Erik Bye

Exposure to Fibres, Crystalline Silica, Silicon Carbide and Sulphur Dioxide in the Norwegian Silicon Carbide Industry

Objectives: The aim of this study was to assess personal exposure to fibres, crystalline silica, silicon carbide (SiC) and sulphur dioxide in the Norwegian SiC industry. Methods: Approximately 720 fibre samples, 720 respirable dust samples and 1400 total dust samples were collected from randomly chosen workers from the furnace, processing and maintenance departments in all three Norwegian SiC plants. The respirable dust samples were analysed for quartz, cristobalite and non-fibrous SiC content. Approximately 240 sulphur dioxide samples were collected from workers in the furnace department. Results: The sorting operators from all plants, control room and cleaning operators in Plant A and charger, charger/mix and payloader operators in Plant C had a geometric mean (GM) of fibre exposure above the Norwegian occupational exposure limit (OEL) (0.1 fibre cm−3). The cleaner operators in Plant A had the highest GM exposure to respirable quartz (20 μg m−3). The charger/mix operators in Plant C had the highest GM exposure to respirable cristobalite (38 μg m−3) and the refinery crusher operators in Plant A had the highest GM exposure to non-fibrous SiC (0.65 mg m−3). Exposure to the crystalline silica and non-fibrous SiC was generally low and between 0.4 and 2.1% of the measurements exceeded the OELs. The cleaner operators in Plant A had the highest GM exposure to respirable dust (1.3 mg m−3) and total dust (21 mg m−3). GM exposures for respirable dust above the Norwegian SiC industry-specific OEL of 0.5 mg m−3 were also found for refinery crusher operators in all plants and mix, charger, charger/mix and sorting operators in Plant C. Only 4% of the total dust measurements exceeded the OEL for nuisance dust of (10 mg m−3). Exposure to sulphur dioxide was generally low. However, peaks in the range of 10–100 p.p.m. were observed for control room and crane operators in Plants A and B and for charger and charger/mix operators in Plant C. Conclusion: Workers in the SiC industry are exposed to a mixture of several agents including SiC fibres, quartz, cristobalite, non-fibrous SiC and sulphur dioxide. Exposure levels were generally below the current Norwegian OELs; however, high exposure to fibres and respirable dust still occurs in the furnace department.

Hazardous peak concentrations of hydrogen sulfide gas related to the sewage purification process.

The concentration of hydrogen sulfide in the atmosphere of a sewage purification plant in Norway was studied. Continuous measurements over several 3-week periods showed that the concentrations generally were lower than 2 ppm, but peak concentrations over 100 ppm were discovered. Rapid onset and decline characterized these peak concentrations, which occurred at regular intervals. Through evaluation of the time pattern of these peaks compared with plant operations, a specific process was identified as the likely causative factor of the spikes. Through simple remedial actions the hydrogen sulfide concentration associated with this activity was reduced from above 100 ppm to less than 2 ppm. Olfactory fatigue to hydrogen sulfide and strong offensive odors from other compounds in the sewage makes smell ineffective for signaling high concentrations. Peak concentrations may therefore pass unnoticed unless detected with continuous measuring equipment. The risk for exposure may be reduced by enclosing processes and through the use of spot extraction ventilation in areas with compacted anaerobic waste material.

Quantitative Determination of Airborne Respirable Non-Fibrous α-Silicon Carbide by X-ray Powder Diffractometry

OBJECTIVES The purpose of the present investigation was to establish a method for the determination of airborne respirable non-fibrous silicon carbide (SiC). The main application is within the industrial production of SiC. METHODS Due to the complex airborne aerosol mixture of crystalline compounds in the SiC industry, X-ray powder diffractometry was selected as the most appropriate method. Without any international standard material for the respirable fraction of non-fibrous SiC, pure and suitable products from three SiC plants in Norway were selected. These products have a median particle diameter in the range 4.4-5.1 mum. The method is based on thin sample technique, with the dust deposited on a polycarbonate filter. Absorption correction is done by standard procedures with the use of a silver filter, situated below the polycarbonate filter. RESULTS The diffraction line used for quantitative determination was selected carefully. This was done to avoid interferences from quartz, cristobalite, and graphite, which all are airborne components present in the atmosphere during the industrial process. The instrumental limit of detection for the method is 12 microg. CONCLUSIONS This method has been used to determine airborne non-fibrous SiC in a comprehensive ongoing project in the Norwegian SiC industry for further epidemiological studies. The method is fully applicable for compliance work.

