Flux Estimates Research Articles

Accuracy of Ocean CO2 Uptake Estimates at a Risk by a Reduction in the Data Collection

AbstractObservation‐based quantification of ocean carbon dioxide (CO2) uptake relies on synthesis data sets such as the Surface Ocean CO2 ATlas (SOCAT). However, the data collection effort has dramatically declined and the number of annual data sets in SOCATv2023 decreased by ∼35% from 2017 to 2021. This decline has led to a 65% increase (from 0.15 to 0.25 Pg C yr−1) in the standard deviation of seven SOCAT‐based air‐sea CO2 flux estimates. Reducing the availability of the annual data to that in the year 2000 creates substantial bias (50%) in the long‐term flux trend. The annual mean CO2 flux is insensitive to the seasonal skew of the SOCAT data and to the addition of the lower accuracy data set available in SOCAT. Our study highlights the need for sustained data collection and synthesis, to inform the Global Carbon Budget assessment, the UN‐led climate negotiations, and measurement, reporting, and verification of ocean‐based CO2 removal projects.

Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1

Abstract. Isoprene, a key biogenic volatile organic compound, plays a pivotal role in atmospheric chemistry. Due to its high reactivity, this compound contributes significantly to the production of tropospheric ozone in polluted areas and to the formation of secondary organic aerosols. The assessment of biogenic emissions is of great importance for regional and global air quality evaluation. In this study, we implemented the biogenic emission model MEGANv2.1 (Model of Emissions of Gases and Aerosols from Nature, version 2.1) in the surface model SURFEXv8.1 (SURface EXternalisée in French, version 8.1). This coupling aims to improve the estimation of biogenic emissions using the detailed vegetation-type-dependent treatment included in the SURFEX vegetation ISBA (Interaction between Soil Biosphere and Atmosphere) scheme. This scheme provides vegetation-dependent parameters such as leaf area index and soil moisture to MEGAN. This approach enables a more accurate estimation of biogenic fluxes compared to the stand-alone MEGAN model, which relies on average input values for all vegetation types. The present study focuses on the assessment of the SURFEX–MEGAN model isoprene emissions. An evaluation of the coupled SURFEX–MEGAN model results was carried out by conducting a global isoprene emission simulation in 2019 and by comparing the simulation results with other MEGAN-based isoprene inventories. The coupled model estimates a total global isoprene emission of 443 Tg in 2019. The estimated isoprene is within the range of results obtained with other MEGAN-based isoprene inventories, ranging from 311 to 637 Tg. The spatial distribution of SURFEX–MEGAN isoprene is consistent with other studies, with some differences located in low-isoprene-emission regions. Several sensitivity tests were conducted to quantify the impact of different model inputs and configurations on isoprene emissions. Using different meteorological forcings resulted in a ±5 % change in isoprene emissions using MERRA (Modern-Era Retrospective analysis for Research and Applications) and IFS (Integrated Forecasting System) compared with ERA5. The impact of using different emission factor data was also investigated. The use of PFT (plant functional type) spatial coverage and PFT-dependent emission potential data resulted in a 12 % reduction compared to using the isoprene emission potential gridded map. A significant reduction of around 38 % in global isoprene emissions was observed in the third sensitivity analysis, which applied a parameterization of soil moisture deficit, particularly in certain regions of Australia, Africa, and South America. The significance of coupling the SURFEX and MEGAN models lies particularly in the ability of the coupled model to be forced with meteorological data from any period. This means, for instance, that this system can be used to predict biogenic emissions in the future. This aspect of our work is significant given the changes that biogenic organic compounds are expected to undergo as a result of changes in their climatic factors.

