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Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Materials".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 29422

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Special Issue Editors

Dr. Abel Silva Vieira

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Guest Editor

Griffith School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, QLD 4222, Australia
Interests: sustainability; optimisation; energy efficiency; water conservation; carbon emission mitigation; artificial intelligence; simulation
Special Issues, Collections and Topics in MDPI journals

Dr. Georgia Khatib

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Guest Editor

Asbestos Safety and Eradication Agency (ASEA), Australian Government, Canberra, Australia
Interests: public health; work health safety; risk assessment; regulatory and policy research; asbestos awareness; asbestos management; asbestos policy

Prof. Dr. Rodney Stewart

E-Mail Website
Guest Editor

School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, QLD 4222, Australia
Interests: digital engineering; building information modelling; digital information asset management; digital utility transformation; smart or intelligent water and energy metering; intelligent sensor networks; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Nicholas Patorniti

E-Mail Website
Guest Editor

School of Engineering and Built Environment and Cities Research Institute, Griffith University, Parklands Drive, Southport, QLD 4222, Australia
Interests: urban planning; sustainable development; urban analytics; predictive modelling; complex systems; asset management; environmental management; resource management; asbestos detection

Special Issue Information

Dear Colleagues,

Asbestos is a hazardous material that presents a range of management challenges. Asbestos exposure is associated with life-threatening public health risks, e.g., asbestosis and various cancers, including mesothelioma. In accordance with the International Agency for Research on Cancer (IARC), all forms of asbestos, including chrysotile, are classified as human carcinogens. Globally, it is estimated that 219,000 deaths annually can be attributed to occupational exposure to asbestos. Asbestos use has been banned in many countries, including Australia, the UK, the EU, Canada and Japan. In countries that have banned asbestos, many public, commercial and residential buildings and infrastructure still contain large amounts of ageing asbestos-containing materials (ACMs). In countries that have not banned asbestos, it can be found in existing and new building stocks, and, hence, presents a risk now and into the future. Advancements in ACM detection, monitoring, management and disposal are required to effectively manage public health risks associated with asbestos exposure. Contributing to the prevention of exposure to asbestos fibres, this Special Issue focuses on studies addressing the development and application of sustainable practices for asbestos detection, management and disposal in the built environment. The World Health Organisation (WHO) considers asbestos as “one of the most important occupational carcinogens” and recommended that the elimination of asbestos-related diseases become a focus from 2003. The International Labour Organisation (ILO) adopted a resolution concerning asbestos in 2006, which calls for: “the elimination of the future use of asbestos and the identification and proper management of asbestos currently in place as the most effective means to prevent future asbestos-related diseases and deaths”. These strategies underpin the common goal for asbestos risk mitigation worldwide and highlight the ongoing need for creating, expanding and sharing research and information on asbestos as part of a forum for international collaboration and leadership on asbestos best practice approaches. The Special Issue of Sustainability welcomes scientific contributions from the following fields: (i) applied asbestos detection and monitoring in the built environment using modelling techniques (e.g., artificial intelligence (AI) deep learning models); (ii) asbestos risk assessments underpinned by metrics, indicators and risk matrices (e.g., risk of exposure, ACM condition assessment); (iii) the development of management and disposal initiatives, approaches and strategies; and (iv) conceptual and practical frameworks for asbestos management and disposal. A large range of research endeavours related to the identification, monitoring, management and disposal of asbestos will be considered. The type of papers accepted in this Special Issue will include original and critical review articles on best practice research for asbestos detection and quantification, qualitative analyses for ACM condition assessment and strategic framework development underpinned by evidence-based and experience-based approaches. The editors encourage contributions from advocacy, industry and government experts on current practices, case studies and strategies for asbestos detection, monitoring and management. The compilation of papers published in this Special Issue is intended to provide readers with a repository of best practice approaches and strategies to identify, manage and dispose ACM, promoting sustainability related to ACM risk mitigation in the built environment.

