Abstract
Absorbance-based sensors produce large raw attenuation datasets. We developed AbspectroscoPY, an open-source Python toolbox to implement semi-automated processing of these data and explore the full potential of high-frequency measurements.
Highlights
Automation plays an essential role in drinking water treatment plants (DWTPs)
Absorbance (UV/vis) spectroscopy is widely used for monitoring natural organic matter in water treatment due its low cost, high sensitivity and speed
Sensors take this technique to the level allowing for continuous measurements to catch rapid changes in water quality
Summary
Automation plays an essential role in drinking water treatment plants (DWTPs). Many process operation decisions, in both manual and automated systems, are based on data acquired from online sensors. The toolbox is adapted to accommodate other instrument outputs (e.g., turbidity and other sensors where the data are contained in a vector instead of a matrix) across environmental research and management disciplines (e.g., water quality monitoring, colour in aqueous solutions, wastewater, watersheds).[7,27,32] AbspectroscoPY currently contains 13 functions for importing, preprocessing, exploring and analysing absorbance-based sensor data and can be expanded by later users as necessary. It can be downloaded from GitHub (https://github.com/ ClaCasc/AbspectroscoPY), along with an example dataset that can be used to test and explore the functions. In this paper we provide a tutorial to guide the user through the AbspectroscoPY toolbox, using a case study of a drinking water dataset
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