Volume 17 Issue 2
Jun.  2024
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Minori Uchimiya. 2024: Big data-driven water research towards metaverse Minori Uchimiya. Water Science and Engineering, 17(2): 101-107. doi: 10.1016/j.wse.2024.02.001
Citation: Minori Uchimiya. 2024: Big data-driven water research towards metaverse Minori Uchimiya. Water Science and Engineering, 17(2): 101-107. doi: 10.1016/j.wse.2024.02.001

Big data-driven water research towards metaverse Minori Uchimiya

doi: 10.1016/j.wse.2024.02.001
  • Received Date: 2023-04-20
  • Accepted Date: 2024-02-05
  • Available Online: 2024-05-14
  • Although big data is publicly available on water quality parameters, virtual simulation has not yet been adequately adapted in environmental chemistry research. Digital twin is different from conventional geospatial modeling approaches and is particularly useful when systematic laboratory/field experiment is not realistic (e.g., climate impact and water-related environmental catastrophe) or difficult to design and monitor in a real time (e.g., pollutant and nutrient cycles in estuaries, soils, and sediments). Data-driven water research could realize early warning and disaster readiness simulations for diverse environmental scenarios, including drinking water contamination.


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  • Azzan, H., Rajagopalan, A.K., L'Hermitte, A., Pini, R., Petit, C., 2022. Simultaneous estimation of gas adsorption equilibria and kinetics of individual shaped adsorbents. Chem. Mater. 34(15), 6671-6686. https://doi.org/10.1021/acs.chemmater.2c01567.
    Bansal, M., Yang, J., Karan, C., Menden, M.P., Costello, J.C., Tang, H., Xiao, G., Li, Y., Allen, J., Zhong, R. et al., 2014. A community computational challenge to predict the activity of pairs of compounds. Nat. Biotechnol 32, 1213-1222. https://doi.org/10.1038/nbt.3052.
    Benes, B., Guan, K., Lang, M., Long, S.P., Lynch, J.P., Marshall-Colon, A., Peng, B., Schnable, J., Sweetlove, L.J., Turk, M.J., 2020. Multiscale computational models can guide experimentation and targeted measurements for crop improvement. Plant J. 103(1), 21-31. https://doi.org/10.1111/tpj.14722.
    Bhandari, M., Chang, A., Jung, J., Ibrahim, A.M.H., Rudd, J.C., Baker, S., Landivar, J., Liu, S., Landivar, J., 2023. Unmanned aerial system-based high-throughput phenotyping for plant breeding. Plant Phenome J. 6(1), e20058. https://doi.org/10.1002/ppj2.20058.
    Burnette, M., Rohde, G.S., Fahlgren, N., Sagan, V., Sidike, P., Kooper, R., Terstriep, J.A., Mockler, T., Andrade-Sanchez, P., Ward, R., Maloney, J.D., et al., 2018. TERRA-REF data processing infrastructure. In:Proceedings of the Practice and Experience on Advanced Research Computing. PEARC Pittsburgh. https://doi.org/10.1145/3219104.3219152.
    CDC, 2022. Waterborne Disease&Outbreak Surveillance Reporting, National Wastewater Surveillance System, Centers for Disease Control and Prevention, National Center for Emerging and Zoonotic Infectious Diseases (NCEZID), Division of Foodborne, Waterborne, and Environmental Diseases (DFWED). CDC, Atlanta. https://covid.cdc.gov/covid-data-tracker/#wastewater-surveillance.
    Chen, W., Zhao, Y., You, T., Wang, H., Yang, Y., Yang, K., 2021. Automatic detection of scattered garbage regions using small unmanned aerial vehicle low-altitude remote sensing images for high-altitude natural reserve environmental protection. Environ. Sci. Technol. 55(6), 3604-3611. https://doi.org/10.1021/acs.est.0c04068.
    Crain, J., Mondal, S., Rutkoski, J., Singh, R.P., Poland, J., 2018. Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. The Plant Genome 11(1), 170043. https://doi.org/10.3835/plantgenome2017.05.0043.
