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body {background-color: #D2D1D5;} Research & Developments is a blog for brief updates that provide context for the flurry of news regarding law and policy changes that impact science and scientists today.

Update, 12 February: At a press conference today, President Donald Trump announced that the EPA has revoked the 2009 Endangerment Finding.

Trump said regulations related to the finding were “crippling,” and designed to “facilitate the green new Scam.”

“Effective immediately, we are repealing the ridiculous Endangerment Finding,” he said.

AGU immediately denounced the repeal.

Revoking the finding repeals the EPA’s authority to regulate greenhouse gas emissions and removes all greenhouse gas emissions regulations for vehicles, according to the EPA.

11 February: The Endangerment Finding is a scientific determination made by the EPA that greenhouse gases threaten public health. It is the legal underpinning for major U.S. climate rules under the Clean Air Act. Revoking the finding repeals the EPA’s authority to regulate greenhouse gas emissions and removes all greenhouse gas emissions regulations for vehicles, according to the EPA. 

“I think it’s a historic low, frankly, for EPA to be taking this stance now,” Benjamin DeAngelo, a former EPA official involved in writing the 2009 finding, told POLITICO. 

Leavitt called the planned finalization the “largest deregulatory action in American history.” She said the repeal of the finding would increase energy affordability and, especially, lower vehicle costs, allegedly saving Americans “$1.3 trillion in crushing regulations.” Businesses and groups prioritizing free markets support the administration’s claim, with the editorial board of the Washington Post writing that rescinding the Endangerment Finding will “end the federal government’s power over cars.”

President Donald Trump and EPA Administrator Lee Zeldin will make the announcement to finalize the repeal on 12 February.

The EPA based its July proposal to revoke the finding on an Energy Department report written by five climate contrarians that downplayed accepted climate science. The National Academies of Sciences, Engineering and Medicine, an independent organization meant to advise the federal government on scientific matters, conducted their own review of the report and found that the 2009 Endangerment Finding was “beyond scientific dispute.”

The science supporting the Endangerment Finding “has only gotten stronger” since 2009, DeAngelo told POLITICO. 

 
Related

•  Trump EPA to Take Its Biggest Swing Yet Against Climate Change Rules
•  EPA ‘Endangerment Finding’ Explained: 5 Facts About the Science and Health Risks
•  Public Speaks Out Against EPA Plan to Rescind Endangerment Finding
•  The EPA is Barreling Toward a Supreme Court Climate Showdown
•  AGU Denounces Trump Administration’s Repeal of the EPA Endangerment Finding
•  Get Involved: AGU Science Policy Action Center
 

In public hearings in August, hundreds of people, including children, scientists, doctors, parents, advocates, and members of Congress, spoke out against the proposal to revoke the Endangerment Finding. Many cited immediate health concerns, worry about the health and safety of future generations, and a fear that the proposal would accelerate environmental degradation.

The move by the EPA will likely be challenged in the courts—which may be one reason the Trump administration has pushed its finalization through so rapidly, according to The New York Times. Legal scholars say the current, conservative-majority Supreme Court is more likely to uphold decisions supporting deregulation while Trump is still in office. 

The administration wants “to not just do what other Republican administrations have done, which is weaken regulations. They want to take the federal government out of the business of regulation, period,” Jody Freedman, director of Harvard Law School’s Environmental and Energy Law Program, told The New York Times.

—Grace van Deelen (@gvd.bsky.social), Staff Writer

These updates are made possible through information from the scientific community. Do you have a story about how changes in law or policy are affecting scientists or research? Send us a tip at eos@agu.org. Text © 2026. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Source: Community Science

Geographic information system (GIS) maps help researchers, policymakers, and community members see how environmental risks are spread throughout a given region. These types of interactive, layered maps can be used for storytelling, education, and environmental activism. When community members are involved in their use and creation, GIS maps can also be a tool for equity.

Lively et al. outlined a project focusing on mapping the features and flooding risks at and around the Tar Creek Superfund site in Ottawa County, Okla. Ottawa County is home to 10 federally recognized Tribal Nations. Residents have experienced decades of health and environmental harm from the region’s legacy of zinc and lead mining, most of which occurred within the Quapaw Reservation. Although mining ceased in 1970, giant piles of mining waste, mine water discharges, and unstable ground have poisoned residents and made entire towns unlivable. For almost a century, floods have spread these contaminants across downstream communities.

