Ocean dissolved oxygen (O-2) is an essential climate variable crucial for sustaining the marine life; thus, changes of O-2 at various spatiotemporal scales should be quantified and understood. Here, we study the climatology and annual cycle of O-2 at regional to global scales using eight available gridded observational products. These datasets are generated by different groups using different primary data selection, quality control, bias correction, and interpolation methods, including statistical and machine-learning-based mapping methods. A common set of metrics was collaboratively developed by the community of the Gridded Observational Dataset Intercomparison Project-Dissolved Oxygen (GODIP-DO) to facilitate the inter-comparison. We find that global mean O-2 profiles are consistent among all products (+/- 3 & micro;mol kg(-1)), with the well-established decrease from high surface values to a minimum similar to 1000 m, and subsequent increase to higher O-2 at depth, although local differences could reach +/- 25 & micro;mol kg(-1) (0-1000 m). The hemispheric O-2 annual cycle correlates strongly with ocean temperature changes, suggesting the key driver of temperature for the O-2 annual cycle. However, there is substantial variation in the global mean 0-100 m O-2 annual cycle, the magnitude ranges from -1 to 0.8 & micro;mol kg(-1), with a standard deviation of the datasets of similar to 0.3 & micro;mol kg(-1). Average oxygen minimum zones (OMZ) volume among the products is 80.92 & times; 10(6) km(3) (+/- 1.95 %) for a 60 & micro;mol kg(-1) threshold and 152.00 & times; 10(6) km(3) (+/- 1.72 %) for a 90 & micro;mol kg(-1) threshold. Our results help to depict and understand the spread among the available O-2 gridded datasets.

The dissolved oxygen (O2) content of the tropical Pacific exhibits substantial variability from interannual to decadal time scales, challenging the detection of ocean deoxygenation in this region. Using a global observational synthesis of O2 along with eddying and noneddying global ocean-sea ice simulations, we examine the interannual variability of O2 and its underlying drivers in the tropical Pacific. We find a tight relationship between El Ni & ntilde;o-Southern Oscillation (ENSO) and the O2 content and distribution across observations and models, with elevated O2 in the eastern and central parts of the basin during El Ni & ntilde;o and reduced O2 in this region during La Ni & ntilde;a. The variability of the O2 content in this region is generally similar across models and observation-based products, though regional patterns differ. ENSO-driven variability of O2 is shown to be the net balance of large and compensating effects between vertical advection and biological consumption that dominate over opposing changes in vertical mixing and lateral advection, such that the O2 content increases during El Ni & ntilde;o despite a major reduction in ventilation. This variability in O2 ventilation is primarily driven by ENSO modulation of shear-driven turbulent mixing and the eastward transport of O2 by the Equatorial Undercurrent (EUC). We also note that ENSO positively couples the tropical Pacific heat and O2 contents, which contrasts sharply with their tight negative relationship at mid-and high latitudes, likely due to a larger role for ocean dynamics and biological processes in modulating the O2 response to climate perturbations in the tropical Pacific.

The ocean is one of the largest sinks for anthropogenic carbon dioxide (C-anth) and its removal of carbon dioxide (CO2) from the atmosphere has been valued at hundreds of billions to trillions of US dollars in climate mitigation annually. The ecosystem impacts caused by planet-wide shifts in ocean chemistry resulting from marine C-anth accumulation are an active area of research. For these reasons, we need accessible tools to quantify ocean C-anth inventories and distributions and to predict how they might evolve in response to future emissions and mitigation activities. Unfortunately, C-anth estimation methods are typically only accessible to trained scientists and modelers with access to significant computational resources. Here, we make modifications to the transit time distribution approach for C-anth estimation that render the method more accessible. We also release software (BRCScienceProducts, 2025) called "Tracer-based Rapid Anthropogenic Carbon Estimation version 1" (TRACEv1) that allows users - with one line of code - to obtain C-anth and water mass age estimates throughout the global open ocean from user-supplied values of geographic location, pressure, salinity, temperature, and the estimate year. We use this code to generate a data product of global gridded open-ocean C-anth distributions (TRACEv1_GGC(anth); Carter, 2025) that ranges from the preindustrial era through 2500 under a range of Shared Socioeconomic Pathways (SSPs, or atmospheric CO2 concentration pathways). We estimated the skill of these estimates by reconstructing C-anth in models with known distributions of C-anth and transient tracers and by conducting perturbation tests. In the model-based reconstruction test, TRACEv1 reproduces the global ocean C-anth inventory to within +/- 10 % in 1980 and 2014. We discuss implications and limitations of the projected C-anth distributions and highlight ways that the estimation strategy might be improved. One finding is that the ocean will continue to increase its net C-anth inventory at least through 2500 due to deep-ocean ventilation, even with the SSP in which intense mitigation successfully decreases atmospheric C-anth by similar to 60 % in 2500 relative to the 2024 concentration. A notable limitation of this and similar projections made with TRACEv1 is that ongoing and potential future warming and changing oceanic circulation patterns with climate change are not captured by the method. The data products generated by this research are available as MATLAB code (https://doi.org/10.5281/zenodo.15692788, BRCScienceProducts, 2025) and a spatially and temporally gridded data product (https://doi.org/10.5281/zenodo.15692788, BRCScienceProducts, 2025).