    Deguchi, A., Hirai, C., Matsuoka, H., Nakano, T., Oshima, K., Tai, M., Tani, S., 2020. What is Society 5.0?In:Society 5.0:A People-centric Super-smart Society. Springer, Singapore, pp. 1-23. https://doi.org/10.1007/978-981-15-2989-4_9.
    Douglas, G.M., Maffei, V.J., Zaneveld, J.R., Yurgel, S.N., Brown, J.R., Taylor, C.M., Huttenhower, C., Langille, M.G.I., 2020. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol 38, 685-688. https://doi.org/10.1038/s41587-020-0548-6.
    Earl, J.P., Adappa, N.D., Krol, J., Bhat, A.S., Balashov, S., Ehrlich, R.L., Palmer, J.N., Workman, A.D., Blasetti, M., Sen, B. et al., 2018. Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes. Microbiome 6, 190. https://doi.org/10.1186/s40168-018-0569-2.
    Fichot, C.G., Downing, B.D., Bergamaschi, B.A., Windham-Myers, L., Marvin-DiPasquale, M., Thompson, D.R., Gierach, M.M., 2016. High-resolution remote sensing of water quality in the San Francisco Bay-Delta Estuary. Environ. Sci. Technol. 50(2), 573-583. https://doi.org/10.1021/acs.est.5b03518.
    Fierer, N., 2017. Embracing the unknown:Disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579-590. https://doi.org/10.1038/nrmicro.2017.87.
    Fisher, J.B., Lee, B., Purdy, A.J., Halverson, G.H., Dohlen, M.B., Cawse-Nicholson, K., Wang, A., Anderson, R.G., Aragon, B., Arain, M.A. et al., 2020. ECOSTRESS:NASA's next generation mission to measure evapotranspiration from the international space station. Water Resour. Res. 56(4), e2019WR026058. https://doi.org/10.1029/2019WR026058.
    Flamholz, A.I., Newman, D.K., 2022. Microbial communities:The metabolic rate is the trait. Curr. Biol. 32, R215-R218. https://doi.org/10.1016/j.cub.2022.02.002.
    Gadepally, K.C., Dhal, S.B., Bhandari, M., Landivar, J., Kalafatis, S., Nowka, K., 2023. A deep transfer learning based approach for forecasting spatio-temporal features to maximize yield in cotton crops. In:Proceedings of 2023 57th Annual Conference on Information Sciences and Systems (CISS). CISS, Baltimore. https://doi.org/10.1109/CISS56502.2023.10089748.
    Gai, J., Xiang, L., Tang, L., 2021. Using a depth camera for crop row detection and mapping for under-canopy navigation of agricultural robotic vehicle. Comput. Electron. Agric. 188, 106301. https://doi.org/10.1016/j.compag.2021.106301.
    Gamon, J.A., Field, C.B., Bilger, W., Bjorkman, O., Fredeen, A.L., Penuelas, J., 1990. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 85, 1-7. https://doi.org/10.1007/BF00317336.
    Goldford, J.E., Lu, N., Bajic, D., Estrela, S., Tikhonov, M., Sanchez-Gorostiaga, A., Segre, D., Mehta, P., Sanchez, A., 2018. Emergent simplicity in microbial community assembly. Science 361, 469-474. https://doi.org/10.1126/science.aat1168.
    Gowda, K., Ping, D., Mani, M., Kuehn, S., 2022. Genomic structure predicts metabolite dynamics in microbial communities. Cell 185(3), 530-546. https://doi.org/10.1016/j.cell.2021.12.036.
    Huang, Y., Wang, X., Xiang, W., Wang, T., Otis, C., Sarge, L., Lei, Y., Li, B., 2022. Forward-looking roadmaps for long-term continuous water quality monitoring:Bottlenecks, innovations, and prospects in a critical review. Environ. Sci. Technol. 56(9), 5334-5354. https://doi.org/10.1021/acs.est.1c07857.
    Kanehisa, M., Sato, Y., Morishima, K., 2016. BlastKOALA and GhostKOALA:KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428(4), 726-731. https://doi.org/10.1016/j.jmb.2015.11.006.
