Asbestos Sensing Review: Emerging technologies for asbestos in waste

The Challenge

The safe reuse and recycling of construction, demolition and recovered waste materials is constrained by the lack of rapid, accurate and practical methods for detecting asbestos contamination in real-world operating environments. Current approaches rely heavily on visual inspection by skilled personnel and laboratory-based analysis of collected samples, creating delays, increasing costs, and introducing uncertainty into waste management and resource recovery decisions. As a result, potentially reusable materials are often rejected as a precaution, while workers remain exposed to health risks associated with undetected asbestos-containing materials. The challenge is further complicated by the wide variety of asbestos and non-asbestos materials, the difficulty of distinguishing fibrous asbestos from chemically similar non-asbestos minerals, and the need to operate at high throughput in recycling facilities. To support safer waste processing and greater material recovery, there is a need for reliable real-time sensing technologies that can accurately identify asbestos contamination in diverse and dynamic waste streams while complementing existing regulatory standards.

The Solution

The report concludes that no single technology can yet deliver reliable, stand-alone real-time asbestos detection across all waste streams. Instead, it identifies a suite of complementary technologies that can be deployed together, with the most appropriate combination depending on the operating environment and stage of the waste processing chain. Key opportunities include AI-assisted microscopy and automated fibre counting to improve the speed and accuracy of laboratory and on-site analysis; near-infrared (NIR) spectroscopy for portable field detection, provided sufficient spectral resolution is achieved; and hyperspectral imaging for high-throughput screening and sorting of mixed waste materials on processing lines. The report also highlights promising developments in machine-learning-based image recognition, fluorescence-assisted fibre detection, and remote sensing approaches such as LiDAR. These technologies could be deployed at different stages of the waste recycling process, from demolition sites through to final quality assurance, providing earlier identification of asbestos risks, reducing unnecessary disposal of reusable materials, and improving worker safety.

The full report can be found here:

https://www.chiefscientist.nsw.gov.au/independent-reports/asbestos-management

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