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NASA NTRS2019

Towards a Satellite Formaldehyde – in situ Hybrid Estimate for Organic Aerosol Abundance

Organic aerosol (OA) is one of the main components of the global particulate burden and intimately links natural and anthropogenic emissions with air quality and climate. It is challenging to accurately represent OA in global models. Direct quantification of global OA abundance is not possible with current remote sensing technology; however, it may be possible to exploit correlations of OA with remotely observable quantities to infer OA spatiotemporal distributions. In particular, formaldehyde (HCHO) and OA share common sources via both primary emissions and secondary production from oxidation of volatile organic compounds (VOCs). Here, we examine OA–HCHO correlations using data from summertime airborne campaigns investigating biogenic (NASA SEAC4RS and DC3), biomass burning (NASA SEAC4RS), and anthropogenic conditions (NOAA CalNex and NASA KORUS-AQ). In situ OA correlates well with HCHO (r=0.59–0.97), and the slope and intercept of this relationship depend on the chemical regime. For biogenic and anthropogenic regions, the OA–HCHO slopes are higher in low NOx conditions, because HCHO yields are lower and aerosol yields are likely higher. The OA–HCHO slope of wildfires is over 9 times higher than that for biogenic and anthropogenic sources. The OA–HCHO slope is higher for highly polluted anthropogenic sources (e.g., KORUS-AQ) than less polluted (e.g., CalNex) anthropogenic sources. Near-surface OAs over the continental US are estimated by combining the observed in situ relationships with HCHO column retrievals from NASA's Ozone Monitoring Instrument (OMI). HCHO vertical profiles used in OA estimates are from climatology a priori profiles in the OMI HCHO retrieval or output of specific period from a newer version of GEOS-Chem. Our OA estimates compare well with US EPA IMPROVE data obtained over summer months (e.g., slope =0.60–0.62, r=0.56 for August 2013), with correlation performance comparable to intensively validated GEOS-Chem (e.g., slope =0.57, r=0.56) with IMPROVE OA and superior to the satellite-derived total aerosol extinction (r=0.41) with IMPROVE OA. This indicates that OA estimates are not very sensitive to these HCHO vertical profiles and that a priori profiles from OMI HCHO retrieval have a similar performance to that of the newer model version in estimating OA. Improving the detection limit of satellite HCHO and expanding in situ airborne HCHO and OA coverage in future missions will improve the quality and spatiotemporal coverage of our OA estimates, potentially enabling constraints on global OA distribution.

anthropogenic emissions↗

NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗

NASA NTRS2020

Understanding and Improving Model Representation of Aerosol Optical Properties for a Chinese Haze Event Measured During KORUS-AQ

KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM2.5, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm−3 in the model vs. observed average of 1.5 g cm−3) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

Pablo E. Saide↗

NASA NTRS2019

Constraining global aerosol emissions using POLDER/PARASOL satellite remote sensing observations

We invert global black carbon (BC), organic carbon (OC) and desert dust (DD) aerosol emissions from POLDER/PARASOL spectral aerosol optical depth (AOD) and aerosol absorption optical depth (AAOD) using the GEOS-Chem inverse modeling framework. Our inverse modeling framework uses standard a priori emissions to provide a posteriori emissions that are constrained by POLDER/PARASOL AODs and AAODs. The following global emission values were retrieved for the three aerosol components: 18.4 Tg/yr for BC, 109.9 Tg/yr for OC and 731.6 Tg/yr for DD for the year 2010. These values show a difference of +166.7 %, +184.0 % and −42.4 %, respectively, with respect to the a priori values of emission inventories used in “standard” GEOS-Chem runs. The model simulations using a posteriori emissions (i.e., retrieved emissions) provide values of 0.119 for global mean AOD and 0.0071 for AAOD at 550 nm, which are +13.3 % and +82.1 %, respectively, higher than the AOD and AAOD obtained using the a priori values of emissions. Additionally, the a posteriori model simulation of AOD, AAOD, single scattering albedo, Ångström exponent and absorption Ångström exponent show better agreement with independent AERONET, MODIS and OMI measurements than the a priori simulation. Thus, this study suggests that using satellite-constrained global aerosol emissions in aerosol transport models can improve the accuracy of simulated global aerosol properties.

