Identification Of Asbestos Research Articles

Presence of asbestos in building materials and soils in postfire areas of Mati, Kineta and Varimbombi in Greece.

The present study was carried out to determine the presence of asbestos in Wildland Urban Interface (WUI) areas of Attica, Greece affected by wildfires between 2018 and 2021. It concerns the first major campaign that took place in Greece. The samples tested in this work were collected from prespecified buildings of the burned area. The samples included different types of building materials such as bricks, wall coatings, insulation plates etc. Soil samples nearby the buildings were also collected to examine dispersion of the fibers. The identification of asbestos was based on the optical properties of the fibrous particles and was performed with stereo microscope and Polarized Light Microscopy according to P401 provided by BOHS (Health and Safety Executive (HSE) 2021). Scanning Electron Microscopy with energy dispersive spectroscopy was also used to validate the presence of asbestos and determine its chemical composition. In this work, 2 different types of asbestos were determined, chrysotile and crocidolite. In some cases, fragments of asbestos containing materials were also detected in the afore-mentioned soil samples indicating dispersion of asbestos containing debris in an area around the burned spots. The presence of asbestos that was identified in this preliminary study could raise concerns to humans in the urban areas. For that reason, a risk assessment for the identified asbestos containing materials (ACMs) was conducted according to the guidelines on management and abatement of ACMs of Health and Safety Authority and the Asbestos: Survey guide, Appendix 4: Material assessment algorithm of Health and Safety Executive (HSE).

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.

Parallelized Nonlinear Target Detection for Asbestos Identification in Large-Scale Remote Sensing Data

Due to the side effects of asbestos on human health and environments, many countries have banned the use of asbestos-containing materials, but there are still illegal products with asbestos in daily life. In order to investigate the distributions of asbestos to facilitate its removal, this paper studies the feasibility of asbestos identification with HyperSpectral (HS) and panchromatic (PAN) data, taking images captured by the PRISMA and ZY1E 2D satellites over Pavia, Italy as examples. In this work, a pansharpening method with guided filter was used to improve HS image quality in terms of spectral fidelity and spatial details. Then, the possible location of asbestos could be obtained by a nonlinear target detector named BSTD. Considering high computational cost for large-scale remote sensing data processing, we further develop BSTD to its parallelized version (denoted as PBSTD). Given the groundtruth of asbestos over Pavia by the Regional Environmental Protection Agency-ARPA Lombardia, our PBSTD and several popular methods are evaluated from both qualitative and quantitative perspectives, showing that most algorithms could correctly detect large-size asbestos roofs, and the nonlinear PBSTD and MSDinter perform better in small-size asbestos identification than other linear detectors. However, the detection accuracy on small-size asbestos is insufficient in practical applications, which indicates that there are still issues to achieve accurate small-size asbestos identification using coarse-spatial-resolution spaceborne remote sensing.

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.

In situ spectroscopic identification of the six types of asbestos

Exposure to asbestos fibres is related to a number of severe lung diseases, and therefore, rapid, accurate and reliable in situ or on-site asbestos detection in real-life samples is of considerable importance. This work presents a comprehensive investigation of all six types of asbestos by mid-infrared ATR–FTIR, NIR spectroscopy and Raman microspectroscopy. Our studies demonstrate that for practical applications, NIR spectroscopy is potentially the most powerful method for asbestos identification in materials utilised by the construction industry. By focusing on the narrow spectral region, 7300–7000 cm−1 (~1370–1430 nm, overtones of O‒H vibrations), which is highly specific to these materials, and optimising the sensitivity and resolution of the instrumentation, we have been able to discriminate and identify each of the six types of asbestos with the level of detection significantly better than 1 wt%. Furthermore, straightforward computational analysis has allowed for automated objective evaluation of the spectroscopic data.

Laser ablation inductively coupled plasma mass spectrometry as a novel clinical imaging tool to detect asbestos fibres in malignant mesothelioma.