    Kim, Y., Evans, R.G., Iversen, W.M., 2008. Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE Trans. Instrum. Meas. 57(7), 1379-1387. https://doi.org/10.1109/TIM.2008.917198.
    Kiruri, L.W., Dellinger, B., Lomnicki, S., 2013. Tar balls from deep water horizon oil spill:Environmentally persistent free radicals (EPFR) formation during crude weathering. Environ. Sci. Technol. 47(9), 4220-4226. https://doi.org/10.1021/es305157w.
    Klapper, L., McKnight, D.M., Fulton, J.R., Blunt-Harris, E.L., Nevin, K.P., Lovley, D.R., Hatcher, P.G., 2002. Fulvic acid oxidation state detection using fluorescence spectroscopy. Environ. Sci. Technol. 36(14), 3170-3175. https://doi.org/10.1021/es0109702.
    Knapp-Wilson, J., Bohn Reckziegel, R., Thapa Magar, S., Bucksch, A., Chavez, D.J., 2023. Three-dimensional phenotyping of peach tree-crown architecture utilizing terrestrial laser scanning. Plant Phenome J. 6(1), e20073. https://doi.org/10.1002/ppj2.20073.
    Langille, M.G.I., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Vega Thurber, R.L., Knight, R. et al., 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol 31, 814-821. https://doi.org/10.1038/nbt.2676.
    Li, X., Luo, J., Li, Y., Wang, W., Hong, W., Liu, M., Li, X., Lv, Z., 2022. Application of effective water-energy management based on digital twins technology in sustainable cities construction. Sustain. Cities Soc. 87, 104241. https://doi.org/10.1016/j.scs.2022.104241.
    Lodge, J.W., Dansie, A.P., Johnson, F., 2023. A review of globally available data sources for modelling the Water-Energy-Food Nexus. Earth-Sci. Rev. 243, 104485. https://doi.org/10.1016/j.earscirev.2023.104485.
    Lovett, G.M., Burns, D.A., Driscoll, C.T., Jenkins, J.C., Mitchell, M.J., Rustad, L., Shanley, J.B., Likens, G.E., Haeuber, R., 2007. Who needs environmental monitoring?Front. Ecol. Environ. 5(5), 253-260. https://doi.org/10.1890/1540-9295(2007)5[253:WNEM]2.0.CO;2.
    Lovley, D.R., Coates, J.D., Blunt-Harris, E.L., Phillips, E.J.P., Woodward, J.C., 1996. Humic substances as electron acceptors for microbial respiration. Nature 382, 445-448. https://doi.org/10.1038/382445a0.
    Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., Fritschi, F.B., 2020. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens. Environ. 237, 111599. https://doi.org/10.1016/j.rse.2019.111599.
    Marshall-Colon, A., Long, S.P., Allen, D.K., Allen, G., Beard, D.A., Benes, B., von Caemmerer, S., Christensen, A.J., Cox, D.J., Hart, J.C. et al., 2017. Crops in silico:Generating virtual crops using an integrative and multi-scale modeling platform. Front. Plant Sci. 8, 786. https://doi.org/10.3389/fpls.2017.00786.
    National Institute of Advanced Industrial Science and Technology (AIST), 2022. 3DDBViewer (Freely Accessible Digital Twin Platform for Archeological Sites around Japan). AIST, Nara. https://gsrt.digiarc.aist.go.jp/nabunken_aist/index.html.
    NEC, 2022. Business Column:Digital Twin in Manufacturing. NEC Solution Innovations, Ltd., Tokyo. https://www.nec-solutioninnovators.co.jp/sp/contents/column/20220701_digital-twin.html.
    Nguyen, N.-P., Warnow, T., Pop, M., White, B., 2016. A perspective on 16S rRNA operational taxonomic unit clustering using sequence similarity. npj Biofilms Microbiomes 2, 16004. https://doi.org/10.1038/npjbiofilms.2016.4.
