RISK ASSESSMENT MODEL OF COASTAL SEA POLLUTION BY BLACK (SEWAGE) WATERS FROM VESSELS

2021 ◽  
Vol 160 (A4) ◽  
Author(s):  
Ž Koboević ◽  
Ž Kurtela ◽  
N Koboević
Keyword(s):  
Risk Assessment ◽  
Risk Index ◽  
International Standards ◽  
Assessment Model ◽  
Sensitivity Factor ◽  
Sensitivity Index ◽  
Economic Activities ◽  
Sea Pollution ◽  
Qualitative Risk Assessment ◽  
Coastal Sea

Qualitative risk assessment using the risk matrices recommended by International Maritime Organization (IMO) and International Standards Organization (ISO) cannot be used for the risk assessment of the pollution of precisely determined part of the coastal sea by black waters from various vessels. Therefore, an original model has been set for risk assessment by means of multiplicative matrices at three levels, allowing risk assessment for very complex assessments with a lot more input factors unlike the classic risk matrix that has two input factors (frequency of occurrence, intensity of consequences). The proposed model of risk assessment uses matrices which first determine the vessel risk index taking into consideration the factor of device for the processing of black waters and the factor of regulations that are applied to the respective vessels. Later, the location sensitivity index is determined, which takes into consideration the sensitivity factor of the location and the factor of impact on the location. Finally, at the third level the assessed risk of sea pollution by black waters is determined according to the type of vessel at precisely defined maritime zone locations. The offered model of risk assessment using multiplicative matrices has practical application and can be used also for many other risk assessments that take into consideration many input factors that affect the risk. The result of risk assessment of the pollution of the coastal sea can be used in decision-making in risk management for undertaking measures in order to protect the coastal sea, human health, and economic activities of a certain area in the coastal sea.

Risk Assessment Model of Coastal Sea Pollution by Black (Sewage) Waters From Vessels

2018 ◽  
Vol Vol 160 (A4) ◽  
Author(s):  
Z Koboević ◽  
Z Kurtela ◽  
N Koboević
Keyword(s):  
Risk Assessment ◽  
Risk Index ◽  
International Standards ◽  
Assessment Model ◽  
Sensitivity Factor ◽  
Sensitivity Index ◽  
Economic Activities ◽  
Sea Pollution ◽  
Qualitative Risk Assessment ◽  
Coastal Sea

Qualitative risk assessment using the risk matrices recommended by International Maritime Organization (IMO) and International Standards Organization (ISO) cannot be used for the risk assessment of the pollution of precisely determined part of the coastal sea by black waters from various vessels. Therefore, an original model has been set for risk assessment by means of multiplicative matrices at three levels, allowing risk assessment for very complex assessments with a lot more input factors unlike the classic risk matrix that has two input factors (frequency of occurrence, intensity of consequences). The proposed model of risk assessment uses matrices which first determine the vessel risk index taking into consideration the factor of device for the processing of black waters and the factor of regulations that are applied to the respective vessels. Later, the location sensitivity index is determined, which takes into consideration the sensitivity factor of the location and the factor of impact on the location. Finally, at the third level the assessed risk of sea pollution by black waters is determined according to the type of vessel at precisely defined maritime zone locations. The offered model of risk assessment using multiplicative matrices has practical application and can be used also for many other risk assessments that take into consideration many input factors that affect the risk. The result of risk assessment of the pollution of the coastal sea can be used in decision-making in risk management for undertaking measures in order to protect the coastal sea, human health, and economic activities of a certain area in the coastal sea.

scholarly journals Risk Assessment of Cold Damage to Maize Based on GIS and a Statistical Model

2015 ◽  
Vol 9 (1) ◽  
pp. 236-242
Author(s):  
Zhewen- Zhao ◽  
Jingfeng- Huang ◽  
Zhuokun- Pan ◽  
Yuanyuan- Chen
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Risk Index ◽  
Growth Period ◽  
Maize Yield ◽  
Assessment Model ◽  
Sensitivity Index ◽  
Climate Data ◽  
Local Climate ◽  
Cold Damage

