February 24, 2006



# Final Report for the Western Regional Air Partnership (WRAP) 2002 Visibility Model Performance Evaluation

WGA Contract Number: 30203

Prepared for:

Western Regional Air Partnership Western Governors’ Association 1515 Cleveland Place, Suite 200 Denver, CO 80202

Prepared by:
Gail Tonnesen, Zion Wang, Mohammad Omary, Chao-Jung Chien, and Yingqun Wang Bourns College of Engineering Center for Environmental Research and

Technology (CE-CERT), Universi ty of California, Riverside 1084 Columbia Avenue, Riverside, CA 92507 http://pah.cert.ucr.edu/aqm/308/
Ralph Morris, Sue Kemball-Cook, Yi qin Jia, Steven Lau, and Bonyoung Koo ENVIRON Internati onal Corporation 101 Rowland Way, Suite 220, Novato, CA 94945 http://www.environcorp.com

Zac Adelman, Andy Holland, and James Wallace UNC-Chapel Hill, Carolina Environmental Program Bank of America Plaza, CB# 6116, 137 E. Franklin Street, Chapel Hill, NC 27599-6116 http://cf.unc.edu/cep/empdFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

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## Executive Summary

The Clean Air Act (CAA) establishes special goa ls for visibility in many national parks, wilderness areas, and international parks. Thr ough the 1977 amendments to the Clean Air Act, Congress set a national goal for visibility as “the prevention of any future, and the remedying of any existing, impairment of vi sibility in mandatory Class I Federal areas which impairment results from manmade air pollution” (40 CFR 51.300). States are requi red to develop State Implementation Plans (SIPs) to attain visibility standards; also, trib es may opt to assume responsibility for visibility programs under 40 CF R Part 49 by developing Tribal Implementation Plans (TIPs). The goal of the U.S. Environmen tal Protection Agency (EPA) Regional Haze Rule (RHR) is to achieve natural vi sibility conditions at 156 Fede rally mandated Class I areas by 2064. To achieve this goal, the RHR has set up milestone years of 2018, 2028, 2038, 2048, 2058, and 2064 to monitor progress to ward natural visibility c onditions. CAA Section 308 (§308) requires that the first visibility SIP/TIP be submitted to EPA by 2007-2008 to demonstrate progress toward natural visibili ty conditions in 2018 using th e 2000-2004 five-year baseline.

The Western Regional Air Partnership (WRAP) Re gional Modeling Center (RMC) is responsible for performing regional air quality modeling simulations for the contiguous WRAP region; the WRAP states and tribes then us e the analytical results to deve lop SIPS or TIPs under the RHR. The contiguous WRAP region includes Arizona , California, Colorado, Idaho, Montana, New Mexico, North Dakota, Or egon, South Dakota, Utah, Washington, and Wyoming. * The air quality and visibility modeling results for the co ntiguous WRAP region will be used by the states and tribes in their technical support documents (TSDs) demonstr ating progress toward attaining visibility goals. Key RMC visibility mode ling work elements include the following:

1. Evaluation of the visibility m odel for a historical episode—in this case, for calendar year 2002. Output from the model simulation is comp ared with ambient air quality data for the historical episode as part of a model performance evaluation (MPE).

2. Development of visibility planning scenar ios for the regional haze baseline period of 2000-04 and for the initial regional haze future projection period, calendar year 2018.

3. Modeling a variety of emissions sensitivity, emissions source apportionment, and emissions control strategies to assess whether pl anned future regional emissions reductions will be sufficient to demonstrate reasonable progress toward achieving visibility goals.

This document is a final repor t that discusses the activitie s the WRAP RMC performed in support of work element 1 above. We used MPEs to assess the suitability of two modeling systems for simulating air quality and visibility for the 2002 calendar year, so that the models could be used for subsequent planning, sensitiv ity, and emissions control strategy modeling. The two models are EPA’s Community Multiscale Air Quality (CMAQ) modeling system and ENVIRON’s Comprehensive Air qu ality Model with extensions (CAMx). This report also

* Modeling analyses and results for the Class I areas in the state of Alaska (which is also included in the WRAP region) and any analyses for native villages in Alaska are described in a separate report(s).Final Report for the WRAP 2002 Visibility Model Performance Evaluation

iii discusses some model intercompa rison work that we performed for the modeling systems. We use all this information as the basis for reco mmending the selection of CMAQ and/or CAMx to complete work elements 2 and 3 above.

The visibility modeling work included deve loping emissions inventories for 2002, developing meteorology data for 2002 using a meteorology mode l, operating the air quality and visibility models, and developing tools and procedures for comparing the model results to ambient monitoring data as part of the model evaluation exerci se. Each of these efforts is summarized below.



### Emissions Modeling

For the emissions modeling work conducted in suppor t of work element 1 above (details are in Section 2), we used improved 2002 emissions data for the United States, Mexico, and Canada to create a final base 2002 annual emissions data base that was used in the CMAQ and CAMx model performance evaluations. Sources for emi ssions inventory and ancillary modeling data included WRAP emissions inventory contractor s, other Regional Planning Organizations (RPOs), and EPA. Building from the WRAP pr eliminary 2002 modeling cases completed earlier (Tonnesen et al., 2005), we integrat ed several updates to the inve ntories and ancillary data to create final 2002 emissions input fi les; this simulation is referre d to as 2002 base case A, or Base02a. We used the Sparse Matrix Operat or Kernel Emissions (SMOKE) version 2.1 processing system (CEP, 2004a) to prepare the inve ntories for input to th e air quality modeling systems. The SMOKE simulations documented in this report include emissions generated for (1) annual CMAQ and CAMx simulations at a 36-k m model grid re solution and (2) a short-term CMAQ test simulation at a 12-km model grid resolution. We performed the modeling and quality assurance (QA) work based on the WRAP RMC emissions QA protocol (Adelman, 2004).

Simulation Base02a represents a significant improvement to the preliminary 2002 emissions modeling previously performed by the RMC. Wh ile the preliminary 2002 modeling served its purpose to develop the infrastructure at the RM C for modeling large emissions data sets and producing annual emissions simulations, much of the input data (bot h as inventories and ancillary data) were placeholders for actual 2002 data th at were being prepared through calendar year 2005. As these actual 2002 data sets became av ailable to us, we inte grated them into the SMOKE modeling and QA system th at we had developed during the preliminary modeling, to produce a high-quality emissions data set for use in the CMAQ and CAMx MPEs. The addition of entirely new inventory categories, like mari ne shipping and oil and gas production sectors, added complexity to the Base02a modeling. By the end of the data collection phase for simulation Base02a, we had identifi ed 22 different emissions categorie s to compose this data set.



### MM5 Modeling

Meteorology data are key input data required for running any ai r quality model. These data include information on wind speed and direction, at mospheric stability and vertical motion in the atmosphere, sunlight intensity, clouds and precipitation, and vert ical mixing. For photochemical grid models, such as CMAQ and CAMx, meteorol ogy data are typically developed by operating a prognostic numerical simulati on model that solves the fundamental equations governingFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

iv conservation of mass, energy, and momentum. Fo r the WRAP modeling we applied the FifthGeneration Pennsylvania State University/Nat ional Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) (Grell et al., 1994) for both a 36-km continental domain and a fine-resolution, nested 12-km domain in th e western U.S. This work, completed in 2004, is summarized in Section 3 of this report, and also described in a final report on the RMC web site:

http://pah.cert.ucr.edu/aqm/308/mm5.shtml

In our original 36-km MM5 simulation, surface humi dity and temperature fields fell within the benchmarks for much of the year for most s ubdomains, but the model had a marked cold, wet bias, especially during the summer, in the we stern subdomains; also, the surface wind fields failed to meet the performance benchmark for the entire year in several model subdomains. We performed extensive MM5 sensitiv ity testing with alternate scie nce configurations to improve the MM5 performance in the WRAP regions. The final 36-km and 12-km MM5 runs show improvement in the modeled precipitation fields, particularly in the summer in the Southwest. With a reduction in the overprediction of summer convective rainfall in both the 36- and 12-km runs, wet biases in the surface humidity and co ld biases in the surf ace temperature are now smaller in the North and desert Southwest subdo mains in the summer months. Although the final WRAP run was optimized for summer perf ormance over the WRAP region, the winter performance in the western U.S. di d not deteriorate significantly.

Based on the upper-air soundings, one of the most serious problems is the difficulty MM5 has in establishing the observed planetary boundary la yer (PBL) structure. The model has trouble getting the PBL depth correct, particularly in the stable nocturnal case. Al so, MM5’s difficulty in simulating the observed fine structure of the dew point temperature profile and the overall level of saturation in the lower troposphere is cause fo r concern. It is important that the model produce cloud decks at the correct height. Errors in humid ity and cloud prediction have a negative impact on the accuracy of downwelling sola r radiation, cause errors in th e temperature profile and the surface fluxes, affect the atmospheric chemistry, and make it difficult for the particulate matter (PM) model to perform properly.

We concluded that the final 36-km and 12- km WRAP MM5 runs exhibited reasonably good performance and were within the bounds of othe r meteorological database s used for prior air quality modeling efforts, and that it was therefore reasonable to proceed w ith their use as inputs for the RMC visibility modeling.



### Visibility Modeling

Visibility impairment occurs wh en fine particulate matter (PM 2.5 ) in the atmosphere scatters and absorbs light, thereby creating haze. PM 2.5 can be emitted into the atmosphere directly as primary particulates, or it can be produced in the atmosphere from photoc hemical reactions of gas-phase precursors and subsequent condensation to form secondary particulates. Examples of primary PM 2.5 include crustal materials and elemental carbon; examples of secondary PM include ammonium nitrate, ammonium sulfates, and s econdary organic aerosols (SOA). Secondary PM 2.5 is generally smaller than primary PM 2.5 , and because the ability of PM 2.5 to scatter light depends on particle size—with light scatteri ng for fine particles being grea ter than for coarse particles—Final Report for the WRAP 2002 Visibility Model Performance Evaluation

v secondary PM 2.5 plays an especially important role in visibility impairment. Moreover, the smaller secondary PM 2.5 can remain suspended in the atmosphe re for longer periods and transported long distances, thereby contributi ng to regional-scale impacts of po llutant emissions on visibility.

The sources of PM 2.5 are difficult to quantify because of th e complex nature of their formation, transport, and removal from the atmosphere. This makes it difficult to simply use emissions data to determine which pollutants shou ld be controlled to most effectively improve visibility. Photochemical air quality models provide a better understanding of the sources of PM 2.5 by simulating the emissions of pollutants and the form ation, transport, and deposition of PM 2.5 . If an air quality model performs well for a historical episode, th e model may then be useful for identifying the sources of PM 2.5 and helping to select the most effective emissions reduction st rategies for attaining future visibility goals. A lthough several types of air quality modeling systems are available, the gridded, three-dimensional, Eulerian models provide the most complete spatial representation and the most comprehensive representation of processes affecting PM 2.5 , especially for situations in which multiple pollutant sources interact to form PM 2.5 . For less complex situations in which a few large point sources of emissi ons are the dominant source of PM 2.5 , trajectory models (such as the California Puff Model [CALPUFF]) ma y also be useful for simulating PM 2.5
. For this study, we compared two Eulerian ai r quality models, CMAQ and CAMx. Both models were operated for calendar year 2002 for the RPO Unified Continental 36-km Modeling Grid domain, shown in Figure ES-1. In addition, we compared CMAQ results from the 36-km model domain with those from a high resolution, nested 12-km domain in the WRAP region. Details on all of this work are provided in Section 4.

For each of these model simulations, we performed extensive comparisons of the modelsimulated PM 2.5 with measured PM 2.5 from several ambient monitoring networks. The ambient data and the networks are descri bed in Section 4.2. Speciated PM 2.5 data from the IMPROVE, CASTNet, NADP/NTN, and STN monitoring networ ks are included, as well as gas-phase data from the AQS network.

Section 4.3 describes the approach used to compar e model data with ambi ent data. Because there is no final guidance from EPA on how to perform a model evaluation for visibility modeling, we experimented with a variety of approaches fo r presenting the model-to-data comparisons. These range from very detailed comparisons at indivi dual monitoring sites to very general summaries showing monthly average bias and error for each monitoring network for a ll sites in the WRAP region. The project web site includ es tens of thousands of plots resulting from the model-to-data comparisons (see http://pah.cert.ucr.edu/aqm/308/cmaq.shtml ); staff at states and tribes can use subsets of these to focus in detail on locations of specific interest. The project web site also includes animations of model and data results that are best reviewed on th e web site because they cannot easily be presented in this report. The report includes a series of appendices (which, for size reasons, must be downloaded separately fr om the main body of the report) that contain examples of the displays of model performance that we found to be most useful for comparing the model data to the ambient data: stacked-b ar time-series plots for each of the model performance evaluations, and stacked-bar plots showing the model perfor mance for the average of the best-20% and the worst- 20% visibility days at each IMPROVE site. The body of this report contains only the more general summary plots that are presented in Section 4, with additional displays of model performance available on the RMC web site listed above.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

vi Figure ES-1. Modeling domains used in th is study. The red grid is the RPO

Unified 36-km domain; the blue grid is the 12-km nested domain used for high-resolution modeling in the WRAP region.