    NSF, 2022. Innovations at the Nexus of Food, Energy and Water Systems (INFEWS). NSF, Alexandria. https://www.nsf.gov/pubs/2018/nsf18545/nsf18545.htm.
    Olivares-Amaya, R., Amador-Bedolla, C., Hachmann, J., Atahan-Evrenk, S., Sanchez-Carrera, R.S., Vogt, L., Aspuru-Guzik, A., 2011. Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics. Energy Environ. Sci. 4, 4849-4861. https://doi.org/10.1039/C1EE02056K.
    Omasa, K., Qiu, G.Y., Watanuki, K., Yoshimi, K., Akiyama, Y., 2003. Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees. Environ. Sci. Technol. 37(6), 1198-1201. https://doi.org/10.1021/es0259887.
    Perlinger, J.A., Angst, W., Schwarzenbach, R.P., 1996. Kinetics of the reduction of hexachloroethane by juglone in solutions containing hydrogen sulfide. Environ. Sci. Technol. 30(12), 3408-3417. https://doi.org/10.1021/es950759o.
    Pitts, J., Gopal, S., Ma, Y., Koch, M., Boumans, R.M., Kaufman, L., 2020. Leveraging big data and analytics to improve food, energy, and water system sustainability. Front. Big Data. 3, 13. https://doi.org/10.3389/fdata.2020.00013.
    Postma, J.A., Kuppe, C., Owen, M.R., Mellor, N., Griffiths, M., Bennett, M.J., Lynch, J.P., Watt, M., 2017. OpenSimRoot:Widening the scope and application of root architectural models. New Phytologist 215(3), 1274-1286. https://doi.org/10.1111/nph.14641.
    Pylianidis, C., Osinga, S., Athanasiadis, I.N., 2021. Introducing digital twins to agriculture. Comput. Electron. Agric. 184, 105942. https://doi.org/10.1016/j.compag.2020.105942.
    Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat, 2019. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195-204. https://doi.org/10.1038/s41586-019-0912-1.
    Saito, K., Rutherford, A.W., Ishikita, H., 2013. Mechanism of proton-coupled quinone reduction in Photosystem II. Proc. Natl. Acad. Sci. USA 110(3), 954-959. https://doi.org/10.1073/pnas.121295711.
    Smith, R.A., Alexander, R.B., Wolman, M.G., 1987. Water-Quality Trends in the Nation's Rivers. Science 235, 1607-1615. https://doi.org/10.1126/science.235.4796.1607.
    Sookhak Lari, K., Davis, G.B., Rayner, J.L., 2022. Towards a digital twin for characterising natural source zone depletion:A feasibility study based on the Bemidji site. Water Research 208, 117853. https://doi.org/10.1016/j.watres.2021.117853.
    Spectee, 2022. Spectee, AI for Real Time Flood Simulation. Spectee, Tokyo (in Japanese). https://spectee.co.jp/report/webinar_20220929/.
    Stockwell, C.E., Bela, M.M., Coggon, M.M., Gkatzelis, G.I., Wiggins, E., Gargulinski, E.M., Shingler, T., Fenn, M., Griffin, D., Holmes, C.D., et al., 2022. Airborne emission rate measurements validate remote sensing observations and emission inventories of western U.S. wildfires. Environ. Sci. Technol. 56(12), 7564-7577. https://doi.org/10.1021/acs.est.1c07121.
    Sun, Y., Fu, R., Dickinson, R., Joiner, J., Frankenberg, C., Gu, L., Xia, Y., Fernando, N., 2015. Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence:Insights from two contrasting extreme events. J. Geophys. Res. Biogeosci. 120(11), 2427-2440. https://doi.org/10.1002/2015JG003150.
    Sun, Y., Frankenberg, C., Wood, J.D., Schimel, D.S., Jung, M., Guanter, L., Drewry, D.T., Verma, M., Porcar-Castell, A., Griffis, T.J. et al., 2017. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747. https://doi.org/10.1126/science.aam5747.
    Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B., 2020. Google Earth Engine for geo-big data applications:A meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152-170. https://doi.org/10.1016/j.isprsjprs.2020.04.001.
