Radium‐226(226Ra) and barium (Ba) exhibit similar chemical behaviors and distributions in the marine environment, serving as valuable tracers of water masses, ocean mixing, and productivity. Despite their similar distributions, these elements originate from distinct sources and undergo disparate biogeochemical cycles, which might complicate the use of these tracers. In this study, we investigate these processes by analyzing a full‐depth ocean section of226Ra activities (
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Abstract T 1/2 = 1,600 years) and barium concentrations obtained from samples collected along the US GEOTRACES GP15 Pacific Meridional Transect during September–November 2018, spanning from Alaska to Tahiti. We find that surface waters possess low levels of226Ra and Ba due to export of sinking particulates, surpassing inputs from the continental margins. In contrast, deep waters have higher226Ra activities and Ba concentrations due to inputs from particle regeneration and sedimentary sources, with226Ra inputs primarily resulting from the decay of230Th in sediments. Further, dissolved226Ra and Ba exhibit a strong correlation along the GP15 section. To elucidate the drivers of the correlation, we used a water mass analysis, enabling us to quantify the influence of water mass mixing relative to non‐conservative processes. While a significant fraction of each element's distribution can be explained by conservative mixing, a considerable fraction cannot. The balance is driven using non‐conservative processes, such as sedimentary, rivers, or hydrothermal inputs, uptake and export by particles, and particle remineralization. Our study demonstrates the utility of226Ra and Ba as valuable biogeochemical tracers for understanding ocean processes, while shedding light on conservative and myriad non‐conservative processes that shape their respective distributions.Free, publicly-accessible full text available June 1, 2025 -
Free, publicly-accessible full text available May 1, 2025
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Abstract. Barium is widely used as a proxy for dissolved silicon and particulateorganic carbon fluxes in seawater. However, these proxy applications arelimited by insufficient knowledge of the dissolved distribution of Ba([Ba]). For example, there is significant spatial variability in thebarium–silicon relationship, and ocean chemistry may influence sedimentaryBa preservation. To help address these issues, we developed 4095 models forpredicting [Ba] using Gaussian process regression machine learning. Thesemodels were trained to predict [Ba] from standard oceanographic observationsusing GEOTRACES data from the Arctic, Atlantic, Pacific, and Southernoceans. Trained models were then validated by comparing predictions againstwithheld [Ba] data from the Indian Ocean. We find that a model trained usingdepth, temperature, and salinity, as well as dissolved dioxygen, phosphate,nitrate, and silicate, can accurately predict [Ba] in the Indian Ocean with amean absolute percentage deviation of 6.0 %. We use this model tosimulate [Ba] on a global basis using these same seven predictors in theWorld Ocean Atlas. The resulting [Ba] distribution constrains the Ba budgetof the ocean to 122(±7) × 1012 mol and revealsoceanographically consistent variability in the barium–silicon relationship. We then calculate the saturation state of seawater with respect to barite. This calculation reveals systematic spatial and vertical variations in marine barite saturation and shows that the ocean below 1000 m is at equilibrium with respect tobarite. We describe a number of possible applications for our model outputs, ranging from use in mechanistic biogeochemical models to paleoproxy calibration. Ourapproach demonstrates the utility of machine learning in accurately simulatingthe distributions of tracers in the sea and provides a framework that couldbe extended to other trace elements. Our model, the data used in training and validation, and global outputs are available in Horner and Mete (2023, https://doi.org/10.26008/1912/bco-dmo.885506.2).more » « less
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Dissolved Ba, Cd, Cu, Ga, Mn, Ni, and V concentration data from the US GEOTRACES Arctic Expedition (GN01, HLY1502) from August to October 2015. Clean seawater samples were collected using a GEOTRACES CTD referred to as GT-C/12L GoFlo. Additional near surface samples were collected using either a small boat or through the ice using Teflon coated Tygon tubing and a trace metal clean pump (IWAKI, model WMD-30LFY-115). This dataset also includes selected stable Ba isotope analyses.more » « less
