In situ spectroscopic identification of the six types of asbestos

Classification of asbestos and their nonasbestiform analogues using FTIR and multivariate data analysis

This study presents a possible application of Fourier transform infrared (FTIR) spectrometry and multivariate data analysis, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) for classifying asbestos and their nonasbestiform analogues. The objectives of the study are: 1) to classify six regulated asbestos types and 2) to classify between asbestos types and their nonasbestiform analogues. The respirable fraction of six regulated asbestos types and their nonasbestiform analogues were prepared in potassium bromide pellets and collected on polyvinyl chloride membrane filters for FTIR measurement. Both PCA and PLS-DA classified asbestos types and their nonasbestiform analogues on the score plots showed a very distinct clustering of samples between the serpentine (chrysotile) and amphibole groups. The PLS-DA model provided ∼95% correct prediction with a single asbestos type in the sample, although it did not provide all correct predictions for all the challenge samples due to their inherent complexity and the limited sample number. Further studies are necessary for a better prediction level in real samples and standardization of sampling and analysis procedures.

Determination of asbestos cement rooftop surface composition using regression analysis and hyper-spectral reflectance data in the visible and near-infrared ranges

The effects of asbestos on human health have spurred numerous studies examining its risks in urban environments. Recent works have shifted towards less-invasive techniques for remote detection and classification of asbestos-cement. In this context, this study combines visible (VIS) and near-infrared (NIR) reflectance data collected in-situ with reference signals from the USGS spectral library, utilizing optimized regression analysis to determine the surface composition of corrugated asbestos-cement rooftops. An outlier filter was successfully implemented to enhance the accuracy of regression calculations, achieving a high level of agreement with actual field observations. The regression analysis revealed varying proportions of weathered cement, hazardous asbestos fibers (specifically chrysotile and cummingtonite), and biological growth (such as lichens and moss). These results are consistent with previous research on the composition of asbestos-cement rooftops, including a comparable field study and XRD analysis conducted in 2019. This underscores the importance of using regression analysis, preceded by an outlier filtering step, on VIS and NIR reflectance data to ascertain the surface composition of asbestos-cement rooftops. This methodology holds potential for application to larger hyperspectral datasets across more extensive sample surfaces and areas.

An AI based smart-phone system for asbestos identification

Asbestos identification is a complex environmental and economic challenge. Typical commercial identification of asbestos involves sending samples to a laboratory where someone learned in the field uses light microscopy and specialized mounting to identify the morphologically distinct signatures of Asbestos. In this work we investigate the use of a portable (30x) microscope which works with a smart phone camera to develop an image recognition system. 7328 images from over 1000 distinct samples of cement sheet from Melbourne, Australia were used to train a phone-based image recognition system for Asbestos identification. Three common CNN's were tested ResNet101, InceptionV3 and VGG_16 with ResNet101 achieving the best result. The distinctiveness of Asbestos was found to be identified correctly 90% of the time using a phone-based system and no specialized mounting. The image recognition system was trained with ResNet101 a convolutional neural network deep learning model which weights layers with a residual function. Resulting in an accuracy of 98.46% and loss of 3.8% ResNet101 was found to produce a more accurate model for this use-case than other deep learning neural networks.

Influence of the Microbiome Metagenomics and Epigenomics on Gastric Cancer

Gastric cancer (GC) is one of the major causes of cancer deaths worldwide. The disease is seldomly detected early and this limits treatment options. Because of its heterogeneous and complex nature, the disease remains poorly understood. The literature supports the contribution of the gut microbiome in the carcinogenesis and chemoresistance of GC. Drug resistance is the major challenge in GC therapy, occurring as a result of rewired metabolism. Metabolic rewiring stems from recurring genetic and epigenetic factors affecting cell development. The gut microbiome consists of pathogens such as H. pylori, which can foster both epigenetic alterations and mutagenesis on the host genome. Most of the bacteria implicated in GC development are Gram-negative, which makes it challenging to eradicate the disease. Gram-negative bacterium co-infections with viruses such as EBV are known as risk factors for GC. In this review, we discuss the role of microbiome-induced GC carcinogenesis. The disease risk factors associated with the presence of microorganisms and microbial dysbiosis are also discussed. In doing so, we aim to emphasize the critical role of the microbiome on cancer pathological phenotypes, and how microbiomics could serve as a potential breakthrough in determining effective GC therapeutic targets. Additionally, consideration of microbial dysbiosis in the GC classification system might aid in diagnosis and treatment decision-making, taking the specific pathogen/s involved into account.

