Rapid Advances in Mobile Mass Spectrometry Enhance Tracer Hydrology and Water Management

Popp, Andrea; Manning, Cara C.; Knapp, Julia L. A.

doi:10.1029/2021wr029890

Cited by 5 publications

(4 citation statements)

References 62 publications

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“…New developments of in situ noble gas measurements (e.g., Ar, Kr, He) via mobile mass spectrometry (Brennwald et al., 2016) provide opportunities for improved hydrological process understanding as recently outlined by Popp et al. (2021). The usefulness of high‐frequency gas tracer measurements for transit time estimates has not yet been explored, but examples assessing the snowmelt contribution in a mountainous catchment show their feasibility (Schilling et al., 2021).…”

Section: Going Forwardmentioning

confidence: 99%

“…As reviewed in Abbott et al (2016) and Sprenger et al (2019), introducing tracers that are more commonly used in groundwater age dating, like CFCs, SF 6 or 85 Kr, can constrain the parameter space accounting for the long tails in transit time modeling (i.e., old water contributions to stream discharge). New developments of in situ noble gas measurements (e.g., Ar, Kr, He) via mobile mass spectrometry (Brennwald et al, 2016) provide opportunities for improved hydrological process understanding as recently outlined by Popp et al (2021). The usefulness of high-frequency gas tracer measurements for transit time estimates has not yet been explored, but examples assessing the snowmelt contribution in a mountainous catchment show their feasibility (Schilling et al, 2021).…”

Section: What We Need From New Field Work and Tracer Techniquesmentioning

confidence: 99%

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Transit Time Estimation in Catchments: Recent Developments and Future Directions

Benettin

Rodriguez

Sprenger

et al. 2022

Water Resources Research

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The transit time of water in catchments is a fundamental descriptor of catchment behavior. If we think of "age" as a label that is attached to any water particle resident within a hydrologic system (e.g., a catchment) and that tracks the time elapsed since arrival (e.g., as precipitation), the transit time is the particle's age when it leaves the system (e.g., as discharge or evapotranspiration). While slow to gain ground as a standard metric, transit times and their analyses now abound in the literature due in part to the increased availability of tracer data used to estimate water transit times. As a result, transit time is now a common means to improve process representation in models and a strong test of model output realism. Once, the review presented in McGuire and McDonnell (2006) was the starting point for newcomers interested in getting to grips with catchment transit time modeling. However, the explosion of the transit time literature since the mid-2000s-with new studies, terminology, theory, and mathematical approaches-means a newcomer to the field now is met with the challenge of absorbing these developments and harmonizing them with earlier approaches.Over the last ∼15 yr, there have indeed been departures and advancements beyond what was summarized by McGuire and McDonnell (2006). That review provided the first "evaluation and review of the transit time literature in the context of catchments and water transit time estimation". It was motivated by "new and emerging interests in transit time estimation in catchment hydrology and the need to distinguish approaches and assumptions used in groundwater applications from catchment applications". At that time, hydrologists were mainly using time-invariant, lumped parameter transit time approaches largely focused on low temporal resolution

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Section: Going Forwardmentioning

confidence: 99%

Section: What We Need From New Field Work and Tracer Techniquesmentioning

confidence: 99%

Transit Time Estimation in Catchments: Recent Developments and Future Directions

Benettin

Rodriguez

Sprenger

et al. 2022

Water Resources Research

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“…with hydrochemical data has great potential to further system understanding of surface water-groundwater interactions and associated biogeochemical cycling (Popp, Manning, & Knapp, 2021;Popp, Pardo-Alvarez, et al, 2021;Popp, Scheidegger, et al, 2019). • Machine learning approaches: The data can serve as a foundation for the development and validation of machine learning models (e.g., Maier et al, 2010;Tyralis et al, 2019).…”

Section: Results and Brief Discussion Of The Datamentioning

confidence: 99%

“…Moreover, the data can be used for model calibration and validation by reducing parameter uncertainty and thereby improving the predictive capacity of the chosen model. Research has shown that hydraulic heads alone do not contain sufficient information to calibrate flow models (Moeck et al., 2020; Schilling et al., 2019), consequently, combining hydraulic with hydrochemical data has great potential to further system understanding of surface water‐groundwater interactions and associated biogeochemical cycling (Popp, Manning, & Knapp, 2021; Popp, Pardo‐Alvarez, et al., 2021; Popp, Scheidegger, et al., 2019). Machine learning approaches: The data can serve as a foundation for the development and validation of machine learning models (e.g., Maier et al., 2010; Tyralis et al., 2019).…”

