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Past work has shown that traffic patterns in the USA and resulting NO x emissions vary by day of week, with NO x emissions typically being higher on weekdays than weekends. This pattern of emissions leads to different levels of ozone on weekends versus weekdays and can be leveraged to understand how local ozone formation changes in response to NO x emission perturbations in different urban areas. Specifically, areas with lower NO x but higher ozone on the weekends (the weekend effect) can be characterized as NO x -saturated and areas with both lower NO x and ozone on weekends (the weekday effect) can be characterized as NO x -limited. In this analysis, we assess maximum daily 8 h average (MDA8) ozone weekend–weekday differences across 51 USA nonattainment areas using 18 years of observed and modeled data from 2002–2019, using the following two metrics: mean MDA8 ozone and percentage of days with MDA8 ozone > 70 ppb (parts per billion). In addition, we quantify the modeled and observed trends in these weekend–weekday differences across this period of substantial NO x emission reductions in the USA. The model assessment is carried out using U.S. Environmental Protection Agency (EPA)'s Air QUAlity TimE Series Project (EQUATES) Community Multiscale Air Quality (CMAQ) model dataset. We identify three types of MDA8 ozone trends occurring across the USA, namely transitioning chemical regime, disappearing weekday effect, and no trend. The transitioning chemical regime trend occurs in a subset of large urban areas that were NO x -saturated (i.e., volatile organic compound (VOC)-limited) at the beginning of the analysis period but transitioned to mixed chemical regimes or NO x -limited conditions by the end of the analysis period. Nine areas have strong transitioning chemical regime trends using both modeled and observed data and with both metrics indicating strong agreement that they are shifting to more NO x -limited conditions: Milwaukee, Houston, Phoenix, Denver, the Northern Wasatch Front, the Southern Wasatch Front, Las Vegas, Los Angeles – San Bernardino County, Los Angeles – South Coast, and San Diego. The disappearing weekday effect was identified for multiple rural and agricultural areas of California which were NO x -limited for the entire analysis period but appear to become less influenced by local day-of-week emission patterns in more recent years. Finally, we discuss a variety of reasons why there are no trends in certain areas including complex impacts of heterogeneous source mixes and stochastic impacts of meteorology. Overall, this assessment finds that the EQUATES modeling simulations indicate more NO x -saturated conditions than the observations but do a good job of capturing year-to-year changes in weekend–weekday MDA8 ozone patterns. [ABSTRACT FROM AUTHOR]

Past work has shown that traffic patterns in the US and resulting NOX emissions vary by day of week, with NOX emissions typically higher on weekdays than weekends. This pattern of emissions leads to different levels of ozone on weekends versus weekdays and can be leveraged to understand how local ozone formation changes in response to NOX emissions perturbations in different urban areas. Specifically, areas with lower NOX but higher ozone on the weekends (the weekend effect) can be characterized as NOX -saturated and areas with both lower NOX and ozone on weekends (the weekday effect) can be characterized as NOX-limited. In this analysis we assess ozone weekend-weekday differences across US nonattainment areas using 18 years of observed and modeled data from 2002–2019 using two metrics: mean ozone and percentage of days > 70 ppb. In addition, we quantify the modeled and observed trends in these weekend-weekday differences across this period of substantial NOX emissions reductions in the US. The model assessment is carried out using EPA's Air QUAlity TimE Series Project (EQUATES) CMAQ dataset. We identify 3 types of ozone trends occuring across the US: disappearing weekend effect, disappearing weekday effect, and no trend. The disappearing weekend effect occurs in a subset of large urban areas that were NOX -saturated (i.e., VOC-limited) at the beginning of the analysis period but transitioned to mixed chemical regimes or NOX-limited conditions by the end of the analysis period. Nine areas have disappearing weekend effect trends in both datasets and with both metrics indicating strong agreement that they are shifting to more NOX-limited conditions: Milwaukee, Houston, Phoenix, Denver, Northern Wasatch Front, Southern Wasatch Front, Las Vegas, Los Angeles – San Bernardino County, Los Angeles – South Coast, and San Diego. The disappearing weekday effect was identified for multiple rural and agricultural areas of California which were NOX -limited for the entire analysis period but appear to become less influenced by local day of week emission patterns in more recent years. Finally, we discuss a variety of reasons why there are no statistically significant trends in certain areas including complex impacts of heterogeneous source mixes and stochastic impacts of meteorology. Overall, this assessment finds that the EQUATES modeling simulations indicate more NOX-saturated conditions than the observations but do a good job of capturing year-to-year changes in weekend-weekday ozone patterns. [ABSTRACT FROM AUTHOR]