Malignant pleural mesothelioma is an extremely aggressive and incurable malignancy associated with prior exposure to asbestos fibres. Difficulties remain in relation to early diagnosis, notably due to impeded identification of asbestos in lung tissue. This study describes a novel laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging approach to identify asbestos within mesothelioma models with clinical significance. Human mesothelioma cells were exposed to different types of asbestos fibres and prepared on plastic slides for LA-ICP-MS analysis. No further sample preparation was required prior to analysis, which was performed using an NWR Image 266 nm laser ablation system coupled to an Element XR sector-field ICP mass spectrometer, with a lateral resolution of 2 μm. Data was processed using LA-ICP-MS ImageTool v1.7 with the final graphic production made using DPlot software. Four different mineral fibres were successfully identified within the mesothelioma samples based on some of the most abundant elements that make up these fibres (Si, Mg and Fe). Using LA-ICP-MS as an imaging tool provided information on the spatial distribution of the fibres at cellular level, which is essential in asbestos detection within tissue samples. Based on the metal counts generated by the different types of asbestos, different fibres can be identified based on shape, size, and elemental composition. Detection of Ca was attempted but requires further optimisation. Detection of asbestos fibres in lung tissues is very useful, if not necessary, to complete the pathological dt9iagnosis of asbestos-related malignancies in the medicolegal field. For the first time, this study demonstrates the successful application of LA-ICP-MS imaging to identify asbestos fibres and other mineral fibres within mesothelioma samples. Ultimately, high-resolution, fast-speed LA-ICP-MS analysis has the potential to be integrated into clinical workflow to aid earlier detection and stratification of mesothelioma patient samples.

Naturally Occurring Asbestos in France: Geological Mapping, Mineral Characterization, and Technical Developments

ABSTRACT In France, asbestos was banned by national decree (no. 96-1133) in 1996. The regulatory texts and standards adopted to implement this ban are concerned primarily with asbestos-containing manufactured products and are difficult to apply to asbestos-bearing natural materials (i.e., rocks and soils). Considering problems related to asbestos-bearing natural materials, the French Ministry of Ecology, Sustainable Development, and Energy has mandated the French Geological Survey to map locations where asbestos-bearing rocks are found. Mapping was prioritized to geological domains where naturally occurring asbestos (NOA) was predictable (e.g., the Western Alps and Corsica). These studies integrated field expertise, sampling, and laboratory analysis data to characterize the potential of geological units to contain NOA. Additionally, studies were conducted on geological formations exploited to produce aggregates. These studies were focused on quarries excavating massive, basic or ultrabasic rocks likely to contain NOA and quarries mining alluvium likely to contain asbestos-bearing rock pebbles. These studies highlight the difficulty of establishing robust analytical procedures for natural materials. The distinction between cleavage fragments (resulting from the fragmentation of non-asbestos particles) and proper asbestos fibers is particularly problematic for laboratories. Thus, a recent study by the National Agency for Health Safety, Food, Environment, and Work recommends applying the asbestos regulation to elongated mineral particles (length/depth > 3:1, length > 5 μm, depth < 3 μm) with chemical composition corresponding to one of the five regulated amphibole species regardless of their mode of crystallization (asbestiform or non-asbestiform). The upcoming regulatory changes are part of a decree published in 2017, including the prior identification of asbestos in natural soils or rocks likely to be impacted by ground-disturbing construction activities. Specific protocols will be defined for sampling, analysis, and characterization of natural materials that may contain asbestos.

Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats.

On the threshold conditions for electron beam damage of asbestos amosite fibers in the transmission electron microscope (TEM)

ABSTRACTAsbestos amosite fibers were investigated to evaluate the damage caused by a transmission electron microscope (TEM) electron beam. Since elemental x-ray intensity ratios obtained by energy dispersive x-ray spectroscopy (EDS) are commonly used for asbestos identification, the impact of beam damage on these ratios was evaluated. It was determined that the magnesium/silicon ratio best represented the damage caused to the fiber. Various tests showed that most fibers have a current density threshold above which the chemical composition of the fiber is modified. The value of this threshold current density varied depending on the fiber, regardless of fiber diameter, and in some cases could not be determined. The existence of a threshold electron dose was also demonstrated. This value was dependent on the current density used and can be increased by providing a recovery period between exposures to the electron beam. This study also established that the electron beam current is directly related to the damage rate above a current density of 165 A/cm2. The large number of different results obtained suggest, that in order to ensure that the amosite fibers are not damaged, analysis should be conducted below a current density of 100 A/cm2.

Consideration of Analysis Method of Asbestos and Existing Condition for Fibrous Materials in Domestic Air

Dust samples collected from air in metropolitan areas of Busan and Ulsan were analyzed by phase contrast microscopy, phase contrast dispersion microscopy and SEM. Asbestos concentration in dust samples was lower than detection limit. Many fibrous materials were observed in dust samples, but most of them were organic fibers. Inorganic fibers such as rock fiber, ceramic fiber and gypsum were contained in the samples and non-fibrous minerals such as quartz, calcite and feldspar were also occasionally observed. Domestic law requires that asbestos in air dust is mainly analyzed by phase contrast microscopy. From this study, however, precise analysis of asbestos was almost impossible by this method only. As indicated in JIS method of Japan, therefore, count and identification of asbestos were more efficient by dispersion staining method after removing organic materials in samples by low temperature incinerator.

