Community Multiscale Air Quality Modeling System Research Articles

Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis

Abstract. There have been consistent efforts to improve the spatiotemporal representations of biogenic/anthropogenic emission sources for photochemical transport modeling for better accuracy of local/regional air quality forecasts. While biogenic emissions, bi-directional NH3 from fertilizer applications, and point source plume rise are dynamically coupled in the Community Multiscale Air Quality (CMAQ) “inline”, there are still known meteorology-induced emissions sectors (e.g., on-road mobile sources, residential heating, and livestock waste), with little or no accounting for the meteorological impacts in the currently operational chemical and aerosol forecasts, but they are represented with static, not weather-aware annual or monthly county total emissions and standard monthly, weekly, or daily temporal allocation profiles to disaggregate them on finer timescales for the hourly air quality forecasts. It often results in poor forecasting performance due to the poor spatiotemporal representations of precursor pollutants during high ozone and PM2.5 episodes. The main focus of this study is to develop a dynamic inline coupler within the CMAQ system for the on-road mobile emission sector that requires significant computational resources in the current modeling application. To improve their accuracy and spatiotemporal representations, we developed the inline coupler module called CMAQ-MetEmis (for meteorology-induced emission sources within CMAQ version 5.3.2 modeling system). It can dynamically estimate meteorology-induced hourly gridded on-road mobile emissions within the CMAQ, using simulated meteorology without any computational burden to the CMAQ modeling system. To understand the impacts of meteorology-driven on-road mobile emissions on local air quality, the CMAQ is applied over the continental U.S. for 2 months (January and July 2019) for two emissions scenarios, namely (a) “static” on-road vehicle emissions based on static temporal profiles and (b) inline CMAQ-MetEmis on-road vehicle emissions. Overall, the CMAQ-MetEmis coupler allows us to dynamically simulate on-road vehicle emissions from the MOtor Vehicle Emission Simulator (MOVES) on-road emission model for CMAQ, with a better spatiotemporal representation based on the simulated meteorology inputs when compared to the static scenario. The domain total of daily volatile organic compound (VOC) emissions from the inline scenario shows that the largest impacts are from the local meteorology, which is approximately 10 % lower than the ones from the static scenario. In particular, the major difference in the VOC estimates was shown over the California region. These local meteorology impacts on the on-road vehicle emissions via CMAQ-MetEmis revealed an improvement in the hourly NO2, daily maximum ozone, and daily average PM2.5 patterns, with a higher agreement and correlation with daily ground observations.

Characterizing variations in ambient PM2.5 concentrations at the U.S. Embassy in Dhaka, Bangladesh using observations and the CMAQ modeling system

We analyze hourly PM2.5 (particles with an aerodynamic diameter of ≤2.5 μm) concentrations measured at the U.S. Embassy in Dhaka over the 2016–2021 time period and find that concentrations are seasonally dependent with the highest occurring in winter and the lowest in monsoon seasons. Mean winter PM2.5 concentrations reached ∼165–175 μg/m3 while monsoon concentrations remained ∼30–35 μg/m3. Annual mean PM2.5 concentration reached ∼5–6 times greater than the Bangladesh annual PM2.5 standard of 15 μg/m3. The number of days exceeding the daily PM2.5 standard of 65 μg/m3 in a year approached nearly 50%. Daily-mean PM2.5 concentrations remained elevated (>65 μg/m3) for more than 80 consecutive days. Night-time concentrations were greater than daytime concentrations. The comparison of results obtained from the Community Multiscale Air Quality (CMAQ) model simulations over the Northern Hemisphere using 108-km horizontal grids with observed data suggests that the model can reproduce the seasonal variation of observed data but underpredicts observed PM2.5 in winter months with a normalized mean bias of -13 to –32%. In the model, organic aerosol is the largest component of PM2.5, of which secondary organic aerosol plays a dominant role. Transboundary pollution has a large impact on the modeled PM2.5 concentration in Dhaka, with an annual mean contribution of ∼40 μg/m3.

