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1. Metrics for quantifying the efficiency of atmospheric CO ₂ reduction by marine carbon dioxide removal (mCDR)

   https://doi.org/10.1088/1748-9326/ad7477  

   Yamamoto, Kana; DeVries, Tim; Siegel, David_A (September 2024, Environmental Research Letters)

   Abstract Marine carbon dioxide removal (mCDR) is gaining interest as a tool to meet global climate goals. Because the response of the ocean–atmosphere system to mCDR takes years to centuries, modeling is required to assess the impact of mCDR on atmospheric CO₂reduction. Here, we use a coupled ocean–atmosphere model to quantify the atmospheric CO₂reduction in response to a CDR perturbation. We define two metrics to characterize the atmospheric CO₂response to both instantaneous ocean alkalinity enhancement (OAE) and direct air capture (DAC): the cumulative additionality (α) measures the reduction in atmospheric CO₂relative to the magnitude of the CDR perturbation, while the relative efficiency (ϵ) quantifies the cumulative additionality of mCDR relative to that of DAC. For DAC,αis 100% immediately following CDR deployment, but declines to roughly 50% by 100 years post-deployment as the ocean degasses CO₂in response to the removal of carbon from the atmosphere. For instantaneous OAE,αis zero initially and reaches a maximum of 40%–90% several years to decades later, depending on regional CO₂equilibration rates and ocean circulation processes. The global meanϵapproaches 100% after 40 years, showing that instantaneous OAE is nearly as effective as DAC after several decades. However, there are significant geographic variations, withϵapproaching 100% most rapidly in the low latitudes whileϵstays well under 100% for decades to centuries near deep and intermediate water formation sites. These metrics provide a quantitative framework for evaluating sequestration timescales and carbon market valuation that can be applied to any mCDR strategy. 

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2. Introducing the Self‐Cleaning FiLtrAtion for Water quaLity SenSors ( SC‐FLAWLeSS ) system

   https://doi.org/10.1002/lom3.10377  

   Khandelwal, Aashish; González‐Pinzón, Ricardo; Regier, Peter; Nichols, Justin; Van Horn, David J. (July 2020, Limnology and Oceanography: Methods)

   Abstract Sensor‐based, semicontinuous observations of water quality parameters have become critical to understanding how changes in land use, management, and rainfall‐runoff processes impact water quality at diurnal to multidecadal scales. While some commercially available water quality sensors function adequately under a range of turbidity conditions, other instruments, including those used to measure nutrient concentrations, cease to function in high turbidity waters (> 100 nephelometric turbidity units [NTU]) commonly found in large rivers, arid‐land rivers, and coastal areas. This is particularly true during storm events, when increases in turbidity are often concurrent with increases in nutrient transport. Here, we present the development and validation of a system that can affordably provide Self‐Cleaning FiLtrAtion for Water quaLity SenSors (SC‐FLAWLeSS), and enables long‐term, semicontinuous data collection in highly turbid waters. The SC‐FLAWLeSS system features a three‐step filtration process where: (1) a coarse screen at the inlet removes particles with diameter > 397 μm, (2) a settling tank precipitates and then removes particles with diameters between 10 and 397 μm, and (3) a self‐cleaning, low‐cost, hollow fiber membrane technology removes particles ≥ 0.2μm. We tested the SC‐FLAWLeSS system by measuring nitrate sensor data loss during controlled, serial sediment additions in the laboratory and validated it by monitoring soluble phosphate concentrations in the arid Rio Grande river (New Mexico, U.S.A.), at hourly sampling resolution. Our data demonstrate that the system can resolve turbidity‐related interference issues faced by in situ optical and wet chemistry sensors, even at turbidity levels > 10,000 NTU. 

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3. Water temperature control on CO₂ flux and evaporation over a subtropical seagrass meadow revealed by atmospheric eddy covariance

   https://doi.org/10.1002/lno.11620  

   Van Dam, Bryce R.; Lopes, Christian C.; Polsenaere, Pierre; Price, René M.; Rutgersson, Anna; Fourqurean, James W. (October 2020, Limnology and Oceanography)

