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

• 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

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