Asbestos containing materials detection and classification by the use of hyperspectral imaging

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.

Assessment of asbestos-cement roof distribution and prioritized intervention approaches through hyperspectral imaging

The discernment of asbestos-cement (AC) roofs within urban areas stands as a pivotal concern pertinent to communal well-being and ecological oversight, particularly in emerging nations where asbestos continues to be extensively employed. Conventional methodologies entailing the recognition of asbestos-cement roofs and the characterization of their degradation status, such as tangible examinations and laboratory assays, prove to be temporally protracted, financially demanding, and arduous to extrapolate comprehensively across expansive urban domains. In this paper, it is presented a novel approach for identifying asbestos-cement roofs in urban areas using hyperspectral airborne acquisition and carry out a diagnosis that allows to identify the state of asbestos-cement roofs and thus provide a tool for the competent authorities to develop and prioritize intervention strategies to mitigate the problem. Four different methodologies were implemented and compared, three of which are new in the literature, to identify the deterioration of asbestos-cement (AC) roof state in large urban areas. This, in turn, furnishes a tool for competent authorities to identify the state of AC roofs, develop and prioritize intervention strategies to mitigate the problem. The control points in field allowed validating the classification and the proposed methodology for the prioritization of intervention in AC roofs. Some neighborhoods in the city showed peaks in the area of asbestos-cement roofs of 47% of the total area of the neighborhood, representing practically all of the roofs present in the neighborhood. On average around 20% of the total area of a neighborhood in Cartagena is covered by AC. Furthermore, it was found a total area of AC roofs throughout the city of more than 9 km2 (9 million square meters). On the other hand, two of the 4 methods used showed encouraging results that demonstrate their ability to identify covers in poor and good condition at a large scale from hyperspectral images. This academic novelty suggests that there is a possibility of practical application of these methods in other urban contexts with high concentrations of AC roofs, helping in the planning and optimization of intervention strategies to mitigate the risk in public and environmental health due to the presence of asbestos.

Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images

In this study, different multivariate classification methods were applied to hyperspectral images acquired, in the short-wave infrared range (SWIR: 1000-2500 nm), to define and evaluate quality control actions applied to construction and demolition waste (C&DW) flow streams, with particular reference to the detection of hazardous material as asbestos. Three asbestos fibers classes (i.e., amosite, chrysotile and crocidolite) inside asbestos-containing materials (ACM) were investigated. Samples were divided into two groups: calibration and validation datasets. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Hierarchical Partial Least Squares-Discriminant Analysis (Hi-PLSDA), Principal Component Analysis k-Nearest Neighbors (PCA-kNN) and Error Correcting Output Coding with Support Vector Machines (ECOC-SVM). The classification results obtained for the three models were evaluated by prediction maps and the values of performance parameters (Sensitivity and Specificity). Micro-X-ray fluorescence (micro-XRF) maps confirmed the correctness of classification results. The results demonstrate how SWIR-HSI technology, coupled with multivariate analysis modelling, is a promising approach to develop both "off-line" and "online" fast, reliable and robust quality control strategies, finalized to perform a quick assessment of ACM presence.

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.

Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods

The exploitation of mineral resources has seriously polluted the environment around mines, notably in terms of heavy metal contamination of tailings pond soil. Hyperspectral remote sensing, as opposed to conventional on-site sampling and laboratory analysis, offers a potent tool for effective monitoring the content of soil heavy metals. Therefore, we investigated the inversion models of heavy metal content in metal tailings area based on measured hyperspectral and multispectral data. Hyperspectral and its transformation, as well as the simulated Landsat8-OLI multispectral were used for model inversion respectively. Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR) and Back Propagation Neuron Network (BPNN) were established to study the spectral inversion of eight heavy metals (Cu, Cd, Cr, Ni, Pb, Zn, As, and Hg). The direct inversion models were established on the basis of correlation analysis and the adjust coefficient of determination (Adjust_R2) and Root Mean Square Error (RMSE) were used for model evaluation. Then the best combination of spectral transformation and inversion model were explored. The model inversion results suggested that: (1) Hyperspectral transformation can generally improve the model accuracy, especially the second derivative spectral, based on which the training Adjust_R2 of Hg SMLR and PLSR models are as high as 0.795 and 0.802. (2) The BP neural network inversion based on the denoised hyperspectrum demonstrate that both the training and testing Adjust_R2 of Cd, Ni and Hg models are all greater than 0.5, indicating good applicability in practical extrapolation. (3) Both the training and testing Adjust_R2 of Cu and Hg PLSR models based on simulated R_Landsat8-OLI multispectral are greater than 0.5, and Hg has lower RMSE and lager Adjust_R2 with training and testing Adjust_R2 values of 0.833 and 0.553 respectively. (4) Multispectral remote sensing detection and mapping of Hg contamination were realized by the optimal simulation model of Hg. Hence, it is feasible to simulate the multispectral with hyperspectral data for investigating heavy metal contamination.

