Risk Assessment Method Research Articles

A response-based internal pressure risk assessment method for PCCV with steel fiber reinforcement

The probabilistic risk assessment (PRA) is a hot topic for highlighting the reliability and safety of infrastructures, especially for nuclear power plants (NPPs). The current internal pressure risk assessment method for prestressed concrete containment vessel (PCCV) generally adopts the internal pressure to characterize its fragility (namely internal-pressure-based fragility), even though the failure of PCCV is factually more tightly associated with PCCV’s realistic responses. This study proposes a novel response-based probabilistic risk assessment method for PCCV under internal pressure. The method adopts the response of the containment to characterize the fragility of components. Additionally, the steel fiber is introduced to form steel fiber reinforced concrete (SFRC) PCCV and the fragility and risk of the SFRC PCCV are quantitatively studied. The results indicate that a real and larger dispersion for the internal pressure risk of the PCCV would be induced by adopting the proposed internal pressure risk assessment method. The strategy of the addition of steel fiber to the conventional PCCV can decrease the internal pressure risk by about 72.1 % on average.

A Framework for a Hazard Taxonomy to Support Risk Assessment of Tangible Outdoor Heritage

The variety of hazards with a potential impact on cultural heritage requires a multidisciplinary approach and a preliminary overview of the existing methods for risk assessment in order to define a comprehensive hazard taxonomy. The starting point of the research thus aims to build a multidisciplinary framework to support the risk assessment process according to the classification of cultural heritage based on the harmonization of European vocabularies’ definitions and protocols. To collect the necessary information, such as hazard classification, indicators, indices and thresholds, a series of methodologies was adopted: analysis of the main international protocols and the EU Research projects related to risk assessment in cultural heritage, expert-based knowledge and a systematic literature review. The research aims to fill a gap in the field of quantitative and indicator-based risk assessment that does not present a unique and all-encompassing framework capable of collecting the main natural and anthropic risks along with the related taxonomy in a single repository. The framework has been set up to be consulted by researchers, professionals and public administrations to support the evaluation process of potential risks on tangible outdoor heritage enabling users to incrementally add exposure and vulnerability data for each specific risk.

On the development of a standardised method for climate vulnerability and risk assessment (CVRA) for buildings and properties

On the development of a standardised method for climate vulnerability and risk assessment (CVRA) for buildings and properties

Assessment of the risk to public health from soil contamination on the territory of solid waste landfills

Introduction. Domestic waste management is an extremely important issue for all countries of the world, especially for the countries of the former Soviet Union, as the current state of solid waste disposal in Ukraine is extremely unsatisfactory. According to the European Commission, the amount of waste in the European Union has increased in recent years to more than 2.5 billion tons per year. The results of the Waste Atlas report on the world's 50 largest landfills show that 64 million people are affected by the 50 largest landfills every day. Ukraine has a total of 6045 landfills and dumpsites. The general trend in Ukraine, in contrast to European countries, is a low level of recycling and utilization of solid waste and a high rate of landfill disposal. The vast majority of landfills (80 to 90%) operate in overload mode. Landfills, in particular solid waste landfills, are the main pollutants of the atmosphere, hydrosphere and soil. Therefore, determining the level of environmental hazard of heavy metals in the soil as a result of the impact of landfills and solid waste dumps is very important. Objective. The purpose of the article is to determine the impact of solid waste landfills on public health due to soil contamination with heavy metals. Methodology. In order to determine the level of environmental hazard of household waste storage at landfills and dumps, the paper assesses the risk to public health from soil contamination in the area of the Hrybovychi landfill (Lviv oblast), Rohan landfill (Kharkiv oblast), and Novovodolazhske landfill (Kharkiv oblast). The risk to public health from soil contamination at solid waste landfills was assessed using the traditional US EPA method and a new method for determining the potential risk to public health from soil contamination with heavy metals. Results. The article compares two fundamentally different methodological approaches to determining the level of environmental hazard of soil contamination with heavy metals. The risk to public health from soil contamination on the territory of solid waste landfills according to the US EPA method corresponds to hazard class 5 (extremely high hazard level) according to all calculations. The results of the assessment of the potential risk to public health from soil contamination using the new method at solid waste landfills correspond to hazard classes 2-4. Comparison of methodological principles for assessing the risk to public health from soil pollution has shown the advantages of using the new method of potential risk assessment. The use of the new method of assessing the potential risk to public health from soil pollution will help improve science-based management of household waste and direct financial resources to reduce the environmental hazard of soil and land pollution in Ukraine. Scientific novelty. The article presents an improved method for assessing the risk to public health from soil pollution, which represents the scientific novelty of the work Practical significance. Determining the environmental hazard of soil pollution due to the impact of landfills and solid waste dumps makes it possible to prioritize the implementation of environmental protection measures and will contribute to the implementation of environmentally sound environmental management, so the research is of practical importance.

