phases of the risk management process. Major functions of IRMS include, risk analysis by Monte Carlo (MC) simulation (probability and rating degree), risk assessment by risk rating, risk re-assessment, response generation. The applicability of the system has been tested by a real case study (three Iraqi construction cases) and its functionality has been demonstrated using the data associated with these cases, study (three cases). Risk carting (RC) processes are reduced from high risk management to moderate ones for the three project cases.

Moving from routine to risk-based official controls in slaughterhouses: Development of a scoring tool for the risk of non-compliance with animal welfare regulations

Slaughter of animals is a necessary step of meat production process that takes place out of public view, but that is a subject of consumers' and civil society organisations’ concern. Public policies are progressing constantly to meet social expectations and optimize official controls in slaughterhouses. Regarding animal welfare, the main objective of official controls of food business operators (FBOs) is to ensure that animals are spared any avoidable pain, distress, or suffering during their killing and related operations. In this context of improving the efficiency of public action, the aim of this study was to develop a risk-based approach for the control of compliance with animal welfare regulations in red meat slaughterhouses. After a preliminary phase of data mapping, data qualitative assessment and expert knowledge elicitation, the statistical study was divided into four steps: (i) identification of risk factors associated with FBOs not being compliant with animal welfare requirements; (ii) development of a risk score formula to predict the risk of non-compliance with animal welfare regulations and assignment of a risk score to each process operating in a slaughterhouse; (iii) ranking of processes and slaughterhouses into different risk groups according to the score; (iv) validation of the model using data from the year 2020.Among the 16 variables included in the statistical model, five were found statistically associated with a higher risk of non-compliance with animal welfare regulations: (i) total annual slaughter volume (intermediate tonnage slaughterhouses being more at risk), (ii) predominance of the category of animals slaughtered (categories representing less than 15% of the total volume of the slaughterhouse being more at risk), (iii) stunning method (head only electrical stunning and slaughter without prior stunning being more risky techniques), (iv) economic situation of the slaughterhouse (a worrying financial situation being more at risk), and (v) the overall evaluation of the previous year's animal welfare control (D-ratings being particularly indicative of high risk). A risk score formula was proposed based on the regression coefficients of each modality of these five variables to predict the risk of non-compliance with animal welfare requirements. Risk scores (RS) calculated for each process ranged from 1 to 8.4. Processes and slaughterhouses were classified into three risk groups based on that score: low risk (RS ≤ 2.5), medium risk (2.5 < RS < 4.5), high risk (RS ≥ 4.5). The validation phase finally allowed to validate the risk score developed. This innovative and comprehensive study will support risk management decisions to improve animal welfare in slaughterhouses, by moving from routine to risk-based inspections and adjusting control efforts frequency and nature of official controls to the level of risk of the establishments. Similar approaches could be developed for poultry slaughterhouses and for food hygiene requirements.

Fall risk management using clinical decision support in a rural nursing-led primary care practice.

The leading cause of injuries among older adults in the United States is unintentional falls. The American Geriatrics Society/British Geriatrics Society promote fall risk management in primary care; however, this is challenging in low-resource settings. Archer Family Health Care (AFHC), an Advanced Practice Registered Nurse (APRN)-managed and federally designated rural health clinic, identified a care gap with falls adherence to guidelines for patients at higher risk for falls. The aim of this quality improvement effort was to integrate an evidence-based fall risk management tool in a rural nurse-managed primary care practice. A standardized fall risk management process with a new brief paper-based clinical decision support (CDS) tool was developed and tested in two phases. Phase 1 focused on developing a fall risk management CDS tool, identifying the primary care visit workflow, communicating the workflow patterns to the AFHC staff, and collaborating with the staff to identify when and who should implement the tool. Phase 2 focused on implementation of the fall risk management CDS tool into standard practice among older adults aged 65 years and older. We found that integrating the tool did not disrupt the workflow of primary care visits at AFHC. The most common recommended intervention for patients at risk of falling was daily vitamin D supplementation. This project revealed that it is feasible to introduce a brief fall risk management decision support tool in an APRN-managed rural primary care practice.

Constraining organic matter composition and dynamics as a dominant driver of hypoxic blackwater risk during river Murray floodplain inundation

AbstractThe ecology of floodplain ecosystems evolved according to the historical frequency and extent of inundation. In many dryland rivers, inundation frequency has reduced due to regulation, competition for water resources and climate change. In some rivers infrastructure has been constructed to enable temporary rises in water level and more frequent floodplain inundation, delivering environmental water to ecosystems that are dependent on surface water. During such events, the release of dissolved organic carbon (DOC) from inundated plant material may result in depletion of dissolved oxygen (DO). If the rate of DO depletion exceeds re‐aeration, hypoxic conditions can occur. Models have been developed to represent these processes, however, there is a wide range of chemical and biological interactions, and hence model parameters, involved. The aim of this research was: (1) identify the dominant parameters through sensitivity analysis, (2) design and implement a field monitoring program to quantify the inherent variability in those parameters, and (3) translate that input variability into the modelled DO, ultimately to support risk management decisions when planning delivery of environmental water. The results indicated that the mass of organic matter on a floodplain had the greatest impact on the modelled DO concentrations. Based on the field monitoring results, the steady state load of organic matter, that is, when the accumulation and decay rates are equal, was estimated, and vegetation mapping used to apply the field monitoring results across the floodplain for a 206 km reach of river of the River Murray, Australia. The model results from multiple, cumulative, operations identified one particular high‐risk operational site. The results are useful to prioritize monitoring effort to focus on the most sensitive model parameters and indicate that the likelihood of hypoxic conditions can be reduced through slower rates of inundation and timing events to coincide with periods with lower water temperature. The methodology developed can be transferred to other sites where hypoxic conditions are a potential outcome associated with delivery of environmental water.

Combined risk assessment method based on spatial interaction: A case for polycyclic aromatic hydrocarbons and heavy metals in Taihu Lake sediments

Owing to the complexity of sources and nonlinear dose-response relationship, evaluating the combined risks of pollutants in lakes remains a considerable challenge. In this study, we developed chose a non-parametric risk assessment method with source diagnostic and spatial interactions (LISA analysis) between the pollutants was developed for zoning the risks of combined pollution. Taihu Lake was chosen as the studied region, and the heavy metals and polycyclic aromatic hydrocarbons (PAHs) in the sediments were observed to test this method. Results showed that the developed method distinguished the “hot spots” of pollution in the eastern and northern Taihu Lake, and more than 50% pollutants were attributed to the combustion of petroleum fossil fuels. The main risk control area for heavy metals is mostly distributed in western Taihu Lake, accounting for 22.5% of the total area, while that for PAHs accounts for the smallest proportion, at only 3.4% of the total area. This study shows that the spatial interaction between pollutants could be a useful tool for evaluating the combined risks of pollutants, which can also provide better decision support for lake risk management.