Chemical mechanisms describe how emissions of gases and particles evolve in the atmosphere and are used within chemical transport models to evaluate past, current, and future air quality. Thus, a chemical mechanism must provide robust and accurate predictions of air pollutants if it is to be considered for use by regulatory bodies. In this work, we provide an initial evaluation of the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMMv1.0) by assessing CRACMMv1.0 predictions of surface ozone (O3) across the northeastern US during the summer of 2018 within the Community Multiscale Air Quality (CMAQ) modeling system. CRACMMv1.0 O3 predictions of hourly and maximum daily 8 h average (MDA8) ozone were lower than those estimated by the Regional Atmospheric Chemistry Mechanism with aerosol module 6 (RACM2_ae6), which better matched surface network observations in the northeastern US (RACM2_ae6 mean bias of + 4.2 ppb for all hours and + 4.3 ppb for MDA8; CRACMMv1.0 mean bias of + 2.1 ppb for all hours and + 2.7 ppb for MDA8). Box model calculations combined with results from CMAQ emission reduction simulations indicated a high sensitivity of O3 to compounds with biogenic sources. In addition, these calculations indicated the differences between CRACMMv1.0 and RACM2_ae6 O3 predictions were largely explained by updates to the inorganic rate constants (reflecting the latest assessment values) and by updates to the representation of monoterpene chemistry. Updates to other reactive organic carbon systems between RACM2_ae6 and CRACMMv1.0 also affected ozone predictions and their sensitivity to emissions. Specifically, CRACMMv1.0 benzene, toluene, and xylene chemistry led to efficient NOx cycling such that CRACMMv1.0 predicted controlling aromatics reduces ozone without rural O3 disbenefits. In contrast, semivolatile and intermediate-volatility alkanes introduced in CRACMMv1.0 acted to suppress O3 formation across the regional background through the sequestration of nitrogen oxides (NOx) in organic nitrates. Overall, these analyses showed that the CRACMMv1.0 mechanism within the CMAQ model was able to reasonably simulate ozone concentrations in the northeastern US during the summer of 2018 with similar magnitude and diurnal variation as the current operational Carbon Bond (CB6r3_ae7) mechanism and good model performance compared to recent modeling studies in the literature. [ABSTRACT FROM AUTHOR]

