Information Security Emergency Response Procedures and Disposal System

In general, the onshore production of oil and gas in North Germany takes place in densely populated areas. Approximately 800 MMSCFD of gas are produced from sour gas reservoirs, with H2S-concentrations of up to 20 Vol.-% being involved. Despite a high technical safety standard and different organizational measures in the production of sour gas, emergency cases cannot be absolutely ruled out. An accidental sour gas release can cause health problems even if inhaled at low concentrations. H2S concentrations of more than 700 ppm will cause immediate death. In Germany the production of hydrocarbons is regulated by the Federal Mining Law. According to these regulations an extensive gas protection plan and gas alarm plan have to be set up for sour gas operations. This includes establishing an emergency response organization and a gas alarm management guide. Beyond the legal requirements Mobil Erdgas-Erdoel GmbH (MEEG) has implemented an emergency response management system in compliance with Mobil Oil's worldwide safety philosophy to minimize the risk for the population, the employees and the environment. The installation of two electronic data systems enables the crisis management team to respond faster, more safely and in a less complicated manner to accidental sour gas releases. The introduction of the Emergency Information and Response System (EMIS) makes possible the display and manipulation of maps in different scales and geographically oriented data. The expansion of the process master computer by installing an H2S-Gas alarm system has achieved the online measurements of H2S concentrations, wind direction and wind velocity at sour gas production facilities. The system, which is operated at the gas dispatcher center, gives the crisis response team the opportunity to predict the location of sour gas release and the area being impacted. Consequently rescue parties will be directed to the location in a safe manner and, if necessary, measures will be managed for evacuating the residents living in the danger zones around sour gas facilities. Introduction The onshore production of sweet and sour gas in North Germany takes place in generally densely populated regions. Among others MEEG operates sour gas fields in the State of Lower Saxony. The production areas of the Scholen concession are located between the cities of Hanover and Bremen (Figure 1). Joint venture partner of the Scholen concession is BEB Erdgas and Erdoel GmbH (50 % Shell and 50 % Exxon). More than 50 billion cubic metres of sour gas have been produced, transported and purified in this area since 1963. Today, sour gas is produced from 12 main reservoirs with an infrastructure of 35 production wells, 9 booster compressor stations and more than 100 km of sour gas pipeline network (Table 1). The sour gas is purified at the MEEG operated purification plant Norddeutsche Erdgas-Aufbereitungs-GmbH (NEAG). After conditioning of the treated gas to sales gas specifications the sweet gas is fed into the sales gas pipeline network. All gas production as well as the gas conditioning process is managed by MEEG's dispatcher center. P. 467

Preparedness is the main criteria for immediate response for any emergency response provider (ERP) and decision support system. Emergency Response System can be either for fire emergency response system, or police station emergency response system and healthcare emergency response system. Emergency decision support system is imperative for the reduction of disaster losses and the efficiency improvement of emergency resources allocation. The objective of this paper is to establish a Geographic Information System (GIS) based healthcare emergency response system for services that can identify the optimal route from the location of incident to any healthcare service providers, and the optimal route was modeled based on the distance (the shortest path) to the closest healthcare service providers. Therefore, the main outcome from this research is to provide immediate response to any request for incidents or accidents. Basic idea behind developing this web application is that a case (patient) or incident occurs at a particular location (latitude and longitude). Knowing those locations and mapping them using (GIS) would greatly help in analyzing and visualizing the various factors associated with the incident and thus would help in providing better decision making and quicker response for any incident or accident using ArcGIS Server and web application. The rest of the paper is organized as follows: Section 2 describes the aims and objective of the research, Section 3 discusses the methodology which includes the description of the study area, data inputs and healthcare emergency response model parameters. In section 4, analyses including shortest path, spatial queries and also service area for healthcare providers are displayed. Results are presented and interpretation is illustrated in Section 4. Conclusions and discussions of future research works are outlined in Section 5

