Dylan B. A. Jones

The American Geophysical Union (AGU) announces the new living collection “Impactful Datasets in Earth, Space, and Environmental Sciences” which recognizes the creation and curation of datasets enabling research, analysis, and decision making beyond what is captured by traditional academic impact metrics. The collection highlights datasets through the dimensions of People, Planet, and Prosperity, and includes a curated set of nominated datasets and accompanying special collection of data papers. Developed with the Earth Science Information Partners (ESIP) community, the nomination and review process initially invites contributors from across research, operations, and public service, to nominate datasets important to their work, and then submit a data paper that expands upon the nomination text for inclusion in the special collection. This commentary highlights the rationale for the living collection, and the process of nomination. The collection serves as a living reference that strengthens data culture, supports reproducible geoscience, and provides enduring access to critical information for science and society.

The Orbiting Carbon Observatory‐3 (OCO‐3) provides precise observations of the column‐averaged dry air mole fraction of carbon dioxide () with the ability to target specific areas using the Snapshot Area Maps (SAMs). This study utilizes OCO‐3 observations in a Bayesian inversion framework, together with the column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model driven by the Weather Research and Forecasting (WRF) model to estimate emissions from the Bełchatów power plant in Poland. Many previous studies quantifying power plant emissions have relied on Gaussian plume modeling (GPM), cross‐sectional flux (CSF), and Integrated Mass Enhancement (IME) approaches. These methods have been effective for estimating point source emissions, but they do not explicitly simulate the emission plume. We used the WRF model at a 1 km resolution to simulate the emission plume from the Bełchatów power plant. We used nine SAMs over Bełchatów between April 2020 and October 2022, together with nitrogen dioxide () observations from the TROPOspheric Monitoring Instrument (TROPOMI) to isolate the plume in the OCO‐3 data. We found that the satellite‐derived emissions are in good agreement with independent emission estimates derived from hourly reported power generation data as well as estimates from previous studies. Our results demonstrate the potential of Bayesian inverse modeling with WRF‐X‐STILT for quantifying emissions from power plants.

Ammonia is an important gaseous air pollutant with many direct and indirect negative effects on human and ecosystem health. The thermodynamic equilibrium between the ammonia in the gas phase and the ammonium ion in the condensed phase complicates the understanding of its ambient concentrations. In this study, long‐term historical data of ammonia concentrations and particle composition at 10 sites from Canada's National Air Pollution Surveillance program was used to show that the simultaneous analysis of both measurements is crucial for properly understanding ammonia's atmospheric budget. Over the multiyear observation period, the ammonia concentration displayed a positive slope or no trend in all sites, while the NHx (sum of ammonia and ammonium) concentration remained almost constant in most sites, and the fraction of NHx in the gas phase increased; that is, the apparent trend in ammonia depends not only on source and sink processes but on the atmospheric conditions that control the gas‐particle equilibrium. Under these circumstances, generalizations about the trends may be incorrect if either the ammonia or ammonium is analyzed in isolation due to their different deposition rates. The comparison of measurements with the output from the GEOS‐Chem High Performance model shows that the seasonality of the ammonia emission inventories and, consequently, the ammonia budget in the model needs to be revised. This work underscores the need for more sites monitoring ammonia concentrations simultaneously with particle composition to understand the temporal and spatial variability and effects of ammonia.

Isoprene is a reactive hydrocarbon emitted to the atmosphere in large quantities by terrestrial vegetation. Annual total isoprene emissions exceed 300 Tg a⁻¹, but emission rates vary widely among plant species and are sensitive to meteorological and environmental conditions including temperature, sunlight, and soil moisture. Due to its high reactivity, isoprene has a large impact on air quality and climate pollutants such as ozone and aerosols. It is also an important sink for the hydroxyl radical which impacts the lifetime of the important greenhouse gas methane along with many other trace gas species. Modeling the impacts of isoprene emissions on atmospheric chemistry and climate requires accurate isoprene emission estimates. These can be obtained using the empirical Model of Emissions of Gases and Aerosols from Nature (MEGAN), but the parameterization of this model is uncertain due in part to limited field observations. In this study, we use ground‐based measurements of isoprene concentrations and fluxes from 11 field sites to assess the variability of the isoprene emission temperature response across ecosystems. We then use these observations in a Metropolis‐Hastings Markov Chain Monte Carlo (MHMCMC) data assimilation framework to optimize the MEGAN temperature response function. We find that the performance of MEGAN can be significantly improved at several high‐latitude field sites by increasing the modeled sensitivity of isoprene emissions to past temperatures. At some sites, the optimized model was nearly four times more sensitive to temperature than the unoptimized model. This has implications for air quality modeling in a warming climate.

