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Research article - Peer-reviewed, 2022

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

Cascone, C.; Murphy, K. R.; Markensten, H.; Kern, J. S.; Schleich, C.; Keucken, A.; Kohler, S. J.


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 in Python"), 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 online spectrophotometers 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 toolbox represents 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.

Published in

Environmental Science: Water Research & Technology
2022, volume: 8, number: 4, pages: 836-848

Authors' information

Cascone, Claudia
Swedish University of Agricultural Sciences, Department of Aquatic Sciences and Assessment
Murphy, K. R.
Chalmers University of Technology
Swedish University of Agricultural Sciences, Department of Aquatic Sciences and Assessment
Kern, J. S.
Royal Institute of Technology
Schleich, C.
Vatten & Miljö i Väst (VIVAB)
Keucken, A.
Lund University
Swedish University of Agricultural Sciences, Department of Aquatic Sciences and Assessment

Sustainable Development Goals

SDG6 Clean water and sanitation

UKÄ Subject classification

Oceanography, Hydrology, Water Resources

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