Impacts of household sources on air pollution at village and regional scales in India

Abstract. Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM2.5 and O3) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m−3, and, on occasion, exceeded 180 µg m−3. PM2.5 levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM2.5 produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM2.5 during the periods studied at the four locations averaged about 30 µg m−3, representing approximately 30 % and 20 % of total PM2.5 levels in the rural and urban stations, respectively.

Potential impacts of cold frontal passage on air quality over the Yangtze River Delta, China

Abstract. Cold frontal passages usually promote quick removal of atmospheric pollutants over North China (e.g. the Beijing–Tianjin–Hebei region). However, in the Yangtze River Delta (YRD), cold fronts may bring air pollutants from the polluted North China Plain (NCP), thereby deteriorating the air quality in the YRD. In this study, a cold frontal passage and a subsequent stable weather event over YRD during 21–26 January 2015 was investigated with in situ observations and Weather Research and Forecasting – Community Multiscale Air Quality Modeling System simulations. Observations showed a burst of PM2.5 pollution and an obvious southward motion of PM2.5 peaks on the afternoon of 21 January, suggesting a strong inflow of highly polluted air masses to YRD by a cold frontal passage. Model simulations revealed an existing warm and polluted air mass over YRD ahead of the frontal zone, which climbed to the free troposphere along the frontal surface as the cold front passed, increasing the PM2.5 concentration at high altitudes. Strong north-westerly frontal airflow transported particles from the highly polluted NCP to the YRD. As the frontal zone moved downstream of YRD, high pressure took control over the YRD, which resulted in a synoptic subsidence that trapped PM2.5 in the boundary layer. After the cold frontal episode, a uniform pressure field took control over the YRD. Locally emitted PM2.5 started to accumulate under the weak winds and stable atmosphere. Tagging of PM2.5 by geophysical regions showed that the PM2.5 contribution from the YRD itself was 35 % and the contribution from the NCP was 29 % during the cold frontal passage. However, under the subsequent stable weather conditions, the PM2.5 contribution from the YRD increased to 61.5 % and the contribution from the NCP decreased to 14.5 %. The results of this study indicate that cold fronts are potential carriers of atmospheric pollutants when there are strong air pollutant sources in upstream areas, which may deteriorate air quality in downstream regions.

Assessment of inter-city transport of particulate matter in the Beijing–Tianjin–Hebei region

Abstract. The regional transport of particulate matter with diameter less than 2.5 µm (PM2.5) plays an important role in the air pollution of the Beijing–Tianjin–Hebei (BTH) region in China. However, previous studies on regional transport of PM2.5 mainly aim at province level, which is insufficient for the development of an optimal joint PM2.5 control strategy. In this study, we calculate PM2.5 inflows and outflows through the administrative boundaries of three major cities in the BTH region, i.e., Beijing, Tianjin and Shijiazhuang, using the WRF (Weather Research and Forecasting model)-CMAQ (Community Multiscale Air Quality) modeling system. The monthly average inflow fluxes indicate the major directions of PM2.5 transport. For Beijing, the PM2.5 inflow fluxes from Zhangjiakou (in the northwest) and Baoding (in the southwest) constitute 57 % of the total in winter, and Langfang (in the southeast) and Baoding constitute 73 % in summer. Based on the net PM2.5 fluxes and their vertical distributions, we find there are three major transport pathways in the BTH region: the northwest–southeast pathway in winter (at all levels below 1000 m), the northwest–southeast pathway in summer (at all levels below 1000 m), and the southwest–northeast pathway in both winter and in summer (mainly at 300–1000 m). In winter, even if surface wind speeds are low, the transport at above 300 m can still be strong. Among the three pathways, the southwest–northeast happens along with PM2.5 concentrations 30 and 55 % higher than the monthly average in winter and summer, respectively. Analysis of two heavy pollution episodes in January and July in Beijing show a much (8–16 times) stronger transport than the monthly average, emphasizing the joint air pollution control of the cities located on the transport pathways, especially during heavy pollution episodes.

A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

Abstract. 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 PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 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 PM2.5 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 PM2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM2.5 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 PM2.5 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.

Development and evaluation of a physics-based windblown dust emission scheme implemented in the CMAQ modeling system.

