Release Of Hazardous Materials Research Articles

Development and Implementation of an Algorithm for Predicting the Intensity of Sorption of Hazardous Gaseous Materials

An algorithm for predicting the intensity of sorption of gaseous materials released into the atmosphere as a result of an accident is proposed. The algorithm consists of three hierarchical levels: monitoring the parameters of gaseous material emission, predicting the consequences of gaseous material emission before and after sorption, and making a management decision. The first hierarchical level includes 4 blocks: obtaining information from the chemical reconnaissance group and the facility representative on the type, amount of hazardous gaseous materials, release intensity and scale of the accident; obtaining information from the hydrometeorological service on temperature, atmospheric pressure, wind direction and speed in the accident area; processing the information received; information on the availability of forces and means for sorption of hazardous gaseous materials. The second hierarchical level also includes 4 blocks: readiness of forces and means for sorption of hazardous gaseous materials; calculation with sorption; calculation without sorption; determination of the boundaries of the chemical damage zone according to the established criteria. At the third hierarchical level, there is 1 block: making a management decision. The software implementation of the proposed algorithm was carried out. The use of the developed algorithm and its software implementation will increase the speed and accuracy of predicting the consequences of the release of hazardous gaseous materials in an accident.

Evaluation of human vulnerability and toxic effects of chronic and acute occupational exposure to ammonia: A case study in an ice factory.

The hazardous material release has frequently occurred worldwide. As a respiratory stimulant and a toxic substance, ammonia has numerous adverse effects on human health. The purpose of this study was to evaluate the human vulnerability and toxic effects of both chronic and acute respiratory exposure to ammonia. This study was conducted in an ice factory. Ammonia reservoirs were selected as the danger center. The scenarios were evaluated from the perspective of the worst-case. The Emergency Response Planning Guidelines 1-3 was used to predict the dangerous concentrations in acute exposure. The probability of human vulnerability was estimated using the Probit model. PHAST 7.2 software was used to model consequences. As a measure of chronic exposure to ammonia, NMAM 6016 was used. A respiratory symptom questionnaire developed by the American Thoracic Society was used for collecting respiratory symptom histories. The ERPG3 level or concentration of 750 ppm was found at a distance of 617.71 and 411.01 meters from tanks, respectively, as a result of a rupture in reservoir 1 over a period of two halves of the year. It was found that the highest probit values for tank 2 at distances of zero, 25, 50, 75, 100, 125, and 150 meters were 9.55, 5.92, 5.47, 4.82, 4.23, 3.56 and 2.96, respectively. The prevalence of pulmonary symptoms, which include coughing, dyspnea, phlegm, and wheezing, was 28%, 19%, 15%, and 26% in the chronic exposure group. In the event that an ammonia reservoir ruptures catastrophically, it may cause human injury at ERPG-2 or ERPG-3 levels. Results revealed that exposure to this substance can impose many pulmonary symptoms on the respiratory system of workers in industries. In order to reduce the vulnerability of humans to potential release scenarios, control measures must be implemented. Also, preventive and mitigation measures can be designed to enhance safety and resilience against the release of hazardous materials.

Comprehensive integrity assessment of spent nuclear fuel cladding during normal and postulated conditions of transportation

As a part of Spent Nuclear Fuel (SNF) management, it is important to maintain the cladding integrity to prevent the potential release of hazardous radioactive materials. Accordingly, assessing the integrity of SNF under the generated shock and vibration loads during normal conditions is essential for safe transportation. In this study, failure criteria for two zirconium alloys were derived by utilizing data from tensile property tests, mechanical model calculations, and machine learning estimations. Integrity assessment under normal and postulated transportation conditions was also performed via finite element analyses. Loading conditions were generated by real time-acceleration data collected during transportation tests in the Republic of Korea. Shock and vibration loads were applied to a simplified light-water reactor assembly. Furthermore, postulated transportation conditions and pre-cracked cladding were assumed for more challenging conditions. Consequently, a comprehensive integrity assessment of SNF cladding under shock and vibration loads for both free-defect and defective (pre-cracked) cladding was conducted. In each case, the resultant maximum principal strains, stress intensity amplitudes, and stress intensity factors were significantly smaller than the strain-based failure criteria, S-N curve, and fracture toughness, respectively. Therefore, the integrity of SNF cladding under transportation conditions remained intact even when assuming further challenging postulated transportation load conditions.