To study the occupational safety and health systems used for emergency response workers in the USA, we performed interviews with related federal agencies and conducted research on related studies. We visited the Federal Emergency Management Agency (FEMA) and National Institute for Occupational Safety and Health (NIOSH) in the USA and performed interviews with their managers on the agencies' roles in the national emergency response system. We also obtained information prepared for our visit from the USA's Occupational Safety and Health Administration (OSHA). In addition, we conducted research on related studies and information on the website of the agencies. We found that the USA had an established emergency response system based on their National Incident Management System (NIMS). This enabled several organizations to respond to emergencies cooperatively using a National Response Framework (NRF) that clarifies the roles and cooperative functions of each federal agency. The core system in NIMS was the Incident Command System (ICS), within which a Safety Officer was positioned as one of the command staff supporting the commander. All ICS staff were required to complete a training program specific to their position; in addition, the Safety Officer was required to have experience. The All-Hazards model was commonly used in the emergency response system. We found that FEMA coordinated support functions, and OSHA and NIOSH, which had specific functions to protect workers, worked cooperatively under NRF. These agencies employed certified industrial hygienists that play a professional role in safety and health. NIOSH recently executed support activities during disasters and other emergencies. The USA's emergency response system is characterized by functions that protect the lives and health of emergency response workers. Trained and experienced human resources support system effectiveness. The findings provided valuable information that could be used to improve the occupational safety and health function in the Japanese system.

Artificial intelligence (AI)-based emergency response systems have emerged as critical enablers of smart infrastructure safety, offering enhanced real-time decision-making, risk assessment, and disaster mitigation strategies across various domains. This systematic literature review, encompassing 424 eligible studies, investigates the integration of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimizing emergency response mechanisms. The study comprehensively examines AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control, providing a detailed assessment of technological advancements and challenges in AI adoption. The findings reveal that AI has significantly improved predictive accuracy, automated hazard detection, and emergency resource optimization, leading to faster response times, minimized human error, and enhanced situational awareness in crisis management. AI-driven predictive analytics models have enabled early warning systems for earthquakes, floods, and wildfires, facilitating proactive disaster preparedness and risk mitigation. In real-time emergency response, AI-powered computer vision and sensor-based surveillance technologies have improved incident detection, reducing intervention delays and ensuring more efficient allocation of emergency resources. In the healthcare sector, AI-enhanced diagnostic tools, triage automation, and geospatial analytics for ambulance dispatch have streamlined medical crisis management, improving survival rates and reducing treatment delays. Additionally, AI-integrated industrial safety frameworks, robotic automation, and cybersecurity intelligence systems have strengthened workplace hazard prevention, cyber threat detection, and emergency communication resilience, ensuring safer and more secure operational environments. Despite these advancements, several challenges related to interoperability, regulatory constraints, cybersecurity vulnerabilities, algorithmic biases, and ethical concerns persist, hindering large-scale AI adoption in emergency response systems. This review provides a comprehensive synthesis of AI’s transformative role in modern emergency management, offering insights into technological developments, limitations, and policy considerations necessary to enhance AI-driven crisis response strategies and ensure more effective, scalable, and resilient emergency safety infrastructures worldwide.

Homeland security, car crashes and traffic incident data: Killing three birds with one IT architecture

Emergency response system is a complex system of systems (SoS). The introduction of connected and autonomous vehicles (CAVs) introduces an extra dimension into the complexity. Future emergency response must be able to take into account of the autonomous vehicles with different automation levels and leverage the increasing connectivity and automation for efficient emergency response. Architecture frameworks have long been used for system engineering for large complex systems. The emerging unified architecture framework converges previous architecture frameworks for a unified one towards both military and civilian use. Based on the scenario of emergency response with CAVs on highways, this paper motivates an enterprise architecture for emergency response system of systems (ERSoS) with identification of the key challenges and opportunities in addition to a proposal of required capabilities. The work is a first iteration of an enterprise architecture for ERSoS with CAVs and forms part of the overall ERSoS architecture development process.

