Sensors for Monitoring Faecal Indicator Bacteria in Bathing Waters

Briciu-Burghina, Ciprian; Regan, Fiona

doi:10.1007/698_2022_946

Cited by 3 publications

(4 citation statements)

References 90 publications

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“…Indirect detection of MPs with commercial online sensors can be performed by e.g., monitoring tryptophan concentrations online with an automatic sensor. (Sorensen et al 2020, Briciu-Burghina et al 2023 Tryptophan is an amino acid used in recognizing wastewater releases to surface or drinking water.…”

Section: Automatic Water Quality Sensorsmentioning

confidence: 99%

Uncertainty in Environmental Micropollutant Modeling

Ahkola,

Kotamäki,

Siivola

et al. 2024

Environmental Management

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Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling.

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Section: Automatic Water Quality Sensorsmentioning

confidence: 99%

Uncertainty in Environmental Micropollutant Modeling

Ahkola,

Kotamäki,

Siivola

et al. 2024

Environmental Management

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show abstract

“…While ocean monitoring systems encompass remote sensing techniques 1 and even community knowledge, 2 the primary source of oceanographic data are in-situ sensors. 3 Such sensors are cost-effective and can be deployed in relatively straightforward manners. Further, applicationspecific sensors can be integrated as part of a larger sensor network, allowing for the interpolation of local and regional measurements to a global scale.…”

Section: Introductionmentioning

confidence: 99%

Assessing magnetic particle content in algae using compact time domain nuclear magnetic resonance

Huggins,

Janvrin,

Martin

et al. 2024

Ocean Sensing and Monitoring XVI

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“…1 Sensors and instrumentation play a key role in water management, and are becoming smaller, smarter and more economical. [2][3][4][5] Optical based sensors are receiving great interest due recent advances in photonics and the ability to work in harsh environments. 2,6 In aquatic environments, uorescence based spectroscopy is a common technique used for detection and quantication of chlorophyll (Chl a), 4,7,8 petroleum compounds, 9 fDOM (uorescent dissolved organic matter), 10,11 tryptophan, 12 algal populations [13][14][15] and faecal indicator bacteria.…”

Section: Introductionmentioning

confidence: 99%

“…[2][3][4][5] Optical based sensors are receiving great interest due recent advances in photonics and the ability to work in harsh environments. 2,6 In aquatic environments, uorescence based spectroscopy is a common technique used for detection and quantication of chlorophyll (Chl a), 4,7,8 petroleum compounds, 9 fDOM (uorescent dissolved organic matter), 10,11 tryptophan, 12 algal populations [13][14][15] and faecal indicator bacteria. 16,17 Chlorophyll measurement in aquatic systems is an indicator of phytoplankton biomass and primary production in response to nutrients and light availability.…”

Section: Introductionmentioning

confidence: 99%