Chemical mechanisms describe how emissions of gases and particles evolve in the atmosphere and are used within chemical transport models to evaluate past, current, and future air quality. Thus, a chemical mechanism must provide robust and accurate predictions of air pollutants if it is to be considered for use by regulatory bodies. In this work, we provide an initial evaluation of the Community Regional Atmospheric Chemical Multiphase Mechanism (CRACMMv1.0) by assessing CRACMMv1.0 predictions of surface ozone (O3) across the Northeast U.S. during the summer of 2018 within the Community Multiscale Air Quality (CMAQ) modeling system. CRACMMv1.0 O3 predictions of hourly and maximum daily 8-hour average (MDA8) ozone were lower than those estimated by the Regional Atmospheric Chemical Mechanism (RACM2_ae6), which better matched surface network observations in the Northeast US (RACM2_ae6 mean bias of +4.2 ppb for all hours and +4.3 ppb for MDA8; CRACMMv1.0 mean bias of +2.1 ppb for all hours and +2.7 ppb for MDA8). Box model calculations combined with results from CMAQ emission reduction simulations indicated high sensitivity of O3 to compounds with biogenic sources. In addition, these calculations indicated the differences between CRACMMv1.0 and RACM2_ae6 O3 predictions were largely explained by updates to the inorganic rate constants (reflecting the latest assessment values) and by updates to the representation of monoterpene chemistry. Updates to other reactive organic carbon systems between RACM2_ae6 and CRACMMv1.0 also affected ozone predictions and their sensitivity to emissions. Specifically, CRACMMv1.0 benzene, toluene, and xylene chemistry led to efficient NOx cycling such that CRACMMv1.0 predicted controlling aromatics reduces ozone without rural O3 disbenefits. In contrast, semivolatile to intermediate volatility alkanes introduced in CRACMMv1.0 acted to suppress O3 formation across the regional background through the sequestration of nitrogen oxides (NOx) in organic nitrates. Overall, these analyses showed that the CRACMMv1.0 mechanism within the CMAQ model was able to reasonably simulate ozone concentrations in the Northeast US during the summer of 2018 with similar magnitude and diurnal variation as the current operational Carbon Bond (CB6r3_ae7) and good model performance compared to recent modelling studies in the literature. [ABSTRACT FROM AUTHOR]

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) O 3 and daily PM 2.5 values from several CMAQ531 simulations to a similarly configured CMAQ521 simulation encompassing 2016. For MDA8 O 3 , CMAQ531 has higher O 3 in the winter versus CMAQ521, due primarily to reduced dry deposition to snow, which strongly reduces wintertime O 3 bias (2–4 ppbv monthly average). MDA8 O 3 is lower with CMAQ531 throughout the rest of the year, particularly in spring, due in part to reduced O 3 from the lateral boundary conditions (BCs), which generally increases MDA8 O 3 bias in spring and fall (∼0.5 µgm-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 O 3 mixing ratios in the regional CMAQ simulation in higher latitudes during winter (due to the decreased O 3 dry deposition to snow in CMAQ53) and lower O 3 mixing ratios in middle and lower latitudes year-round (due to reduced O 3 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 O 3 mixing ratios and higher PM 2.5 concentrations (0.1–0.25 µgm-3) throughout the year. Finally, comparisons of the M3Dry and STAGE deposition models showed that MDA8 O 3 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 NH 3 , 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. [ABSTRACT FROM AUTHOR]

Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow tends to be time-consuming, error-prone, inconsistent among model users and difficult to document while consuming increased computer storage space. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g. energy system models, reduced-form models). [ABSTRACT FROM AUTHOR]

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/iodine chemistry; updated simple halogen chemistry; addition of dimethyl sulfide (DMS) chemistry in the CB6r3 chemical mechanism; updated M3Dry bi-directional deposition model; and the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bi-directional deposition model. In addition, support for the Weather Research and Forecasting (WRF) model's hybrid vertical coordinate (HVC) system 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 CMAQ53 was performed by comparing monthly and seasonal mean daily 8-hr average (MDA8) O3 and PM2.5 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. 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. For daily 24-hr average PM2.5, CMAQ531 has lower concentrations on average in spring and fall, higher concentrations in summer, and similar concentrations in winter as CMAQ521, which slightly increases bias in spring and fall and reduces bias in summer. Comparisons isolating updates to several specific aspects of the modeling system, namely the lateral BCs, meteorology model version, and the deposition model used, were also performed. 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 mid 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 system resulted in consistently higher (1.0-1.5 ppbv) MDA8 O3 mixing ratios and higher PM2.5 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 PM2.5 is consistently higher with STAGE due to differences in the assumptions of particle deposition velocities to some surfaces 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. [ABSTRACT FROM AUTHOR]

