Integrating air quality information from models, satellites, and in-situ monitors allows for both better estimation of air quality and better quantification of uncertainties in this estimation. Uncertainty quantification is important to appropriately convey confidence in these estimates and forecasts to users who will base decisions on these. Uncertainty quantification also allows tracing the value of information provided by different data sources. This can identify gaps in the monitoring network where additional data could further reduce uncertainties. This paper presents a framework for data fusion with uncertainty quantification, applicable to multiple air-quality-relevant pollutants. Testing of this framework in the context of nitrogen dioxide forecasting at sub-city scales shows promising results, with confidence intervals typically encompassing the expected number of actual measurements during cross-validation. The framework is now being implemented into an online tool to support local air quality management decision-making. Future work will also include the incorporation of low-cost air sensor data and the quantification of uncertainty at hyper-local scales.

Chemical cycling drives the production and loss of many important atmospheric constituents. The speed of atmospheric chemical cycling is a particularly valuable indicator for characterizing and measuring the effects of such cycles on oxidant chemistry, air quality, and climate. Here, we apply graph theoretical methods to explicitly quantify and analyze the characteristic timescales of chemical cycles in the atmosphere, as simulated by the GEOS-Chem chemical mechanism. We identify all two-, three-, and four-reaction cycles in the mechanism and calculate a characteristic timescale for each individual cycle. We find that the speed of chemical cycling varies by orders of magnitude at any given location but tends to be faster in urban- and biogenically-dominated chemical regions, and slower during the night. We further quantify the fraction of cycling that contains a rate-determining step, and explicitly demonstrate the large potential for mechanisms to recycle oxidants like OH.

During polar spring, periods of elevated tropospheric bromine known as “bromine explosion events” are associated with near complete removal of surface ozone. The satellite-based Ozone Monitoring Instrument (OMI) provides total column measurements of bromine monoxide (BrO) with daily global coverage. In this study, we estimate springtime bromine emissions over the Arctic using OMI retrievals of BrO in combination with the GEOS-Chem (version 12.0.1) chemical mechanism, run online within the GEOS Earth System Model. Tropospheric hotspots of BrO are identified over the Arctic where the difference between OMI and modeled columns of BrO exceeds the bias observed over regions not impacted by bromine explosion emissions. The resulting hotspot columns are a lower-limit estimate for the portion of the OMI BrO signal attributable to bromine explosion events and are well correlated with BrO measured in the lower troposphere by buoy-based instruments. Daily flux of molecular bromine is calculated from hotspot columns of BrO based on the modeled atmospheric lifetime of inorganic bromine in the lower troposphere and partitioning of bromine species into BrO at OMI overpass time. Following the application of Arctic emissions in GEOS-Chem, OMI-based tropospheric hotspots of BrO are successfully modeled for 2008 – 2012 and periods of isolated, large (> 50%) decreases in surface ozone are captured during April and May. While this technique does not fully capture the low ozone observed at coastal stations, if a lower threshold is used to identify tropospheric hotspots of BrO, the representation of surface ozone in late spring is improved.

The final version of this paper has been published and is available (open-access) from the Earth & Space Science JournalWhile multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is therefore necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this paper, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the EPA network in the continental United States (US), the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.

Lightning plays a major role in tropospheric oxidation, and its role on modulating tropospheric chemistry was thought to be emissions of nitrogen oxides (NOx). Recent field and laboratory measurements demonstrate that lightning generates extremely large amounts of oxidants, including hydrogen oxides (HOx) and O3. We here implement the lightning-produced oxidants in a global chemical transport model to examine its global impact on tropospheric composition. We find that lightning-produced oxidants can increase global mass weighted OH by 0.3-10%, and affect CO, O3, and reactive nitrogen substantially, depending on the emission strength of oxidants from lightning. Our work highlights the importance and uncertainties of lightning-produced oxidants, as well as the need for rethinking the role of lightning in tropospheric oxidation chemistry.

