Published on in Vol 1, No 1 (2017): Jan-Dec
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/8370, first published
.
![Identifying Asbestos-Containing Materials in Homes: Design and Development of the ACM Check Mobile Phone App Identifying Asbestos-Containing Materials in Homes: Design and Development of the ACM Check Mobile Phone App](https://asset.jmir.pub/assets/204778261143a8073d9987a9dd748517.png 480w,https://asset.jmir.pub/assets/204778261143a8073d9987a9dd748517.png 960w,https://asset.jmir.pub/assets/204778261143a8073d9987a9dd748517.png 1920w,https://asset.jmir.pub/assets/204778261143a8073d9987a9dd748517.png 2500w)
Journals
- Govorko M, Fritschi L, Reid A. Using a Mobile Phone App to Identify and Assess Remaining Stocks of In Situ Asbestos in Australian Residential Settings. International Journal of Environmental Research and Public Health 2019;16(24):4922 View
- Govorko M, Fritschi L, Reid A. Accuracy of a mobile app to identify suspect asbestos-containing material in Australian residential settings. Journal of Occupational and Environmental Hygiene 2018;15(8):598 View
- Wu P, Mjörnell K, Mangold M, Sandels C, Johansson T. A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories. Sustainability 2021;13(14):7836 View
- Bloise A, Miriello D. Distinguishing asbestos cement from fiber-reinforced cement through portable µ-Raman spectroscopy and portable X-ray fluorescence. Environmental Monitoring and Assessment 2022;194(10) View
- Wu P, Sandels C, Johansson T, Mangold M, Kristina Mjörnell . Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings. Resources, Conservation and Recycling 2023;199:107253 View