Shallow, unit process open water (UPOW) constructed wetlands effectively remove a range of water contaminants. Yet, much remains to be learned about internal carbon and nitrogen cycling within these systems and their diel functionality. The current study focused on light/dark fluctuations in water column and biomat geochemistry and N cycle processes in a field-scale UPOW. Dissolved oxygen concentrations fluctuated daily from 50 to 250 % air saturation, while nitrate (NO3-) and ammonium (NH3+4) concentrations cyclically ranged from 30 to 125 μM and 2-20 μM, respectively. NO3- concentrations increased at night and decreased during the day, while N isotope values (δ15N-[NO3-]) varied inversely with concentration. Ammonium concentrations and N isotope values (δ15N-[NH3+4]) both increased at night, decreasing during the day. Column incubations containing biomat and overlying water, conducted in situ with 15N-NO2-, determined nearly equivalent rates of nitrite production and denitrification in the dark (6.6 and 7.2 μmol L-1 d-1, respectively), but both decreased 82-84 % in daylight. Combined nighttime rates of anammox and dissimilatory NO3- reduction to NH3+4 (DNRA) were nearly equal to denitrification. A time-step N process model was used to reproduce diel N species concentration and stable isotope changes, and N dissimilatory rates. Best-fit results to field data indicated that N photoassimilation rates far outpaced dissimilatory rates in either light or dark conditions while denitrification and DNRA rates were greater than incubation-measured rates. Redox gradients and potential diatom migration within the biomat, coupled with diatom DNRA respiration, are factors that complicate the diel function and wetland efficacy by short-circuiting N removal as nitrogen gas.

Sensor-based oxygen (O2) measurements in the ocean have become the dominant data source for in situ O2 since 2010. We examine the overall quality of the shipborne conductivity-temperature-depth (CTD)-and Argo-measured O2 in the World Ocean Database (WOD) and evaluate biases by comparing them to independently measured reference bottle-sampled profiles. No significant O2 difference is found between CTD and bottle data for the global ocean, suggesting high quality of CTD O2 observations generally. A negative residual bias is found in both postcorrected delayed-mode (-1.69 +/- 5.15 mu mol kg-1, fitted mean +/- standard deviation) and real-time-adjusted (-4.68 +/- 6.99 mu mol kg-1) Argo O2. Residual biases have both spatial (basin scale and vertical) and temporal variations, indicating the complexity of residual biases. We also examine Argo O2 biases based on the type of calibration methods. The Argo delayed-mode O2 profiles calibrated using the World Ocean Atlas (WOA), on average, are offset by-1.60 +/- 5.30 mu mol kg-1, while a larger bias (-3.29 +/- 4.86 mu mol kg-1) is found for profiles calibrated using in-air measurements for the global ocean. The Argo delayed-mode data calibrated with O2 profiles from the WOD have a slight positive bias (0.33 +/- 4.16 mu mol kg-1). Further analysis demonstrates that the O2 profiles without matched reference profiles also have negative bias similar to those with matched reference profiles when they are compared to WOA. This analysis suggests the negative residual biases in Argo postcorrected O2 should be carefully considered when using Argo O2 data to draw conclusive inferences about anthropogenic impacts on ocean oxygen concentrations and/or derived quantities in a changing ocean.