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R² and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2:5, 0.88 and 0.65 for NO3-, 0.78 and 0.23 for SO42-, 1.00 and 1.01 for NHC4+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R² results for the satellite-based PM2.5 improve by 30% and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO42- cross-validation values showed the largest spatial and spatiotemporal R² improvement, with a 43% increase. Assessing this physical technique in a wellinstrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent. [ABSTRACT FROM AUTHOR]

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NOx (NO+NO2), VOC and SOx (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions. [ABSTRACT FROM AUTHOR]

This paper describes the development and implementation of an extendable aqueous-phase chemistry option (AQCHEM - KMT(I)) for the Community Multiscale Air Quality (CMAQ) modeling system, version 5.1. Here, the Kinetic PreProcessor (KPP), version 2.2.3, is used to generate a Rosenbrock solver (Rodas3) to integrate the stiff system of ordinary differential equations (ODEs) that describe the mass transfer, chemical kinetics, and scavenging processes of CMAQ clouds. CMAQ's standard cloud chemistry module (AQCHEM) is structurally limited to the treatment of a simple chemical mechanism. This work advances our ability to test and implement more sophisticated aqueous chemical mechanisms in CMAQ and further investigate the impacts of microphysical parameters on cloud chemistry. Box model cloud chemistry simulations were performed to choose efficient solver and tolerance settings, evaluate the implementation of the KPP solver, and assess the direct impacts of alternative solver and kinetic mass transfer on predicted concentrations for a range of scenarios. Month-long CMAQ simulations for winter and summer periods over the US reveal the changes in model predictions due to these cloud module updates within the full chemical transport model. While monthly average CMAQ predictions are not drastically altered between AQCHEM and AQCHEM - KMT, hourly concentration differences can be significant. With added in-cloud secondary organic aerosol (SOA) formation from biogenic epoxides (AQCHEM - KMTI), normalized mean error and bias statistics are slightly improved for 2-methyltetrols and 2-methylglyceric acid at the Research Triangle Park measurement site in North Carolina during the Southern Oxidant and Aerosol Study (SOAS) period. The added in-cloud chemistry leads to a monthly average increase of 11-18% in "cloud" SOA at the surface in the eastern United States for June 2013. [ABSTRACT FROM AUTHOR]

A new windblown dust emission treatment was incorporated in the Community Multiscale Air Quality (CMAQ) modeling system. This new model treatment has been built upon previously developed physics-based parameterization schemes from the literature. A distinct and novel feature of this scheme, however, is the incorporation of a newly developed dynamic relation for the surface roughness length relevant to small-scale dust generation processes. Through this implementation, the effect of nonerodible elements on the local flow acceleration, drag partitioning, and surface coverage protection is modeled in a physically based and consistent manner. Careful attention is paid in integrating the new windblown dust treatment in the CMAQ model to ensure that the required input parameters are correctly configured. To test the performance of the new dust module in CMAQ, the entire year 2011 is simulated for the continental United States, with particular emphasis on the southwestern United States (SWUS) where windblown dust concentrations are relatively large. Overall, the model shows good performance with the daily mean bias of soil concentrations fluctuating in the range of ±1 µg m−3 for the entire year. Springtime soil concentrations are in quite good agreement (normalized mean bias of 8.3%) with observations, while moderate to high underestimation of soil concentration is seen in the summertime. The latter is attributed to the issue of representing the convective dust storms in summertime. Evaluations against observations for seven elevated dust events in the SWUS indicate that the new windblown dust treatment is capable of capturing spatial and temporal characteristics of dust outbreaks. [ABSTRACT FROM AUTHOR]