Identification of Asbestos Using Laser-Induced Breakdown Spectroscopy: A Viable Alternative to the Conventional Approach?

Laser-induced breakdown spectroscopy has been performed on isolated samples of six types of asbestos—Chrysotile, Crocidolite, Amosite, Anthophyllite, Actinolite, and Tremolite—with analysis of optical emission in the visible region of the spectrum. The principal elements of Mg, Fe, Si, Na, and Ca have all been identified. By examining peak intensity ratios of these elements it is possible to identify the type of asbestos under examination solely from internal examination of the sample spectra. LIBS offers some significant advantages of speed of analysis and removal of subjectivity with potential for on-site rapid analysis.

Method validation for the identification of asbestos–cement roofing

The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping asbestos–cement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as asbestos–cement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of asbestos–cement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of asbestos–cement in the studied area has been also collected. The asbestos–cement surfaces of buildings vary from 100 to 5,000 m2, totalling to 30,800 m2, which is approximately 400,400 kg of asbestos surfaces in an area of 5.2 km2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of asbestos-cement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.

Optical Identification of Asbestos by Dispersion Staining Method

Optical Identification of Asbestos by Dispersion Staining Method

Identification of Asbestos by Laser-Induced Fluorescence Microscopy

蛍光顕微分光分析によるアスベスト粒子（クリソタイル）の同定，識別計測を目的として，顕微鏡観察下での紫外域のレーザー照射によるアスベスト粒子とその代替建材粒子（グラスウール）の蛍光特性を調査した．その結果，顕微鏡で観測される各粒子の蛍光寿命と蛍光量においてクリソタイルとグラスウールと間に顕著な差異が観測された．これら蛍光特性の差異を蛍光顕微分析に利用することによってアスベスト粒子をアスベスト以外の建材粒子と識別して同定できることが分かった．

Feasibility Study on Identification of Asbestos Using Laser-Induced Fluorescence

レーザー誘起蛍光によるアスベストの識別計測を目的として，3種類（クリソタイル，クロシドライト及びアモサイト）のアスベスト，アスベスト代替建材試料であるグラスウール，ロックウール，及び建材試料のタルク，石膏等の蛍光スペクトルを調査した．その結果，波長266 nmの紫外レーザー光照射において，それらすべての試料から波長350から700 nmに及ぶ幅の広い蛍光スペクトルが観測された．各試料の蛍光特性を比較した結果，スペクトル形状，蛍光量及び蛍光寿命において，アスベストとそれ以外の建材試料との間に識別可能な差異が確認された．特にグラスウール等の繊維状試料との蛍光スペクトル形状と蛍光量の差異は著しく，蛍光量はアスベストのものの約10倍以上であった．これらの蛍光特性の差異を利用するアスベスト識別法として，二つの離れた波長間の強度比，蛍光の減衰比，及び蛍光量比の3つの比較法について紹介する．

Detoxification of industrial asbestos waste by low-temperature heating in a vacuum

Industrial asbestos waste such as asbestos cement board, sprayed crocidolite and sprayed amosite was heated in a vacuum. The detoxification conditions were then established in reference to JIS A 1481:2006 and JIS K 3850-1:2006. When asbestos cement board was heated at 700°C for 1 h, detoxification of the cement board was completed. On the other hand, in the case of amphibolic asbestos waste, such as sprayed crocidolite and sprayed amosite, was heated, a phase-contrast microscope analysis was not applicable for identification of asbestos heated at more than 500°C. According to XRD analysis, crocidolite was eliminated by heating at 800°C for 3 h. Furthermore, amosite was not detected after heating at 700°C for 3 h. Finally, the grindability of heated amphibolic asbestos was investigated. When sprayed crocidolite was heated at 900°C for 3 h and then ground with a mortar for 300 s, the crushed grains had a length of less than 5 μm and an aspect ratio of 3. In addition, when sprayed amosite was heated at 1000°C for 3 h and then ground, the crushed grains had a length of less than 5 μm and an aspect ratio of 3.