Mapping soil arsenic pollution at a brownfield site using satellite hyperspectral imagery and machine learning

Heavy metal contamination is ubiquitous in brownfields. Traditional site investigation employs geostatistical interpolation methods (GIMs) to predict the distribution of soil pollutants after soil sampling and chemical analysis. However, the heterogeneity of soil pollution in brownfields makes the assumptions of GIMs no longer valid and further undermines the accuracy of soil investigation. In the present study, a satellite hyperspectral image processing and machine learning method was developed to map arsenic pollution at a brownfield site. To eliminate the noise caused by atmospheric factors and increase the efficiency of spectral data, 1.3 million spectral indexes (SIs) were constructed and 1171 of them were selected due to their high correlations with soil arsenic. Five machine learning methods, i.e., Random forest (RF), ExtraTrees, Adaptive Boosting, Extreme Gradient Trees, and Gradient Descent Boosting Trees (GDB) were built to predict soil arsenic. The RF method was found to render the best performance (r = 0.78), reducing 30 % of prediction errors compared with traditional GIMs. RF also maintained a relatively higher level of accuracy (r = 0.56) when the sampling grids increased to 100 m, which was higher than that of GIMs under a 50 m sampling grid (r = 0.42), revealing that the proposed method can provide more accurate results with fewer sampling points, namely less investigation cost. It was indicated that the second derivate was the most efficient preprocessing method to remove spectral noise and normalized difference (ND) was the most reliable spectral index construction strategy. Based on uncertainty analysis, the heterogeneity of soil arsenic distribution was considered the most influential factor causing prediction errors. This study demonstrates that machine learning based on satellite visible and near-infrared reflectance spectroscopy (VNIR) is a promising approach to map soil arsenic contamination at brownfield sites with high accuracy and low cost.

Environmental management criteria, aimed at public policymaking, for the removal and disposal of asbestos-containing building materials in Colombia

Asbestos is a carcinogenic mineral banned in Colombia since 1 January 2021; however, there is a considerable amount of asbestos-containing building materials (ACBM) installed across the country in products such as roof tiles, tanks, pipes, and downspouts. Installed ACBM represent an exposure risk when the mineral fibers are released into the air through deterioration, damage, or disturbance of the cement matrix within which the asbestos is contained. Due to potential detrimental impacts on human health, safe management and correct handling of ACBM is a matter of vital importance. This article proposes evidence-based environmental management guidelines, aimed at public policymaking, for the removal and final disposal of installed ACBM in Colombia. A descriptive study was carried out, with a qualitative approach, based on an integrative literature review of international practices applied in the removal and disposal of installed ACBM. Forty scientific publications were reviewed, as well as the regulations for removal, transport, and final disposal of installed asbestos-cement from Australia, the USA, Italy, Chile, the UK, and Canada. Guidelines for the removal and final disposal of installed ACBM are proposed, suggesting the following stages: (a) diagnosis and management plan of installed ACBM, (b) removal of installed ACBM, (c) transport of ACBM waste, and (d) final disposal of ACBM waste. Expert opinion was collected to assess the local feasibility of the proposed guidelines. These guidelines may help direct national and regional agencies to establish comprehensive strategies with clear, measurable, and achievable goals for future replacement of installed ACBM. Integr Environ Assess Manag 2023;00:1-10. © 2023 SETAC.

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.

Physical, Mechanical and Perforation Resistance of Natural-Synthetic Fiber Interply Laminate Hybrid Composites

Natural and synthetic fibres have emerged in high demand due to their excellent properties. Natural fibres have good mechanical properties and are less expensive, making them a viable substitute for synthetic fibers. Owing to certain drawbacks such as their inconsistent quality and hydrophilic nature, researchers focused on incorporating these two fibres as an alternative to improve the limitations of the single fibre. This review focused on the interply hybridisation of natural and synthetic fibres into composites. Natural fibres and their classifications are discussed. The physical and mechanical properties of these hybrid composites have also been included. A full discussion of the mechanical properties of natural/synthetic fibre hybrid composites such as tensile, flexural, impact, and perforation resistance, as well as their failure modes, is highlighted. Furthermore, the applications and future directions of hybrid composites have been described in details.

