Fine Scale Modeling of Agricultural Air Quality over the Southeastern ...

Aerosol and Air Quality Research, 13: 1231–1252, 2013 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2012.12.0346

Fine Scale Modeling of Agricultural Air Quality over the Southeastern United States Using Two Air Quality Models. Part I. Application and Evaluation Yang Zhang*, Kristen M. Olsen+, Kai Wang Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA ABSTRACT Two air quality models, the U.S. EPA Community Multiscale Air Quality (CMAQ) model and ENVIRON’s Comprehensive Air Quality Model with extensions (CAMx), are evaluated for their applications in simulating ambient air quality, in particular, the fate and transport of agriculturally-emitted NH3 over an area in the southeastern U.S. in January and July 2002 using a fine-scale horizontal grid resolution of 4-km. Both models moderately overpredict maximum 1-hr and 8-hr ozone (O3) and fine particulate matter (PM2.5) in January, due likely to a weaker vertical mixing and insufficient dry and wet removal of PM2.5 species simulated by the models. They either slightly underpredict or overpredict O3 but significantly underpredict PM2.5 in July. The large underprediction in PM2.5 is due to an excess wet deposition removal of sulfate, an excess dry deposition removal of precursors, and an underestimation of emissions of primary PM and precursors of secondary PM and secondary organic aerosol concentrations. Both models show large biases in the simulated concentrations of several gases (e.g., CO in CAMx, NO in CMAQ, NO2 in both models in both months and NH3 by both models in July) and PM species (in particular, nitrate in both months and carbonaceous PM in July), visibility indices, and dry and wet deposition fluxes. They also show some inaccuracies in reproducing temporal variations of NH3, PM2.5, dry and wet deposition fluxes. Differences in model performance between the two models are attributed to different model treatments such as vertical mixing, wet and dry deposition, SOA formation, and PM size representations. These results indicate a need to improve accuracies of the emissions and measurements of NH3, the emissions of primary PM and precursors of secondary PM, as well as model treatments of vertical mixing and dry and wet removal processes. Keywords: CMAQ; CAMx; Air quality; Agricultural emissions; Fine scale modeling.

INTRODUCTION Agriculturally-emitted species such as ammonia (NH3), hydrogen sulfide (H2S), methane (CH4), nitrous oxide (N2O), and volatile organic compounds (VOCs) have important impacts ambient air quality, the eutrophication of the ecosystems, as well as global and regional warming. Among these species, NH3 is most concerned, because it is the most abundant alkaline gas in the atmosphere and plays an important role in the nitrogen cycle in the ecosystem, neutralization of acids in the air, and the formation of particulate matter having an aerodynamic diameter of 2.5 μm or less (PM2.5). The wet and dry deposition of NH3 to the soil is a source of nitrogen, providing nutrients to plants; however, too much nitrogen runoff into coastal waterways and estuaries can lead to eutrophication, increasing harmful

*

Corresponding author. E-mail address: [email protected] + Now at Department of Environmental Quality, Richmond, VA 23218, USA

algal blooms (Kinzig and Socolow, 1994; Paerl, 1997). NH3 in the atmosphere neutralizes acids, such as nitric acid (HNO3) and sulfuric acid (H2SO4), creating salts (i.e., ammonium sulfate ((NH4)2SO4) and ammonium nitrate (NH4NO3)), which are a major component of PM2.5. Some studies have also indicated that NH3 may play an important role in the formation of new particles through nucleation (Napari et al., 2002; Zhang et al., 2010). The ammonium ion (NH4+) has a longer lifetime (up to 15 days) (Aneja et al., 2001) than NH3 (up to 10 days) (Seinfeld and Pandis, 2006); it is thus capable to be transported and deposited to regions further from the source. NH3 emissions can also cause odors, impacting the lifestyles of people in the area. For these reasons, an accurate understanding of the emissions, fate, and transport of agricultural livestock NH3 (AL-NH3) emissions is important in improving the air quality and ecosystem of the surrounding areas. The role of NH3 further increases as the emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) are being reduced in many regions in the world as a result of air pollution control policy. Agricultural sources such as livestock, including cattle, poultry, swine and sheep contribute to 81% of NH3-nitrogen emissions in the U.S. (Battye et al., 1994). High NH3

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emissions are of major concern in the southeastern United States (U.S.), in particular, in the eastern North Carolina (NC) and northeastern Georgia (GA) because of a high density of animal feeding operations. While most air quality modeling for the State Implementation Plans (SIPs) is performed at a horizontal grid resolution of 12-km, the U.S. EPA has suggested that SIP modeling (U.S. EPA, 2007), particularly over areas with high gaseous precursor emissions and primary PM sources, may benefit from increased grid resolution from 12-km to 4-km (U.S. EPA, 2007). Given high emissions of SO2 and NH3 in the southeastern U.S., 3-D agricultural air quality modeling at a fine-scale (< 12-km) will be necessary from both scientific and regulation perspectives. In this work, two commonly-used air quality modeling systems are applied to simulate agricultural air quality at a horizontal grid resolution of 4-km over a portion of the southeastern U.S. They are the U.S. EPA Community Multiscale Air Quality (CMAQ) (Binkowski and Roselle, 2003; Byun and Schere, 2006) modeling system and the ENVIRON’s Comprehensive Air Quality Model with extensions (CAMx) (ENVIRON, 2006), both are driven by meteorological predictions from the Pennsylvania State University (PSU)/National Center for Atmospheric Research (NCAR) 5th generation Mesoscale Model (MM5) (Grell et al., 1995). A simulation with CMAQ at a horizontal grid resolution of 1.33-km is also conducted over a nested domain. Four additional CMAQ simulations are conducted at 4-km with 50% of reduction in the emissions of PM2.5 precursors to study the responses of PM2.5 predictions to these precursors. The objectives of this work are to perform a comprehensive model evaluation to assess the models’ capability in simulating agricultural air quality, examine the sensitivity of model predictions to different air quality models (i.e., CMAQ vs. CAMx) and horizontal grid resolutions (i.e., 12-km vs. 4-km vs. 1.33 km), and assess the relative importance of the major PM precursors (i.e., SO2, NOx, and NH3) in PM2.5 formation. Part I describes the model setup, including episode description, model configuration, evaluation database and protocol, and performance evaluation of the baseline simulations using both models at 4-km. Part II describes the sensitivity evaluations conducted, including

sensitivity to grid resolution and reductions of emissions of precursors of PM2.5. DESCRIPTION OF EPISODE, MODEL CONFIGURATIONS, AND EVALUATION DATABASE AND PROTOCOL Episode and Model Configurations MM5/CMAQ v4.5.1 and MM5/CAMx v4.42 simulations are conducted at a 4-km horizontal grid resolution in January and July 2002. The January and July 2002 episodes are selected in order to compare the fine scale simulation results with the coarser scale CMAQ simulations at a 12-km horizontal grid resolution previously completed by the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) (Morris et al., 2007). When possible, the 4-km CMAQ and CAMx simulations use the model configurations and physics that are consistent with those of CMAQ used in the Phase II of the VISTAS modeling study at 36-km and 12-km horizontal grid resolution. MM5 version 3.7 with Four Dimensional Data Assimilation (FDDA) is used to drive the meteorology fields supplied to the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system, CMAQ and CAMx. Table 1 summarizes the configuration of MM5. The cumulus scheme and shallow convection are turned off because all clouds are assumed to be resolved at a 4-km grid resolution. Analysis nudging is used aloft for temperature, moisture, and winds and at the surface for winds using reanalysis data from NCAR (ds464.0 and ds353.4) (Olerud and Sims, 2004). The initial and boundary conditions (ICON and BCON) are derived from the VISTAS 12-km MM5 and CMAQ simulations and prepared for CAMx using the cmaq2camx tool. Emissions, based on the 1999 National Emission Inventory version 2 and additional data for VISTAS states (MACTEC, 2008), are prepared for CMAQ using SMOKE version 2.1. Table 2 summarizes the major model configurations used in model simulations. When available, both models use the same or similar options, e.g., the Carbon Bond IV gas-phase mechanism, the ISORROPIA inorganic aerosol thermodynamic module, and the Regional Acid Deposition

