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.

The National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) is on its way to deploying various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/, last access: 18 June 2022), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in collaboration between the Global Systems Laboratory, Chemical Science Laboratory, Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component version 2 (NGACv2) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in the GEFS is based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). GEFS-Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke based on WRF-Chem was implemented. The GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL). The Blended Global Biomass Burning Emissions Product (GBBEPx version 3) provides biomass burning emission and fire radiative power (FRP) data. The global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid-scale transport and deposition are handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by the NCEP's operational FV3GFS. This paper describes the details of GEFS-Aerosols model development and evaluation of real-time and retrospective runs using different observations from in situ measurement and satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGACv2 system with the fundamental updates (e.g., dust and fire emission) in the atmospheric and chemical transport model.

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM_2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM_2.5 concentrations near the source region but higher AOD and PM_2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %–30 % near the source regions and 5 %–10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM_2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 µg m⁻³) predictions. Higher plume heights generally produce larger downwind PM_2.5 exceedance areas. The PM_2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM_2.5 concentration and predicted PM_2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM_2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.

The so‐called accumulation‐size range of airborne particles is the center of a continuing disagreement about the formulation of dry deposition. Some contemporary meteorological and air quality models use theoretical developments based on early wind tunnel and other controlled experiments, while other models consider the bulk properties of the underlying surface and the ability of atmospheric turbulence to deliver particles to it. This dichotomy arose when the first micrometeorological measurements of particle deposition velocities became available, yielding numbers exceeding the highest expectations of the then‐current models based on assumptions about inertial impaction and interception. The model predictions had previously been shown to be in accord with theoretical treatments of filtration. A common reaction was to distrust the field experimental results, but the experimental findings were supported by subsequent studies. The difference between model predictions and field measurements appears greatest for densely vegetated canopies. Ongoing research is investigating factors that could give rise to the discrepancy, e.g., turbulence intermittency, leaf orientation, leaf morphology, leaf flutter, electrical charges, and a number of phoretic effects. In the meantime, many investigators are faced with a decision as to whether to make use of parameterized field results or theoretical descriptions of behaviors that are not yet well examined. Here the history of the ongoing disagreement is reviewed, and some possible resolutions are presented.

The COVID-19 stay-at-home orders issued in the United States caused significant reductions in traffic and economic activities. To understand the pandemic's perturbations on US emissions and impacts on urban air quality, we developed near-real-time bottom-up emission inventories based on publicly available energy and economic datasets, simulated the emission changes in a chemical transport model, and evaluated air quality impacts against various observations. The COVID-19 pandemic affected US emissions across broad-based energy and economic sectors and the impacts persisted to 2021. Compared with 2019 business-as-usual emission scenario, COVID-19 perturbations resulted in annual decreases of 10–15% in emissions of ozone (O₃) and fine particle (PM_2.5) gas-phase precursors, which are about two to four times larger than long-term annual trends during 2010–2019. While significant COVID-induced reductions in transportation and industrial activities, particularly in April–June 2020, resulted in overall national decreases in air pollutants, meteorological variability across the nation led to local increases or decreases of air pollutants, and mixed air quality changes across the United States between 2019 and 2020. Over a full year (April 2020 to March 2021), COVID-induced emission reductions led to 3–4% decreases in national population-weighted annual fourth maximum of daily maximum 8-h average O₃ and annual PM_2.5. Assuming these emission reductions could be maintained in the future, the result would be a 4–5% decrease in premature mortality attributable to ambient air pollution, suggesting that continued efforts to mitigate gaseous pollutants from anthropogenic sources can further protect human health from air pollution in the future.

This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM_2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM_2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM_2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM_2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM_2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM_2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.