Isoprene is a reactive hydrocarbon emitted to the atmosphere in large quantities by terrestrial vegetation. Annual total isoprene emissions exceed 300 Tg a-1, but emission rates vary widely among plant species and are sensitive to meteorological and environmental conditions including temperature, sunlight, and soil moisture. Due to its high reactivity, isoprene has a large impact on air quality and climate pollutants such as ozone and aerosols. It is also an important sink for the hydroxyl radical which impacts the lifetime of the important greenhouse gas methane along with many other trace gas species. Modelling the impacts of isoprene emissions on atmospheric chemistry and climate requires accurate isoprene emission estimates. These can be obtained using the empirical Model of Emissions of Gases and Aerosols from Nature (MEGAN), but the parameterization of this model is uncertain due in part to limited field observations. In this study, we use ground-based measurements of isoprene concentrations and fluxes from 11 field sites to assess the variability of the isoprene emission temperature response across ecosystems. We then use these observations in a Metropolis-Hastings Markov Chain Monte Carlo (MHMCMC) data assimilation framework to optimize the MEGAN temperature response function. We find that the performance of MEGAN can be significantly improved at several high-latitude field sites by increasing the modelled sensitivity of isoprene emissions to past temperatures. At some sites, the optimized model was nearly 4 times more sensitive to temperature than the unoptimized model. This has implications for air quality modelling in a warming climate.

In September 2020, the Western United States experienced anomalously severe wildfires that resulted in carbon monoxide (CO) emissions almost three times the 2001–2019 average. In this study, we investigate the influence of wildfires on atmospheric carbon monoxide (CO) variability through a comparative analysis of observations from the Measurements of Pollution in the Troposphere (MOPITT), the Infrared Atmospheric Sounding Interferometer (IASI), and the Tropospheric Monitoring Instrument (TROPOMI). Our focus is on the North American domain, aiming to understand the differences among these products. In general, all instruments show excellent agreement under typical atmospheric CO conditions. However, notable discrepancies were observed in the CO data from the three sensors, particularly in regions with elevated CO total column (TC) values. IASI and TROPOMI consistently showed higher CO values over the western U.S. compared to MOPITT. During the fire episodes, we found that the IASI retrievals suggested higher CO abundances near the surface than the MOPITT thermal infrared retrievals that are probably the result of the differences in the covariance matrices used in IASI and MOPITT retrievals. We also found that the high IASI and TROPOMI CO observations over the western U.S. coincided with high values of the TROPOMI aerosol index (AI), suggesting the presence of absorbing aerosols. The analysis exhibited better agreement between TROPOMI and MOPITT CO TC when the AI values were low. Our results suggest that appropriate quality filtering should be employed when analyzing pollution events with these data. In particular, utilizing the AI for quality filtering may be useful when analyzing extreme pollution events with these satellite products.

The long‐term trends and seasonality of many tropospheric pollutants are not well characterized in the high Arctic due to a dearth of trace‐gas measurements in this remote region. In this study, the inter‐ and intra‐annual variabilities of carbon monoxide (CO), acetylene (C₂H₂), ethane (C₂H₆), methanol (CH₃OH), formaldehyde (H₂CO), formic acid (HCOOH), and peroxyacetyl nitrate (PAN) in the high Arctic region were derived from the total column time‐series of ground‐based Fourier transform infrared (FTIR) measurements at Eureka, Nunavut (80.05°N, 86.42°W, 2006–2020) and Thule, Greenland (76.53°N, 68.74°W, 1999–2022). Consistent seasonal cycles were observed in the FTIR measurements at both sites for all species. Negative trends were observed for CO, C₂H₂, and CH₃OH at both sites, and for HCOOH at Eureka. Positive trends were detected for C₂H₆ and H₂CO at both sites, and for PAN at Eureka. Additionally, a 19‐year simulation was performed using the novel GEOS‐Chem High Performance model v14.1.1 for the period of 2003–2021. The model was able to reproduce the observed seasonality of all gases, but all species showed negative biases relative to observations, and CH₃OH was found to have a particularly large bias of approximately −70% relative to the FTIR measurements. The GEOS‐Chem modeled trends broadly agreed with observations for all species except C₂H₆, H₂CO, and PAN, which were found to have opposite trends in the model. For some species, the measurement‐model differences are suspected to be the result of errors or underestimations in the emissions inventories used in the simulation.