The comparison of the 36-km and 12-km CMAQ simulations is presented in Section 4.6. A key finding is that model performance does not appearto benefit significantly from using the finerresolution grid for modeling th e lower concentrations of PM 2.5 that typically occur in the Class I

areas. These results are consistent with prev ious WRAP model comparisons using the 1996 modeling year and the preliminar y 2002 modeling, which also s howed that there is no clear improvement in model performance when using finer grid resolutions. There does remain a possibility that the 12-km model might respond differently to emissions controls than the 36-km model does, even though there is no substantial difference in the model performance at the Class I sites. However, when we investigated this possibility in previous model emissions reduction sensitivity simulations using 12-km and 36-km mode l scenarios in the southeastern U.S., in work conducted for the Visibility Improvement – State and Tribal Associations of the Southeast (VISTAS) RPO (the WRAP equi valent for that region; see http://www.vistas-sesarm.org/ ), we found that the model grid resolution had only sm all effects on the model response to emissions reductions. A key finding of the current study is that because of the substantially higher resources and costs associated with perfor ming high-resolution modeling, we do not recommendFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

vii the routine application of additional 12-km modeling as part of the WRAP regional haze planning effort. However, this conclusion applies onl y to the relatively clean conditions experienced at Class I areas. It is likely that the finer-resolution m odel would provide some benefit for modeling higher ozone and PM 2.5 concentrations relevant to health-based air quality standards in urban or suburban areas.

The results presented in Section 4 for the CMAQ and CAMx model performance evaluations show that both models have some skill in simulating several of the PM species that contribute to regional haze. A key finding is that these model results ar e also significantly improved compared to results from the Section 309 modeling t hat was performed for calendar year 1996 (see http://wrapair.org/ 309/031215Final309TSD.pdf ). The improved model performance may be due to a combination of improvements in the 2002 meteorology modeling, new data sets and emissions models developed for the 2002 emissi ons inventories, and updates and improvements to the CMAQ and CAMx models. Both these mode ls met many of the chosen performance goals for most months of the year and for most of the ambient monitoring networks for sulfate, nitrate, organic carbon (OC), elemental carbon (EC), and so il. Both models failed to meet performance goals for coarse mass (CM) and also for most PM species measured at the STN network, including soil, EC, OC, and nitrate. The STN m onitoring sites, however, are usually located in areas influenced by urban emissions. Thus, poor performance at the STN sites may not be indicative of the model performance at Class I areas. We conclude from th ese results that the CMAQ and/or the CAMx 36-km modeling can be used, in combinati on with the relative reduction factor (RRF) approach, to evaluate the benefits of emi ssions reduction strategies for all PM species other than CM, in order to projec t visibility changes at Cl ass I areas for regional haze planning purposes.

In the comparison of the CMAQ and the CAMx 36-km model simulations with ambient data, neither model exhibited superior model perfor mance across all species and seasons. Whereas CMAQ exhibited better sulfate pe rformance in the winter, CAMx summer sulfate performance appeared better than CMAQ’s. CMAQ nitrate pe rformance was better in the winter, while both models exhibited large summer nitrate underp rediction biases. CMAQ exhibited better OC model performance than CAMx, but uncertainties with the OMC/OC factor (see Section 4.4.3) and known missing processes in both the CMAQ and CAMx SOA modules (e.g., missing SOA from biogenic sesquiterpenes and isoprene) make these comparisons difficult to interpret. The models exhibited similar fairly good performan ce for EC, but the performance of both models for soil and CM was fairly poor. Combined, all of these results were al so consistent with previous model comparisons that showed no cl ear improvement in model performance when using either the CMAQ or the CAMx model. T ypically, model performance improves slightly for some species and degrades slightly for other species. Based on these results, a key finding is that both CMAQ and CAMx are acceptable for vi sibility modeling. The choice of model should be based in part on factors other than model perf ormance, such as computer run times, disk storage requirements, source apportionment and/or sensitivity analysis needs, and any other factors that might make it more effective or convenient to use one model over the other.

Table ES-1 summarizes all of th e key findings highlighted above.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

viii Table ES-1. Summary of key findings from the mo del performance evaluation study.
Key Findings Model performance does not appear to benefit signi ficantly from using the finer-resolution grid for modeling the lower concentrations of PM 2.5 that typically occur in the Class I areas. We do not recommend the routine application of additional 12-km modeling as part of the WRAP regional haze planning effort, due to the substa ntially higher resources and costs associated with performing high-resolution modeling. The 2002 model results are significantly improved co mpared to results from the Section 309 modeling that was performed for calendar year 1996. The CMAQ and/or the CAMx 36-km modeling can be u sed, in combination with the RRF approach, to evaluate the benefits of emissions reduction strategies for all PM sp ecies other than CM, in order to project visibility changes at Class I areas for regional haze planning purposes. Both CMAQ and CAMx are acceptable for visibility modeling. The choice of model should be basedin part on factors other than model performance, such as computer run times, disk storage requirements, and source apportionment and/ or sensitivity analysis needs.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

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## Contents

Executive Summary ___________________________________________________________ ii
Tables _____________________________________________________________________ xiii
Figures_____________________________________________________________________ xiv
Abbreviations ______________________________________________________________ xvii

1. Introduction ______________________________________________________________ 1
1.1 Background 1
1.1.1 Need for regional haze modeling 1
1.1.2 Role of the Western Regional Air Partnership 2
1.1.3 WRAP strategic plan 4
1.1.4 Organization of the Regional Modeling Center 4
1.2 Overview of Visibility Modeling Data Sets 5
1.3 Description of WRAP Modeling Domain 6

2. Final 2002 Emissions Inventories ____________________________________________ 10
2.1 Introduction 10
2.1.1 Emissions data overview 10
2.1.2 Emissions modeling overview 12
2.1.3 Simulation Base02a data co llection and preparation 13
2.2 Stationary-Point-Source Emissions 18
2.2.1 Data sources 18
2.2.2 Emissions processing 19
2.2.3 Uncertainties and future work 19
2.3 Stationary-Area-Source Emissions 20
2.3.1 Data sources 20
2.3.2 Emissions processing 22
2.3.3 Uncertainties and future work 23
2.4 On-Road Mobile-Source Emissions 23
2.4.1 Data sources 23
2.4.2 Emissions processing 24
2.4.3 Uncertainties and future work 25
2.5 Nonroad Mobile-Source Emissions 26
2.5.1 Data sources 26
2.5.2 Emissions processing 27Final Report for the WRAP 2002 Visibility Model Performance Evaluation

x 2.5.3 Uncertainties and future work 28
2.6 Biogenic Emissions 28
2.6.1 Data sources 28
2.6.2 Emissions processing 29
2.6.3 Uncertainties and future work 29
2.7 Fire Emissions 29
2.7.1 Data sources 29
2.7.2 Emissions processing 30
2.7.3 Uncertainties and future work 31
2.8 Dust Emissions 31
2.8.1 Data sources 31
2.8.2 Emissions processing 32
2.8.3 Uncertainties and future work 33
2.9 Ammonia Emissions 33
2.9.1 Data sources 33
2.9.2 Emissions processing 34
2.9.3 Uncertainties and future work 34
2.10 Oil and Gas Emissions 35
2.10.1 Data sources 35
2.10.2 Emissions processing 36
2.10.3 Uncertainties and future work 36
2.11 Offshore Area and Point Emissions 36
2.11.1 Data sources 36
2.11.2 Emissions processing 36
2.11.3 Uncertainties and future work 37
2.12 Commercial Marine Shipping Emissions 37
2.12.1 Data sources 37
2.12.2 Emissions processing 37
2.12.3 Uncertainties and future work 38
2.13 Summary 38
2.14 Effects of Uncertainties and Future Work 39

3. Meteorology Data ________________________________________________________ 40
3.1 Summary of Meteorolog y Evaluation Results 40
3.1.1 Preliminary sensitivity tests 42
3.1.2 Revised sensitivity tests 42
3.1.3 Final model configurations 44
3.1.4 Final MM5 model performance 44Final Report for the WRAP 2002 Visibility Model Performance Evaluation

xi 3.2 Uncertainties and Effects on Visibility Modeling 45

4. Air Quality and Visibility Modeling __________________________________________ 47
4.1 Description of the CMAQ and CAMx Models 47
4.1.1 Description of model versions 48
4.1.2 Selection of model science options 49
4.1.3 Description of model boundary cond itions and initial conditions 50
4.2 Summary of Ambient Monitoring Data 51
4.3 Model Performance Evaluation Approach 59
4.3.1 The most useful plot types for the MPEs 59
4.3.2 Subdomains analyzed for the MPEs 67
4.3.3 Performance time periods 68
4.3.4 Description of performance metrics 68
4.4 CMAQ Model Performance Evaluation in th e WRAP States for Calendar Year 2002 70
4.4.1 Sulfate (SO 4 ) 71
4.4.2 Nitrate (NO 3 ) 74
4.4.3 Organic carbon (OC) 77
4.4.4 Elemental carbon (EC) 80
4.4.5 Other fine PM (soil) 83
4.4.6 Coarse mass (CM) 85
4.4.7 Ozone 87
4.5 Evaluation of CMAQ at Class I Area s for the Best-20% and Worst-20% Visibility Days 88
4.6 Grid Resolution Sensitivity Analysis : CMAQ 12-km Visibility Modeling for the WRAP Region 89
4.7 CAMx Model Performance Evalua tion and Comparison to CMAQ 101
4.7.1 Sulfate (SO 4 ) 102
4.7.2 Nitrate (NO 3 ) 105
4.7.3 Organic carbon (OC) 108
4.7.4 Elemental carbon (EC) 111
4.7.5 Other fine PM (soil) 114
4.7.6 Coarse mass (CM) 116
4.8 Uncertainties, Errors, and Biases and Their Effects on Future Visibility Projection 118
4.9 Conclusions of Model Performance Evaluation 119

5. Computational Aspects of the Modeling ______________________________________ 121
5.1 Description of Hardware and Software Used 121
5.1.1 Hardware 121Final Report for the WRAP 2002 Visibility Model Performance Evaluation

xii 5.1.2 Software 123
5.2 Summary of Procedures for Modeling 124
5.2.1 The Meteorology-Chemistry In terface Processor (MCIP) 125
5.2.2 Initial condition (ICON) and boundary condition (BCON) processors 125
5.2.3 CMAQ photolysis rates processor (JPROC) 125
5.2.4 CMAQ emissions processing 125
5.2.5 CMAQ Chemical Transport Model (CCTM) 126
5.3 Computational Performance Benchmarks 126
5.4 Technology Transfer 127

6. Conclusions ____________________________________________________________ 128
References_________________________________________________________________ 130
Appendices to the Report _____________________________________________________ 134 A: Emissions Modeling and Quality Assurance B: Stacked-Bar Plots for CMAQ—Annual Ti me Series of Contributions to Light Extinction C: Stacked-Bar Plots for CMAQ—Contributi ons to Light Extinction for the Average of Best-20% Visibility Days, with Data Relaxed in Time D: Stacked-Bar Plots for CMAQ—Contributi ons to Light Extinction for the Average of Worst-20% Visibility Days, with Data Relaxed in Time E: Stacked-Bar Plots for CMAQ—Contributi ons to Light Extinction for the Average of Best-20% Visibility Days, with Data Relaxed in Time and Space F: Stacked-Bar Plots for CMAQ—Contributi ons to Light Extinction for the Average of Worst-20% Visibility Days, with Data Relaxed in Time and Space

G: Stacked-Bar Plots for CAMx—Annual Ti me Series of Contributions to Light Extinction

H: Stacked-Bar Plots for CAMx—Contributi ons to Light Extinction for the Average of Best-20% Visibility Days, with Data Relaxed in Time

I: Stacked-Bar Plots for CAMx—Contributi ons to Light Extinction for the Average of Worst-20% Visibility Days, with Data Relaxed in TimeFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

xiii 

## Tables

Table ES-1. Summary of key findings from the model performance evaluation study. ............. viii
Table 1-1. WRAP 2003 Stra tegic Plan (WGA, 2003)..................................................................... 4
Table 1-2. RPO Unified do main grid definition. ............................................................................. 6 Table 1-3. Grid definitions for MM5 and CMAQ. .......................................................................... 8 Table 1-4. Vertical layer defin ition for MM5 simulations (left five columns), and approach for reducing CMAQ/CAMx layers by collapsing multiple MM5 layers (right five columns).......................................................................................................................

.............. 9
Table 2-1. WRAP Base02a emissions categories.......................................................................... 12 Table 2-2. WRAP Base02a anthropogeni c emissions inventory contacts..................................... 14 Table 2-3. New Canadian spat ial surrogates for Base02a. ............................................................ 17 Table 2-4. Refueling SCCs removed from the non-WRAP U.S. stationa ry-area inventory. ........ 21
Table 2-5. Base02a nonroad mobile-source inventory temporal configuration. ........................... 27 Table 2-6. Base02a fugitive and road dust SCCs. ......................................................................... 32 Table 3-1. MM5 configurations from origin al (WRAP_0) and modi fied (WRAP_5) MM5

runs...........................................................................................................................

................ 42 Table 3-2. MM5 Final Configuration for th e WRAP 2002 36-km MM5 run after revised sensitivity tests were completed............................................................................................... 43 Table 3-3. MM5 Final Configuration fo r the WRAP 2002 36/12-km MM5 run. ......................... 44 Table 4-1. Model configurations fo r the CMAQ and CAMx simulations..................................... 49 Table 4-2. Summary of ambient database ava ilable for the model performance evaluation......... 53 Table 4-3a. Mapping of gaseous model spec ies to gaseous observation species. ......................... 56 Table 4-3b. Mapping of particulate model speci es to particulate observation species.................. 57
Table 4-4. Recommended statistical measur es for model performance evaluation....................... 69Final Report for the WRAP 2002 Visibility Model Performance Evaluation

xiv 

## Figures

Figure ES-1. Modeling domains used in this st udy. The red grid is the RPO Unified 36-km domain; the blue grid is the 12-km nested domain used for high-resolution modeling in the WRAP region................................................................................................................

