WRAP/WAQS 2014 v1 Shakeout Study

The objectives of the WRAP/WAQS 2014 Platform Development and Shake-Out Study (“Shake-Out Study”) is to develop regional photochemical grid model (PGM) modeling platforms for the western U.S. and 2014 calendar year using existing information. The Shake-Out Study 2014 PGM modeling platforms will be used for regional haze State Implementation Plans (SIPs) and potentially other air quality issues in the western states. The first two phases of the Shake-Out Study were performed during the first four months of 2019 and culminated in version 1 (2014v1) of the annual CMAQ and CAMx PGM 2014 base case simulations, model performance evaluation and documentation as described below.

NOTE: The 2014v1 Shake-Out PGM modeling databases are not being made available on the IWDW. Work updating the PGM databases is ongoing and the 2014v2 PGM databases are expected to be made available on the IWDW later in 2019.

2014v1 Shake-Out Study Modeling Plan

The initial draft Modeling Plan for the 2014 Shake-Out Study was dated December 31, 2018 and distributed to State, Federal and Local agencies who provided comments that were implemented in a second draft Modeling Plan that was also reviewed. The final 2014 Shake-Out Study Modeling Plan was dated March 9, 2019 and represented the implementation of two rounds of comments. The final Modeling Plan and Response-to-Comments documents can be found at the following links.

2014v1 Shake-Out Study Modeling Plan Addendum

An addendum to the 2014v1 Shake-Out Study Modeling Plan was prepared that discusses the types of analyses that could be undertaken using the WRAP/WAQS 2014 as well as the EPA/MJO 2016 modeling platforms. The types of analyses discussed included Representative Baseline and future year emissions modeling, and sensitivity and source apportionment modeling to examine contributions and impacts of U.S. and international anthropogenic, fire and natural emissions by geographic region and source sectors. One recommendation based on experience from the first round of regional haze SIP modeling and subsequent WAQS and EPA modeling was to not try and answer all questions in a single source apportionment/sensitivity simulation that takes a long time to conduct, but rather conduct a series of more computationally efficient analyses focusing on answering specific questions and take advantage of model updates and improvements as they occur.

2014v1 Shake-Out Study Final Report

The 2014v1 Shake-Out Study Draft Final Report was dated May 21, 2019 and documents the first phases of the WRAP Shake-Out Study. The Draft Final Report provides details on the 2014v1 PGM platform development, sensitivity modeling for Boundary Conditions (BCs), meteorology and emissions and documents the final annual 2014v1 CMAQ and CAMx 36/12-km Shake-Out simulations and model performance evaluation. The draft final report can be found at the following link.

2014v1 PGM Platform Development

The existing information used to develop the 2014v1 Shake-Out Study PGM modeling platforms was primarily based on EPA’s 2014 PGM Modeling Platform and 2014 WRF meteorological modeling conducted by the Western Air Quality Study (WAQS).

2014 Meteorological Data

There are two existing 2014 Weather Research Forecast (WRF) meteorological model databases that were available for developing 2014 meteorological input for the two PGMs: (1) the EPA 2014 WRF 12-km database for the 12US2 continental U.S. domain; and (2) the WAQS 2014 36/12-km database for the 36-km 36US continental U.S. and 12-km 12WUS2 western U.S. modeling domains. Figure 1 displays the PGM modeling domains that were developed using the WAQS 36-km 36US and 12-km 12WUS2 and EPA 12-km 12US2 WRF data.

Figure 1. 36-km 36US, 12-km 12US2 and 12-km 12WUS2 modeling domains use in the 2014v1 Shake-Out PGM modeling.

The WAQS 12-km 12WUS2 and EPA 12-km 12US2 WRF output was evaluated against observed surface wind speed, wind direction, temperature and humidity (water mixing ratio) at surface monitoring sites in the western U.S. The WAQS and EPA WRF precipitation were evaluated against monthly and daily PRISM spatial maps in the western U.S. This resulted in 10s of thousands of WRF model evaluation graphical displays. The WAQS and EPA comparative model performance evaluation is summarized in Chapter 3 of the 2014v1 Shake-Out Draft Final Report and in a PDF that was presented to the RTOWG on a January 31, 2019 webinar. All WRF MPE graphical displays are available for viewing and download on the IWDW Website where the user can drill down and analyze the 2014 WRF MPE to individual states or even individual sites. There are three types of WAQS and EPA 2014 MPE products available: (1) soccer plots that display surface meteorological variability statistical performance measures against performance goals and criteria across all western states, within each western states and at individual monitoring sites; (2) time series plots of surface meteorological variable model performance; and (3) comparison of observed and predicted spatial plots of precipitation fields in the western U.S. by month and by individual day. Figures 2 and 3 displays example soccer and time series plots for the two 2014 WRF simulations and wind speed across the western U.S. An example of WRF performance against PRISM data for August monthly precipitation is shown in Figure 4.