Remote measurement of transient heat flux based on the thermomagnetic effect

Abstract Heat flux is a physical quantity that characterises the strength of heat transfer per unit area. Heat transfer occurs with a temperature difference. Herein, a remote heat flux measurement method based on the thermomagnetic effect is proposed. Using Bloch’s law, Faraday’s law of electromagnetic induction, and Fourier’s law, a temperature measurement model from heat flux to magnetisation of the magnetic film and finally to the induced electromotive force in the Helmholtz coil is established. A compression corner was fabricated, and the heat flux measurement method was experimentally verified in a Mach 6 shock tube wind tunnel. In addition, a Schlieren experiment was performed for comparison. In the experiments, the temperature resolution of the magnetic film reached 0.36 K, and the temperature response reached 100 kHz. The range of the proposed heat flux measurement method is up to 5 MW m m − 2 , and the measurement error is less than 10%. At a resolution of 10 μs, the change in heat flow is mapped when there is a transition for the magnetic film from flat to turbulent flow, which shows the different effects of hypersonic heat flux on the boundary layer and attachment position. The magnetic thin film shows the potential for heat flux estimation in our experiment.

Reconstructing Balloon‐Observed Gravity Wave Momentum Fluxes Using Machine Learning and Input From ERA5

AbstractGlobal atmospheric models rely on parameterizations to capture the effects of gravity waves (GWs) on middle atmosphere circulation. As they propagate upwards from the troposphere, the momentum fluxes associated with these waves represent a crucial yet insufficiently constrained component. The present study employs three tree‐based ensemble machine learning (ML) techniques to probe the relationship between large‐scale flow and small‐scale GWs within the tropical lower stratosphere. The measurements collected by eight superpressure balloons from the Strateole 2 campaign, comprising a cumulative observation period of 680 days, provide valuable estimates of the gravity wave momentum fluxes (GWMFs). Multiple explanatory variables, including total precipitation, wind, and temperature, were interpolated from the ERA5 reanalysis at each balloon's location. The ML methods are trained on data from seven balloons and subsequently utilized to estimate reference GWMFs of the remaining balloon. We observed that parts of the GW signal are successfully reconstructed, with correlations typically around 0.54 and exceeding 0.70 for certain balloons. The models show significantly different performances from one balloon to another, whereas they show rather comparable performances for any given balloon. In other words, limitations from training data are a stronger constraint than the choice of the ML method. The most informative inputs generally include precipitation and winds near the balloons' level. However, different models highlight different informative variables, making physical interpretation uncertain. This study also discusses potential limitations, including the intermittent nature of GWMFs and data scarcity, providing insights into the challenges and opportunities for advancing our understanding of these atmospheric phenomena.

Divergent drivers of the spatial variation in greenhouse gas concentrations and fluxes along the Rhine River and the Mittelland Canal in Germany

Lotic ecosystems are sources of greenhouse gases (GHGs) to the atmosphere, but their emissions are uncertain due to longitudinal GHG heterogeneities associated with point source pollution from anthropogenic activities. In this study, we quantified summer concentrations and fluxes of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and dinitrogen (N2), as well as several water quality parameters along the Rhine River and the Mittelland Canal, two critical inland waterways in Germany. Our main objectives were to compare GHG concentrations and fluxes along the two ecosystems and to determine the main driving factors responsible for their longitudinal GHG heterogeneities. The results indicated that the two ecosystems were sources of GHG fluxes to the atmosphere, with the Mittelland Canal being a hotspot for CH4 and N2O fluxes. We also found significant longitudinal GHG flux discontinuities along the mainstems of both ecosystems, which were mainly driven by divergent drivers. Along the Mittelland Canal, peak CO2 and CH4 fluxes coincided with point pollution sources such as a joining river tributary or the presence of harbors, while harbors and in-situ biogeochemical processes such as methanogenesis and respiration mainly explained CH4 and CO2 hotspots along the Rhine River. In contrast to CO2 and CH4 fluxes, N2O longitudinal trends along the two lotic ecosystems were better predicted by in-situ parameters such as chlorophyll-a concentrations and N2 fluxes. Based on a positive relationship with N2 fluxes, we hypothesized that in-situ denitrification was driving N2O hotspots in the Canal, while a negative relationship with N2 in the Rhine River suggested that coupled biological N2 fixation and nitrification accounted for N2O hotspots. These findings stress the need to include N2 flux estimates in GHG studies, as it can potentially improve our understanding of whether nitrogen is fixed through N2 fixation or lost through denitrification.

