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

BACKGROUND: The hazardous material release has frequently occurred worldwide. As a respiratory stimulant and a toxic substance, ammonia has numerous adverse effects on human health. OBJECTIVE: The purpose of this study was to evaluate the human vulnerability and toxic effects of both chronic and acute respiratory exposure to ammonia. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Does exposure to ammonia concentrations lower than the threshold limit value cause acute pulmonary effects?

One of the major toxic effects of exposure to ammonia is the resulting pulmonary acute and chronic effects. This study investigated the acute pulmonary effects of exposure to ammonia lower than the recommended threshold limit value (TLV). This cross-sectional study was conducted in 2021 in four chemical fertilizer production industries using ammonia as the main raw material. A total of 116 workers who were exposed to ammonia were investigated. The level of exposure to ammonia was measured by NMAM 6016, and the evaluation of pulmonary symptoms and function parameters was done using the American Thoracic Society and European Respiratory Society protocols in four sessions. The paired-sample t-test, repeated measures test, Chi-square, and Fisher's exact test were run to analyze the collected data. The prevalence rates of pulmonary symptoms, including cough, dyspnea, phlegm, and wheezing, were 24.14, 17.24, 14.66, and 16.38%, respectively, after one exposure shift. It was observed that all pulmonary function parameters were reduced after one exposure shift to ammonia. The results revealed that the parameters of vital capacity, forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), the FEV1/FVC ratio, and peak expiratory flow significantly decreased (p < 0.05) across four exposure shifts. The findings indicated that exposure to ammonia at concentrations lower than one-fifth of TLV could bring about acute pulmonary effects and reduce pulmonary function parameters, similar to the pattern observed in obstructive pulmonary diseases.

Occupational safety risk analysis in construction sites based on fuzzy analytic hierarchy process: A case study in a large construction project.

BACKGROUND: Construction projects are one of the most critical occupational sectors that experienced many challenges in occupational accidents and safety performance. OBJECTIVE: This study was designed to assess safety risk in construction projects based on fuzzy analytic hierarchy process. METHODS: This study was conducted with 12 construction and occupational safety experts in one of the largest construction projects in Tehran-Iran in 2020. The process of this study included (1) risk identification, (2) measurement of risk parameters and sub-parameters, and (3) risk assessment based on a fuzzy analytic hierarchy process. Risk-forming parameters in this study included the probability and severity of the event. The probability of occurrence was estimated based on four sub-parameters of technical inspection, accident experience, detection probability, and human reliability. Sub-parameters of severity included human injury, cost imposition, tarnishing the organization's esteem, and impact on project timing and work stoppage. RESULTS: Twenty-eight identified hazards were examined in the studied construction project, including falling from a height (9-hazard), falling objects (2-hazard), electric shock (6-hazard), falling crane or load (6-hazard), elevator crashes (2-hazard), and soil fall (3-hazard). Safety risk assessment revealed that 27 risk sources were at the tolerable level and one risk source was unacceptable. CONCLUSION: This study demonstrated that the risk levels in the studied construction project were tolerable and unacceptable. The obtained model in this study demonstrated that using parameters that determine the probability and severity of risk according to the nature of the working environments can be a practical step in risk evaluating and implementing control measures.

Construction accident data mining: A retrospective study using structural equation modeling based on 10-year data.

BACKGROUND: The data mining of construction accidents based on a robust modeling process can be used as a practical technique for reducing the frequency of construction accidents. OBJECTIVE: This study was designed to data-mine construction accidents. METHODS: This study was conducted in 2020 on construction accidents in Iran for ten years (2009-2018). The instruments to collect the required data were the checklists and descriptive reports of the accidents. The dependent variables of the study included reactive safety indicators related to construction accidents (lost working days (LWD) and total accident costs (TAC)). The independent variables consisted of four latent factors: personal variables, organizational variables, unsafe working conditions, and unsafe acts. The data were collected based on the conceptual model designed for data mining. The data mining process was carried out based on the structural equation modeling by IBM AMOS V. 23.0. RESULTS: A total of 5742 construction accidents occurring in 10 years were analyzed. The means of TAC and LWD indicators were estimated to be 248.20±52.60 days and 1893.10±152.22 $. These two indicators directly correlated with the two latent factors of unsafe conditions and unsafe acts and their related variables and were indirectly influenced by latent personal and organizational factors. The relationship between unsafe conditions and unsafe acts was significantly positive. The relationship between latent personal and organizational factors and the two construction accident indicators was significantly negative (p <0.05). CONCLUSION: The model results showed that personal and organizational variables could, directly and indirectly, affect reactive safety indicators in construction projects. Thus, these findings can be used to design and improve safety strategies to prevent and decrease construction accidents and incidents.

