Factors affecting prevalence of neurological symptoms among workers at gasoline stations in Rayong Province, Thailand

Comparison of acetylcholinesterase among employees based on job positions and personal protective equipment in fuel station

The purpose of this study was to compare the levels of acetylcholinesterase (AChE) among employees based on job positions and the wearing of personal protective equipment (PPE) in fuel station areas. The sample group consisted of 200 people sorted into various groups, including (i) Inside fuel dispenser area (I-FDA) group consisting of 100 employees for refueling, cashier, and loading fuel into storage tanks, and (ii) Outside fuel dispenser area (O-FDA) group, consisting of 100 employees working in convenience stores, food stores, coffee shops, and offices, as well as general staff and car washers. Data were collected using interview questionnaires and blood samples were analyzed for AChE activity. The results showed that the I-FDA group had a significantly lower mean of serum AChE (7.38±1.73 U/ml) than the O-FDA group (7.85±1.49 U/ml) (p<0.05). The I-FDA group had a 2.43 times higher risk of abnormal serum AChE than the O-FDA group (OR=2.43, 95%CI=1.05-5.60). When considering the risk factors for job positions, it was found that refueling and cashier positions had lower AChE activity levels than those who did not (p<0.05). In the part, employees for food sales in a closed building and those wearing PPE masks had significantly higher AChE activity levels than those who did not or who wore them (p<0.05). Based on the aforementioned, employers at fuel stations should strictly determine the appropriate measures for wearing a PPE mask. Further, employees should be provided accommodation away from oil supply or enclosed buildings during breaks to reduce the effects on the neurotransmitter.

A sustainable Decision Support System for soil bioremediation of toluene incorporating UN sustainable development goals

Decision Support System (DSS) is a novel approach for smart, sustainable controlling of environmental phenomena and purification processes. Toluene is one of the most widely used petroleum products, which adversely impacts on human health. In this study, Fusarium Solani fungi are utilized as the engine of the toluene bioremediation procedure for the monitoring part of DSS. Experiments are optimized by Central Composite Design (CCD) - Response Surface Methodology (RSM), and the behavior of the mentioned fungi is estimated by M5 Pruned model tree (M5P), Gaussian Processes (GP), and Sequential Minimal Optimization (SMOreg) algorithms as the prediction section of DSS. Finally, the control stage of DSS is provided by integrated Petri Net modeling and Failure Modes and Effects Analysis (FMEA). The findings showed that Aeration Intensity (AI) and Fungi load/Biological Waste (F/BW) are the most influential mechanical and biological factors, with P-value of 0.0001 and 0.0003, respectively. Likewise, the optimal values of main mechanical parameters include AI, and the space between pipes (S) are equal to 13.76 m³/h and 15.99 cm, respectively. Also, the optimum conditions of biological features containing F/BW and pH are 0.001 mg/g and 7.56. In accordance with the kinetic study, bioremediation of toluene by Fusarium Solani is done based on a first-order reaction with a 0.034 s-1 kinetic coefficient. Finally, the machine learning practices showed that the GP (R2 = 0.98) and M5P (R2 = 0.94) have the most precision for predicting Removal Percentage (RP) for mechanical and biological factors, respectively. At the end of the present research, it is found that by controlling seven possible risk factors in bioremediation operation through the FMEA- Petri Net technique, efficiency of the process can be adjusted to optimum value.