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.

Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0

Abstract. 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 model-derived 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 overall did well replicating the observed soil concentrations in the western United States (mean bias generally around ±0.5 μg m−3); however, the model consistently overestimated the observed soil concentrations in the eastern United States (mean bias generally between 0.5–1.5 μg m−3), regardless of season. The performance of the individual trace metals was highly dependent on the network, species, and season, with relatively small biases for Fe, Al, Si, and Ti throughout the year at the Interagency Monitoring of Protected Visual Environments (IMPROVE) sites, while Ca, K, and Mn were overestimated and Mg underestimated. For the urban Chemical Speciation Network (CSN) 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 (380%), Ti (370%) and Si (470%) 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 emission sources in the emissions inventory.

Examination of the Community Multiscale Air Quality (CMAQ) model performance over the North American and European domains

Abstract The CMAQ modeling system has been used to simulate the air quality for North America and Europe for the entire year of 2006 as part of the Air Quality Model Evaluation International Initiative (AQMEII). The operational model performance of tropospheric ozone (O 3 ), fine particulate matter (PM 2.5 ) and total particulate matter (PM 10 ) for the two continents has been assessed. The model underestimates daytime (8am–8pm LST) O 3 mixing ratios by 13% in the winter for North America, primarily due to an underestimation of daytime O 3 mixing ratios in the middle and lower troposphere from the lateral boundary conditions. The model overestimates winter daytime O 3 mixing ratios in Europe by an average of 8.4%. The model underestimates daytime O 3 by 4–5% in the spring for both continents, while in the summer daytime O 3 is overestimated by 9.8% for North America and slightly underestimated by 1.6% for Europe. The model overestimates daytime O 3 in the fall for both continents, grossly overestimating daytime O 3 by over 30% for Europe. The performance for PM 2.5 varies both seasonally and geographically for the two continents. For North American, PM 2.5 is overestimated in the winter and fall, with an average Normalized Mean Bias (NMB) greater than −30%, while performance in the summer is relatively good, with an average NMB of −4.6%. For Europe, PM 2.5 is underestimated throughout the entire year, with the NMB ranging from −24% in the fall to −55% in the winter. PM 10 is underestimated throughout the year for both North America and Europe, with remarkably similar performance for both continents. The domain average NMB for PM 10 ranges between −45% and −65% for the two continents, with the largest underestimation occurring in the summer for North American and the winter for Europe.

Predictions of U.K. Regulated Power Station Contributions to Regional Air Pollution and Deposition: A Model Comparison Exercise

ABSTRACT Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics–Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study. IMPLICATIONS Modeling tools are required to assess the contribution of industrial sources to ambient levels of air pollution, acid deposition, and eutrophication. This study evaluates the performance characteristics of regional air quality modeling systems in predicting contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition. It contrasts acid deposition modeling approaches used in the United Kingdom and demonstrates the sensitivity of the modeling systems to large emission changes. This work suggests considering an ensemble average of model calculations to provide an estimate of the uncertainty associated with an industrial source footprint.

The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1

Abstract. The Community Multiscale Air Quality (CMAQ) modeling system, a state-of-the-science regional air quality modeling system developed by the US Environmental Protection Agency, is being used for a variety of environmental modeling problems including regulatory applications, air quality forecasting, evaluation of emissions control strategies, process-level research, and interactions of global climate change and regional air quality. The Meteorology-Chemistry Interface Processor (MCIP) is a vital piece of software within the CMAQ modeling system that serves to, as best as possible, maintain dynamic consistency between the meteorological model and the chemical transport model (CTM). MCIP acts as both a post-processor to the meteorological model and a pre-processor to the emissions and the CTM in the CMAQ modeling system. MCIP's functions are to ingest the meteorological model output fields in their native formats, perform horizontal and vertical coordinate transformations, diagnose additional atmospheric fields, define gridding parameters, and prepare the meteorological fields in a form required by the CMAQ modeling system. This paper provides an updated overview of MCIP, documenting the scientific changes that have been made since it was first released as part of the CMAQ modeling system in 1998.