Table 1. MM5 model configurations used in this work. Attributes Horizontal Resolution Vertical Resolution Land Surface Model Planetary Boundary Layer Model Cloud Microphysics Cumulus Scheme Shallow Convection Longwave Radiation Shortwave Radiation Analysis Nudging Temperature Moisture Wind (u and v) Observational Nudging

MM5 v3.7 Configuration 4- and 1.33-km 34 layers from 0 to approximately 15 km, with first model layer height of 36 m Pleim-Xu Pleim-Xu (ACM) Reisner 1 (Mixed Phase) None None Rapid Radiative Transfer Model (RRTM) Cloud-Radiation Aloft (nudging coefficient: 1 × 10–4) Aloft (nudging coefficient: 1 × 10–5) Surface and Aloft (nudging coefficient: 1 × 10–4) None


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Table 2. CMAQ and CAMx model configurations used in this work. Attributes Horizontal Advection Horizontal Diffusion Vertical Advection Vertical Diffusion Gas-Phase Chemistry Aqueous-Phase Chemistry PM Size Representation Inorganic Aerosol Thermodynamics Secondary Organic Aerosol Dry Deposition Wet Deposition

CMAQ v4.51 Yamartino-Blackman Cubic Explicit Yamartino-Blackman Cubic Semi-implicit K-theory CB-IV RADM Modal (i.e., nuclei, accumulation, coarse)

CAMx v4.42 PPM Explicit Implicit Implicit CB-IV RADM

ISORROPIA v1.5

ISORROPIA v1.6

SORGAM M3DRY Accumulation and coarse mode particles completely absorbed in cloud water; Nuclei mode slowly scavenged; Henry’s law equilibrium for gases

SOAP The Wesely (1989) resistance theory All PM assumed in cloud water; Henry’s law equilibrium for gases

Model (RADM) aqueous chemistry. One of the major differences between the models is the treatment of vertical advection. The Yamartino-Blackman Cubic advection option in CMAQ utilizes the Piecewise Parabolic Method (PPM) for horizontal advection, and then uses the density from MM5 to calculate the vertical velocity at each grid cell that satisfies the continuity equation. While CAMx also uses PPM for horizontal advection, the vertical diffusion and advection are calculated implicitly (ENVIRON, 2006). The difference in the treatment of vertical mixing between the models results in weaker vertical mixing of pollutants, and thus higher pollutant concentrations near the surface, by CAMx, as compared with CMAQ (Zhang et al., 2004). The second difference between the two models is the representation of PM size distribution. CMAQ represents PM using a modal distribution (i.e., three log-normal modes: nuclei, accumulation, and coarse), whereas CAMx uses a sectional approach (e.g., 2 or more bins as specified by the user). Increasing the number of sections can improve the model representation of PM distribution; however, it also increases the computational time. Two size sections are used in the CAMx simulation in this study. The third difference between the models is the treatment of secondary organic aerosols (SOA). Both models simulate SOA formation from VOCs included in the CB-IV gas-phase mechanism. ENVIRON enhanced the SOA module in CMAQ by including sesquiterpenes, additional SOA formation from isoprene, and polymerization of SOA species (Morris et al., 2006, 2007). The models also treat dry and wet deposition differently. The dry deposition of gases in CAMx is based on Wesely’s (1989) resistance model, which calculates the deposition velocity using the aerodynamic, boundary layer, and surface resistances. The dry deposition of gases in CMAQ v4.5.1 is the Models-3 dry (M3DRY) deposition model, which is coupled with the Pleim-Xiu land-surface model (LSM) to use the aerodynamic and boundary layer resistances simulated from this LSM in MM5 in order to improve the stomatal resistance over a non-coupled model (Otte and Pleim, 2010). A more advanced treatment for NH3

Sectional (2 bins)

such as the bi-directional air-surface exchange algorithm for NH3 is not included in CMAQ v4.5.1 used in this work, but it has been included in CMAQ v. 4.7 or newer (Cooter et al., 2010). CAMx simulates PM dry deposition using the approach of Slinn and Slinn (1980). CMAQ uses the approach from the Regional Particulate Model developed by Binkowski and Shankar (1995). The wet deposition of gases in both models is based on Henry’s law equilibrium; however, the absorption of particles into cloud water is treated differently between the models. CAMx assumes that all particles in a cloudy grid cell are contained within cloud water, while CMAQ assumes the accumulation and coarse mode particles are completely absorbed by cloud water and particles in the nuclei mode are slowly scavenged (Byun and Schere, 2006). These differences lead to differences in predictions using the two models. More details on model setup can be found in Olsen (2009). Evaluation Database and Protocol The meteorology and air quality model results are evaluated against available observations to assess model performance. Table 3 summarizes data from the surface networks for model evaluation. These include national networks (e.g., the Clean Air Status and Trends Network (CASTNET), the Speciation Trends Network (STN), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Aerometric Information Retrieval System – Air Quality Subsystem (AIRS-AQS), and the National Atmospheric Deposition Program (NADP), as well as state, local, and private agencies (e.g., the Southeastern Aerosol Research and Characterization (SEARCH) study, the North Carolina Department of Environmental and Natural Resources (NCDENR), and the North Carolina State Climate Office (NC SCO). The meteorological variables evaluated include hourly temperature at 1.5-m (T1.5), relative humidity at 1.5-m (RH1.5), wind speed (WS10) and direction at 10-m (WD10), and weekly total precipitation (Precip). Chemical variables evaluated include 1-hr and 8-hr maximum O3, carbon monoxide (CO), nitrogen oxide (NO), nitrogen


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Table 3. The observational networks used in model evaluation along with the variables evaluated, the sampling frequency, and the number of sites within the 4-km and 1.33-km modeling domains. Network AIRS-AQS IMPROVE (mostly remote sites)

Variables Met. -----

Gas O3

-----

-----

CASTNET (mostly rural sites)

T1.5, RH1.5, WS10, WD10

STN (urban sites)

T1.5

NADP

Precip

PM ----PM2.5, SO42–, NO3–, NH4+, EC, OC, TC, βext, HI

Sampling Frequency Hourly 24-hour average (1-in-3 day)

hourly for meteorological variables and O3; weeklyConcentrations of dry deposition of SO42–, NO3– average for other species concentrations; weekly total , NH4+ for dry deposition fluxes PM2.5, SO42–, NO3–, 24-hour average (daily, 1-in----NH4+, EC, TC 3, or 1-in-6 day) Wet Deposition of SO42–, ----Weekly total NO3–, NH4+ O3, CO, Hourly for meteorological SO2, PM2.5, SO42–, NO3–, variables and gases; daily NOx, NH4+, EC, OC average for PM HNO3 and components O3, CO, SO2, Hourly, 24-hour average (1PM2.5 NO2, in-3 day) NH3 O3, SO2, HNO3