造岩鉱物学からみるアスベスト同定法とその問題点

Asbestos consists of fibrous aggregates of serpentines and/or amphiboles including chrysotile, anthophyllite-gedrite, cummingtonite-grunerite, tremolite-actinolite and riebeckite. The dispersion staining method, X-ray diffractometry, electron-probe microanalysis and Raman spectroscopy are briefly reviewed to assess their potential use for asbestos identification. Optical, crystal chemical and spectrographic characteristics of asbestos are summarized along with their powder X-ray diffraction patterns, energy dispersive spectra and Raman spectra data.

Use of a fibre‐optic probe for the identification of asbestos fibres in bulk materials by Raman spectroscopy

AbstractWithin the UK, a large number of surveys are being carried out for the identification of asbestos in building materials. Pre‐demolition or major refurbishment and building sampling surveys require that representative samples be analysed for asbestos. The primary technique for the analysis of asbestos fibres in bulk materials is polarized light microscopy (PLM). Since this method is based on visual estimation, it depends on the competence and experience of the analyst. Furthermore, PLM, in common with the other analytical methods used for the identification of asbestos in bulk materials, is a laboratory‐based technique requiring samples to be collected and then sent to a laboratory for analysis. Raman spectroscopy is a powerful tool for mineral characterization and provides a molecular fingerprint for material. In recent years, new developments in optics and electronics have permitted the development of compact Raman spectrometers allowing in situ measurements. We report the performance of a robust, laboratory‐based fibre‐optic probe for the identification of asbestos in bulk materials by Raman spectroscopy. Thirty‐eight samples were analysed, and the results showed that 95% of the samples were successfully identified as either asbestos free or containing one or more asbestos components. A success rate of 100% was found for asbestos insulating board and asbestos cement materials. The results indicated that a Raman probe device for detecting asbestos does have potential, but a number of issues need to be addressed before an on‐site system can be developed. Copyright © 2004 John Wiley & Sons, Ltd.

Dr. Walter C. McCrone's Contribution to the Characterization and Identification of Explosives

Dr. McCrone was an amazing individual, possessing many talents and having many interests. He especially loved applying polarized light microscopy (PLM) to answering the question-at-hand and solving problems. He applied PLM to many different fields including the identification of air pollution particles, asbestos identification, art conservation, pharmaceuticals, industry problems and forensic sciences. A field that I believe he enjoyed the most was the characterization and identification of explosives. Throughout his life he worked on, gave presentations and published articles on the characterization and identification of explosives. Also, he encouraged other scientists to give presentations and publish on the subject by providing "behind the scene" advice and/or be a co-author on a paper. He unselfishly taught others how to apply PLM and incorporate this invaluable tool into their analytical scheme.

The Polarized Light Microscope: Should We Teach the use of a 19th Century Instrument in the 21st Century?

The polarizing light microscope (PLM) has no doubt contributed more to our knowledge of minerals and rocks than any other single instrument. Then why is the use of the PLM, and the teaching of optical mineralogy in general, decreasing? Probably one of the main reasons is educators seek to present the newest, most technologically advanced techniques and methods to their students. Also, the geoscience curriculum has changed to include environmental geology, essentially hydrogeology. However, in presenting these newer materials, or new courses, we must exclude something, and it appears that one of the things excluded is instruction in use of the PLM. Another possibility is the professors teaching these courses do not have an adequate understanding of optical mineralogy. Excluding the subject of optical microscopy will be the biggest mistake we ever make in the geosciences curriculum. This statement is justified because of the fundamentally important concepts presented in optical mineralogy: 1) three-dimensional visualization, 2) inquiry-based learning, and 3) hands-on use of an analytical instrument. No other single course in our curriculum provides so many of these fundamental skills to our students. An example of inquiry-based learning is determining the best technique to identify a mineral. For example, powder X-ray diffraction provides a diffraction pattern searchable in a database, but the result might not be correct. In the past 20–30 years, the spindle stage has allowed for more detailed single crystal studies than ever before. Optical techniques are also used to study such diverse mineralogical problems as cation-diffusion in zeolites and asbestos identification, or they may be incorporated into other areas of research, such as synchrotron experiments on oriented single crystals. Also, any serious petrologic study must begin with a thorough examination of the samples by PLM, before other types of characterization can proceed. A diversity of employment opportunities exist for students who are trained in use of the PLM, such as in the fields of forensics, material science, manufacturing, the food industry, medical technology, and the emerging field of environmental mineralogy.