Single-shot hyperspectral imaging based on dual attention neural network with multi-modal learning

Hyperspectral imaging is being extensively investigated owing to its promising future in critical applications such as medical diagnostics, sensing, and surveillance. However, current techniques are complex with multiple alignment-sensitive components and spatiospectral parameters predetermined by manufacturers. In this paper, we demonstrate an end-to-end snapshot hyperspectral imaging technique and build a physics-informed dual attention neural network with multimodal learning. By modeling the 3D spectral cube reconstruction procedure and solving that compressive-imaging inverse problem, the hyperspectral volume can be directly recovered from only one scene RGB image. Spectra features and camera spectral sensitivity are jointly leveraged to retrieve the multiplexed spatiospectral correlations and realize hyperspectral imaging. With the help of integrated attention mechanism, useful information supplied by disparate modal components is adaptively learned and aggregated to make our network flexible for variable imaging systems. Results show that the proposed method is ultra-faster than the traditional scanning method, and 3.4 times more precise than the existing hyperspectral imaging convolutional neural network. We provide theory for network design, demonstrate training process, and present experimental results with high accuracy. Without bulky benchtop setups and strict experimental limitations, this simple and effective method offers great potential for future spectral imaging applications such as pathological digital stain, computational imaging and virtual/augmented reality display, etc.

Risk assessment of asbestos containing materials in a deteriorated dwelling area using four different methods

The release of asbestos fibers in old buildings, during demolition, or remodeling is associated with severe public health risks to building occupants and workers. In Korea, asbestos was used in several building materials during the 20th century. Although the use of asbestos is currently banned, its widespread earlier use and the current government initiatives to revitalize dilapidated areas make it essential to accurately evaluate the location and status of asbestos-containing materials (ACMs). This study surveyed buildings in an area of deteriorated dwellings targeted for renewal and determined the status and distribution of ACMs in that area. Asbestos distribution maps were generated and asbestos characteristics were analyzed. In addition, the risk posed by the identified ACMs was assessed using four international methods (the Korean Ministry of Environment, US Environmental Protection Agency, American Society for Testing and Materials, and UK Health and Safety Executive methods), and the results were compared. Notable differences between the assessment results were identified and were found to reflect the specific characteristics of buildings in the study area. These findings suggest ACM risk assessments should be specifically tailored to the regions in which they are applied, thereby improving ACM management and promoting both worker and occupant health.

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.

Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways

Malignant pleural mesothelioma (MPM) is a rare neoplasm, mainly caused by asbestos exposure, with a high mortality rate. The management of patients with MPM is controversial due to a long latency period between exposure and diagnosis and because of non-specific symptoms generally appearing at advanced stage of the disease. Breath analysis, aimed at the identification of diagnostic Volatile Organic Compounds (VOCs) pattern in exhaled breath, is believed to improve early detection of MPM. Therefore, in this study, breath samples from 14 MPM patients and 20 healthy controls (HC) were collected and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). Nonparametric test allowed to identify the most weighting variables to discriminate between MPM and HC breath samples and multivariate statistics were applied. Considering that MPM is an aggressive neoplasm leading to a late diagnosis and thus the recruitment of patients is very difficult, a promising data mining approach was developed and validated in order to discriminate between MPM patients and healthy controls, even if no large population data are available. Three different machine learning algorithms were applied to perform the classification task with a leave-one-out cross-validation approach, leading to remarkable results (Area Under Curve AUC = 93%). Ten VOCs, such as ketones, alkanes and methylate derivates, as well as hydrocarbons, were able to discriminate between MPM patients and healthy controls and for each compound which resulted diagnostic for MPM, the metabolic pathway was studied in order to identify the link between VOC and the neoplasm. Moreover, five breath samples from asymptomatic asbestos-exposed persons (AEx) were exploratively analyzed, processed and tested by the validated statistical method as blinded samples in order to evaluate the performance for the early recognition of patients affected by MPM among asbestos-exposed persons. Good agreement was found between the information obtained by gold-standard diagnostic methods such as computed tomography CT and model output.

Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest

Hyperspectral imaging, with the hundreds of bands and high spectral resolution, offers a promising approach for estimation of heavy metal concentration in agricultural soils. Using airborne imagery over a large-scale area for fast retrieval is of great importance for environmental monitoring and further decision support. However, few studies have focused on the estimation of soil heavy metal concentration by airborne hyperspectral imaging. In this study, we utilized the airborne hyperspectral data in LiuXin Mine of China obtained from HySpex VNIR-1600 and HySpex SWIR-384 sensor to establish the spectral-analysis-based model for retrieval of heavy metals concentration. Firstly, sixty soil samples were collected in situ, and their heavy metal concentrations (Cr, Cu, Pb) were determined by inductively coupled plasma-mass spectrometry analysis. Due to mixed pixels widespread in airborne hyperspectral images, spectral unmixing was conducted to obtain purer spectra of the soil and to improve the estimation accuracy. Ten of estimated models, including four different random forest models (RF)-standard random forest (SRF), regularized random forest (RRF), guided random forest (GRF), and guided regularized random forest (GRRF)-were introduced for hyperspectral estimated model in this paper. Compared with the estimation results, the best accuracy for Cr, Cu, and Pb is obtained by RF. It shows that RF can predict the three heavy metals better than other models in this area. For Cr, Cu, Pb, the best model of RF yields R_p² values of 0.75,0.68 and 0.74 respectively, and the values of RMSE_p are 5.62, 8.24, and 2.81 (mg/kg), respectively. The experiments show the average estimated values are close to the truth condition and the high estimated values concentrated near several industries, valifating the effectiveness of the presented method.

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

Asbestos-Containing Materials (ACMs) are hazardous and prohibited to be sold or used as recycled materials. In the past, asbestos was widely used, together with cement, to produce “asbestos cement-based” products. During the recycling process of Construction and Demolition waste (C&DW), ACM must be collected and deposited separately from other wastes. One of the main aims of the recycling strategies applied to C&DW was thus to identify and separate ACM from C&DW (e.g., concrete and brick). However, to obtain a correct recovery of C&DW materials, control methodologies are necessary to evaluate the quality and the presence of harmful materials, such as ACM. HyperSpectral Imaging (HSI)-based sensing devices allow performing the full detection of materials constituting demolition waste. ACMs are, in fact, characterized by a spectral response that nakes them is different from the “simple” matrix of the material/s not embedding asbestos. The described HSI quality control approach is based on the utilization of a platform working in the short-wave infrared range (1000–2500 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: Principal Component Analysis for data exploration and hierarchical Partial Least-Square-Discriminant Analysis (PLS-DA) to build classification models. Following this approach, it was possible to set up a repeatable, reliable and efficient technique able to detect ACM presence inside a C&DW flow stream. Results showed that it is possible to discriminate and identify ACM inside C&DW. The recognition is potentially automatic, non-destructive and does not need any contact with the investigated products.

Digitalisation and intelligent robotics in value chain of circular economy oriented waste management - A review

The general aim of circular economy is the most efficient and comprehensive use of resources. In order to achieve this goal, new approaches of Industry 4.0 are being developed and implemented in the field of waste management. The innovative K-project: Recycling and Recovery of Waste 4.0 - "ReWaste4.0" deals with topics such as digitalisation and the use of robotic technologies in waste management. Here, a summary of the already published results in these areas, which were divided into the four focused topics, is given: Collection and Logistics, Machines and waste treatment plants, Business models and Data Tools. Presented are systems and methods already used in waste management, as well as technologies that have already been successfully applied in other industrial sectors and will also be relevant in the waste management sector for the future. The focus is set on systems that could be used in waste treatment plants or machines in the future in order to make treatment of waste more efficient. In particular, systems which carry out the sorting of (mixed) waste via robotic technologies are of interest. Furthermore "smart bins" with sensors for material detection or level measurement, methods for digital image analysis and new business models have already been developed. The technologies are often based on large amounts of data that can contribute to increase the efficiency within plants. In addition, the results of an online market survey of companies from the waste management industry on the subject of waste management 4.0 or "digital readiness" are summarized.

Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image Classification

Hyperspectral image (HSI) provides both spatial structure and spectral information for classification, but many traditional methods simply concatenate spatial features and spectral features together that usually lead to the curse-of-dimensionality and unbalanced representation of different features. To address this issue, a new dimensionality reduction (DR) method, termed multi-feature manifold discriminant analysis (MFMDA), was proposed in this paper. At first, MFMDA explores local binary patterns (LBP) operator to extract textural features for encoding the spatial information in HSI. Then, under graph embedding framework, the intrinsic and penalty graphs of LBP and spectral features are constructed to explore the discriminant manifold structure in both spatial and spectral domains, respectively. After that, a new spatial-spectral DR model for multi-feature fusion is built to extract discriminant spatial-spectral combined features, and it not only preserves the similarity relationship between spectral features and LBP features but also possesses strong discriminating ability in the low-dimensional embedding space. Experiments on Indian Pines, Heihe and Pavia University (PaviaU) hyperspectral data sets demonstrate that the proposed MFMDA method performs significantly better than some state-of-the-art methods using only single feature or simply stacking spectral features and spatial features together, and the classification accuracies of it can reach 95.43%, 97.19% and 96.60%, respectively.

Hyperspectral Imaging as Powerful Technique for Investigating the Stability of Painting Samples

The aim of this work is to present the utilization of Hyperspectral Imaging for studying the stability of painting samples to simulated solar radiation, in order to evaluate their use in the restoration field. In particular, ready-to-use commercial watercolours and powder pigments were tested, with these last ones being prepared for the experimental by gum Arabic in order to propose a possible substitute for traditional reintegration materials. Samples were investigated through Hyperspectral Imaging in the short wave infrared range before and after artificial ageing procedure performed in Solar Box chamber under controlled conditions. Data were treated and elaborated in order to evaluate the sensitivity of the Hyperspectral Imaging technique to identify the variations on paint layers, induced by photo-degradation, before they could be detected by eye. Furthermore, a supervised classification method for monitoring the painted surface changes, adopting a multivariate approach was successfully applied.

Breath analysis as a diagnostic and screening tool for malignant pleural mesothelioma: a systematic review

Malignant pleural mesothelioma (MPM) is a tumour related to a historical exposure to asbestos fibres. Currently, the definite diagnosis is made only by the histological examination of a biopsy obtained through an invasive thoracoscopy. However, diagnosis is made too late for curative treatment because of non-specific symptoms mainly appearing at advanced stage disease. Hence, due to its biologic aggressiveness and the late diagnosis, survival rate is low and the patients' outcome poor. In addition, radiological imaging, like computed tomographic scans, and blood biomarkers are found not to be sensitive enough to be used as an early diagnostic tool. Detection in an early stage is assumed to improve the patients' outcome but is hampered due to non-specific and late symptomology. Hence, there is a need for a new screening and diagnostic test which could improve the patients' outcome. Despite extensive research has focused on blood biomarkers, not a single has been shown clinically useful, and therefore research recently shifted to "breathomics" techniques to recognize specific volatile organic compounds (VOCs) in the breath of the patient as potential non-invasive biomarkers for disease. In this review, we summarize the acquired knowledge about using breath analysis for diagnosing and monitoring MPM and asbestos-related disorders (ARD). Gas chromatography-mass spectrometry (GC-MS), the gold standard of breath analysis, appears to be the method with the highest accuracy (97%) to differentiate MPM patients from at risk asbestos-exposed subjects. There have already been found some interesting biomarkers that are significantly elevated in asbestosis (NO, 8-isoprostane, leukotriene B4, α-Pinene…) and MPM (cyclohexane) patients. Regrettably, the different techniques and the plethora of studies suffer some limitations. Most studies are pilot studies with the inclusion of a limited number of patients. Nevertheless, given the promising results and easy sampling methods, we can conclude that breath analysis may become a useful tool in the future to screen for MPM, but further research is warranted.

Multi-Analytical Approach for Identifying Asbestos Minerals In Situ

An innovative and, as yet, untested approach is to analyze serpentinite and metabasite rocks containing asbestos using a portable multi-analytical device, which combines portable digital microscopy (p-DM), portable X-ray Fluorescence (p-XRF) and portable micro-Raman Spectroscopy (p-µR). The analyses were carried out in two inactive quarries of serpentinitic and metabasitic rocks from the Gimigliano-Mount Reventino Unit (Southern Italy) already characterized in previous studies, with the aim of testing the efficiency of these portable tools. In this study, a portable X-ray fluorescence analyzer was used to obtain the in situ rapid chemical discrimination of serpentinite and metabasite rocks. The characterization of outcropping rocks using portable devices enabled us to detect the presence of chrysotile and asbestos tremolite. The results obtained were consistent with the findings from previous research studies and therefore combining p-DM, p-XRF and p-µR could be a useful approach for discriminating asbestos contained in outcropping rocks, especially when sampling is prohibited or for field-based sampling.