Source Apportionment of Particulate Matter in a Metal Workshop.

Metal workshops are workplaces with the substantial production of particulate matter (PM) with high metal content, which poses a significant health risk to workers. The PM produced by different metal processing techniques differs considerably in its elemental composition and size distribution and therefore poses different health risks. In some previous studies, the pollution sources were isolated under controlled conditions, while, in this study, we present a valuable alternative to characterize the pollution sources that can be applied to real working environments. Fine PM was sampled in five units (partially specializing in different techniques) of the same workshop. A total of 53 samples were collected with a temporal resolution of 30 min and 1 h. The mass concentrations were determined gravimetrically, and the elemental analysis, in which the concentrations of 14 elements were determined, was carried out using the X-ray fluorescence technique. Five sources of pollution were identified: background, steel grinding, metal active gas welding, tungsten inert gas welding, and machining. The sources were identified by positive matrix factorization, a statistical method for source apportionment. The identified sources corresponded well with the work activities in the workshop and with the actual sources described in previous studies. It is shown that positive matrix factorization can be a valuable tool for the identification and characterization of indoor sources.

Deposition of heavy metals in biological tissues of workers in metal workshops.

Welding and cutting of metals produce large amounts of particulate matter (PM), which poses a significant health risk to exposed workers. Appropriate biological markers to estimate exposure are of great interest for occupational health and safety. Here, hair and nail samples from metal workers were analyzed, which appear to be more suitable than blood or urine samples for assessing long-term exposure. Four workshops working with steel components were included in the study. The hair and nail samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS) to measure the concentrations of 12 elements. At the workplaces, the concentrations of 15 elements in particulate matter were determined using X-ray fluorescence (XRF) and particle-induced X-ray emission (PIXE) techniques. The hair and nail samples of the workers contained significantly higher metal concentrations than the analytical results of a nonexposed control group. The most significant difference between the groups was found for Ti, Mn, Fe, and Co.

BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies.

BACKGROUND: The software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing. RESULTS: Here we describe BactImAS - a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree. CONCLUSIONS: The presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. It will be of interest to the community of both end-users and developers of time-lapse microscopy software.