Technical experts and community members with local knowledge worked together to build a GIS map that can be used by community members and leaders. It depicts how floodwaters run through former mining sites, which then ferry toxic waste throughout the region’s creeks and soils.

The map is viewable in various layers that show the locations of different kinds of mining waste, tribal land boundaries, and flood zones designated by the Federal Emergency Management Agency (FEMA). Users can also view layers showing soil types and the locations of aquifers, fault lines, and wells.

Between 2021 and 2023, members of the Local Environmental Action Demanded Agency (LEAD), a community-led organization, connected with GIS professionals through AGU’s Thriving Earth Exchange. This program partners local organizations with volunteer scientists and experts to address environmental or geoscience-related issues in their communities. Many members of the project team contributing to the Tar Creek project were local to the Miami, Okla., region.

Though much of the actual map building was completed by the GIS expert team member, decisions on what to include in each layer of the map were made by LEAD representatives and nonscientist community members. This coproduction defined equity not only by who built or contributed to the map but also by how it is used by the community as a key storytelling tool—helping to educate officials and residents about the ongoing environmental and health risks when flooding occurs in the region. For the team, it was important not to just make the map but also to use it: Production without activism, the researchers said, would make for an unfinished project. (Community Science, https://doi.org/10.1029/2024CSJ000077, 2026)

—Rebecca Owen (@beccapox.bsky.social), Science Writer

Citation: Owen, R. (2026), Making a map to make a difference, Eos, 107, https://doi.org/10.1029/2026EO260035. Published on 11 February 2026. Text © 2026. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Earth and Space Science

The color of the oceans is an important diagnostic parameter as it reflects the health of oceans, monitors CO2 variability, and tracks ecosystem changes due to environmental stressors. Remote observations of the ocean color (OC) are routinely performed, but rapid changes in this parameter are difficult to capture. Geostationary platforms are uniquely suited for this purpose, because they monitor the same area and can therefore detect changes in real time. However, measurements of OC from geostationary satellites are not routinely performed.

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary instrument monitors air quality and pollution over North America. Using a new approach, Fasnacht et al. [2025] apply a combination of statistical and machine learning techniques to TEMPO hyperspectral hourly measurements, and obtain OC values across the USA coastal regions and the Great Lakes.

Thus, the authors demonstrate the feasibility of capturing hourly variability of environmental parameters from deep space. This reinforces the scientific value of future dedicated geostationary ocean color missions, such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR), and the Geostationary Extended Observations (GeoXO) Ocean Color Instrument (OCX).  

Citation: Fasnacht, Z., Joiner, J., Bandel, M., Ibrahim, A., Heidinger, A., Himes, M. D., et al. (2025). Exploiting machine learning to develop ocean color retrievals from the tropospheric emissions: Monitoring of pollution instrument. Earth and Space Science, 12, e2025EA004341. https://doi.org/10.1029/2025EA004341

—Graziella Caprarelli, Editor-in-Chief, Earth and Space Science

Text © 2026. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Author(s): Luisa Izquierdo, Felipe Veloso, Miguel Escalona, Vicente Valenzuela-Villaseca, Gonzalo Avaria, and Julio Valenzuela

The influence of conical wire array geometry on the formation and dynamics of pulsed-power driven plasma jets is investigated. In the experiments, the jet becomes isolated from the inflows as it passes through an aperture, allowing the study of its intrinsic evolution for different conical angles. O…

