Impacts of the COVID-19 economic slowdown on ozone pollution in the U.S.

In this work, we use observations and experimental emissions in a version of NOAA's National Air Quality Forecasting Capability to show that the COVID-19 economic slowdown led to disproportionate impacts on near-surface ozone concentrations across the contiguous U.S. (CONUS). The data-fusion methodology used here includes both U.S. EPA Air Quality System ground and the NASA Aura satellite Ozone Monitoring Instrument (OMI) NO2 observations to infer the representative emissions changes due to the COVID-19 economic slowdown in the U.S. Results show that there were widespread decreases in anthropogenic (e.g., NOx) emissions in the U.S. during March-June 2020, which led to widespread decreases in ozone concentrations in the rural regions that are NOx-limited, but also some localized increases near urban centers that are VOC-limited. Later in June-September, there were smaller decreases, and potentially some relative increases in NOx emissions for many areas of the U.S. (e.g., south-southeast) that led to more extensive increases in ozone concentrations that are partly in agreement with observations. The widespread NOx emissions changes also alters the O3 photochemical formation regimes, most notably the NOx emissions decreases in March-April, which can enhance (mitigate) the NOx-limited (VOC-limited) regimes in different regions of CONUS. The average of all AirNow hourly O3 changes for 2020-2019 range from about +1 to -4 ppb during March-September, and are associated with predominantly urban monitoring sites that demonstrate considerable spatiotemporal variability for the 2020 ozone changes compared to the previous five years individually (2015-2019). The simulated maximum values of the average O3 changes for March-September range from about +8 to -4 ppb (or +40 to -10%). Results of this work have implications for the use of widespread controls of anthropogenic emissions, particularly those from mobile sources, used to curb ozone pollution under the current meteorological and climate conditions in the U.S.

Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011.

UNLABELLED: We employed an optimal interpolation (OI) method to assimilate AIRNow ozone/PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) data into the Community Multi-scale Air Quality (CMAQ) model to improve the ozone and total aerosol concentration for the CMAQ simulation over the contiguous United States (CONUS). AIRNow data assimilation was applied to the boundary layer, and MODIS AOD data were used to adjust total column aerosol. Four OI cases were designed to examine the effects of uncertainty setting and assimilation time; two of these cases used uncertainties that varied in time and location, or "dynamic uncertainties." More frequent assimilation and higher model uncertainties pushed the modeled results closer to the observation. Our comparison over a 24-hr period showed that ozone and PM2.5 mean biases could be reduced from 2.54 ppbV to 1.06 ppbV and from -7.14 µg/m³ to -0.11 µg/m³, respectively, over CONUS, while their correlations were also improved. Comparison to DISCOVER-AQ 2011 aircraft measurement showed that surface ozone assimilation applied to the CMAQ simulation improves regional low-altitude (below 2 km) ozone simulation. IMPLICATIONS: This paper described an application of using optimal interpolation method to improve the model's ozone and PM2.5 estimation using surface measurement and satellite AOD. It highlights the usage of the operational AIRNow data set, which is available in near real time, and the MODIS AOD. With a similar method, we can also use other satellite products, such as the latest VIIRS products, to improve PM2.5 prediction.

Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16.

A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a "state-of-the-science" CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.

Evaluation of the offline-coupled GFSv15-FV3-CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States.

As a candidate for the next-generation National Air Quality Forecast Capability (NAQFC), the meteorological forecast from the Global Forecast System with the new Finite Volume Cube-Sphere dynamical core (GFS-FV3) will be applied to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modeling system. CMAQv5.0.2, a historical version of CMAQ, has been coupled with the North American Mesoscale Forecast System (NAM) model in the current operational NAQFC. An experimental version of the NAQFC based on the offline-coupled GFS-FV3 version 15 with CMAQv5.0.2 modeling system (GFSv15-CMAQv5.0.2) has been developed by the National Oceanic and Atmospheric Administration (NOAA) to provide real-time air quality forecasts over the contiguous United States (CONUS) since 2018. In this work, comprehensive region-specific, time-specific, and categorical evaluations are conducted for meteorological and chemical forecasts from the offline-coupled GFSv15-CMAQv5.0.2 for the year 2019. The forecast system shows good overall performance in forecasting meteorological variables with the annual mean biases of -0.2 °C for temperature at 2 m, 0.4% for relative humidity at 2 m, and 0.4 m s-1 for wind speed at 10 m compared to the METeorological Aerodrome Reports (METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts, particularly in spring. Although the monthly accumulated precipitation forecasts show generally consistent spatial distributions with those from the remote-sensing and ensemble datasets, moderate-to-large biases exist in hourly precipitation forecasts compared to the Clean Air Status and Trends Network (CASTNET) and METAR. While the forecast system performs well in forecasting ozone (O3) throughout the year and fine particles with a diameter of 2.5 µm or less (PM2.5) for warm months (May-September), it significantly overpredicts annual mean concentrations of PM2.5. This is due mainly to the high predicted concentrations of fine fugitive and coarse-mode particle components. Underpredictions in the southeastern US and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semivolatile or intermediate-volatility organic compounds. This work demonstrates the ability of FV3-based GFS in driving the air quality forecasting. It identifies possible underlying causes for systematic region- and time-specific model biases, which will provide a scientific basis for further development of the next-generation NAQFC.