Synergistic interaction between scrap tyre and plastics for the production of sulphur-free, light oil from fast co-pyrolysis

Fast co-pyrolysis offers a sustainable solution for upcycling polymer waste, including scrap tyre and plastics. Previous studies primarily focused on slow heating rates, neglecting synergistic mechanisms and sulphur transformation in co-pyrolysis with tyre. This research explored fast co-pyrolysis of scrap tyre with polypropylene (PP), low-density polyethylene (LDPE), and polystyrene (PS) to understand synergistic effects and sulphur transformation mechanisms. A pronounced synergy was observed between scrap tyre and plastics, with the nature of the synergy being plastic-type dependent. Remarkably, blending 75 wt% PS or LDPE with tyre effectively eliminated sulphur-bearing compounds in the liquid product. This reduction in sulphur content can substantially mitigate the release of hazardous materials into the environment, emphasizing the environmental significance of co-pyrolysis. The synergy between PP or LDPE and tyre amplified the production of lighter hydrocarbons, while PS's interaction led to the creation of monocyclic aromatics. These findings offer insights into the intricate chemistry of scrap tyre and plastic interactions and highlight the potential of co-pyrolysis in waste management. By converting potential pollutants into valuable products, this method can significantly reduce the release of hazardous materials into the environment.

Synthesis of pyrimido[4,5-b]quinolindiones and formylation: ultrasonically assisted reactions.

Ultrasound-assisted synthesis of pyrimido‌quinolindione derivatives via a multicomponent reaction and subsequent formylation with Vilsmeier-Haack reagent were performed. Compounds were prepared by a one-pot method from aminopyrimidinones, dimedone and aromatic aldehydes through a Mannich-type reaction sequence, and then functionalized under ultrasound irradiation and Vilsmeier-Haack conditions to give β-chlorovinylaldehyde products. Ultrasonically assisted reactions, experimental simplicity, good yields without using metallic catalysts and the control of hazardous material release are features of this simple procedure.

Mitigating marine hazardous contaminants: A sustainable management perspective

Marine hazardous contaminants, such as cadmium (Cd) and lead (Pb), pose significant risks to both human health and aquatic organisms. Traditional methods may not remove contaminants to safe levels, leading to the release of hazardous materials into marine environments. This research proposes polymeric membrane bioreactors as a potential solution to this problem. We determined Cd and Pb levels in three freshwater fish species (Rita, Ompok bimaculatus, and Heteropneustes fossils) from two distinctive regions (Zone 1 and Zone 2). Additionally, Cd and Pb concentrations in feeding materials, water, and sediments were analyzed to estimate daily intake and potential hazardous effects of these contaminants on the fish species. These findings underscore the need for effective regulatory measures and policies to reduce the discharge of hazardous contaminants into freshwater and marine environments, protecting both human health and the environment. Implementing polymeric membrane bioreactors in wastewater treatment and industrial facilities could mitigate the risks associated with consuming contaminated fish species. Significantly, the Cd and Pb levels in all three fish species from both fishponds exceeded the Food and Agriculture Organization's (FAO) maximum permissible limits. These findings carry important implications for policymakers, regulators, and industries, urging them to act appropriately to ensure the safety of the environment and public health. This study suggests that polymeric membrane bioreactors are a promising technological approach to address marine contamination, emphasizing their potential role in safeguarding human health and aquatic ecosystems.

The Implementation of SDGs Program on Liquid Waste Processing for the Pasteurized Milk Industry

The SDGs (Sustainable Development Goals) program is the result of an agreement between nations in the United Nations (United Nations) which is very important to save nature. This study aims to discuss in more depth the implementation of the SDGs program in the wastewater treatment of the pasteurized milk industry. The type of research used is field research supported by a study of literature with a qualitative approach. The main data collection technique in this study was through field visits, observations, interviews, and documentation supported by searching for materials in the form of articles, journals, scientific papers, theses, final assignments, or books related to case studies to draw conclusions and develop theoretical aspects. and aspects of practical benefits. The results of the study show that the implementation of the Sustainable Development Goals (SDGs) program in the processing of wastewater from the pasteurized milk industry can be in the form of reducing pollution of the proportion of liquid waste and minimizing the release of hazardous materials or materials by recycling significant liquid waste and reusing the recycled liquid waste as a whole and safe and global.