..... vi Figure 1-1. Regional Planning Organizations engaged in regional haze modeling......................... 3 Figure 1-2. Locations of the 116 Class I areas requiring regional haze plans by December 2007 within the WRAP region................................................................................................... 3 Figure 1-3. Nesting of the 36-km CMAQ/CAMx grid within the MM5 36-km grid...................... 7 Figure 1-4. Nesting of the WRAP region 12-km grid within the RPO Unified 36-km grid. .......... 7 Figure 2-1. Example Base02a NH 3 emissions spatial plot, show ing artificial concentration gradients that follow so me political boundaries. ..................................................................... 35 Figure 3-1. Definition of subdoma ins used in MM5 modeling. .................................................... 41 Figure 4-1. Locations of sites in each of the PM ambient monitoring networks........................... 54 Figure 4-2. Locations of sites in the AQS ga seous species ambient monitoring network............. 54 Figure 4-3. Example time-series plot of sulfate at the Grand Canyon IMPROVE site showing ambient data and two sets of model results for June 2002 (Julian dates 152 to 180). ..........................................................................................................................

............... 60 Figure 4-4. Example stacked-bar time-series plot for the Grand Canyon IMPROVE site showing ambient data (top) and model output (b ottom) contributions to light extinction. ..... 61 Figure 4-5. Example scatter plot compari ng CMAQ v4.4 and CMAQ v4.5 model results to ambient data...................................................................................................................

.......... 62 Figure 4-6. Example PAVE spatial plot with ambient data shown in symbols overlaid on model results shown in background for the 12-km CMAQ MPE. .......................................... 63 Figure 4-7. Example bar plot showing monthly variability in mean fractional bias (MFB) for two model simulations. .....................................................................................................

. 64 Figure 4-8. Example bugle plot showing monthly average sulfate MFB for the CMAQ v4.5 model simulation as a function of the aver age of the modeled and observed sulfate concentration for the CASTNet, IMPROVE, and STN data. .................................................. 65 Figure 4-9. Example stacked-bar pl ot of contributions to light extinction for the average of the worst-20% visibility da ys at an IMPROVE site, fo r data relaxed in time. ........................ 67 Figure 4-10. Sulfate monthly MFB for the 36- km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data. ............................................................................................ 72 Figure 4-11. Monthly average sulfate MFB fo r CMAQ version 4.5 36-km results compared to the CASTNet, IMPROVE, and STN networks.................................................................... 72 Figure 4-12. Bugle plot of monthly average sulfate MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the CA STNet, IMPROVE, and STN networks. ......... 73Final Report for the WRAP 2002 Visibility Model Performance Evaluation

xv Figure 4-13. Nitrate monthly MFB for the 36- km results for CMAQ for versions 4.4 and 4.5 compared to the IMPROVE data. ...................................................................................... 75 Figure 4-14. Monthly average nitrate MFB fo r CMAQ version 4.5 36-km results compared to the CASTNet, IMPROVE, and STN networks.................................................................... 75 Figure 4-15. Bugle plot of monthly average nitrate MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the CA STNet, IMPROVE, and STN networks. ......... 76 Figure 4-16. OC monthly MFB for the 36- km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data. ............................................................................................ 78 Figure 4-17. Monthly average OC MFB for CM AQ version 4.5 36-km results compared to the IMPROVE and STN networks........................................................................................... 78 Figure 4-18. Bugle plot of monthly average OC MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the IMPROVE and STN networks. ......................................... 79 Figure 4-19. EC monthly MFB for the 36- km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data. ............................................................................................ 81 Figure 4-20. Monthly average EC MFB for CM AQ version 4.5 36-km results compared to the IMPROVE and STN networks........................................................................................... 81 Figure 4-21. Bugle plot of monthly average EC MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the IMPROVE and STN networks. ......................................... 82 Figure 4-22. Soil monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data. ............................................................................................ 83 Figure 4-23. Bugle plot of monthly average soil MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the IMPROVE network. .......................................................... 84 Figure 4-24. CM monthly MFB for the 36- km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data. ............................................................................................ 85 Figure 4-25. Bugle plot of monthly average CM MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results for the IMPROVE network. ........................................................... 86 Figure 4-26. Ozone monthly MFB for the 36- km results for CMAQ versions 4.4 and 4.5 compared to the AQS hourly average ozone data , using a filter to exclude all hours with data values less than 60 ppb...................................................................................................

.. 87 Figure 4-27. MFB for the 12-km and 36-km CMAQ sulfate compared to each monitoring network. .......................................................................................................................

............ 92 Figure 4-28. MFB for the 12-km and 36-km CMAQ nitrate compared to each monitoring network. .......................................................................................................................

............ 93 Figure 4-29. MFB for the 12-km and 36-km CMAQ compared to IMPROVE data for OC, EC, soil, and CM...............................................................................................................

....... 94 Figure 4-30. Mean fractional bias for the 12-km and 36-km CMAQ compared to AQS gasphase ozone and CO data........................................................................................................

. 95 Figure 4-31. Comparison of spatial plots fo r the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE sulfate data. ........................................................................................... 96 Figure 4-32. Comparison of spatial plots fo r the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE nitrate data............................................................................................. 97Final Report for the WRAP 2002 Visibility Model Performance Evaluation

xvi Figure 4-33. Comparison of spatial plots fo r the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE OC data. ................................................................................................ 98 Figure 4-34. Comparison of spatial plots fo r the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE EC data.................................................................................................. 99 Figure 4-35. Comparison of spatial plots fo r the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE soil data. .............................................................................................. 100 Figure 4-36. Sulfate monthly MFB for CAMx co mpared to ambient data at the CASTNet, IMPROVE, and STN sites. .................................................................................................... 103 Figure 4-37. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE sulfate data. ..................................................................................................................

.......... 103 Figure 4-38. Bugle plot of monthly average sulfate MFB (top) and MFE (bottom) for CAMx 36-km results and the CASTNe t, IMPROVE, and STN networks............................ 104 Figure 4-39. Nitrate monthly MFB for CAMx co mpared to ambient data at the CASTNet, IMPROVE, and STN sites. .................................................................................................... 106 Figure 4-40. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE nitrate data...................................................................................................................

........... 106 Figure 4-41. Bugle plot of monthly average nitrate MFB (top) and MFE (bottom) for CAMx 36-km results versus the CASTNet, IMPROVE, and STN networks. ...................... 107 Figure 4-42. OC monthly MFB fo r CAMx compared to ambien t data at the IMPROVE and STN sites......................................................................................................................

.......... 109 Figure 4-43. Comparison of CAMx and CM AQ 36-km monthly MFB for IMPROVE OC data...........................................................................................................................

.............. 109 Figure 4-44. Bugle plot of m onthly average OC MFB (top) and MFE (bottom) for CAMx 36-km results versus the IM PROVE and STN networks....................................................... 110 Figure 4-45. EC monthly MFB for CAMx compar ed to ambient data at the IMPROVE and STN sites......................................................................................................................

.......... 112 Figure 4-46. Comparison of CAMx and CM AQ 36-km monthly MFB for IMPROVE EC data...........................................................................................................................

.............. 112 Figure 4-47. Bugle plot of m onthly average EC MFB (top) a nd error (bottom) for CAMx 36-km results versus the IM PROVE and STN networks....................................................... 113 Figure 4-48. Comparison of CAMx and CM AQ 36-km monthly MFB for IMPROVE soil data...........................................................................................................................

.............. 114 Figure 4-49. Bugle plot of m onthly average soil MFB (top) and MFE (bottom) for CAMx 36-km results versus the IMPROVE network. ...................................................................... 115 Figure 4-50. Comparison of CAMx and CM AQ 36-km monthly MFB for IMPROVE CM data...........................................................................................................................

.............. 116 Figure 4-51. Bugle plot of m onthly average CM MFB (top) and MFE (bottom) for CAMx 36-km results versus the IMPROVE network. ...................................................................... 117Final Report for the WRAP 2002 Visibility Model Performance Evaluation

xvii 

## Abbreviations

ACM ....................Asymmetric Convective Model ADEC...................Alaska Department of Environmental Conservation AIM......................Aerosol Inorganic Module AIRMoN ..............Atmospheric Integrated Research Monitoring Network AIRS ....................Aerometric Information Retrieval System AML.....................ARC Macro Language AoH......................Attribution of Haze AQS......................Air Quality System ARDB ..................Acid Rain Database AVHRR ...............Advanced Very High Resolution Radiometer BART...................Best Available Retrofit Technology BC ........................boundary condition BEIS.....................Biogenic Emissions Inventory System BELD ...................Biogenic Emissions Landcover Database BM .......................Betts-Miller BRAVO................Big Bend Regional Aerosol and Visibility Observations Study CAA .....................Clean Air Act CAFO...................confined animal feed operations CALMET .............California Meteorological Model CALPUFF............California Puff Model CAMx ..................Comprehensive Air quality Model with extensions CAPE ...................convective available potential energy CARB...................California Air Resources Board CASTNet..............Clean Air Stat us and Trends Network CB-IV...................Carbon Bond IV (also referred to in the modeling community as CBIV, CB4) CCM2...................Community Climate Model 2 CCTM ..................CMAQ Chemical Transport Model CEM ....................continuous emissions monitoring CENRAP..............Central Regional Air Planning Association CEP ......................Carolina Environmental Program CM .......................coarse mass (a term used in the cont ext of the IMPROVE network; see also PMc) CMAQ..................Community Multi scale Air Quality CMAS ..................Community Modeling and Analysis System CMC.....................Chemical Mechanism Compiler CMU ....................Carnegie Mellon University CNG .....................compressed natural gas CPC......................Climate Prediction Center CPU......................central processing unitFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

xviii CVS......................Concurrent Versions System CY02....................calendar year 2002 DDM ....................decoupled direct method DEAD ..................Dust Entrainment and Deposition dV.........................deciview EC ........................elemental carbon EDAS ...................Eta Data Assimilation System EEA......................European Environment Agency EGAS ...................Economic Growth Analysis System EGU .....................electric generating unit EPA......................U.S. Environmental Protection Agency ESSC....................Earth System Science Center FAQ......................frequently asked question FDDA...................four-dimensional data assimilation FEJF .....................Fire Emissions Joint Forum FIPS......................Federal Implementation Standards GCVTC................Grand Canyon Visibility Transport Commission GEOS ...................Goddard Earth Observing System GIS .......................geographic information system GUI ......................graphical user interface IC..........................initial condition IDA ......................Inventory Data Analyzer IDNR....................Iowa Department of Natural Resources IEH.......................Implicit-Explicit Hybrid IMPROVE............Interagency Monitoring of Protected Visual Environments IOA ......................index of agreement IPR .......................Integrated Process Rates IRR.......................Integrated Reaction Rates KF ........................Kain-Fritsch LAC......................light-absorbing carbon LADCO................Lake Michigan Air Directors Consortium lat-lon ...................latitude-longitude LPG......................liquefied petroleum gas LSM .....................land-surface model LULC ...................land use/land cover M4........................Mechanism 4 MADM.................Multicomponent Aerosol Dynamics Model MADRID .............Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution MANE-VU...........Mid-Atlantic/Northeast Visibility Union MDN ....................Mercury Deposition Network MFB .....................mean fractional bias MFE .....................mean fractional error MIMS...................Multimedia Integrated Modeling SystemFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

xix MM5 ....................Fifth-Generation PSU/N CAR Mesoscale Model M-MRF ................modified Medium-Range Forecast MMS ...................Minerals Management Service MPE .....................model performance evaluation MRF .....................Medium-Range Forecast MRLC ..................Multi-Resolution Land Characterization MRPO ..................Midwest Regional Planning Organization MWSS..................Monday-weekday-Saturday-Sunday NADP...................National Atmospheric Deposition Program NAMS..................National Air Monitoring Stations NASS ...................National Agricultu ral Statistics Service NCAR ..................National Center for Atmospheric Research NCEP ...................National Centers for Environmental Prediction NEI.......................National Emissions I nventory (NEI96 = 1996 versi on; NEI99 = 1999 version) NIF .......................National Emissions Inve ntory (NEI) Input Format NLCD...................National Land Cover Database NNRP...................NCAR/NCEP Reanalysis Project NORTAD.............North American Tr ansportation Atlas Data NTN .....................National Trends Network NWS.....................National Weather Service OC........................organic carbon OMC ....................organic matter carbon ORD .....................Office of Resear ch and Development OSAT ...................Ozone Source Apportionment Technology PAMS...................Photochemical Assessment Monitoring Stations PAVE ...................Program for the Analysis and Vi sualization of Environmental data PBL ......................planetary boundary layer PDM.....................Plume Dynamics Model PinG .....................plume-in-grid PM........................particulate matter PM 10 .....................particulate matter ≤ 10 μm in diameter PM 2.5 ....................particulate matter ≤ 2.5 μm in diameter (referred to in this report as “fine particulate matter”) PMc......................coarse particulate matter: PM that ra nges from 2.5 to10 μm in diameter (note that PMc is also referred to as CM in the context of the IMPROVE network) PPM......................Piecewise Parabolic Method ppmv ....................parts per million by volume PSAT....................PM Source Apportionment Technology PSU ......................Pennsylvania State University PX ........................Pleim-Xiu QA........................quality assurance QAPP ...................quality assurance project plan QC........................quality controlFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

xx QSSA ...................Quasi Steady State Approximation RADM..................Regional Acid Deposition Model RAMS ..................Regional Atmospheric Modeling System REMSAD.............Regional Modeling System for Aerosols and Deposition RHR .....................Regional Haze Rule RMC.....................Regional Modeling Center RMSE...................root mean squared error ROG .....................reactive organic gas RPO......................Regional Planning Organization RRF......................relative reduction factor RRTM ..................Rapid Radiative Transfer Model Rx.........................prescribed (used with “wildfires”) SAPRC.................Statewide Air Pollution Research Center SCC......................source classification code SEARCH..............Southeastern Aerosol Research a nd Characterization (monitoring network) SEARCH_H.........SEARCH monitoring ne twork collecting hourly data (vs. daily [24-hour] data) SIP........................State Implementation Plans SLAMS ................State and Local Air Monitoring Stations SMOKE................Sparse Matrix Operat or Kernel Emissions SMVGEAR..........Sparse-Matrix Vectorized Gear SOA......................secondary organic aerosols SST.......................sea-surface temperature STATSGO............State Soil Geographic STN......................Speciated Trends Network STP.......................standard temperature and pressure TIP........................Tribal Implementation Plan TM........................thematic mapper TOG .....................total organic gas TSD......................Technical support document TSSA....................Tagged Species Source Apportionment UAF......................University of Alaska Fairbanks UCR .....................University of California, Riverside UNC .....................University of North Carolina USDA...................U.S. Department of Agriculture USGS ...................U.S. Geological Survey VIEWS.................Visibility Information Exchange Web System VISTAS................Visibility Improvement – State and Tribal Associations of the Southeast VMT.....................vehicle miles traveled VOC .....................volatile organic compound(s) VRSM ..................Variable Size-Resolution Model WEQ ....................Wind Erosion Equation WRAP..................Western Regional Air PartnershipFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

1 

## 1. Introduction

The Western Regional Air Partnership (WRAP) Re gional Modeling Center (RMC) is responsible for performing air quality modeli ng simulations for the states a nd tribes in the WRAP region (which includes 14 western U.S. st ates, as described below) in orde r to provide analytical results used in developing implementation plans under the U.S. Environmental Protection Agency (EPA) Regional Haze Rule (RHR). RMC responsibilities include:

• meteorological modeling;

• emissions processing and modeling;

• air quality and visibili ty modeling simulations;

• analysis, display, and reporti ng of modeling results; and

• storage and quality assurance of th e modeling input and output files.