Quantitative microbial risk assessment (QMRA) of the work of manual pit emptiers, commonly known as bayakous.

In Haiti, manual pit emptiers, known as bayakous, face significant health risks. They work by descending naked into latrine pits, exposing themselves to pathogens and contributing to environmental contamination. This study employs the quantitative microbial risk assessment (QMRA) method to evaluate the microbial risks associated with this practice, considering nine prevalent pathogens in Haiti. Three ingestion scenarios were developed: hand-to-mouth contact, ingestion while immersed in excreta, and a combination of both. A sensitivity analysis assessed the impact of input data on study outcomes. The results indicate a high probability of infection and illness during pit emptying operations annually for all scenarios and pathogens. Recommendations include adopting personal protective equipment (PPE) and using a manual Gulper waste pump to eliminate the need to descend directly into the pits, thereby reducing the risk of injury from sharp objects. The study proposes the establishment of intermediate disposal points approximately 5 km from collection sites to deter illegal dumping. National regulations and professionalization of the bayakou profession are suggested, along with awareness campaigns to promote PPE and Gulper pump usage. Addressing these issues is crucial for safeguarding the health of bayakou and public health in Haiti.

Measures and results of prevention and control on casing deformation and frac-hit in deep shale gas wells in southern Sichuan Basin

Casing deformation and frac-hit pose significant challenges to the development of deep shale gas in southern Sichuan Basin. By analyzing the mechanism and main control factors of casing deformation and frac-hit, two kinds of risk assessment methods were defined, and the overall prevention and control concept and practice were formulated. The results show that initial stress, pore pressure, fault development and large scale fracturing in local block are the main factors leading to the deformation. The development of fracture through well group and uncontrolled fracturing fluid volume are the main factors leading to pressure channeling. Based on this, the risk classification technology of casing deformation and frac-hit is established, and the dual-optimal, dual-control concept and technology are formed. In terms of the prevention and control of casing deformation, the formation of small-diameter bridge plug fracturing, large section combined fracturing, glass beads cementing, single-well staggered and platform straddle fracturing mode, dual-dimension controlled and lift fracturing, hyperbolic diagnosis, etc. Frac-hit prevention and control formed pump sequence optimization mode, physical and chemical temporary plugging and other methods. The above technology achieved casing deformation rate decreased from 50.4% to 25.4%, frac-hit rate decreased from 58.6% to 33.9%, and the average well kilometer EUR reached 0.52–0.7 million square meters, an increase of 7.7% compared with the previous research, with remarkable results.

Risk Assessment and Analysis of New Power System Operation Under Extreme Disasters

Abstract In recent years, new power systems have been developing continuously, and the operational risk assessment of new power systems under extreme disaster conditions has attracted much attention. This paper uses Latin hypercube sampling technology to establish a power system failure model under extreme disasters and proposes a new power system risk assessment method that applies the power system power flow model to establish a comprehensive system risk index. In addition, taking the power grid of a certain province in eastern China as an example, a case analysis is conducted on the IEEE39 node system to verify the effectiveness of the method.