Number of Sites 4-km 1.33-km 14 4 6

0

9

1

18

9

19

5

T1.5, RH1.5, 2 0 SEARCH WS10, WD10 T1.5, RH1.5, a NCDENR 73 60 WS10, WD10 T1.5, RH1.5, NC SCO --------Hourly 117 45 WS10, WD10 a There are a total of 73 NCDENR sites in the 4-km domain, some with collocated observations (46 sites with hourly O3 observations, 37 sites with 24-hour average PM observations, 8 sites with hourly PM observations, and 8 sites with hourly meteorology observations). dioxide (NO2), HNO3, SO2, NH3, PM2.5, ammonium (NH4+), sulfate (SO42–), nitrate (NO3–), elemental carbon (EC), organic carbon (OC), total carbon (TC = EC + OC), the dry deposition (DD) of NH4+, SO42–, NO3–, SO2, and HNO3, and the wet deposition (WD) of NH4+, SO42–, and NO3–. The two visibility parameters evaluated are the extinction coefficient (βext) and haziness index (HI). The evaluation of model performance is conducted using statistics, spatial distributions, and temporal analysis. The statistics, including mean observation, mean simulation, correlation, and normalized mean bias and error (NMB and NME, respectively), are separated by networks because of their varying characteristics in terms of sampling frequency and resolution, monitoring approach, and type of area (e.g., urban vs. rural) following several studies (e.g., Eder and Yu, 2006; Zhang et al., 2009). The simulated dry deposition velocities and fluxes of some species are compared with the results of the Multilayer Model (MLM), which uses species concentrations and leaf area index recorded at the CASTNET sites. More details on the MLM are provided in Section 3.4, as well as Meyers et al. (1998) and Finkelstein et al. (2000). The statistics for evaluation of simulated dry deposition velocities and fluxes is calculated against values from MLM. Nine locations are selected for temporal

analysis; a coastal site (Beaufort (BFT), NC), a mountain site (Great Smoky Mountain (GRS), TN), two urban sites (Raleigh (RAL), NC, and the Jefferson Street (JST), downtown Atlanta, GA), two rural sites (Yorkville (YRK), GA, and Candor (CND), NC), and three sites in the eastern NC (Kinston-Lenoir (LCC), Clinton (CLT), and Jamesville (JMV)) where NH3 emissions are high and the measurements of NH3 mixing ratios are available. Among them, JST and YRK are SEARCH sites, BFT, GRS, and CND are CASTNET sites, and RAL, LCC, CLT, and JMV are NCDENR sites. PERFORMANCE EVALUATION Meteorology Table 4 summarizes the performance statistics for meteorological variables. For WS10, a cut-off value of 1.5 knots (i.e., 0.771 m/s) is used because of instrumentation limitations in reporting calm wind speeds following Olerud and Sims (2004). When the observed WS is less than this value, the data pair is not included in the statistical calculations. The NMBs of T1.5 and RH1.5 are generally within ± 10%, with a few exceptions in January. The large cold bias in T1.5 in January is likely due to too cold soil

Variable Network

CAST

T1.5 (°C) STN SEARCH

SCO

WD10 (°) Precip (mm) RH1.5 (%) WS10 (m/s) CAST SEARCH CAST SEARCH SCO CAST SEARCH SCO NADP January 2002 Number 5157 60 1360 623 6857 1455 6089 1295 700 7098 1455 744 72 MeanObs 7.9 6.3 10.0 8.5 70.7 69.6 3.0 3.0 2.7 200.5 210.3 212.2 29.3 MeanMod 7.0 6.9 7.0 7.1 75.1 80.3 3.8 3.3 3.4 212.6 210.6 215.2 26.1 corr 0. 9 0.9 0.9 0.9 0.7 0.7 0.6 0.6 0.7 0.5 0.6 0.9 0.8 MB –0.9 0.6 –3.0 –1.3 4.4 10.7 0.8 0.3 0.8 12.1 0.3 3.1 –3.2 RMSE 2.6 2.0 4.2 2.9 16.7 18.4 1.7 1.3 1.1 103.0 90.3 40.4 17.7 NMB (%) –11 9 –30 –16 6 15 28 10 29 6 0 1 –10.9 NME (%) 25 26 33 26 17 20 43 36 36 31 26 7 40.1 July 2002 Number 7410 134 1393 744 6741 1164 5425 946 697 7140 1182 744 81 MeanObs 23.4 26.0 28.5 26.3 76.4 75.1 2.0 2.0 2.1 183.5 222.8 197.5 31.0 MeanMod 24.4 25.6 26.0 26.0 70.7 71.8 2.9 2.6 2.9 184.5 216.4 186.1 77.0 corr 0.9 0.9 0.9 1.0 0.7 0.7 0.5 0.3 0.7 0.3 0.6 0.6 0.3 MB 1.0 –0.5 –2.5 –0.3 –5.7 –3.4 0.9 0.6 0.8 0.9 –6.5 –11.3 46.0 RMSE 2.1 1.4 3.3 0.9 13.6 12.4 1.5 1.3 1.0 116.4 74.3 79.7 96.8 NMB (%) 4 –2 –9 –1 –7 –5 48 32 37 1 –3 –6 148.5 NME (%) 7 4 10 3 14 12 63 53 40 41 20 16 199.0 Obs: Observations, Mod: Model, corr: Correlation, MB: Mean Bias, NMB: Normalized Mean Bias, NME: Normalized Mean Error, RMSE: Root Mean Square Error, T1.5: Temperature at 1.5-m, RH1.5: Relative Humidity at 1.5-m, Precip: Precipitation, WS10: Wind Speed at 10-m, WD10: Wind Direction at 10-m, CAST: CASTNET Clean Air Status and Trends Network, STN: Speciation Trends Network, SEARCH: Southeastern Aerosol Research Characterization, SCO: NC State Climate Office.

Table 4. Performance statistics for meteorological variables at a 4-km horizontal grid resolution for January and July 2002. Zhang et al., Aerosol and Air Quality Research, 13: 1231–1252, 2013 1235


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initial temperatures and inappropriate snow treatments (Liu et al., 2010). Larger cold biases at the SEARCH and SCO sites may be due to an additional model limitation in capturing an urban heat island effect. For a similar reason, the overprediction in RH1.5 at the SEARCH sites is larger than that at the CASTNET sites. WS10 is overpredicted in both months with a better performance in January. The MBs for WD10 are within ± 12°, indicating an overall small deviation from observations. Precip is underpredicted with an NMB of –10.9% in January but significantly overpredicted with an NMB of 148.5% in July due to the limitation of MM5 in capturing convective rainfall in terms of its frequency and intensity (Olerud and Sims, 2004). This overprediction indicates an inability of MM5 in reproducing a drier than historical average season in the region. It affects the removal of pollutants through wet deposition, as shown in Section 3.4. Chemical Concentrations of Gaseous and PM Species The statistics for several gaseous (i.e., O3, CO, NO, NO2, HNO3, SO2, and NH3) and PM species (i.e., PM2.5, NH4+, SO42–, NO3– , EC, OC, and TC) are provided in Tables 5 and 6. Despite underpredicted T1.5 by MM5 at most sites, the maximum 1-hr and 8-hr O3 mixing ratios are slightly overpredicted by both models in January

