Biological monitoring for occupational acrylamide exposure from acrylamide production workers.

OBJECTIVE: We conducted a repeated-measurement study to (1) investigate the correlation between occupational exposure to airborne acrylamide (AA) and the time-dependent behavior of urinary AAMA, GAMA2, and GAMA3 and (2) calculate the estimated biological exposure index at the permissible exposure limit (PEL) level of 30 µg/m(3). METHODS: Forty-four workers were recruited--8 were AA-exposed and 36 were controls. Pre- and post-shift urine samples were collected from the exposed group in parallel with personal sampling for 8 consecutive days and only 1 day for the control group and analyzed using liquid chromatography-electrospray ionization/tandem mass spectrometry (LC-ESI-MS/MS). RESULTS: Post-shift urinary AAMA level was significantly associated with personal AA exposure (p < 0.001), indicating that urinary AAMA was a better AA exposure biomarker. The estimated urinary excretion of AAMA was 3.0 mg/g creatinine for nonsmoking workers exposed to the PEL of 30 µg/m(3). The median GAMA (the sum of GAMA2 and GAMA3)/AAMA ratio for exposed workers was 0.03 (range, 0.005-0.14), relatively lower than that of the nonoccupational group. CONCLUSIONS: Although sample size in this study was small, the repeated-measurement data provide useful reference for future studies related to biological monitoring of occupational exposure to AA.

Establishing aerosol exposure predictive models based on vibration measurements.

This paper establishes particulate exposure predictive models based on vibration measurements under various concrete drilling conditions. The whole study was conducted in an exposure chamber using a full-scale mockup of concrete drilling simulator to simulate six drilling conditions. For each drilling condition, the vibration of the three orthogonal axes (i.e., a(x), a(y), and a(z)) was measured from the hand tool. Particulate exposure concentrations to the total suspended particulate (C(TSP)), PM(10) (C(PM10)), and PM(2.5) (C(PM2.5)) were measured at the downwind side of the drilling simulator. Empirical models for predicting C(TSP), C(PM10) and C(PM2.5) were done based on measured a(x), a(y), and a(z) using the generalized additive model. Good agreement between measured aerosol exposures and vibrations was found with R(2)>0.969. Our results also suggest that a(x) was mainly contributed by the abrasive wear. On the other hand, a(y) and a(z) were mainly contributed by both the impact wear and brittle fracture wear. The approach developed from the present study has the potential to provide a cheaper and convenient method for assessing aerosol exposures from various emission sources, particularly when conducting conventional personal aerosol samplings are not possible in the filed.