Improving the AML/CFT system within the framework of the RMS of the subject of a financial and credit institution: shortcomings and vulnerabilities

The article is devoted to the analysis of the shortcomings and vulnerabilities of the AML/CFT system (countering the legalization (laundering) of proceeds from crime and the financing of terrorism) within the framework of the risk management system (RMS) of the subject of a financial and credit organization (FCO). In the context of globalization and the increasing complexity of financial transactions, traditional approaches to AML/CFT are becoming insufficient, which requires improving risk management systems taking into account new challenges. Attention is focused on the fact that today the issue of money laundering and financing of terrorist activity is a global threat to the financial security of both individual states and the entire global economic system. In addition, this study emphasizes that according to the National Security Strategy of the Russian Federation, in the context of the transformation of modern society and increasing international uncertainty, it is necessary to increase the efficiency of using existing achievements and competitive aspects of the country, taking into account long-term trends in global development. The article examines the key shortcomings of the AML/CFT system within the framework of the FCO RMS, including the lack of a unified approach to risk assessment, insufficient integration of AML/CFT into general risk management processes, lack of resources and staff qualifications, as well as difficulties in applying new technologies in combating money laundering and terrorist financing. The paper analyzes the impact of these shortcomings on the effectiveness of the AML/CFT system and identifies key vulnerabilities that can be used by criminals for money laundering and terrorist financing. The article offers recommendations for improving the AML/CFT system within the framework of the FCO RMS, including the introduction of more effective risk assessment methods, improved control and monitoring processes, staff training and the introduction of new technologies into the AML/CFT system.

Incorporating ergonomic and psychosocial stressors: A comprehensive model for assessing miners’ work-related musculoskeletal disorders

Although the influence of ergonomic and psychosocial stressors on work-related musculoskeletal disorders (WMSDs) has been widely recognized, a risk assessment method that comprehensively considers these stressors is still lacking. As such, this study developed a fuzzy Bayesian network (FBN) model that incorporated a comprehensive set of ergonomic and psychosocial stressors to investigate how they interacted to affect frontline miners’ WMSDs risk. A methodology combining fuzzy set theory, Leaky Noisy-OR gate, and an expert weight dynamic adjustment algorithm was employed to deal with fuzziness, uncertainty, limited data, and conflicting judgments and to provide a probabilistic assessment. Meanwhile, the FBN was validated using 10-fold cross-validation through data collected from 542 frontline miners. Results indicated that the probability of frontline miners suffering from WMSDs is 79.7%. Vibration, awkward posture, inadequate organizational support, inadequate organizational justice, and bad organizational culture were the most influential stressors to frontline miners’ WMSDs. The proposed FBN model can be a powerful decision-support tool to improve frontline miners’ musculoskeletal health by managing a more targeted subset of ergonomic and psychosocial stressors.

Application of the hazard identification risk assessment and risk control method in the welding frame body process (a case study in indonesian company)

Application of the hazard identification risk assessment and risk control method in the welding frame body process (a case study in indonesian company)

Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces

The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets.

Heart Disease Prediction using Machine Learning

Abstract: Heart disease remains a leading cause of morbidity and mortality worldwide. Early detection and accurate prediction of heart disease risk factors are crucial for effective prevention and timely intervention. In recent years, machine learning techniques have emerged as powerful tools for predictive analytics in healthcare. This paper presents a comprehensive review of recent advances in the application of machine learning algorithms for heart disease prediction. The review begins by discussing the significance of heart disease prediction and the limitations of traditional risk assessment methods.

