Abstract
This chapter delves into the recent advancements, challenges, applications, and future outlook of real-time online monitoring sensing technologies for detecting emerging contaminants (ECs) in aquatic environments. The initial section underscores the criticality of implementing real-time online sensing monitoring for tracing ECs and delineates the scope of the review. It sets the stage by highlighting the necessity for real-time tracking in tackling the escalating challenge of ECs and outlines the contours of the ensuing discussion. Then, this chapter offers an in-depth exploration of the sensing principles underpinning commonly deployed real-time online sensors, encompassing fluorescence sensors, colorimetric sensors, surface-enhanced Raman sensors, and infrared sensors. It assesses the merits and constraints of each sensor type, providing a comprehensive perspective on these critical tools in monitoring ECs. We also illuminate how diverse sensing technologies are harnessed to trace ECs, framed within the categories of ECs. It draws upon many research cases to describe the practical deployments of these technologies, critically examining their effectiveness and limitations in real-world applications. The penultimate section grapples with the challenges confronting real-time online sensing technologies for ECs. It scrutinizes issues spanning sensor-specific considerations, data management and interpretation, and relevant statutory and regulatory constraints. The potential threats that ECs pose to environmental ecology and human health should be considered. These invisible assailants can have a devastating impact if not monitored and managed effectively. The evolution of real-time online monitoring sensing technologies promises to furnish real-time control and early warning against such risks, underscoring the urgency and importance of advancement in this field.
| Original language | English |
|---|---|
| Title of host publication | Water Security |
| Subtitle of host publication | Big Data-Driven Risk Identification, Assessment and Control of Emerging Contaminants |
| Publisher | Elsevier |
| Pages | 185-204 |
| Number of pages | 20 |
| ISBN (Electronic) | 9780443141706 |
| ISBN (Print) | 9780443141713 |
| DOIs | |
| State | Published - 1 Jan 2024 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Real-time
- emerging contaminants
- machine learning
- online sensing
- optical
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