Dr. Abel Silva Vieira
Dr. Georgia Khatib
Prof. Dr. Rodney Stewart
Dr. Nicholas Patorniti
Guest Editors

Manuscript Submission Information

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Keywords

abestos-containing materials (ACM)
built environment
environmental management
predictive modelling
artificial intelligence
deep learning
public health
work health safety

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Published Papers (9 papers)

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Research

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12 pages, 990 KiB

Open AccessArticle

The Past, Present and Future of Asbestos-Related Diseases in Australia: What Are the Data Telling Us?

by Kathleen Mahoney, Tim Driscoll, Julia Collins and Justine Ross

Sustainability 2023, 15(11), 8492; https://doi.org/10.3390/su15118492 - 23 May 2023

Cited by 5 | Viewed by 4573

Abstract

Exposure to asbestos fibres causes asbestosis, mesothelioma and several other cancers, which together are commonly referred to as asbestos-related diseases (ARDs). The use of asbestos increased rapidly in Australia and overseas throughout the 1900s, but knowledge about the health effects of exposure and subsequent controls came about more gradually. In Australia today, an estimated 4000 people still die annually from ARDs. While most of these deaths are due to past occupational exposures, there is ongoing concern about the many potential sources of asbestos exposure remaining in homes and the broader built environment as a legacy of past use. Current evidence indicates that Australians will continue to be exposed to legacy asbestos occupationally and non-occupationally, and continue to develop ARDs, without targeted action to prevent it. Evidence of ongoing exposure highlights the importance of better understanding how and why such exposures might still occur, and how they can be effectively prevented or controlled, with the aim of preventing the disease in the future. A better characterisation of this risk is also necessary to enable effective risk management and appropriate risk communication that is relevant to the current Australian context. This article explores the past, present and future of ARDs in Australia, considers the risk of a new wave of ARDs from legacy asbestos, and identifies where further study is required so that sustainable policies and practices can be developed to prevent a future wave of diseases. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

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16 pages, 3001 KiB

Open AccessArticle

Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery

by Gordana Kaplan, Mateo Gašparović, Onur Kaplan, Vancho Adjiski, Resul Comert and Mohammad Asef Mobariz

Sustainability 2023, 15(7), 6067; https://doi.org/10.3390/su15076067 - 31 Mar 2023

Cited by 4 | Viewed by 2300

Abstract

Detecting asbestos-containing roofs has been of great interest in the past few years as the substance negatively affects human health and the environment. Different remote sensing data have been successfully used for this purpose. However, RGB and thermal data have yet to be investigated. This study aims to investigate the classification of asbestos-containing roofs using RGB and airborne thermal data and state-of-the-art machine learning (ML) classification techniques. With the rapid development of ML reflected in this study, we evaluate three classifiers: Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). We have used several image enhancement techniques to produce additional bands to improve the classification results. For feature selection, we used the Boruta technique; based on the results, we have constructed four different variations of the dataset. The results showed that the most important features for asbestos-containing roof detection were the investigated spectral indices in this study. From a ML point of view, SVM outperformed RF and XGBoost in the dataset using only the spectral indices, with a balanced accuracy of 0.93. Our results showed that RGB bands could produce as accurate results as the multispectral and hyperspectral data with the addition of spectral indices. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

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11 pages, 3468 KiB

Open AccessArticle

Awareness and Profiling of High-Risk Asbestos Exposure Groups in Australia

by Katrina Khamhing, Shane McArdle and Justine Ross

Sustainability 2023, 15(7), 5806; https://doi.org/10.3390/su15075806 - 27 Mar 2023

Cited by 1 | Viewed by 1896

Abstract

The increase in home improvement activity during the COVID-19 pandemic gave rise to concerns of increased asbestos exposure risk. This paper describes high-risk asbestos exposure groups based on current home improvement trends in Australia. A series of quantitative and qualitative studies were commissioned to better understand the attitudes, motivations, and behaviours of home improvers in Australia. In 2021, two in three Australian adults were inclined to undertake home improvement projects—big or small—with or without professional help, underscoring the importance of improving the asbestos safety knowledge and capacity of this cohort. The studies commissioned across 2020 and 2021 provide a deep analysis into this cohort, defining who they are and the segments that make up home improvers, their behaviours, and their asbestos awareness and attitudes. This knowledge enables the development and implementation of a range of targeted campaigns to increase asbestos awareness and prevent potential exposure to asbestos fibres. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

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23 pages, 5740 KiB

Open AccessArticle

Artificial Intelligence for the Detection of Asbestos Cement Roofing: An Investigation of Multi-Spectral Satellite Imagery and High-Resolution Aerial Imagery

by Mia V. Hikuwai, Nicholas Patorniti, Abel S. Vieira, Georgia Frangioudakis Khatib and Rodney A. Stewart