The air quality and visibility modeling will be used by the WRAP states and tribes as part of their technical support documents (TSDs) to de monstrate reasonable prog ress toward attaining visibility goals. Key work elements of the RMC visibility modeling include the following:

1. Evaluation of the visibility m odel for a historical episode—in this case, for calendar year 2002. Output from the model simulation is compared with ambient air quality data for the historical episode as part of a model performance evaluation (MPE).

2. Development of visibility planning scenar ios for the regional haze baseline period of 2000-04 and for the initial regional haze future projection period, calendar year 2018.

3. Modeling a variety of emissions sensitivity, emissions source apportionment, and emissions control strategies to assess whether pl anned future regional emissions reductions will be sufficient to demonstrate reasonable progress toward achieving visibility goals.

This final report discusses the activities the WR AP RMC performed in support of work element 1. We used MPEs to evaluate the historical 2002 modeling in order to a ssess the suita bility of two modeling system for subsequent planning, se nsitivity, and emissions control strategy modeling. The two models are EPA’s Community Mult iscale Air Quality (CMAQ) modeling system (Byun and Ching, 1999) and ENVIR ON’s Comprehensive Air quality Model with extensions (CAMx) (ENVIRON, 2004). This report also disc usses some model intercomparison work that we performed for the two modeling systems. We use all this information as the basis for recommending the selection of CMAQ and/or CAMx to complete work elements 2 and 3 above.



### 1.1 Background
1.1.1 Need for regional haze modeling

The Clean Air Act (CAA) establishes special goa ls for visibility in many national parks, wilderness areas, and international parks. Thr ough the 1977 amendments to the Clean Air Act,Final Report for the WRAP 2002 Visibility Model Performance Evaluation

2 Congress set a national goal for visibility as “the prevention of any future, and the remedying of any existing, impairment of vi sibility in mandatory Class I Federal areas which impairment results from manmade air pollution” (40 CFR 51.300). States are requi red to develop State Implementation Plans (SIPs) to attain visibility standards; also, tribes may opt to assume responsibility for visibility programs under 40 CFR Pa rt 49 by developing Tribal Implementation Plans (TIPs). The goal of the EPA RHR is to achieve natural visibility conditions at 156 Federally mandated Class I areas by 2064. To achieve this goal, the RHR has set up milestone years of 2018, 2028, 2038, 2048, 2058, and 2064 to monitor progress toward natural visibility conditions. CAA Section 308 (§308) requires that the first vi sibility SIP/TIP be submitted to EPA by 2007- 2008 to demonstrate progress toward natural vi sibility conditions in 2018 using the 2000-2004 five-year baseline.

Regional haze is linked to fi ne particulate matter (PM 2.5 , which is particulate matter ≤ 2.5 μm in diameter), for which EPA has a health-based standard. PM 2.5 SIPs/TIPs are to be submitted three years after designation of PM 2.5 nonattainment areas. EPA performed the final designations of the PM 2.5 nonattainment areas in December 2004, so the PM 2.5 SIPs / TIPs are due by December 2007. As regional haze is intricately linked to PM 2.5, the PM 2.5 and regional haze SIPs / TIPs are in the process of being aligned so that they would both be due by approximately December 2007 (January 2008). EPA designated 8-h ozone nonatt ainment areas in April 2004, which makes 8-h ozone SIPs / TIPs also due in 2007. States and tribes may use the integrated one-atmosphere WRAP RMC base-year modeling as the regiona l component of the 8-h ozone attainment activities as well as for PM 2.5 and regional haze. Those decisions will be based on schedule constraints, model performance, appropriatene ss of the episodes, feasibility, and need.



### 1.1.2 Role of the Western Regional Air Partnership

WRAP is one of five Regional Pl anning Organizations (RPOs) (Figur e 1-1); the other four are the Central Regional Air Planning Association (CEN RAP), the Midwest Regional Planning Organization (MRPO), the Visibility Improvement – Stat e and Tribal Associati ons of the Southeast (VISTAS), and the Mid-Atlantic /Northeast Visibility Union (MANE-VU). The RPOs are responsible for coordinating development of SIPs and TIPs in selected areas of the U.S. to address the requirements of the RHR. The WRAP region is compos ed of states and tribal lands located within the boundaries of Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, New Mexico, North Dakota, Oregon, South Dakota, Utah, Wash ington, and Wyoming; it includes the Class I areas indicated in Figure 1-2. WRAP is a regional partnership of states, tribes, Federal agencies, stakeholders, and citizen groups th at was established to initiate and coordinate activities associated with the management of regional haze and other air quality issues within the WRAP states; their role includes providing tech nical and policy tools to help st ates and tribes. The members of WRAP committees and forums ( http://wrapair.org/about/orgchart.htm ) consist of representatives from the affected stakeholder groups identifi ed above. Committee and fo rum members are urged to communicate freely with their peers and professi onal associations so that they can adequately represent the views of that segment of the public. Each committee/forum is expected to translate technical materials into a form accessible to the public. More information about the WRAP organizational structure and activities is available at http://wrapair.org/facts/forumdescribe.html

.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

3 Figure 1-1. Regional Planning Organizatio ns engaged in regional haze modeling.

Figure 1-2. Locations of the 116 Class I areas requiring regional haze plans by December 2007 within the WRAP region.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

4 

### 1.1.3 WRAP strategic plan

On September 29, 2003, WRAP released their st rategic plan for the years 2003-2008 (WGA, 2003): http://wrapair.org/WRAP/meetings/03101 4board/Tab_4_Strategic_Plan_Final.pdf . Prior to 2003, WRAP’s activities focused on developing the Section 309 (§309) SIPs/TIPs that nine WRAP states may elect to opt into and ther eby adopt the recommendati ons of the Grand Canyon Visibility Transport Commission (GCVTC). Thes e §309 SIPs/TIPs address visibility at the 16 Class I areas on the Colorado Plateau (see Figure 1-2). In 2003, WRAP’s efforts turned to performing the technical analyses needed to develo p the §308 SIPs/TIPs that address all 116 Class I areas in the WRAP region. The WRAP 2003-2008 stra tegic plan is broken down into two phases (Table 1-1). It will be reviewed and revised as needed in 2006-07.

Table 1-1. WRAP 2003 Strategic Plan (WGA, 2003).

Phase I 2003-2005 Phase II 2005-2007 Purpose: Dry run for Phase II. Hedge against earlier due dates. Refine and apply Phase I approaches for SIP/TIP purposes.
Scale: Regional. Regional and subregional. Apportionment: 1996 & 2002 source contributions. Areas each plan to address. 2002 source contributions. Reduction obligations. Strategies: Identify options, screen. Perform cost/benefit, select, design.
Communications: Public education. Public acceptance. Major state/tribe submittals: 2002 emissions inventories. Modeling run specifications.



### 1.1.4 Organization of the Regional Modeling Center

The WRAP RMC is composed of researchers fr om the University of California, Riverside (UCR), ENVIRON International Corporation, and the University of North Carolina’s Carolina Environmental Program (UNC-CEP). Dr. Gail T onnesen of UCR is the Project Manager and Principal Investigator for the RMC. Mr. Ra lph Morris and Mr. Zac Adelman lead the RMC efforts at ENVIRON and UNC-CEP, respectively. We are performing mete orological, emissions, and air quality modeling to support WRAP’s effo rts to comply with the requirements of the RHR. UCR hosts the RMC’s computer center, which includes numerous Linux computer platforms and RAID disk storage systems. In the past , training of states, trib es, and others has been performed at UCR’s facilities.

The primary modeling tools used by the RMC in clude the Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) meteorological modeling system, the Sparse Ma trix Operator Kernel Emissions (SMOKE)emissions modeling system, and the CMAQ air quality modeling system. We have also performed some sensitivity tests using CAMx and the Regional Modeling System for Aerosols andFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

5 Deposition (REMSAD). In addition, there are plans to apply MM5 and the California Meteorological Model/California Puff Model (CALM ET/CALPUFF) modeling system to Alaska.

During 2002 and 2003, the RMC focused on performing the technical analysis needed to develop the §309 SIPs/TIPs for those states and tribes that elected to pursue the §309 process. We set up the SMOKE emissions and CMAQ modeling system for 1996 for the we stern states using 36-km-resolution MM5 data, and performed the tec hnical analysis needed for the §309 SIPs and TIPs. More details can be found in the Section 309 TSD (WRAP, 2003) located at http://wrapair.org/309 /031215Final309TSD.pdf

. In 2003, the RMC then turned its efforts to set ting up the modeling tools needed to address the technical requirements of the §308 SIPs/TIPs. This included setting up the MM5, SMOKE, and CMAQ modeling systems for calendar year 2002 (C Y02) using a 36-km-resolution grid for the continental United States and a 12-km-resolutio n grid for the WRAP states. During 2004 and early 2005, we continued setting up the modeling in frastructure for CY02 to address the requirements of the WRAP strategic plan.

The RMC notes and understands the importance of the guidance contained in the WRAP Tribal Template ( http://wrapair.org/WRAP/reports/Gen-Tribe-Temp.pdf ). Work products from the RMC are reviewed by the WRAP Modeling Fo rum and any interested stakeholders.



### 1.2 Overview of Visibility Modeling Data Sets

Data used in the visibility modeling discussed in this report include the following categories:

• Emissions Data: Emissions input data were derive d from many different sources, including data sets developed by other WRAP contractors and mode led emissions data from emissions models developed by EPA, WRAP, the RMC, and others. The emissions input data were then processed using th e SMOKE system. Emissions input data and emissions processing are described in Section 2.

• Meteorology Data: Meteorology data are a key input both for de veloping some components of the emissions inventory and for conducting air quality visibility modeling. Meteorology data for this study were devel oped by operating MM5 (Grell et al., 1994) as described in Section 3.

• Visibility Model Results: Model-simulated visibility data are developed by running an air quality simulation model. The models used here were CMAQ and CAMx, both described in Section 4.1.

• Ambient Air Quality Monitoring Data: Ambient data for gaseous and PM species are collected from a variety of monitoring ne tworks (see Section 4.2) . Ambient data were compared to the model-simulated data us ing procedures described in Section 4.3.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

6 

### 1.3 Description of WRAP Modeling Domain

The 36-km coarse-grid horizontal domain used for the air quality modeling was the RPO Unified Continental 36-km Modeling Grid do main (abbreviated in this repo rt as “RPO Unified domain”), which consists of the contiguous 48 U.S. states, contiguous lands and waters of southern Canada and northern Mexico, portions of the Pacific and Atlantic Oceans, most of the Gulf of Mexico, all of the Gulf of California, and the southern Hudson Bay region. Table 1-2 lists the parameters defining the RPO Unified grid. This grid resulted from discussi ons between the five RPOs and represents a compromise designed to provide an acceptable modeling domain for the Class I areas in the contiguous 48 U.S. states.

Table 1-2. RPO Unified domain grid definition.

Parameter Value Map projection Lambert conformal Alpha 33°N

Beta 45°N
Gamma 97°W

x

center 97°W

y center 40°N

This CMAQ/CAMx air quality modeling domain is nested within the MM5 meteorological modeling domain. The selection of the MM5 doma in is described in the WRAP MM5 modeling protocol (ENVIRON and UCR, 2004a). Figure 1-3 shows the MM5 horizontal domain as the outermost (aqua) grid. Also shown in Figure 1- 3 is the CMAQ 36-km grid (gray grid) nested within the MM5 domain.