This paper describes the development and implementation of an extendable aqueous phase chemistry option (AQCHEM-KMT(I)) for the Community Multiscale Air Quality (CMAQ) modeling system, version 5.1. Here the Kinetic PreProcessor (KPP), version 2.2.3, is used to generate a Rosenbrock solver (Rodas3) to integrate the stiff system of ODEs that describe the mass transfer, chemical kinetics, and scavenging processes of CMAQ clouds. CMAQ's standard cloud chemistry module (AQCHEM) is structurally limited to the treatment of a simple chemical mechanism. This work advances our ability to test and implement more sophisticated aqueous chemical mechanisms in CMAQ and further investigate the impacts of microphysical parameters on cloud chemistry. Box model cloud chemistry simulations were performed to choose efficient solver and tolerance settings, evaluate the implementation of the KPP solver, and assess the direct impacts of alternative solver and kinetic mass transfer on predicted concentrations for a range of scenarios. Month-long CMAQ simulations for winter and summer periods over the U.S. reveal the changes in model predictions due to these cloud module updates within the full chemical transport model. While monthly average CMAQ predictions are not drastically altered between AQCHEM and AQCHEM-KMT, hourly concentration differences can be significant. With added in-cloud secondary organic aerosol (SOA) formation from biogenic epoxides (AQCHEM-KMTI), normalized mean error and bias statistics are slightly improved for 2-methyltetrols and 2-methylglyceric acid at the Research Triangle Park measurement site in North Carolina during the SOAS field campaign period. The added in-cloud chemistry leads to a monthly-average increase of 11-18 % in "cloud" SOA at the surface in the eastern U.S. for June 2013. [ABSTRACT FROM AUTHOR]

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multi-pollutant air quality modeling system developed and maintained by the U.S. Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public which incorporates a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ; updates to the gas and aerosol chemistry; revisions to the calculations of clouds and photolysis; and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations generally result in greater afternoon and early evening mixing in the model, times when the model historically underestimates mixing. The result is higher ozone (O3) mixing ratios on average due to reduced NO titration and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g. elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and reduced attenuation of photolysis in the model. Updates to the aerosol chemistry results in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing PM2.5 bias, while updates to the gas chemistry result in generally increased O3 in January and July (small) and slightly higher PM2.5 concentrations on average in both January and July. Overall, seasonal variation in simulated PM2.5 generally improves in the new model version, as concentrations decrease in the winter (when PM2.5 is overestimated by CMAQ v5.0.2) and increase in the summer (when PM2.5 is underestimated by CMAQ v5.0.2). Ozone mixing ratios are higher on average with v5.1 versus v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low), however both the error and correlation are largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios showed that v5.1 tends to be slightly more responsive to reductions in NOx (NO + NO2), VOC and SOx (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions. Finally, the computational efficiency of the model was significantly improved in v5.1, which keeps runtimes similar to v5.0.2 despite the added complexity to the model. [ABSTRACT FROM AUTHOR]

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the modelderived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface based measurements from several air quality networks. The CMAQ modeling system generally did well replicating the observed soil concentrations in the western United States; however the model consistently overestimated the observed soil con centrations in the eastern United States, regardless of season. The performance of the individual trace metals was generally good at the rural network sites, with relatively small biases for Fe, Al, Si and Ti throughout the year, while Ca, K and Mn were overestimated and Mg underestimated. For the urban sites, Fe, Mg and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al, Ti and Si in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust related emissions sources in the emissions inventory. [ABSTRACT FROM AUTHOR]

The evaluation of regional air quality models is a challenging task, not only for the intrinsic complexity of the topic but also in view of the difficulties in finding sufficiently abundant, harmonized and time/space-well-distributed measurement data. This study, conducted in the framework of AQMEII (Air Quality Model Evaluation International Initiative), evaluates 4-D model predictions obtained from 15 modelling groups and relating to the air quality of the full year of 2006 over the North American and European continents. The modelled variables are ozone, CO, wind speed and direction, temperature, and relative humidity. Model evaluation is supported by the high quality in-flight measurements collected by instrumented commercial aircrafts in the context of the MOZAIC programme. The models are evaluated at five selected domains positioned around major airports, four in North America (Portland, Philadelphia, Atlanta, Dallas) and one in Europe (Frankfurt). Due to the extraordinary scale of the exercise (number of models and variables, spatial and temporal extent), this study is primarily aimed at illustrating the potential for using MOZAIC data for regional-scale evaluation and the capabilities of models to simulate concentration and meteorological fields in the vertical rather than just at the ground. We apply various approaches, metrics, and methods to analyze this complex dataset. Results of the investigation indicate that, while the observed meteorological fields are modelled with some success, modelling CO in and above the boundary layer remains a challenge and modelling ozone also has room for significant improvement. We note, however, that the high sensitivity of models to height, season, location, and metric makes the results rather difficult to interpret and to generalize. With this work, though, we set the stage for future process-oriented and in-depth diagnostic analyses. [ABSTRACT FROM AUTHOR]