Quantitative determination of silica mixtures by multivariate calibration applied to infrared spectroscopy

Abstract Mixtures of crystalline and amorphous silica cannot be quantified by standard infrared spectroscopic techniques alone. This is due to the severe overlap of the primary spectral SiO2 band in the most characteristic region, 850-750 cm−1. This problem may be solved with the use of multivariate calibration by partial least squares regression (PLSR). Using this method, the complete relevant infrared spectral profile of the sample may be used for quantitative determinations of silica mixtures. The three silica modifications of quartz, cristobalite and fumed amorphous silica have been simultaneously quantified in mixtures using this technique. The standard errors of prediction are 25, 26 and 24 μg, respectively, for the three silica components in 1 mg dust samples. Multivariate calibration with the PLSR technique thus offers an accurate, rapid and low cost method for the determination of silica mixtures using infrared spectroscopy.

Determinants of Exposure to Dust and Dust Constituents in the Norwegian Silicon Carbide Industry

INTRODUCTION The aim of this study was to identify important determinants of dust exposure in the Norwegian silicon carbide (SiC) industry and to suggest possible control measures. METHODS Exposure to total dust, respirable dust, quartz, cristobalite, SiC, and fiber was assessed in three Norwegian SiC plants together with information on potential determinants of exposure. Mixed-effect models were constructed with natural log-transformed exposure as the dependent variable. RESULTS The exposure assessment resulted in about 700 measurements of each of the sampled agents. Geometric mean (GM) exposure for total dust, respirable dust, fibers, and SiC for all workers was 1.6mg m(-3) [geometric standard deviation (GSD) = 3.2], 0.30mg m(-3) (GSD = 2.5), 0.033 fibers cm(-3) (GSD = 5.2), and 0.069mg m(-3) (GSD = 3.1), respectively. Due to a large portion of quartz and cristobalite measurements below the limit of detection in the processing and maintenance departments (>58%), GM for all workers was not calculated. Work in the furnace department was associated with the highest exposure to fibers, quartz, and cristobalite, while work in the processing department was associated with the highest total dust, respirable dust, and SiC exposure. Job group was a strong determinant of exposure for all agents, explaining 43-82% of the between-worker variance. Determinants associated with increased exposure in the furnace department were location of the sorting area inside the furnace hall, cleaning tasks, building and filling furnaces, and manual sorting. Filling and changing pallet boxes were important tasks related to increased exposure to total dust, respirable dust, and SiC in the processing department. For maintenance workers, increased exposure to fibers was associated with maintenance work in the furnace department and increased exposure to SiC was related to maintenance work in the processing department. CONCLUSION Job group was a strong determinant of exposure for all agents. Several tasks were associated with increased exposure, indicating possibilities for exposure control measures. Recommendations for exposure reduction based on this study are (i) to separate the sorting area from the furnace hall, (ii) minimize manual work on furnaces and in the sorting process, (iii) use remote controlled sanders/grinders with ventilated cabins, (iv) use closed systems for filling pallet boxes, and (v) improve cleaning procedures by using methods that minimize dust generation.

Evaluation of the direct and autocorrelated use of physicochemical descriptors in quantitative structure-property relationship studies. A partial least squares prediction of log P for chlorinated alkylbenzenes

Abstract Bye, E., Heiberg, A.B. and Vogt, N.B., 1990. Evaluation of the direct and autocorrelated use of physicochemical descriptors in quantitative structure—property relationship studies. A partial least squares prediction of log P for chlorinated alkylbenzenes. Chemometrics and Intelligent Laboratory Systems, 9: 185–199. The use of global and substituent physicochemical structure descriptors for quantitative structure—property relationship (QSPR) studies has been evaluated through direct and autocorrelated applications. Partial least squares (PLS) analysis has been used to predict the partition coefficient for octanol/water, log P, for chlorinated alkylbenzenes (CABs). This investigation revealed that various of the physicochemical parameters considered were highly correlated with the partition coefficient and that PLS models based on global, direct or autocorrelated substituent parameters all seem to be adequate for prediction of log P, as compared to results obtained experimentally or by means of standard substituent calculation schemes. However, the use of autocorrelation vectors of connectivity, steric and electronic effects may be preferably due to a high degree of explanation of the molecular descriptor variance.