Background The last six years of India’s National Rural Health Mission has witnessed the emergence of a wide variety of public funded, privately managed ambulance services across the states. Today there are over 3850 government financed emergency response ambulances and another 3000 just about to start up, whereas just five years ago there were almost none. States have chosen different models for emergency response systems with varying levels of investments and effectiveness. Objectives of the Study The main objective if this study to analyze the three different business models of Emergency Response Systems that has evolved under NRHM across the country. The study compares the strengthens and weaknesses of each of these models in terms of (1) Coverage, timeliness, prioritization and quality of emergency response (2)Provision of assured cashless transport for pregnant-women and sick-newborn (3)Costs and sustainability of the models(4)Equity of access to these services (5)Outcomes-with respect to the rest of the emergency healthcare chain. Based on the analysis of these models, the possible roadmap to ERS systems in the country and the principles of design that could be used to improve the efficiency and effectiveness of each business-model for the coming five year plan period is also laid down. Methodology The NHSRC published its first evaluation of 108 programme in 2009 and has since conducted three more studies. The first study provided policy makers with a clear description of the program and an objective evaluation of its claims. A document analysis was undertaken to understand the various types of publically financed partnership models across the country. Based on this, three distinct business models of public-private partnership in ERS were identified. To further understand the three business models, three separate case studies in states where they are most mature in terms of design, number of years of running, continuity and efficiency of management. The business models studied includes (a) The “Dial 108” Model Was studied at its most mature site-Andhra Pradesh. Where we chose to study it in three districts, each from one of the three different regions- coastal Andhra, Rayalaseema, and Telengana regions. In addition a qualitative study and secondary data collection in these three districts. Haryana Swasthya Vahan Sewa(HSVS) Model For a district model with assured referral transport(RT) for pregnancy as primary focus, and emergency response as secondary, we studied the HSVS model-the more mature and established amongst the three states-which have opted for this approach (Haryana, Jharkhand & Chhattisgarh). This model was studied in three districts, one each from North, South and west central regions of the State. Janani express Model For a Local Partnerships based model of assured RT, we studied the programme in Nabrangpur, where the model had matured and developed to its best through a considerable brilliance in local innovation and adaptation with a continuity of local leadership. Discussion and Recommendations This study was used by the Ministry of Health and Family Welfare, Government of India to re-define its financing policy, giving states a greater ownership and risk, leading to improved financing and efficiency. The studies call for a more sound financing policy, caution against a provider monopoly and point out that a disproportionate attention to rescue without sufficient attention to the rest of the emergency management provision is counter-productive Legal challenges to existing policies of procurement also referred to evidence from NHSRC studies. Subsequent to this eight more states adopted a more competitive approach to procurement and financing. More new players entered the market. Alternate models have also emerged. Comparison across models in the second generation studies provided inputs into strengths and limitations of each and helped improve designs. This mechanism of evaluation and feedback is essential to ensure constant improvements in effectiveness, efficiency and governance of such a massive public health effort.

The emergency responses required during large-scale crises or disasters are extremely knowledge-intensive processes and are usually characterized by a high degree of unpredictability and unrepeatability. An emergency response is mission- and time-critical, unstructured, very dynamic, and it is very difficult to predefine or even to anticipate all possible response scenarios. Therefore, designing and implementing a software system to support such a response system is highly complicated and challenging. This research aims to investigate and discuss how Adaptive Case Management (ACM) can be leveraged in the design and implementation of a case-based emergency response system. In particular, this research considers the best practices of the National Incident Management System (NIMS), which is an essential part of the National Response Framework (NRF) developed in the United States. As a proof-of-concept, a prototype demonstration has been carried out on a leading commercial ACM platform. In addition, a walkthrough scenario is discussed to elaborate how ACM can support emergency response activities in real settings using the Incident Command System (ICS) organizational structure. The key benefit of this research is to guide the development and implementation of cased-based emergency response systems with a flexible and agile approach.

The Advanced Fire Monitoring and Emergency Response System (AFMERS) presents an IoT-driven solution designed to reduce fire emergency response times and enhance firefighter safety. This integrated system includes automated fire detection, robotic firefighting, firefighter health monitoring, and a centralized command center. The system employs sensors to detect fires, assess type and severity, and immediately relay alerts to a firefighting robot and command center dashboard. The robot, equipped with adaptive extinguishing capabilities, is controlled by the operators in the command center to contain early-stage fires, while a wearable device monitors firefighter vitals and facilitates communication. A web-based and mobile dashboard consolidates live health, fire, and video data for real-time decision-making. Operators, using the AFMERS app, can activate the Help Me function to directly alert the Fire Department if the robot is unable to control the fire. Initial testing shows this research improves response efficiency and personnel safety, highlighting its potential to advance fire emergency response technology.