Cold damage to maize is the primary meteorological disaster in northwest China. In order to establish a comprehensive risk assessment model for cold damage to maize, in this study, risk models and indices were developed from average daily temperature and maize yield and acreage data in 1991-2012. Three northwest provinces were used to calculate the temperature sum during the growth period, temperature departure over the years and relative meteorological yield in order to obtain the climate risk index, risk sensitivity index and damage assessment index. Using the geographic information system (GIS) and cold damage risk indices obtained from the statistical assessment model, the studied area was divided into four risk regions: low, medium, medium-high and high. Northeast and southwest Gansu were grouped to the high-risk region; west Shaanxi and north NHAR were grouped into to the low-risk region; all other areas fell into medium and medium-high risk regions. Our results can help growers avoid cold damage to maize using local climate data and optimize the structure and layout of maize planting. It is of significance in guiding the agricultural production in the three northwest provinces in China and also can serve as a reference in modeling risk assessment in other regions.

scholarly journals Qualitative Risk Assessment of Cybersecurity and Development of Vulnerability Enhancement Plans in Consideration of Digitalized Ship

2021 ◽  
Vol 9 (6) ◽  
pp. 565
Author(s):  
Yunja Yoo ◽  
Han-Seon Park
Keyword(s):  
Risk Assessment ◽  
Safety Management ◽  
International Standards ◽  
Security Risk ◽  
Analytic Hierarchy ◽  
Improvement Plan ◽  
Qualitative Risk Assessment ◽  
Cyber Risk ◽  
Hierarchy Process ◽  
Ship Safety

The International Maritime Organization (IMO) published the Guidelines on Maritime Cyber Risk Management in 2017 to strengthen cybersecurity in consideration of digitalized ships. As part of these guidelines, the IMO recommends that each flag state should integrate and manage matters regarding cyber risk in the ship safety management system (SMS) according to the International Safety Management Code (ISM Code) before the first annual verification that takes place on or after 1 January 2021. The purpose of this paper is to identify cybersecurity risk components in the maritime sector that should be managed by the SMS in 2021 and to derive priorities for vulnerability improvement plans through itemized risk assessment. To this end, qualitative risk assessment (RA) was carried out for administrative, technical, and physical security risk components based on industry and international standards, which were additionally presented in the IMO guidelines. Based on the risk matrix from the RA analysis results, a survey on improving cybersecurity vulnerabilities in the maritime sector was conducted, and the analytic hierarchy process was used to analyze the results and derive improvement plan priority measures.

scholarly journals MATHEMATICAL RISK ASSESSMENT MODEL FOR BIODIESEL PRODUCTION PROJECTS IN UKRAINE AGRICULTURE

2021 ◽  
Vol 2 (37) ◽  
pp. 280-286
Author(s):  
O. Halytskyi ◽  
М. Polenkova ◽  
O. Fedirets ◽  
O. Brezhnieva-Yermolenko ◽  
S.` Hanziuk
Keyword(s):  
Mathematical Model ◽  
Risk Assessment ◽  
Jel Classification ◽  
Assessment Model ◽  
Biodiesel Production ◽  
Economic Activities ◽  
Methodological Foundation ◽  
Agriculture Risk ◽  
Single Standard ◽  
Level Of Risk

Abstract. One of the trends in the development of the market of alternative motor fuels is the production and use of biofuels, biodiesel in particular. Biodiesel which is used by domestic farmers is mainly self-produced. The current situation is related, first of all, to the lack of a single standard (regulation) for biodiesel production technology and is not enshrined in any legal act in Ukraine. In the conditions of the market functioning, agricultural producers face various risk factors, in particular, instability of prices for fuels and lubricants, monopolization of certain regions or market segments by traders, low quality of fuel, etc. Conditions of biodiesel production, as well as other economic activities, usually require the creation or involvement of labor, financial and material resources, which also affects the change in the level of risk. These problems can be solved by adapting and improving the existing mathematical apparatus to risk assessment for biodiesel production projects by agricultural enterprises. The main legal act that allows to determine and assess the level of risk is the state standard of Ukraine «Risk Management. Methods of general risk assessment», which served as the methodological foundation of the study. We propose to use three main technological schemes of biodiesel production, namely: cyclic scheme of production with the use of catalysts; non-catalytic cyclic circuit and multi-reactor continuous circuit scheme. In order to analyze each of these schemes, it is proposed to analyze the feasibility of investment in terms of their effectiveness and tie-in to the risks of introducing innovative technologies. The developed methodology provides a substantiation for the choice of technological option for biodiesel production. An algorithm for calculating risks has been proposed for the introduction of biodiesel production, the preparation of business plans and the assessment of criticality of possible losses for the production. The use of methods of vector algebra and fuzzy logic in the formation of the mathematical model makes it possible to estimate the probability indicators of each risk. Keywords: biodiesel, risks, mathematical model, agriculture, risk assessment, risk assessment methods. JEL Classification C60, Q42 Formulas: 8; fig.: 0; tabl.: 0; bibl.: 18.