A study on modeling of boiling heat transfer in core debris bed of SFR

In case of a hypothetical severe accident in a Sodium-cooled Fast Reactor (SFR), coolability of the debris bed in the post-accident phase plays a vital role in mitigating the accident and ensuring the structural integrity of the reactor vessel. Few numerical studies are reported in literature, in which the boiling heat transfer in debris bed is expressed as equivalent heat conduction using similarity law between heat conduction and two-phase heat transfer. However, these studies assumed steady state mass conservation for the boiling zone and neglected the gravity force. Hence, a detailed study has been carried out for various particle sizes and porosities of SFR debris to investigate the influence of above considerations. The effect of gravity on debris bed coolability is studied using steady state model of Lipinski, which showed that gravity has a non-negligible effect, for particle size of 0.3 mm and porosity of 0.5. However, the gravitation force was found to have a negligible effect in dryout heat flux estimation for the bottom cooled configuration. A transient numerical model is developed for simulating the boiling phenomena in debris beds and validated with the published experimental results. The assumption of steady state mass conservation is verified by carrying out transient analysis, which indicated early prediction of the dryout inception. For time dependent heat generation case, the unsteady mass conservation predicted higher DHF compared to constant heat generation.

Atmospheric oxygen as a tracer for fossil fuel carbon dioxide: a sensitivity study in the UK

Abstract. We investigate the use of atmospheric oxygen (O2) and carbon dioxide (CO2) measurements for the estimation of the fossil fuel component of atmospheric CO2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O2 and CO2, minimizing the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which make APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the exchange ratio of O2 to CO2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compare three ocean O2 flux estimates from the NEMO–ERSEM, the ECCO–Darwin ocean model, and the Jena CarboScope (JC) APO inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios is investigated through Monte Carlo sampling within literature uncertainty ranges and by comparing different inventory estimates. We focus our model–data analysis on the year 2015 as ocean fluxes are not available for later years. As APO measurements are only available for one UK site at this time, our analysis focuses on the Weybourne station. Model–data comparisons for two additional UK sites (Heathfield and Ridge Hill) in 2021, using ocean flux climatologies, are presented in the Supplement. Of the factors that could potentially compromise simulated APO-derived fossil fuel CO2 (ffCO2) estimates, we find that the ocean O2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable in magnitude to the contribution of simulated fossil fuel CO2 to simulated APO. We find that simulations using different ocean fluxes differ from each other substantially. No single model estimate, or a model estimate that assumed zero ocean flux, provided a significantly closer fit than any other. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources needs to be better accounted for in model simulations of APO in the UK to reduce the potential influence on inferred fossil fuel CO2 using APO.

Estimation of heat flux entering the bone during the drilling process using the inverse heat transfer method

Drilling is one of the most important surgical steps and bone fracture repair. In this step, the temperature may exceed 47 °C and thermal necrosis may occur, which has irreversible consequences. Knowing the amount of heat transferred to the bone and its changes can be effective in recognizing this heat damage and minimizing it. In this research, it was tried to estimate the amount of heat flux entering the bone during the drilling process using the inverse heat transfer method. Drilling tests were performed on cow bone samples and the temperature changes at the hole location were measured using the inverse heat conduction transfer method. The amount of heat input was estimated. The K-type thermocouple is used to measure temperature. The diaphysis fragment of the cow's thigh (middle part), which has a length of about 90 mm and the thickness of the cortical bone in this range is about 8–10 mm, was used. Also, to ease the tests, the beginning and end parts of the fresh femur were cut with a saw. The results show that the maximum heat flux occurs earlier than the maximum temperature. It was observed that the maximum heat flux and the maximum temperature rise with the increase of the rotational speed and the diameter of the tool. As the feed speed rises, first the maximum temperature and maximum heat flux decrease and then increase. By tripling the feed speed, the maximum heat flux decreases by about 20%. The highest percentage rise in temperature and heat flux occurs with a rise in rotational speed, which is around 130–140%. After the rotational speed, increasing the drill diameter increases the temperature up to about 66.7%. The results can be used to prevent thermal necrosis because the maximum heat flux is visible before the occurrence of the maximum temperature.

