IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i12p2160-d120044.html
   My bibliography  Save this article

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

Author

Listed:
  • Juan Manuel Ponce Romero

    (School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK)

  • Stephen H. Hallett

    (School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK)

  • Simon Jude

    (School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK)

Abstract

The water utility sector is subject to stringent legislation, seeking to address both the evolution of practices within the chemical/pharmaceutical industry, and the safeguarding of environmental protection, and which is informed by stakeholder views. Growing public environmental awareness is balanced by fair apportionment of liability within-sector. This highly complex and dynamic context poses challenges for water utilities seeking to manage the diverse chemicals arising from disparate sources reaching Wastewater Treatment Plants, including residential, commercial, and industrial points of origin, and diffuse sources including agricultural and hard surface water run-off. Effluents contain broad ranges of organic and inorganic compounds, herbicides, pesticides, phosphorus, pharmaceuticals, and chemicals of emerging concern. These potential pollutants can be in dissolved form, or arise in association with organic matter, the associated risks posing significant environmental challenges. This paper examines how the adoption of new Big Data tools and computational technologies can offer great advantage to the water utility sector in addressing this challenge. Big Data approaches facilitate improved understanding and insight of these challenges, by industry, regulator, and public alike. We discuss how Big Data approaches can be used to improve the outputs of tools currently in use by the water industry, such as SAGIS (Source Apportionment GIS system), helping to reveal new relationships between chemicals, the environment, and human health, and in turn provide better understanding of contaminants in wastewater (origin, pathways, and persistence). We highlight how the sector can draw upon Big Data tools to add value to legacy datasets, such as the Chemicals Investigation Programme in the UK, combined with contemporary data sources, extending the lifespan of data, focusing monitoring strategies, and helping users adapt and plan more efficiently. Despite the relative maturity of the Big Data technology and adoption in many wider sectors, uptake within the water utility sector remains limited to date. By contrast with the extensive range of applications of Big Data in in other sectors, highlight is drawn to how improvements are required to achieve the full potential of this technology in the water utility industry.