chemical transport model (CTM) simulations↗

NASA NTRS2021

The impact of Los Angeles Basin pollution and stratospheric intrusions on the surrounding San Gabriel Mountains as seen by surface measurements, lidar, and numerical models

In this work, the impact of Los Angeles Basin pollution transport and stratospheric intrusions on the surface ozone levels observed in the San Gabriel Mountains is investigated based on a combination of surface and lidar measurements as well as WRF-Chem (Weather Research and Forecasting with Chemistry) and WACCM (Whole Atmosphere Community Climate Model) runs. The number of days with observed surface ozone levels exceeding the National Ambient Air Quality Standards exhibit a clear seasonal pattern, with a maximum during summer, when models suggest a minimum influence of stratospheric intrusions and the largest impact from Los Angeles Basin pollution transport. Additionally, measured and modeled surface ozone and PM10 were analyzed as a function of season, time of the day, and wind direction. Measurements and models are in good qualitative agreement, with maximum surface ozone observed for southwest and west winds. For the prevailing summer wind direction, slightly south of the ozone maximum and corresponding to south-southwest winds, lower ozone levels were observed. Back trajectories suggest that this is associated with transport from the central Los Angeles Basin, where titration limits the amount of surface ozone. A quantitative comparison of the lidar profiles with WRF-Chem and WACCM models revealed good agreement near the surface, with models showing an increasing positive bias as function of altitude, reaching 75 % at 15 km above sea level. Finally, three selected case studies covering the different mechanisms affecting the near-surface ozone concentration over the San Gabriel Mountains, namely stratospheric intrusions and pollution transport, are analyzed based on surface and ozone lidar measurements, as well as co-located ceilometer measurements and models.

Impact↗

NASA NTRSDate not supplied

NASA's High-Resolution GEOS Forecasting and Reanalysis Products: A Unified Tool from Local to Global Scales

NASA's GMAO produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric chemical composition at unprecedented horizontal resolution of 0.25 degrees (~25 km), freely available to the public. This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). GEOS-CF also assimilated satellite observations into the system for improved representation of weather and smoke. The assimilation system is currently being expanded to include chemically reactive trace gases. While the main focus of this new product is on tropospheric air quality information, the GEOS-Chem chemistry model used in this system includes the unified tropospheric stratospheric chemistry mechanism for improved forecasts of total column ozone during anomalous dynamical and chemical events. I will discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions for GEOS-CF and reanalysis products. In addition, I will show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.

Co-DAS↗

NASA NTRS2021

Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction with Satellite Data for Rural Areas

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS-Chem, WRF-Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non rural areas, referred to as “extended ground truth” or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two-part study develops a multimodel and multi-AOD bias-correction technique that has the potential to improve PM2.5 forecasts in both rural and non rural areas in near real time, and be readily implemented at state levels.

Huanxin Zhang↗

NASA NTRS2022

Exploring the Inorganic Composition of the Asian Tropopause Aerosol Layer Using Medium-Duration Balloon Flights