SAFETY MANAGEMENT AT THE WORKPLACE IN PUBLIC AND PRIVATE HEALTHCARE FACILITIES IN THE CANTON OF SARAJEVO

Introduction: Workplace safety means implementing a modern risk assessment method automatically included in the risk management activity. When a risk is discovered, it is necessary to reduce it or eliminate it completely through preventive measures or interventions. Aim: To examine to what extent healthcare organizations care about their workers, to what extent workers are aware of the risks, whether they have adequate working conditions and whether they respect them. Participants and methods: The study was included 144 healthcare professionals employed in public and private healthcare institutions in the Sarajevo Canton. The sample was selected by the method of random selection. The study is descriptive and comparative. Results: The total sample (N=144) included 140 or 97.2% respondents working in public healthcare institutions, and 4 or 2.8% respondents working in private healthcare institutions. Almost ¾ of respondents are familiar with the implementation of risk assessment in the workplace, and in 25.0% of cases they are familiar with some strategic document on risk management and assessment of risky workplaces. The largest number of respondents, 63.9%, state that they respect the protection measures provided by the employer. None of the respondents gave reasons for non-compliance. Conclusion: Respondents confirmed the existence of workplace risks in public and private healthcare institutions and awareness about them, they are fully or partially aware of the risks and partially practice protection against them. A significant number of employees mention illnesses that are potentially caused by a risky workplace.Keywords:workplace safety, management, healthcare institutions.

Comparative Analysis of Methods of Evaluating Human Fatigue

AbstractThe present study used four different methods to estimate fatigue. Forty-seven volunteers (45 men and 2 women), 41.3 ± 7.5 years old, truck operators for 11.5 ± 6.0 years, were included. All participants accepted the invitation to be included in the study. Actigraphy and core temperature were evaluated. The 5-minute psychomotor vigilance test, the Karolinksa Sleepiness Scale (KSS), and the postural assessment using the Light Sonometer™ (Belo Horizonte, Minas Gerais, Brazil) were performed. Fatigue prediction was performed using the Fatigue Avoidance Scheduling Tool (FAST) program. In response to the Pittsburgh Sleep Quality Index (PSQI), 51.06% had good sleep quality and 48.94% had poor sleep quality with an average efficiency of 81.6%. In response to the actigraphy, workers slept an average of 7.2 hours a day with 93.5% efficiency. The workers' core body temperature (CBT) cosinor analysis showed a preserved circadian curve. Core body temperature showed differences between the 6 hours worked in each shift. Similarly, the light sound level meter showed lower risk scores for fatigue in day shifts. Only the variable of the fastest 10% of the Psychomotor Vigilance Test (PVT) showed worse results, while no significant differences were observed by the KSS. The risk analysis by FAST showed a strong influence of the circadian factor. In conclusion, each method has positive and negative points, and it is up to the evaluator/manager to identify the method that best suits the purpose of the evaluation, as well as the local culture and conditions. We recommend using different methods of risk assessment and management in combination with fatigue prediction by Sonometer as well as carrying out assessments, which enable researchers to estimate performance and fatigue throughout the working day, since these may change over the duration of the working day.

Research on the Quantitative Assessment Method of HVDC Transmission Line Failure Risk during Wildfire Disaster

It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters.

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach.

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.

Occupational exposure, carcinogenic and non-carcinogenic risk assessment of formaldehyde in the pathology labs of hospitals in Iran

Formaldehyde, a known carcinogenic compound, is commonly used in various medical settings. The objective of this study was to assess the carcinogenic and non-carcinogenic risks associated with occupational exposure to formaldehyde. This study was conducted in the pathology labs of four hospitals in Tehran. Cancer and non-cancer risks were evaluated using the quantitative risk assessment method proposed by the United States environmental protection agency (USEPA), along with its provided database known as the integrated risk information system (IRIS). Respiratory symptoms were assessed using the American thoracic society (ATS) questionnaire. The results indicated that 91.23% of exposure levels in occupational groups exceed the NIOSH standard of 0.016 ppm. Regarding carcinogenic risk, 41.03% of all the studied subjects were in the definite carcinogenic risk range (LCR > 10−4), 23.08% were in the possible carcinogenic risk range (10−5 < LCR < 10−4), and 35.90% were in the negligible risk range (LCR < 10−6). The highest index of occupational carcinogenesis was observed in the group of lab technicians with a risk number of 3.7 × 10−4, followed by pathologists with a risk number of 1.7 × 10−4. Furthermore, 23.08% of the studied subjects were within the permitted health risk range (HQ < 1.0), while 76.92% were within the unhealthy risk range (HQ > 1.0). Overall, the findings revealed significantly higher carcinogenic and non-carcinogenic risks among lab technicians and pathologists. Therefore, it is imperative to implement control measures across various hospital departments to mitigate occupational formaldehyde exposure levels proactively. These findings can be valuable for policymakers in the health sector, aiding in the elimination or reduction of airborne formaldehyde exposure in work environments.

Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network.

In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems.

Quantifying risks of lane-changing behavior in highways with vehicle trajectory data under different driving environments

Improper lane-changing behavior may have a great impact on the safety of the surrounding vehicles, and accurate risk assessment of lane-changing behavior can detect improper lane-changing behavior. Many existing risk assessment methods are based on the traditional minimum safe distance model as the theoretical foundation, not taking into account the effects of different weather and road conditions. Moreover, the initially calculated safe lane-changing distance through the model is generally the limit safety distance, which deviates to some extent from the initial distance maintained when the vehicle begins to change lanes during the actual driving process. The ratio of these two distances can reflect the degree of risk between the changing vehicle and its neighboring vehicles during lane-changing. For this reason, a lane-changing safety coefficient is defined and lane-changing risk assessment model based on the hyperbolic tangent function is constructed by combining the change characteristics of this coefficient. An environmental adjustment factor was introduced into the model to consider the influence of different driving weather and road conditions on the lane-changing risk assessment. Then, based on survey data from the survey questionnaire, the weights of the influencing factors were determined by using the hierarchical analysis method, and the environmental adjustment factor in the model was calibrated by using the fuzzy comprehensive assessment method. Finally, to verify the effectiveness of the model, the lane-changing trajectories of 373 vehicles were extracted from the HighD dataset. The risk assessment value of lane-changing was calculated by using the constructed model and was classified into high-risk, medium-risk and low-risk by using the K-means++ clustering algorithm. Various evaluation results were compared with the evaluation results from the expert evaluation method and the classical risk evaluation indexes, and the evaluation results are basically the same, which verifies the effectiveness of the lane-changing risk model established.

Assessment of Heavy Metal Contamination and Ecological Risk in Soil within the Zheng–Bian–Luo Urban Agglomeration

As urbanization accelerates, the contamination of urban soil and the consequent health implications stemming from urban expansion are increasingly salient. In recent years, a plethora of cities and regions nationwide have embarked on rigorous soil geological surveys with a focus on environmental quality, yielding invaluable foundational data. This research aims to develop scientifically robust and rational land-use planning strategies while assessing the levels of heavy metal pollution and associated risks. The urban agglomeration encompassing Zhengzhou, Luoyang, and Kaifeng (referred to as Zheng–Bian–Luo Urban Agglomeration) in Henan Province was designated as the study area. Leveraging the Nemerow comprehensive index method alongside the Hakanson potential ecological risk assessment method, this study delved into the pollution levels and potential ecological ramifications of nine heavy metals, namely Cr, Mn, Ni, Cu, Zn, As, Cd, Pb, and Co. Research indicates that the hierarchy of individual potential ecological risks ranges from most to least significant as follows: Cd &gt; Pb &gt; Cr &gt; Ni &gt; Cu &gt; Zn &gt; As &gt; Mn &gt; Co. The concentrations of Cd in both Zhengzhou and Kaifeng surpassed the established background levels. Furthermore, the mean single-factor pollution index values for the heavy metals Cd and Zn exceeded 1, signifying a state of minor pollution. The Nemerow comprehensive index P of Cd and Zn is between 1 &lt; Pcomp ≤ 2, which is considered mild pollution. The comprehensive P values of the other seven metal elements are all less than 0.7, reaching a clean (alert) level. Predominantly, the primary potential risk factor in the superficial soil of the Zheng–Bian–Luo urban agglomeration is Cd, while the ecological risk implications associated with other heavy metal elements are comparatively minimal. The soil environmental quality within the designated study area remains secure, although certain localized areas pose potential risks of heavy metal pollution. A comprehensive assessment of the current state of soil heavy metal pollution is essential to establish a theoretical foundation and provide technical support for soil environmental protection, pollution mitigation, and sustainable utilization.