Data‐driven methods have been extensively applied to predict atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O₃) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O₃ observations in China and the US in 2015–2018. The DL model was applied to predict hourly surface O₃ over three continents in 2015–2022. Compared to baseline simulations using GEOS‐Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 μg/m³ with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 μg/m³ with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015–2018 and 2019–2022, respectively. The comparable performances between DL and GC indicate the potential of DL to make reliable predictions over spatial and temporal domains where a wealth of local observations for training is not available.

Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of urban ozone (O₃) responses to nitrogen oxide (NO_x) changes in China and the United States (US) over 2015–2020 by integrating various data‐ and model‐based methods. The data‐based deep learning (DL) model exhibited good performance in simulating urban air quality: the correlation coefficients (R) of O₃ daily variabilities with respect to independent O₃ observations are 0.88 and 0.79 over N. China, 0.87 and 0.90 over S. China, and 0.87 and 0.49 over E. United States by the DL and GEOS‐Chem chemical transport models, respectively. Furthermore, the data‐based methods suggest volatile organic compound (VOC)‐limited regimes in urban areas over northern inland China and transitional regimes over eastern US urban areas; in contrast, GEOS‐Chem model suggests strong NO_x‐limited regimes. Sensitivity analysis indicates that the inconsistent O₃ responses are partially caused by the inaccurate representation of O₃ precursor concentrations at the locations of urban air quality stations in the simulations, while the data‐based methods are driven by the variabilities in local O₃ precursor concentrations and meteorological conditions. The O₃ responses to NO_x changes reported here provide a better understanding of urban O₃ pollution; for example, reductions in NO_x emissions are suggested to have resulted in an increase in surface O₃ by approximately 7 ppb in the Sichuan Basin in 2014–2020.

Extreme enhancements in the total columns of carbon monoxide (CO), peroxyacetyl nitrate (PAN), ethylene (C₂H₄), methanol (CH₃OH), and formic acid (HCOOH) were observed over the Canadian high Arctic during the period of 17–22 August 2017 by a ground‐based Fourier transform infrared (FTIR) spectrometer at Eureka, Nunavut (80.05°N, 86.42°W), and by the Infrared Atmospheric Sounding Interferometer (IASI) satellite instruments. These enhancements have been attributed to wildfires in British Columbia (BC) and the Northwest Territories (NWT) of Canada, and represent the largest short‐term perturbations of PAN, C₂H₄, and HCOOH above ambient concentrations over the 14‐year (2006–2020) Eureka time‐series. Enhancement ratios, emission ratios, and emission factors relative to CO were calculated for all species for both FTIR and IASI observations. The C₂H₄ and HCOOH emission factors are significantly larger than previous studies, suggesting unusually high emissions from these fires. The wildfire plumes were also simulated using the GEOS‐Chem model. Initial GEOS‐Chem simulations displayed a severe under‐estimation relative to observations for these fire plumes resulting from the injection height scheme of the model. Sensitivity tests highlighted that injection heights of 12.5 km for BC (based on previous studies) and 10 km for the NWT fires yielded the strongest correlations with ground‐based measurements. Applying these injection heights to the model significantly improves the simulated plume transport and agreement with ground‐ and space‐based observations. GEOS‐Chem was also used to estimate the magnitude of secondary in‐plume production of CH₃OH and HCOOH; it was found to be an important component (∼18%) of the enhanced HCOOH columns at Eureka.