Simulating lightning NO production in CMAQv5.2: evolution of scientific updates

Examining the impact of dimethyl sulfide emissions on atmospheric sulfate over the continental U.S

We examine the impact of dimethylsulfide (DMS) emissions on sulfate concentrations over the continental U.S. by using the Community Multiscale Air Quality (CMAQ) model version 5.4 and performing annual simulations without and with DMS emissions for 2018. DMS emissions enhance sulfate not only over seawater but also over land, although to a lesser extent. On an annual basis, the inclusion of DMS emissions increase sulfate concentrations by 36% over seawater and 9% over land. The largest impacts over land occur in California, Oregon, Washington, and Florida, where the annual mean sulfate concentrations increase by ~25%. The increase in sulfate causes a decrease in nitrate concentration due to limited ammonia concentration especially over seawater and an increase in ammonium concentration with a net effect of increased inorganic particles. The largest sulfate enhancement occurs near the surface (over seawater) and the enhancement decreases with altitude, diminishing to 10-20% at an altitude of ~5 km. Seasonally, the largest enhancement of sulfate over seawater occurs in summer, and the lowest in winter. In contrast, the largest enhancements over land occur in spring and fall due to higher wind speeds that can transport more sulfate from seawater into land.

Volatile Chemical Product Enhancements to Criteria Pollutants in the United States

Volatile chemical products (VCPs) are a significant source of reactive organic carbon emissions in the United States with a substantial fraction (>20% by mass) serving as secondary organic aerosol (SOA) precursors. Here, we incorporate a new nationwide VCP inventory into the Community Multiscale Air Quality (CMAQ) model with VCP-specific updates to better model air quality impacts. Model results indicate that VCPs mostly enhance anthropogenic SOA in densely populated areas with population-weighted annual average SOA increasing 15-30% in Southern California and New York City due to VCP emissions (contribution of 0.2-0.5 μg m^-3). Annually, VCP emissions enhance total population-weighted PM_2.5 by ∼5% in California, ∼3% in New York, New Jersey, and Connecticut, and 1-2% in most other states. While the maximum daily 8 h ozone enhancements from VCP emissions are more modest, their influence can cause a several ppb increase on select days in major cities. Printing Inks, Cleaning Products, and Paints and Coatings product use categories contribute ∼75% to the modeled VCP-derived SOA and Cleaning Products, Paints and Coatings, and Personal Care Products contribute ∼81% to the modeled VCP-derived ozone. Overall, VCPs enhance multiple criteria pollutants throughout the United States with the largest impacts in urban cores.

Assessment of Airport-Related Emissions and Their Impact on Air Quality in Atlanta, GA, Using CMAQ and TROPOMI

Impacts of emissions from the Atlanta Hartsfield-Jackson Airport (ATL) on ozone (O₃), ultrafine particulates (UFPs), and fine particulate matter (PM_2.5) are evaluated using the Community Multiscale Air Quality (CMAQ) model and high-resolution satellite observations of NO₂ vertical column densities (VCDs) from TROPOMI. Two airport inventories are compared: an inventory using emissions where landing and take-off (LTO) processes are allocated to the surface (default) and a modified (3D) inventory that has LTO and cruise emissions vertically and horizontally distributed, accounting for aircraft climb and descend rates. The 3D scenario showed reduced bias and error between CMAQ and TROPOMI VCDs compared to the default scenario [i.e., normalized mean bias: -43%/-46% and root mean square error: 1.12/1.21 (1015 molecules/cm²)]. Close agreement of TROPOMI-derived observations to modeled NO₂ VCDs from two power plants with continuous emissions monitors was found. The net effect of aviation-related emissions was an increase in UFP (j mode in CMAQ), PM_2.5 (i + j mode), and O₃ concentrations by up to 6.5 × 10² particles/cm³ (∼38%), 0.7 μg/m³ (∼8%), and 2.7 ppb (∼4%), respectively. Overall, the results show (1) that the spatial allocation of airport emissions has notable effects on air quality modeling results and will be of further importance as airports become a larger part of the total urban emissions and (2) the applicability of high-resolution satellite retrievals to better understand emissions from facilities such as airports.

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 Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation

The Community Multiscale Air Quality (CMAQ) model version 5.3 (CMAQ53), released to the public in August 2019 and followed by version 5.3.1 (CMAQ531) in December 2019, contains numerous science updates, enhanced functionality, and improved computation efficiency relative to the previous version of the model, 5.2.1 (CMAQ521). Major science advances in the new model include a new aerosol module (AERO7) with significant updates to secondary organic aerosol (SOA) chemistry, updated chlorine chemistry, updated detailed bromine and iodine chemistry, updated simple halogen chemistry, the addition of dimethyl sulfide (DMS) chemistry in the CB6r3 chemical mechanism, updated M3Dry bidirectional deposition model, and the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bidirectional deposition model. In addition, support for the Weather Research and Forecasting (WRF) model's hybrid vertical coordinate (HVC) was added to CMAQ53 and the Meteorology-Chemistry Interface Processor (MCIP) version 5.0 (MCIP50). Enhanced functionality in CMAQ53 includes the new Detailed Emissions Scaling, Isolation and Diagnostic (DESID) system for scaling incoming emissions to CMAQ and reading multiple gridded input emission files. Evaluation of CMAQ531 was performed by comparing monthly and seasonal mean daily 8 h average (MDA8) O₃ and daily PM_2.5 values from several CMAQ531 simulations to a similarly configured CMAQ521 simulation encompassing 2016. For MDA8 O₃, CMAQ531 has higher O₃ in the winter versus CMAQ521, due primarily to reduced dry deposition to snow, which strongly reduces wintertime O₃ bias (2-4 ppbv monthly average). MDA8 O₃ is lower with CMAQ531 throughout the rest of the year, particularly in spring, due in part to reduced O₃ from the lateral boundary conditions (BCs), which generally increases MDA8 O₃ bias in spring and fall ( 0.5 μg m^-3). For daily 24 h average PM_2.5, CMAQ531 has lower concentrations on average in spring and fall, higher concentrations in summer, and similar concentrations in winter to CMAQ521, which slightly increases bias in spring and fall and reduces bias in summer. Comparisons were also performed to isolate updates to several specific aspects of the modeling system, namely the lateral BCs, meteorology model version, and the deposition model used. Transitioning from a hemispheric CMAQ (HCMAQ) version 5.2.1 simulation to a HCMAQ version 5.3 simulation to provide lateral BCs contributes to higher O₃ mixing ratios in the regional CMAQ simulation in higher latitudes during winter (due to the decreased O₃ dry deposition to snow in CMAQ53) and lower O₃ mixing ratios in middle and lower latitudes year-round (due to reduced O₃ over the ocean with CMAQ53). Transitioning from WRF version 3.8 to WRF version 4.1.1 with the HVC resulted in consistently higher (1.0-1.5 ppbv) MDA8 O₃ mixing ratios and higher PM_2.5 concentrations (0.1-0.25 μg m^-3) throughout the year. Finally, comparisons of the M3Dry and STAGE deposition models showed that MDA8 O₃ is generally higher with M3Dry outside of summer, while PM_2.5 is consistently higher with STAGE due to differences in the assumptions of particle deposition velocities to non-vegetated surfaces and land use with short vegetation (e.g., grasslands) between the two models. For ambient NH₃, STAGE has slightly higher concentrations and smaller bias in the winter, spring, and fall, while M3Dry has higher concentrations and smaller bias but larger error and lower correlation in the summer.

Evaluation of 15 years of modeled atmospheric oxidized nitrogen compounds across the contiguous United States

Atmospheric nitrogen oxide and nitrogen dioxide (NO + NO2, together termed as NO X ) estimates from annual photochemical simulations for years 2002-2016 are compared to surface network measurements of NO X and total gas-phase-oxidized reactive nitrogen (NO Y ) to evaluate the Community Multiscale Air Quality (CMAQ) modeling system performance by U.S. region, season, and time of day. In addition, aircraft measurements from 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality are used to evaluate how emissions, chemical mechanism, and measurement uncertainty each contribute to the overall model performance. We show distinct seasonal and time-of-day patterns in NO X performance. Summertime NO X is overpredicted with bimodal peaks in bias during early morning and evening hours and persisting overnight. The summertime morning NO X bias dropped from between 28% and 57% for earlier years (2002-2012) to between -2% and 7% for later years (2013-2016). Summer daytime NO X tends to be unbiased or underpredicted. In winter, the evening NO X overpredictions remain, but NO X is unbiased or underpredicted overnight, in the morning, and during the day. NO X overpredictions are most pronounced in the Midwestern and Southern United States with Western regions having more of a tendency toward model underpredictions of NO X . Modeled NO X performance has improved substantially over time, reflecting updates to the emission inputs and the CMAQ air quality model. Model performance improvements are largest for years simulated with CMAQv5.1 or later and for emission inventory years 2014 and later, coinciding with reduced onroad NO X emissions from vehicles with newer emission control technologies and improved treatment of chemistry, deposition, and vertical mixing in CMAQ. Our findings suggest that emissions temporalization of specific mobile source sectors have a small impact on model performance, while chemistry updates improve predictions of NO Y but do not improve summertime NO X bias in the Baltimore/DC area. Sensitivity runs performed for different locations across the country suggest that the improvement in summer NO X performance can be attributed to updates in vertical mixing incorporated in CMAQv5.1.

Estimating US Background Ozone Using Data Fusion

US background (US-B) ozone (O3) is the O3 that would be present in the absence of US anthropogenic (US-A) emissions. US-B O3 varies by location and season and can make up a large, sometimes dominant, portion of total O3. Typically, US-B O3 is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O3. We develop and apply a method to fuse observed O3 with US-B O3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O3. Trends in O3 bias are explored across different simulation years and varying model scales. We found that the CTM US-B O3 estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O3 was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O3. Across the four estimates, we found annual mean CTM-simulated US-B O3 ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O3 ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.