scholarly journals Determining the Risk Level of Heavy Rain Damage by Region in South Korea

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 219
Author(s):  
Jongsung Kim ◽  
Donghyun Kim ◽  
Myungjin Lee ◽  
Heechan Han ◽  
Hung Soo Kim
Keyword(s):  
Risk Assessment ◽  
South Korea ◽  
Risk Index ◽  
National Level ◽  
Heavy Rain ◽  
Quantitative Risk Assessment ◽  
Risk Level ◽  
Disaster Prevention ◽  
Qualitative Risk Assessment ◽  
Regional Risk

For risk assessment, two methods, quantitative risk assessment and qualitative risk assessment, are used. In this study, we identified the regional risk level for a disaster-prevention plan for an overall area at the national level using qualitative risk assessment. To overcome the limitations of previous studies, a heavy rain damage risk index (HDRI) was proposed by clarifying the framework and using the indicator selection principle. Using historical damage data, we also carried out hierarchical cluster analysis to identify the major damage types that were not considered in previous risk-assessment studies. The result of the risk-level analysis revealed that risk levels are relatively high in some cities in South Korea where heavy rain damage occurs frequently or is severe. Five causes of damage were derived from this study—A: landslides, B: river inundation, C: poor drainage in arable areas, D: rapid water velocity, and E: inundation in urban lowlands. Finally, a prevention project was proposed considering regional risk level and damage type in this study. Our results can be used when macroscopically planning mid- to long-term disaster prevention projects.

scholarly journals Spatial Distribution, Pollution Source, and Health Risk Assessment of Heavy Metals in Atmospheric Depositions: A Case Study from the Sustainable City of Shijiazhuang, China

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 222 ◽  
Author(s):  
Kui Cai ◽  
Chang Li ◽  
Sanggyun Na
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Health Risk ◽  
Health Risk Assessment ◽  
Risk Index ◽  
Assessment Model ◽  
Pollution Level ◽  
Pb Isotope ◽  
Atmospheric Depositions ◽  
Skin Contact

Samples of atmospheric depositions from five types of functional areas in Shijiazhuang, Hebei Province, China, were collected, and the concentrations of six toxic heavy metals (Cd, Cr, Cu, Pb, Ni, and Zn) were measured. Geographic information system, Pb isotope assessment, multivariate statistical analysis (principal component analysis, PCA), the geoaccumulation index (Igeo), potential ecological risk index (PERI), and a health risk assessment model were used to study the degree of pollution, identify sources of pollution, and assess the health risks to children and adults via three pathways (hand–mouth intake, skin contact, and respiration). The results show that the high traffic volume and exhaust gas emissions have led to high concentrations of heavy metals. The Igeo and PERI values of Cd (0.38–2.0 and 108–4531, respectively), indicating the present high pollution level and potential risk, respectively, varied the most. Pb isotope and PCA showed that Pb, Zn, and Cd from atmospheric deposition come from power plants and traffic—Cu is related to traffic, and Ni and Cr come mainly from soil particles (natural source). The health risk assessment showed that heavy metals in atmospheric depositions are at a safe level in the study area.

scholarly journals Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

10.2196/18186 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e18186
Author(s):  
Weijia Chen ◽  
Zhijun Lu ◽  
Lijue You ◽  
Lingling Zhou ◽  
Jie Xu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Surgical Site Infection ◽  
Risk Index ◽  
External Validation ◽  
The Self ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Site Infection ◽  
Attention Network ◽  
The Third