A fuzzy Bayesian network DEMATEL model for predicting safety behavior.

Objectives. Safety behavior significantly affects safety performance in the workplace. This study aimed to develop a Bayesian network (BN) model for managing and improving safety behavior. Methods. This study was carried out in the chemical industries in Iran. The data were gathered by a questionnaire consisting of 13 variables including organization safety priority, systems design, safety communication, safety education, work strategy, human-system interaction, mental workload, environmental distractions, work pressure, fatigue, sleepiness, safety knowledge and locus of control. The BN structure was created using the fuzzy decision-making trial, evaluation laboratory method and expert opinions. Belief updating was used to determine variables with the strongest effect on safety behavior. Results. Locus of control, organization safety priority and safety knowledge were the best predictors of safety behavior. Moreover, it was found that improving organization safety priority and safety knowledge is the best intervention strategy to improve safety behavior significantly. Conclusions. BN is a powerful tool that can model causal relationships among variables. Improving organization safety priority and safety knowledge can lead to the maximum possible level of safety behavior.

Evaluating the potential severity of biogas toxic release, fire and explosion: consequence modeling of biogas dispersion in a large urban treatment plant.

Objectives. Biogas production in treatment plants for energy generation has increased in recent years. This study aimed to model the consequence of biogas release in a large urban treatment plant. Methods. The study modeled biogas storage tank consequences in a large urban treatment plant in Iran. Due to potential for biogas harmfulness, three consequences of toxic release, fire and explosion were evaluated. Scenarios were evaluated in the worst-case situation. All modeling steps were performed using PHAST version 7.2. Results. In the case of catastrophic reservoir rupture in summer, distances of 3788.94, 128.86 and 91.72 m from the reservoir in the wind direction will be in the range of 100, 500 and 1000 ppm biogas, respectively. Study of pressure values due to explosion in the catastrophic rupture scenario revealed that distances of 57.19, 14.70 and 115.84 m from the biogas reservoir were in the range of 0.02, 0.13 and 0.2 bar pressure increase, respectively. Conclusion. Due to the treatment plant's location in a dense urban area, biogas dispersion could lead to exposure of many people to high-risk areas. Therefore, taking control measures comparable with the consequence modeling output can be a practical step toward reducing vulnerability against such incidents.

Developing a risk assessment method for infectious diseases focusing on COVID-19 based on the Delphi method and fuzzy analytical hierarchy process.

BACKGROUND: Given the coronavirus 2019 (COVID-19) risk, it is essential to develop a comprehensive risk assessment method to manage the risk of the infectious diseases. OBJECTIVE: This study aimed to develop a risk assessment method for infectious diseases focusing on COVID-19. METHOD: This study was based on the fuzzy Delphi method (FDM) and fuzzy analytical hierarchical process (FAHP) in three steps: (a) designing the preliminary risk assessment algorithm by reviewing the literature, (b) corroborating the designed structure based on the majority opinions of the expert panel and assigning scores to different factors according to the Delphi method, and (c) determining the weight of components and their factors based on the FAHP. RESULTS: The COVID-19 risk index (CVRI) was found to be affected by four components and 19 factors. The four components consisted of the probability of getting sick (5 factors), disease severity (4 factors), health beliefs level (3 factors), and exposure rate (6 factors). The identified components and their relevant factors had different weights and effects on the CVIR. The weights of probability, severity, health beliefs level, and exposure rate components were 0.27, 0.20, 0.14, and 0.38, respectively. The CVRI was found to range from 0.54 to 0.82, defined in three levels. CONCLUSION: Given the significant effects of identified components, factors, and parameters on the incidence of COVID-19 on the one hand and using the FDM and FAHP on the other, the proposed method can be considered as an appropriate method for managing the risk of COVID-19 and other infectious diseases.

Linking mental health to safety behavior in construction workers: The mediating effect of work ability and sleep quality.