MELCOR-TMAP: The integration of MELCOR for fusion and TMAP4 for fusion reactor systems safety analysis and tritium inventory tracking

The design of fusion reactors requires the prediction of radioactive tritium migration during normal operating conditions and accident scenarios. Safety standards such as those from the U.S. Department of Energy (DOE) require that fusion reactor designs limit the release of hazardous material to the environment. In addition to safety requirements, the rarity of tritium supplies for burning D-T fusion plasma requires tritium breeding blankets. Computational modeling of migration of tritium and various reaction products throughout the fusion reactor serves to create designs that fulfill these requirements. MELCOR-TMAP, owned and developed at Idaho National Laboratory (INL), is an integrated system-level engineering code adapted for the analysis of fusion reactor systems. The history of the conception and evolution of the MELCOR-TMAP code, as well as its current capabilities, recent updates including a corrected composite material wall nodalization scheme, and plans for future development, are discussed.

Human and Environmental Bias Affecting Risk Perception in Military Radiological and Nuclear Operations

The urban military operating environment may offer favorable conditions for combat involving asymmetric actions or 4th generation warfare which includes the chemical, biological, radiological, or nuclear (CBRN) environment. The CBRN environment is characterized by threats and hazards capable of producing negative impacts to the operations. The detriment may be produced by intentional release of hazardous materials into the operational environment. This study deals with an environment deliberately contaminated by radioactive materials because of the activation of a radiological dispersive device (RDD). A computer simulation of the radioactive scenario was performed in order to produce useful information which in turn can be used to support decision-making. The main goal was to assess the risk of developing radio-induced solid cancer by studying the articulation between local environmental variables and a cognitive bias, the Dunning-Kruger effect. The findings highlight intuitive correlations between local atmospheric stability and cognitive bias affecting the threat level (radiation dose), the responsiveness and the perceived risk. The findings also allow us to infer that, especially in the military-operational context, the methodology proposed for the assessment of environment-human interactions may be decisive for correctly direct available resources, both human and material, reducing operational risk.

Estimation of the influence of protective behaviour on expected injuries in transport accidents with hazardous materials

The release of hazardous materials due to transport accidents can have major health consequences for bystanders. The number of casualties will partly be determined by their protective behaviour.This study describes a three-step approach to predict health consequences of protective behaviour (for example, hiding or escaping) of bystanders in the first minutes of a transport accident with hazardous material. First, a discrete choice experiment (DCE) was used to predict protective behaviour. Second, a gas dispersion model (SeReMo) was used to estimate the distribution of casualties for different protective behaviours. Third, results of the DCE and SeReMo were combined to estimate the distribution of casualties in the population-at-risk. This approach was applied to a hypothetical marine accident with different hazardous material scenarios on a large waterway in a close vicinity of a beach/quay with bystanders.An important finding of our study was that in general a short reaction time and escaping in cross-wind direction, as protective behaviours, are of vital importance to reduce the number of casualties. A scenario with a short reaction time and a visible cloud towards the beach/quay resulted in a protective behaviour with the largest reduction of casualties.A dynamic risk assessment approach considering that people threatened by hazardous material are not ‘stationary observers’, but will exhibit protective behaviour, and a risk assessment that takes into account empirical information on expected protective behaviour will present a more realistic estimate of the number and severity of casualties when a large transport accident with hazardous materials would occur.

Analysis of Vulnerability during Business and Off-business Hours in Case of Hazardous Materials Release Accidents

Korea has been experiencing human and environmental damage caused by accidental release of toxic chemicals such as the Gumi hydrofluoric acid accident and the Ulsan chemical accident. As the government managers do not know when, where, and how such accidents will occur, they have been trying to reduce the level of damage by recognizing victims in advance. In this study, assuming a chlorine gas release accident occurs in facilities handling hazardous chemicals, the impact range was analyzed considering the worst-case scenario of chemical release. After analyzing social, physical, and place-dependent vulnerabilities by applying the multi-criteria evaluation method, a vulnerability map of chemical release was prepared. The study focused on proposing a method for vulnerability assessment during non-economic and economic activity time by considering dynamic factors when evaluating vulnerability. The results of dynamic vulnerability analysis based on GIS are expected to support decision making that could respond differently to chemical release accidents depending on the time of occurrence, thereby minimizing human casualties.

Emergency management and HAZMAT mitigation: Strategic planning for new developments.

In cases of accidental hazardous materials (HAZMAT) release, several emergency management responses can be taken to maintain the safety of the living environment. This study focuses on building a long-range planning, urban development model for hazards addressing where development zones should be located to mitigate exposure to HAZMAT and minimize evacuations. Interviews and meta-analytic manipulation of data were used to demonstrate flaws in three industrial cities' disaster management plans. These data were also used to influence development in plain model areas where hydrogen sulfide or other hazardous materials could accidentally be released. The Areal Location of Hazardous Atmospheres (ALOHA) model was used to simulate physical conditions affecting the hydrogen sulfide gas released from Mesaieed Industrial City in the State of Qatar, and Mapping Application for Response, Planning, and Local Operational Tasks mapping was used to display ALOHA threat estimates. Findings represent a considerable improvement to the urban developments in areas near HAZMAT risk zones and can be applied to other international industrial areas.