The MM5 36-km grid has 164 cells in the east-w est dimension and 128 cells in the north-south dimension. The CMAQ/CAMx 36-km grid includ es 148 cells in the east-west dimension, 112 cells in the north-south dimension. Because the MM5 model grid is also nested within the Eta model grid, there is a possibili ty of modeling artifacts in th e MM5 boundary cells as the Etaderived meteorological variab les along the MM5 boundaries come into balance with the MM5 algorithms. To eliminate any errors in the meteorology from these boundary effects, we chose an MM5 domain larger than the CMAQ/CAMx 36- km air quality domain; the MM5 domain provides a buffer of 8 to 9 grid cells outsi de each boundary of the CMAQ/CAMx domain. The buffer region used here exceeds the EPA suggesti on of at least a 5-grid-cell buffer at each boundary. Figure 1-4 shows the 36-km CMAQ/CAMx continental grid an d the high-resolution, nested 12-km grid used for high-re solution modeling in the WRAP region.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

7 Figure 1-3. Nesting of the 36-km CMAQ/ CAMx grid within the MM5 36-km grid.

Figure 1-4. Nesting of the WRAP region 12-km grid within the RPO Unified 36-km grid.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

8 Table 1-3 lists the numbers of rows (running east-west) and columns (running north-south) and the definitions of the x

- y origin (i.e., the southwest corne r) for the 36-km and 12-km grids for both MM5 and CMAQ/CAMx. For each domain, the values indicated as the x-y origin are the distances in meters of the domain’s southwest co rner from the center of the RPO Unified grid. Note that the CMAQ/CAMx grid is rotated 90° relative to the MM5 grid, so rows and columns are reversed between the two grid s. In Table 1-3, “dot” refers to the grid mesh defined at the vertices of the grid cells, while “cross” refers to the grid mesh defined by the grid cell centers. Thus, the dimension of the dot mesh is equal to the cross mesh plus one.

Table 1-3. Grid definitions for MM5 and CMAQ.

Southwest Corner Model Columns 1 Dot (Cross) 2 Rows 1 Dot (Cross) 2 x Origin y Origin

MM5 36-km 129 (128) 165 (164) -2,952,000 -2,304,000 CMAQ/CAMx 36-km 149 (148) 113 (112) -2,736,000 -2,088,000

MM5 12-km 199 (198) 220 (219) -2,448,000 -1,008,000 CMAQ/CAMx 12-km 208 (207) 187 (186) -2,376,000 -936,000

1 Dot nodes are defined at grid cell vertices; cross nodes are defined at grid cell centers.

2 Note that the CMAQ/CAMx grid is rotated 90° re lative to the MM5 grid, so rows and columns are reversed between the two grids.

Finally, we note that the grid definitions for the CMAQ Meteorology-Chemistry Interface Processor (MCIP) and CMAQ Chemical Trans port Model (CCTM) are identical. The CMAQ vertical structure is defined pr imarily by the vertical grid us ed in the MM5 modeling. The MM5 simulations employed a terrain-fo llowing coordinate system define d by pressure, using 34 layers that extend from the surface to the model top at 100 mb. Table 1-4 lists the layer definitions for both MM5 and CMAQ. To reduce computational cost in the CMAQ modeling, a layer-averaging scheme was adopted for CMAQ simulations. The e ffects of layer averaging were previously evaluated (Wang et al., 2002) and found to ha ve a relatively minor effect on the model performance metrics when both the 34-layer and 19-layer CMAQ models were compared to ambient monitoring data. Note that CAMx was conf igured with exactly the same horizontal grid system and vertical layer structure as CMAQ.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

9

Table 1-4. Vertical layer definition for MM5 simulations (left five columns), and approach for reducing CMAQ/CAMx layers by collapsing mu ltiple MM5 layers (right five columns).

MM5 CMAQ 19L

Layer Sigma Pressure (mb) Height (m) Depth (m) Layer Sigma Pressure (mb) Height (m) Depth (m)
34 0.000 100 14662 1841 19 0.000 100 14662 6536

33 0.050 145 12822 1466 0.050 145
32 0.100 190 11356 1228 0.100 190
31 0.150 235 10127 1062 0.150 235
30 0.200 280 9066 939 0.200 280
29 0.250 325 8127 843 18 0.250 325 8127 2966

28 0.300 370 7284 767 0.300 370
27 0.350 415 6517 704 0.350 415
26 0.400 460 5812 652 0.400 460
25 0.450 505 5160 607 17 0.450 505 5160 1712

24 0.500 550 4553 569 0.500 550
23 0.550 595 3984 536 0.550 595
22 0.600 640 3448 506 16 0.600 640 3448 986

21 0.650 685 2942 480 0.650 685
20 0.700 730 2462 367 15 0.700 730 2462 633

19 0.740 766 2095 266 0.740 766
18 0.770 793 1828 259 14 0.770 793 1828 428

17 0.800 820 1569 169 0.800 820
16 0.820 838 1400 166 13 0.820 838 1400 329

15 0.840 856 1235 163 0.840 856
14 0.860 874 1071 160 12 0.860 874 1071 160

13 0.880 892 911 158 11 0.880 892 911 158
12 0.900 910 753 78 10 0.900 910 753 155

11 0.910 919 675 77 0.910 919
10 0.920 928 598 77 9 0.920 928 598 153

9 0.930 937 521 76 0.930 937
8 0.940 946 445 76 8 0.940 946 445 76
7 0.950 955 369 75 7 0.950 955 369 75
6 0.960 964 294 74 6 0.960 964 294 74
5 0.970 973 220 74 5 0.970 973 220 74
4 0.980 982 146 37 4 0.980 982 146 37
3 0.985 986.5 109 37 3 0.985 986.5 109 37
2 0.990 991 73 36 2 0.990 991 73 36
1 0.995 995.5 36 36 1 0.995 995.5 36 36
0 1.000 1000 0 0 0 1.000 1000 0 0Final Report for the WRAP 2002 Visibility Model Performance Evaluation

10 

## 2. Final 2002 Emissions Inventories

2.1 Introduction

For the emissions modeling work, the WRAP RM C used improved 2002 emissions data for the United States, Mexico, and Canada to create a fi nal base 2002 annual emissions database for use in the CMAQ and CAMx model performance eval uations. Sources for em issions inventory and ancillary modeling data included WRAP emissions inventory contractors, other RPOs, and EPA. Building from the WRAP preliminary 2002 modeli ng cases completed earlier (Tonnesen et al., 2005), we integrated several updat es to the inventories and anci llary data to create final 2002 emissions input files; this simulation is referr ed to as 2002 base case A, or Base02a. We used SMOKE version 2.1 (CEP, 2004a) to prepare the inve ntories for input to the air quality modeling systems. The SMOKE simulations documented he re include the generation of emissions for (1) annual CMAQ and CAMx simulations at a 36-k m model grid re solution and (2) a short-term CMAQ test simulation at a 12-km grid resolution . Definitions of the WRAP modeling grids used for these simulations are available in Secti on 1.3. We performed the modeling and quality assurance (QA) work based on the WRAP RM C emissions QA protocol (Adelman, 2004).

This section documents all work performed to co llect, prepare, and quali ty-assure the emissions for the Base02a annual simulation. We first give a brief synopsis of the modeling concepts used to prepare and model the data, and then provide details on the sources of the inventories and ancillary emissions data, where “an cillary data” refers to all of the SMOKE input data other than the emissions inventories. Organized by the major emissions modeling categories (e.g., stationary point, stationary area, on-road mobile), Sections 2.2 through 2.12 describe the modeling for each category, problems encountered and resoluti ons, known uncertainties, and directions for future work. Section 2 concludes with a summary of the work that we performed, and a section discussing the effects of uncertainties and futu re work. Throughout this section we refer to Appendix A, which includes summary graphics and tables of the emissions inventory data used for simulation Base02a.



### 2.1.1 Emissions data overview

Emissions inventories are typically divided into area, on-road mob ile, point, and biogenic source categories. These divisions arise from differing methods for prepar ing the inventories, different characteristics and attributes of the categories, and how the emissions are processed through models. Generally, emissions inve ntories are divided into the fo llowing source categories, which we refer to later as “SMOKE processing categories.”

• Stationary Area Sources: Sources that are treated as be ing spread over a spatial extent (usually a county or air distri ct) and that are not movable (a s compared to nonroad mobile and on-road mobile sources). Because it is not possible to collect the emissions at each point of emission, they are estimated over larg er regions. Examples of stationary area sources are residential heating and architectural coatings. Nu merous sources, such as dry cleaning facilities, may be treated either as st ationary area sources or as point sources.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

11 • Mobile Sources: Vehicular sources that travel on roadways. These sources can be computed either as being spread over a spatial extent or as be ing assigned to a line location (called a link). Data in on-road inventories can be either emissions or activity data. Activity data consist of vehicle miles tr aveled (VMT) and, optionally, vehicle speed. Activity data are used when SMOKE will be computing emission factors via another model, such as MOBILE6 (U.S. EPA, 2005a). Examples of on-road mobile sources include light-duty gasoline vehicles and heavy-duty diesel vehicles.

• Point Sources: These are sources that are identifie d by point locations, typically because they are regulated and their locations are available in regulator y reports. In addition, elevated point sources will have their emissions allocated vertically through the model layers, as opposed to being emitted into only the first model layer. Point sources are often further subdivided into electric genera ting unit (EGU) sources and non-EGU sources, particularly in criteria inventories in which EGUs are a primary source of NO x and SO 2
. Examples of non-EGU point sources include chemical manufacturers and furniture refinishers. Point sources are included in both criteria and toxics inventories.

• Biogenic Land Use Data: Biogenic land use data characteri ze the types of vegetation that exist in either county-total or grid cell values. The biogenic land use data in North America are available using two different sets of land use cate gories: the Biogenic Emissions Landcover Database (BELD) vers ion 2 (BELD2), and the BELD version 3 (BELD3) (CEP, 2004b).

In addition to these standard SMOKE processing categories, we have added other categories, either to represent specific em issions processes more accurately or to integrate emissions data that are not compatible with SMOKE. Examples of emissions sectors that fall outside of the SMOKE processing categories incl ude emissions generated from process-based models for representing windblown dust a nd agricultural ammonia (NH 3 ) sources. An emissions category with data that are not compatible with SMOKE is one with gridded emissions data sets, such as commercial marine sources. All of the emissions categories that we used to build WRAP simulation Base02a are described in detail in the following sections.

Continuing the enhancement of the emissions s ource categories that we initiated during the preliminary 2002 modeling (Tonnesen et al., 2005), we further refi ned the categories from the standard definitions listed above to include mo re explicit emissions sectors. The advantage of using more detailed definitions of the source cate gories is that it leads to more flexibility in designing control strategies, subs tituting new inventory or profile data into the modeling, and managing the input and output datafrom SMOKE. The major drawback to defining more emissions source categories is the in creased level of complexity that results from having a larger number of input data sets. A nother motivation behind separating the various emissions categories is related to the size and flexibility of the inpu t data. Some data sets, like the CENRAP on-road mobile inventory, were so large that we had to process them separately from the rest of the sources in the on-road sector due to computational constraints. We also separated the nonroad mobile sector into yearly and monthly inventor ies to facilitate the application of uniform monthly temporal profiles to the monthly data . Additional details about how we prepared the emissions inventories and ancillary data for modeling are described in Sections 2.2 through 2.12. Table 2-1 summarizes the entire group of source sectors that composed simulation Base02a.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

12 Each emissions sector listed in the table repres ents an explicit SMOKE simulation. As discussed in Section 2.1.2, after finishing all of the source-specific simu lations we used SMOKE to combine all of the data into a singl e file for each day for input to the air quality modeling systems. Each subsection below on the emissions sectors de scribes each sector in terms of the SMOKE processing category, the year covered by the invent ory, and the source(s) of the data. Additional details about the inventories ar e also provided, including any modi fications that we made to prepare them for input into SMOKE.

Table 2-1. WRAP Base02a emissions categories.

Emissions Sector Abbreviation* Stationary area, including VISTAS county-level fires ar

CENRAP county-level fires arf WRAP point-source fires awf Biogenic bg Ontario, Canada, point-source fires bsf Fugitive dust fd
WRAP on-road mobile mb
CENRAP on-road mobile mbvcen
Other U.S. on-road mobile mbv
Non-U.S. on-road mobile nusm CENRAP/MRPO anthropogenic NH 3 nh3 WRAP anthropogenic NH 3 nh3wrap Seasonal/monthly nonroad mobile nrm Annual nonroad mobile nry Non-WRAP point-source fires nwf
Offshore area ofsar Offshore point ofspt Commercial marine shipping ofsmar Stationary point pt
Road dust rd
Windblown dust wbd WRAP oil and gas wog
*These abbreviations are used in the file naming of the SMOKE output files for each sector.



### 2.1.2 Emissions modeling overview

Emissions models such as SMOKE are computer programs that convert a nnual or daily estimates of emissions at the state or c ounty level to hourly emissions fluxe s on a uniform spatial grid thatFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

13 are formatted for input to an air quality model. For the Base02a simulation, we prepared emissions for CMAQ version 4.4 using SMOKE ve rsion 2.1 on the WRAP RMC Linux computing cluster (see Section 5). SMOKE integrates annu al county-level emissions inventories with source-based temporal, spatial, and chemical al location profiles to creat e hourly emissions fluxes on a predefined model grid. For elevated sources that require allocation of the emissions to the vertical model layers, SMOKE integrates meteorol ogy data to derive dynamic vertical profiles. In addition to its capacity to represent the st andard emissions processing categories, SMOKE is also instrumented with the Biogenic Emissions Inventory System, version 3 (BEIS3) model for estimating biogenic emissions fluxes (U.S. EPA, 2004a) and the MOBILE6 model for estimating on-road mobile emissions fluxes from county-leve l vehicle activity data (U.S. EPA, 2005a).

To supplement the emissions estimates produc ed by SMOKE, we developed enhanced or additional emissions estimates for sources eith er misrepresented in the SMOKE modeling or completely missing from it. We used outputs from stand-alone, process-based emissions models to generate emissions estimates for windblown dust and agricultural NH 3 sources. The WRAP windblown dust model generates gridded estimates of dust emissions from wind erosion for input to an air quality model (Mansell et al., 2004) . Before developing this model, we neglected dust emissions estimates from wind eros ion in the WRAP modeling. The WRAP NH 3 model estimates anthropogenic NH 3 emissions for input to an air quality model (Mansell, 2005). We used these modeled NH 3 emissions to replace the estimate s provided in the stationary-areasource inventory. Finally, we included an offshore commercial shipping inventory for the Pacific Ocean in simulation Base02a; commercial shipping is another source category that we had never represented in the WRAP modeling until simulation Base02a.