    Uchimiya, M., Arai, M., Masunaga, S., 2007. Fingerprinting localized dioxin contamination:Ichihara Anchorage case. Environ. Sci. Technol. 41(11), 3864-3870. https://doi.org/10.1021/es062998p.
    Uchimiya, M., Stone, A.T., 2009. Reversible redox chemistry of quinones:Impact on biogeochemical cycles. Chemosphere 77, 451-458. https://doi.org/10.1016/j.chemosphere.2009.07.025.
    Uchimiya, M., 2020. Proton-coupled electron transfers of defense phytochemicals in Sorghum (Sorghum bicolor (L.) Moench). J. Agric. Food Chem. 68(46), 12978-12983. https://doi.org/10.1021/acs.jafc.9b07816.
    Uchimiya, M., Bannon, D., Nakanishi, H., McBride, M.B., Williams, M.A., Yoshihara, T., 2020. Chemical speciation, plant uptake, and toxicity of heavy metals in agricultural soils. J. Agric. Food Chem. 68(46), 12856-12869. https://doi.org/10.1021/acs.jafc.0c00183.
    University of Copenhagen, 2022. Public Data Sets for Multivariate Data Analysis. University of Copenhagen, Copenhagen. http://www.models.life.ku.dk/datasets.
    USDA-NRCS, 2022. Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. USDA, Washington DC. https://websoilsurvey.sc.egov.usda.gov/.
    USGS, 2022. USGS Water Data for the Nation. USGS, Reston. https://dashboard.waterdata.usgs.gov.
    Van Henten, E.J., Hemming, J., Van Tuijl, B.A.J., Kornet, J.G., Meuleman, J., Bontsema, J., Van Os, E.A., 2002. An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots 13, 241-258. https://doi.org/10.1023/A:1020568125418.
    Watanabe, K., Guo, W., Arai, K., Takanashi, H., Kajiya-Kanegae, H., Kobayashi, M., Yano, K., Tokunaga, T., Fujiwara, T., Tsutsumi, N. et al., 2017. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front. Plant Sci. 8, 421. https://doi.org/10.3389/fpls.2017.00421.
    White, G., Zink, A., Codeca, L., Clarke, S., 2021. A digital twin smart city for citizen feedback. Cities 110, 103064. ttps://doi.org/10.1016/j.cities.2020.103064.
    Xie, C., Li, C., Ding, X., Jiang, R., Sung, S., 2021. Chemistry on the cloud:From wet labs to web labs. J. Chem. Educ. 98(9), 2840-2847. https://doi.org/10.1021/acs.jchemed.1c00585.
    Yokohama National University, 2022. The Profile Database of Environmental Contaminants (PDEC) for Environmental Forensics, Remediation and Risk Management. Yokohama National University, Yokohama. http://risk.kan.ynu.ac.jp/21coe_database/index_e.html.
    Young, D.W., Bender, A., Hoyt, J., McWhinnie, E., Chirn, G.-W., Tao, C.Y., Tallarico, J.A., Labow, M., Jenkins, J.L., Mitchison, T.J. et al., 2008. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol. 4, 59-68. https://doi.org/10.1038/nchembio.2007.53.
    Yuan, S., Tang, H., Fu, L.J., Tan, J.L., Govindjee, G., Guo, Y., 2022. An open Internet of Things (IoT)-based framework for feedback control of photosynthetic activities. Photosynthetica 60, 77-85. https://doi.org/10.32615/ps.2021.066.
    Zhu, J.-J., Borzooei, S., Sun, J., Ren, Z.J., 2022. Deep learning optimization for soft sensing of hard-to-measure wastewater key variables. ACS ES&T Engineering 2(7), 1341-1355. https://doi.org/10.1021/acsestengg.1c00469.
    Zwanenburg, A., Vallieres, M., Abdalah, M.A., Aerts, H.J.W.L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R.J., Boellaard, R. et al., 2020. The image biomarker standardization initiative:Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295, 328-338. https://doi.org/10.1148/radiol.2020191145.
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