Volume 17 Issue 3
Sep.  2024
Turn off MathJax
Article Contents
Jacky Cao, Xiaoli Liu, Xiang Su, Jonas Eilertsen Hædahl, Thomas Berg Fjellestad, Donjete Haziri, André Hoang-An Vu, Jari Koskiaho, Satu Maaria Karjalainen, Anna-kaisa Ronkanen, Sasu Tarkoma, Pan Hui. 2024: Head-mounted display-based augmented reality for water quality visualisation. Water Science and Engineering, 17(3): 236-248. doi: 10.1016/j.wse.2023.12.002
Citation: Jacky Cao, Xiaoli Liu, Xiang Su, Jonas Eilertsen Hædahl, Thomas Berg Fjellestad, Donjete Haziri, André Hoang-An Vu, Jari Koskiaho, Satu Maaria Karjalainen, Anna-kaisa Ronkanen, Sasu Tarkoma, Pan Hui. 2024: Head-mounted display-based augmented reality for water quality visualisation. Water Science and Engineering, 17(3): 236-248. doi: 10.1016/j.wse.2023.12.002

Head-mounted display-based augmented reality for water quality visualisation

doi: 10.1016/j.wse.2023.12.002
Funds:

This work was supported by the Freshwater Competence Centre, Academy of Finland (Decision No. 345008) and the Nordic University Cooperation on Edge Intelligence (Grant No. 168043).

  • Received Date: 2023-02-06
  • Accepted Date: 2023-11-30
  • Available Online: 2024-08-24
  • Water covers most of the Earth’s surface and is nowhere near a good ecological or recreational state in many areas of the world. Moreover, only a small fraction of the water is potable. As climate change-induced extreme weather events become ever more prevalent, more and more issues arise, such as worsening water quality problems. Therefore, protecting invaluable and useable drinking water is critical. Environmental agencies must continuously check water sources to determine whether they are in a good or healthy state regarding pollutant levels and ecological status. The currently available tools are better suited for stationary laboratory use, and domain specialists lack suitable tools for on-site visualisation and interactive exploration of environmental data. Meanwhile, data collection for laboratory analysis requires substantial time and significant effort. We, therefore, developed an augmented reality system with a Microsoft HoloLens 2 device to explore the visualisation of water quality and status in situ. The developed prototype visualises geo-referenced sensor measurements incorporated into the perspective of the surroundings. Any users interested in water bodies’ conditions can quickly examine and retrieve an overview of water body status using augmented reality and then take necessary steps to address the current situation.

     