SMOKE uses C-shell scripts as user interfaces to set configuration options and call executables. SMOKE is designed with flexible QA capabilities to generate standard and custom reports for checking the emissions modeling process. After m odeling all of the source categories individually, including those categories generated outside of SMOKE, we used SMOKE to merge all of the categories together to crea te a single CMAQ input file per simulation day. Also, for use in the CAMx modeling, we converted the CMAQ-rea dy emissions estimates to CAMx-ready files using a combination of SMOKE and stand-alone Perl scripts developed by ENVIRON. We used SMOKE to generate CAMx inputs for all low- level emissions (incl uding low-level point sources) and the CMAQ2CAMX converter to creat e the CAMx inputs for the elevated sources (including fires). Additio nal technical details about the vers ion of SMOKE used for simulation Base02a are available from CEP ( 2004b). All scripts, data, and executables used to generate the Base02a emissions for CMAQ and CAMx are archived in a CVS repository on the WRAP RMC computing cluster.



### 2.1.3 Simulation Base02a data collection and preparation

This section describes the pro cedures that the WRAP RMC followed to collect and prepare all emissions data for simulation Base02a. We discuss the sources of all invent ory and ancillary data used for this simulation. The WRAP RMC worked with emissions inventory contractors, other RPOs, and EPA to collect all of the data that constitute simulation Base02a. Table 2-2 lists all of the contacts for the various U.S. anthropogenic emission inventories we used. For the WRAP inventories, this table lists the contacts for the contractors who prepared the inventories; for theFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

14 non-WRAP inventories it lists th e contacts at the RPOs who pr ovided us inventory data. We obtained the emissions inventories for Canada a nd Mexico from the U.S. EPA Emissions Factors and Inventory Group (EFIG) vi a the Clearinghouse for Invent ories and Emissions Factors (CHIEF) website ( http://www.epa.gov/ttn/chief/index.html ).

Table 2-2. WRAP Base02a anthropogenic emissions inventory contacts.
Source Category Emissions Data Contact

WRAP Stationary area (including fugitive dust), stationary point, WRAP oil and
gas Paula Fields, ERG, Inc., Sacramento, CA USA phone: 916-361-6751; email: Paula.Fields@erg.com On-road and nonroad mobile, in-port, near-port, and commercial marine shipping, windblown dust, road dust, anthropogenic NH 3 , Alison Pollack, ENVIRON International Corporation, Novato, CA USA phone: 415-899-0710; email: a pollack@environcorp.com

All point-source fires Dave Randall, Air Sciences, Inc., Golden, CO USA phone: 303-988-2960; email: drandall@airsci.com
CENRAP All Lee Warden, Oklahoma DEQ, Oklahoma City, OK USA phone: 405-702-4201; email: lee. warden@deq.state.ok.us
VISTAS All Greg Stella, Alpine Geophysics, LLC, phone: 828-675-9045; email: gm s@alpinegeophysics.com
MANE-VU All Megan Schuster, MARAMA, Baltimore, MD USA phone: 410-467-0170; email: mschuster@marama.org
MRPO All Marc Janssen, LADCO, Des Plaines, IL, USA phone: 847-296-2181; email:janssen@ladco.org

As mentioned above, the refinement of these invent ories involved splitting some of the inventory files into more specific source sectors. As the stationary-area-sour ce emissions sector has traditionally been a catch-all for many types of sources, this is the inventory sector that required the greatest amount of preparation. Upon receivi ng all stationary-area-source inventories we extracted fugitive dust, road dust, anthropogenic NH 3 , and for the non-WRAP U.S. inventories, stage II refueling sources. We retained the dus t sources as separate cat egories that we would further refine with the application of transpor t factors (see Section 2.8 ). We discarded the NH 3Final Report for the WRAP 2002 Visibility Model Performance Evaluation

15 and refueling sources, as these emissions are represented in the WRAP NH 3 model and in the onroad mobile-source inventories, re spectively. We also split the WRAP fire inventories into the six distinct fire categories: wildfires, agricultu ral fires, wildland fire use, natural prescribed, anthropogenic prescribed, and non- Federal rangeland fires. While we modeled these sources collectively for simulation Base02a, by splitting them into distinct categories we prepared them for fire sensitivity simulations to be completed later.

We collected the ancillary data used for SMOKE modeling from several sources. SMOKE ancillary modeling data include

• temporal and chemical allocation factors by state, county, and sour ce classification code (SCC)

• spatial surrogates and cross-reference files fo r allocating county-level emissions to the model grid

• hourly, gridded meteorology data

• stack defaults for elevated point sources

• MOBILE6 configuration files

• a Federal Implementation Sta ndards (FIPS) codes (i.e., c ountry/state/county codes) definition file

• an SCC definition file

• a pollutant definition file

Except for the meteorology data and the MOBILE6 conf iguration files, we us ed default data sets provided by EPA as the basis for all of the ancillary data. The WRAP RMC provided the meteorology data for the Base02a simulations at 36-km and 12-km grid resolutions (Tonnesen et al., 2005). The inventory contra ctor who prepared the MOBI LE6 inventories provided the MOBILE6 configuration files either directly or via an RPO representative; details about the sources of the MOBILE6 inputs are provided in Section 2.4. We made minor modifications to the chemical allocation, pollutant definition, and country/state/county codes files for new sources, pollutants, or counties c ontained in the Base02a inventorie s that we had not previously modeled. We made major modifications to the temporal and spatial allocation inputs, as described below.

2.1.3.1 Temporal allocation data

Temporally allocating annual, daily, or hour ly emissions inventories in SMOKE involves combining a temporal cross-reference file and a temporal profiles file.

• Temporal cross-reference files associate m onthly, weekly, and diurnal temporal profile codes with specific inventory sources, thr ough a combination of a FIPS code, an SCC, and sometimes for point sources, faci lity and unit identification codes.

• Temporal profiles files contain coded monthly, weekly, and diur nal profiles in terms of a percentage of emissions allocated to each te mporal unit (e.g., percentage of emissions per month, weekday, or hour).Final Report for the WRAP 2002 Visibility Model Performance Evaluation

16 As a starting point for the temporal allocation data for simulation Base02a, we used the files from the WRAP preliminary 2002 modeling (Tonnese n et al., 2005), which originated from EPA (2005b). Based on guidance from the developers of so me of the inventory files, we enhanced the temporal profiles and assignments for some source categories.

We modified the temporal allocation data for simulation Base02a to improve the representation of temporal emissions patterns for certain sour ce categories. With the delivery of the WRAP nonroad mobile-source inventories, we also rece ived weekday/weekend split factors to allocate these data to the various days of the week (Pollack et al., 2006) . Using these split factors, we modified the standard EPA temporal profiles fo r a series of nonroad SCCs specified by the inventory contractor. We implemented the ad justed profiles in SMOKE by modifying the temporal cross-reference file for the applicab le FIPS and SCC combinations. Section A.1 in Appendix A contains additional information about how the WRAP RMC modified these profiles. Equations A-1 and A-2 illustrate how we applied the adjustments to standard weekly temporal profiles. Table A-1 lists the modi fied profiles by source, including the original profile number for reference. Table A-2 lists the weekday/weekend ad justment factors with the SCCs to which they were applied. Figures A-1 and A-2 illustrate the new profiles relative to the standard temporal profiles from which they were derived.

Other additions to the Base02a temporal allocati on data included updates that we made for some WRAP sources and updates made by other RPOs that are applicable to their inventories. These other updates to the temporal allocation files included

• VISTAS continuous emissions monitoring (C EM)-specific profiles for EGUs in the VISTAS states

• VISTAS agricultural burning profiles

• Wildfire and prescribed fire profiles developed by VISTAS for the entire U.S.

• MANE-VU on-road mobile profiles

• WRAP weekly and diurnal road dust profiles

• WRAP diurnal wildfire, agricultural fire, and prescribed fire profiles

• WRAP on-road mobile weekly and diurnal profiles

• CENRAP non-Federal rangeland fire profiles

Finally, for all of the monthly and seasonal emis sions inventories, we modified the temporal cross-reference files to apply uni form monthly profiles to the s ources contained in these inventories. The monthly variability is inherent in monthly and seasonal inventories and does not need to be reapplied through the temporal allocation process in SMOKE. The inventories to which we applied uniform monthly te mporal profiles included

• WRAP, CENRAP, and MRPO nonroad mobile sources

• WRAP on-road mobile sources

• WRAP road dustFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

17 2.1.3.2 Spatial allocation data

SMOKE uses spatial surrogates and SCC cross-refe rence files to allocate county-level emissions inventories to model grid cells . Geographic information system (GIS)-calculated fractional land use values define the percentage of a grid cell that is covere d by standard sets of land use categories. For example, spatial surrogates can de fine a grid cell as bei ng 50% urban, 10% forest, and 40% agricultural. In addition to land use categories, spatia l surrogates can also be defined by demographic or industrial units, such as populati on or commercial area. Similar to the temporal allocation data, an accompanying spatial cross-reference file a ssociates the spatial surrogates (indexed with a numeric code) to SCCs. Spatial a llocation with surrogates is applicable only to area and mobile sources. Point sources are loca ted in the model grid cells by SMOKE based on the latitude-longitude coordinates of each sour ce. Biogenic emissions are estimated based on 1-km 2 gridded land use information that is mapped to the model grid using a processing program such as the Multimedia Integrated Modeling System (MIMS) Spatia l Allocator (CEP, 2005).

We used various sources of sp atial surrogate information for the U.S., Canada, and Mexico inventories in simulation Base02a. For the U.S. and Canadian sources, we used the EPA unified surrogates available through the EFIG web site (EPA, 2005c). For the 36-km grid, EPA provides these data already formatted for SMOKE on the RPO Unified 36-km domain that we used for simulation Base02a. For the WRAP region’s 12-km grid, we used the EPA 12-km surrogates, extracting the WRAP domain in a preprocessi ng step before implementing these data in SMOKE. We modified the spatial surrogates for Canada on the RPO Unified 36-km domain by adding several surrogate categorie s that we felt were lacking in the EPA data set. Table 2-3 provides details about the new Canadian spatia l surrogates that we developed for simulation Base02a. For modeling Mexico, we used Shap efiles developed for the Big Bend Regional Aerosol and Visibility Observat ions Study (BRAVO) modeling to create surrogates for Mexico on the RPO Unified 36-km domain (U.S. EPA, 2005c).

Table 2-3. New Canadian spatial surrogates for Base02a.

Attribute Base02a Code Shapefile Reference Land area 950 can_land93_land Natu ral Resources Canada (1993) AVHRR land cover data Water area 951 can_land93_water Na tural Resources Canada (1993) AVHRR land cover data Forest land area 952 can_land93_forest Natural Resources Canada (1993) AVHRR land cover data Agricultural land area 953 can_land93_agr i Natural Resources Canada (1993) AVHRR land cover data Urban land area 954 can_land93_urban Natural Resources Canada (1993) AVHRR land cover data Rural land area 955 can_land93_rural Natural Resources Canada (1993) AVHRR land cover dataFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

18 Attribute Base02a Code Shapefile ReferenceAirports 956 can_airport U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 1:1,000,000 scale dataPorts 957 can_port U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 1:1,000,000 scale data Roads 958 can_road1m Natural Resources Canada (2001) National Scale Frameworks data Rail 959 can_rail1m Natural Resources Canada (1999) National Scale Frameworks data

For a description of the 36-km and 12-km mode ling grids, refer to Section 1.3. The following sections provide information about the inventory data, preparation, and modeling for each of the major source categories that compose simulation Ba se02a. For a more detailed description of the emissions inventory data files, re fer to Section A.2 in Appendix A.



### 2.2 Stationary-Point-Source Emissions

Stationary-point-source emissions data for SM OKE consist of (1) Inventory Data Analyzer (IDA)-formatted inventory files; (2) ancillary data for allocating the inventories in space, in time, and to the Carbon Bond-IV chemistry mechanis m used in CMAQ and CAMx; and (3) meteorology data for calculating plume rise from the el evated point sources. This section describes where the RMC obtained these data, how we mode led them, and the types of QA that we performed to ensure that SMOKE pr ocessed the data as expected.



### 2.2.1 Data sources

For the stationary-point-source inventories in simulation Base02a, we used actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the BRAVO 1999 Mexican invent ory. The WRAP stationary-point inventory consisted of annual county-level and tribal data provided to us by ERG, Inc. (2006). The CENRAP (E.H. Pechan and CEP, 2005a) and VISTAS (Stella, 2005) stationary- point inventories consis ted of an annual data set and monthly CEM data for selected EGUs. CENRAP and Alpine Geophysics provided these data directly to the RMC. We downloaded the MANE-VU and MRPO 2002 stationary-point inventories from the MANE-VU and the Lake Michigan Air Directors Consortium (LADCO) web sites, respectively. We opted to use the summer season invent ory to model the entire year for the MANE-VU states (E.H. Pechan et al., 2005b) , and from the MRPO data we used the Base I stationary-point inventory. The WRAP RMC entered into a nondisclosure agreement with Environment Canada to obtain version 2 of th e 2000 Canadian point-source inventory. This inventory represented a major improvement over the version of the data that we had used in the preliminary 2002 modeling. For Mexico we used the same BRAVO 1999 inventory used in the preliminary 2002 modeling (Tonnesen et al., 2005).Final Report for the WRAP 2002 Visibility Model Performance Evaluation

19 Simulation Base02a used updated meteorology da ta and improved the temporal allocation information relative to th e preliminary 2002 modeling; the rest of the ancillary data for modeling stationary point sources stayed the same (Tonne sen et al., 2005). The meteorology data that we used to calculate plume rise for the elevat ed sources is version 2 of the 2002 MM5 data preprocessed for SMOKE and CMAQ with MCIP version 2.3 (Kemball-Cook et al., 2005). We made one major improvement to the temporal allocation data based on information provided by the VISTAS RPO: for the VISTAS sources, we added EGU-based CEM profiles developed by Alpine Geophysics. These additi ons included new monthly profile s and month-specific weekly and diurnal profiles.