The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP.

The NOAA National Centers for Environmental Prediction (NCEP) implemented NEMS GFS Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC). NGAC Version 1.0 has been providing 5 day dust forecasts at 1°×1° resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC), since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered.

Study of atmospheric mercury budget in East Asia using STEM-Hg modeling system.

East Asia is the largest source region of global anthropogenic mercury emissions, and contributes to atmospheric mercury concentration and deposition in other regions. Similarly, mercury from the global pool also plays a role in the chemical transport of mercury in East Asia. Annual simulations of atmospheric mercury in East Asia were performed using the STEM-Hg modeling system to study the mass budgets of mercury in the region. The model results showed strong seasonal variation in mercury concentration and deposition, with signals from large point sources. The annual mean concentrations for gaseous elemental mercury, reactive gaseous mercury and particulate mercury in central China and eastern coastal areas were 1.8 ng m(-3), 100 pg m(-3) and 150 pg m(-3), respectively. Boundary conditions had a strong influence on the simulated mercury concentration and deposition, contributing to 80% of the concentration and 70% of the deposition predicted by the model. The rest was caused by the regional emissions before they were transported out of the model domain. Using different oxidation rates reported for the Hg(0)-O(3) reaction (i.e., by Hall, 1995 vs. by Pal and Ariya, 2004) led to a 9% difference in the predicted mean concentration and a 40% difference in the predicted mean deposition. The estimated annual dry and wet deposition for East Asia in 2001 was in the range of 590-735 Mg and 482-696 Mg, respectively. The mercury mass outflow caused by the emissions in the domain was estimated to be 681-714 Mg yr(-1). This constituted 70% of the total mercury emission in the domain. The greatest outflow occurred in spring and early summer.

Asian aerosols: current and year 2030 distributions and implications to human health and regional climate change.

Aerosol distributions in Asia calculated over a 4-year period and constrained by satellite observations of aerosol optical depth (AOD) are presented. Vast regions in Asia that include > 80% of the population have PM2.5 concentrations that exceed on an annual basis the WHO guideline of 10 microg/m3, often by factors of 2 to 4. These high aerosol loadings also have important radiative effects, causing a significant dimming at the surface, and mask approximately 45% of the warming by greenhouse gases. Black carbon (BC) concentrations are high throughout Asia, representing 5-10% of the total AOD, and contributing significantly to atmospheric warming (its warming potential is approximately 55% of that due to CO2). PM levels and AODs in year 2030, estimated based on simulations that consider future changes in emissions, are used to explore opportunities for win-win strategies built upon addressing air quality and climate change together. It is found that in 2030 the PM2.5 levels in significant parts of Asia will increase and exacerbate health impacts; but the aerosols will have a larger masking effect on radiative forcing, due to a decrease in BC and an increase in SO2 emissions.

Adjoint sensitivity analysis of ozone nonattainment over the continental United States.

An application of the adjoint method in air quality management is demonstrated. We use a continental scale chemical transport model (STEM) to calculate the sensitivities of a nationwide U.S. ozone national ambient air quality standard (NAAQS) nonattainment metric to precursor emissions for the period July 1 to August 15, 2004. The model shows low bias and error (-4 and 24%, respectively), particularly for areas with high ozone concentrations. The nonattainment metric accounts for both 1-h and 8-h ozone standards, but is dominated by the 8-h exceedances (97% of the combined metric). Largest values of sensitivities are found to be with respect to emissions in the south and southeast U.S., Ohio River Valley, and California. When nonattainment sensitivities are integrated over the entire U.S., NOx emissions account for the largest contribution (62% of the total), followed by biogenic and anthropogenic VOCs (24% and 14%, respectively). For NOx emissions, point/area and mobile sources account for 54% and 46% of the total sensitivities, respectively. We also provide a state-by-state comparison for the nonattainment magnitude, nonattainment sensitivity, and emission magnitudes to explore the influence of interstate transport of ozone and its precursors, and policy implications of the results. Our analysis of the nationwide ozone nonattainment metric suggests that simple cap-and-trade programs may prove inadequate in achieving sought-after air quality objectives.