• Environmental Analysis Health and Toxicology
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• v.17 no.3
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• pp.197-205
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• 2002

Volatile organic compounds (VOCs) are an important public health problem throughout the world. Many important questions remain to be addressed in assessing exposure to these compounds. Because they are ubiquitous and highly volatile, special techniques must be applied in the analytical determination of VOCs. Personal exposure measurements are needed to evaluate the relationship between microenvironmental concentrations and actual exposures. It is also important to investigate exposure frequency, duration, and intensity, as well as personal exposure characteristics. In addition to air monitoring, biological monitoring may contribute significantly to risk assessment by allowing estimation of absorbed doses, rather than just the external exposure concentrations, which are evaluated by environmental and personal monitoring. This study was conducted to establish the analytic procedure of VOCs in air, blood, urine and exhaled breath and to evaluate the relationships among these environmental media. The subjects of this study were selected because they are occupationally exposed to high levels of VOCs. Environmental, personal, blood, urine and exhalation samples were collected. Purge ＆ trap, thermal desorber, gas chromatography and mass selective detector were used to analyze the collected samples. Analytical procedures were validated with the“break through test”, 'quot;recovery test for storage and transportation”,“method detection limit test”and“inter-laboratory QA/QC study”. Assessment of halogenated compounds indicted that they were significantly correlated to each other (p value < 0.01). In a similar manner, aromatic compounds were also correlated, except in urine sample. Linear regression was used to evaluate the relationships between personal exposures and environmental concentrations. These relationships for aromatic and halogenated are as follows: Halogen $s_{personal}$ = 3.875+0.068Halogen $s_{environmet}$, ($R^2$= .930) Aromatic $s_{personal}$ = 34217.757-31.266Aromatic $s_{environmet}$, ($R^2$= .821) Multiple regression was used to evaluate the relationship between exposures and various exposure deter-minants including, gender, duration of employment, and smoking history. The results of the regression model-ins for halogens in blood and aromatics in urine are as follows: Halogen $s_{blood}$ = 8.181+0.246Halogen $s_{personal}$+3.975Gender ($R^2$= .925), Aromatic $s_{urine}$ = 249.565+0.135Aromatic $s_{personal}$ -5.651 D.S ($R^2$ = .735), In conclusion, we have established analytic procedures for VOC measurement in biological and environmental samples and have presented data demonstrating relationships between VOCs levels in biological media and environmental samples. Abbreviation GC/MS, Gas Chromatography/Mass Spectrometer; VOCs, Volatile Organic Compounds; OVM, Organic Vapor Monitor; TO, Toxic Organicsapor Monitor; TO, Toxic Organics.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.93-101
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• 2017

The distribution of inter-annual variation in temperature would help evaluate the likelihood of a climatic risk and assess suitable zones of crops under climate change. In this study, we evaluated two methods to estimate the standard deviation of temperature in the areas where weather information is limited. We calculated the monthly standard deviation of temperature by collecting temperature at 0600 and 1500 local standard time from 10 automated weather stations (AWS). These weather stations were installed in the range of 8 to 1,073m above sea level within a mountainous catchment for 2011-2015. The observed values were compared with estimates, which were calculated using a geospatial correction scheme to derive the site-specific temperature. Those estimates explained 88 and 86% of the temperature variations at 0600 and 1500 LST, respectively. However, it often underestimated the temperatures. In the spring and fall, it tended to had different variance (e.g., increasing or decreasing pattern) from lower to higher elevation with the observed values. A regression analysis was also conducted to quantify the relationship between the standard deviation in temperature and the topography. The regression equation explained a relatively large variation of the monthly standard deviation when lapse-rate corrected temperature, basic topographical variables (e.g., slope, and aspect) and topographical variables related to temperature (e.g., thermal belt, cold air drainage, and brightness index) were used. The coefficient of determination for the regression analysis ranged between 0.46 and 0.98. It was expected that the regression model could account for 70% of the spatial variation of the standard deviation when the monthly standard deviation was predicted by using the minimum-maximum effective range of topographical variables for the area.