Humans ; Mining ; Noise, Occupational ; statistics & numerical data ; Occupational Exposure ; statistics & numerical data ; Work Schedule Tolerance

OBJECTIVETo explore the relationship between sample size and variance of means for personal noise exposure in weaving workers as to contributing evidence for establishing personal noise exposure measurement guideline.

METHODSA personal noise exposure measurement database from a group of weaving workers was used in the randomized re-sampling data analysis. The sampling cases were one number selecting from one to fifteen at each randomized re-sampling procedure. The randomized re-sampling was one thousand times from original personal noise exposure measurement database to get one thousands of re-sampling database. One thousands of L(Aeq.8 h) mean were calculated by re-sampling databases. The variation of randomized re-sampling means was analyzed for different re-sampling numbers.

RESULTSThe change for narrow trend of maximum, minimum, 95 percent number, 5 percent number of L(Aeq.8 h) mean was faster when randomized re-sampling number was smaller in variation vs randomized re-sampling number curve analysis. After that, the change for narrow trend of L(Aeq.8 h) mean was smooth for increasing the randomized re-sampling numbers. The 95% - 5% of L(Aeq.8 h) mean was about half for randomized re-sampling four cases (3.30 dB) vs one case (7.40 dB), and about one third for seven cases (2.44 dB), and about one fourth for eleven cases (1.85 dB).

CONCLUSIONThe sample size in personal noise exposure measurement guideline could be selected from four to eleven.

Humans ; Noise, Occupational ; statistics & numerical data ; Occupational Exposure ; statistics & numerical data ; Sample Size ; Sampling Studies

OBJECTIVETo measure and analyze the personal noise exposure of city metro station workers by using noise dosimeter.

METHODSAccording to job category and work type, all workers were divided into 4 groups. The workers from each group were selected as subjects for personal noise exposure measurement. CEL-320 dosimeters were worn by each subject and noise data collected by a phone fixed at collar. All subjects were asked to take notes about their working activities when they were wearing CEL-320 dosimeters. Each worker's one workday LAeq, geometric mean and range of each group were computed.

RESULTSThere were many noise sources in the metro station, and the noise exposure was unstable. The varieties of personal noise levels were recorded among 48 workers, the highest LAeq work type was of the instrument room, (81.8 +/- 2.5) dB (A), and the biggest LAeq rang was of the hall, 8.1 dB (A). The lowest LAeq was of the station control room (68.7 +/- 1.8) dB (A) and the lowest LAeq rang also was there, 4.0 dB (A).

CONCLUSIONThe personal noise exposure of metro station should be implicated. Measuring personal noise exposure individually with dosimeters might obtain the noise exposure level more integrally in the complicated environment.

Humans ; Noise, Occupational ; statistics & numerical data ; Occupational Exposure ; analysis ; statistics & numerical data ; Railroads ; Urban Population

China ; Humans ; Occupational Exposure ; statistics & numerical data ; Solvents

China ; Humans ; Industry ; statistics & numerical data ; Lead ; blood ; Occupational Exposure

OBJECTIVETo analyze the results of an occupational hazards survey of specially supervised enterprises (156 enterprise-times) during 2011-2012 in one district of Shenzhen, China and find out the changes in occupational hazards in these enterprises, and to put forward countermeasures for the prevention and control of occupational hazards.

METHODSOccupational hazards monitoring results for specially supervised enterprises (156 enterprise-times) during 2011-2012 were included. Comparison and analysis were performed between different years, different industries, different occupational hazards, and different sizes of enterprises.

RESULTSA total of 1274 monitoring sites from these specially supervised enterprises were included, of which qualification rate was 73.55% (937/1274), and the noise monitoring sites showed the lowest qualification rate. The overall qualification rate in 2012 (70.37%) was significantly lower than that in 2011 (80.94%) (χ(2) = 15.38, P < 0.01). In electronics industry, the qualification rate in 2012 was significantly lower than that in 2011 (χ2 = 11.27, P = 0.001). Comparison of various hazards in different industries indicated that electronic enterprises and furniture enterprises had the lowest qualification rate in noise monitoring, printing enterprises had the lowest qualification rate in organic solvent monitoring, and furniture enterprises had the lowest qualification rate in dust monitoring. Comparison between different sizes of enterprises indicated that the qualification rate of large and medium enterprises in 2012 was significantly lower than that in 2011, while the qualification rate of small enterprises in 2012 was significantly higher than that in 2011 (P < 0.01 or P < 0.05).

CONCLUSIONIn the prevention and control of occupational hazards in specially supervised enterprises, special attention should be paid to the control of organic solvents in printing enterprises and noise and dust in furniture enterprises.

Air Pollutants, Occupational ; China ; Dust ; Industry ; statistics & numerical data ; Noise, Occupational ; Occupational Exposure ; statistics & numerical data ; Occupational Health ; statistics & numerical data ; Solvents

Air Pollutants, Occupational ; analysis ; Female ; Hexanes ; analysis ; poisoning ; Humans ; Male ; Occupational Exposure ; statistics & numerical data ; Shoes

Dust ; Humans ; Iron ; Mining ; Noise, Occupational ; Occupational Exposure ; statistics & numerical data ; Vibration

Humans ; Occupational Exposure ; statistics & numerical data ; Occupational Health ; Physical Examination ; Surveys and Questionnaires

BACKGROUNDNoise is widespread occupational hazard in iron and steel industry. Overhead-traveling cranes are widely used in this industry, but few studies characterized the overhead-traveling crane drivers' noise exposure level so far. In this study, we assessed and characterized personal noise exposure levels of overhead-traveling crane drivers in two steel-rolling mills.

METHODSOne hundred and twenty-four overhead-traveling crane drivers, 76 in the cold steel-rolling mill and 48 in the hot steel-rolling mill, were enrolled in the study. Personal noise dosimeters (AIHUA Instruments Model AWA5610e, Hangzhou, China) were used to collect full-shift noise exposure data from all the participants. Crane drivers carried dosimeters with microphones placed near their collars during the work shifts. Work logs had been taken by the drivers simultaneously. Personal noise exposure data were divided into segments based on lines in which they worked. All statistical analyses were done using SPSS 13.0.

RESULTSThe average personal noise exposure (L(Aeq.8h)) of overhead-traveling crane drivers in the hot steel-rolling mills ((85.03 +/- 2.25) dB (A)) was higher than that in the cold one ((83.05 +/- 2.93) dB (A), P < 0.001). There were 17 overhead traveling cranes in the hot steel-rolling mill and 24 cranes in the cold one, of which carrying capacities varied from 15 tons to 100 tons. The average noise exposure level based on different lines in the hot and cold steel-rolling mills were (85.2 +/- 2.61) dB (A) and (83.3 +/- 3.10) dB (A) respectively (P = 0.001), which were similar to the average personal noise exposure in both mills. The noise exposure levels were different among different lines (P = 0.021).

CONCLUSIONNoise exposure levels, depending upon background noise levels and the noise levels on the ground, are inconstant. As the noise exposure levels are above the 85 dB (A) criteria, these drivers should be involved in the Hearing Conservation Program to protect their hearing.

Environmental Monitoring ; instrumentation ; methods ; statistics & numerical data ; Female ; Humans ; Male ; Noise, Occupational ; Noise, Transportation ; Occupational Exposure ; analysis ; statistics & numerical data ; Occupational Health ; statistics & numerical data ; Steel