by increasingly advanced chemometric solutions. This chapter provides a brief
overview of the fundamentals behind these technologies and reviews their use in
water quality monitoring applications.
Keywords Optical sensors, Smart water, Spectroscopy, Water quality
1 Introduction
Understanding and monitoring the quantity and quality of one of the world’s most
precious resources, water, are essential. Currently, the most common method of
analysis of water quality consists of (grab or composite) sampling followed by
laboratory investigation. This approach fails to fully indicate the dynamics of
water quality, since it only provides snapshots of specific points in time. Furthermore, due to delays in transportation, sample preparation and analysis, laboratory
analysis only reveals a history of water quality, and not its current state.
Management of water and wastewater networks, meeting operational demands
and regulatory compliance while simultaneously minimising costs, is becoming
increasingly challenging. The water industry is beginning to recognise that further
progress will be limited as long as assets are operated independently of each other
and retrospectively with regard to changes in load and failures. An integrated
approach to water management potentially offers major advantages to the water
industry, hence the quickly expanding interest in smart water solutions.
The holistic approach that is at the cornerstone of smart water systems requires
greater system knowledge and improved control. The use of real-time control will
enable more flexible and efficient use of existing assets and will provide the ability to
respond proactively to both short-term changes and longer-term challenges. Examples of areas where smart water solutions can achieve gains include in treatment
processes, e.g. to enable more efficient operation, achieve compliance and reduce the
carbon footprint; in water distribution systems, e.g. allowing reaction to operational
problems and threats to public health and safety and reducing operational costs; in
the sewerage network, e.g. to deal with (rain) events in real-time and moving from
hydraulic to quality control; and in asset monitoring, e.g. to allow better forecasting
and targeting of asset maintenance. For a brief introduction, see Peleg [1]; for a more
detailed review, see Owen [2]. Additionally, there is a trend in the industry towards
small-scale, distributed water systems [3]; although these systems remove the need
for capital-intensive distribution and collection systems, they pose a special challenge to monitoring as there is no skilled workforce to supervise them and smaller
volumes mean lower equipment costs are required. Without reliable automation and
control solutions, small-scale systems will not be viable.
Real-time information about the water quality and quantity is the basis for smart
water solutions. This information is collected by sensors distributed throughout the
water network. Not all sensors are suited for use in smart water systems. The
deployment of sensors in large numbers, possibly in locations that are difficult to
284
J. van den Broeke and T. Koster
overview of the fundamentals behind these technologies and reviews their use in
water quality monitoring applications.
Keywords Optical sensors, Smart water, Spectroscopy, Water quality
1 Introduction
Understanding and monitoring the quantity and quality of one of the world’s most
precious resources, water, are essential. Currently, the most common method of
analysis of water quality consists of (grab or composite) sampling followed by
laboratory investigation. This approach fails to fully indicate the dynamics of
water quality, since it only provides snapshots of specific points in time. Furthermore, due to delays in transportation, sample preparation and analysis, laboratory
analysis only reveals a history of water quality, and not its current state.
Management of water and wastewater networks, meeting operational demands
and regulatory compliance while simultaneously minimising costs, is becoming
increasingly challenging. The water industry is beginning to recognise that further
progress will be limited as long as assets are operated independently of each other
and retrospectively with regard to changes in load and failures. An integrated
approach to water management potentially offers major advantages to the water
industry, hence the quickly expanding interest in smart water solutions.
The holistic approach that is at the cornerstone of smart water systems requires
greater system knowledge and improved control. The use of real-time control will
enable more flexible and efficient use of existing assets and will provide the ability to
respond proactively to both short-term changes and longer-term challenges. Examples of areas where smart water solutions can achieve gains include in treatment
processes, e.g. to enable more efficient operation, achieve compliance and reduce the
carbon footprint; in water distribution systems, e.g. allowing reaction to operational
problems and threats to public health and safety and reducing operational costs; in
the sewerage network, e.g. to deal with (rain) events in real-time and moving from
hydraulic to quality control; and in asset monitoring, e.g. to allow better forecasting
and targeting of asset maintenance. For a brief introduction, see Peleg [1]; for a more
detailed review, see Owen [2]. Additionally, there is a trend in the industry towards
small-scale, distributed water systems [3]; although these systems remove the need
for capital-intensive distribution and collection systems, they pose a special challenge to monitoring as there is no skilled workforce to supervise them and smaller
volumes mean lower equipment costs are required. Without reliable automation and
control solutions, small-scale systems will not be viable.
Real-time information about the water quality and quantity is the basis for smart
water solutions. This information is collected by sensors distributed throughout the
water network. Not all sensors are suited for use in smart water systems. The
deployment of sensors in large numbers, possibly in locations that are difficult to
284
J. van den Broeke and T. Koster