[Phys. Rev. E 113, 025203] Published Wed Feb 11, 2026

SummaryIn the last two decades, the improvement of both instruments and theory, as well as the broadened scope of applications, led to a spectacular development of the use of induced polarization. In particular, the richness of complex conductivity spectra is driving the scientific community towards vast deployment of this measurement method often referred to as Spectral Induced Polarization (SIP). In this contribution, we describe an innovative multichannel instrument that we develop for fast monitoring of critical zone processes. The spectral content of a signal with line spectrum resulting from square-wave current is exploited by injecting successively three square-wave currents with periods of 1, 10 and 100 s, covering the frequency range of 10−2 to 102 Hz in less than four minutes. One dataset consists of eight successive current injections at different depths. For each current injection, the electrical potential is simultaneously measured at seven dipoles. The time-series are recorded with a 2 kHz sampling rate, allowing to calculate by Fourier transform the amplitude and phase spectra up to 1 kHz for each quadrupole. The complex conductivity data was validated by a comparison with the commercial SIP-Fuchs instrument, despite a significant discrepancy below 0.1 Hz which may be due to a worse signal-to-noise ratio at low frequencies. The prototype version of the instrument has been installed in 2018 at a wetland at Ploemeur-Guidel hydrogeological observatory to monitor reactive processes with high spatial resolution across the top meter of soil. The instrumental device, controlled by a Gantner data acquisition system connected to a solar panel, is fully autonomous and consumes little energy. Acquisitions are made several times a day and recorded on a SD card. Seven-year continuous monitoring highlights significant temporal variations of both subsurface resistivity and phase angle. The absence of correlation between resistivity and phase variations in the continuously saturated soil thickness highlights the potential of the system to monitor and separate different types of dynamics processes, such as groundwater/surface water mixing and mineral precipitation/dissolution.

SummaryFrom April until the end of June 2025, we deployed a dense seismic network of 271 three-component stations within an 8 km radius around Lavey-les-Bains, Switzerland, to investigate the structure of the country’s hottest known natural geothermal system. The site hosts a 3 km-deep exploration well (Lavey-1), drilled in 2022, that revealed unexpectedly low flow rates despite temperatures exceeding 120°C, prompting the suspension of the project. The site lies within the narrow Rhône Valley, characterized by steep topography, strong lateral structural heterogeneity, and elevated anthropogenic noise, complicating seismic imaging. The dense nodal array was complemented by a distributed acoustic sensing (DAS) system along a buried telecommunication cable, providing a hybrid dataset suited for passive seismic imaging. We describe the network geometry, instrumentation and deployment logistics; assess data completeness and noise characteristics and present first examples of ambient noise and earthquake recordings. Preliminary analyses demonstrate a high data quality and spatial coverage. This experiment establishes a benchmark dataset for developing advanced passive imaging techniques in complex Alpine environments.

Water is made of hydrogen and oxygen, and sometimes these atoms are slightly heavier than usual. These heavier forms are called isotopes. As water evaporates or moves through the atmosphere, the amount of these isotopes changes in predictable ways. This can act as a fingerprint, allowing researchers to trace the movement of water at global scales.

Commercial shipping not only affects the Baltic Sea on the surface, but also has a significant impact on the water column and the seabed. A study by the Leibniz Institute for Baltic Sea Research Warnemünde (IOW) and Kiel University (CAU) now shows for the first time that wake turbulence from large ships in heavily trafficked areas of the western Baltic Sea significantly alters water stratification and leads to marked sea floor erosion. The research team has therefore documented a previously underestimated human impact on shallow marine areas. The results are published in the journal Nature Communications.

Abstract

To meet the high-precision and high-integrity positioning demands of safety–critical applications, monitoring the quality of precise satellite products in global navigation satellite system (GNSS) precise point positioning (PPP) is crucial. This work employs ionosphere-free (IF) PPP with ambiguity resolution (PPP-AR) phase residuals to construct test statistics for monitoring the quality of precise satellite corrections. By utilizing precise satellite orbit and clock products from CODE, WUM, and GRG, the PPP-AR phase residuals were first analyzed with sample moments, Allan variance and power spectral density (PSD). The key findings are as follows: (1) The skewness and kurtosis results indicate that ambiguity-fixed phase residuals deviate from an ideal zero-mean Gaussian distribution and exhibit a super-Gaussian distribution. (2) Allan variance and PSD analysis reveal that flicker noise dominates the phase residuals. (3) The noise amplitudes are similar for all satellites, but certain differences are observed among different GNSS systems and satellite types. (4) The noise level of phase residuals is influenced by the receiver types, antenna types, and precise products from different analysis centers. Leveraging the error characteristics, the two-step Gaussian overbounding (OB) method was employed to estimate the corresponding OB parameters of the phase residuals. The overbounding results demonstrate that, under similar conditions, phase residuals can be bounded by the calculated bound within the acceptable integrity risk after removing the detected outliers. Anomaly monitoring experiments further show that phase residuals can effectively capture anomalies in precise satellite corrections, with the set threshold successfully detecting such anomalies.