Background Surgical site infection (SSI) is one of the most common types of health care–associated infections. It increases mortality, prolongs hospital length of stay, and raises health care costs. Many institutions developed risk assessment models for SSI to help surgeons preoperatively identify high-risk patients and guide clinical intervention. However, most of these models had low accuracies. Objective We aimed to provide a solution in the form of an Artificial intelligence–based Multimodal Risk Assessment Model for Surgical site infection (AMRAMS) for inpatients undergoing operations, using routinely collected clinical data. We internally and externally validated the discriminations of the models, which combined various machine learning and natural language processing techniques, and compared them with the National Nosocomial Infections Surveillance (NNIS) risk index. Methods We retrieved inpatient records between January 1, 2014, and June 30, 2019, from the electronic medical record (EMR) system of Rui Jin Hospital, Luwan Branch, Shanghai, China. We used data from before July 1, 2018, as the development set for internal validation and the remaining data as the test set for external validation. We included patient demographics, preoperative lab results, and free-text preoperative notes as our features. We used word-embedding techniques to encode text information, and we trained the LASSO (least absolute shrinkage and selection operator) model, random forest model, gradient boosting decision tree (GBDT) model, convolutional neural network (CNN) model, and self-attention network model using the combined data. Surgeons manually scored the NNIS risk index values. Results For internal bootstrapping validation, CNN yielded the highest mean area under the receiver operating characteristic curve (AUROC) of 0.889 (95% CI 0.886-0.892), and the paired-sample t test revealed statistically significant advantages as compared with other models (P<.001). The self-attention network yielded the second-highest mean AUROC of 0.882 (95% CI 0.878-0.886), but the AUROC was only numerically higher than the AUROC of the third-best model, GBDT with text embeddings (mean AUROC 0.881, 95% CI 0.878-0.884, P=.47). The AUROCs of LASSO, random forest, and GBDT models using text embeddings were statistically higher than the AUROCs of models not using text embeddings (P<.001). For external validation, the self-attention network yielded the highest AUROC of 0.879. CNN was the second-best model (AUROC 0.878), and GBDT with text embeddings was the third-best model (AUROC 0.872). The NNIS risk index scored by surgeons had an AUROC of 0.651. Conclusions Our AMRAMS based on EMR data and deep learning methods—CNN and self-attention network—had significant advantages in terms of accuracy compared with other conventional machine learning methods and the NNIS risk index. Moreover, the semantic embeddings of preoperative notes improved the model performance further. Our models could replace the NNIS risk index to provide personalized guidance for the preoperative intervention of SSIs. Through this case, we offered an easy-to-implement solution for building multimodal RAMs for other similar scenarios.

scholarly journals A risk assessment model of WSMC projects under emergencies

2021 ◽  
Author(s):  
Xiaosheng Wang ◽  
Wei Li ◽  
Haiying Guo ◽  
Ran Li
Keyword(s):  
Risk Assessment ◽  
Risk Index ◽  
Water Saving ◽  
Risk Tolerance ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Water Users ◽  
Theoretical Support ◽  
Management Contract ◽  
The Impact

Abstract As a novel market-based water-saving mechanism, the Water Saving Management Contract (WSMC) project faces interruption risk caused by emergencies like the coronavirus disease-2019 (COVID-19) pandemic. An interruption risk assessment model of WSMC projects is established through a quantitative evaluation of the impact of emergencies on water users based on input-output theory. First, the concept of the interruption risk index (IRI) is defined as a function of the duration of enterprise shutdown (DES). Second, the DES is divided into two parts: the duration caused by COVID-19 and the that under other types of emergencies. Third, the risk tolerance threshold is given to estimate the interruption result, and its different consequences are discussed. Finally, a WSMC project in China is taken as a case study, and its interruption risks are analysed. The results show that the IRIs of this WSMC in both 2020 and 2021 are theoretically greater than the risk tolerance thresholds, and the high pandemic prevention standards and conservative pandemic estimates are the main reasons for the above results. The model established in this study provides a reference for WSMC participants to deal with emergencies and provides the theoretical support for the extension of the WSMC.