Uncertainties in global ocean oxygen inventories are assessed by a coordinated intercomparison of dissolved oxygen inventories derived from two observational data sets with distinct quality control (QC) protocols and five different statistical interpolation methods. We investigate key sources of uncertainty including mapping interpolation schemes and data QC methods, which contribute more significantly than measurement or sampling errors. Local differences in mapped oxygen content can reach up to 10 mu mol/kg (about 4% of the surface climatological mean), especially in the regions of high variability and poor sampling such as the eastern tropical Pacific and the coastal Antarctica. Globally integrated differences, however, are small (<= 0.17% above 2,000 m depth). Mapping methods are likely the largest contributor of the uncertainty for the annual mean, but both mapping and QC methods are important for the seasonal cycle. These results are limited by only including two sets of QC methods and only statistical interpolation techniques. Future incorporation of machine learning-based methods and time-dependent oxygen maps will be critical for tracking deoxygenation trends and for providing observational constraints to validate Earth System Models.

The release of tannery wastewater contributes to chromium (Cr) pollution globally. Herein, we conduct a novel consolidation of research from the Arequipa region of southern Peru that integrates university theses written in Spanish alongside peer-reviewed journal articles. The objective is to provide a place-based complement to existing research in English scientific journals focused on effective tools for Cr treatment from tannery wastewater. Our consolidation categorized a total of 75 publications (70 theses and five peer-reviewed) into five distinct strategies for Cr treatment: adsorption (twenty-three studies), phytoremediation (eighteen studies), bioremediation (thirteen studies), electrocoagulation (five studies), and other techniques (fifteen studies). This synthesis highlighted potentially promising approaches that could be sustainably tailored to regional resources and waste products. This includes sorptive materials derived from food waste such as native achiote peels (B. orellana) and avocado seeds (P. americana) either used directly or as a feedstock for biochar. Other technologies include phytoremediation using microalgae and resident vascular plants and microbial bioremediation that capitalizes on indigenous bacteria and fungi. Promise was also discerned in studies that incorporated a combination of abiotic and biotic mechanisms tailored toward the region, such as infiltration using selective and bioactive materials, wetlands, solar distillation, iron-based coagulation and flocculation, and bioreactors. These findings provide a sustainable complement to prior global investigations for effective attenuation strategies by adding novel materials and techniques that could be further explored to assess the viability of implementation at pilot and larger scales. These promising technologies and the ability to tailor sustainable treatments toward local resources highlight the opportunity to prioritize the treatment of tannery wastewater to ensure a cleaner environment by informing policy makers, academics, and industry on technologies that could be adopted for implementation in the region.

The subpolar North Atlantic (SPNA) is one of the few regions where the deep ocean is in direct contact with the atmosphere, making it a key location for interior ocean ventilation through gas exchange. We use a novel observation-based data product to analyze large-scale patterns of the air-sea flux of oxygen, finding a mean annual flux of 48.1 14.6 Tmol from the atmosphere into the ocean integrated over the SPNA (N-N). An analysis of a fully-closed oxygen budget from the data-assimilative ECCO-Darwin ocean biogeochemistry model suggests that the net uptake is counteracted by oxygen removal through ocean circulation and mixing. Over an annual cycle, a SPNA oxygen uptake of 63.6 13.8 Tmol at densities greater than 26.7 kg drives a wintertime oxygen increase in corresponding mode and deep water layers. 87% of this net annual uptake occurs in the density range of subpolar mode water (SPMW), 26.7 kg 27.63 kg , in the upper branch of the Atlantic Meridional Overturning Circulation (AMOC). Our results demonstrate that oxygen is injected during mode water formation throughout the subpolar gyre's cyclonic pathway from the North Atlantic Current toward the Labrador Sea. Along this path, SPMW becomes progressively denser and more oxygenated, and is ultimately transformed into Labrador Sea Water which exports the accumulated oxygen to the global ocean in the lower branch of the AMOC.

