BackgroundFatigue and sleepiness are inter-related and common among road transport drivers. In this study, sleep deprivation and fatigue among chemical transportation drivers were examined.MethodsA cross-sectional study surveying 107 drivers from three hazardous types of chemical production and transportation industries (nonflammable gases, flammable gases, and flammable liquids) was conducted. Data on sleep deprivation were collected using questionnaires of the Stanford Sleeping Scale and the Groningen Sleep Quality Scale. Fatigue was assessed using an interview questionnaire and a flicker fusion instrument.ResultsChemical drivers had a mean sleeping scale (Stanford Sleeping Scale) of 1.98 (standard deviation 1.00) and had a mean score of 1.89 (standard deviation 2.06) on the Groningen Sleep Quality Scale. High-risk drivers had higher scores in both the Stanford Sleeping Scale and the Groningen Sleep Quality Scale with a mean score of 2.59 and 4.62, respectively, and those differences reached statistical significance (p < 0.05). The prevalence of fatigue, as assessed through a critical flicker fusion analyzer, subjective fatigue question, and either of the instruments, was 32.32%, 16.16%, and 43.43%, respectively. Drivers who slept <7 hours and had poor sleep quality were found to have more fatigue than those who slept enough and well. Drivers who had a more sleepiness score resulted in significantly more objective fatigue than those who had a less sleepiness score.ConclusionSleep quality and sleeping hour can affect a driver's fatigue. Optimization of work–rest model should be considered to improve productivity, driver retention, and road safety.

Background. E-waste sorting workers usually separate electronic waste. Therefore, they can be exposed to heavy metals. Objectives. This study compared monoamine oxidase (MAO) levels affected by the levels of lead (Pb), cadmium (Cd), and nickel (Ni) in the blood and their workplace among e-waste sorting workers (EWSW). Material and methods. The exposed group included 76 EWSW, and the non-exposed group included 49 village health volunteers. An interview form was used to assess the risk factors. We measured Pb, Cd, and Ni on the work surfaces and in the blood, and MAO levels as a neurological enzymes. Results. Among the EWSW, 42 were males (55.3%), and the mean age (SD) 48.0 (12.64) years, and income were 156.37 ± 88.08 USD. In the work areas of the exposed group, the concentration of Pb, Cd, and Ni were 245.042 (± 613.910), 0.375 (± 0.662), and 46.115 (± 75.740) μg/100 cm2, respectively, while the non-exposed group, the concentration of Pb, Cd, and Ni were 0.609 (± 0.934), 0.167 (± 1.171) and 1.020 (± 0.142) μg/100 cm2. Pb and Ni concentrations in the workplace of the exposed groups were statistically different from that of the non-exposed group. Pb, Cd, and Ni concentrations in serum were 6.411 ± 1.492 μg/dL, 0.9480 ± 0.350 μg/L, 2.568 ± 0.468 μg/L, respectively, while in the non-exposed group, the heavy metal concentrations were 6.411 ± 1.620 μg/dL, 0.909 ± 0.277 μg/L, 2.527 ± 0.457 μg/L. The MAO in the exposed group was 362.060 ± 97.981 U/L, while that in the non-exposed group was 369.771 ± 86.752 U/L. Moreover, MAO concentration was significantly different from Ni concentration (p < 0.05). Conclusion. The electronic waste sorting workers should clean their work areas to reduce the Pb, Cd, and Ni levels on the working surfaces, and health surveillance should be performed.

This research was conducted to estimate exposure and risk of exposure to BTEX (benzene, toluene, ethylbenzene and xylene)by a cross-sectional studying among pregnant women who lived in the vicinity of a petro-chemical industrial estatearea. Personal exposure was monitored by environmental and biological sampling.Other relevant data were gathered by a structured questionnaire for analysis. The relationship between BTEX exposure and potential risk factors was determined by multiple linear regression. Average BTEX exposure levels were 18.89, 29.40, 10.26, and 17.85μg m-3, respectively, which were significantlyhigher than the control group. Urinary metabolite levels of muconic acid(t,t-MA), hippuric acid (HA),mandelic acid (MA), andmethyl-hippuric acid (mHA)were significantly higher in pregnant residents living in the vicinity of the petro-chemical industrial estate area.The correlation between women’s individual exposure to BTEX and theirurinary metabolites showed significantlydifference between groups.Risks of benzene and ethylbenzene exposure for these pregnant subjects were approximately 4.37x10-7 and 1.92x10-8lower than the acceptable limits of United States Environmental Protection Agency(US.EPA.) guidelines(10-6). The hazard index (HI) of these pregnant subjects a lifetime rate was 0.555 which was below the reference level(HI < 1).Multiple linear regression analysis found that t,t-MA, HA, MA, and mHAin the urine of all models were significantly (p<0.05)and positively related with BTEX in ambient air.The study showed that there was a low cancer risk for pregnant women in the petro-chemical industrial estatezone. However, when compared with the control area, a statistically significant difference was found in the relationship between BTEX content in ambient air and metabolites in the urine of pregnantwomen. The implication is that the responsible government agencies should implement health surveillancecontinuously.

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Driver fatigue is a recognized risk factor for commercial road transport industry drivers. The aim of this cross-sectional research was to assess the fatigue and its determining factors among 99 chemical transportation drivers in Chonburi. Driving fatigue was assessed by both subjective questionnaire (n = 99) and flicker fusion instrument (n = 88). The association between driving fatigue and related factors were analyzed by Pearson’s correlation and Chi-square (χ2). The results revealed that the prevalence of fatigue as assessed by critical flicker fusion analyzer, subjective fatigue question and either one of the instruments were 32.32 %, 16.16 % and 43.43 %, respectively. Multiple regression analysis indicated that the predictive factors of objective fatigue were alcohol drinking, musculoskeletal disorder and road accident history. The results suggest thatscreening for alcohol use and musculoskeletal disorders was further needed for policy settings and routine checks.