Figure 2. Example wind speed performance across western states for EPA (left) and WAQS (right) 2014 12-km WRF simulations.
Figure 3. Time series of daily predicted and observed wind speeds and wind speed bias (m/s) averaged across all sites in western U.S. for WAQS (blue) and EPA (red) WRF simulations and 2014 Q2 (left) and Q3 (right).
Figure 4. Comparison of August monthly total precipitation from PRISM (left) based on observations, EPA WRF (middle) and WAQS WRF (right).

The performance of the two 2014 WRF 12-km simulations varied with no one WRF simulation exhibiting better performance across all meteorological variables and locations than the other. For example, EPA WRF produced better temperature performance, whereas WAQS WRF produced better wind speed and summer precipitation performance. PGM sensitivity modeling of the two 12-km WRF inputs that are presented in Chapter 5 of the 2014v1 Shake-Out Draft Final Report found that one set of WRF fields did not produce better air quality performance than the other across all species and locations. Ultimately the WAQS 12-km 12WUS2 domain meteorological fields were selected for the final 2014v1 Shake-Out CMAQ and CAMx simulations based on better summer precipitation performance than the EPA 12-km WRF simulation.

2014v1 Base Case Emissions

The WRAP Shake-Out 2014v1 Base Case emissions are based on Version 2 of the 2014 National Emissions Inventory (2014NEIv2) with updates provided by the WRAP western states. The CMAQ-ready 2014 emissions inputs for 2014 and the 12-km 12US2 domain from the EPA 2014 Modeling Platform were used for all source sectors but Point and Non-Point that had western state updates so had to be re-processed. Details on the development of EPA’s 2014 modeling platform emissions are contained in their Technical Support Document (TSD). The SMOKE emissions model was used to process the 2014NEIv2 emissions with western state updates to generate the 2014 12-km 12US2 domain CMAQ-ready emissions. The 2014NEIv2 Mexico, Canada and off-shore shipping and oil and gas emission sources were processed for the 36-km 36US domain. The CMAQ 2014 12-km 12US2 domain emissions were disaggregated to 36-km resolution and used to replace the emissions in the 36-km 36US domain to make CMAQ-ready emissions for 2014 and the 36-km 36US domain. The 2014v1 CMAQ-ready emissions were converted to the CAMx format using the CMAQ2CAMx tool.

The WRAP Fire and Smoke Work Group made updates (e.g., classify fires as either wildfire, prescribed burns and agricultural burning) to the 2014NEIv1 open land burning emissions that were then processed for input into CMAQ and CAMx. The CAMx windblown dust (WBD) processor was used to generate WBD emissions for both CMAQ and CAMx. The CAMx oceanic emissions processor was used to generate sea salt (SSA) and dimethyl sulfide (DMS) emissions that were used in CAMx. For CMAQ, the in-line SSA algorithm was used that does not include DMS emissions. Note that because the version of CAMx (v6.5) used in the 2014v1 Shake-Out modeling does not include explicit DMS chemistry, DMS was renamed to SO2 when input into CAMx.

Chapter 2 of the 2014v1 Shake-Out Draft Final Report details the 2014v1 emissions data and modeling with a summary contained in a PDF that was presented at the April 5, 2019 2014v1 Shake-Out Close-Out meeting.

