溶解有机碳
化学
吸光度
校准
水质
遥感
采样(信号处理)
外推法
分光光度法
分水岭
环境化学
分析化学(期刊)
色谱法
滤波器(信号处理)
生态学
统计
机器学习
地质学
生物
计算机科学
数学
计算机视觉
作者
Xudan Zhu,Liang Chen,Jukka Pumpanen,Markku Keinänen,Hjalmar Laudon,Anne Ojala,Marjo Palviainen,Mikko Kiirikki,Kimmo Neitola,Frank Berninger
出处
期刊:Talanta
[Elsevier]
日期:2020-12-03
卷期号:224: 121919-121919
被引量:12
标识
DOI:10.1016/j.talanta.2020.121919
摘要
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.
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