Sustainability 2023, 15(5), 4276; https://doi.org/10.3390/su15054276 - 27 Feb 2023

Cited by 5 | Viewed by 3555

Abstract

Artificial Intelligence (AI) is providing the technology for large-scale, cost-effective and current asbestos-containing material (ACM) roofing detection. AI models can provide additional data to monitor, manage and plan for ACM in situ and its safe removal and disposal, compared with traditional approaches alone. Advances are being made in AI algorithms and imagery applied to ACM detection. This study applies mask region-based convolution neural networks (Mask R-CNN) to multi-spectral satellite imagery (MSSI) and high-resolution aerial imagery (HRAI) to detect the presence of ACM roofing on residential buildings across an Australian case study area. The results provide insights into the challenges and benefits of using AI and different imageries for ACM detection, providing future directions for its practical application. The study found model 1, using HRAI and 460 training samples, was the more reliable model of the three with a precision of 94%. These findings confirm the efficacy of combining advanced AI techniques and remote sensing imagery, specifically Mask R-CNN with HRAI, for ACM roofing detection. Such combinations can provide efficient methods for the large-scale detection of ACM roofing, improving the coverage and currency of data for the implementation of coordinated management policies for ACM in the built environment. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

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15 pages, 13230 KiB

Open AccessArticle

Understanding and Ending the Lethal Asbestos Legacy

by Simone Peta Stevenson, Oonagh Barron, Andrew Pakenham and Masayoshi Hashinaka

Sustainability 2023, 15(3), 2507; https://doi.org/10.3390/su15032507 - 31 Jan 2023

Cited by 3 | Viewed by 2226

Abstract

The Victorian Asbestos Eradication Agency (VAEA) was established to develop a long-term plan for the prioritised removal of asbestos containing materials (ACMs) from Victorian government-owned buildings. The safest and most sustainable way to end the lethal asbestos legacy is through prioritised, planned, and safe removal of ACMs from the built environment. In this article, we describe our consolidated asbestos register (AIRSystem); our custom risk assessment model that informs prioritised removal, and our work towards ending the lethal asbestos legacy. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

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Review

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23 pages, 2170 KiB

Open AccessReview

Australia’s Ongoing Challenge of Legacy Asbestos in the Built Environment: A Review of Contemporary Asbestos Exposure Risks

by Georgia Frangioudakis Khatib, Julia Collins, Pierina Otness, James Goode, Stacey Tomley, Peter Franklin and Justine Ross

Sustainability 2023, 15(15), 12071; https://doi.org/10.3390/su151512071 - 7 Aug 2023

Cited by 2 | Viewed by 3232

Abstract

Asbestos remains ubiquitous in the Australian built environment. Of the 13 million tonnes of asbestos products installed in earlier decades, an estimated 50% remain in situ today. Because of the extensive past use of asbestos, and the increasing age of these products, the potential for exposure to asbestos fibres in both indoor and outdoor environments remains high, even while the actual asbestos exposure levels are mostly very low. Sources of these exposures include disturbance of in situ asbestos-containing materials (ACMs), for example during renovations or following disaster events such as fires, cyclones and floods. Our understanding of the risk of asbestos-related disease arising from long-term low-level or background exposure, however, is poor. We provide the most up-to-date review of asbestos exposure risks currently affecting different groups of the Australian population and the settings in which this can manifest. From this, a need for low-level asbestos monitoring has emerged, and further research is required to address whether current exposure monitoring approaches are adequate. In addition, we make the case for proactive asbestos removal to reduce the risk of ongoing asbestos contamination and exposure due to deteriorating, disturbed or damaged ACMs, while improving long-term building sustainability, as well as the sustainability of limited resources. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

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29 pages, 3630 KiB

Open AccessReview

Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review

by Mohammad Abbasi, Sherif Mostafa, Abel Silva Vieira, Nicholas Patorniti and Rodney A. Stewart

Sustainability 2022, 14(13), 8068; https://doi.org/10.3390/su14138068 - 1 Jul 2022

