METHODOLOGY: The test was conducted for two different road conditions, tarmac and dirt roads. HAV exposure was measured using a Brüel & Kjær Type 3649 vibration analyzer, which is capable of recording HAV exposures from steering wheels. The data was analyzed using I-kaz Vibro to determine the HAV values in relation to varying speeds of a truck and to determine the degree of data scattering for HAV data signals.
RESULTS: Based on the results obtained, HAV experienced by drivers can be determined using the daily vibration exposure A(8), I-kaz Vibro coefficient (Ƶ(v)(∞)), and the I-kaz Vibro display. The I-kaz Vibro displays also showed greater scatterings, indicating that the values of Ƶ(v)(∞) and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Ƶ(v)(∞). The results of the regression model showed that Ƶ(v)(∞) increased when the vehicle speed and HAV exposure increased.
DISCUSSION: For model validation, predicted and measured noise exposures were compared, and high coefficient of correlation (R(2)) values were obtained, indicating that good agreement was obtained between them. By using the developed regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring.
METHODS: A cross-sectional study was conducted in a steel factory in Terengganu, Malaysia to assess the metal dust exposure and its relationship to lung function values among 184 workers. Metal dust concentrations values (Co, Cr, and Ni) for each worker were collected using air personal sampling. Lung function values (FEV1, FVC, and %FEV1/FVC) were determined using spirometer.
RESULTS: Exposure to cobalt and chromium were 1-3 times higher than permissible exposure limit (PEL) while nickel was not exceeding the PEL. Cumulative of chromium was the predictor to all lung function values (FEV1, FVC, and %FEV1/FVC). Frequency of using mask was positively associated with FVC (Adj b = 0.263, P = 0.011) while past respiratory illnesses were negatively associated with %FEV1/FVC (Adj b = -1.452, P = 0.026). Only few workers (36.4%) were found to wear their masks all times during the working hours.
CONCLUSIONS: There was an exposure-response relationship of cumulative metal dust exposure with the deterioration of lung function values. Improvement of control measures as well as proper and efficient use or personal protection equipment while at work could help to protect the respiratory health of workers.
MATERIAL AND METHODS: Urine samples were collected from plastic factory workers and from control subjects after their shift. Air samples were collected using gas analyzers from 5 sampling positions in the injection molding unit work area and from ambient air. The level of BPA in airborne and urine samples was quantified by the gas chromatography mass spectrometry - selected ion monitoring (GCMS-SIM) analysis.
RESULTS: Bisphenol A was detected in the median range of 8-28.3 ng/m³ and 2.4-3.59 ng/m³ for the 5 sampling points in the plastic molding factory and in the ambient air respectively. The median urinary BPA concentration was significantly higher in the workers (3.81 ng/ml) than in control subjects (0.73 ng/ml). The urinary BPA concentration was significantly associated with airborne BPA levels (ρ = 0.55, p < 0.01).
CONCLUSIONS: Our findings provide the first evidence that workers in a molding factory in Malaysia are occupationally exposed to BPA. Int J Occup Med Environ Health 2017;30(5):743-750.
METHODS: A pulmonary function test using a spirometer was carried out to measure the lung function of the traffic policemen. The personal exposure level to PM2.5 was measured using a pump with a PVC filter and 5.0μm pore size. A questionnaire requesting the background data, such as age, height, and weight, was also used for testing lung function abnormalities.
RESULTS: The PM2.5 personal exposure level was found to be significantly related to lung function (predicted FEV1 and predicted FVC) at p-value