(except at the SEARCH sites) and by CAMx in July, due likely to a weaker than actual vertical mixing that prevents the dispersion of precursor species. The maximum 1-hr and 8-hr O3 mixing ratios are slightly underpredicted by CMAQ in July, due partly to underpredicted peak T1.5 by MM5 on most days at most sites and partly to the underestimate in emissions of precursors, particularly at the AIRS-AQS sites, that dominates over the effects caused by a weaker than actual vertical mixing simulated by CMAQ. This weaker vertical mixing can be reflected in the overprediction of CO mixing ratio by both models. The CO statistics also indicate that CAMx has a much weaker vertical mixing than CMAQ, resulting in a much larger overprediction of CO in both months. In January, both models underpredict the mixing ratios of NO and overpredict those of NO2. In July, the mixing ratios of NO are underpredicted by CMAQ but overpredicted by CAMx; both overpredict those of NO2. Despite overpredicted NO2 mixing ratios in both months, the underprediction and overprediction in Precip in January and July, respectively, lead to an overprediction of HNO3 mixing ratios (which is highly soluble) in January at all sites and an underprediction of HNO3 mixing ratios at the SEARCH sites in July by CMAQ. CAMx underpredicts the mixing ratio of HNO3 at all sites in both months, due partly to different dry deposition treatments as compared

Table 5. Performance statistics for trace gases a 4-km horizontal grid resolution in January and July 2002. Species

Network AIRS-AQS

1-h max O3 (ppb)

CASTNET SEARCH AIRS-AQS

8-h max O3 (ppb)

CASTNET SEARCH

CO (ppb)

SEARCH

NO (ppb)

SEARCH

NO2 (ppb)

SEARCH

HNO3 (μg/m3 at CASTNET, ppb at SEARCH)

CASTNET

SO2 (μg/m3 at CASTNET, ppb at SEARCH)

CASTNET

NH3 (ppb)

NCDENR

SEARCH

SEARCH

Model CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx

Number MeanObs January 2002 384

37.0

301

37.0

62

34.4

384

32.0

381

34.0

61

27.5

1377

416.1

1408

20.6

1343

12.1

39

1.6

1390

0.6

39

6.8

1340

5.2

2038

5.3

MeanMod

corr

NMB (%)

NME (%)

39.0 41.0 41.0 42.0 31.8 34.2 35.0 36.0 38.0 39.0 27.5 28.7 502.6 775.7 8.0 20.1 19.1 21.8 2.4 1.6 0.8 0.5 9.0 9.7 5.6 6.8 3.0 4.1

0.5 0.5 0.7 0.7 0.6 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.5 0.6 0.2 0.4 0.7 0.7 0.2 0.2 0.2 0.2 0.9 0.9 0.5 0.5 –0.18 –0.23

4.6 10.1 9.0 12.2 –7.6 –0.3 9.3 12.1 13.2 15.8 0.1 4.4 20.8 86.4 –61.1 –2.1 57.5 79.9 44.8 0.0 19.0 –17.6 32.6 42.1 7.1 30.5 –43.4 –23.7

17.9 21.4 15.4 17.8 22.3 24.5 20.7 23.4 19.2 21.0 22.4 25.4 63.6 101.4 93.3 105.7 72.6 92.1 50.5 37.1 95.9 79.6 33.2 42.1 69.8 82.3 93.7 112.3


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Table 5. (continued). Species

Network AIRS-AQS

1-h max O3 (ppb)

CASTNET SEARCH AIRS-AQS

8-h max O3 (ppb)

CASTNET SEARCH

CO (ppb)

SEARCH

NO (ppb)

SEARCH

NO2 (ppb)

SEARCH

HNO3 (μg/m3 at CASTNET, ppb at SEARCH)

CASTNET

SO2 (μg/m3 at CASTNET, ppb at SEARCH)

CASTNET

NH3 (ppb)

NCDENR

SEARCH

SEARCH

Model CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx

Number

MeanObs July 2002

3216

71.1

300

65.5

62

73.9

3215

62.7

300

58.4

57

60.3

1374

259.8

1351

3.9

1088

10.2

40

2.1

1365

1.3

40

3.0

1368

3.0

1534

19.6

MeanMod

corr

NMB (%)

NME (%)

62.1 68.8 59.8 64.9 65.0 74.7 56.3 63.0 55.7 60.6 57.6 67.3 308.8 441.4 1.7 4.6 14.2 17.0 2.2 1.8 1.0 1.0 4.2 4.2 4.2 4.5 5.9 4.0

0.7 0.6 0.7 0.6 0.8 0.8 0.7 0.6 0.7 0.6 0.7 0.7 0.7 0.7 0.6 0.6 0.7 0.8 0.8 0.8 0.5 0.6 0.8 0.8 0.4 0.3 –0.04 –0.04

–12.6 –3.2 –8.8 –1.0 –12.0 1.0 –10.1 0.5 –4.7 3.8 –4.5 11.5 18.9 69.8 –55.7 17.6 39.6 67.4 2.8 –16.5 –21.4 –20.7 42.3 41.9 40.6 52.5 –69.9 –79.8

17.2 16.3 14.5 13.7 15.4 12.5 16.2 16.6 14.5 15.4 16.8 18.9 44.0 78.2 82.4 104.4 66.3 82.8 26.8 29.0 55.7 58.3 57.8 56.0 110.5 122.0 77.4 85.9

Table 6. Performance statistics for PM species a 4-km horizontal grid resolution in January and July 2002. (a) January Species

Network IMPROVE

PM2.5 (μg/m3)

STN SEARCH CASTNET

NH4+ (μg/m3)

STN SEARCH IMPROVE

SO42– (μg/m3)

CASTNET STN SEARCH

Model CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx

Number

MeanObs

49

6.1

76

12.4

1216

10.2

39

1.0

79

1.1

822

0.91

49

2.1

39

2.3

79

2.9

1334

2.2

MeanMod 6.9 7.2 14.8 16.5 15.0 18.7 0.9 0.9 1.8 1.8 1.5 1.3 2.2 2.8 1.9 2.3 2.6 3.3 2.8 3.6

corr 0.8 0.7 0.4 0.2 0.4 0.3 0.8 0.7 0.02 –0.05 0.3 0.1 0.7 0.6 0.6 0.3 0.3 0.2 0.2 0.2

NMB (%) 13.8 19.0 19.0 32.8 46.9 83.0 –6.9 –8.0 59.3 58.4 61.5 41.9 5.3 33.4 –18.2 0.9 –10.3 13.7 23.5 63.6

NME (%) 29.6 29.2 37.5 48.4 74.2 101.6 25.0 24.5 80.6 77.2 93.6 80.4 32.1 47.8 22.6 23.5 39.3 47.4 68.5 98.0


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Table 6. (continued). (a) January Species

Network IMPROVE

NO3– (μg/m3)

CASTNET STN SEARCH

EC (μg/m3)

IMPROVE SEARCH

OC (μg/m3)

IMPROVE SEARCH IMPROVE

TC (μg/m3)

SEARCH STN

*

βext (Mm–1)*

IMPROVE

HI (dcv)

IMPROVE

Model CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx

Number

MeanObs

49

0.6

39

1.3

79

1.6

1229

1.7

49

0.3

743

1.5

49

1.4

62

3.4

49

1.8

1132

5.4

79

6.9

42

52.4

42

17.7

Model CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx

Number

MeanObs

55

15.8

127

19.3

1203

19.7

9

1.7

40

2.3

135

2.0

1412

2.1

56

7.1

40

8.3

135

7.2

986

5.9

56

0.2

MeanMod 0.8 0.4 1.3 1.0 3.2 2.4 2.7 1.8 0.3 0.3 1.7 3.1 1.2 1.2 2.6 3.3 1.5 1.5 4.1 5.9 3.5 4.3 102.6 65.1 16.9 17.7

corr 0.8 0.8 0.6 0.7 0.3 0.3 0.2 0.1 0.7 0.7 0.1 0.1 0.8 0.8 0.8 0.8 0.8 0.8 0.4 0.4 0.5 0.4 0.3 0.6 0.6 0.7