Time and size resolved data of trace elements were obtained from measurements with a rotating drum impactor (RDI) and subsequent X-ray fluorescence spectrometry. Trace elements can act as indicators for the identification of sources of particulate matter <10 µm (PM10) in ambient air. Receptor modeling was performed with positive matrix factorization (PMF) for trace element data from an urban background site in Zürich, Switzerland. Eight different sources were identified for the three examined size ranges (PM1-0.1, PM2.5-1 and PM10-2.5): secondary sulfate, wood combustion, fire works, road traffic, mineral dust, de-icing salt, industrial and local anthropogenic activities. The major component was secondary sulfate for the smallest size range; the road traffic factor was found in all three size ranges. This trace element analysis is complemented with data from an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (AMS), assessing the PM1 fraction of organic aerosols. A separate PMF analysis revealed three factors related to three of the sources found with the RDI: oxygenated organic aerosol (OOA, related to inorganic secondary sulfate), hydrocarbon-like organic aerosol (HOA, related to road traffic) and biomass burning organic aerosol (BBOA), explaining 60 %, 22 % and 17 % of total measured organics, respectively. Since different compounds are used for the source classification, a higher percentage of the ambient PM10 mass concentration can be apportioned to sources by the combination of both methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the details of the Atmospheric Model Evaluation Tool (AMET) v1.1 created by scientists in the Atmospheric Modeling and Analysis Division (AMAD) of the U.S. Environmental Protection Agency (EPA). AMET was first developed to evaluate the performance of the 5th Generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological model output and was later extended to include capabilities for evaluating output data from the Community Multiscale Air Quality (CMAQ) model as well. AMET is designed to leverage several open-source software packages that are used in combination to 1) pair the modeled and observed values in time and space, 2) store these paired datasets in an easily accessible and searchable database and 3) access and analyze these data using a statistical package. Through this process, AMET is able to provide a convenient method for evaluating meteorological and air quality model predictions. The use of a searchable, relational database allows the entire dataset to be quickly subset into only those data that are of the most interest for the current analysis, a process that is often tedious and time consuming without the use of a database. In addition to common summary statistics (e.g. RMSE, bias, and correlation), several of the many analysis features available in AMET include scatter plots, time series plots, box plot and spatial plots as part of operational model evaluation. Additionally, several unique analysis functions are also available in AMET, and the system provides a framework within which users may extend the current functionality for their own custom analyses. While AMET was designed to work specifically with MM5, WRF and CMAQ model output, it could easily be modified to work with output data from other meteorological and air quality models. [Copyright &y& Elsevier]

The article presents a study regarding the scientific and structural updates of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 (v4.7). The study evaluated the said updates through observations and results of the assessment conducted for the effect of the changes in the modeling system after it underwent updates. The results of the study indicate that the CMAQv4.7 model has worse performance in ozone predictions.