2014v1 Boundary Conditions

Boundary Condition (BC) concentrations for the most outer 36-km 36US PGM domain were defined using output from a 2014 GEOS-Chem global chemistry model simulation EPA performed to define BCs for their CMAQ modeling used in the 2014 National Air Toxics Assessment (NATA). EPA used version 11-01 of GEOS-Chem with a horizontal resolution of 4 x 5 degrees (~440 km) and 47 vertical levels. More details on EPA’s 2014 GEOS-Chem simulation can be found in the 2014 NATA TSD. The output from EPA’s 2014 GEOS-Chem simulation were processed to obtain BC inputs for CMAQ and CAMx and the 36-km 36US domain. CAMx no emissions, inert chemistry and with deposition BC-only 2014 36/12-km simulations were conducted and compared with observed concentrations. Since there were no emissions, the CAMx BC-only simulations were expected to under-predict the observed concentrations, however two overestimation problems were revealed: (1) an ozone overestimation bias that occurs year-round; and (2) a sulfate and SO2 overestimation bias during June and July. Additional CAMx BC-only sensitivity simulations were performed with the 2014 GEOS-Chem BC concentrations capped for some species as follows: ozone = 0.500 ppm (500 ppb); sulfate = 2 µg/m3; and SO2 = 6 ppb. The ozone cap only affected stratospheric ozone BC concentrations and the sulfate/SO2 caps only affected a portion of the western BCs at the end of June and in early July. The cap on the ozone BCs had little effect on the ozone overestimation bias that was due to 2014 GEOS-Chem overstating tropospheric ozone concentrations. The cap on sulfate/SO2 BC concentrations alleviated with worst June/July SO4 overestimation bias.

Discussions with EPA revealed that they were aware of the 2014 GEOS-Chem ozone and sulfate performance issues and since it has minimal effect on air toxics concentrations they did not try to correct them for their 2014 NATA modeling platform. The June/July sulfate/SO2 overestimation bias was caused by using volcano eruption emissions in the 2014 GEOS-Chem run that uses climatological volcano eruption emissions data that included a large eruption in 2009 that was responsible for the sulfate/SO2 overestimation. EPA re-ran their 2014 GEOS-Chem set-up turning off volcano eruption emissions whose results were used for the June/July BCs in the final 2014v1 Shake-Out PGM simulations. Details on the use of the 2014 GEOS-Chem BCs and sensitivity modeling are contained in Chapter 4 of the 2014v1 Shake-Out Draft Final Report.

2014 PGM Sensitivity Tests

A limited amount of PGM sensitivity modeling was conducted to investigate ways to improve model performance and identify model inputs and model options for the final 2014v1 Shake-Out annual CMAQ and CAMx annual simulations. The three main inputs examined in the sensitivity modeling were BCs, meteorology and biogenic emissions that are documented in Chapters 4, 5 and 6, respectively, of the 2014v1 Shake-Out Draft Final Report and summarized in the PDF file that was presented at the April 5, 2019 2014v1 Shake-Out Study Close-Out meeting. Results of the comparison of the CAMx January and July 2014 BC, meteorological and biogenic emissions sensitivity tests with observations are available in an Excel spreadsheet:

As discussed above, the PGM BC sensitivity modeling identified that the 2014 GEOS-Chem BCs overstated ozone year-round and SO4/SO2 in the summer. The SO4/SO2 overstatement was corrected by having EPA rerun GEOS-Chem for June and July 2014 without volcano emissions. Figure 5 displays predicted and observed time series sulfate concentration at Yellowstone and Weiminuche IMPROVE sites for the CAMx July 2014 BC sensitivity tests using 2014 GEOS-Chem BCs with and without volcano eruption emissions that clearly shows the improved sulfate performance when volcano eruption emissions are not included.

Figure 5. Predicted and observed sulfate concentrations at Yellowstone (left) and Weiminuche (right) IMPROVE sites for the CAMx July BC sensitivity tests using 2014 GEOS-Chem results with (orange) and without (blue) volcano eruption emissions.

The PGM meteorological sensitivity modeling ran CAMx for January and July using the WAQS and EPA 12-km WRF meteorological inputs and compared the CAMx model estimates with observations in a model performance evaluation. Neither CAMx simulation using the two WRF meteorological inputs exhibited consistently better model performance than the other (see Chapter 5 of 2014v1 Shake-Out Draft Final Report). For ozone, the comparison of model performance was confounded by the overstated 2014 GEOS-Chem BCs. Ultimately the WAQS 12-km WRF meteorological inputs were selected for the final 2014v1 Shake-Out PGM simulations based on better WRF summer precipitation model performance.