NMB (%) 45.5 –21.2 1.6 –21.7 103.5 55.3 57.2 6.2 –9.4 4.5 11.4 105.8 –15.1 –17.7 –24.8 –4.0 –14.0 –13.6 –25.2 8.5 –49.7 –38.6 96.0 24.3 –4.4 0.0

NME (%) 104.9 68.3 54.6 52.8 126.9 95.3 114.1 95.8 33.1 35.7 91.6 148.1 30.9 33.0 32.9 35.5 28.9 30.0 60.5 72.4 52.4 49.1 119.6 32.1 27.3 12.9

MeanMod 6.7 9.9 9.7 13.1 12.3 16.2 0.7 0.9 1.1 1.3 1.2 1.5 1.6 1.9 4.3 6.5 6.3 8.3 4.7 6.9 5.8 7.8 0.09 0.05

corr 0.5 0.5 0.5 0.7 0.22 0.31 0.6 0.9 0.7 0.7 0.4 0.6 0.2 0.4 0.5 0.6 0.7 0.8 0.5 0.7 0.1 0.2 0.2 0.2

NMB (%) –57.8 –37.1 –49.6 –32.0 –37.4 –17.4 –57.3 –48 –52.4 –44.7 –41.2 –25.5 –23.8 –10.7 –39.1 –8.6 –23.9 –1 –34.3 –4.3 –1.4 32.4 –61.8 –80.3

NME (%) 60.0 46.1 51.2 37.2 51.3 43.8 60.3 52 52.4 45.7 50.0 36.0 53.6 48.3 50.2 40.2 26.9 18 44.4 30.4 76.2 79.4 107.1 99.4

Mm = 1,000 km

(b) July Species

Network IMPROVE

PM2.5 (μg/m3)

STN SEARCH IMPROVE

NH4+ (μg/m3)

CASTNET STN SEARCH IMPROVE

SO42– (μg/m3)

CASTNET STN SEARCH

NO3– (μg/m3)

IMPROVE


1239

Table 6. (continued). (b) July Species

Network CASTNET

NO3– (μg/m3)

STN SEARCH

EC (μg/m3)

IMPROVE SEARCH

OC (μg/m3)

IMPROVE SEARCH IMPROVE

TC (μg/m3)

SEARCH STN

*

βext (Mm–1)*

IMPROVE

HI (dcv)

IMPROVE

Model CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx

Number

MeanObs

40

0.2

135

0.7

927

0.7

43

0.3

1233

0.8

43

2.3

57

3.4

43

2.6

1256

5.0

134

7.2

32

107.9

32

23.5

MeanMod 0.06 0.03 0.09 0.05 0.3 0.2 0.2 0.2 0.8 1.2 0.6 1.2 1.5 2.4 0.8 1.4 2.2 3.6 1.9 2.9 53.6 80.8 14.0 19.2

corr 0.02 –0.1 0.1 0.3 –0.07 –0.01 0.4 0.4 0.4 0.5 0.7 0.7 0.5 0.3 0.7 0.7 0.3 0.3 0.4 0.4 0.3 0.6 0.5 0.7

NMB (%) –73.4 –89 –86.9 –92.9 –56.9 –70.7 –46.6 –33 –3.1 42.9 –72.5 –46.5 –56.7 –28.4 –69.6 –44.9 –55.4 –28.9 –73.3 –60.1 –50.3 –25.1 –40.3 –18.3

NME (%) 89.7 93 86.9 92.9 104.3 103.8 63.2 58 68.8 89.5 75.8 52.3 57.1 37.3 73.8 51.5 61.5 49.4 74.6 62.6 57.8 41.1 41.0 22.3

Mm = 1,000 km

with CMAQ. CAMx gives higher (by 60% on average) dry deposition velocity of HNO3 than CMAQ (see Table 7 and Figs. 5–7), resulting in lower mixing ratios of HNO3. Both models overpredict SO2 mixing ratios in both months, with higher values by CAMx than by CMAQ, due mainly to a weaker vertical mixing and a lower (by 69% on average) dry deposition velocity of SO2 calculated by CAMx. Both models significantly underpredict NH3 mixing ratios, particularly in July. One possible reason is underestimation of NH3 emissions in the eastern NC, despite significant efforts by the VISTAS program on improving NH3 emissions (e.g., Morris et al., 2007). Another possible reason is the large uncertainties in the NH3 measurements. The difficulties of measuring NH3 with high temporal resolution, due to its “sticky” nature, are well documented (e.g., von Bobrutzki et al., 2010). The concentrations of NH3 measured at LCC in July using a Thermo Environmental Instruments (TEI) Model 17C Ammonia analyzer (Shendrikar, 2006) are much higher (e.g., by a factor of 8.7 for monthly mean values, as compared with the mean values of Walker et al. (2004) than measurements using other methods such as the annular denuder system during summers 1999–2000 at the same site and at Clinton, an agricultural site with the highest emission density that is located 52 miles southwest of Kinston (e.g., Robarge et al., 2002; Walker et al., 2004). Furthermore, the concentrations during late July are higher than concentrations typically observed in high emission density areas of eastern NC (e.g., using the ALPHA passive sampler

and the Tropospheric Emission Spectrometer (TES), Pinder et al., 2011). Shendrikar (2006) clearly showed that the extremely high NH3 concentrations in 2002 does not reflect the typical annual cycle observed at LCC and CLT sites. According to the historic climate records at the NOAA’s National Climatic Data Center, June through August 2002 was much warmer and drier than average summers in the U.S. and North Carolina. The emissions of NH3 would likely high or the sampling artifact by the TEI Model 17C Ammonia analyzer may be high under extremely high temperature conditions. At present it is uncertain whether the high summertime concentrations at LCC are the result of a temporary local source or a sampling artifact. The weaker vertical mixing simulated by both models can also impact PM2.5, which is also overpredicted by both models in January. Despite a weaker vertical mixing in July, other factors, such as the underestimation in the emissions of primary EC and OC and SOA concentrations, and the overprediction in the removal of SO42– through wet deposition, are dominant, resulting in the underprediction of PM2.5 in July. The performance of individual species vary, e.g., the overprediction of SO42– by CAMx but underprediction by CMAQ at the CASTNET and STN sites in January and much larger underprediction of SO42– by CMAQ than CAMx in July. This indicates several other factors and/or differences between the models are more influential to PM species in both months than vertical mixing alone. First, the oxidation of SO2 to SO42– may be


1240

Table 7. Performance statistics for dry and wet deposition of species a 4-km horizontal grid resolution in January and July 2002. Variable DV_SO2 (cm/s) DV_HNO3 (cm/s) DD_SO2 (g/ha) DD_HNO3 (g/ha) DD_NH4+ (g/ha) DD_SO42– (g/ha) DD_NO3– (g/ha) WD_NH4+ (g/ha) WD_SO42– (g/ha) WD_NO3– (g/ha)

Network CASTNET CASTNET CASTNET CASTNET CASTNET CASTNET CASTNET NADP NADP NADP

Model CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx CMAQ CAMx

Number

MeanObs January 2002

6455

0.3

6455

1.1

6450

0.8

6450

0.7

6450

0.03

6450

0.08

6450

0.04

62

32.6

62

278.3

62

186.2

MeanMod

corr

NMB (%)