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Abstract North African dust is known to be deposited in the Gulf of Mexico, but its deposition rate and associated supply of lithogenic dissolved metals, such as the abiotic metal thorium or the micronutrient metal iron, have not been well‐quantified.232Th is an isotope with similar sources as iron and its input can be quantified using radiogenic230Th. By comparing dissolved232Th fluxes at three sites in the northern Gulf of Mexico with upwind sites in the North Atlantic, we place an upper bound on North African dust contributions to232Th and Fe in the Gulf of Mexico, which is about 30% of the total input. Precision on this bound is hindered by uncertainty in the relative rates of dust deposition in the North Atlantic and the northern Gulf of Mexico. Based on available radium data, shelf sources, including rivers, submarine groundwater discharge, and benthic sedimentary releases are likely as important if not more important than dust in the budget of lithogenic metals in the Gulf of Mexico. In other words, it is likely there is no one dominant source of Th and Fe in the Gulf of Mexico. Finally, our estimated Fe input in the northern Gulf of Mexico implies an Fe residence time of less than 6 months, similar to that in the North Atlantic despite significantly higher supply rates in the Gulf of Mexico.
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Abstract Coastal ecosystems are highly dynamic areas for carbon cycling and are likely to be negatively impacted by increasing ocean acidification. This research focused on dissolved inorganic carbon (DIC) and total alkalinity (TA) in the Mississippi Sound to understand the influence of local rivers on coastal acidification. This area receives large fluxes of freshwater from local rivers, in addition to episodic inputs from the Mississippi River through a human‐built diversion, the Bonnet Carré Spillway. Sites in the Sound were sampled monthly from August 2018 to November 2019 and weekly from June to August 2019 in response to an extended spillway opening. Prior to the 2019 spillway opening, the contribution of the local, lower alkalinity rivers to the Sound may have left the study area more susceptible to coastal acidification during winter months, with aragonite saturation states (Ωar) < 2. After the spillway opened, despite a large increase in TA throughout the Sound, aragonite saturation states remained low, likely due to hypoxia and increased CO2concentrations in subsurface waters. Increased Mississippi River input could represent a new normal in the Sound's hydrography during spring and summer months. The spillway has been utilized more frequently over the last two decades due to increasing precipitation in the Mississippi River watershed, which is primarily associated with climate change. Future increases in freshwater discharge and the associated declines in salinity, dissolved oxygen, and Ωarin the Sound will likely be detrimental to oyster stocks and the resilience of similar ecosystems to coastal acidification.
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Abstract Metal cations are potent environmental pollutants that negatively impact human health and the environment. Despite advancements in sensor design, the simultaneous detection and discrimination of multiple heavy metals at sub‐nanomolar concentrations in complex analytical matrices remain a major technological challenge. Here, the design, synthesis, and analytical performance of three highly emissive conjugated polyelectrolytes (CPEs) functionalized with strong iminodiacetate and iminodipropionate metal chelates that operate in challenging environmental samples such as seawater are demonstrated. When coupled with array‐based sensing methods, these polymeric sensors discriminate among nine divalent metal cations (CuII, CoII, NiII, MnII, FeII, ZnII, CdII, HgII, and PbII). The unusually high and robust luminescence of these CPEs enables unprecedented sensitivity at picomolar concentrations in water. Unlike previous array‐based sensors for heavy metals using CPEs, the incorporation of distinct π‐spacer units within the polymer backbone affords more pronounced differences in each polymer's spectroscopic behavior upon interaction with each metal, ultimately producing better analytical information and improved differentiation. To demonstrate the environmental and biological utility, a simple two‐component sensing array is showcased that can differentiate nine metal cation species down to 500 × 10−12
m in aqueous media and to 100 × 10−9m in seawater samples collected from the Gulf of Mexico.