   Abstract Subtropical seagrass meadows play a major role in the coastal carbon cycle, but the nature of air–water CO₂exchanges over these ecosystems is still poorly understood. The complex physical forcing of air–water exchange in coastal waters challenges our ability to quantify bulk exchanges of CO₂and water (evaporation), emphasizing the need for direct measurements. We describe the first direct measurements of evaporation and CO₂flux over a calcifying seagrass meadow near Bob Allen Keys, Florida. Over the 78‐d study, CO₂emissions were 36% greater during the day than at night, and the site was a net CO₂source to the atmosphere of 0.27 ± 0.17 μmol m⁻²s⁻¹(x̅ ± standard deviation). A quarter (23%) of the diurnal variability in CO₂flux was caused by the effect of changing water temperature on gas solubility. Furthermore, evaporation rates were ~ 10 times greater than precipitation, causing a 14% increase in salinity, a potential precursor of seagrass die‐offs. Evaporation rates were not correlated with solar radiation, but instead with air–water temperature gradient and wind shear. We also confirm the role of convective forcing on night‐time enhancement and day‐time suppression of gas transfer. At this site, temperature trends are regulated by solar heating, combined with shallow water depth and relatively consistent air temperature. Our findings indicate that evaporation and air–water CO₂exchange over shallow, tropical, and subtropical seagrass ecosystems may be fundamentally different than in submerged vegetated environments elsewhere, in part due to the complex physical forcing of coastal air–sea gas transfer. 

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4. Lotic‐SIPCO2 : Adaptation of an open‐source CO₂ sensor system and examination of associated emission uncertainties across a range of stream sizes and land uses

   https://doi.org/10.1002/lom3.10600  

   Robison, Andrew L.; Koenig, Lauren E.; Potter, Jody D.; Snyder, Lisle E.; Hunt, Christopher W.; McDowell, William H.; Wollheim, Wilfred M. (February 2024, Limnology and Oceanography: Methods)

   Abstract River networks play a crucial role in the global carbon cycle, as relevant sources of carbon dioxide (CO₂) to the atmosphere. Advancements in high‐frequency monitoring in aquatic environments have enabled measurement of dissolved CO₂concentration at temporal resolutions essential for studying carbon variability and evasion from these dynamic ecosystems. Here, we describe the adaptation, deployment, and validation of an open‐source and relatively low‐cost in situpCO₂sensor system for lotic ecosystems, the lotic‐SIPCO2. We tested the lotic‐SIPCO2 in 10 streams that spanned a range of land cover and basin size. Key system adaptations for lotic environments included prevention of biofouling, configuration for variable stage height, and reduction of headspace equilibration time. We then examined which input parameters contribute the most to uncertainty in estimating CO₂emission rates and found scaling factors related to the gas exchange velocity were the most influential when CO₂concentration was significantly above saturation. Near saturation, sensor measurement ofpCO₂contributed most to uncertainty in estimating CO₂emissions. We also found high‐frequency measurements ofpCO₂were not necessary to accurately estimate median emission rates given the CO₂regimes of our streams, but daily to weekly sampling was sufficient. High‐frequency measurements ofpCO₂remain valuable for exploring in‐stream metabolic variability, source partitioning, and storm event dynamics. Our adaptations to the SIPCO2 offer a relatively affordable and robust means of monitoring dissolved CO₂in lotic ecosystems. Our findings demonstrate priorities and related considerations in the design of monitoring projects of dissolved CO₂and CO₂evasion dynamics more broadly. 

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5. Instrument Bias Correction With Machine Learning Algorithms: Application to Field-Portable Mass Spectrometry

   https://doi.org/10.3389/feart.2020.537028  

   Loose, B.; Short, R. T.; Toler, S. (December 2020, Frontiers in Earth Science)

   null (Ed.)

   In situ sensors for environmental chemistry promise more thorough observations, which are necessary for high confidence predictions in earth systems science. However, these can be a challenge to interpret because the sensors are strongly influenced by temperature, humidity, pressure, or other secondary environmental conditions that are not of direct interest. We present a comparison of two statistical learning methods—a generalized additive model and a long short-term memory neural network model for bias correction of in situ sensor data. We discuss their performance and tradeoffs when the two bias correction methods are applied to data from submersible and shipboard mass spectrometers. Both instruments measure the most abundant gases dissolved in water and can be used to reconstruct biochemical metabolisms, including those that regulate atmospheric carbon dioxide. Both models demonstrate a high degree of skill at correcting for instrument bias using correlated environmental measurements; the difference in their respective performance is less than 1% in terms of root mean squared error. Overall, the long short-term memory bias correction produced an error of 5% for O 2 and 8.5% for CO 2 when compared against independent membrane DO and laser spectrometer instruments. This represents a predictive accuracy of 92–95% for both gases. It is apparent that the most important factor in a skillful bias correction is the measurement of the secondary environmental conditions that are likely to correlate with the instrument bias. These statistical learning methods are extremely flexible and permit the inclusion of nearly an infinite number of correlates in finding the best bias correction solution. 

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