### 2.2.2 Emissions processing

For simulation Base02a we configured SMOKE to process the annual inventories for the U.S., Canada, and Mexico and process hourly CEM da ta for the VISTAS and CENRAP states. We configured SMOKE to allocate these emissions up to model layer 15, wh ich roughly corresponds to the maximum planetary boundary layer (PBL) heights across the entire domain throughout the year. As coarse particulate matter (PMc) is not an inventory pollutant but is required by the air quality models as an input species, we used SMOKE to calculate PMc during the processing as (PM 10 - PM 2.5 ). With the SMOKE option WKDAY_NORMA LIZE set to “No,” we treated the annual inventories based on the assumption that they represent average-day data based on a seven-day week, rather than average weekday data . We also assumed that all of the volatile organic compound (VOC) emissions in the inventor ies are reactive organic gas (ROG), and thus used SMOKE to convert the VOC to total orga nic gas (TOG) before converting the emissions into CB-IV speciation for the air quality models. To capture the differences in diurnal patterns that are contained in the CEM temporal prof iles for the VISTAS and CENRAP states, we configured SMOKE to generate daily tempor al matrices, as opposed to using a Mondayweekday-Saturday-Sunday (MWSS) temporal allocation approach.

To QA the stationary-point emissions, we us ed the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summarie s. We used tabulated summaries of the input data and SMOKE script settings to document the data and configuration of SMOKE used. The graphical QA summaries include, for all emi ssions output species, daily spatial plots summed across all model layers, daily time-series plots, annual time-series plots, and daily vertical profiles. These QA graphics are av ailable on the RMC web site at http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.p lots/pt/plots/

.



### 2.2.3 Uncertainties and future work

There were a group of issues with the stationary-point emissions that we left unresolved at the completion of simulation Base02a either because we did not feel they would have a major impact on the modeling results in the WRAP states or be cause we did not have alternative approaches and they represented that best available informati on. All of the issues were related to the sources outside of the WRAP region. Fo r the non-WRAP inventories th at originated as National Emissions Inventory (NEI) Input Format (NIF) f iles, incompatibilities between the NIF and IDA formats resulted in the omission of fields that uniquely identified the sources. As a result, SMOKE flagged thousands of sour ces outside of the WRAP states as duplicates. SMOKE treatedFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

20 these sources correctly, summing their emissions. In future modeling, all of these sources should be given unique identification fields so that they will be treated as separate stacks or units.

SMOKE also flagged thousands of sources in the MRPO, MANE-VU, and CENRAP regions as having PM 10 emissions less than their PM 2.5 emissions, which is physically impossible because PM 2.5 is a subset of PM 10 . This problem is indicated when the PMc values calculated by SMOKE are negative. SMOKE resets the value of PMc to 0 for these sources and leaves PM 10 and PM 2.5 unchanged. We notified the RPO representatives about this problem with their inventories before proceeding with these data.

For the CEM data in the VISTAS and CENRAP regions, we encountered several inventory sources with entries in the hour ly inventory file but no corres ponding entry in the annual inventory file. SMOKE requires entries in both files, with the hourly file containing the hourly emissions values and the annual file containing the location and stack parameters. When SMOKE does not find an entry in the annual file for sour ces in the hourly file, it has no information about how to locate these emissions on the model grid or how to calculate the plume rise. Sources that have this inconsistency are ignored by SMOKE. We notified the RPO representatives about these problems with their inventories before proceeding with these data.

The BRAVO 1999 Mexico inventory that we used is known to be deficient in that it contains only 110 sources for the entire country and doe s not contain any stack parameters. SMOKE applies default U.S. stack parameters to these sources based on the SCCs in the inventory. We anticipate receiving an u pdated Mexican inventory to use in future 2002 emissions simulations.

Along with improved Mexican point-source em issions, we anticipate receiving improved 2002 inventories for California, Alaska, Arizona, Mo ntana, New Mexico, Or egon, Utah, Washington, Wyoming, and the WRAP tribes. We will also recei ve temporal profiles based on CEM data for WRAP, CENRAP, VISTAS, and MANE-VU EGUs.



### 2.3 Stationary-Area-Source Emissions

Stationary-area-source emissions data for SMOKE consist of IDA-formatte d inventory files and ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This sec tion describes where we obtained these data, how we modeled them, and the ty pes of QA that we performed to ensure that SMOKE processed the data as expected.



### 2.3.1 Data sources

For the stationary-area-source inventories in simulation Base 02a, we used actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the BRAVO 1999 Mexican invent ory. The WRAP stationary-area inventory consisted of annual county-level and tribal data provided to us by ER G, Inc. (2006). Due to the small contribution of the WRAP tribal inventories to the total domai n emissions and the lack of readily available spatial allocation data for these parts of the dom ain, we did not use the WRAP tribal data for simulation Base02a. The CENRAP (E.H. Pechan and CEP, 2005a) and VIST AS Phase II (Stella,Final Report for the WRAP 2002 Visibility Model Performance Evaluation

21 2005) stationary-area inventories also consisted of an annua l data set and were provided directly to us by these RPOs. We downloaded the MANE-VU and MRPO 2002 stationary-area inventories from the MANE-VU and LADCO websites, respectivel y. We used the MRPO Base I stationary-area inventory for simulation Base 02a. We received the Canadian 2000 inventory version 2 from the U.S. EPA EFIG (U.S. EPA, 2005d). For Mexico we used the same BRAVO 1999 inventory that we used in the prel iminary 2002 modeling (Tonnesen et al., 2005).

To prepare the stationary-area inventories for modeling, we made several modifications to the files by removing selected sources either to model them as separa te source categories or to omit them from simulation Base02a completely. Us ing guidance provided by EPA (U.S. EPA, 2004b) we extracted fugitive and road dust sources from all stationary-area inventories for adjustment by transport factors and modeling as separate source categories (see Section 2.8). We also extracted and discarded the stage II refueling sources (Tab le 2-4) from the non-WRAP U.S. inventories; we modeled these sources with MOBILE6 as pa rt of the on-road mobile-source emissions. We left the stage II refueling emissions in the WRAP stationary-area invent ory because the on-road mobile inventory that we received for this region did not cont ain these emissions.

Table 2-4. Refueling SCCs removed from the non-WRAP U.S. stationary-area inventory.

SCC Description 2501060100 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service Stations Stage 2: Total 2501060101 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service Stations Stage 2: Displacement Loss/Uncontrolled 2501060102 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service Stations Stage 2: Displacement Loss/Controlled 2501060103 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service Stations Stage 2: Spillage 2501070100 Storage and Transport Petroleum a nd Petroleum Product Storage Diesel Service Stations Stage 2: Total 2501070101 Storage and Transport Petroleum a nd Petroleum Product Storage Diesel Service Stations Stage 2: Displacement Loss/Uncontrolled 2501070102 Storage and Transport Petroleum a nd Petroleum Product Storage Diesel Service Stations Stage 2: Displacement Loss/Controlled 2501070103 Storage and Transport Petroleum a nd Petroleum Product Storage Diesel Service Stations Stage 2: Spillage

Other steps that we took to prep are the stationary-area inventor ies included moving oil and gas sources from the California inve ntory to a separate file for explicit treatment (Section 2.10), confirming that there is no overl ap between the anthropogenic NH 3 inventory (Section 2.9) and stationary-area sources, moving several nonroad mobile SCCs from the Mexico inventory to the nonroad mobile sector, and moving area-source fires in each regional inventory to separate files.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

22 In addition to the above inventor y modifications, we made the fo llowing changes to the ancillary data files for simulation Base02a: We used impr oved temporal and spatial allocation information relative to the preliminar y 2002 modeling; the rest of the anc illary data for modeling stationaryarea sources stayed the same as in the pr eliminary modeling (Tonne sen et al., 2005). We enhanced the spatial allocation data with addi tional area-based surrogate s for Canada (Table 2-3), and added surrogates for a missing county in Colorado. As part of the SMOKE modeling QA process we look at the assignment of emissions to spatial surrogates, focusing on the largest (top 10% by mass) inventory SCCs . We noticed when looking at the Canadian data for the preliminary 2002 modeling that forest fire emi ssions from the Canadian area-source inventory, which are relatively la rge sources of CO, NO x , and PM 2.5 , were being allocated to a surrogate for logging activities. We found similar discrepancie s for other area and nonroad SCCs in Canada. To improve the representation of the Canadian emissions, we added several land-area-based surrogates for Canada, such as forested land area, urban land area, and ru ral land area, and made the accompanying additions to the spatial cross-refe rence file to associate inventory SCCs with these surrogates. We also added spatial surroga tes for Broomfield County, CO; this county was included in the inventory but wa s not included in the base EPA surrogates. To create surrogates for Broomfield County, we filtered the EPA Shap efiles (U.S. EPA, 2005c) that were used to create surrogates for the rest of the domain with a county boundary file for Broomfield and surrounding counties. In adding Br oomfield County to the modeli ng domain, we also subtracted parts of the surrounding four Colorado countie s: Adams, Boulder, Jefferson, and Weld.

Improvements to the temporal allocation data for simulation Base02a included the addition of several FIPS-specific profiles provided by VI STAS and CENRAP. These temporal profiles targeted mainly fire and agricultural NH 3 sources in these regions, such as open burning and livestock operations, respectively.



### 2.3.2 Emissions processing

We configured SMOKE to process the annual st ationary-area-source inventories for the U.S., Canada, and Mexico. As PMc is not an inventory pollutant but is required by the air quality models as input species, we used SMOKE to calculate PMC during the processing as (PM 10 - PM 2.5 ). With the SMOKE option WKDAY_NORMALIZE set to “Yes,” we treated the annual stationary-area inventories based on the assumpti on that they represent average weekday data, causing SMOKE to renormalize the data to a seve n-day estimate before applying any temporal adjustments. We also assumed that all of the VOC emissions in the invent ories are ROG and thus used SMOKE to convert the VOC to TOG be fore converting the emissions into CB-IV speciation for the air quality models. We c onfigured SMOKE to use a MWSS temporal allocation approach, as opposed to a daily temporal approach.

To QA the stationary-area emissions, we used the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summarie s. We used tabulated summaries of the input data and SMOKE script settings to document the data and configuration of SMOKE used. The graphical QA summaries include, for all emi ssions output species, daily spatial plots summed across all model layers, daily time-series plots, and annual time-series plots. These QA graphics are available on the RMC web site at http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.plots/ar/plots/

.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

23 

### 2.3.3 Uncertainties and future work

Most of the issues that we encountered with the stationary area sources re lated to the removal of certain SCCs from the base inventories for in clusion in other source categories or complete omission from simulation Base02a. After removing the dust sources from th e area inventories for modeling as separate sectors, we noted that seve ral SCCs related to wind erosion remained in the Mexican inventory. As geogenic wind erosion emissions are covered by the WRAP windblown dust model, we removed these area-source inventory dust sources completely from simulation Base02a. We spent considerable effort on ensuring that we did not have overlap between the area inventory and the other sectors that explicitly repr esent sources traditionally contained in the area inventory, such as NH 3 and dust.

Both the Canadian and Mexican inventories pr esented minor problems that we resolved for simulation Base02a but that ca n be addressed more thoroughl y in future simulations. The Canadian inventory we us ed contained data only at the provin ce level—it was the equivalent of a statewide, rather than county-le vel, inventory. A higher-resoluti on inventory would have allowed us to use higher-resolution and more accurate sp atial allocation data. Future modeling that uses Canadian data should move to the newly releas ed municipality-level year 2000 inventories for Canada.

We found a discrepancy between the state and co unty coding in the Mexi can inventory and the SMOKE file that defines acceptable FIPS codes. Di fferences in the ordering of the Mexican state names between these two data sets led to so me of the Mexican i nventory sources being mislabeled in the SMOKE QA reports. We changed the state codes in the inventory and spatial surrogate files for two Mexican states to be consistent with the SMOKE country/state/county codes file. The Mexican inventory also covered only the two northern ti ers of Mexican states, causing the southern portion of Mexico within the 36-km mo deling domain to be without emissions. Future modeling of Mexico should use an inventory with full coverage of the country and a revised SMOKE country/state /county codes file that used co rrect spatial codes for Mexico.



### 2.4 On-Road Mobile-Source Emissions

On-road mobile-source emissions data for SM OKE consist of IDA-formatted emissions and vehicle activity inventory files, a nd ancillary data for allocating the inventories in space, in time, and to the Carbon Bond-IV chemistry mechan ism used in CMAQ and CAMx. This section describes where we obtained these data, how we modeled them, and the types of QA that we performed to ensure that SMOKE processed the data as expected.