The River Runner: a low-cost sensor prototype for continuous dissolved greenhouse gas measurements

Abstract. Freshwater ecosystems are sources of the two most relevant greenhouse gases (GHGs): CO2 and CH4. Understanding the importance of freshwater ecosystems in the global carbon cycle and their role in global warming trends requires the accurate quantification of gas fluxes from the water phase to the atmosphere. These fluxes depend on the gas exchange velocity and the concentration gradient between the phases, which both cause high spatio-temporal variability in fluxes. On a global scale, the estimation of fluxes is limited by the lack of cheap and accurate methods to measure dissolved gas concentrations. Low-cost sensors, as an alternative to expensive gas analysers, are available; however, to date, the in situ performance of such sensors has been poorly examined. Here, we present an inexpensive data-logging sensor prototype that provides continuous measurements of dissolved CO2 and CH4 in submerged environments. Gas measurements are done in a confined gas space, which is rapidly equilibrated with the water phase through a single-layer polytetrafluoroethylene (PTFE) membrane, by a miniature non-dispersive infrared (NDIR) sensor for CO2 (Sunrise sensor, Senseair, Sweden) and a cheap metal oxide sensor for CH4 (TGS2611-E, Figaro Engineering Inc., Japan). Pressure, temperature and humidity are measured to correct raw sensor readings. For freshwater, the dissolved gas concentration is directly obtained from the measured molar fraction and temperature and pressure readings. In air, we measured the molar fraction of CO2 in a range from 400 to 10 000 ppm and the molar fraction of CH4 in a range from 2 to 50 ppm with an accuracy of ± 58 and ± 3 ppm respectively. We successfully used our prototype to measure diurnal variations in dissolved CO2 in a natural stream. We further calibrated the CH4 sensor for in situ use at concentrations ranging from 0.01 to 0.3 µmol L−1. Underwater, we were able to measure the molar fraction of CH4 in the prototype head with an accuracy of ± 13 ppm in the range from 2 to 172 ppm. The underwater measurement error of CH4 is always higher than for the same concentration range in air, and CH4 is highly overestimated below 10 ppm. At low CH4, humidity was the most important influence on the TGS2611-E sensor output in air, whereas temperature became the predominant factor underwater. We describe the response behaviour of low-cost sensors in submerged environments and report calibration methods to correct for temperature and humidity influence on the sensor signal if used underwater. Furthermore, we provide do-it-yourself instructions to build a sensor for submerged continuous measurements of dissolved CO2 and CH4. Our prototype does not rely on an external power source, and we anticipate that such robust low-cost sensors will be useful for future studies of GHG emissions from freshwater environments.

An examination of point-particle Lagrangian simulations for assessing time-resolved hydroacoustic particle flux measurements in sediment-laden flows.

Accurate modelling and prediction of sediment transport in aquatic environments is essential for sustainable coastal and riverine management. Current capabilities rely on physical process-based numerical models and fine-scale sediment flux measurements. High-resolution hydroacoustic instrumentation has emerged as a promising tool for such measurements. However, challenges arise due to the inherent complexity of ultrasound scattering processes. This study introduces a numerical modelling using a point-particle approach to simulate the echoes backscattered by such instrumentation in sediment-laden flow conditions. The model considers geometric, statistical, particle cloud, and flow-induced effects on sediment velocity, concentration, and flux estimates using an acoustic concentration and velocity profiler as a reference. The model performance is assessed here under unidirectional constant flow conditions in terms of velocity, concentration, and time-resolved sediment flux estimates for a large range of the particles' advection speed and sampled volume sizes. Application to the estimation of the measurement accuracy of sediment flux in these flows is also considered, with a final error on the flux seen to be partially controlled by the residence time of particles within the sampled volumes. The proposed model provides insights into scattering processes and offers a tool for investigating robust sediment flux estimation techniques in various flow conditions.

How well does surface magnetism represent deep Sun-like star dynamo action?

