Estimating cobalt exposure in respirable dust from cobalt in inhalable dust.

Cobalt is a commonly used element in metal industry. Exposure to workers occurs mainly by inhalation of cobalt-containing dust. For the evaluation of cobalt exposure, risk assessment and investigations on occupational diseases, measurements of cobalt in respirable dust are needed. Up to now, often only data for cobalt in inhalable dust are available, which is due to the earlier classification of the limit value in this fraction. Therefore, a possibility to convert cobalt concentrations mathematically from inhalable into respirable concentrations is desirable. In this study, 639 parallel measurements of cobalt concentrations in inhalable (cI(Co)) and respirable dust fractions (cR(Co)) were extracted from the non-public exposure database MEGA (Measurement data relating to workplace exposure to hazardous substances, maintained at the Institute for Occupational Safety and Health of the German Social Accident Insurance) and investigated by regression analysis. For the whole dataset regression shows high quality measures (correlation coefficient R = 0.888, adjusted coefficient of determination adj. R2 = 0.788 - R2 is adjusted to sample size). Further description of the data is achieved by splitting the dataset according to the type of sampling ('stationary' and 'personal') and three working activity groups, 'high temperature processing', 'filling/transport/storage', and 'machining/abrasive techniques' (0.845 ≤ R ≤ 0.876; 0.711 ≤ adj. R2 ≤ 0.762). As subgroups of 'high temperature processing' and 'machining/abrasive techniques' two further groups could be determined. These groups are called heuristic groups, since they have to be formed non-systematically by trial and error. These heuristic groups are 'welding' and 'grinding'. They are more selective on the included working activities with adj. R2 of 0.703 and 0.748 respectively. The resulting conversion functions of all groups are power functions with exponents between 0.704 and 0.794. For the estimation of cobalt in respirable dust in other studies, it is possible to use the conversion functions of the heuristic and working activity groups. Limitations of the possibility to use the conversion functions are discussed.

Estimating nickel exposure in respirable dust from nickel in inhalable dust.

The conversion of dust components is of high importance for retrospective evaluations of exposure levels, of occupational diseases or the time trend of occupational dust exposure. For this purpose, possibilities to convert nickel concentrations from inhalable to respirable dust are discussed in this study. Therefore, 551 parallel measurements of nickel concentrations in inhalable and respirable dust fractions were extracted from the exposure database MEGA (maintained at the Institute for Occupational Safety and Health of the German Social Accident Insurance) and investigated by linear regression analysis of ln-transformed concentrations. Inhalable dust is the most important predictor variable, showing an adjusted coefficient of determination (adj. R2) of 0.767 (R2 adjusted to sample size). Since multilinear regression analysis, cannot be applied, further description of data is gained by splitting the whole dataset into working activity groups (e. g. 'high temperature processing', adj. R2 = 0.628,' filling/transport/storage' adj. R2 = 0.741, 'machining/abrasive techniques', adj. R2 = 0.777). From these groups, four task restrictive subgroups, so-called 'heuristic groups', can be derived by pooling similar working tasks with similar regression coefficients and enhanced quality measures (adj. R2 between 0.724 and 0.924): 'welding (grinding time fraction [GTF] < 5%)', 'welding (grinding time fraction [GTF] > 5%)', 'high temperature cutting' and 'grinding'. For the working activity group 'high temperature processing' and the heuristic group 'welding' the determination of the grinding time fraction and its inclusion or exclusion from a dataset has a huge impact on the description of data and whether a transformation of nickel concentrations using the natural logarithm (ln) is necessary or not. In case of GTF < 5%, the conversions functions are linear, all other conversion functions are power functions with exponents between 0.713 and 0.986. It is possible to develop conversion functions for estimating the nickel concentration in the respirable dust fraction (cR(Ni)) out of the nickel concentration in the inhalable dust fraction (cI(Ni)). For the estimation of Nickel in respirable dust other studies, it is recommend to use the conversion functions of the heuristic trial and error groups. Limitations of the possibility to use the conversion functions are discussed.

Estimating Respirable Dust Exposure from Inhalable Dust Exposure.

In the sector of occupational safety and health only a limited amount of studies are concerned with the conversion of inhalable to respirable dust. This conversion is of high importance for retrospective evaluations of exposure levels or of occupational diseases. For this reason a possibility to convert inhalable into respirable dust is discussed in this study. To determine conversion functions from inhalable to respirable dust fractions, 15 120 parallel measurements in the exposure database MEGA (maintained at the Institute for Occupational Safety and Health of the German Social Accident Insurance) are investigated by regression analysis. For this purpose, the whole data set is split into the influencing factors working activity and material. Inhalable dust is the most important predictor variable and shows an adjusted coefficient of determination of 0.585 (R2 adjusted to sample size). Further improvement of the model is gained, when the data set is split into six working activities and three material groups (e.g. high temperature processing, adj. R2 = 0.668). The combination of these two variables leads to a group of data concerned with high temperature processing with metal, which gives rise to a better description than the whole data set (adj. R2 = 0.706). Although it is not possible to refine these groups further systematically, seven improved groups are formed by trial and error, with adj. R2 between 0.733 and 0.835: soldering, casting (metalworking), welding, high temperature cutting, blasting, chiseling/embossing, and wire drawing. The conversion functions for the seven groups are appropriate candidates for data reconstruction and retrospective exposure assessment. However, this is restricted to a careful analysis of the working conditions. All conversion functions are power functions with exponents between 0.454 and 0.946. Thus, the present data do not support the assumption that respirable and inhalable dust are linearly correlated in general.

Driving under the influence of synthetic phenethylamines: a case series.

New psychoactive drugs, so-called legal highs, have gained more and more popularity during the last years. One of the most important groups of these legal high substances are the synthetic phenethylamines that share a common phenethylamine moiety. Based on certain structural characteristics, these synthetic phenethylamines can be divided into further subclasses, among which the synthetic cathinones ('bath salts') are particularly noteworthy. Synthetic cathinones are characterized by an additional carbonyl group attached at the beta position on the amino alkyl chain. Consumption of synthetic phenethylamines can lead to impairments similar to those observed after the use of, for instance, amphetamine or 3,4-methylenedioxy-N-methylamphetamine (MDMA, 'ecstasy'). These impairments include diverse neurological and psychological symptoms which can affect a safe driving behaviour. Although several reports on clinical symptoms and poisonings due to these substances have been published, most of these publications do not contain any analytical data. Additionally, there is still a lack of information concerning pharmacological and toxicological effects of these rather new psychoactive substances. In particular, the knowledge of the impact on the ability to drive following consumption of synthetic phenethylamines is relevant for the police as well as for forensic toxicologists. In this publication, several cases of individuals driving under the influence (DUI) of synthetic phenethylamines (4-fluoroamphetamine, mephedrone (4-methylmethcathinone, 4-MMC), 2-DPMP (desoxypipradol), methylenedioxypyrovalerone (MDPV), benzedrone, N-ethylamphetamine (etilamfetamine), 3-methylmethcathinone (3-MMC)) are presented, focusing on analytical results and signs of impairment.