This paper is Part II in a pair of papers that examines the results of the Community Multiscale Air Quality (CMAQ) model version 4.5 (v4.5) and discusses the potential explanations for the model performance characteristics seen. The focus of this paper is on fine particulate matter (PM2.5) and its chemical composition. Improvements made to the dry deposition velocity and cloud treatment in CMAQ v4.5 addressing compensating errors in 36-km simulations improved particulate sulfate (SO4 2−) predictions. Large overpredictions of particulate nitrate (NO3 −) and ammonium (NH4 +) in the fall are likely due to a gross overestimation of seasonal ammonia (NH3) emissions. Carbonaceous aerosol concentrations are substantially underpredicted during the late spring and summer months, most likely due, in part, to a lack of some secondary organic aerosol (SOA) formation pathways in the model. Comparisons of CMAQ PM2.5 predictions with observed PM2.5 mass show mixed seasonal performance. Spring and summer show the best overall performance, while performance in the winter and fall is relatively poor, with significant overpredictions of total PM2.5 mass in those seasons. The model biases in PM2.5 mass cannot be explained by summing the model biases for the major inorganic ions plus carbon. Errors in the prediction of other unspeciated PM2.5 (PMOther) are largely to blame for the errors in total PM2.5 mass predictions, and efforts are underway to identify the cause of these errors. [Copyright &y& Elsevier]

This study examines ozone (O3) predictions from the Community Multiscale Air Quality (CMAQ) model version 4.5 and discusses potential factors influencing the model results. Daily maximum 8-h average O3 levels are largely underpredicted when observed O3 levels are above 85ppb and overpredicted when they are below 35ppb. Using a clustering approach, model performance was examined separately for several different synoptic regimes. Under the most common synoptic conditions of a typical summertime Bermuda High setup, the model showed good overall performance for O3, while associations have been identified here between other, less frequent, synoptic regimes and the O3 overprediction and underprediction biases. A sensitivity test between the CB-IV and CB05 chemical mechanisms showed that predictions of daily maximum 8-h average O3 using CB05 were on average 7.3% higher than those using CB-IV. Boundary condition (BC) sensitivity tests show that the overprediction biases at low O3 levels are more sensitive to the BC O3 levels near the surface than BC concentrations aloft. These sensitivity tests also show the model performance for O3 improved when using the global GEOS-CHEM BCs instead of default profiles. Simulations using the newest version of the CMAQ model (v4.6) showed a small improvement in O3 predictions, particularly when vertical layers were not collapsed. Collectively, the results suggest that key synoptic weather patterns play a leading role in the prediction biases, and more detailed study of these episodes are needed to identify further modeling improvements. [Copyright &y& Elsevier]

Abstract: Significant uncertainty exists in the seasonal distribution of NH3 emissions since the predominant sources are animal husbandry and fertilizer application. Previous studies that estimated bottom–up and top–down NH3 emissions have provided the most comprehensive information available about the seasonality of NH3 emissions. In this study, this bottom–up and top–down emission information is combined with the most recent 2001 USEPA National Emission Inventory (NEI) to construct a best prior estimate of seasonal NH3 emissions. These emission estimates are then used in an annual 2001 USEPA Community Multiscale Air Quality (CMAQ) model simulation for the continental United States. A key objective of this study is to evaluate these prior NH3 emission estimates and test the top–down inverse modeling method for a different year and a larger modeling domain than used previously. Based on the final posterior NH3 emission estimates, the inverse modeling results suggest that the annual total NEI NH3 emissions are reasonable and that a previous high bias in older USEPA emission inventories has been addressed in the updated inventory. Inverse modeling results suggest that the prior NH3 emission estimates should be increased in the summer and decreased in the winter, while results for the spring and fall are questionable due to precipitation prediction biases. A final conclusion from this study is that total NH x (NH3 and aerosol NH4 +) air concentration data are essential for quantitative top–down analyses of NH3 emissions that can extend beyond what is possible using precipitation chemistry data. [Copyright &y& Elsevier]