NME (%)

0.8 0.2 2.3 3.5 1.3 0.6 1.9 2.2 0.011 0.005 0.04 0.01 0.02 0.01 37.4 15.9 313.6 163.0 282.7 10.3

0.2 0.2 0.6 0.5 0.2 0.2 0.3 0.2 0.1 0.05 0.2 0.1 0.1 0.08 0.41 0.64 0.55 0.63 0.47 0.48

159.2 –20.9 102.7 216.6 58.2 –20.6 151.4 194.1 –68.0 –85.6 –55.7 –82.1 –63.2 –89.0 14.7 –51.3 12.7 –41.4 51.8 –94.5

218.5 76.6 117.2 229.7 131.4 81.5 187.6 230.7 86.7 87.7 96.6 85.9 102.1 92.2 68.9 60.9 51.4 52.8 81.0 94.5

July 2002 CMAQ 0.6 –0.04 91.4 149.3 DV_SO2 CASTNET 6285 0.3 (cm/s) CAMx 0.3 0.06 –0.8 72.6 DV_HNO3 CMAQ 2.3 –0.02 62.1 127.4 CASTNET 6284 1.4 (cm/s) CAMx 3.7 0.07 164.3 204.8 CMAQ 0.7 –0.03 56.1 159.1 DD_SO2 CASTNET 6239 0.5 (g/ha) CAMx 0.5 0.39 16.1 100.7 DD_HNO3 CMAQ 1.8 –0.04 58.5 157.2 CASTNET 6239 1.1 (g/ha) CAMx 2.5 0.57 124.6 159.6 DD_NH4+ CMAQ 0.011 –0.03 –89.0 96.2 CASTNET 6239 0.1 (g/ha) CAMx 0.006 0.27 –93.9 95.0 DD_SO42– CMAQ 0.07 –0.04 –80.1 96.8 CASTNET 6239 0.4 (g/ha) CAMx 0.04 0.42 –88.7 91.4 DD_NO3– CMAQ 0.00 –0.02 –98.0 99.7 CASTNET 6239 0.01 (g/ha) CAMx 0.00 –0.05 –99.4 99.8 CMAQ 108.2 0.05 18.8 106.4 WD_NH4+ NADP 68 91.1 (g/ha) CAMx 73.0 0.15 –19.8 88.5 CMAQ 92.5 –0.10 46.5 120.9 WD_SO42– NADP 68 63.2 CAMx 80.5 0.05 27.5 111.0 (g/ha) – CMAQ 28.4 –0.04 –34.9 75.9 WD_NO3 NADP 68 43.6 (g/ha) CAMx 6.9 –0.06 –98.4 98.4 1 Dry deposition fluxes and dry deposition velocities are not observed measurements, but calculated from measurements using the Multilayer Model (MLM) (Meyers et al., 1998; Finkelstein et al., 2000). DV: Deposition Velocity; DD: Dry Deposition; WD: Wet Deposition. underestimated because SO2 mixing ratio is overpredicted and SO42– concentration is underpredicted by both models. Second, CMAQ and CAMx contain different treatments for dry and wet removals of NH4+, SO42–, and NO3–, which can partly explain differences in their predicted concentrations. For example, compared with CMAQ, CAMx gives lower dry deposition fluxes for all these species in both months and lower wet deposition fluxes for all species in January

but higher wet deposition fluxes for NH4+ and SO42– in July (see Table 7). In January, the lower dry and wet deposition fluxes of SO42– explain higher concentrations of SO42– by CAMx than by CMAQ. In July, despite a higher wet deposition flux of SO42–, the dry deposition flux of SO2 is lower and the vertical mixing is weaker by CAMx, both factors lead to higher mixing ratios of H2SO4 and thus higher concentrations of SO42– by CAMx than by CMAQ.


The statistics for EC, OC, and TC in both months indicate a generally higher prediction by CAMx than CMAQ, which is likely affected by a weaker vertical mixing and additional SOA formation from VOCs in CAMx, as well as the differences in removal processes between the two models. Both models, however, underpredict OC, EC, and TC, due likely to the underestimation in the emissions of primary EC and OC and SOA formation. Among all PM species, both models generally perform the worst for NO3–. For example, NO3– concentrations are significantly overpredicted by both models at the STN sites in January, which is associated with the overpredictions in NH4+ concentrations. In July, although large underprediction occurs for the concentrations of NO3–, it has little impact on the PM underprediction because it is the smallest component of PM. Underprediction in the concentrations of SO42– and NO3– can explain underprediction in the concentration of NH4+ at all sites in July by both models. The simulated monthly-mean PM2.5 concentrations by CMAQ and CAMx are overlaid with observations in January and July in Fig. 1. In both months, CAMx predicts higher values than CMAQ throughout the domain, likely due to a weaker vertical mixing and lower dry and wet deposition fluxes of secondary PM2.5 (except for the wet deposition flux of SO42– in July) by CAMx than CMAQ. In January, the overprediction of PM2.5 by both models is significant near Atlanta, GA, which may be partly due to overestimate of SO2 emissions from the Bowen power plant in Bartow, GA. The Bowen plant has been ranked among the top 50 emitters of SO2 in the U.S. In July, the underprediction occurs throughout the domain. In the eastern portion of the domain, PM2.5 predictions are higher in January than July, contradictory to observations, which may be attributed to a weaker vertical mixing in January than in July. Fig. 2 shows observed and simulated hourly O3 mixing ratios at six sites. In January, the predicted O3 mixing ratios by both models are similar at non-urban sites such as YRK, GRS, and BFT but are apparently different at urban sites such as JST and RAL and a site with high NH3 mixing ratio, i.e., LCC. CAMx gives higher O3 mixing ratios during the daytime at these sites due to higher precursor mixing ratios as a result of a weaker daytime mixing. CMAQ tends to give higher O3 mixing ratios at nights than CAMx due to less titration of O3 by NOx as a result of a stronger nocturnal vertical mixing. The minimum O3 mixing ratios are generally captured at JST, YRK, and GRS by CAMx and overpredicted by both models at BFT. The maximum O3 mixing ratios are underpredicted throughout the month at JST and during the first half of the months at other sites by both models. In July, CMAQ also gives higher O3 mixing ratios at night and lower daytime O3 for the same reasons. Similar to January, the minimum O3 mixing ratios are generally captured at JST, YRK, RAL, and GRS by CAMx and overpredicted by both models at BFT and LCC. The maximum O3 mixing ratios are underpredicted by both models in the beginning of the month and overpredicted at the end of the month. The diurnal cycle is better captured by both models at the urban, rural, and mountain sites (i.e.,

