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

Cascone, Claudia and Murphy, K. R. and Markensten, Hampus and Kern, J. S. and Schleich, C. and Keucken, A. and Köhler, Stephan (2022). AbspectroscoPY, a Python toolbox for absorbance-based sensor data in water quality monitoring. Environmental Science: Water Research & Technology. 8 :4 , 836-848
[Research article]

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Abstract

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.

Authors/Creators:Cascone, Claudia and Murphy, K. R. and Markensten, Hampus and Kern, J. S. and Schleich, C. and Keucken, A. and Köhler, Stephan
Title:AbspectroscoPY, a Python toolbox for absorbance-based sensor data in water quality monitoring
Series Name/Journal:Environmental Science: Water Research & Technology
Year of publishing :2022
Volume:8
Number:4
Page range:836-848
Number of Pages:13
Publisher:ROYAL SOC CHEMISTRY
ISSN:2053-1400
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 3.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Oceanography, Hydrology, Water Resources
URN:NBN:urn:nbn:se:slu:epsilon-p-116334
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-116334
Additional ID:
Type of IDID
DOI10.1039/d1ew00416f
Web of Science (WoS)000759677000001
ID Code:27526
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Aquatic Sciences and Assessment
Deposited By: SLUpub Connector
Deposited On:11 Apr 2022 10:42
Metadata Last Modified:11 Apr 2022 10:51

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