Assessment of the Integrated ARPS–CMAQ Modeling System through Simulating PM10 Concentration in Beijing, China

In this study, the Advanced Regional Prediction System (ARPS) model was introduced to replace the default weather module in Models-3 Community Multiscale Air Quality (CMAQ) modeling system, to provide a complete meteorological dataset required for urban air quality simulations. The ARPS model was chosen because of its nonhydrostatic dynamics, the generalized terrain-following height vertical coordinate, and the Arakawa-C horizontal grids, which are identical to the ones used in CMAQ, as well as its capabilities of simulating assorted-scale meteorological circulations within a large spectrum (especially at small scales). To assess its performance, the integrated ARPS–CMAQ modeling system was applied to Beijing, China, for simulating its PM10 concentration. The study region was delineated into a two-level nested-grid domain with spatial resolutions of 12 and 4 km, respectively. Three monitoring stations within the urban area of Beijing representing different categories of city function zones were selected t...

Impact of Domain Size on Modeled Ozone Forecast for the Northeastern United States

Abstract This study investigates the impact of model domain extent and the specification of lateral boundary conditions on the forecast quality of air pollution constituents in a specific region of interest. A developmental version of the national Air Quality Forecast System (AQFS) has been used in this study. The AQFS is based on the NWS/NCEP Eta Model (recently renamed the North American Mesoscale Model) coupled with the U.S. Environmental Protection Agency Community Multiscale Air Quality (CMAQ) model. This coupled Eta–CMAQ modeling system provided experimental air quality forecasts for the northeastern region of the United States during the summers of 2003 and 2004. The initial forecast over the northeastern United States was approved for operational deployment in September 2004. The AQFS will provide forecast coverage for the entire United States in the near future. In a continuing program of phased development to extend the geographical coverage of the forecast, the developmental version of AQFS has undergone two domain expansions. Hereinafter, this “developmental” domain-expanded forecast system AQFS will be dubbed AQFS-β. The current study evaluates the performance of AQFS-β for the northeastern United States using three domain sizes. Quantitative comparisons of forecast results with compiled observation data from the U.S. Aerometric Information Retrieval Now (AIRNOW) network were performed for each model domain, and interdomain comparisons were made for the regions of overlap. Several forecast skill score measures have been employed. Based on the categorical statistical metric of the critical success index, the largest domain achieved the highest skill score. This conclusion should catapult the implementation of the largest domain to attain the best forecast performance whenever the operational resource and criteria permit.

Impact of an Updated Carbon Bond Mechanism on Predictions from the CMAQ Modeling System: Preliminary Assessment

Abstract An updated and expanded version of the Carbon Bond mechanism (CB05) has been incorporated into the Community Multiscale Air Quality (CMAQ) modeling system to more accurately simulate wintertime, pristine, and high-altitude situations. The CB05 mechanism has nearly 2 times the number of reactions relative to the previous version of the Carbon Bond mechanism (CB-IV). While the expansions do provide more detailed treatment of urban areas, most of the new reactions involve biogenics, toxics, and species potentially important to particulate formation and acid deposition. Model simulations were performed using the CB05 and the CB-IV mechanisms for the winter and summer of 2001. For winter with the CB05 mechanism, ozone, aerosol nitrate, and aerosol sulfate concentrations were within 1% of the results obtained with the CB-IV mechanism. Organic carbon concentrations were within 2% of the results obtained with the CB-IV mechanism. However, formaldehyde and hydrogen peroxide concentrations were lower by 25% and 32%, respectively, during winter with the CB05 mechanism. For the summer, ozone concentrations increased by 8% with the CB05 mechanism relative to the CB-IV mechanism. The aerosol sulfate, aerosol nitrate, and organic carbon concentrations with the CB05 mechanism decreased by 8%, 2%, and 10%, respectively. The formaldehyde and hydrogen peroxide concentrations with the CB05 mechanism were lower by 12% and 47%, respectively, during summer. Model performance with the CB05 mechanism improved at high-altitude conditions and in rural areas for ozone. Model performance also improved for organic carbon with the CB05 mechanism.