Nitrate is an essential nutrient for phytoplankton growth and is a primary component of ocean carbon cycling. In this study, we developed a neural network constrained by the high spatial and temporal coverage of BGC-Argo floats to predict nitrate in a consistent way throughout space and time in the Southern Ocean, a key area for ocean carbon uptake and controlling global ocean nutrient distributions. After correcting for physical and sampling biases using the Biogeochemical Southern Ocean State Estimate model, we show that annual net community production (ANCP), originally calculated from seasonal nitrate drawdown, reveals the greatest production around the 45-55 degrees S meridional band, and an average basin-wide ANCP of 3.91 +/- 0.13 PgC y-1 with a significant increase of 0.67% y-1 from 2004 to 2022. We also highlight that using the common nitrate seasonal drawdown method to derive ANCP might underestimate the true carbon export at depth by about one third. Our findings align with previous studies, which indicate an increase in surface satellite chlorophyll-a and model export fluxes. Our results demonstrate the potential of leveraging machine learning constrained by BGC-Argo observations to study long-term changes of biogeochemical processes in the ocean.

Strategies to resolve conflicts around water supply and quality in mining-impacted communities include a better understanding of the role and mitigation of mining activities proximal to surface water bodies. The Ocoña watershed of Arequipa (Peru) is one such region where there are conflicts between artisanal miners, fishers, and agriculture over local water resources. The aim of this study is to develop a workflow that leverages existing water quality monitoring and geological mapping to identify major processes that adversely impact water quality with a focus on the relative contribution of mining-related activities on the Ocoña watershed. This study integrates a statistical evaluation of water chemistry data, concentration-discharge relationships, and detailed mineralogy with geochemical modeling to identify sources and mechanisms of contaminant contributions to surface waters in the Ocoña watershed. This revealed that primary deposit mineralogy and seasonal variations exert direct effects on water quality in this mining-active watershed. Multivariate statistical analysis reveals two distinct hydrogeochemical signatures in Ocoña River water that likely represent natural geothermal activity in the northern portion and acid mine drainage contribution in the southern portion of the watershed. In both cases, anthropogenic activities such as mining and agricultural may increase contaminant mobilization. Interestingly, despite perceived challenges, water quality in the Ocoña watershed is better than that of neighbors potentially due to pH-controlled precipitation of Fe and resultant sorption of metals onto precipitants. However, copper released from acid mine drainage, and in particular peak concentrations during the high flow season, exert a potential for adverse ecotoxicological effects. This workflow could potentially be extrapolated to other regions with similar available data to better identify the relative contributions of natural and anthropogenic pressures to the release of toxic compounds in watersheds of concern.

A collaborative analysis of constructed wetlands in Latin America and the Caribbean, published in Spanish, brought together insights from 10 different countries in the region. The collective reports focused on both subsurface and surface flow wetlands. Treatment targets included industrial and agricultural discharges, yet the emphasis was on the treatment of domestic wastewater as a pollutant, and a lack of sufficient municipal wastewater treatment infrastructure. Common macrophyte genera as well as unique species were highlighted for their potential contributions to treatment and ecological diversity. Finally, a growing body of legal frameworks for establishment and protection were reported.

The metalloids boron and arsenic are ubiquitous and difficult to remove during water treatment. As chemical pretreatment using strong base and oxidants can increase their rejection during membrane-based nanofiltration (NF), we examined a nature-based pretreatment approach using benthic photosynthetic processes inherent in a unique type of constructed wetland to assess whether analogous gains can be achieved without the need for exogenous chemical dosing. During peak photosynthesis, the pH of the overlying clear water column above a photosynthetic microbial mat (biomat) that naturally colonizes shallow, open water constructed wetlands climbs from circumneutral to approximately 10. This biological increase in pH was reproduced in a laboratory bioreactor and resulted in analogous increases in NF rejection of boron and arsenic that is comparable to chemical dosing. Rejection across the studied pH range was captured using a monoprotic speciation model. In addition to this mechanism, the biomat accelerated the oxidation of introduced arsenite through a combination of abiotic and biotic reactions. This resulted in increases in introduced arsenite rejection that eclipsed those achieved solely by pH. Capital, operation, and maintenance costs were used to benchmark the integration of this constructed wetland against chemical dosing for water pretreatment, manifesting long-term (sub-decadal) economic benefits for the wetland-based strategy in addition to social and environmental benefits. These results suggest that the integration of nature-based pretreatment approaches can increase the sustainability of membrane-based and potentially other engineered treatment approaches for challenging water contaminants.

