Assessment of Health, Safety and Environmental Risks of Zahedan City Gasoline Stations

Abstract

The purpose of this study was to assess the risk and determine the health, safety and environmental status of fuel stations in Zahedan. In this study, failure mode and effects analysis (FMEA) method was used for risk assessment in accordance with the HSE guidelines, national and international standards and laws. In this cross-sectional study, 2 governmental stations and 6 active private stations were evaluated after the necessary coordination with the relevant units. As a result of risk assessment, 27 health risks, 55 safety risks and 22 environmental risks were identified. From among all the identified risks, 67 risks had a Risk Priority Number (RPN) of less than 91, 31 risks had an RPN ranging between 91 and 201, and 6 risks had an RPN of over 201. The findings of the study indicated that compliance with the HSE requirements was 51.85%, in the area of health, 47.57% in the area of safety and 27.45% in the environmental area. Overall compliance with the HSE requirements was 42.54%. In order to distribute fuel considering health, reducing risk and increasing compliance with the requirements for safety improvement, health and environmental conditions of fuel supplies are essential.

Utilization of Machine Learning in Supporting Occupational Safety and Health Decisions in Hospital Workplace

“…The proposed change is related to the transfer of coded paper records into a DSS which is promising for OSH decisions. This process could result in faster and more accurate predictions according to OSH injuries and interventions, in accordance with [9,34,62], emphasizing the significance of quantitative analysis of empirical injury data in the safety science [25]. Another advantage of the proposed model is the fact that it can be adjusted to various working sectors, depending on the training sets and the existing records of incidents and accidents in each of them.…”

“…Their potential in occupational accident analysis has been highlighted from several studies, as they have been reviewed in [7]. Indicatively, statistical methods have been used for analyzing health and safety issues of women in industry [8], the OSH risk in fuel stations has been assessed through Failure Mode and Effects Analysis (FMEA) method [9], Naive Bayesian (NB) model has been used for coding causation of workers' compensation claims [10], Bayesian Networks (BNs) have been used for analyzing data on occupational accidents [11][12][13], and decision trees have been used in the industrial mining sector for predicting the type of accident [14]. Artificial Neural Networks (ANNs) have been used to correlate causes and OSH conditions [15][16][17][18][19] and they have been combined with Support Vector Machines (SVMs) for the prediction of occupational accidents [20].…”

Evaluation of Occupational Health Risk Management and Performance in China: A Case Study of Gas Station Workers

“…Risk factors such as anxiety, irritability, sadness, bacteria, viruses, parasites and fatigue are of central importance in order to protect the occupational health of gas workers [ 2 , 4 , 47 ]. Various precautionary measures have been suggested in published literature [ 6 , 56 , 58 ]. However, practical implementation of such measures is yet to become a prerequisite in China.…”

“…Studies associated with workers’ perceived risk demonstrated that managing materials scattered on slippery floors and working spaces is a good strategy for minimizing the risk of slick at fuel stations [ 2 ]. Besides, gas stations should try to alleviate the risk of collision between cars and workers by ensuring proper vehicles flow [ 56 ]. Proper work shifts and schedules should be established to eliminate severe fatigue problems caused due to repetitive movements, and long periods of standing required for the job [ 49 ].…”

New risk assessment and prioritization failure modes based approach in a gas turbine system

“…The notion of fuzzy sets that was developed by Zadeh is the foundation for the fuzzy risk assessment model. It gives a more reliable to assess risk related with various failure modes [21], where the different factors utilized in the conventional technique will be fuzzified by using a proper membership function that employs knowledge rules IF -THEN that come from expert opinion, where the relation is represented as: criticality = frequency × severity × detection (1)…”