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OBJECTIVE: To improve the standard detection method for acetaldehyde, butyraldehyde and isobutyraldehyde in the air of workplace. METHODS: Acetaldehyde, butyraldehyde and isobutyraldehyde in the air of workplace were collected using silica gel tube, desorbed with 45.0% ethanol, separated by a capillary column and detected by a flame ionization detector. RESULTS: The linear range of this method for detecting acetaldehyde, butyraldehyde and isobutyraldehyde were 1.57-1 568.00, 1.60-1 600.00 and 1.59-1 588.00 mg/L, respectively. All the correlation coefficients were greater than 0.999. The detection limits were 0.52, 0.46 and 0.54 mg/L, respectively. The desorption efficiency was 91.0%-103.0%. The within-run relative standard deviation(RSD) was 0.7%-1.7%.The between-run RSD was 2.0%-3.7%. The samples could be stored for at least 10 days at room temperature. CONCLUSION: This method can be used for simultaneous detection of acetaldehyde, butyraldehyde and isobutyraldehyde in the air of workplace.
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OBJECTIVE: To improve the standard detection method for acetonitrile in workplace air. METHODS: Acetonitrile in the air of workplace was collected by silica gel, eluted with methanol, separated and determined by gas chromatography with flame ionization detection. RESULTS: After the improvement of the method, the linear range of acetonitrile was 1.57-1 574.00 mg/L, and the correlation coefficient was 0.999 98. The detection limit was 0.29 mg/L and the minimum detection concentration was 0.19 mg/m~(3 )(collected sample volume was 1.5 L). The average desorption efficiency was 93.1%-98.9%. The within-run and between-run precision was 2.6%-3.3% and 1.7%-3.6%, respectively. The samples could be stored at room temperature for at least 10 days. CONCLUSION: The improved method is precisied, accurate and simple to operate, which is suitable for determination of acetonitrile in workplace air.
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Objective @#To explore the spatial distribution of occupational diseases in Guangdong Province and to provide evidence for the policy development of occupational disease prevention and control. @*Methods @#A database of occupational disease incidence from 2009 to 2016 in Guangdong Province was built. The distribution of occupational diseases in Guangdong Province was displayed based on the geographic information system（GIS）, then spatial autocorrelation analysis and trend-surface analysis were carried out to explore the clustering areas and spatial epidemic characteristics of occupational diseases in Guangdong Province.@* Results @#The number of cases with occupational diseases was 5 231 and was increasing year by year from 2009 to 2016 in Guangdong Province. The high-incidence areas were located in Guangzhou,Shenzhen,Foshan and Dongguan. Through global spatial autocorrelation analysis,it was found that there were spatial clustering of occupational diseases in Guangdong Province in each year（P<0.05）,and the cumulative incidence was also clustered（Moran's I=0.492,P<0.05）. The number of cases in Guangzhou,Shenzhen,Foshan and Dongguan had local spatial autocorrelation,and the local Moran's I values were 10.329,8.614,3.725 and 9.811,respectively（P<0.05）. The results of trend surface analysis showed that the overall incidence of occupational disease had a slight increase from west to east,and the Pearl River Delta region was a high-incidence area. @*Conclusion @#The incidence of occupational diseases in Guangdong Province had an obvious spatial clustering,the Pearl River Delta region was a high-incidence area.
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Objective@#To explore the occupational disease spatial distribution characteristics in Guangzhou and Foshan city in 2006-2013 with Geographic Information System and to provide evidence for making control strategy.@*Methods@#The data on occupational disease diagnosis in Guangzhou and Foshan city from 2006 through 2013 were collected and linked to the digital map at administrative county level with Arc GIS12.0 software for spatial analysis.@*Results@#The maps of occupational disease and Moran’s spatial autocor-relation analysis showed that the spatial aggregation existed in Shunde and Nanhai region with Moran’s index 1.727, -0.003. Local Moran’s I spatial autocorrelation analysis pointed out the "positive high incidence re-gion" and the "negative high incidence region" during 2006~2013. Trend analysis showed that the diagnosis case increased slightly then declined from west to east, increase obviously from north to south, declined from? southwest to northeast, high in the middle and low on both sides in northwest-southeast direction.@*Conclusions@#The occupational disease is obviously geographical distribution in Guangzhou and Foshan city. The corresponding prevention measures should be made according to the geographical distribution.
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<p><b>OBJECTIVE</b>To explore the application of the gray series model GM (1, 1) in predicting trends in the incidence of pneumoconiosis and evaluate its degree of predicted precision.</p><p><b>METHODS</b>Analyzing the incidence of pneumoconiosis in this region from 2009 to 2013, and predicting the incidence of pneumoconiosis of the area in 2014-2016 by establishing GM (1, 1) according to the gray system theory.</p><p><b>RESULTS</b>Using occupational pneumoconiosis population data from 2009 to 2013, to establish GM (1, 1) model: yt = 1396.89e(0.12(t-1)), α = -0.12, µ = 147.2. The pneumoconiosis in 2014, 2015, 2016 were predicted respectively 51, 47, 43 cases based on the GM (1, 1) model, and C value of model is 0.15, P value is 1, all of them meet the requirements of model predictions. It shows the cases of pneumoconiosis are rising significantly.</p><p><b>CONCLUSION</b>GM (1, 1) model can be used to predict the recent trend in the incidence of pneumoconiosis.</p>