Computational models of the Earth System are critical tools for modern scientific inquiry. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Recent work has shown that these errors can be effectively predicted using modern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previous studies to apply an interpretable A.I. technique to not only predict model errors but also move toward understanding the underlying reasons for successful error prediction. We use XGBoost classification trees and SHapley Additive exPlanations (SHAP) analysis to explore the errors in the prediction of lightning occurrence in the NASA GEOS model, a widely used Earth System Model. This interpretable error prediction system can effectively predict the model error and indicates that the errors are strongly related to convective processes and the characteristics of the land surface.

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25 degree) global constituent prediction system from NASA’s Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA’s broad range of space-based and in-situ observations and to support flight campaign planning, support of satellite observations, and air quality research. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide analyses and 5-day forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community. Evaluation of GEOS-CF against satellite, ozonesonde and surface observations show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to -0.3, normalized root mean square errors (NRMSE) between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants under a variety of meteorological conditions, yet also highlight current limitations, such as an overprediction of summertime ozone over the Southeast United States. GEOS-CF v1.0 generally overestimates aerosols by 20-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions. The 5-day hourly forecasts have skill scores comparable to the analysis. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS-CF) provides recent estimates and five-day forecasts of atmospheric composition to the public in near-real time. To do this, the GEOS Earth system model is coupled with the GEOS-Chem tropospheric-stratospheric unified chemistry extension (UCX) to represent composition from the surface to the top of the GEOS atmosphere (0.01 hPa). The GEOS-CF system is described, including updates made to the GEOS-Chem UCX mechanism within GEOS-CF for improved representation of stratospheric chemistry. Comparisons are made against balloon, lidar and satellite observations for stratospheric composition, including measurements of ozone (O3) and important nitrogen and chlorine species related to stratospheric O3 recovery. The GEOS-CF nudges the stratospheric O3 towards the GEOS Forward Processing (GEOS FP) assimilated O3 product; as a result the stratospheric O3 in the GEOS-CF historical estimate agrees well with observations. During abnormal dynamical and chemical environments such as the 2020 polar vortexes, the GEOS-CF O3 forecasts are more realistic than GEOS FP O3 forecasts because of the inclusion of the complex GEOS-Chem UCX chemistry. Overall, the spatial pattern of the GEOS-CF simulated concentrations of stratospheric composition agrees well with satellite observations. However, there are notable biases – such as low NOx and HNO3 in the polar regions and generally low HCl throughout the stratosphere – and future improvements to the chemistry mechanism and emissions are discussed. GEOS-CF is a new tool for the research community and instrument teams observing trace gases in the stratosphere and troposphere, providing near-real-time three-dimensional gridded information on atmospheric composition.

Diesel-powered vehicles emit several times more nitrogen oxides than comparable gasoline-powered vehicles, leading to ambient nitrogen dioxide (NO2) pollution and adverse health impacts. The COVID-19 pandemic and ensuing changes in emissions provide a natural experiment to test whether NO2 reductions have been starker in Europe, a region with larger diesel passenger vehicle shares. Here we use a semi-empirical approach that combines in-situ NO2 observations from urban areas and an atmospheric composition model within a machine learning algorithm to estimate business-as-usual NO2 during the first wave of the COVID-19 pandemic in 2020. These estimates account for the moderating influences of meteorology, chemistry, and traffic. Comparing the observed NO2 concentrations against business-as-usual estimates indicates that diesel passenger vehicle shares played a major role in the magnitude of NO2 reductions. European cities with the five largest shares of diesel passenger vehicles experienced NO2 reductions ~2.5 times larger than cities with the five smallest diesel shares. Extending our methods to a cohort of non-European cities from the C40 Cities network reveals that NO2 reductions in these cities were generally smaller than reductions in European cities, which was expected given their small diesel shares. We identify potential factors such as the deterioration of engine controls associated with older diesel vehicles to explain spread in the relationship between cities’ shares of diesel vehicles and changes in NO2 during the pandemic. Our results provide a glimpse of potential NO2 reductions that could accompany future deliberate efforts to phase out or remove passenger vehicles from cities.