  • loading
  • Team Aventior, 2020. Object Detection of Water Bodies. Aventior.
    Azuma, R.T., 1997. A survey of augmented reality. Presence Teleoperators Virtual Environ. 6(4), 355-385. https://doi.org/10.1162/pres.1997.6.4.355.
    Biswas, A.K., Tortajada, C., 2019. Water quality management: A globally neglected issue. Int. J. Water Resour. Dev. 35, 913-916. https://doi.org/10.1080/07900627.2019.1670506.
    Bloschl, G., Bierkens, M.F.P., Chambel, A., Cudennec, C., Destouni, G., Fiori, A., Kirchner, JW., McDonnell, J.J., Savenije, H.H.G., Sivapalan, M., et al., 2019. Twenty-three unsolved problems in hydrology (UPH) - a community perspective. Hydrol. Sci. J. 64(10), 1141-1158. https://doi.org/10.1080/02626667.2019.1620507.
    Cham, H., Malek, S., Milow, P., Ramli, M.R., 2020. Web-based system for visualisation of water quality index. Life 13(1), 426-432. https://doi.org/10.1080/26895293.2020.1788998.
    Destouni, G., Jaramillo, F., Prieto, C., 2013. Hydroclimatic shifts driven by human water use for food and energy production. Nat. Clim. Change 3, 213-217. https://doi.org/10.1038/nclimate1719.
    Doughty, M., Ghugre, N.R., 2022. Head-mounted display-based augmented reality for image-guided media delivery to the heart: A preliminary investigation of perceptual accuracy. J. Imag. 8(2), 33. https://doi.org/10.3390/jimaging8020033.
    Frazier, P.S., Page, K.J., 2000. Water body detection and delineation with Landsat TM data. Photogramm. Eng. Rem. Sens. 66(12), 1461-1468.
    Goldsmith, D., Liarokapis, F., Malone, G., Kemp, J., 2008. Augmented reality environmental monitoring using wireless sensor networks. In: Proceedings of the 2008 12th International Conference Information Visualisation. IEEE, London, pp., 539-544. https://doi.org/10.1109/iv.2008.72.
    Google, 2023. Get Started | Maps Static API | Google Developers. Google, Mountain View. https://developers.google.com/maps/documentation/maps-static/start.
    Gray, A., Robertson, C., Feick, R., 2021. CWDAT-An open-source tool for the visualization and analysis of community-generated water quality Data. ISPRS Int. J. Geo-Inf. 10(4), 207. https://doi.org/10.3390/ijgi10040207.
    Gul, M.U., Paul, A., Manimurugan, S., Chehri, A., 2023. Hydrotropism: Understanding the impact of water on plant movement and adaptation. Water 15(3), 567. https://doi.org/10.3390/w15030567.
    Haynes, P., Hehl-Lange, S., Lange, E., 2018. Mobile augmented reality for flood visualisation. Environ. Model. Software 109, 380-389. https://doi.org/10.1016/j.envsoft.2018.05.012.
    Hirsh, M.B., Baron, J.L., Mietzner, S.M., Rihs, J.D., Yassin, M.H., Stout, J.E., 2020. Evaluation of recommended water sample collection methods and the impact of holding time on legionella recovery and variability from healthcare building water systems. Microorganisms 8(11), 1770. https://doi.org/10.3390/microorganisms8111770.
    Horsburgh, J.S., Reeder, S.L., Jones, A.S., Meline, J., 2015. Open source software for visualization and quality control of continuous hydrologic and water quality sensor data. Environ. Model. Software 70, 32-44. https://doi.org/10.1016/j.envsoft.2015.04.002.
    Kawagoshi, Y., Suenaga, Y., Chi, N.L., Hama, T., Ito, H., Duc, L.V., 2019. Understanding nitrate contamination based on the relationship between changes in groundwater levels and changes in water quality with precipitation fluctuations. Sci. Total Environ. 657, 146-153. https://doi.org/10.1016/j.scitotenv.2018.12.041.
    Kritzberg, E.S., Hasselquist, E.M., Skerlep, M., Lofgren, S., Olsson, O., Stadmark, J., Valinia, S., Hansson, L.A., Laudon, H., 2020. Browning of freshwaters: Consequences to ecosystem services, underlying drivers, and potential mitigation measures. Ambio 49, 375-390. https://doi.org/10.1007/s13280-019-01227-5.
    Li, W., Zhang, W., Li, Z., Wang, Y., Chen, H., Gao, H., Zhou, Z., Hao, J., Li, C., Wu, X., 2022. A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model. Eur. J. Rem. Sens 55(1), 303-312. https://doi.org/10.1080/22797254.2022.2062054.
    Lindqvist, A.N., Fornell, R., Prade, T., Tufvesson, L., Khalil, S., Kopainsky, B., 2021. Human-water dynamics and their role for seasonal water scarcity - a case study. Water Resour. Manag. 35, 3043-3061. https://doi.org/10.1007/s11269-021-02819-1.
    Lloyd, C., Freer, J., Johnes, P., Collins, A., 2016. Using hysteresis analysis of high-resolution water quality monitoring data, including uncertainty, to infer controls on nutrient and sediment transfer in catchments. Sci. Total Environ. 543, 388-404. https://doi.org/10.1016/j.scitotenv.2015.11.028.
    Mach, P., Becvar, Z., 2017. Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. 19(3), 1628-1656. https://doi.org/10.1109/comst.2017.2682318.
    Manz, B.J., Rodriguez, J.P., Maksimovic, C., McIntyre, N., 2013. Impact of rainfall temporal resolution on urban water quality modelling performance and uncertainties. Water Sci. Technol. 68(1), 68-75. https://doi.org/10.2166/wst.2013.224.
    Marttila, H., Lepisto, A., Tolvanen, A., Bechmann, M., Kyllmar, K., Juutinen, A., Wenng, H., Skarboevik, E., Futter, M., Kortelainen, P., et al., 2020. Potential impacts of a future Nordic bioeconomy on surface water quality. Ambio 49, 1722-1735. https://doi.org/10.1007/s13280-020-01355-3.
    Maure, E.R., Terauchi, G., Ishizaka, J., Clinton, N., DeWitt, M., 2021. Globally consistent assessment of coastal eutrophication. Nat. Commun. 12, 6142. https://doi.org/10.1038/s41467-021-26391-9.
