Special Issue "Sustainable Practices for Asbestos Detection, Management and Disposal in the Built Environment"
Deadline for manuscript submissions: 30 June 2022.
Interests: environmental engineering; lifecycle sustainability; sustainable development; urban analytics; predictive modelling; artificial intelligence (AI); deep learning; asset management; environmental management; resource management; public health; asbestos detection.
Interests: public health; work health safety; risk assessment; regulatory and policy research; asbestos awareness; asbestos management; asbestos policy
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
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Interests: urban planning; sustainable development; urban analytics; predictive modelling; complex systems; asset management; environmental management; resource management; asbestos detection
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- abestos-containing materials (ACM)
- built environment
- environmental management
- predictive modelling
- artificial intelligence
- deep learning
- public health
- work health safety