Satellite observations have revealed an enhanced aerosol layer near the tropopause over Asia during the summer monsoon, called the “Asian Tropopause Aerosol Layer” (ATAL). In this work, aerosol particles in the ATAL were collected with a balloon-borne impactor near the tropopause region over India, using extended-duration balloon flights, in summer 2017 and winter 2018. The chemical composition of these particles was further investigated by quantitative analysis using offline ion chromatography. Nitrate (NO_3 ^1) and nitrite (NO_2 ^-) were found to be the dominant ions in the collected aerosols with values ranging between 87 and 343 ng m^(−3) at STP (standard temperature and pressure) during the summer campaign. In contrast, sulfate (SO_4 ^(2-)) levels were found to be above the detection limit (>10 ng m^(−3) at STP) only in winter. In addition, we determined the origin of the air masses sampled during the flights using the analysis of back trajectories as well as a convective proxy from cloud-top temperature fields derived from a geostationary satellite. The results obtained from this analysis were put into the context of large-scale transport and aerosol distribution using GEOS-Chem chemical transport model simulations. The first flight in summer 2017 which sampled an air mass within the Asian monsoon anticyclone (AMA), influenced by convection over Western China, was associated with particle size diameters from 0.05 to 0.15 µm. In contrast, the second flight sampled air masses at the edge of the AMA associated with a larger particle size radius (>2 µm) with a higher NO_2 ^- concentration. The sampled air masses in winter 2018 were likely affected by smoke from the Pacific Northwest fire event in Canada, which occurred 7 months before our campaign, associated with concentration enhancements of SO_4 (^2-) and Ca^(2+). Overall, our results suggest that nitrogen-containing particles represent a large fraction of cloud-free and in-cloud aerosols populating the ATAL, which is partially in agreement with the results from aircraft measurements during the StratoClim (Stratospheric and upper tropospheric processes for better climate predictions) campaign. The exact nature of those particles is still unknown, but their coincidences with subvisible cirrus clouds and their sizes suggest nitric acid trihydrate (NAT) as a possible candidate, as NAT has already been observed in the tropical upper troposphere and lower stratosphere in other studies. Furthermore, GEOS-Chem model simulations indicate that lightning NOx emissions could have significantly impacted the production of nitrate aerosols sampled during the summer of 2017.

Hazel Vernier↗

NASA NTRSDate not supplied

Time-Accurate Computational Fluid Dynamics Analysis of a Supersonic Turbine to Assess Structural Mode Excitation

Gas turbines can experience significant unsteady loading during operation which can cause damage such as blade cracking or bearing wear. While computational fluid dynamics (CFD) modeling of full three-dimensional gas turbine flow to verify meanline design performance is standard practice within the industry, the use of time-accurate CFD makes it possible to assess the unsteady loading which can be especially problematic at operating conditions where unsteady fluid loading aligns with structural modes. The Fluid Dynamics branch (ER42) at NASA Marshall Space Flight Center has developed a time-accurate CFD analysis methodology using the unstructured, density-based, Loci-CHEM solver which is capable of simulating the relative motion required for turbomachinery applications through sliding interface meshes. The highly-parallel unstructured solver allows for full three-dimensional, time-accurate simulation of turbines including upstream and downstream components such as manifolds, nozzles, and stators, any of which could excite a turbine structural mode. Recently, Loci-CHEM was used to analyze the FASTRAC supersonic turbine to assess the loading on the rotor. Overall performance and time-averaged loading on the rotor was computed, including the axial force over the entire rotor disk which is required for force balance calculations on the turbopump. The FASTRAC turbine has supersonic nozzles upstream of the rotor that result in significant unsteady loading on the rotor blades at multiples of the nozzle pass frequency. The unsteady blade pressure loading was decomposed into the frequency domain for direct use in structural forced response analysis. Utilizing this type of CFD analysis coupled with structural analysis has proven to be a valuable tool in identifying structural modes that are being excited by unsteady fluid loading.

M.V. Fischels↗

NASA NTRSDate not supplied

Time-Accurate Computational Fluid Dynamics Analysis of a Supersonic Turbine to Assess Structural Mode Excitation

M V Fischels↗

NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite NO2 Data Assimilation and Improvements in Background NOx Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NOx=NO+NO2) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

V. Shah↗

NASA NTRSDate not supplied

Joint Assimilation of CO, O3, NO2, SO2, PM, AOD, NH4, PAN, and HNO3 in Support of the Tropospheric Regional Atmospheric Composition and Emissions Reanalysis (2005 - 2024) (TRACER-I)