BACKGROUND: The severity of occupational accidents and injuries in the construction industry is the greatest across all industries. Few studies have examined the causal relationships among physical and psychological variables affecting the safety behavior of construction workers. OBJECTIVE: This study aimed to investigate the relationships among three factors (metal health, work ability and sleep quality) influencing the safety behavior of construction workers. METHODS: This cross-sectional study was conducted on 204 workers in civil construction projects in Iran. The data were gathered via self-reporting questionnaires. The acquired data were analyzed by the Smart PLS3 software using structural equation modeling with the partial least squares (PLS) regression approach. RESULTS: The R square revealed an acceptable fineness of the structural model. Similarly, as Q square values were higher than 0.15 for all factors, the predictability of the model was approved. The direct effect of mental health on safety behavior was not significant, but work ability and sleep quality had significant direct effects on safety behavior. Moreover, a decrease in mental health significantly reduced construction workers' sleep quality and work ability. CONCLUSION: The results of this study prove that sleep quality and work ability mediate the relationship between mental health and safety behavior. The deterioration of construction workers' mental health can increase the risk of unsafe behavior and occupational injuries. Therefore, appropriate measures need to be taken to maintain and improve the mental health of construction workers.

The mediating effect of workers' situation awareness on the relationship between work-related factors and human error: a path analysis approach.

Situation awareness is the main reason behind different patterns of unsafe behavior among workers and can play a mediating role in the relationship between predictive situational variables and human error. This questionnaire-based study carried out with Iranian workers investigated the direct and indirect effects of work pressure, mental workload, human-systems interaction and environmental distractions on three types of human error (i.e., slips, lapses and mistakes). The results of path analysis showed that, based on model fit indexes, the model is appropriately fit (χ2 / df = 3, comparative fit index = 099, Tucker-Lewis index = 0.99, root mean square error of approximation = 026). It was also observed that the effects of four independent variables on all three types of human error are indirect and mediated by situation awareness. Consequently, it is confirmed that situation awareness plays a key role in the occurrence of human error and is a sharp-end causal factor for human error in industrial workplaces.

Evaluation of the toxic effects of ammonia dispersion: consequence analysis of ammonia leakage in an industrial slaughterhouse.

Ammonia is a toxic compound and has many toxic effects on humans and the environment. This study was designed to model the consequences of ammonia leakage in an industrial slaughterhouse. Given the potential hazard of ammonia, only the toxic dimension of this gas was evaluated. The scenarios were evaluated in the worst possible condition and in the case of the complete rupture. Findings showed that in case of a catastrophic rupture scenario in reservoir 1 in the first and second 6 months of the year, the distances of 920.37 and 569.38 m from the reservoir in the wind direction were at Emergency Response Planning Guidelines, level 3 (ERPG3), respectively. In reservoir 2, in the first and second 6 months of the year, the distances of 699.58 and 384.86 m from the reservoir were at the ERPG3 level, respectively. In reservoir 3, in the first and second 6 months of the year, the distances of 203.48 and 748.28 m from the reservoir were at the ERPG3 level, respectively. Examination of the probit values showed that in reservoirs 1 and 2, the probit values were more than 4.28 up to 100 m from the reservoirs, and in the reservoir 3, the mortality rates were lower. The findings revealed that the catastrophic rupture of ammonia reservoirs in the studied slaughterhouse and the release of ammonia could lead to the fatality of large numbers of people in ERPG2 and ERPG3 areas. Therefore, it is necessary to take control measures to reduce the vulnerability against such accidents.

A wavelet-based random forest approach for indoor BTEX spatiotemporal modeling and health risk assessment.

This study reports on BTEX concentrations in one of the largest parking garages in Iran with a peak traffic flow reaching up to ~9300 vehicles in the last few days of the Nowruz holidays. Samples were obtained on different days of the week at three main locations in the Zaer Parking Garage. A novel wavelet-based random forest model (WRF) was trained to estimate BTEX concentrations by decomposing temperature, day of the week, sampling location, and relative humidity data with a maximal overlap discrete wavelet transform (MODWT) function and subsequently inputted into the WRF model. The results suggested that the WRF model can reasonably estimate BTEX trends and variations based on high R2 values of 0.96, 0.95, and 0.98 for training, validation, and test data subsets, respectively. The carcinogenic (LTCR) and non-carcinogenic health risk (HI) assessment results indicated a definite carcinogenic risk of benzene (LTCR = 2.22 × 10-4) and high non-carcinogenic risk (HI = 4.51) of BTEX emissions. The results of this study point to the importance of BTEX accumulation in poorly ventilated areas and the utility of machine learning in forecasting air pollution in diverse airsheds such as parking garages.