1241

JST, RAL, YRK, and GRS) but not well represented at the costal site (BFT) and the site with high NH3 (LCC). Fig. 3 shows observed and simulated mixing ratios of NH3 at three sites in the eastern NC. Large differences exist in simulated mixing ratios of NH3, particularly at LCC and JMV, due to different treatments in vertical mixing, dry and wet deposition, and gas-to-particle conversion processes (as a result of different size representations and different versions of ISORROPIA). While both models overpredict mixing ratios of NH3 at CLT in January, they significantly underpredict those at LCC in January and CLT in July on most days, and at LCC in July and JMV in both months throughout the months. In particular, the model fails to capture the extremely high mixing ratios of NH3 (up to 183 ppb) at LCC in July. The large discrepancies indicate large uncertainties in NH3 emissions in terms of both magnitudes and spatial distributions (e.g., a possible underestimate in winter and an overestimate in summer, Zhang et al. (2006)) and in NH3 measurements as mentioned previously. Fig. 4 shows the observed and simulated hourly concentrations of PM2.5 at five sites. In January, the models show an overall good agreement with the observations at YRK and GRS in terms of both magnitude and temporal variations, but a large overprediction exists at other sites, particularly at JST by CAMx, because of overprediction in the concentrations of SO42–, NH4+, and EC. In July, both models significantly underpredict the concentrations of PM2.5 at most sites during most time periods, particularly at RAL and GRS, although they do capture some of the long term trends (i.e., the increase and decrease of PM2.5 at JST, YRK, and RAL during some periods and the three peaks at LCC). The large underprediction at all sites in the beginning of July is likely due to the long range transport of PM2.5 mass from a forest fire in Canada (DeBell et al., 2004) that is not accurately represented in the emission inventories used by both models. While forest fire emissions are included in the inventory, many assumptions are made (MACTEC, 2008) in estimating the emissions, resulting in possible errors in the inventory. Other factors contributing to underpredictions may include underestimate of primary PM emissions and the formation of secondary inorganic aerosol and SOA. Visibility CMAQ uses two methods to calculate these optical properties: one calculates βext based on aerosol size distribution and the other is based on the species mass concentration (Binkowski and Roselle, 2003; Mebust et al., 2003). The latter method is selected here for the evaluation because it is similar to the calculation used by IMPROVE and can be readily adapted for CAMx. The relative humidity factor (i.e., f(RH)) is needed to calculate βext. While f(RH) is obtained from a lookup table based on Malm et al. (1994) in CMAQ, it is calculated for each site by CAMx based on U.S. EPA (2003). In a pristine environment, βext = 0.01 km–1 (Mebust et al., 2003). The HI, reported in deciviews (dcv), is calculated based on βext. In a pristine environment, the HI is equal to 0. CAMx does not internally calculate any optical properties but the mass concentrations

1242



1243

Fig. 2. Observed and simulated hourly O3 mixing ratios at a 4-km horizontal grid resolution in January (left) and July (right) at Jefferson Street (JST), Atlanta, GA, Yorkville (YRK), GA, Great Smoky Mountains (GRS), TN, Raleigh (RAL), NC, Beaufort (BFT), NC, and Kinston-Lenoir (LCC), NC (no observations available in January 2002 at RAL and LCC).

1244


Fig. 3. Observed and simulated hourly NH3 mixing ratios a 4-km horizontal grid resolution in January (left) and July (right) at Clinton (CLT), Kinston-Lenoir (LCC), and Jamesville (JMV), NC. of PM species at each IMPROVE site can be used to calculate βext and HI. Table 6 shows performance statistics of βext and HI at the IMPROVE sites from both models. Both models simulate optical properties worse than observed, i.e., higher βext, due to the overpredicted concentrations of PM2.5 in January, with a cleaner environment simulated by CAMx than CMAQ. This may be a result of using different f(RH) values in CAMx than those used in CMAQ. The HI was slightly overpredicted by CMAQ and underpredicted by CAMx. In July, the underpredictions in the simulated βext and HI values are consistent with the underpredictions of PM2.5 concentrations, with lower predictions by CMAQ than by CAMx. Dry and Wet Deposition Fluxes The dry deposition velocity and flux are calculated using MLM, which is based on the concentrations and vegetation data collected by CASTNET. Compared with the M3Dry or Wesely modules which treat the canopy as one layer, MLM is a more detailed model that uses 20 layers within the canopy layer and calculates individual boundary and stomatal layer resistances for each canopy layer (Meyers et al., 1998). MLM has been evaluated against some limited observations and found to have varying performance, ranging from an underestimation of SO2 dry deposition velocity by 35% (Finkelstein et al., 2000) to an overestimation by 18.3% (Meyers et al., 1998). The MLM predictions are

used as a benchmark to evaluate dry deposition fluxes and velocities simulated by CMAQ and CAMx in this work, as shown in Table 7 and Figs. 5–8. Compared to MLM in both months, M3Dry in CMAQ shows some improvement in dry deposition velocities and fluxes of HNO3 over the Wesely’s deposition module in CAMx, although they are significantly overpredicted by both models. While the NMBs for SO2 dry deposition velocity in CMAQ indicate significant overpredictions (159% and 91% in January and July, respectively), those by CAMx are within the biases of the MLM estimations against observations (–21% and –1%, respectively). MLM contains dry deposition velocities of SO2 and HNO3 only, which are shown along with those of CMAQ and CAMx in Fig. 5 at GRS, CND, and BFT. CMAQ gives much higher dry deposition velocities of SO2 than those of MLM and CAMx on some days at GRS and most days at CND and BFT in January and some days at all sites in July but to a lesser extent. CAMx gives lower values than MLM on some days at CND and most days at GRS and BFT in January and agree quite well at all sites in July. For dry deposition velocities of HNO3, CAMx gives slightly higher values than CMAQ at all sites, both give higher values than MLM on most days in both months, with better agreement between MLM calculations and both model predictions in July than in January. These discrepancies in dry deposition velocities will propagate into dry deposition fluxes calculations.


The NMBs of SO2 dry deposition fluxes simulated by CMAQ and CAMx are 58% and –21% in January and 56% and 16% in July, respectively. This is due partly to the overpredictions in the concentrations of SO2 (see Table 5)

1245

and partly to the overestimation of the dry deposition velocity of SO2 in both months. The latter can limit the amount of the gas available for gas-to-particle conversion. Since the degree of overpredictions of the concentrations

Fig. 4. Observed and simulated PM2.5 concentrations a 4-km horizontal grid resolution in January (left) and July (right) at Jefferson Street (JST), Atlanta, GA, Yorkville (YRK), GA, Great Smoky Mountains (GRS), TN, Raleigh (RAL), NC, and Kinston-Lenoir (LCC), NC. Only 24-hr average observations are available on some days at GRS and LCC.

1246



1247

Fig. 6. Simulated dry deposition fluxes of SO2, HNO3, NH4+, SO42–, and NO3– by MLM, CMAQ, and CAMx a 4-km horizontal grid resolution in January (left) and July (right) at Great Smoky Mountains (GRS), TN.

1248


Fig. 7. Simulated dry deposition fluxes of SO2, HNO3, NH4+, SO42–, and NO3– by MLM, CMAQ, and CAMx a 4-km horizontal grid resolution in January (left) and July (right) at Candor (CND), NC.


1249

Fig. 8. Simulated dry deposition fluxes of SO2, HNO3, NH4+, SO42–, and NO3– by MLM, CMAQ, and CAMx a 4-km horizontal grid resolution in January (left) and July (right) at Beaufort (BFT), NC.