Context. For Sun-like stars, the generation of toroidal magnetic field from poloidal magnetic field is an essential piece of the dynamo mechanism powering their magnetism. Previous authors have estimated the net toroidal flux generated in each hemisphere of the Sun by exploiting its conservative nature. This only requires observations of the photospheric magnetic field and surface differential rotation. Aims. We explore this approach using a 3D magnetohydrodynamic dynamo simulation of a cool star, for which the magnetic field and its generation are precisely known throughout the entire star. Methods. Changes to the net toroidal flux in each hemisphere were evaluated using a closed line integral bounding the cross-sectional area of each hemisphere, following the application of Stokes theorem to the induction equation; the individual line segments correspond to the stellar surface, base, equator, and rotation axis. We evaluated the influence of the large-scale flows, the fluctuating flows, and magnetic diffusion on each of the line segments, along with their depth-dependence. Results. In the simulation, changes to the net toroidal flux via the surface line segment typically dominate the total line integral surrounding each hemisphere, with smaller contributions from the equator and rotation axis. The surface line integral is governed primarily by the large-scale flows, and the diffusive current; the latter acting like a flux emergence term due to the use of an impenetrable upper boundary in the simulation. The bulk of the toroidal flux is generated deep inside the convection zone, with the surface observables capturing this due to the conservative nature of the net flux. Conclusions. Surface magnetism and rotation can be used to produce an estimate of the net toroidal flux generated in each hemisphere, allowing us to constrain the reservoir of magnetic flux for the next magnetic cycle. However, this methodology cannot identify the physical origin or the location of the toroidal flux generation. In addition, not all dynamo mechanisms depend on the net toroidal field produced in each hemisphere, meaning this method may not be able to characterise every magnetic cycle.

Fractionation Mechanism and Flux Estimation of Strontium Isotopes During Basalt Weathering

AbstractThe fluxes of metal cations and isotopes released by weathering of silicate rocks are crucial and a prerequisite for constraining geochemical fluxes to rivers and oceans. This study presents mineral and elemental compositions along with 87Sr/86Sr and δ88Sr data from a basaltic weathering regolith on Hainan Island, South China to elucidate Sr isotope fractionation and weathering fluxes. The 87Sr/86Sr ratios vary from 0.703936 to 0.706338 as a result of differential weathering of the minerals. δ88Sr values in the weathering regolith range from −0.29 to 0.37‰, with the majority of the weathering regolith having lower δ88Sr values than the parent rock. Sr is leached into the soil solution during plagioclase decomposition, while 86Sr is preferentially adsorbed on the surface of secondary minerals. As weathering progresses, smectite decomposes and kaolinite desorbs under weakly acidic conditions, releasing the previously adsorbed 86Sr into the soil solution. The differential weathering of kaolinite and smectite controls the δ88Sr values of the weathering regolith, with pH being an important determinant of isotope fractionation. Furthermore, Sr elemental fluxes (SrFlux) and Sr isotopic fluxes (δ88SrFlux) of this weathering regolith were calculated using a mass balance model, yielding mean values of 0.20 (mg cm−3 Myr−1) and 0.052 (‰ (mg cm−3 Myr−1)), respectively. The δ88SrFlux exhibits a nonlinear positive correlation with the Chemical Index of Alteration (CIA), indicating that enhanced weathering leads to significant stable Sr isotope fractionation at CIA values below 95%. Our research promotes the understanding of Sr recycling and the fractionation behavior of stable Sr isotopes during consecutive weathering.

Studies on acceleration algorithms for numerical simulations of hypersonic conjugate heat transfer problems

This research proposes acceleration algorithms for the coupled numerical simulation of the long-time hypersonic conjugate heat transfer problem from two perspectives. First, the newly developed disturbance region update method (DRUM) is introduced to enhance the convergence speed of the steady flow field. The numerical investigations reveal that, in the context of hypersonic CHT issues, the disturbance prompted by variations in wall temperature remains confined post the detached shock wave. The implementation of DRUM has been shown to substantially diminish the computational demand per iteration and the total iterations required for convergence. DRUM could reduce the time needed to calculate the flow field by over 60 % for a typical CHT case. Second, a new aerodynamic heat flux estimate method is introduced based on the local wall temperature within a coupled time step to increase the coupled time step. Combining this method with the adaptive coupled time step method, a long-time CHT problem with a total time of 1100s requires only 25 coupled time steps, much less than the 220 steps needed for the existing method. Meanwhile, the average error of the obtained solid temperature field, when compared to the temperature obtained from an accurate simulation with a fixed small coupled time step, is maintained below 0.22 %.

Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model

This study explores the potential of integrating satellite retrievals of surface soil moisture (SSM) and vegetation conditions into the Noah-MP land surface model. In total, five data assimilation (DA) experiments were carried out. One of the experiments only assimilates SSM retrievals from the Soil Moisture Active Passive mission, two experiments only assimilate retrievals of vegetation conditions: either optical retrievals of leaf area index (LAI) from the Copernicus Global Land Service, or X-band microwave-based retrievals of vegetation optical depth (VOD) from the Advanced Microwave Scanning Radiometer 2. Additionally, two joint DA experiments are performed, each incorporating SSM and one of the vegetation products. The DA experiments are compared with a model-only run, and all experiments are evaluated using independent ground reference data of soil moisture, evapotranspiration, net ecosystem exchange and gross primary production (GPP). Assimilating only SSM improves estimates of the soil moisture profile (median SSM anomaly correlation improves with 0.02 compared to a model-only run), whereas assimilating LAI predominantly improves GPP estimates (reduction in median RMSD of 0.024 gC m−2 day−1 compared to a model-only run). The joint assimilation of SSM and vegetation conditions captures both of these improvements in a single, physically consistent analysis product. The DA increments show that this combined setup allows one satellite product to compensate for potential degradations introduced into the system by the other product. Furthermore, the joint SSM and VOD DA experiment has the smallest ensemble spread in its estimates (21% reduction in SSM spread compared to a model-only run). Overall, our results underline the potential of multi-sensor and multivariate DA, in which information from different sources is combined to improve the estimates of several land surface states and fluxes simultaneously.

Abstract 18: The polyamine-hypusine axis is a promising avenue to improve T-cell metabolism and persistence for adoptive cell therapy

Abstract There are significant barriers to the application of Adoptive Cell Therapy (ACT) to many cancer types. One of these barriers is a result of poor metabolic fitness of T cells, leading to poor proliferation, persistence, and dampened anti-tumor function. Leveraging T-cell metabolism to improve their function is becoming increasingly important to make significant strides in immunotherapy. Stat5a has been shown to be a master regulator of energy and metabolism in T cells, highlighting it as a promising avenue to improve T-cell metabolism for ACT. We have established that the expression of Constitutively Active Signal Transducer and Activator of Transcription 5A (CASTAT5) on tumor-specific CD4+ T cells improves their persistence, polyfunctionality, and anti-tumor effects in a mouse model. This study further examines Stat5a and its downstream metabolic targets in the context of ACT. We carried out scRNAseq analysis and found that Stat5a directly interacts with genes involved in polyamine metabolism. More specifically, Stat5a activation increases the expression of ornithine decarboxylase (Odc1), spermidine synthase (Srm), deoxyhypusine synthase (Dhps), and deoxyhypusine hydroxylase (Dohh) in a subset of CD4+ T cells expressing CASTAT5. Cleavage Under Targets and Tagmentation (Cut&Tag) analysis of histone H3K27ac also demonstrated increased H3K27ac at promoter and enhancer regions of polyamine pathway genes in CASTAT5 compared to control CD4+ T cells. To further examine the function of Stat5a, the murine pro-B cell line, Ba/F3, is being used as a model to study Stat5a-mediated signaling. We have found that upon withdrawal of IL-3 from Ba/F3 cells, Stat5a activation is significantly downregulated. Our scRNA-seq-based metabolic flux estimation showed a significant reduction in metabolic fluxes and related genes, including polyamines, following IL-3 withdrawal. Correspondingly, Stat5a ChIP-Seq and Cut&Tag analyses of H3K27ac revealed Stat5a binding to active enhancer regions in the presence of IL-3 that are lost following its withdrawal. Untargeted metabolomics confirmed the marked Stat5a-mediated reduction in metabolite abundance after IL-3 deprivation, in a time-dependent manner. The polyamine-hypusine axis revealed in our studies is a promising avenue to improve T-cell function. This axis has been shown to regulate T-cell function, and potentially modulate mitochondrial respiration and T-cell activation. Hence, our studies have identified polyamine supplementation and gene modulation as promising strategies to improve the metabolism of Adoptive T cells in a Stat5a-mediated manner. Further studies will evaluate the effectiveness of this avenue in improving T-cell function, improving their efficacy for ACT, and improving patient outcomes. Citation Format: Mercy Kehinde-Ige, Ogacheko Okoko, Gang Zhou, Huidong Shi. The polyamine-hypusine axis is a promising avenue to improve T-cell metabolism and persistence for adoptive cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 18.

