AbspectroscoPY, a Python toolbox for absorbance-based sensor data in water quality monitoring

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AbspectroscoPY, a Python toolbox for absorbance-based sensor data in water quality monitoring

Cascone, Claudia (author): Swedish University of Agricultural Sciences

Murphy, Kathleen R. (author): Chalmers University of Technology

Markensten, H. (author): Swedish University of Agricultural Sciences

Kern, J.S. (author): KTH Royal Institute of Technology

Schleich, C. (author): Vatten och Miljö i Väst AB (VIVAB)

Keucken, Alexander (author): Lund University,Lunds universitet,Avdelningen för Teknisk vattenresurslära,Institutionen för bygg- och miljöteknologi,Institutioner vid LTH,Lunds Tekniska Högskola,Division of Water Resources Engineering,Department of Building and Environmental Technology,Departments at LTH,Faculty of Engineering, LTH,Vatten och Miljö i Väst AB (VIVAB)

Köhler, S.J. (author): Swedish University of Agricultural Sciences,Norrvatten

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2022
2022
English 13 s.
In: Environmental Science: Water Research & Technology. - 2053-1419. ; 8:4, s. 836-848

Related links:: http://dx.doi.org/10... (free); show more...; https://lup.lub.lu.s...; https://doi.org/10.1...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

The long-term trend of increasing natural organic matter (NOM) in boreal and north European surface waters represents an economic and environmental challenge for drinking water treatment plants (DWTPs). High-frequency measurements from absorbance-based online spectrophotometers are often used in modern DWTPs to measure the chromophoric fraction of dissolved organic matter (CDOM) over time. These data contain valuable information that can be used to optimise NOM removal at various stages of treatment and/or diagnose the causes of underperformance at the DWTP. However, automated monitoring systems generate large datasets that need careful preprocessing, followed by variable selection and signal processing before interpretation. In this work we introduce AbspectroscoPY (“Absorbance spectroscopic analysis inPython”), a Python toolbox for processing time-series datasets collected by in situ spectrophotometers. The toolbox addresses some of the main challenges in data preprocessing by handling duplicates, systematic time shifts, baseline corrections and outliers. It contains automated functions to compute a range of spectral metrics for the time-series data, including absorbance ratios, exponential fits, slope ratios and spectral slope curves. To demonstrate its utility, AbspectroscoPY was applied to 15-month datasets from three onlinespectrophotometers in a drinking water treatment plant. Despite only small variations in surface water quality over the time period, variability in the spectrophotometric profiles of treated water could be identified, quantified and related to lake turnover or operational changes in the DWTP. This toolboxrepresents a step toward automated early warning systems for detecting and responding to potential threats to treatment performance caused by rapid changes in incoming water quality.

Subject headings

TEKNIK OCH TEKNOLOGIER -- Miljöbioteknik -- Vattenbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Environmental Biotechnology -- Water Treatment (hsv//eng)

Keyword

Python, Scientific Computing
NOM removal
Drinking Water

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By the author/editor: Cascone, Claudia; Murphy, Kathleen ...; Markensten, H.; Kern, J.S.; Schleich, C.; Keucken, Alexand ...; show more...; Köhler, S.J.; show less...

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ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Environmental Bi ...; and Water Treatment

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By the university: Lund University

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