The Effect of Remediation of Crude Oil Contaminated Land on Agricultural Resources in Umuechem in Etche Local Government Area of Rivers State, Nigeria

Contamination is the release of hazardous materials into the environment; which could be in the form of: waste, petroleum hydrocarbons and other unwanted materials. The release of these hazardous materials could affect soil nutrients and aggravate biodiversity loss. When the soil nutrient is impacted, it negatively affects its crop yield and production capabilities. Once there is contamination of the environment, remediation becomes necessary. This paper examines the effects of remediation of crude oil contaminated land on agricultural resources in Umuechem in Etche Local Government Area of Rivers State, Nigeria. Purposive sampling technique and a survey research design was adopted with the use of structured questionnaires administered on a sample size of sixty two (62) persons whose farmlands and other agricultural resources were contaminated and thereafter remediated. The sample size was made up of fifty four (54) literate and eight (8) illiterate persons. The questionnaire was interpreted in native language to the illiterate persons by either their children or any other relative. The data obtained from the survey were analysed using descriptive statistics such as tables and statistical package for social sciences (SPSS).The findings obtained indicated that there was a post-remediation low-crop-yield and recommended that remediation consultants should adopt techniques capable of completely removing contaminants from the environment.

Discharge and atmospheric dispersion modelling in case of hazardous material releases

One of the most important tools for improving the OHS level in process industries is represented by risk analysis and assessment. Within industrial units in operation or in the ones which find themselves in the design phase, risk assessment is carried out for determining the hazards which may occur and which may lead to unwanted events, such as hazardous toxic releases, fires and explosions. Accidental releases of toxic/flammable/explosive substances may have serious consequences on workers or on the neighbouring population, therefore the need to establish safety areas based on best practices in the field and on scientific grounds is fully justified. Pressure tanks containing hazardous materials represent one of the most relevant industrial facilities within process plants, being most of the time exposed to hazardous toxic releases, fire and explosion risks. The current study aims to evaluate the consequences and discuss the safety distances required in case of an accidental release of a hazardous material from a tank located within a process plant, using process analysis software tools. Accident scenarios are modelled for comparison purposes with consequence modelling software widely used in safety engineering.

A Screening Methodology for the Identification of Critical Units in Major-Hazard Facilities Under Seismic Loading

The complexity of process industry and the consequences that Na-Tech events could produce in terms of damage to equipment, release of dangerous substances (flammable, toxic, or explosive), and environmental consequences have prompted the scientific community to focus on the development of efficient methodologies for Quantitative Seismic Risk Analysis (QsRA) of process plants. Several analytical and numerical methods have been proposed and validated through representative case studies. Nevertheless, the complexity of this matter makes their applicability difficult, especially when a rapid identification of the critical components of a plant is required, which may induce hazardous material release and thus severe consequences for the environment and the community. Accordingly, in this paper, a screening methodology is proposed for rapid identification of the most critical components of a major-hazard plant under seismic loading. It is based on a closed-form assessment of the probability of damage for all components, derived by using analytical representations of the seismic hazard curve and the fragility functions of the equipment involved. For this purpose, fragility curves currently available in the literature or derived by using low-fidelity models could be used for simplicity, whereas the parameters of the seismic hazard curve are estimated based on the regional seismicity. The representative damage states (DS) for each equipment typology are selected based on specific damage states/loss of containment (DS/LOC) matrices, which are used to individuate the most probable LOC events. The risk is then assessed based on the potential consequences of a LOC event, using a classical consequence analysis, typically adopted in risk analysis of hazardous plants. For this purpose, specific probability classes will be used. Finally, by associating the Probability Class Index (PI) with Consequence Index (CI), a Global Risk Index (GRI) is derived, which provides the severity of the scenario. This allows us to build a ranking of the most hazardous components of a process plant by using a proper risk matrix. The applicability of the method is shown through a representative case study.

