data upon which the whole system is built. As smart systems are expected to acquire
information not only from traditional monitoring locations such as water treatment
plants, but actually from the entire water system, there is a requirement for durable,
autonomous, networked and affordable sensor technology. Optical sensor systems
provide a good basis for this sensor generation: they are robust and low maintenance.
In this chapter a selection of technologies has been described, which have proven
they can provide valuable information on the composition and quality of water.
Although the current application of these methodologies in real-time online sensing
remains limited, such sensors, UV/Vis absorbance and fluorescence devices in
particular, have become widely accepted and established in the water industry.
Their use is on the rise, especially in process monitoring and control and as early
warning systems. This is expected to gain further momentum as the performance and
cost-effectiveness of these systems increase further.
References
1. Peleg A (2014) Investment in smart water networks on the rise. Water Technology
2. Owen DAL (2018) Smart water technologies and techniques – data capture and analysis for
sustainable water management. Wiley, Oxford
3. Makropoulos CK, Butler D (2010) Distributed water infrastructure for sustainable communities.
Water Resour Manag 24:2795–2816
4. van den Broeke J, Carpentier C, Moore C, Carswell L, Jonsson J, Sivil D, Rosen JS, Cade L,
Mofidi A, Swartz C, Coomans N (2015) Compendium of sensors and monitors and their use in
the global water industry. IWA Publishing, London
5. Sun S, Bertrand-Krajewski J-L, Lynggaard Jensen A, van den Broeke J, Edthofer F,
do Céu Almeida M, Silva Ribeiro À, Menaia J (2011) Literature review for data validation
methods. Deliverable 3.1.1, PREPARED (EC FP7 Programme project number 244232)
6. Hyndman RJ, Athanasopoulos G (2018) Online open access textbooks. www.otexts.org/fpp2/
7. Bettencourt LMA, Hagberg AA, Larkey LB (2007) Separating the wheat from the chaff:
practical anomaly detection schemes in ecological applications of distributed sensor networks.
In: Aspnes J, Scheideler C, Arora A, Madden S (eds) Lecture notes in computer science, vol
4549. Springer, Berlin
8. Otto M (1999) Chemometrics: statistics and computer application in analytical chemistry.
Wiley, Weinheim
9. Thomas O, Burgess C (2017) UV-visible spectrophotometry of water and wastewater, 2nd edn.
Elsevier, Amsterdam
10. Langergraber G, Fleischmann N, Hofstaedter F (2003) A multivariate calibration procedure for
UV/Vis spectrometric quantification of organic matter and nitrate in wastewater. Water Sci
Technol 47(2):63–71
11. Rice EW, Baird RB, Eaton AD (2017) Standard methods for the examination of water and
wastewater. AWWA Catalog no. 10086, American Public Health Association, American Water
Works Association and Water Environment Federation
12. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
13. Mesquita DP, Quintelas C, Amaral AL, Ferreira EC (2017) Monitoring biological wastewater
treatment processes: recent advances in spectroscopy applications. Rev Environ Sci Biotechnol.
https://doi.org/10.1007/s1115701794399
14. Huber E, Frost M (1998) Light scattering by small particles. J Water Supply Res Technol 47
(2):87–94
312
J. van den Broeke and T. Koster
information not only from traditional monitoring locations such as water treatment
plants, but actually from the entire water system, there is a requirement for durable,
autonomous, networked and affordable sensor technology. Optical sensor systems
provide a good basis for this sensor generation: they are robust and low maintenance.
In this chapter a selection of technologies has been described, which have proven
they can provide valuable information on the composition and quality of water.
Although the current application of these methodologies in real-time online sensing
remains limited, such sensors, UV/Vis absorbance and fluorescence devices in
particular, have become widely accepted and established in the water industry.
Their use is on the rise, especially in process monitoring and control and as early
warning systems. This is expected to gain further momentum as the performance and
cost-effectiveness of these systems increase further.
References
1. Peleg A (2014) Investment in smart water networks on the rise. Water Technology
2. Owen DAL (2018) Smart water technologies and techniques – data capture and analysis for
sustainable water management. Wiley, Oxford
3. Makropoulos CK, Butler D (2010) Distributed water infrastructure for sustainable communities.
Water Resour Manag 24:2795–2816
4. van den Broeke J, Carpentier C, Moore C, Carswell L, Jonsson J, Sivil D, Rosen JS, Cade L,
Mofidi A, Swartz C, Coomans N (2015) Compendium of sensors and monitors and their use in
the global water industry. IWA Publishing, London
5. Sun S, Bertrand-Krajewski J-L, Lynggaard Jensen A, van den Broeke J, Edthofer F,
do Céu Almeida M, Silva Ribeiro À, Menaia J (2011) Literature review for data validation
methods. Deliverable 3.1.1, PREPARED (EC FP7 Programme project number 244232)
6. Hyndman RJ, Athanasopoulos G (2018) Online open access textbooks. www.otexts.org/fpp2/
7. Bettencourt LMA, Hagberg AA, Larkey LB (2007) Separating the wheat from the chaff:
practical anomaly detection schemes in ecological applications of distributed sensor networks.
In: Aspnes J, Scheideler C, Arora A, Madden S (eds) Lecture notes in computer science, vol
4549. Springer, Berlin
8. Otto M (1999) Chemometrics: statistics and computer application in analytical chemistry.
Wiley, Weinheim
9. Thomas O, Burgess C (2017) UV-visible spectrophotometry of water and wastewater, 2nd edn.
Elsevier, Amsterdam
10. Langergraber G, Fleischmann N, Hofstaedter F (2003) A multivariate calibration procedure for
UV/Vis spectrometric quantification of organic matter and nitrate in wastewater. Water Sci
Technol 47(2):63–71
11. Rice EW, Baird RB, Eaton AD (2017) Standard methods for the examination of water and
wastewater. AWWA Catalog no. 10086, American Public Health Association, American Water
Works Association and Water Environment Federation
12. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
13. Mesquita DP, Quintelas C, Amaral AL, Ferreira EC (2017) Monitoring biological wastewater
treatment processes: recent advances in spectroscopy applications. Rev Environ Sci Biotechnol.
https://doi.org/10.1007/s1115701794399
14. Huber E, Frost M (1998) Light scattering by small particles. J Water Supply Res Technol 47
(2):87–94
312
J. van den Broeke and T. Koster