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The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers, and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m−3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (about 25 km). This GEOS Composition Forecast (GEOS-CF) system combines the GEOS weather analysis and forecasting system with the state-of-the-science GEOS-Chem chemistry module (Bey et al., 2001; Keller et al., 2014; Long et al., 2015) to provide detailed chemical analysis of a wide range of air pollutants including ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Full model description and evaluation of the GEOS-CF tropospheric simulation and forecast skill is given in Keller et al. (2021). For evaluation of GEOS-CF stratospheric composition see Knowland et al. (2021).

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 regions of Europe 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 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.

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision-makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods either rely on chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time-series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product – NASA’s Goddard Earth Observing System “Composition Forecasting” (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next five days at a 1-km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next two days had overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following three forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with validation normalized mean bias close to zero, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

Air pollution poses a serious threat to children’s respiratory health around the world. Satellite remote-sensing technology and air quality models can provide pollution data on a global scale, necessary for riskcommunication efforts in regions without ground-based monitoring networks. Several large centers, including NASA, produce global pollution forecasts that may be used alongside air quality indices to communicate local, daily risk information to the public. Here we present a health-based, globally applicable air quality index developed specifically to reflect the respiratory health risks among children exposed to elevated outdoor air pollution. Additive, excess-risk air quality indices were developed using 51 different coefficients derived from time-series health studies evaluating the impacts of ambient fine particulate matter, nitrogen dioxide, and ozone on children's respiratory morbidity outcomes. A total of four indices were created which varied based on whether or not the underlying studies controlled for co-pollutants and in the adjustment of excess risks of individual pollutants. Combined with historical estimates of air pollution provided globally at a 25x25 km2 spatial resolution from the NASA's Goddard Earth Observing System composition forecast (GEOS-CF) model, each of these indices were examined in a global sample of 664 small and 140 large cities for study year 2017. Adjusted indices presented the most normal distributions of locally-scaled index values, which has been shown to improve associations with health risks, while indices based on coefficients controlling for co-pollutants had little effect on index performance. We provide the steps and resources need to apply our final adjusted index at the local level using freely-available forecasting data from the GEOS-CF model, which can provide risk communication information for cities around the world to better inform individual behavior modification to best protect children's respiratory health.

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. Here we implement these 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.

Modeling atmospheric chemistry at fine resolution globally is computationally expensive; the capability to focus on specific geographic regions using a multi scale grid is desirable. Here, we develop, validate, and demonstrate stretched grids in the GEOS-Chem atmospheric chemistry model in its high-performance implementation (GCHP). These multiscale grids are specified at runtime by four parameters that offer users nimble control of the region that is refined and the resolution of the refinement. We validate the stretched-grid simulation versus global cubed-sphere simulations. We demonstrate the operation and flexibility of stretched-grid simulations with two case studies that compare simulated tropospheric NO2column densities from stretched-grid and cubed-sphere simulations to retrieved column densities from the TROPOspheric Monitoring Instrument (TROPOMI). The first case study uses a stretched grid with a broad refinement covering the contiguous US to produces imulated columns that perform similarly to a C180 (∼50 km) cubed-sphere simulation at less than one-ninth the computational expense. The second case study experiments with a large stretch-factor for a global stretched-grid simulation with a highly localized refinement with∼10 km resolution for California. We find that the refinement improves spatial agreement with TROPOMI columns compared to a C90 cubed-sphere simulation of comparable computational demands. Overall we find that stretched grids in GEOS-Chem are a practical tool for fine resolution regional- or continental-scale simulations of atmospheric chemistry. Stretched grids are available in GEOS-Chem version 13.0.0

Tropospheric ozone (O3) negatively impacts human health and is also a greenhouse gas. It is formed photochemically by reactions of nitrogen oxides (NOx) and volatile organic compounds (VOCs), of which wildfires are an important source. This study presents data from research flights sampling wildfires in West and Central African savannah regions, both close to the fires and after the emissions had been transported several days over the tropical North Atlantic Ocean. Emission factors (EFs) in g kg-1 for NOx (as NO), six VOCs and formaldehyde were calculated from enhancement to mole fractions in data taken close to the fires. For NOx, the emission factor was calculated as 2.05±0.43 g kg-1 for Senegal and 1.20±0.28 g kg-1 for Uganda, both higher than the average value of 1.13±0.6 g kg-1 for previous studies of African savannah regions. For most VOCs (except acetylene), EFs in Uganda were lower by factors of 20-50% compared to Senegal, with almost all the values below those in the literature. O3 enhancement in the fire plumes was investigated by examining the ΔO3/ΔCO enhancement ratio, with values ranging from 0.07 - 0.14 close to the fires up to 0.25 for measurements taken over the Atlantic Ocean up to 200 hours downwind. In addition, measurements of O3 and its precursors were compared to the output of a global chemistry transport model (GEOS-CF) for the flights over the Atlantic Ocean. Normalised mean bias (NMB) comparison between the measured and modelled data was good outside of the fire plumes, with CO showing a model under-prediction of 4.6% and O3 a slight over-prediction of 0.7% (both within the standard deviation of the data). For NOx the agreement was poorer, with an under-prediction of 9.9% across all flights. Inside the fire plumes the agreement between modelled and measured values is worse, with the model being biased significantly lower for all three species. In total across all flights, there was an under-prediction of 29.4%, 16.5% and 37.5% for CO, O3 and NOx respectively. Finally, the measured ΔO3/ΔCO enhancement ratios were compared those in the model for the equivalent flight data, with the model showing a lower value of 0.17±0.03 compared to an observed value of 0.29±0.05. The results detailed here show that the O3 burden to the North Atlantic Ocean from African wildfires may be underestimated and that further study is required to better study the O3 precursor emissions and chemistry.

Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO can operate in offline stand-alone mode, but more importantly it provides an online facility for models to compute emissions at runtime. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present a new version here, HEMCO 3.0, that features an improved three-layer architecture to facilitate implementation into any atmospheric model and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) the Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data, (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid, and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust or vegetation). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets. We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem “Classic” chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), as well as the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in CESM2 contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales.

Social-distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business-as-usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO2) and ozone (O3) at 5,756 observation sites in countries from January through June 2020. Reductions in NO2 coincide with timing and intensity of COVID-19 restrictions, ranging from 60% in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO2 concentrations were (13-23) % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5% to the business-as-usual estimate. NO2 reductions in Europe and the US have been more gradual with a halting recovery starting in late March. We estimate that the global NOx (NO+NO2) emission reduction during the first 6 months of 2020 amounted to 3.1 (2.6-3.6) TgN, equivalent to 5.5(4.7-6.4) % of the annual anthropogenic total. The response of surface O3 is complicated by competing influences of non-linear atmospheric chemistry. While surface O3 increased by up to 50% in some locations, we find the overall net impact on daily average O3 between February -June 2020 to be small. However, our analysis indicates a flattening of the O3 diurnal cycle with an increase in nighttime ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production.

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multi-model composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8,834 sites globally)in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multi-model composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2=0.81 at test point, 0.63 at 0.1°,0.62 at 0.5°) than the multi-model mean (R2=0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

The global response to the COVID-19 pandemic has led to a sudden reduction of both GHG emissions and air pollutants. Here, using national mobility data, we estimate global emission reductions for ten species during the period February to June 2020. We estimate that global NOx emissions declined by as much as 30% in April, contributing a short-term cooling since the start of the year. This cooling trend is offset by ~20% reduction in global SO2 emissions that weakens the aerosol cooling effect, causing short-term warming. As a result, we estimate that the direct effect of the pandemic-driven response will be negligible, with a cooling of around 0.01 ± 0.005 °C by 2030 compared to a baseline scenario that follows current national policies. In contrast, with an economic recovery tilted towards green stimulus and reductions in fossil fuel investments, it is possible to avoid future warming of 0.3 °C by 2050.

The C-13 isotopic ratio of methane, δC-13 of CH4, provides additional constraints on the CH4 budget to complement the constraints from CH4 observations. The interpretation of δC-13 observations is complicated, however, by uncertainties in the methane sink. The reaction of CH4 with Cl is highly fractionating, increasing the relative abundance of 13CH4, but there is currently no consensus on the strength of the tropospheric Cl sink. Global model simulations of halogen chemistry differ strongly from one another in terms of both the magnitude of tropospheric Cl and its geographic distribution. This study explores the impact of the inter-model diversity in Cl fields on the simulated δC-13 of CH4. We use a set of GEOS global model simulations with different predicted Cl fields to test the sensitivity of the δC-13 of CH4 to the diversity of Cl output from chemical transport models. We find that δC-13 is highly sensitive to both the amount and geographic distribution of Cl. Simulations with Cl providing 0.28% or 0.66% of the total CH4 loss bracket the δC-13 observations for a fixed set of emissions. Thus, even when Cl provides only a small fraction of the total CH4 loss and has a small impact on total CH4, it provides a strong lever on δC-13. Consequently, it is possible to achieve a good representation of total CH4 using widely different Cl concentrations, but the partitioning of CH4 loss between the OH and Cl reactions leads to strong differences in isotopic composition depending on which model’s Cl field is used. Comparing multiple simulations, we find that altering the tropospheric Cl field leads to approximately a 0.5‰ increase in δ(13)CH4for each percent increase in how much CH4 is oxidized by Cl. The geographic distribution and seasonal cycle of Cl also impacts the hemispheric gradient and seasonal cycle of δC-13. The large effect of Cl on δC-13 compared to total CH4 broadens the range of CH4 source mixtures that can be reconciled with δC-13 observations. Stronger constraints on tropospheric Cl are necessary to improve estimates of CH4 sources from δC-13 observations.