Modeling the causes-effect relationships among major accident predictors based on a fuzzy multi-criteria decision-making method.

BACKGROUND: Finding the best practices for accident prevention is possible by identifying the influential factors affecting accident occurrence and their interactions as well as implementing corrective actions for the root cause factors. OBJECTIVE: This study was aimed to determine the cause-effect relationships and the interaction of the influential factors affecting accident occurrence and determine the critical root factors. METHODS: This study was carried out based on the opinions of a panel of experts and used the fuzzy DEMATEL method. RESULTS: The results showed that "organization safety attitude", "safety communication", "work and safety training" and "safe design of systems" are root cause variables. Also, "work and safety knowledge" and "experience in the job" are individual cause variables. CONCLUSIONS: Organizational factors and some of individual variables are the critical factors that affect the occurrence of accidents. Therefore, corrective actions for accident prevention should primarily focus on the correction of these variables.

Explanation and prediction of accidents using the path analysis approach in industrial units: The effect of safety performance and climate.

BACKGROUND: The safety climate in an organization depends on people's understanding of the safety policies and procedures, as well as the value, importance, and priority of safety in the workplace. OBJECTIVE: This study aimed to describe and predict accidents using the path analysis model (PAM) in industrial units though the analysis of the effect of safety performance and climate. METHODS: This cross-sectional study was conducted on 294 workers in industrial units in Hamadan, a province in the western part of Iran. The data on safety performance and climate was collected using a questionnaire. The first part of the questionnaire was a short version inventory (with 25 items on safety climate) that was used to assess five variables of management commitment, supportive environment, training, personal safety prioritization, and perceived work pressure. Moreover, the safety performance was measured using 10 items on safety rules and participation. The PAM was used to describe the effects of safety climate and performance on accidents. RESULTS: The results showed that the safety climate had the strongest negative impact on work pressure and safety compliance toward accident, followed by safety participation, and quality of training. Moreover, the negative influence of safety climate on accident was mainly mediated by two variables: work pressure and safety participation toward accident. The work pressure had the strongest indirect and total influence on accidents. However, none of the variables had a direct effect on accidents. Training was the most important direct cause of promoting personal safety priority. The safety compliance was more effective than safety participation in reducing accidents rates. CONCLUSIONS: Therefore, it seems that perceived work pressure has an indirect effect on accidents which is mediated by other variables, mainly personal safety priority and safety performance.

Respiratory Disorders Resulting From Exposure to Low Concentrations of Ammonia: A 5-Year Historical Cohort Study.

OBJECTIVE: This study aimed to investigate respiratory disorders associated with exposure to low concentrations of ammonia. METHOD: This cohort study was conducted on 122 industrial workers. Data gathering were based on the officially registered data, the NMAM 6016 and the American Thoracic Society and European Respiratory Society protocols. RESULTS: The prevalence rate of respiratory disorders significantly increased in the exposure group over 5 years (P < 0.05). The frequencies of these symptoms in the exposure group were significantly higher than non-exposure group (P < 0.05). Respiratory functions were decrease significantly in the exposure group and were lower than non-exposure group (P < 0.05). Exposure and the amount of exposure to ammonia had a significant relationship with respiratory disorders and respiratory functions (P < 0.05). CONCLUSION: Exposure to ammonia lower than threshold limit value (TLV), can act as a risk factor of respiratory disorders.

Data on emerging sulfur dioxide in the emission of natural gas heater in winter.

Natural gas is a kind of fuel that is used in various heating systems for combustion processes. Combustion of natural gas produce such air pollutants as CO2, NOx, SOx, PM, CO, and HC. During cold days, total gas consumption in Iran goes up. Thus, in these days it is likely to make some changes in gas properties that can affect the emissions from gas heaters. Portable flue gas analyzer (LANCOM III) was used for, SO2, NOX, and CXHY measurement from December 2012 to April 2013. Data show that emissions of pollutants from the gas heater were different during the cold seasons. On colder days, total gas consumption in the country increased, SO2 emerged in heater stack, and the concentration of hydrocarbons (CXHY) significantly increased as well. This emergence could be attributed to the changes in gas properties in the colder days. In these days, the transient flow and high changes of speed and pressure in the gas pipes could lead to release of some deposited sulfur in gas flow. Therefore, sulfur dioxide will be generated in the combustion process.