Section: Results and Brief Discussion Of The Datamentioning

confidence: 99%

Assessing Hydrology, Biogeochemistry, and Organic Micropollutants in an Urban Stream‐Aquifer System: An Interdisciplinary Data Set

Popp,

Weatherl,

Moeck

et al. 2024

JGR Biogeosciences

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Urban expansion leads to increasing water pollution, impacting both human health and ecosystems. This decline in water quality often stems from insufficient wastewater treatment, along with runoff from both urban and agricultural areas. Water quality degradation challenges our efforts for sustainable water management and hinders progress toward the UN's Sustainable Development Goals (SDGs), particularly SDG6. Within aquatic environments, the hyporheic zone—the subsurface area where surface water and groundwater mix—plays a crucial role in facilitating pollutant turnover and overall aquatic health. Mixing between surface water and groundwater generates diverse microhabitats in the streambed with varying levels of oxygen, temperature, and chemical composition, which in turn allows diverse microbial communities to strive. The complexity of exchange flows within the hyporheic zone and associated turnover processes of pollutants and nutrients can only be assessed with detailed, cross‐disciplinary data sets including data about hydrology, climatology, biogeochemistry, and the subsurface composition. However, integrated data sets of this kind are seldom available. As a result, the drivers behind pollutant dynamics in stream‐aquifer systems are still not fully understood. Addressing this knowledge deficit, we present a comprehensive unique data set from an urban stream‐aquifer system in Switzerland spanning over 6 months. Incorporating hydrometric, tracer, nutrient, microbial and organic micropollutant data, our data set can help to shed light on the intricate mechanisms governing hyporheic exchange flows, as well as nutrient and organic micropollutant cycling in urban environments.

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Evaluating O₂ : Ar, N₂ : Ar, and ^29,30N₂ using membrane inlet mass spectrometry configured to minimize oxygen interference

Boedecker,

Taylor,

Tappenbeck

et al. 2024

Limnology & Ocean Methods

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Membrane inlet mass spectrometry (MIMS) provides detailed measures of dissolved 28,29,30N2, O2, and argon (Ar) for estimating important gas fluxes and concentrations in aquatic ecosystems. Previous studies demonstrated a large O2 scavenging effect while using a MIMS, where varying concentrations of O2 can affect measured N2 : Ar because O2 interacts with N2 in the ion source to produce NO+ (m/z = 30), potentially decreasing the detected current for 28,29N2 and increasing the detected current for 30N2. A common solution is to use a muffle furnace heated to 600°C with a copper reduction column to reduce the concentration of O2 to minimal levels and accurately measure 28,29,30N2. However, this solution eliminates the detection of O2 in environmental samples, which is a major benefit of using a MIMS. We questioned whether the MIMS was sensitive enough to provide accurate O2 estimates when using the furnace and whether the O2 scavenging effect was real and consistent among MIMS. We conducted four separate experiments on three different MIMS to test the O2 scavenging effect and the potential detection of O2 when using a MIMS with furnace. The furnace removed ~ 99% of O2, and O2 scavenging had little to no detectable effect on N2 : Ar and an unclear effect on 29N2 : 28N2, but increased 30N2 : 28N2. In most cases, accurate O2 data could be retrieved despite using the furnace. The need for O2 reduction may be limited to measuring accurate 30N2 : 28N2 in isotope pairing studies, but without substantial loss of MIMS measurements used to describe O2 dynamics.

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Rapid Advances in Mobile Mass Spectrometry Enhance Tracer Hydrology and Water Management

Cited by 5 publications

References 62 publications

Transit Time Estimation in Catchments: Recent Developments and Future Directions

Transit Time Estimation in Catchments: Recent Developments and Future Directions

Assessing Hydrology, Biogeochemistry, and Organic Micropollutants in an Urban Stream‐Aquifer System: An Interdisciplinary Data Set

Evaluating O₂ : Ar, N₂ : Ar, and ^29,30N₂ using membrane inlet mass spectrometry configured to minimize oxygen interference

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Rapid Advances in Mobile Mass Spectrometry Enhance Tracer Hydrology and Water Management

Cited by 5 publications

References 62 publications

Transit Time Estimation in Catchments: Recent Developments and Future Directions

Transit Time Estimation in Catchments: Recent Developments and Future Directions

Assessing Hydrology, Biogeochemistry, and Organic Micropollutants in an Urban Stream‐Aquifer System: An Interdisciplinary Data Set

Evaluating O2 : Ar, N2 : Ar, and 29,30N2 using membrane inlet mass spectrometry configured to minimize oxygen interference

Contact Info

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Resources

About

Evaluating O₂ : Ar, N₂ : Ar, and ^29,30N₂ using membrane inlet mass spectrometry configured to minimize oxygen interference