NASA Ames Research Center is collaborating with the NOAA Chemical Systems Laboratory (NOAA/CSL), the NASA Jet Propulsion Laboratory (JPL), the NASA Goddard Modeling and Assimilation Office, and the National Center for Atmospheric Research to prepare a 20-year Tropospheric Regional Atmospheric Composition and Emissions Reanalysis (2005 – 2024) (TRACER-I) for the continental United States during the summer ozone (O3) and wildfire seasons (April to October). TRACER-I will be a regional complement to JPL’s global Tropospheric Chemistry Reanalyses II and III. We will use WRF-Chem/DART with NOAA/CSL’s WRF-Chem setup at 12 km x 12 km horizontal resolution, 51 vertical levels, and 30 ensemble members. We will assimilate: (i) conventional meteorological observations; (ii) EPA’s Air Quality System in situ measurements of carbon monoxide (CO), O3, nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM) with diameters less than 10 µm (PM10), and PM with diameters less than 2.5 µm (PM2.5); and (iii) MOPITT, MODIS, OMI, TROPOMI, GOME-2a, MLS, TES, SCIAMACHY, CrIS, and TEMPO total/partial column and/or profile retrievals of CO, O3, NO2, SO2, aerosol optical depth (AOD), formaldehyde (HCHO), ammonia (NH4), peroxyacetyl nitrate (PAN), and/or nitric acid (HNO3) with 3-hr cycling. We will present an overview of this project, its status, and available results (likely the analysis of sensitivity experiment results from the assimilation/emissions estimation system).

data assimilation↗

NASA NTRS2023

Sources of Formaldehyde in U.S. Oil and Gas Production Regions

We analyzed observational and model data to study the sources of formaldehyde over oil and gas production regions and to investigate how these observations may be used to constrain oil and gas VOC emissions. The analysis of aircraft and satellite data consistently found that formaldehyde over oil and gas production regions during spring and summer is mostly formed by the photooxidation of precursor VOCs. Formaldehyde columns over the Permian basin, one of the largest oil and gas producing regions in the United States, are correlated with production locations. Formaldehyde simulations by the atmospheric chemistry and transport model WRF-Chem, which included oil and gas NOx and VOC emissions from the fuel-based oil and gas inventory, were in very good agreement with TROPOMI satellite measurements. Sensitivity studies illustrated that VOCs released from oil and gas activities are important precursors to formaldehyde, but other sources of VOCs contribute as well, and that the formation of secondary formaldehyde is highly sensitive to NOx. We also investigated the ability of the chemical mechanism used in WRF-Chem to represent formaldehyde formation from oil and gas hydrocarbons by comparing against the Master Chemical Mechanism. Further, our work provides estimates of primary formaldehyde emissions from oil and gas production activities, with per basin averages ranging from 0.07 kg h -1 to 2.2 kg h -1 in 2018. A separate estimate for natural gas flaring found that flaring emissions could contribute 5% to 12% to the total primary formaldehyde emissions for the Permian basin in 2018.

SAF↗

NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite Data Assimilation and Improvements in Background NO x Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NO x =NO+NO 2 ) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO 2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

Viral Shah↗

NASA NTRSDate not supplied

Expanding SPoRT RGBs and Machine Learning Techniques to Enhance Air Quality Monitoring in Southern Asia

Air pollution poses significant environmental, public health, and societal concerns in the Hindu Kush Himalaya (HKH) region of south-central Asia, notably during the dry monsoon months (~November to May). Key contributors to poor air quality include dust from the Middle East and western India, persistent nocturnal fog/smog, and biomass burning. To address this issue, we established a robust air quality and chemistry observation and modeling product suite utilizing multi-spectral red-green-blue (RGB) composite satellite products from Korea’s GEO-KOMPSAT-2A satellite, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for dust transport forecasts, and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model to predict aerosols and chemical species concentrations. Our team employed similar RGB recipes transitioned by the NASA Short-term Prediction Research and Transition (SPoRT) Center for the GOES-R era products over the Western Hemisphere, with significant success in depicting dust and nocturnal fog / low clouds, and to a lesser extent smoke and fire hot spots. We will extend these capabilities for the HKH region by applying an artificial intelligence (AI) model that objectively identifies dust from multi-spectral satellite data over the Southwestern United States. The AI model will be calibrated for automated dust detection over HKH from GEO-KOMPSAT-2A satellite data, with a goal of developing a similar AI model for objectively identifying smoke as well. The ultimate goal of this effort is to enhance dust and smoke predictions in the region by establishing improved emission initializations in HYSPLIT and/or WRF-Chem through the automated AI detection of dust and smoke.