Biogenic emissions sensitivity modeling conducted two CAMx simulations of the 12-km 12WUS2 domain for January and July 2014, one using the BEIS and the other using MEGAN3 biogenic emissions. The BEIS biogenic isoprene emissions in the western U.S. tended to be approximately 20% lower than MEGAN3. However, the BEIS terpene emissions were 4-5 times greater than MEGAN3. There was a small effect of the biogenic emissions on ozone model performance but whether one biogenic emissions model produced better ozone performance was difficult to interpret due to the overstated ozone BCs. The different biogenic emission models had a bigger effect on summer Organic Aerosol (OA) performance due to the large differences in the biogenic terpene emissions that is a precursor to Secondary Organic Aerosol (SOA). At most IMPROVE sites, the July 2014 CAMx OA model performance was better using the BEIS biogenic emissions than MEGAN3 due to higher OA concentrations that better matched the observed values. Figure 6 shows example OA time series plots at four IMPROVE sites in the western U.S. with CAMx clearly showing better OA performance at the sites in CA (SEQU), WA (NOCA) and CO (WEIM) but worse OA performance at the NM site (SAGU). Based on the better summer OA model performance at most sites, the BEIS biogenic emissions were selected for the final 2014v1 Shake-Out CMAQ and CAMx annual simulations.

Figure 6. Predicted and observed time series of Organic Aerosol concentrations in July 2014 at the Sequoia CA, North Cascades WA, Weiminuche CO and Saguaro NM IMPROVE sites for the CAMx BEIS (green) and MEGAN (orange) biogenic emission sensitivity tests.

Final 2014v1 Shake-Out CMAQ and CAMx Simulations and Model Performance Evaluation

The final configuration for the 2014v1 Shake-Out annual CMAQ and CAMx simulations used the revised 2014 GEOS-Chem simulation without volcano eruption emissions for June and July to define BCs for the 36-km 36US. The meteorological inputs for the 36/12-km domains were based on the WAQS 2014 WRF simulation. BEIS biogenic emissions and the 2014v1 emissions with western state updates were used. The table below summarizes the CMAQ and CAMx configuration used in the final annual 2014v1 Shake-Out simulations and MPE.

Science Options CAMx CMAQ Comment
Model Codes CAMx V6.5 - Apr 2018 CMAQ v5.2.1 – Apr 2018 CMAQ v5.3 Beta is available but will not be used as it needs more testing.
Horizontal Grid Mesh
36 km grid 148 x 112 cells 148 x 112 cells  36-km RPO CONUS
12 km grid 225 x 213 cells 225 x 213 cells 12-km WESTUS (12WUS2) WAQS
Vertical Grid Mesh 25 vertical layers, defined by WAQS WRF 25 vertical layers, defined by WAQS WRF Layer 1 thickness 20/24 m. Model top at ~19 km AGL
Grid Interaction One-way grid nesting One-way grid nesting CAMx and CMAQ 12-km 12WUS2 domain BCs based on 36-km CONUS CAMx and CMAQ simulations
Initial Conditions 10 day spin-up on 36/12-km domains before January 1, 2014 10 day spin-up on 36/12-km domains before January 1, 2014  
Boundary Conditions Updated 2014 GEOS-Chem for 36-km CONUS domain Updated 2014 GEOS-Chem for 36-km CONUS domain EPA re-ran June and July 2014 GEOS-Chem without volcano eruptions
Baseline Processing SMOKE, BEIS, CAMx Natural Emissions Processors SMOKE, BEIS, CMAQ in-line SSA and LNOx processors (CAMx WBD)  2014NEIv2 with western state updates for Point and Non-Point
Sub-grid-scale Plumes Plume-in-Grid for major NOX sources (~300 in WUS) No Plume-in-Grid CMAQ does not have a Plume-in-Grid module
Gas Phase Chemistry CB6r4 CB6r3 Latest chemical reactions and kinetic rates with halogen chemistry (Yarwood et al., 2010)
Meteorological Processor WRFCAMx MCIP  Compatible with CAMx v6.5 and CMAQ V5.2.1
Horizontal Diffusion Spatially varying Spatially varying K-theory with Kh grid size dependence
Vertical Diffusion CMAQ-like Kv MCIP Minimum Kv 0.1 to 1.0 m2/s
Diffusivity Lower Limit Kz-min = 0.1 to 1.0 m2/s or 2.0 m2/s Kz-min = 0.1 to 1.0 m2/s or 2.0 m2/s Depends on urban land use fraction
Deposition Schemes      
Dry Deposition Zhang dry deposition scheme CMAQ formulation (Zhang et. al, 2001; 2003)
Wet Deposition CAMx -specific formulation CMAQ formulation rain/snow/graupel
Gas Phase Chemistry Solver Euler Backward Iterative (EBI) EBI EBI fast and accurate solver
Vertical Advection Scheme Implicit scheme w/ vertical velocity update CMAQ Emery et al., (2009a,b; 2011)
Horizontal Advection Scheme Piecewise Parabolic Method (PPM) scheme PPM Colella and Woodward (1984)
Integration Time Step Wind speed dependent Wind speed dependent 1-5 min (12-km), 5-15 min (36-km)


