Assessment of a portable UV-Vis spectrophotometer's performance in remote areas: Stream water DOC, Fe content and spectral data

This paper presents data for the assessment of a portable UV-Vis spectrophotometer's performance on predicting stream water DOC and Fe content. The dataset contains DOC and Fe concentrations by laboratory methods, in-situ and ex-situ spectral absorbances, monitoring environmental indexes such as water depth, temperature, turbidity and voltage. The records in Yli-Nuortti river (Cold station, Finland) took place during the hydrological year 2018-2019 and in Krycklan (C4 and C5, Sweden) during the hydrological years 2016-2019. The data analyses were conducted with 'pls' and 'caret' package in R. The correlation coefficient (R), root-mean-square deviation (RMSD), standard deviation (STD) and bias were used to check the performance of the models. This dataset can be combined with datasets from other regions around the world to build more universal models. For discussion and more information of the dataset creation, please refer to the full-length article "Assessment of a portable UV-Vis spectrophotometer's performance for stream water DOC and Fe content monitoring in remote areas" [1].

Assessment of a portable UV-Vis spectrophotometer's performance for stream water DOC and Fe content monitoring in remote areas

Quantification of dissolved organic carbon (DOC) and iron (Fe) in surface waters is critical for understanding the water quality dynamics, brownification and carbon balance in the northern hemisphere. Especially in the remote areas, sampling and laboratory analysis of DOC and Fe content at a sufficient temporal frequency is difficult. Ultraviolet-visible (UV-Vis) spectrophotometry is a promising tool for water quality monitoring to increase the sampling frequency and applications in remote regions. The aim of this study was (1) to investigate the performance of an in-situ UV-Vis spectrophotometer for detecting spectral absorbances in comparison with a laboratory benchtop instrument; (2) to analyse the stability of DOC and Fe estimates from UV-Vis spectrophotometers among different rivers using multivariate methods; (3) to compare site-specific calibration of models to pooled models and investigate the extrapolation of DOC and Fe predictions from one catchment to another. This study indicates that absorbances that were measured by UV-Vis sensor explained 96% of the absorbance data from the laboratory benchtop instrument. Among the three tested multivariate methods, multiple stepwise regression (MSR) was the best model for both DOC and Fe predictions. Accurate and unbiased models for multiple watersheds for DOC were built successfully, and these models could be extrapolated from one watershed to another even without site-specific calibration for DOC. However, for Fe the combination of different datasets was not possible.