Distinguishing asbestos cement from fiber-reinforced cement through portable µ-Raman spectroscopy and portable X-ray fluorescence

It is now widely acknowledged that asbestos can adversely affect human health; accordingly, in recent decades, fiber-reinforced cement (FRC) has been used as a substitute for asbestos cement (AC). This manuscript focuses on portable micro-Raman spectroscopy coupled with portable microscopy (p-µR) and portable X-ray fluorescence (p-XRF) as a means to identify chrysotile fibers in AC (Eternit) and fibers present in the asbestos-free FRC used as a substitute. Our results show that the simultaneous use of portable devices such as p-µR and p-XRF may be useful in quickly identifying fibrous chrysotile asbestos in Eternit, as well as polyvinyl fibers in new material FRC used as substitutes for Eternit. Chrysotile shows bands in the 800-200 cm^-1 range, whereas polyvinyl alcohol fibers show bands in the 3000-800 cm^-1 range. The p-XRF data on the two types of cement could possibly be used as a chemical fingerprint for the two different materials. Given that exposure to asbestos is a serious health hazard, its rapid and reliable detection in situ on residential buildings is important both for citizens and for administrative bodies charged with safeguarding public health. We believe that our study provides valuable insight into the potential use of portable devices for identifying asbestos and asbestos-free materials.

Asbestos in soil and water: A review of analytical techniques and methods

In this review the main standard and novel analytical techniques and methods for sampling, sample preparation, detection and quantification of asbestos in soil and water are described, compared and discussed in terms of advantages and limitations. An overview of common analytical methods applied for identification and quantification of airborne asbestos is preliminary provided, as they have been widely studied, due to the well-known human pathologies related to fibers inhalation. Despite the presence of asbestos in soil and water may also constitute a health risk, it has been less investigated and regulated. For these environmental matrices, the methods adopted at international and national scale, covering the whole analytical process, from sampling to management of data, are reported in depth, highlighting their limitations like sensitivity, reliability and reproducibility. Finally, different promising novel/unconventional methods, that may substitute or support traditional ones for asbestos detection both in environmental and anthropic matrices, are presented and critically evaluated.

On-site detection of asbestos at the surface of building materials wasted at disaster sites by staining

We have developed a method to detect asbestos by staining the surface of building materials in order to quickly detect asbestos-containing building materials at disaster sites. After staining, asbestos was easily detected by the color and characteristic shape of the images observed under a stereomicroscope. The type of asbestos was confirmed to be chrysotile by polarized light microscopy, X-ray diffraction patterns, and Raman spectra. The percentage of the area of asbestos at the surface of building materials was also determined by an image analyzer after the dye staining, and the distribution percentage of asbestos increased with its total concentration in the building material. Three-dimensional X-ray computed tomography images showed that asbestos was mainly distributed at the surface of building materials. This result suggests that the asbestos at the surface of debris of building materials is more easily and sensitively detected than total asbestos analysis by pulverization. The present method was applied to detect and determine asbestos in debris of building materials wasted at temporary storage sites after disaster and on the wall of a building in use. Therefore, this method can contribute to the classification of asbestos-containing and non-asbestos-containing building materials at disaster sites and demolition sites, as well as to preliminary inspections for the detection of asbestos-containing building materials before demolition of houses and buildings.

Portable Raman Spectrometer for In Situ Analysis of Asbestos and Fibrous Minerals

Asbestos inhalation is associated with fatal respiratory diseases and raises concerns from the perspective of workplace safety and environmental impacts. Asbestos and asbestos-like minerals naturally occur in rocks and may become airborne when outcrops or soils are disturbed by anthropic activities. In situ detection of these minerals is a crucial step for the risk evaluation of natural sites. We assess here whether a portable Raman spectrometer (pRS) may be used in the identification of asbestos and asbestos-like minerals at the mining front during exploitation. pRS performance was tested at three geologically different mining sites in Italy and New Caledonia and compared with a high-resolution micro-Raman spectrometer (HRS). About 80% of the overall in situ analyses at the mining front were successfully identified by pRS, even when intermixed phases or strongly disaggregated and altered samples were analyzed. Chrysotile and tremolite asbestos, asbestos-like antigorite, and balangeroite were correctly detected during surveys. The major difficulties faced during in situ pRS measurements were fluorescence emission and focussing the laser beam on non-cohesive bundles of fibers. pRS is adequate for discriminating asbestos and asbestos-like minerals in situ. pRS may support risk assessment of mining sites to better protect workers and environment.