Cited by 15 | Viewed by 4165

Abstract

Building roofing produced with asbestos-containing materials is a significant concern due to its detrimental health hazard implications. Efficiently locating asbestos roofing is essential to proactively mitigate and manage potential health risks from this legacy building material. Several studies utilised remote sensing imagery and machine learning-based image classification methods for mapping roofs with asbestos-containing materials. However, there has not yet been a critical review of classification methods conducted in order to provide coherent guidance on the use of different remote sensing images and classification processes. This paper critically reviews the latest works on mapping asbestos roofs to identify the challenges and discuss possible solutions for improving the mapping process. A peer review of studies addressing asbestos roof mapping published from 2012 to 2022 was conducted to synthesise and evaluate the input imagery types and classification methods. Then, the significant challenges in the mapping process were identified, and possible solutions were suggested to address the identified challenges. The results showed that hyperspectral imagery classification with traditional pixel-based classifiers caused large omission errors. Classifying very-high-resolution multispectral imagery by adopting object-based methods improved the accuracy results of ACM roof identification; however, non-optimal segmentation parameters, inadequate training data in supervised methods, and analyst subjectivity in rule-based classifications were reported as significant challenges. While only one study investigated convolutional neural networks for asbestos roof mapping, other applications of remote sensing demonstrated promising results using deep-learning-based models. This paper suggests further studies on utilising Mask R-CNN segmentation and 3D-CNN classification in the conventional approaches and developing end-to-end deep semantic classification models to map roofs with asbestos-containing materials. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

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Other

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10 pages, 239 KiB

Open AccessViewpoint

Managing Asbestos Waste Using Technological Alternatives to Approved Deep Burial Landfill Methods: An Australian Perspective

by Georgia Frangioudakis Khatib, Ian Hollins and Justine Ross

Sustainability 2023, 15(5), 4066; https://doi.org/10.3390/su15054066 - 23 Feb 2023

Cited by 5 | Viewed by 2569

Abstract

Given Australia’s significant and aged asbestos legacy, the long-term sustainability of effective and accessible asbestos waste management is a national priority of Australia’s Asbestos National Strategic Plan. The current policy for managing hazardous asbestos waste is via deep burial in landfill. Technological alternatives to approved deep burial landfill methods exist and could be considered innovative and sustainable additional options for managing asbestos waste, where these are proven viable, and where appropriate policy and regulatory changes are implemented. We present a summary of alternative asbestos waste management technologies and discuss issues influencing their potential application in the Australian context. Increasing the options for asbestos waste management in Australia may additionally facilitate the safe, planned removal of asbestos-containing materials (ACMs) from the built environment. Altogether, this will reduce the potential for exposure to asbestos fibres and work towards eliminating asbestos-related disease in Australia, therefore contributing towards achieving the overarching aim of Australia’s Asbestos National Strategic Plan. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

9 pages, 1844 KiB

Open AccessBrief Report

Asbestos Stocks and Flows Legacy in Australia

by Belinda Brown, Ian Hollins, Joe Pickin and Sally Donovan

Sustainability 2023, 15(3), 2282; https://doi.org/10.3390/su15032282 - 26 Jan 2023

Cited by 8 | Viewed by 2645

Abstract

Information about asbestos stocks and flows is paramount for effective legacy management, both for understanding potential asbestos exposure risks from the different product types remaining in the built environment and proactive resource planning for their safe decommissioning, removal and disposal. This paper provides an overview of the Australian Stocks and Flows Model for Asbestos, a national model that provides best estimates to examine asbestos legacy stocks remaining in the built environment and flows to waste, now and into the future in Australia. The model was updated in 2021 to reflect new information from literature and input from industry experts and includes a Monte Carlo analysis to better reflect the range in the value estimates, as well as allowing for input of data from asbestos removal programs. Australia’s total asbestos stocks peaked at approximately 11 million tonnes in the 1980s. Over 95% of stocks comprise asbestos cement products, such as wall sheeting and water pipes. Australia’s current remaining asbestos stocks in the built environment are estimated at 6.2 million tonnes, with just under half of total consumption estimated to have gone to landfill as waste. The model can continue to be used with updated information to help track how much of Australia’s hazardous asbestos legacy is remaining and by how much it is reducing. The model can also be used to test scenarios and implications for predicted development trends and waste infrastructure needs. It is a valuable resource to assist with sustainable planning across a range of government departments that are responsible for managing asbestos waste in Australia. Full article

(This article belongs to the Special Issue Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment)

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