Proposed Resolution to the Solar Open Magnetic Flux Problem

The solar magnetic fields emerging from the photosphere into the chromosphere and corona are comprised of a combination of closed (field lines with both ends rooted at the Sun) and open (field lines with only one end at the Sun) fields. Since the early 2000s, the magnitude of total unsigned open magnetic flux estimated by coronal models has been in significant disagreement with in situ spacecraft observations, especially during solar maximum. Estimates of total open unsigned magnetic flux using coronal hole observations (e.g., using extreme ultraviolet or helium (He) I) are in general, in average agreement with the coronal model results and thus show similar disagreements with in situ observations. This paper provides a brief overview of the problem, summarizes the proposed explanations for the discrepancies, and presents results that strongly support the explanation that the discrepancy is due to dynamics at the open-closed boundary. These results are derived from the determination of the total unsigned open magnetic flux, utilizing the Wang–Sheeley–Arge model at a particular spatial resolution and different field-line tracing methods. One of these methods produces excellent agreement with in situ observations. Our results imply that strong magnetic fields in close proximity to active regions and residing near the boundaries of mid-latitude coronal holes are the primary source of the missing open flux. Furthermore, the results outlined here resolve many of the seemingly contradictory facts that have made the open-flux problem so difficult.

Designing additional CO2 in-situ surface observation networks over South Korea using bayesian inversion coupled with Lagrangian modelling

Efforts to enhance greenhouse gas (GHG) emission reduction in East Asia play a pivotal role on both global and regional scales in advancing climate mitigation strategies. This study aimed to better constrain anthropogenic CO2 emission estimates by expanding the network of near-surface in-situ stations for CO2 observations across South Korea. To achieve an optimal CO2 network design, we conducted an Observing System Simulation Experiment (OSSE) coupled with the Stochastic Lagrangian Transport model (STILT), utilizing meteorological data from the Korean Integrated Model (KIM). Our inversion setup incorporated two CO2 emission datasets with a 0.1o resolution: EDGAR v6 for prior emissions and GRACED for truth emissions. A uniform model-mismatch error of 3 ppm was introduced across sites. The effectiveness of the existing five in-situ stations, termed the base network, in South Korea was evaluated to gauge their ability to constrain CO2 surface flux estimates. However, the findings revealed a reduction in flux uncertainty of only 29.2%, which fell short of the desired uncertainty reduction goal. In this base network, the Lotte World Tower (LWT: 37.5126°E, 127.1025°E) in Seoul and the Anmyeondo (AMY: 36.538576° N, 126.330071° E) site in Taean county stood as major contributors, with estimated reductions of 17.48% and 6.35%, respectively. Consequently, we proposed and developed an extended network, identifying seven candidate sites based on consideration of logistical factors, existing infrastructures, and proximity to the emission source regions. An incremental optimization scheme ranked their contributions, resulting in an additional 25% reduction, bringing the total to 54.13%. However, it is noteworthy that diminishing returns (ranging from 13% to less than 0.1%) were observed with an increase in station count mainly due to the possibility that adding a station earlier in the sequence might render subsequent stations redundant. Despite this, the proposed CO2 network successfully reduced uncertainty in emissions, narrowing the gap with the objectives of the Global Greenhouse Gas Watch (G3W).