    Microsoft, 2022a. Color, Light, and Materials - Mixed Reality. Microsoft. https://learn.microsoft.com/en-us/windows/mixed-reality/design/color-light-and-materials.
    Microsoft, 2022b. HoloLens 2 - Overview, Features, and Specs | Microsoft HoloLens. Microsoft. https://www.microsoft.com/en-us/hololens/hardware.
    Microsoft, 2022c. MRTK2 - Unity Developer Documentation - MRTK 2. Microsoft. https://learn.microsoft.com/en-us/windows/mixed-eality/mrtk-unity/mrtk2/.
    Milgram, P., Kishino, F., 1994. A taxonomy of mixed reality visual displays. IEICE Trans. Info Syst. E77-D (12), 1321-1329.
    Miller, L., 2018. Recognition and Outlining a Lake with Opencv and python. GitHub. https://github.com/lmiller1990/python-opencv-lake-recognition.
    Mirauda, D., Erra, U., Agatiello, R., Cerverizzo, M., 2017. Applications of mobile augmented reality to water resources management. Water 9(9), 699. https://doi.org/10.3390/w9090699.
    Mishra, V., Limaye, A.S., Muench, R.E., Cherrington, E.A., Markert, K.N., 2020. Evaluating the performance of high-resolution satellite imagery in detecting ephemeral water bodies over West Africa. Int. J. Appl. Earth Obs. Geoinf. 93, 102218. https://doi.org/10.1016/j.jag.2020.102218.
    Nachimuthu, G., Watkins, M.D., Hulugalle, N., Finlay, L.A., 2020. Storage and initial processing of water samples for organic carbon analysis in runoff. MethodsX 7, 101012. https://doi.org/10.1016/j.mex.2020.101012.
    Nayebi, F., Desharnais, J.M., Abran, A., 2012. The state of the art of mobile application usability evaluation. In: Proceedings of the 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). CCECE, Montreal, pp. 1-4. https://doi.org/10.1109/ccece.2012.6334930.
    Nicolaidis Lindqvist, A., Fornell, R., Prade, T., Khalil, S., Tufvesson, L., Kopainsky, B., 2022. Impacts of future climate on local water supply and demand - a socio-hydrological case study in the Nordic region. J. Hydrol.: Reg. Stud. 41, 101066. https://doi.org/10.1016/j.ejrh.2022.101066.
    Park, J., Kim, K.T., Lee, W.H., 2020. Recent advances in information and communications technology (ICT) and sensor technology for monitoring water quality. Water 12(2), 510. https://doi.org/10.3390/w12020510.
    Peterson, K.T., Sagan, V., Sloan, J.J., 2020. Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GIScience Remote Sens. 57(4), 510-525. https://doi.org/10.1080/15481603.2020.1738061.
    Pokric, B., Krco, S., Drajic, D., Pokric, M., Rajs, V., Mihajlovic, Z., Knezevic, P., Jovanovic, D., 2015. Augmented reality enabled IoT services for environmental monitoring utilising serious gaming concept. J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. 6(1), 37-55.
    Pope, M.L., Bussen, M., Feige, M.A., Shadix, L., Gonder, S., Rodgers, C., Chambers, Y., Pulz, J., Miller, K., Connell, K., Standridge, J., 2003. Assessment of the effects of holding time and temperature on Escherichia coli densities in surface water samples. Appl. Environ. Microbiol. 69(10), 6201-6207. https://doi.org/10.1128/AEM.69.10.6201-6207.2003.
    Rode, M., Wade, A.J., Cohen, M.J., Hensley, R.T., Bowes, M.J., Kirchner, J.W., Arhonditsis, G.B., Jordan, P., Kronvang, B., Halliday, S.J., et al., 2016. Sensors in the stream: The high-frequency wave of the present. Environ. Sci. Technol. 50, 10297-10307. https://doi.org/10.1021/acs.est.6b02155.
    Sarp, G., Ozcelik, M., 2017. Water body extraction and change detection using time series: A case study of lake Burdur, Turkey. J. Taibah Univ. Sci. 11(3), 381-391. https://doi.org/10.1016/j.jtusci.2016.04.005.
    Schwarzenbach, R.P., Egli, T., Hofstetter, T.B., Von Gunten, U., Wehrli, B., 2010. Global water pollution and human health. Annu. Rev. Environ. Resour. 35, 109-136. https://doi.org/10.1146/annurev-environ-100809-125342.
    Selvakumar, A., Borst, M., Boner, M., Mallon, P., 2004. Effects of sample holding time on concentrations of microorganisms in water samples. Water Environ. Res. 76(1), 67-72.
    Speicher, M., Hall, B.D., Nebeling, M., 2019. What is mixed reality? In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, pp. 1-15. https://doi.org/10.1145/3290605.3300767.
    Veas, E., Grasset, R., Ferencik, I., Grunewald, T., Schmalstieg, D., 2013. Mobile augmented reality for environmental monitoring. Personal Ubiquitous Comput. 17, 1515-1531. https://doi.org/10.1007/s00779-012-0597-z.
    Xu, H., Berres, A., Liu, Y., Allen-Dumas, M.R., Sanyal, J., 2022. An overview of visualization and visual analytics applications in water resources management. Environ. Model. Software 153, 105396. https://doi.org/10.1016/j.envsoft.2022.105396.
    Yao, J., Sun, S., Zhai, H., Feger, K.H., Zhang, L., Tang, X., Li, G., Wang, Q., 2022. Dynamic monitoring of the largest reservoir in north China based on multi-source satellite remote sensing from 2013 to 2022: Water area, water level, water storage and water quality. Ecol. Indicat. 144, 109470. .
    Zhang, D., Adipat, B., 2005. Challenges, methodologies, and issues in the usability testing of mobile applications. Int. J. Hum. Comput. Interact. 18(3), 293-308. https://doi.org/10.1207/s15327590ijhc1803_3.
    Zhang, Q., Miao, L., Wang, H., Hou, J., Li, Y., 2020. How rapid urbanization drives deteriorating groundwater quality in a provincial capital of China. Pol. J. Environ. Stud. 29(1), 441-450. https://doi.org/10.15244/pjoes/103359.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(2)

    Article Metrics

    Article views (37) PDF downloads(0) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return