The 2014v1 Shake-Out CMAQ and CAMx annual 36/12-km simulations were subjected to a multi-phased MPE. The Atmospheric Model Evaluation Tool (AMET) was used to evaluate the CMAQ and CAMx 2014v1 12-km modeling results using observed gaseous and particle concentration data from the AQS, CSN, CASTNet and IMPROVE monitoring networks. The IMPROVE observed particulate matter species concentration data were then loaded into a spreadsheet to generate visualization of the concentration data for the Most Impaired Days (MID) and Worst 20% days and then processed into light extinction in a second spreadsheet to analyze seasonal and daily performance. The Chapter 8 of the 2014v1 Shake-Out Draft Final Report discussed the CMAQ and CAMx 2014v1 MPE, with the full suite of MPE products available in the links below.

AMET MPE Evaluation Products

AMET was run to generate scatter plots and spatial maps of model performance statistics. The scatter plots included model performance statistics and were generated for the annual period, by season and by month. The spatial maps of model performance statistics were for the annual period. The species considered in the AMET MPE analysis were gaseous Maximum Daily Average 8-hour (MDA8) ozone concentrations and particle total PM2.5, SO4, NO3, NH4, EC, OA, Na and Cl. All of the AMET graphical evaluation products can be viewed using the Image Viewer below.

Figure 7 and 8 show example AMET MPE products for the 2914v1 CMAQ and CAMx simulation and DMA8 ozone and SO4 concentrations, respectively. The DMA8 ozone scatter plots (Figure 7, top) are for all CASTNet sites in the western U.S. and show a net overestimation bias (NMB) that is greater for CAMx (+19%) than CMAQ (+11%). The spatial NMB bias plot for DMA8 ozone (Figure 7, bottom) are all grey (≤±15%) or yellow and warmer colors with no cooler (green-blue) colors indicating an ozone overestimations bias.

Figure 7. Example AMET MPE products for MDA8 ozone (O3_8hrmax) and CMAQ (left) and CAMx (right) 2014v1 Shake-Out runs showing scatter plots of CASTNet ozone (top) and Normalized Mean Bias (NMB) (bottom) across the 2014 annual period.

Both CMAQ and CAMx show fairly good SO4 performance across the western U.S. for the 2014v1 base case simulations. CAMx has a few large overpredictions that are mainly due to overstated SO4 at coastal sites (see NMB bias plot in Figure 8, bottom right). This 2014v1 CAMx coastal summer SO4 overestimation will be investigated in the next phase of the study.

Figure 8. Example AMET MPE products for SO4 and CMAQ (left) and CAMx (right) 2014v1 Shake-Out runs showing scatter plots of IMPROVE SO4 (top) and Normalized Mean Bias (NMB) (bottom) across the 2014 annual period.

Time Series Evaluation Spreadsheet Tool

The AMET predicted and observed PM species concentration pairs from the IMPROVE network were loaded into an Excel spreadsheet so that time series comparisons could be made that the user could manipulate the time period and concentration scale that is not possible with the AMET static time series graphics. For example, when an IMPROVE site is impacted by concentrations from file the automatic scaling is set to capture the high fire impact conditions rendering the time series comparisons for the rest of the time difficult to interpret. For example, Figure 9 shows time series of OA concentrations at the Grand Canyon IMPROVE site using the auto-scaling approach and then with the user setting the scale to a lower value so the comparisons can be seen for the other days. The time series evaluation spreadsheet for the 2014v1 Shake-Out CMAQ and CAMx simulations is available at the following link.

Figure 9. Time series of predicted and observed 24-hour average OA concentrations at Grand Canyon IMPROVE site for 2014v1 Shake-Out CMAQ (blue) and CAMx (orange) annual simulations using autoscaling (top) and user-defined scaling (bottom).