Suggested Citation

  • Juan Manuel Ponce Romero & Stephen H. Hallett & Simon Jude, 2017. "Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector," Sustainability, MDPI, vol. 9(12), pages 1-19, November.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2160-:d:120044
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/12/2160/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/9/12/2160/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edward L. Glaeser & Andrew Hillis & Scott Duke Kominers & Michael Luca, 2016. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy," American Economic Review, American Economic Association, vol. 106(5), pages 114-118, May.
    2. Mohsen Bayati & Mark Braverman & Michael Gillam & Karen M Mack & George Ruiz & Mark S Smith & Eric Horvitz, 2014. "Data-Driven Decisions for Reducing Readmissions for Heart Failure: General Methodology and Case Study," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-9, October.
    3. Fytili, D. & Zabaniotou, A., 2008. "Utilization of sewage sludge in EU application of old and new methods--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 116-140, January.
    4. Beal, C.D. & Flynn, J., 2015. "Toward the digital water age: Survey and case studies of Australian water utility smart-metering programs," Utilities Policy, Elsevier, vol. 32(C), pages 29-37.
    5. Mark Gerstein, 2012. "ENCODE leads the way on big data," Nature, Nature, vol. 489(7415), pages 208-208, September.
    6. McKenna, Eoghan & Richardson, Ian & Thomson, Murray, 2012. "Smart meter data: Balancing consumer privacy concerns with legitimate applications," Energy Policy, Elsevier, vol. 41(C), pages 807-814.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cahn, Amir & Katz, David & Ghermandi, Andrea & Prevos, Peter, 2023. "Initiating data-as-a-service adoption in water utilities: A service design approach," Utilities Policy, Elsevier, vol. 84(C).
    2. Bhandari, Pratik & Creighton, Douglas & Gong, Jinzhe & Boyle, Carol & Law, Kris M.Y., 2023. "Evolution of cyber-physical-human water systems: Challenges and gaps," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    3. Luis Arismendy & Carlos Cárdenas & Diego Gómez & Aymer Maturana & Ricardo Mejía & Christian G. Quintero M., 2020. "Intelligent System for the Predictive Analysis of an Industrial Wastewater Treatment Process," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    4. Gabriel Koman & Martin Holubcik & Milan Kubina, 2018. "Descriptive representation about transformation of company by using current technologies and tools for analytical processing and evaluation of diverse data," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 19(1), pages 89-101.
    5. Cahn, Amir & Katz, David & Ghermandi, Andrea & Prevos, Peter, 2023. "Adoption of data-as-a-service by water and wastewater utilities," Utilities Policy, Elsevier, vol. 81(C).
    6. Jorge Alejandro Silva, 2022. "Implementation and Integration of Sustainability in the Water Industry: A Systematic Literature Review," Sustainability, MDPI, vol. 14(23), pages 1-28, November.
    7. Pedrini, Giulio & Cappiello, Giuseppe, 2022. "The impact of training on labour productivity in the European utilities sector: An empirical analysis," Utilities Policy, Elsevier, vol. 74(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Jui-Sheng & Gusti Ayu Novi Yutami, I, 2014. "Smart meter adoption and deployment strategy for residential buildings in Indonesia," Applied Energy, Elsevier, vol. 128(C), pages 336-349.
    2. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    3. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
    4. Xu, Xiaojing & Chen, Chien-fei & Zhu, Xiaojuan & Hu, Qinran, 2018. "Promoting acceptance of direct load control programs in the United States: Financial incentive versus control option," Energy, Elsevier, vol. 147(C), pages 1278-1287.
    5. Chamaret, Cécile & Steyer, Véronique & Mayer, Julie C., 2020. "“Hands off my meter!” when municipalities resist smart meters: Linking arguments and degrees of resistance," Energy Policy, Elsevier, vol. 144(C).
    6. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    7. Shahbeig, Hossein & Nosrati, Mohsen, 2020. "Pyrolysis of municipal sewage sludge for bioenergy production: Thermo-kinetic studies, evolved gas analysis, and techno-socio-economic assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    8. Sanchez, M.E. & Otero, M. & Gómez, X. & Morán, A., 2009. "Thermogravimetric kinetic analysis of the combustion of biowastes," Renewable Energy, Elsevier, vol. 34(6), pages 1622-1627.
    9. Maria R. Ibanez & Michael W. Toffel, 2020. "How Scheduling Can Bias Quality Assessment: Evidence from Food-Safety Inspections," Management Science, INFORMS, vol. 66(6), pages 2396-2416, June.
    10. Fiona Burlig & Christopher Knittel & David Rapson & Mar Reguant & Catherine Wolfram, 2020. "Machine Learning from Schools about Energy Efficiency," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(6), pages 1181-1217.
    11. Seongmin Kang & Changsang Cho & Ki-Hyun Kim & Eui-chan Jeon, 2018. "Fossil Carbon Fraction and Measuring Cycle for Sewage Sludge Waste Incineration," Sustainability, MDPI, vol. 10(8), pages 1-8, August.
    12. Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
    13. Zhou, Kaile & Yang, Shanlin, 2015. "A framework of service-oriented operation model of China׳s power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 719-725.
    14. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
    15. Allison Lassiter & Nicole Leonard, 2022. "A systematic review of municipal smart water for climate adaptation and mitigation," Environment and Planning B, , vol. 49(5), pages 1406-1430, June.
    16. Jiawen Zhang & Zhiyi Liang & Toru Matsumoto & Tiejia Zhang, 2022. "Environmental and Economic Implication of Implementation Scale of Sewage Sludge Recycling Systems Considering Carbon Trading Price," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    17. Maurer, Jona & Tschuch, Nicolai & Krebs, Stefan & Bhattacharya, Kankar & Cañizares, Claudio & Hohmann, Sören, 2023. "Toward transactive control of coupled electric power and district heating networks," Applied Energy, Elsevier, vol. 332(C).
    18. Bidart, Christian & Fröhling, Magnus & Schultmann, Frank, 2014. "Electricity and substitute natural gas generation from the conversion of wastewater treatment plant sludge," Applied Energy, Elsevier, vol. 113(C), pages 404-413.
    19. Ioanna-M. Chatzigeorgiou & Christos Diou & Kyriakos C. Chatzidimitriou & Georgios T. Andreou, 2021. "Demand Response Alert Service Based on Appliance Modeling," Energies, MDPI, vol. 14(10), pages 1-15, May.
    20. Wang, Liping & Chang, Yuzhi & Li, Aimin, 2019. "Hydrothermal carbonization for energy-efficient processing of sewage sludge: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 423-440.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2160-:d:120044. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.