The measurement of virtual height of the sporadic E layer (h'Es) is very sensitive to the type of ionosonde used and the calibration processes. The ionosondes used by the national institute of communication an...

Abstract

Solar photospheric abundances and CI-chondrite compositions are reviewed and updated to obtain representative solar system abundances of the elements and their isotopes. The new photospheric abundances obtained here lead to higher solar metallicity. Full 3D NLTE photospheric analyses are only available for 11 elements. A quality index for analyses is introduced. For several elements, uncertainties remain large. Protosolar mass fractions are H (X = 0.7060), He (Y = 0.2753), and for metals Li to U (Z = 0.0187). The protosolar (C+N)/H agrees within 13% with the ratio for the solar core from the Borexino experiment. Elemental abundances in CI-chondrites were screened by analytical methods, sample sizes, and evaluated using concentration frequency distributions. Aqueously mobile elements (e.g., alkalis, alkaline earths, etc.) often deviate from normal distributions indicating mobilization and/or sequestration into carbonates, phosphates, and sulfates. Revised CI-chondrite abundances of non-volatile elements are similar to earlier estimates. The moderately volatile elements F and Sb are higher than before, as are C, Br and I, whereas the CI-abundances of Hg and N are now significantly lower. The solar system nuclide distribution curves of s-process elements agree within 4% with s-process predictions of Galactic chemical evolution models. P-process nuclide distributions are assessed. No obvious correlation of CI-chondritic to solar elemental abundance ratios with condensation temperatures is observed, nor is there one for ratios of CI-chondrites/solar wind abundances.

The January 2022 Tonga volcanic eruption generated atmospheric pressure waves that propagated over the ocean’s surface and triggered a meteotsunami. This meteotsunami caused significant amplitudes exceeding 10...

Abstract

Accurately modeling and prediction the nonlinear motion of GNSS (Global Navigation Satellite System) coordinate time series holds significant theoretical and practical value for the study of geodynamics. A novel integrated network, named Ensemble Learning method based on Signal Source Driver (ELSSD), is proposed, which leverages the strengths of Long Short-Term Memory (LSTM) and Deep Self-Attention Neural Network (DSANN), while integrating GNSS loading data as an additional data source. Additionally, a multi-track synchronous sliding window data processing strategy is designed to address the challenge of multi-source data fusion input. The effectiveness of this algorithm is validated using GNSS coordinate time series from 186 global stations over a period of 10 years. Experimental results initially illustrate that, when accounting for displacement caused by environmental loading effects, there is a marked improvement in the modeling and prediction accuracy compared with GNSS input-only. Furthermore, the application of three ensemble network strategies-Bagging, Boosting, and Stacking-have further been demonstrated to enhance modeling and prediction accuracy. Compared with LSTM and DSANN networks, the proposed ELSSD algorithm achieves an average RMSE (Root Mean Square Error) of 3.6 mm for both modeling and prediction, with modeling accuracy improvements of 4.8% and 6.2%, while prediction accuracy improvements of 5.4% and 5.9%, respectively. With respect to the traditional Least Square method, there is an improvement of 22.1% and 27.9% in modeling and prediction accuracy, respectively. Regarding noise characteristics, there is a significant reduction in colored noise amplitude, with decreases of 36.7% and 36.0% observed in modeling and prediction, respectively. Simultaneously, the velocity uncertainty experiences an average reduction of 27.1% and 27.5%. The average velocity differences are measured at 0.06 mm/year and 0.24 mm/year, respectively. Hence, our findings suggest that the ELSSD algorithm emerges as an effective methodology for handling multi-source data input in GNSS coordinate time series, presenting promising practical applications in the field.

The Mw 7.5 Noto Peninsula earthquake, which occurred on January 1, 2024, was considerably hazardous to the peninsula and surrounding regions owing to a strong motion, large-scale crustal deformation, and subse...

Nature Geoscience, Published online: 21 February 2025; doi:10.1038/s41561-025-01653-z

Quartz-rich clasts in Martian meteorite NWA 7533 indicate the presence of granitic rocks on early Mars that formed via hydrothermal activity and impact melting, according to petrologic and in situ geochemical analyses.