Numerical experiments on MM5–CMAQ sensitivity to various PBL schemes

The Community Multiscale Air Quality (CMAQ) modeling system developed by the United States Environmental Protection Agency (USEPA) was used along with the PSU/NCAR mesoscale modeling system MM5 to study air quality modeling sensitivity to various planetary boundary layer (PBL) schemes implemented in MM5. Two 37-day periods, representing typical summer and winter periods, were selected for the simulations. The MM5 domain covered the continental US while the CMAQ domain covered much of the central and eastern US. There were 34 vertical layers in MM5 and 18 in CMAQ. The horizontal resolution was 36 km for both. MM5 was run with five of the seven commonly used PBL schemes to generate a series of gridded meteorological fields. The data were then processed and converted by the meteorology–chemistry interface processor (MCIP) to produce meteorological input for CMAQ, in conjunction with the emissions data prepared by the Sparse Matrix Operator Kernel Emissions (SMOKE) System. The sensitivity of MM5–CMAQ modeling to the various PBL schemes was assessed and quantified by comparing model output against observations from the meteorological and the air quality monitoring networks within the CMAQ domain. The meteorological variables evaluated included surface (2-m) and 850-mb temperature, 10-meter and 850 mb wind speed and direction, 2-m mixing ratio, surface pressure, and cloud fraction over 50 surface sites and 21 upper air sounding sites. The CMAQ variables included gaseous species O 3, NO 2, NO x , SO 2 and fine particulate matter PM 2.5 for over 2000 monitoring stations. A new 3-2-1 objective evaluation approach was developed to enhance the model sensitivity analysis. Results of evaluation of these variables indicate that domain-wide the performance of neither model was sensitive to the PBL schemes. Although large differences of PBL height existed across the meteorological simulations, they did not appreciably affect the CMAQ performance. On an urban scale, differences across the CMAQ simulations were large enough for the model to be considered sensitive, although no favorable PBL scheme was identified. It is suggested that domain-wide the lack of sensitivity of the MM5–CMAQ modeling system to the PBL schemes was mainly attributed to the parameterization of vertical eddy diffusivity in CMAQ and the four-dimensional data assimilation (FDDA) in MM5.

Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System

Abstract This article describes the governing equations, computational algorithms, and other components entering into the Community Multiscale Air Quality (CMAQ) modeling system. This system has been designed to approach air quality as a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including tropospheric ozone, fine particles, acid deposition, and visibility degradation. CMAQ was also designed to have multiscale capabilities so that separate models were not needed for urban and regional scale air quality modeling. By making CMAQ a modeling system that addresses multiple pollutants and different spatial scales, it has a “one-atmosphere” perspective that combines the efforts of the scientific community. To implement multiscale capabilities in CMAQ, several issues (such as scalable atmospheric dynamics and generalized coordinates), which depend on the desired model resolution, are addressed. A set of governing equations for compressible nonhydrostatic atmospheres is available to better resolve atmospheric dynamics at smaller scales. Because CMAQ is designed to handle scale-dependent meteorological formulations and a large amount of flexibility, its governing equations are expressed in a generalized coordinate system. This approach ensures consistency between CMAQ and the meteorological modeling system. The generalized coordinate system determines the necessary grid and coordinate transformations, and it can accommodate various vertical coordinates and map projections. The CMAQ modeling system simulates various chemical and physical processes that are thought to be important for understanding atmospheric trace gas transformations and distributions. The modeling system contains three types of modeling components (Models-3): a meteorological modeling system for the description of atmospheric states and motions, emission models for man-made and natural emissions that are injected into the atmosphere, and a chemistry-transport modeling system for simulation of the chemical transformation and fate. The chemical transport model includes the following process modules: horizontal advection, vertical advection, mass conservation adjustments for advection processes, horizontal diffusion, vertical diffusion, gas-phase chemical reactions and solvers, photolytic rate computation, aqueous-phase reactions and cloud mixing, aerosol dynamics, size distributions and chemistry, plume chemistry effects, and gas and aerosol deposition velocity estimation. This paper describes the Models-3 CMAQ system, its governing equations, important science algorithms, and a few application examples. This review article cites 114 references.