Abstract Deposition and accumulation of aerosol particles on photovoltaics (PV) panels, which is commonly referred to as "soiling of PV panels," impacts the performance of the PV energy system. It is desirable to estimate the soiling effect at different locations and times for modeling the PV system performance and devising cost-effective mitigation. This study presents an approach to estimate the soiling effect by utilizing particulate matter (PM) dry deposition estimates from air quality model simulations. The Community Multiscale Air Quality (CMAQ) modeling system used in this study was developed by the U.S. Environmental Protection Agency (U.S. EPA) for air quality assessments, rule-making, and research. Three deposition estimates based on different surface roughness length parameters assumed in CMAQ were used to illustrate the soling effect in different land-use types. The results were analyzed for three locations in the U.S. for year 2011. One urban and one suburban location in Colorado were selected because there have been field measurements of particle deposition on solar panels and analysis on the consequent soiling effect performed at these locations. The third location is a coastal city in Texas, the City of Brownsville. These three locations have distinct ambient environments. CMAQ underestimates particle deposition by 40% to 80% when compared to the field measurements at the two sites in Colorado due to the underestimations in both the ambient PM 10 concentration and deposition velocity. The estimated panel transmittance sensitivity due to the deposited particles is higher than the sensitivity obtained from the measurements in Colorado. The final soiling effect, which is transmittance loss, is estimated as 3.17 ± 4.20% for the Texas site, 0.45 ± 0.33%, and 0.31 ± 0.25% for the Colorado sites. Although the numbers are lower compared to the measurements in Colorado, the results are comparable with the soiling effects observed in U.S. Graphical abstract Unlabelled Image Highlights • CMAQ model overestimates PM 2.5 concentration but underestimates PM 10 concentrations at selected three sites in U.S. • CMAQ model underestimates PM 10 deposition at three sites. • PV panel transmittance losses estimated based on the modeled particle deposition are lower compared to on-site measurements. • The transmittance loss estimates are comparable to other observations in U.S., indicating applicability of the approach. [ABSTRACT FROM AUTHOR]

In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher-resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000–2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method’s use for future air quality projections. [ABSTRACT FROM AUTHOR]

Dry deposition of atmospheric aerosols in large-scale models is a critical, but highly uncertain, sink process with a strong dependence on particle size, meteorological conditions, and land surface properties. This study investigates the particle dry deposition scheme implemented in the standard Community Multiscale Air Quality (CMAQ) model v5.2.1, characterizes its underlying parameterized components with comparison to a similar scheme in a contemporary regional-scale model, and proposes two updated schemes that are then evaluated with available ambient particle deposition velocity (V d) measurements. Both updated schemes reduce the surprisingly strong dependence of deposition velocity on the aerosol mode width, with one scheme further introducing a dependence on vegetation coverage that is broadly consistent with variability in observations between vegetated and non-vegetated surfaces. Compared to the base scheme, the updated scheme with vegetation dependence increases V d for submicron particles and decreases it for larger particles by an average of 37% and −66%, respectively. This scheme performs statistically better than the base scheme, reducing fractional biases by 56%–97% for vegetated land-use types and has roughly equivalent performance over water. The base and updated schemes are tested with three annual CMAQ (v5.2.1) simulations for the year 2011; predicted ambient aerosol concentrations are evaluated with routine monitoring network observations and predicted dry deposition fluxes are evaluated with data from the Clean Air Status and Trends Network (CASTNET). The updated scheme with vegetation dependence reduces negative fractional biases for PM 10 by 41% and positive fractional biases for PM 2.5 organic carbon by 15%. This scheme has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3 and beyond) to replace the scheme used in previous versions of CMAQ (v4.5 through v5.2.1). • A 0-D box model is designed to characterize the parameterized components of particle dry deposition in CTMs. • The source of the differences between CAMx and CMAQ particle dry deposition schemes is characterized in the box model. • Incremental revisions to the particle dry deposition scheme are updated in CMAQ. • The behaviors of updated schemes are characterized in the 0-D box model and full 3-D CMAQ. • Key issues for current applications and future development of particle dry deposition are highlighted. [ABSTRACT FROM AUTHOR]