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr−1 and pH decrease of 0.0014 ± 0.0004 yr−1. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.

Seawater carbonate chemistry observations are increasingly necessary to study a broad array of oceanographic challenges such as ocean acidification, carbon inventory tracking, and assessment of marine carbon dioxide removal strategies. The uncertainty in a seawater carbonate chemistry observation comes from unknown random variations and systematic offsets. Here, we estimate the magnitudes of these random and systematic components of uncertainty for the discrete open-ocean carbonate chemistry measurements in the Global Ocean Data Analysis Project 2022 update (GLODAPv2.2022). We use both an uncertainty propagation approach and a carbonate chemistry measurement “inter-consistency” approach that quantifies the disagreement between measured carbonate chemistry variables and calculations of the same variables from other carbonate chemistry measurements. Our inter-consistency analysis reveals that the seawater carbonate chemistry measurement community has collected and released data with a random uncertainty that averages about 1.7 times the uncertainty estimated by propagating the desired “climate-quality” random uncertainties. However, we obtain differing random uncertainty estimates for subsets of the available data, with some subsets seemingly meeting the climate-quality criteria. We find that seawater pH measurements on the total scale do not meet the climate-quality criteria, though the inter-consistency of these measurements improves (by 38%) when limited to the subset of measurements made using purified indicator dyes. We show that GLODAPv2 adjustments improve inter-consistency for some subsets of the measurements while worsening it for others. Finally, we provide general guidance for quantifying the random uncertainty that applies for common combinations of measured and calculated values.

Though surface water quality has been monitored in southern Peru over the past and current century, it has been implemented by multiple organizations. The data lacks a centralized repository and access requires logistical and temporal hurdles associated with official requests. A substantial portion of the data has not been quality assured and is in difficult-to-access formats such as scanned PDF documents. These obstacles collectively make it challenging to maximize the impact of these monitoring efforts such as efficiently evaluating long-term water quality trends. To address this opportunity, we gathered available surface water quality information from five watersheds in the Arequipa Region of southern Peru: Camaná, Chili, Ocoña, Tambo, and Yauca. The effort required entry of more than 130,000 records of water quality properties across 274 monitoring stations with data including the concentration of select nutrients, metals, organic compounds, and biological taxa. The water quality records in the Chili watershed go back as far as 1905, while data for the other watersheds was largely confined to the years 2012-2021. This document describes how the surface water quality information was assimilated with quality control and provides a centralized Excel database so that the data can be efficiently used for research and decision making purposes.

The ocean carbonate system is critical to monitor because it plays a major role in regulating Earth's climate and marine ecosystems. It is monitored using a variety of measurements, and it is commonly understood that all components of seawater carbonate chemistry can be calculated when at least two carbonate system variables are measured. However, several recent studies have highlighted systematic discrepancies between calculated and directly measured carbonate chemistry variables and these discrepancies have large implications for efforts to measure and quantify the changing ocean carbon cycle. Given this, the Ocean Carbonate System Intercomparison Forum (OCSIF) was formed as a working group through the Ocean Carbon and Biogeochemistry program to coordinate and recommend research to quantify and/or reduce uncertainties and disagreements in measurable seawater carbonate system measurements and calculations, identify unknown or overlooked sources of these uncertainties, and provide recommendations for making progress on community efforts despite these uncertainties. With this paper we aim to (1) summarize recent progress toward quantifying and reducing carbonate system uncertainties; (2) advocate for research to further reduce and better quantify carbonate system measurement uncertainties; (3) present a small amount of new data, metadata, and analysis related to uncertainties in carbonate system measurements; and (4) restate and explain the rationales behind several OCSIF recommendations. We focus on open ocean carbonate chemistry, and caution that the considerations we discuss become further complicated in coastal, estuarine, and sedimentary environments.