Autonomous in situ measurements of freshwater alkalinity

  • Qipei Shangguan
  • , Chun Ze Lai
  • , Cory M. Beatty
  • , Fischer L. Young
  • , Reggie S. Spaulding
  • , Michael D. DeGrandpre

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Total alkalinity (AT) is an important parameter in the study of aquatic biogeochemical cycles, chemical speciation modeling, and many other important fundamental and anthropogenic (e.g., industrial) processes. We know little about its short-term variability, however, because studies are based on traditional bottle sampling typically with coarse temporal resolution. In this work, an autonomous AT sensor, named the Submersible Autonomous Moored Instrument for Alkalinity (SAMI-alk), was tested for freshwater applications. A comprehensive evaluation was conducted in the laboratory using freshwater standards. The results demonstrated excellent precision and accuracy (± 0.1%–0.4%) over the AT range from 800 to 3000 μmol L−1. The system had no drift over an 8 d test and also demonstrated limited sensitivity to variations in temperature and ionic strength. Three SAMI-alks were deployed for 23 d in the Clark Fork River, Montana, with a suite of other sensors. Compared to discrete samples, in situ accuracy for the three instruments were within 10–20 μmol L−1 (0.3–0.6%), indicating good performance considering the challenges of in situ measurements in a high sediment, high biofouling riverine environment with large and rapid changes in temperature. These data reveal the complex AT dynamics that are typically missed by coarse sampling. We observed AT diel cycles as large as 60–80 μmol L−1, as well as a rapid change caused by a runoff event. Significant errors in inorganic carbon system modeling result if these short-term variations are not considered. This study demonstrates both the feasibility of the technology and importance of high-resolution AT measurements.

Original languageEnglish
Pages (from-to)51-66
Number of pages16
JournalLimnology and Oceanography: Methods
Volume19
Issue number2
DOIs
StatePublished - Feb 2021

Funding

We thank the University of Montana Valett Aquatic Ecology Lab for assistance with sample collection and analysis. This research was supported in part by the U.S. National Science Foundation Long Term Research in Environmental Biology program (DEB‐1655197), Montana NSF EPSCoR program (OIA‐1757351), Sunburst Sensors, LLC, and the University of Montana through a teaching assistantship to Qipei Shangguan. The critical comments from the associate editor and two reviewers helped to improve this manuscript. Data are available from Michael D. DeGrandpre. We thank the University of Montana Valett Aquatic Ecology Lab for assistance with sample collection and analysis. This research was supported in part by the U.S. National Science Foundation Long Term Research in Environmental Biology program (DEB-1655197), Montana NSF EPSCoR program (OIA-1757351), Sunburst Sensors, LLC, and the University of Montana through a teaching assistantship to Qipei Shangguan. The critical comments from the associate editor and two reviewers helped to improve this manuscript. Data are available from Michael D. DeGrandpre.

Funder number
1655197, 1655198, DEB‐1655197, 1757351
OIA‐1757351

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