Impact of Lightning NOx Emissions on Atmospheric Composition and Meteorology in Africa and Europe

NOx emissions from lightning have been added to the CHIMERE v2020r1 model using a parameterization based on convective clouds. In order to estimate the impact of these emissions on pollutant concentrations, two simulations, using the online coupled WRF-CHIMERE models with and without NOx emissions from lightning, have been carried out over the months of July and August 2013 and over a large area covering Europe and the northern part of Africa. The results show that these emissions modify the pollutant concentrations as well as the meteorology. The changes are most significant where the strongest emissions are located. Adding these emissions improves Aerosol Optical Depth in Africa but has a limited impact on the surface concentrations of pollutants in Europe. For the two-month average we find that the maximum changes are localized and may reach ±0.5 K for 2 m temperature, ±0.5 m s−1 for 10 m wind speed, 10 W m−2 for short wave radiation surface flux, and 50 and 2 μg m−3 for dust and sea salt surface concentrations, respectively. This leads to maximum changes of 1 μg m−3 for surface concentrations of PM2.5.

Significant ground-level ozone attributed to lightning-induced nitrogen oxides during summertime over the Mountain West States

Using lightning flash data from the National Lightning Detection Network with an updated lightning nitrogen oxides (NOx) emission estimation algorithm in the Community Multiscale Air Quality (CMAQ) model, we estimate the hourly variations in lightning NOx emissions for the summer of 2011 and simulate its impact on distributions of tropospheric ozone (O3) across the continental United States. We find that typical summer-time lightning activity across the U.S. Mountain West States (MWS) injects NOx emissions comparable to those from anthropogenic sources into the troposphere over the region. Comparison of two model simulation cases with and without lightning NOx emissions show that significant amount of ground-level O3 in the MWS during the summer can be attributed to the lightning NOX emissions. The simulated surface-level O3 from a model configuration incorporating lightning NOx emissions showed better agreement with the observed values than the model configuration without lightning NOx emissions. The time periods of significant reduction in bias in simulated O3 between these two cases strongly correlate with the time periods when lightning activity occurred in the region. The inclusion of lightning NOx increased daily maximum 8 h O3 by up to 17 ppb and improved model performance relative to measured surface O3 mixing ratios in the MWS region. Analysis of model results in conjunction with lidar measurements at Boulder, Colorado during July 2014 corroborated similar impacts of lightning NOx emissions on O3 emissions estimated for other summers is comparable to the 2011 air quality. The magnitude of lightning NOx estimates suggesting that summertime surface-level O3 levels in the MWS region could be significantly influenced by lightning NOx.

Simulating lightning NO production in CMAQv5.2: performance evaluations

This study assesses the impact of the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model on ground-level air quality as well as aloft atmospheric chemistry through detailed evaluation of model predictions of nitrogen oxides (NO x ) and ozone (O3) with corresponding observations for the US. For ground-level evaluations, hourly O3 and NO x values from the U.S. EPA Air Quality System (AQS) monitoring network are used to assess the impact of different LNO schemes on model prediction of these species in time and space. Vertical evaluations are performed using ozonesonde and P-3B aircraft measurements during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign conducted in the Baltimore- Washington region during July 2011. The impact on wet deposition of nitrate is assessed using measurements from the National Atmospheric Deposition Program's National Trends Network (NADP NTN). Compared with the Base model (without LNO), the impact of LNO on surface O3 varies from region to region depending on the Base model conditions. Overall statistics suggest that for regions where surface O3 mixing ratios are already overestimated, the incorporation of additional NO from lightning generally increased model overestimation of mean daily maximum 8 h (DM8HR) O3 by 1-2 ppb. In regions where surface O3 is underestimated by the Base model, LNO can significantly reduce the underestimation and bring model predictions close to observations. Analysis of vertical profiles reveals that LNO can significantly improve the vertical structure of modeled O3 distributions by reducing underestimation aloft and to a lesser degree decreasing overestimation near the surface. Since the Base model underestimates the wet deposition of nitrate in most regions across the modeling domain with the exception of the Pacific Coast, the inclusion of LNO leads to reduction in biases and errors and an increase in correlation coefficients at almost all the NADP NTN sites. Among the three LNO schemes described in Kang et al. (2019), the hNLDN scheme, which is implemented using hourly observed lightning flash data from National Lightning Detection Network (NLDN), performs best for comparisons with ground-level values, vertical profiles, and wet deposition of nitrate; the mNLDN scheme (the monthly NLDN-based scheme) performed slightly better. However, when observed lightning flash data are not available, the linear regression-based parameterization scheme, pNLDN, provides an improved estimate for nitrate wet deposition compared to the base simulation that does not include LNO.