Emergency Response System for Pollution Accidents in Chemical Industrial Parks, China

A supplementary assessment system of AQI-V for comprehensive management and control of air quality in chemical industrial parks

Volatile organic compounds (VOCs) are the dominant pollutants in industrial parks. However, they are not generally considered as part of the air quality index (AQI) system, which leads to a biased assessment of pollution in industrial parks. In this study, a supplementary assessment system of AQI-V was established by analyzing VOCs characteristics with vehicle-mounted PTR-TOFMS instrument, correlation analysis and the standards analysis. Three hourly and daily scenarios were considered, and the hierarchical parameter setting was further optimized by field application. The hourly and daily assessments revealed the evaluation factors for the discriminability of different air quality levels, practiced value for regional air quality improvement, and the reservation of general dominant pollutants. Finally, the universality testing in ZPIP successfully recognized most of the peaks, with 54.76%, 38.39% and 6.85% for O₃, VOCs and NO₂ as the dominant pollutant, and reflected the daily ambient air quality condition, together with the dominant pollutant. The AQI-V system with VOCs sub-index is essential for air quality evaluation in industrial parks, which can further provide scientific support to control the pollution of VOCs and the secondary pollutant, therefore significantly improve the air quality in local industrial parks.

An emergency supplies scheduling for chemical industry park: based on super network theory

In a concentrated area of chemical industry parks (CIPs), emergency relief efficiency is not only affected by the rescue capability of themselves, but also their coordination relationships with other CIPs. Previous studies focus on the location of resource warehouse and the scheduling of logistics transportation, in the relief process after unexpected events, but rarely integrate them ideally in practice. This paper utilizes the super network theory to propose a regional emergency scheduling model to improve collaboration efficiency among primary relief centers (PRC), local relief centers (LRC), and CIPs. So, the proposed super network model fills the research gap of only considering emergency logistic supply chain and provides decision scheme regarding the emergency material dispatch plan. We developed a modified projection algorithm to solve the scheduling problem by turning it to a variational inequality and compare the performance under several disaster scenarios. The practicability of the model is proved by the result of the numerical example given.

Research on the Construction and Application Mode of Digital Plans for Sudden Water Pollution Events

Water pollution is an important aspect of a national water treatment. Sudden water safety incidents are random and destructive, often bringing about huge losses of life and property. Due to the uncertainty of sudden water pollution, it is difficult to respond in a timely and rapid manner. Emergency personnel must deal with emergencies quickly and effectively to reduce the harm caused by these emergencies. The randomness and uncertainty of sudden water pollution events make emergency work more complicated; it is difficult for current emergency plans to play guiding roles in complex responses. The decision-making and use of traditional water safety procedures largely depend on the experiences of command personnel, as well as on the emergency plan, which often has poor applicability. This can result in ineffective implementation of emergency actions and use of resources stemming from the high subjectivity and low efficiency of emergency plans. In this paper, we summarize previous research on digital planning and platform component technology exploration in order to evaluate the use of sudden water safety emergency procedures. We first analyze the main problems in the construction and use of emergency plans (e.g., the lack of experience and adaptability). Secondly, based on the decision-making support platform, a digital emergency plan database for water pollution emergencies was established by using component technology and knowledge map technology. In doing so, the decision support platform could enable the rapid construction of digital plans that improve application efficiency in an actual response scenario. Finally, through the system example, this system model can be quickly matched from the plan database to the emergency plan that meets the current scenario. It is a recommended model used to provide rapid and effective assistance for emergency management and improve emergency efficiency.

Multirisk: What trends in recent works? - A bibliometric analysis

The issue of multirisk is coming under increasing scrutiny in the scientific literature and is of great concern for governments. Multirisk embraces different meanings: domino and cascade effects, NaTech events and the consideration of several natural hazards and their interactions. Scientific production relating to multirisk has been growing over the last 15 years. This review, based on 191 articles, proposes a new way of analyzing and presenting bibliographic results by the use of a global textual analysis. This analysis leads to identify seven main themes of research in the literature: three concern Domino Effects (46.6% of the articles), two are dedicated to the assessment of Multi-(hazard/vulnerability) Risk (28.7%), one deals with Natech issues (13.5%) and one concerns Cascade Effects in critical infrastructures (11.2%). A cross-issue analysis was performed on the basis of four criteria: objectives, hazards, the elements at risk considered, and the approaches used or developed in the articles. It provides general lessons on these items and proposes themes for future research on the topic of multirisk.