Using satellite imagery to estimate CO2 partial pressure and exchange with the atmosphere in the Songhua River

Rivers play a vital role in the global carbon cycle, even though they account for only 0.58 % of the Earth's non-glaciated land surface area. Owing to the significant spatial and temporal variability of the partial pressure of CO2 (pCO2) in river water, there is a large degree of uncertainty in the estimation of CO2 flux from the river water-atmosphere interface. This study assembled more than 200 in-situ measured pCO2 in the mainstream water of Songhua River and matched with Sentinel-2 overpasses within ± 7 days from the GEE platform. Multiple linear regression (MLR) and two machine learning models (Random Forest and XGBoost) algorithms were used to estimate pCO2 in Songhua River. MLR, Random Forest, and XGBoost all demonstrated good performance and have the potential to map pCO2 for different years using high-quality Sentinel-2 images. In all the tested modeling approaches, the XGBoost model exhibited stable performance in the validation set (R2 > 0.79, RMSE < 228 µatm, MAPE < 42.93 %, SMAPE < 30.64 %). The distribution of mapped pCO2 by XGBoost model significantly varied with seasonal change, and the pCO2 in summer (445 ± 125 µatm) was higher than in autumn (373 ± 58 µatm) and spring (351 ± 57 µatm). CO2 emission flux from the mainstream water of Songhua River was calculated based on the inverse pCO2 value with a net annual uptake of 15.6 Gg C during the ice-free period. The findings suggest that the Songhua River functions as a carbon sink during both spring and autumn but as a carbon source during the summer. In addition, the potential control of annual CO2 flux spatial variability was attributed to the characteristics of the watershed landscapes. The study is critical to clarify the role of river systems in the global carbon cycle.

Space-specific flux estimation of atmospheric chemicals from point sources to soil

Predicting chemical flux to soil from industrial point sources accurately at a regional scale has been a significant challenge due to high uncertainty in spatial heterogeneity and quantification. To address this challenge, we developed an innovative approach by combining California Air Resources Board Puff (CALPUFF) and mass balance models, leveraging their complementary strengths in quantitative accuracy and spatial precision. Specifically, CALPUFF was used to predict the polycyclic aromatic hydrocarbons (PAHs) flux to soil due to industrial sources. Additionally, the spatial distribution coefficient of PAHs flux (e.g., si for spatial unit i) was calculated by neural network and combined with the mass balance model to obtain the results of total PAHs fluxes, which were then combined with the results predicted by CALPUFF to effectively estimate the contribution of industrial sources to soil PAHs flux. Taking a petrochemical industry region located in Zhejiang province, China as a case study, results showed the input Phenanthrene (Phe) and Benzo(a)pyrene (BaP) fluxes predicted by CALPUFF were generally lower than those by the mass balance model, with slightly different distribution patterns. CALPUFF results, based on 36 industrial sources, partially represent those of the mass balance model, which includes all sources and pathways. It was suggested that industrial sources contributed 49%–89% and 65%–100% of soil Phe and BaP, respectively across the study area. The average Phe flux from point sources by deposition averaged 2.68 mg m−2∙a−1 in 2021, accounting for approximately 60% of the total Phe flux to soil. The average BaP flux from point sources by deposition averaged 0.0755 mg m−2∙a−1, accounting for only 0.1%–3.65% of the total BaP flux to soil. Thereby, our approach fills up a gap between the relevance to point sources and the accuracy of deposition quantification in estimating chemical flux from specific point sources to soil at a regional scale.

First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK

Abstract. Atmospheric methane (CH4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ∼ 60 % of global CH4 emissions, of which 20 % come from oil and gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat's satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK, in the spring and summer of 2023 and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME), to estimate the flux from both satellite- and ground-based observation methods and assess the leak's contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat's emission estimates to be in broad agreement with those made from the in situ measurements. During the study period (March–June 2023) GHGSat's emission estimates are 236–1357 kg CH4 h−1, whereas the mobile surface measurements are 634–846 kg CH4 h−1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 h−1, which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change.