Jonathan L. Case↗

DOE OSTI2022

Impact of f-element complexation on the radiolytic robustness of separations ligands

Impact of f-element complexation on the radiolytic robustness of separations ligands Gregory P. Horne, Makayla R. Baxter, Corey D. Pilgrim, Travis S. Grimes, Center for Radiation Chemistry Research, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID, 83415, USA Cristian Celis Barros, Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306, USA. E-mail: ccelisbarros@fsu.edu Andrew R. Cook, Department of Chemistry, Brookhaven National Laboratory, Upton, New York, 11973, USA Stephen P. Mezyk, Department of Chemistry and Biochemistry, California State University Long Beach, 1250 Bellflower Boulevard, Long Beach, California, 90840-9507, USA The effects of ionizing radiation are ubiquitous throughout all aspects of a nuclear fuel cycle. However, the complexity and intensity of these effects are greatest during the management of used nuclear fuel, owing to the presence of a wide spectrum of radionuclides from neutron capture and fission processes. With regards to used nuclear fuel (UNF) reprocessing, radiation-induced processes typically promote the destruction of active compounds (e.g., complexants and additives) with the concomitant formation of potentially detrimental degradation products and corresponding changes in physical and chemical properties, which ultimately impact the effectiveness and longevity of a given reprocessing system. Concerning UNF complexants, radiation chemistry studies have historically focused on their radiation robustness in the absence of the metal ions they were designed to selectively complex. This knowledge gap is worrisome as previous studies on aqueous phase complexants have demonstrated significant changes in radiolytic behavior upon metal ion complexation.1-4 More recently, the rate of reaction of the n-dodecane radical cation—believed to be the major organic phase radiation-induced transient species responsible for complexant radiolysis in n-dodecane based solvent systems—with hexa-n-octylnitrilo-triacetamide (HONTA) was shown to increase by an order-of-magnitude upon complexation of europium or americium.5 These findings have significant implications on the projected longevity of complexants in UNF reprocessing solvent systems. Consequently, a thorough understanding of metal ion complexation effects on the radiolytic integrity of UNF complexants is essential to evaluate their potential for process application. Presented here are two recent studies from the Idaho National Laboratory Center for Radiation Chemistry Research group that demonstrate the various impacts of f-element complexation (uranium, americium, and lanthanides) on the radiolytic integrity (gamma and electron pulse) of tributyl phosphate (TBP), N,N-di-(2-ethylhexyl)butyramide (DEHBA), N,N-di-2-ethylhexylisobutryamide (DEHiBA), and 2-ethylhexylphosphonic acid mono-2-ethylhexyl ester (HEH[EHP]) under UNF reprocessing conditions. References 1) Bhattacharyya and Kundu, Int. J. Radiat. Phys. Chem., 1971, 3, 1. 2) Kundu and Matuura, Int. J. Radiat. Phys. Chem., 1975, 7, 565. 3) Ilan and Czapski, Biochimica et Biophysica Acta, 1977, 498, 386. 4) Buettner, Doherty, and Patterson, Fed. Euro. Biochem. Soc., 1983, 158 (1), 143. 5) Toigawa, Peterman, Meeker, Grimes, Zalupski, Mezyk, Cook, Yamashita, Kumagai, Matsumura, Horne, PCCP, 2021, 23, 1343.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2024

Unique impacts of strong and westward-extended western Pacific subtropical high on ozone pollution over eastern China

