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Occupational exposure to benzene was measured in two gasoline marketing terminals and five major refineries in Singapore. A total of 280 workers were monitored over two years. This assessment was carried out with two primary objectives: (1) To find out the extent of occupational exposure to benzene in the petroleum industry in Singapore, (2) To identify suitable biomarkers for monitoring of low levels of benzene exposure. The exposure was measured in five different categories of petroleum and petrochemical workers, i.e., truck drivers, despatch assistant, process operators, oil movements operators and laboratory technicians. The results revealed wide variations in exposure, from 0.01 to 13.6 ppm for personal time weighted average (TWA) exposure over the whole workshift. The exposure of truck drivers appeared to be the highest, with geometric mean (GM) of 1.98 ppm (ranged from 0.25 to 13.6 ppm). The average benzene exposure for process operators was relative low with a GM of 0.04 ppm. Lowest benzene exposure was found in the laboratory technicians, with a GM of 0.02 ppm. As cigarette smoking is known to affect metabolism of benzene, data analyses on the relationships with environmental exposure were conducted only on the 190 nonsmokers. The results showed that urinary trans, trans-muconic acid (ttMA), unmetabolized benzene in urine (UBZ) and benzene in blood (BBZ) were better biomarkers for low level benzene exposure as compared to urinary phenolic metabolites in urine, such as hydroquinone, phenol and catechol.  相似文献   
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A number of publications report statistical summaries for environmental tobacco smoke (ETS) concentrations. Despite compelling evidence for the data not being normally distributed, these publications typically report the arithmetic mean and standard deviation of the data, thereby losing important information related to the distribution of values contained in the original data. We were interested in the frequency distributions of reported nicotine concentrations in hospitality environments and subjected available data to distribution analyses. The distribution of experimental indoor airborne nicotine concentration data taken from hospitality facilities worldwide was fit to lognormal, Weibull, exponential, Pearson (Type V), logistic, and loglogistic distribution models. Comparison of goodness of fit (GOF) parameters and indications from the literature verified the selection of a lognormal distribution as the overall best model. When individual data were not reported in the literature, statistical summaries of results were used to model sets of lognormally distributed data that are intended to mimic the original data distribution. Grouping the data into various categories led to 31 frequency distributions that were further interpreted. The median values in nonsmoking environments are about half of the median values in smoking sections. When different continents are compared, Asian, European, and North American median values in restaurants are about a factor of three below levels encountered in other hospitality facilities. On a comparison of nicotine concentrations in North American smoking sections and nonsmoking sections, median values are about one-third of the European levels. The results obtained may be used to address issues related to exposure to ETS in the hospitality sector.  相似文献   
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