Abstract

The predictive learning of total electron content (TEC) spatiotemporal sequences aims to generate future TEC maps by learning from historical data, where both the spatial appearances and temporal variations are crucial for accurate predictions. However, the state-of-the-art TEC map prediction models typically employ sequential stacking of ConvLSTM, ConvGRU, and their variants. These models focus more on modeling temporal variations, and the spatial features extracted from the historical sequence are highly abstracted, resulting in the fine-grained spatial appearances not being adequately memorized or transmitted, leading to fuzzy prediction results during storm time. In this paper, we used PredRNN to propose a storm-time ionospheric TEC spatiotemporal prediction model with multichannel features, named Multichannel PredRNN, which can simultaneously remember the temporal patterns and spatial appearances in input sequence. The temporal memory as well as the spatial memory are updated repeatedly over time, ensuring that both temporal memory and spatiotemporal memory are fully utilized in prediction. According to Dst index, 60 magnetic storm events from 2011 to 2019 were selected as the dataset. We first discussed the impact of feature combinations on predictive performance. The results show that using multichannel feature (TEC + Dst&F10.7), the Multichannel PredRNN and the comparison models ConvGRU and ConvLSTM have the best prediction performance. Then we used the optimal feature combination for prediction. We compared Multichannel PredRNN with IRI-2016, COPG, ConvLSTM and ConvGRU under various conditions, including the entire test magnetic events, periods of quiet and storm, different phases of geomagnetic storms, and the most severe geomagnetic storms. Finally, we compared the performance of different output steps. The experimental results indicate that in all cases, Multichannel PredRNN with dual memory state and zigzag flow is superior to four compared models.

Abstract

The gravimetry measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission provide an essential way to monitor changes in ocean bottom pressure ( \(p_b\) ), which is a critical variable in understanding ocean circulation. However, the coarse spatial resolution of the GRACE(-FO) fields blurs important spatial details, such as \(p_b\) gradients. In this study, we employ a self-supervised deep learning algorithm to downscale global monthly \(p_b\) anomalies derived from GRACE(-FO) observations to an equal-angle 0.25  \( ^{\circ }\) grid in the absence of high-resolution ground truth. The optimization process is realized by constraining the outputs to follow the large-scale mass conservation contained in the gravity field estimates while learning the spatial details from two ocean reanalysis products. The downscaled product agrees with GRACE(-FO) solutions over large ocean basins at the millimeter level in terms of equivalent water height and shows signs of outperforming them when evaluating short spatial scale variability. In particular, the downscaled \(p_b\) product has more realistic signal content near the coast and exhibits better agreement with tide gauge measurements at around 80% of 465 globally distributed stations. Our method presents a novel way of combining the advantages of satellite measurements and ocean models at the product level, with potential downstream applications for studies of the large-scale ocean circulation, coastal sea level variability, and changes in global geodetic parameters.

Abstract

Data-driven technologies have shown promising potential for improving GNSS positioning, which can analyze observation data to learn the complex hidden characteristics of system models, without rigorous prior assumptions. However, in complex urban areas, the input observation data contain task-irrelevant noisy GNSS measurements arising from stochastic noise, such as signal reflections from tall buildings. Moreover, the problem of data distribution shift between the training and testing phases exists for dynamically changing environments. These problems limit the robustness and generalizability of the data-driven GNSS positioning methods in urban areas. In this paper, a novel deep reinforcement learning (DRL) method is proposed to improve the robustness and generalizability of the data-driven GNSS positioning. Specifically, to address the data distribution shift in dynamically changing environments, the robust Bellman operator (RBO) is employed into the DRL optimization to model the deviations in the data distribution and to enhance generalizability. To improve robustness against task-irrelevant noisy GNSS measurements, the long-term reward sequence prediction (LRSP) is adopted to learn robust representations by extracting task-relevant information from GNSS observations. Therefore, we develop a DRL method with robust augmented reward sequence prediction to correct the rough position solved by model-based methods. Moreover, a novel real-world GNSS positioning dataset is built, containing different scenes in urban areas. Our experiments were conducted on the public dataset Google smartphone decimeter challenge 2022 (GSDC2022) and the built dataset Guangzhou GNSS version 2 (GZGNSS-V2), which demonstrated that the proposed method can outperform model-based and state-of-the-art data-driven methods in terms of generalizability across different environments.