Source term determination with elastic plume bias correction

Estimation of a source term, i.e. release rate, of atmospheric radionuclide emissions is of key interest for nuclear emergency response and further accident analysis. The source term estimate is, however, often very inaccurate due to biases in atmospheric transport and used meteorological analysis. We propose a method for atmospheric plume bias correction which uses not only concentrations modeled at a measuring site but also the information on concentration gradient from the neighborhood of each measuring site, i.e. information already available from the atmospheric transport model. To properly regularize the model, we propose an elastic model of the plume bias correction based on regularization with the use of known topology of the measurement network. The proposed plume bias correction method can be coupled with an arbitrary source term estimation algorithm and can be instantly applied to any other atmospheric release of hazardous material. We demonstrate the method in two real cases. First, we use data from the European Tracer Experiment to validate the methodology. Second, we use data from the 106Ru occurrence over Europe in 2017 to demonstrate the methodology in a more demanding case where agreement with state-of-the-art estimates is shown with much better reconstruction of measurements.

Does the Zero Carry Essential Information for Artificial Neural Network learning to simulate the contaminant transport in Urban Areas?

The release of hazardous materials in urbanized areas is a considerable threat to human health and the environment. Therefore, it is vital to detect the contamination source quickly to limit the damage. In systems localizing the contamination source based on the measured concentrations, the dispersion models are used to compare the simulated and registered point concentrations. These models are run tens of thousands of times to find their parameters, giving the model output’s best fit to the registration. Artificial Neural Networks (ANN) can replace in localization systems the dispersion models, but first, they need to be trained on a large, diverse set of data. However, providing an ANN with a fully informative training data set leads to some computational challenges. For example, a single simulation of airborne toxin dispersion in an urban area might contain over 90% of zero concentration in the positions of the sensors. This leads to the situation when the ANN target includes a few percent positive values and many zeros. As a result, the neural network focuses on the more significant part of the set - zeros, leading to the non-adaptation of the neural network to the studied problem. Furthermore, considering the zero value of concentration in the training data set, we have to face many questions: how to include zero, scale a given interval to hide the zero in the set, and include zero values at all; or limit their number? This paper will try to answer the above questions and investigate to what extend zero carries essential information for the ANN in the contamination dispersion simulation in urban areas. For this purpose, as a testing domain, the center of London is used as in the DAPPLE experiment. Training data is generated by the Quick Urban & Industrial Complex (QUIC) Dispersion Modeling System.

Dual Control for Exploitation and Exploration (DCEE) in autonomous search

This paper proposes an optimal autonomous search framework, namely Dual Control for Exploration and Exploitation (DCEE), for a target at an unknown location in an unknown environment. Source localisation is to find sources of atmospheric hazardous material release in an unknown environment. This paper proposes a control theoretic approach to this autonomous search problem. To cope with an unknown target location, at each step, the target location is estimated by Bayesian inference. Then a control action is taken to minimise the error between future robot position and the predicted future estimation of the target location. The latter is generated by hypothesised measurements at the corresponding future robot positions (due to the control action) with the current estimation of the target location as a prior. It shows that DCEE can take into account both the error between the next robot position and the estimated target location, and the uncertainty of the estimate. This approach is further extended to deal with both an unknown source location and unknown local environment (e.g. wind speed and direction). Different from current information theoretic approaches, this new control theoretic approach achieves the optimal trade-off between exploitation and exploration in an unknown environment with an unknown target by driving the robot moving towards estimated target location while reducing its estimation uncertainty. Simulation and experimental studies demonstrate promising performance of the proposed approach. The relationships between the proposed approach, informative path planning, dual control, and classic model predictive control are discussed and compared. This work opens a door for developing control systems operating in unknown environments, or performing tasks with unknown parameters.

Emergency responder and public health considerations for plastic sewer lining chemical waste exposures in indoor environments

The cured‐in‐place pipe (CIPP) manufacturing process is used to repair buried pipes, and its waste commonly discharged into the air can enter nearby buildings. Exposure can prompt illness and the need for medical care. A mass balance model was applied to estimate indoor styrene concentrations due to intrusion of CIPP emissions through plumbing under different bathroom ventilation conditions. To better understand building contamination and recommend emergency response actions, calculations to estimate chemical intrusion through plumbing were developed. Field reports and study calculations showed that contractor-applied external pressures during plastic manufacture have and can displace plumbing trap water seals. Modeled styrene vapor concentrations that entered the building (1, 300, 1000 ppm) were similar to those measured at CIPP worksites. Modeling revealed that in some cases, bathroom exhaust fan operation during a CIPP project may increase indoor styrene concentrations due to enhanced entrainment of styrene-laden air from the sink and toilet. However, styrene concentrations decreased with increasing air leakage across the bathroom door due to reduced suction from the plumbing system. CIPP waste discharge should be treated as a hazardous material release and can pose a threat to human health. Immediate building evacuation, respiratory protection, provision of medical assistance, source elimination, and building decontamination are recommended.

Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models

In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.