As a subtropical anticyclonic high-pressure system that typically forms over the northwestern Pacific Ocean in summer, the Western Pacific subtropical high (WPSH) affects meteorological conditions and ozone pollution in China. The relationship between maximum daily 8-h average ozone (MDA8 O 3 ) concentrations and the extremely strong and westward-extended WPSH occurred in 2022 is investigated using observations, reanalysis data and GEOS-Chem model simulations. The intensity of WPSH has a significant positive correlation with MDA8 O3 over southern China during July-August in 2022, with a correlation coefficient of +0.44, but the correlation is negative (–0.40) in northern China. During the strong WPSH days, MDA8 O 3 increased by 16.5µgm -3 (16.4% relative to July-August average) over southern China and decreased by 19.0µgm -3 (14.5%) in northern China compared to the weak WPSH days. The unique dipole pattern in the relationship between ozone levels and the WPSH in 2022 exhibited a contrast to that during 2015–2021. The difference is primarily due to the extremely strong WPSH intensity and its unusual westward expansion in 2022. In this case, an anomalous anticyclone at 500 hPa dominates over southern China, which creates conditions conducive for ozone formation and accumulation. The anticyclone weakened horizontal winds and reduced the dispersion of ozone, alongside a high temperature and low relative humidity, which favored the chemical production of ozone. In contrast, abnormal northerly winds enhanced ozone diffusion in northern China and the low temperature reduced ozone chemical production. Here, this study reveals the mechanism for the significant impact of strong and westward-extended WPSH on ozone concentrations over China, emphasizing the role of the WPSH location in modulating meteorology and ozone levels.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Reconciliation of asynchronous satellite-based $\mathrm{NO_2}$ and $\mathrm{XCO_2}$ enhancements with mesoscale modeling over two urban landscapes

Fossil fuel carbon dioxide (CO 2ff ), the main driver of global warming and climate change, is often co-emitted with nitrogen oxides (NO x ) and precursors to ground-level ozone from anthropogenic sources like power plants or vehicles. In urban and suburban areas, satellite-based NO 2 can be used as a proxy to track the emissions of CO 2ff . Because of NO 2 ’s shorter lifetime, urban NO 2 plumes are more distinguishable from backgrounds and more sensitive to variations in emissions. However, the combination of these two gases is limited by the asynchrony among NO 2 and CO 2 monitoring satellites. We used CO 2ff simulated by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model to reconcile the tropospheric NO 2 vertical column density (VCD) from the Tropospheric Monitoring Instrument (TROPOMI) and column-averaged dry-air mole fractions of carbon dioxide enhancements (ΔXCO 2 ) from Orbiting Carbon Observatory 3 (OCO-3) Snapshot Area Maps (SAMs) over a multicity area, Washington D.C.-Baltimore (DC-Balt), and a basin city, Mexico City. NO 2 /CO 2ff ratios over DC-Balt are smaller than Mexico City, indicative of stricter emission restrictions, a more combustion-efficient vehicle fleet, and higher combustion efficiency due to lower altitude in DC-Balt. For single-track cases, the spatial correlations between NO 2 and ΔXCO 2 over Mexico City are stronger than DC-Balt because the NO 2 and CO 2 are mostly trapped in the valley of Mexico City, while DC-Balt is severely affected by distant sources (i.e., US East Coast cities). Using multi-track averaging, spatial correlation coefficients increase with the number of days used for averaging. The correlations reached a maximum when averaging >12 continuous images for DC-Balt and > 10 continuous images for Mexico City. This finding indicates that multi-track averaging using modeled CO 2ff as a proxy is helpful to filter the noise in single-track images, to cancel the interference from distant sources, and to magnify correlations between NO 2 and CO 2ff . Mexico City showed stronger spatial correlations but weaker temporal correlations than DC-Balt due to biomass burning hot spots and large transport errors caused by the trapping effects of the surrounding mountains. Tracking the 20-day moving average of CO 2ff emissions using TROPOMI NO 2 seems technically feasible, considering the relationship between correlation coefficients and the number of available satellite images.

54 ENVIRONMENTAL SCIENCES↗