1250


of SO2 by both models is similar, the larger overprediction in its dry deposition fluxes by CMAQ is likely dominated by the larger overprediction in the dry deposition velocities of SO2 than CAMx. The NMBs of HNO3 dry deposition fluxes simulated by CMAQ and CAMx are 151.4% and 194.1% in January and 62.1% and 164.3% in July, respectively. While CMAQ overpredicts the concentrations of HNO3 in both months, CAMx gives zero bias in January and moderately underpredicts them. The overprediction in dry deposition fluxes of HNO3 is therefore caused primarily by the overpredicted dry deposition velocity in CAMx but mostly by overpredicted dry deposition velocity and to a lesser degree by the overpredicted concentrations of HNO3 in CMAQ. The dry deposition fluxes of all three PM species (i.e., NH4+, SO42–, and NO3–) are significantly underpredicted by both models in both months, which may be influenced by several factors including their underpredicted mass concentrations in July (see Table 6(b)), the underestimates in their estimated dry deposition velocities, and the overestimation of dry deposition fluxes of their gaseous precursors. The hourly dry deposition fluxes of SO2, HNO3, NH4+, SO42–, and NO3– calculated by MLM, CMAQ, and CAMx are shown in Figs. 6–8 at GRS, CND, and BFT, respectively. Significant discrepancies exist in calculated dry deposition fluxes of all these species at all sites. CMAQ tends to give the highest dry deposition fluxes of SO2 and CAMx tends to give the highest dry deposition fluxes of HNO3 at most sites during most time periods in both months. Because NH4+ is often associated with SO42–, their dry deposition fluxes are generally similar in terms of magnitudes and variation patterns. However, both models fail to capture the maximum fluxes for NH4+, SO42–, and NO3– determined by MLM at all three sites in both months. CMAQ tends to predict a much larger daily variation in NH4+ and SO42– dry deposition fluxes than CAMx and MLM. Among the three PM species, the dry deposition fluxes of NO3– exhibit the largest discrepancy against the MLM values at all sites in both months, due partly to the worst performance in the predicted concentrations of NO3–. The overall dry deposition results indicate large uncertainty in the simulated dry deposition using different dry deposition models with different levels of details in their treatments (e.g., the single-layer, bulk, and 20 layers). Table 7 also shows the statistical evaluation of wet deposition fluxes of NH4+, SO42–, and NO3– against the NADP measurements. Despite underpredicted precipitation, CMAQ overpredicts the wet deposition fluxes of all species in January, indicating that wet deposition is a more efficient removal process than dry deposition for those species. CAMx, however, underpredicts their wet deposition fluxes in January. The underpredictions of dry and wet deposition fluxes by CAMx also contribute to the higher PM2.5 concentrations than CMAQ in January. Both models ovepredict the wet deposition fluxes of SO42– in July because there is sufficient ambient SO42– to be removed from the atmosphere by the excess precipitation simulated by MM5. Despite the overpredicted precipitation, NO3– wet deposition fluxes are underpredicted by both models

because there is limited NO3– available to be removed from the atmosphere in the southeast in July. There is a mixed performance for the wet deposition fluxes of NH4+, with an overprediction by CMAQ and an underprediction by CAMx. These differences may be attributed to the differences in PM size representations and influential model treatments such as wet and dry deposition and vertical mixing. Comparing with dry deposition flux predictions, the correlation between the biases in the predicted concentrations and wet deposition fluxes is not strong, and in many cases their biases indicate an opposite trend. For example, in July, both models underpredict the concentrations of NH4+, SO42–, and NO3–, however, the wet deposition of SO42– is overpredicted by both models. This indicates the high non-linearity in the wet deposition flux prediction, which depends on the rate limiting impacts of chemical concentrations and/or wet scavenging (i.e., the wet deposition fluxes are limited by insufficient ambient concentrations or less precipitation or both). CONCLUSIONS The performance of two commonly-used air quality modeling systems, MM5/CMAQ and MM5/CAMx, is evaluated against observations from a number of networks for their applications over an area in the southeastern U.S.at a horizontal grid resolution of 4-km in January and July, 2002. Simulated 1.5-m temperature and relative humidity using MM5 are generally within ± 10% of the observations. Wind speed at 10-m is moderately overpredicted in both months. Precipitation in July is significantly overpredicted, which affects the removal of pollutants through wet deposition. Differences in simulated major air pollutants between CMAQ and CAMx simulation results can be attributed to several differences in model treatments, such as the treatment of vertical mixing and SOA formation, PM size representation, and wet and dry deposition modules. Both models overpredict surface concentrations of PM2.5 and maximum O3 mixing ratios in January, with higher values by CAMx. This is likely due to a weaker vertical mixing simulated by the models, particularly CAMx, as indicated by the overprediction in CO. Despite a weaker vertical mixing simulated in July by both models, the concentrations of PM2.5 and O3 are underpredicted. This is due to an excess removal of pollutants through wet deposition (i.e., SO42–), an excess removal of precursors through dry deposition (e.g., SO2), and an underestimation of emissions of primary PM and precursors of O3 and secondary PM and SOA concentrations. While both models reasonably capture the diurnal variations of O3, they show inaccuracies in simulating those of NH3 and PM2.5. Uncertainty in the emission estimates and the spatial distribution of the emissions and measurements may be responsible for large biases in simulated NH3 mixing ratios, indicating a need to improve the accuracy of NH3 emissions and measurements. Difficulties in simulating PM2.5 arise from the volatile species (i.e., NO3–), lack of information in the formation mechanisms (i.e., SOA), and large uncertainties in the emissions of primary PM (e.g., EC and OC). Large discrepancies exist in the dry and wet


deposition predictions from CMAQ and CAMx and from MLM and NADP. The discrepancies in dry deposition fluxes between the two models and between model predictions and MLM/NADP values are likely dominated by discrepancies in the dry deposition velocities of species. Those in wet deposition fluxes are likely dominated by differences in the wet scavenging of species, in particularly in July. These differences may be attributed to the differences in PM size representations and treatments for wet and dry deposition and vertical mixing. Since both models have been used for regulatory applications, the results from this work provide an assessment of their capability in reproducing observed concentrations and deposition fluxes as well as visibility and identify several areas of improvement, which would be useful for their continuous development and applications in support of the SIP modeling efforts and the development of other emission control policies as well as in agricultural air quality modeling. The areas of improvement identified through this work should be given more research attentions to enhance the models’ capability and fidelity. ACKNOWLEDGEMENTS This project is supported by National Research Initiative Competitive Grant no. 2008-35112-18758 from the USDA Cooperative State Research, Education, and Extension Service Air Quality Program. Thanks are due to Pat Brewer, Mike Abraczinskas, George Bridgers, Bebhinn Do, Chris Misenis, Hoke Kimball, and Wayne Cornelius, NC Department of Environmental and Natural Resources, for providing 12-km VISTAS CMAQ inputs, outputs, and observational data for NC; Don Olerud, Baron Advanced Meteorological Systems, for providing 12-km MM5 outputs; Dennis McNally and Cyndi Loomis, AlpineGeophysics, Inc., for providing the source code of the CMAQSOAmods v4.51 and updated VISTAS emission inventories; Ryan Boyles, NC State Climate Office, for providing NC CRONOS observational data; Steve Howard and Shao-Cai Yu, the U.S. EPA, for providing observational data and FORTRAN scripts used for statistical calculations; and Wayne Robarge, Department of Soil Science, NCSU and John Walker, the U.S. EPA, for their insightful discussions and technical contributions. REFERENCES Aneja, V.P., Roelle, P.A., Murray, G.C., Southerland, J., Erisman, J.W., Fowler, D., Asman, W.A.H. and Patni, N. (2001). Atmospheric Nitrogen Compounds II: Emissions, Transport, Transformation, Deposition and Assessment. Atmos. Environ. 35: 1903–1911. Battye, R., Battye, W., Overcash, C. and Fudge, S. (1994). Development and Selection of Ammonia Emission Factors, EPA/600/R-94/190, U.S. Environmental Protection Agency, Research Triangle Park, NC. Binkowski, F.S. and Shankar, U. (1995). The Regional Particulate Matter Model 1. Model Description and Preliminary Results. J. Geophys. Res. 100: 26191–26209.

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