Scenario-Based Marine Oil Spill Emergency Response Using Hybrid Deep Reinforcement Learning and Case-Based Reasoning

Case-based reasoning (CBR) systems often provide a basis for decision makers to make management decisions in disaster prevention and emergency response. For decades, many CBR systems have been implemented by using expert knowledge schemes to build indexes for case identification from a case library of situations and to explore the relations among cases. However, a knowledge elicitation bottleneck occurs for many knowledge-based CBR applications because expert reasoning is difficult to precisely explain. To solve these problems, this paper proposes a method using only knowledge to recognize marine oil spill cases. The proposed method combines deep reinforcement learning (DRL) with strategy selection to determine emergency responses for marine oil spill accidents by quantification of the marine oil spill scenario as the reward for the DRL agent. These accidents are described by scenarios and are considered the state inputs in the hybrid DRL/CBR framework. The challenges and opportunities of the proposed method are discussed considering different scenarios and the intentions of decision makers. This approach may be helpful in terms of developing hybrid DRL/CBR-based tools for marine oil spill emergency response.

A Systematic Literature Review on Safety Research Related to Chemical Industrial Parks

The increasing demand for chemical products has driven the construction and development of chemical industrial areas, or so-called ‘chemical industrial parks’ (CIPs), but this has intrinsically raised the risk of major accidents. Therefore, it is significant and urgent to summarize the state of art and research needs in the field of CIP safety. In this paper, a keyword co-occurrence analysis of 116 scientific articles was conducted to support the classification of research topics in this field, then an overview of those research topics was presented to investigate the evolution of safety research with respect to CIPs. Specifically, the way that safety assessments are conducted, as well as how safety management and safety technology in such areas are classified and investigated, followed by detailed descriptions of representative methods and their contributions to CIP safety, are discussed. An integrated safety framework for CIPs is proposed to organize safety approaches and measures systematically. Based on the classification and analysis of studies on management, assessment, and technology related to CIP safety, the research trends and future directions and challenges are discussed and outlined. Those results are useful for improving theoretical method and industrial strategies, and can advance the safety and sustainability development of CIPs.

An Ontological and Semantic Foundation for Safety and Security Science

When discussing the concepts of risk, safety, and security, people have an intuitive understanding of what these concepts mean, and, to a certain level, this understanding is universal. However, when delving into the real meaning of these concepts, one is likely to fall into semantic debates and ontological discussions. In industrial parks, it is important that (risk) managers from different companies, belonging to one and the same park, have the same understanding of the concepts of risk, safety, and security. It is even important that all companies in all industrial parks share a common understanding regarding these issues. As such, this paper explores the similarities and differences behind the perceptions of these concepts, to come to a fundamental understanding of risk, safety, and security, proposing a semantic and ontological ground for safety and security science, based on an etymological and etiological study of the concepts of risk and safety. The foundation has been induced by the semantics used in the ISO 31000 risk management guidance standard. Hence, this article proposes a coherent, standardized set of concepts and definitions with a focus on the notion “objectives” that can be used as an ontological foundation for safety and security science, linking “objectives” with the concepts of safety, security, risk, performance and also failure and success, theoretically allowing for an increasingly more precise understanding and measurement of (un)safety across the whole range of individuals, sectors and organizations, or even society as a whole.

Integration Multi-Model to Evaluate the Impact of Surface Water Quality on City Sustainability: A Case from Maanshan City in China

Water pollution is a worldwide problem that needs to be solved urgently and has a significant impact on the efficiency of sustainable cities. The evaluation of water pollution is a Multiple Criteria Decision-Making (MCDM) problem and using a MCDM model can help control water pollution and protect human health. However, different evaluation methods may obtain different results. How to effectively coordinate them to obtain a consensus result is the main aim of this work. The purpose of this article is to develop an ensemble learning evaluation method based on the concept of water quality to help policy-makers better evaluate surface water quality. A valid application is conducted to illustrate the use of the model for the surface water quality evaluation problem, thus demonstrating the effectiveness and feasibility of the proposed model.

Tackling Complex Emergency Response Solutions Evaluation Problems in Sustainable Development by Fuzzy Group Decision Making Approaches with Considering Decision Hesitancy and Prioritization among Assessing Criteria

In order to be prepared against potential balance-breaking risks affecting economic development, more and more countries have recognized emergency response solutions evaluation (ERSE) as an indispensable activity in their governance of sustainable development. Traditional multiple criteria group decision making (MCGDM) approaches to ERSE have been facing simultaneous challenging characteristics of decision hesitancy and prioritization relations among assessing criteria, due to the complexity in practical ERSE problems. Therefore, aiming at the special type of ERSE problems that hold the two characteristics, we investigate effective MCGDM approaches by hiring interval-valued dual hesitant fuzzy set (IVDHFS) to comprehensively depict decision hesitancy. To exploit decision information embedded in prioritization relations among criteria, we firstly define an fuzzy entropy measure for IVDHFS so that its derivative decision models can avoid potential information distortion in models based on classic IVDHFS distance measures with subjective supplementing mechanism; further, based on defined entropy measure, we develop two fundamental prioritized operators for IVDHFS by extending Yager's prioritized operators. Furthermore, on the strength of above methods, we construct two hesitant fuzzy MCGDM approaches to tackle complex scenarios with or without known weights for decision makers, respectively. Finally, case studies have been conducted to show effectiveness and practicality of our proposed approaches.