MID/W20% Days Concentration Evaluation Spreadsheet

The AMET predicted and observed PM species concentrations from the IMPROVE network were loaded into an Excel spreadsheet and sorted by the observed Most Impaired Days (MID) and Worst 20 percent (W20%) days so comparisons could be made. These were some of the data that were used in the PDF file that presented the 2014v1 Shake-Out CMAQ and CAMx MPE at the April 5, 2019 Close-Out meeting. Figure 10 shows example concentrations at Yellowstone averaged across and for each individual observed MID and W20% days that shows how the new MID visibility metric eliminates many observed days with high wildfire contributions that are included in the W20% days (e.g., July 19). The MID-W20% interactive spreadsheet of concentrations can be found at the following link.

Figure 10. Predicted and observed PM concentrations at Yellowstone IMPROVE site average across the observed W20% (top left) and MID (top right) days and for each individual W20% (middle) and MID (bottom) days.

Light Extinction Evaluation Spreadsheet

The predicted and observed PM concentrations at the IMPROVE sites were processed using the revised IMPROVE light extinction equation so that the model performance for total PM extinction and extinction due to individual components could be evaluated. The 2014v1 CMAQ and CAMx MPE for light extinction is discussed in Chapter 8 of the 2014v1 Shake-Out Draft Final Report. The interactive spreadsheet of light extinction (Mm-1) model performance can generate stacked bar charts of total light extinction (without Rayleigh) and scatter and time series plots of light extinction due to each PM component. Example displays of model performance for the Mount Zirkel, Colorado IMPROVE site from the light extinction MPE spreadsheet are shown in Figures 11 through 13 with the spreadsheet available at the following link.

Figure 11. Predicted and observed stacked bar charts of seasonal light extinction (Mm-1) at Mount Zirkel IMPROVE site for the 2014v1 CMAQ and CAMx annual simulations.
Figure 12. Scatter plot of predicted and observed daily ammonium sulfate (left) and ammonium nitrate (right) light extinction (Mm-1) at Mount Zirkel IMPROVE site for the 2014v1 CMAQ and CAMx annual simulations.
Figure 13. Time series plot of predicted and observed daily ammonium sulfate light extinction (Mm-1) at Mount Zirkel IMPROVE site for the 2014v1 CMAQ and CAMx annual simulations.

Major Model Performance Issues and Potential Resolution

The major model performance issues of the 2014v1 Shake-Out CMAQ and CAMx annual simulations and recommendations for improvements are presented in Chapter 9 of the 2014v1 Shake-Out Draft Final Report. They are summarized as follows:

  • Ozone: The ozone performance is dominated by the overstated BCs from EPA’s 2014 GEOS-Chem simulation. Rerunning GEOS-Chem using updated emissions and version of the model may alleviate this issue. Ozone in fire plumes frequently appears to be too high and the NOx speciation of fire emissions should be investigated and updated as needed.
  • Sulfate: Once the problems with the June and July SO4/SO2 concentrations along the western BC was resolved by re-running GEOS-Chem without volcano eruptions, the SO4 performance was generally good with the exception of overstated SO4 at coastal sites by CAMx. Targeted sensitivity tests need to be performed using CAMx to identify the cause of this coastal SO4 overestimation bias so that corrective action can be taken. Because CAMx used different primary SO4 emissions from Sea Salt than CMAQ and included DMS emissions that were not in CMAQ, those are the first two variables to investigate in the CAMx coastal SO4 overestimation sensitivity tests.
  • Nitrate: NO3 tends to be underestimated, with the underestimation greater in CAMx than CMAQ. The use of a bi-directional ammonia flux algorithm should be investigated along with the treatment of mineral (e.g., Ca and Na) nitrate.
  • Organic Aerosol: In the absence of OA from fires, summer OA is overstated in the desert southwest that is believed to overstated SOA from biogenic terpene emissions. The treatment of biogenic emissions using BEIS and MEGAN should be re-visited.
  • Elemental Carbon: EC performance is generally fairly good, although very uncertain when fire impacts occur.
  • Other PM2.5/Soil: There and model-observed incommensurability issues with the Soil species that make them difficult to compare. The modeling of fine crustal species is challenging and can have local contribution that a grid model has difficulty simulating.
  • Coarse Mass: Due to higher deposition rates, Coarse Mass (CM) is frequently dominated by local sources (e.g., road dust) that is subgrid-scale to the model so not well simulated and frequently underestimated.