P1.001 Developing the City Emergency-health Response Capability (CERC) Tool

In the last several years, the large-scale evolution of emergency response system (ERS) has been enabled by technological advances in the area of Information and Communication Technology (ICT). The ultimate goal of such an ERS is to optimize compute resources to meet customer requirements. Specifically, emergency network traffic should be delivered in a timely manner to help with rescue efforts and recovery once a crisis takes a place. The delivered emergency traffic is usually categorized according to the priority level. In this paper, we consider the adoption of Software Defined Networking (SDN) to support preemptive emergency responses, where we develop an SDN framework to optimize the end-to-end (E2E) delay for emergency service delivery according to the urgency level of services. The emergency service is delivered based on preemptive policies. The proposed SDN-based ERS framework is evaluated on the Global Environment for Network Innovations (GENI) testbed with real-world emergency data whose urgency levels range from 0 to 50. Our experimental evaluation demonstrates that the proposed framework is efficient in delivering emergency service depending on data urgency level. Specifically, our framework provides an improvement of average E2E delays where the preemptive response service guarantees approximately 30% in E2E delay reduction for data with the highest priority. The proposed framework can be applicable to other cyber-physical systems and smart city-enabled applications.

An Emergency Assessment and Response System (EARS) has been developed which provides live-time quantifications and graphical display of radioactivity releases from the Diablo Canyon Power Plant. The EARS system consists of offsite and onsite monitors, a minicomputer for data acquisition and dose calculations, and a color graphics display network to provide plume projection maps and other assessment information to offsite locations. EARS is interfaced to selected channels of the plant radiation monitoring system, and to a mobile laboratory which has both digital and voice RF communications links. EARS calculates doses for offsite locations based on data received from the various interfacing systems. These computed isodose plume results, along with input data, source terms, and meteorological parameters, are transmitted via data communications to computational equipment located at several offsite emergency response centers. Each offsite location will have the capability to graphically display plume projection maps and data, and to provide hard copy to the user. Displays at different locations can be of an identical form for the purpose of response planning and coordination among various agencies. Displayed information may be selected from several forms of calculated data and map presentations. Mapping capabilities are provided to superimpose plume boundaries, dose rates, population densities, and evacuation routes. Color graphics and printouts are used to increase the information content and readability of information.

Hazardous materials road transportation accidents would cause great harm to neighboring population and environment. It is essential to develop a decision support system (DSS) for emergency response of hazardous materials road transportation accidents to improve emergency response ability and reduce accidents hazards. A framework based on quality function deployment (QFD) method to design DSS scheme selection was proposed. First of all, this paper identified DSS design scheme selection problem, which took into the requirements of government departments and transport enterprises, then applied the method based on QFD to lead the user's requirements into technical attributes of DSS design. In addition, Fuzzy set theory was applied in the QFD method, to evaluate the linguistic terms and variables in questionnaire and evaluation. Finally, a real case was given to illustrate the feasibility of the proposed method.

In recent years, advances in computer technology allowed the development of sophisticated software applications able to simulate the effects of oil spills on the marine environment. The need for such modelling arises from the perception of oil spills as long lasting and highly damaging events because of their wide range of potential impacts on the environment, human health and economic activities. Modelling tools can predict the consequences of these events and allow a targeted Response Planning to be made. This paper presents an innovative application of oil spill modelling tools aimed at supporting the Emergency Response System along the different stages of the project life-cycle. Three case studies are presented, from exploration, to response planning during production, to contingency response during an emergency. The oil spill modelling approach for each case study is detailed, as well as the specific support provided to the decision making process. Two different methods for oil spill modelling and environmental consequences assessment in eni exploration & production activities are shown. Preventive modelling, based on a statistical approach, identifies the range of possible outcomes of an event. Through this simulation, the consequence assessment of a spill provides useful information for a targeted Emergency Response Planning. Emergency modelling, on the other hand, is based on a deterministic approach and simulates the evolution of a single event, whose characteristics are known. In this case, oil spill models can predict the fate of an oil spill in an emergency, providing an insight on its future evolution and driving the decision on the most appropriate response actions. The results highlight the contribution of oil spill modelling in making the Emergency Response System an active player during all the project life-cycle, enabling its complete integration within the project management structure. Oil & Gas operators may benefit of this advanced approach that contributes to the improvement of their Emergency Response Management system. Through a prompt and proper response, the effect of oil spills can be excluded or mitigated, saving human lives as well as protecting the environment, the facilities and Company's reputation.

This position paper outlines an ongoing research project that aims to incorporate crowdsourcing as part of an emergency response system. The proposed system's novelty is that it integrates crowdsourcing into its architecture to analyze and structure social media content posted by microbloggers and service users, including emergency response coordinators and victims, during the event or disaster. An important challenge in this approach is identifying appropriate tasks to crowdsource, and adopting effective motivation strategies. Author Keywords Emergency response, social media, crowdsourcing, text mining.