Institute of Analytical and Bioanalytical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89081, Ulm, Germany.
Pittsburgh Mining Research Division, Centers for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health, Pittsburgh, PA, 15236, USA.
Anal Bioanal Chem. 2020 May;412(14):3499-3508. doi: 10.1007/s00216-020-02565-0. Epub 2020 Apr 14.
Due to the global need for energy and resources, many workers are involved in underground and surface mining operations where they can be exposed to potentially hazardous crystalline dust particles. Besides commonly known alpha quartz, a variety of other materials may be inhaled when a worker is exposed to airborne dust. To date, the challenge of rapid in-field monitoring, identification, differentiation, and quantification of those particles has not been solved satisfactorily, in part because conventional analytical techniques require laboratory environments, complex method handling, and tedious sample preparation procedures and are in part limited by the effects of particle size. Using a set of the three most abundant minerals in limestone mine dust (i.e., calcite, dolomite, and quartz) and real-world dust samples, we demonstrate that Fourier transform infrared (FTIR) spectroscopy in combination with appropriate multivariate data analysis strategies provides a versatile tool for the identification and quantification of the mineral composition in relative complex matrices. An innovative analytical method with the potential of in-field application for quantifying the relative mass of crystalline particles in mine dust has been developed using transmission and diffuse reflection infrared Fourier transform spectroscopy (DRIFTS) within a unified multivariate model. This proof-of-principle study shows how direct on-site quantification of crystalline particles in ambient air may be accomplished based on a direct-on-filter measurement, after mine dust particles are collected directly onto PVC filters by the worker using body-mounted devices. Without any further sample preparation, these loaded filters may be analyzed via transmission infrared (IR) spectroscopy and/or DRIFTS, and the mineral content is immediately quantified via a partial least squares regression (PLSR) algorithm that enables the combining of the spectral data of both methods into a single robust model. Furthermore, it was also demonstrated that the size regime of dust particles may be classified into groups of hazardous and less hazardous size regimes. Thus, this technique may provide additional essential information for controlling air quality in surface and underground mining operations. Graphical Abstract.
由于全球对能源和资源的需求，许多工人从事地下和地表采矿作业，在这些作业中，他们可能会接触到潜在危险的结晶粉尘颗粒。除了通常已知的α石英外，当工人接触到空气中的粉尘时，还可能吸入各种其他材料。迄今为止，快速现场监测、识别、区分和量化这些颗粒的挑战尚未得到满意解决，部分原因是常规分析技术需要实验室环境、复杂的方法处理以及繁琐的样品制备程序，部分原因是受到颗粒尺寸的影响。我们使用一组石灰岩矿尘中最丰富的三种矿物质（即方解石、白云石和石英）和实际粉尘样本，证明傅里叶变换红外（FTIR）光谱结合适当的多元数据分析策略为识别和量化相对复杂基质中的矿物质组成提供了一种多功能工具。已经开发了一种具有创新分析方法的潜力，该方法具有在现场应用中量化矿尘中结晶颗粒相对质量的潜力，该方法使用透射和漫反射红外傅里叶变换光谱（DRIFTS）在统一的多元模型中进行。这项原理验证研究展示了如何在直接对过滤的测量基础上，基于工人使用身体佩戴设备直接将矿尘颗粒收集到 PVC 过滤器上，直接在现场对环境空气中的结晶颗粒进行直接现场定量。无需进一步的样品制备，这些负载的过滤器可以通过透射红外（IR）光谱和/或 DRIFTS 进行分析，并且通过偏最小二乘回归（PLSR）算法立即对矿物质含量进行量化，该算法允许将两种方法的光谱数据组合到单个稳健模型中。此外，还证明了粉尘颗粒的尺寸范围可以分为危险和较危险的尺寸范围。因此，该技术可能为控制地表和地下采矿作业中的空气质量提供额外的重要信息。
