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AbspectroscoPY, a P...
AbspectroscoPY, a Python toolbox for absorbance-based sensor data in water quality monitoring
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- Cascone, C. (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för vatten och miljö,Department of Aquatic Sciences and Assessment,IVL Svenska Miljöinstitutet AB,IVL Swedish Environmental Research Institute,Sveriges lantbruksuniversitet (SLU),Swedish University of Agricultural Sciences (SLU)
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- Murphy, Kathleen, 1972 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Markensten, Hampus (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för vatten och miljö,Department of Aquatic Sciences and Assessment,Sveriges lantbruksuniversitet (SLU),Swedish University of Agricultural Sciences (SLU)
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- Kern, J. Simon (författare)
- KTH,Teknisk mekanik,Kungliga Tekniska Högskolan (KTH),Royal Institute of Technology (KTH)
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Schleich, C. (författare)
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- Keucken, A. (författare)
- Lunds universitet,Lund University
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- Köhler, Stephan (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för vatten och miljö,Department of Aquatic Sciences and Assessment,Sveriges lantbruksuniversitet (SLU),Swedish University of Agricultural Sciences (SLU)
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(creator_code:org_t)
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- 2022
- 2022
- Engelska.
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Ingår i: Environmental Science. - : Royal Society of Chemistry (RSC). - 2053-1400 .- 2053-1419. ; 8:4, s. 836-848
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Environmental Sciences (hsv//eng)
Nyckelord
- Automation
- Biogeochemistry
- Curve fitting
- Data handling
- High level languages
- Large dataset
- Monitoring
- Organic compounds
- Potable water
- Signal processing
- Spectroscopic analysis
- Surface waters
- Time series
- Time series analysis
- Water quality
- Water treatment
- Water treatment plants
- Absorbances
- Dissolved organic matters
- Drinking water treatment plants
- Economic challenges
- Environmental challenges
- High-frequency measurement
- Long-term trend
- Natural organic matters
- Sensors data
- Water quality monitoring
- Python
- drinking water
- organic matter
- pollutant removal
- removal experiment
- surface water
- water treatment plant
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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