scholarly journals The Risk Characteristics of Heavy Metals in Urban Soil

2021 ◽  
Author(s):  
Yan Li ◽  
Dike Feng ◽  
Meiying Ji ◽  
Zhanpeng Li ◽  
Ruocheng Zhang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Heavy Metal ◽  
High Risk ◽  
Human Health ◽  
Rapid Development ◽  
Risk Index ◽  
Principal Component ◽  
Research Area ◽  
Assessment Model

Abstract With the rapid development of China's industrial economy, heavy metals and other pollutants continue to accumulate in the environment, which has created serious threats for the ecological environment and human health. To comprehensively evaluate the ecological risks from heavy metals in the soil in Nanjing, China, as well as the status of the risks to human health, this study randomly collected 50 surface soil samples, and the contents of Al, Ca, Fe, Mg, Mn, Ni, Ti, Cd, Cr, Cu, Pb and Zn in the samples were determined, combined with the ecological risk index and the USEPA health risk assessment model for a comprehensive risk assessment of soil heavy metals in Nanjing. The results show that there has been heavy metal enrichment of Mn, Pb, Zn and other heavy metals in the research area in Nanjing city, and the variation coefficients of Pb and Cu are distinctly large; that is, the distribution of Pb and Cu in the research area shows a great fluctuation. These elements are all slightly polluting, among which the Cu heavy metal pollution degree is different, and Pb element pollution is the most serious. Children are at a high risk of exposure in various ways, among which Pb and Cu elements have a high risk of causing non-carcinogenic issues. Overall, Pb and Cu in Nanjing are important risk elements that should be monitored and controlled. The results of the correlation analysis showed that the content changes of Pb, Zn and Cu; Ni, Ti and Fe; and Zn and Pb had extremely significant correlations, indicating that they may have the same source; while Ti and Ca, Ti and Cu, and Pb and Zn showed opposite changes, indicating that their concentrations were inversely related. The results of the principal component analysis showed that industrial sources in Nanjing contributed the most heavy metals, reaching 34.4%. The second largest source was from parent material and fertilizer, which contributed 32.3% and 19.6%, respectively. The sources with the lowest contributions were from weathering and deposition, which reached 13.7%.

scholarly journals Investigation of Cadmium Content in Rice in Heilongjiang Province and Health Risk Assessment

2020 ◽  
Author(s):  
Dongmei Cao ◽  
Chang Zhang ◽  
Dongjie Zhang
Keyword(s):  
Risk Assessment ◽  
Health Risk ◽  
Health Risk Assessment ◽  
Risk Index ◽  
Pollution Index ◽  
Assessment Model ◽  
Cadmium Content ◽  
Heilongjiang Province ◽  
Adults And Children ◽  
Risk Of Cancer

Abstract We investigated the cadmium content in soils and rice in Cha Hayang, Wuchang, Fangzheng, Xiangshui, and Jiansanjiang areas of Heilongjiang Province, and characterized the effect of rice intake on human health. The samples were analyzed by ICP-MS, and the cadmium transfer in soil-rice system was modeled by the Nemero comprehensive pollution index method. The health risk assessment model was used to study the status of cadmium pollution in rice and its health risk assessment for adults and children. The results showed that the average contents of cadmium in rice were 0.003 (Cha Hayang), 0.016 (Wuchang), 0.006 (Fangzheng), 0.006 (Xiangshui), and 0.005 (Jiansanjiang) mg kg-1. The prediction model developed in this study, including the total heavy metals and pH value of the soil, effectively described the transfer of cadmium in the soil-rice system of Wuchang, Chahayang and Xiangshui paddy fields (with R2 between 0.256 and 0.468). The pollution index of the study area was less than 1. The comprehensive pollution index was 0.037<1, sugesting no pollution, and the comprehensive pollution index was between 0.059 and 0.158. The health risk index of carcinogenic heavy metal cadmium to adults and children in Cyang and JianSanjiang areas was lower than that recommended by USEPA (1 × 10-4), suggesting no risk of cancer. However, the mean values in Wuchang, Fang Zheng and Xiangshui were higher than the maximum acceptable risk recommended by USEPA, suggesting a risk of cancer.

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