### 2.4.1 Data sources

For the on-road mobile-source inventories in si mulation Base02a, we divided the modeling into three categories based on common data attri butes: WRAP precomputed emissions, non-WRAP U.S. activities for processing with MOBILE6, and non-U.S. precomputed emissions. For the WRAP emissions we used actual 2002 data sp lit into California and non-California seasonal inventories. In addition to the standard criteria pollutants, th ese files contained prespeciated PM 2.5 emissions. For the rest of the U.S. we us ed annual, county-level activity and speedFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

24 inventories with monthly, count y-level MOBILE6 inputs, and hourly meteorology to estimate the hourly emissions with MOBILE6. For the non-U .S. inventories, we used version 2 of the year 2000 Canadian inventory and the BRAVO 1999 Mexican i nventory. The WRAP on-road mobile inventory consisted of seasonal count y emissions data provi ded to us by ENVIRON (Pollack et al., 2006). The CENR AP (E.H. Pechan and CEP, 2005a ), VISTAS Phase II (Stella, 2005), and MANE-VU (E.H. Pechan, 2005c) on-road mobile activity invent ories consisted of annual county-level activities and speeds, and m onthly county-level roadway data. We received these inventories and ancillary MOBILE6 inputs directly from the RPO inventory representatives. We used annual, county-level MRPO 2002 activity inventories that we received from Alpine Geophysics with the data that they pr ovided for their VISTAS modeling (Stella, 2005). We received the Canadian 2000 inventory versio n 2 from the U.S. EPA EFIG (U.S. EPA, 2005d). For Mexico we used the same BRAVO 1999 i nventory that we used in the preliminary 2002 modeling (Tonnesen et al., 2005).

Simulation Base02a used improved temporal and spatial allocation information relative to the preliminary 2002 modeling; the rest of the ancilla ry data for modeling on-road mobile sources stayed the same as in the preliminary m odeling (Tonnesen et al., 2005).We added spatial surrogates for Broomfield County, CO, a county that was included in the inventory but was not included in the base EPA surrogates. To create surrogates for Broomfield County, we filtered the EPA Shapefiles (U.S. EPA, 2005c) that were used to create surrogates for the rest of the domain with a county boundary file for Broomfield a nd surrounding counties. In adding Broomfield County to the modeling domain, we also subtra cted parts of the surrounding four Colorado counties: Adams, Boulder, Jefferson, and Weld.

We received temporal allocation profiles and cros s-reference files directly from ENVIRON for use with the WRAP on-road mobile inventories (P ollack et al., 2006). We also received temporal profiles for the MANE-VU on-road mobile sources.



### 2.4.2 Emissions processing

We configured SMOKE to process the annual on -road mobile emissions inventory data for WRAP, Canada, and Mexico as precomputed area -like inventories. Fo r the non-WRAP states, we used the SMOKE/MOBILE6 integration to process the annual activity inventories and monthly, county-based roadway information for input to CMAQ. As the WRAP inventories contained precomputed PM emissions, we di d not have to use SMOKE to compute PM. ENVIRON prepared the WRAP on-road mobile inventories to represent seven-day average emissions (different from the area inventory, which represented average weekday emissions). With actual weekly average emissions, we c onfigured SMOKE to process them by setting WKDAY_NORMALIZE to “No.” We also assumed that all of the VOC emissions in the inventories are ROG and used SMOKE to conve rt the VOC to TOG before converting the emissions into CB-IV speciation for the air quality models. We configured SMOKE to create daily, rather than MWSS, temporal intermediate s because the WRAP on-road mobile temporal profiles that we received from ENVIRON contain weekly profiles that vary across the weekdays and do not exhibit flat weekday profiles.

The large number of county roadway inputs for MOBILE6 we received from CENRAP forced us to split the non-WRAP U.S. mobile-source proc essing into three subsets because of memoryFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

25 limitations with SMOKE. The three subsets consiste d of two sets of CENRAP simulations and a simulation that computed on-road mobile em issions for the MRPO, VISTAS, and MANE-VU states. We configured MOBILE6 to use weekly temperature averaging for computing these emissions within SMOKE.

To QA the on-road mobile emissions, we used the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summarie s. We used tabulated summaries of the input data and SMOKE script settings to document the data and configuration of SMOKE used. The graphical QA summaries include, for all emissions output species, daily sp atial plots, daily timeseries plots, and annual ti me-series plots. These gr aphics are available at http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.plots/mb/plots/

.



### 2.4.3 Uncertainties and future work

We approached the on-road mobile emissions preparation for simulation Base02a from three different directions, which were based on the forms of the input inventories and ancillary emissions data for different re gions of the modeling domain:

• The WRAP region used emissions estimates precomputed with MOBILE6 and processed like area sources with SMOKE.

• The non-WRAP U.S. states used county-level activity data to compute emissions with MOBILE6.

• The non-U.S. parts of the domain used an ar ea-source approach for representing the onroad mobile emissions.

Using different approaches for modeling a si ngle emissions sector adds complexity and additional sources of error to th e modeling because of the different assumptions that went into the preparation of the input da ta. For example, refueling emissions from the on-road mobile sector are represented in the WRAP area-source sector but are computed with MOBILE6 for the rest of the U.S. Not using MOBILE6-based em issions for the non-U.S. portion of the domain neglects the effects of the actual 2002 meteorology on thes e emissions. Applying MOBILE6 outside of the U.S. is currently not possible because MOBILE6 is instrumented only for calculating U.S. emissions. The result of using MOBILE6 to calculate U.S. emissions and not using it to calculate the non-U.S. on-road mob ile emissions estimates is that the non-U.S. emissions are not specific to this modeling ye ar while the U.S. emi ssions are 2002-specific.

While we used the best available information to compute the on-road mobile emissions for the various portions of the modeling domain, inconsis tent approaches for representing these emissions may lead to unnatural emissions gradie nts along political boundari es. We recommend for future work a unified approach for at least the U.S. inventories, where either we use MOBILE6 in SMOKE for the entire domain, or we calcu late the emissions with MOBILE6 outside of SMOKE and then use the resulting c ounty-based emissions inventories.

The Mexican inventory covered only the two no rthern tiers of Mexican states, causing the southern portion of Mexico w ithin the 36-km modeling domain to be without emissions. Future modeling of Mexico should use an invent ory with full coverage of the country.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

26 

### 2.5 Nonroad Mobile-Source Emissions

Nonroad mobile-source emissions data for SMOKE consist of annual, seasonal, and monthly IDA-formatted emissions inventory files and anci llary data for allocating the inventories in space, in time, and to the Carbon Bond-IV ch emistry mechanism used in CMAQ and CAMx. This section describes where we obtained these data, how we modeled them, and the types of QA that we performed to ensure that SM OKE processed the data as expected.



### 2.5.1 Data sources

For the nonroad mobile-source inventories in si mulation Base02a, we used actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the BRAVO 1999 Mexican invent ory. The U.S. inventories consis ted of annual, seasonal, and monthly inventories; the non-U. S. inventories were annual da ta. ENVIRON provided the RMC with the WRAP inventories divided between Calif ornia and non-California seasonal inventories, further subdivided into aircraft, locomotives , shipping, and all other nonroad mobile sources (Pollack et al., 2006). With these data ENVIRON al so provided temporal adjustments to apply to the inventories to split them between weekday and weekend emi ssions (see Section A.1). We used these weekday/weekend splits to derive new weekly temporal profiles for these WRAP sources. CENRAP provided both the CENRAP a nd MRPO base J monthly nonroad inventories in SMOKE format (E.H. Pechan and CEP, 2005a ). The VISTAS Phase II (Stella, 2005) and MANE-VU (E.H. Pechan et al., 2005c) 2002 nonroa d mobile inventories consisted of annual county-level data. We received these inventorie s directly from the respective RPO inventory representatives. We received the Canadian 2000 inventory version 2 from the U.S. EPA EFIG (U.S. EPA, 2005d). For Mexico we used the same BRAVO 1999 inventory that we used in the preliminary 2002 modeling (T onnesen et al., 2005).

We had to reformat the Base02a WRAP nonroad m obile inventories before they could be input to SMOKE. The WRAP nonroad inventories that we received from ENVIRON contained more pollutants than SMOKE requires for generating CMAQ and CAMx emissions. In addition to the standard gas-phase and prespeciated PM 2.5 pollutants, these files al so contained prespeciated NO x , PM 10 , total VOC, evaporative VOC, and exhaus t VOC pollutants. As evaporative and exhaust VOC use the same speciation profiles, we discarded these pollutants and used only the total VOC pollutant, which is the sum of ev aporative and exhaust VOC emissions for the nonroad mobile sources. We also discarded the prespeciated NO x and PM 10 pollutants, as these are not used by CMAQ or CAMx.

Along with adding the WRAP weekday/weekend em issions splits to th e temporal allocation files, we also created temporal input files that apply a flat, uniform mont hly profile to the monthly and seasonal nonroad inventorie s. With the monthly and seasonal variability inherent in these inventories, we avoided applyi ng redundant monthly profiles by splitting the inventories into seasonal/monthly and annual data. We applied th e uniform monthly tempor al profiles to the seasonal/monthly inventories and nonuniform mont hly temporal profiles to the annual inventories. For the non-WRAP inventories, the week ly and diurnal temporal profiles remained unchanged from the preliminary 2002 modeling. Tabl e 2-5 summarizes the temporal distribution of the nonroad inventories used in simulation Base02a.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

27 Table 2-5. Base02a nonroad mobile-source inventory temporal configuration.
Region Source Temporal Coverage WRAP (non-CA) Nonroad mobile Seasonal WRAP (CA) Nonroad mobile Seasonal
WRAP Aircraft Seasonal
WRAP Locomotive Annual WRAP In-port and near-shore shipping Annual CENRAP All nonroad Monthly
VISTAS All nonroad Annual MRPO All nonroad Monthly

MANE-VU All nonroad Annual
Canada All nonroad Annual
Mexico All nonroad Annual



### 2.5.2 Emissions processing

For simulation Base02a we configured SMOKE to process all of the nonroad mobile emissions inventory data as area-like i nventories. As the WRAP invent ories contained precomputed PM emissions, we did not have to use SMOKE to compute PMc. ENVIRON prepared the WRAP nonroad mobile inventories to re present seven-day average emissi ons (different from the area inventory, which represented weekday averag e emissions). With actual weekly average emissions, we configured SMOKE to pro cess them by setting WKDAY_NORMALIZE to “No.” For the rest of the nonroad mobile inventories we processed the data as weekday average data by setting WKDAY_NORMALIZE to “Yes.” We also a ssumed that all of the VOC emissions in the inventories are ROG and used SMOKE to conve rt the VOC to TOG before converting the emissions into CB-IV speciation for the air quality models. We configured SMOKE to create MWSS temporal intermediates ra ther than daily temporal f iles because the nonroad mobile sources do not use weekly temporal prof iles that vary across the weekdays.

We divided the nonroad mobile emissions modeli ng based on whether the data were annual or seasonal/monthly inventories. This split facilitate d the application of uniform monthly temporal profiles to the seasonal/monthly inventories. After processing the nonroad emissions as two separate categories—nonroad year ly and nonroad monthly—we combin ed them with the rest of the emissions sectors to create mode l-ready emissions for CMAQ and CAMx.

The WRAP shipping inventories consisted of new data for offshore commercial marine emissions, new data for in-port and near-shore shipping emissions for Oregon and Washington, and old data for in-port and near-shore shipping emissions in California. The new gridded marine shipping inventory that we received for simulati on Base02a (Section 2.12) covers the open ocean in the WRAP 36-km modeling domain all the wa y to the coastlines of Oregon and Washington, but only up to 25 miles from the coast of Californi a. The in-port shipping inventory delivered toFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

28 the RMC by ENVIRON is consistent with th e gridded marine shipping inventories for Washington and Oregon in that it covers only emi ssions up to the Pacific coastlines for these states; the near-shore and offshore emissions are contained in the gridde d shipping inventory. We did not receive new California in -port and near-shore emissions for simulation Base02a, to cover inland and 25 miles off the coast, and so had to use the inventory from the preliminary 2002 modeling for these emissions.

To QA the nonroad mobile emissions, we used the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summarie s. We used tabulated summaries of the input data and SMOKE script settings to document the data and configuration of SMOKE used. The graphical QA summaries include, for all emissions output species, daily sp atial plots, daily timeseries plots, and annual ti me-series plots. These QA gr aphics are available at http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.plots/nr/plots/

.



### 2.5.3 Uncertainties and future work

We prepared nonroad mobile emissions using a combination of inventories having different temporal resolutions and various forms of anc illary data. These different combinations of information may lead to inconsistencies in how these emissions are represented across the modeling domain. Regarding the quality of the inve ntories and temporal information, there is a discrepancy between the WRAP states and th e rest of the modeling domain; the WRAP inventories, spatial surrogates, and temporal profiles are mo re highly developed and quality assured than similar information for the othe r RPOs. In addition, the Canadian inventories contain only province-level information and t hus have low-resolution spatial and temporal profiles applied to them. The Mexican nonroad emi ssions are deficient in the number of different SCCs contained in the inventory a nd the availability of spatial su rrogates that are applicable to nonroad mobile sources. Improvements to the tempor al profiles and spatia l surrogates outside of the WRAP states could provide a more consis tent approach to re presenting the nonroad emissions across the entire modeling domain.



### 2.6 Biogenic Emissions

Biogenic emissions data for SMOKE consist of input files to the BE IS3 model (U.S. EPA, 2004a). BEIS3 is a system integrated into SMOKE for deriving emissions estimates of biogenic gas-phase pollutants from land use information, em issions factors for diffe rent plant species, and hourly, gridded meteorology data. The results of BEIS3 modeling are hourly, gridded emissions fluxes formatted for input to CMAQ or CAMx. This section describes the sources of the BEIS3 input data that we used for simulation Base02a, how we modeled these data, and the types of QA that we performed to ensure that SM OKE processed the data as expected.



### 2.6.1 Data sources

For simulation Base02a we used the BELD3 land use data and biogenic em issions factors that we collected during the WRAP preliminary 2002 modeling (Tonne sen et al., 2005). These data included BELD3 1-km resolution land use estima tes and version 0.98 of the BELD emissionsFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

29 factors. The Base02a biogenic emissions mode ling differed from the preliminary 2002 modeling in the use of improved 2002 meteorology data th at we developed in 2005 (Kemball-Cook et al., 2005).



### 2.6.2 Emissions processing

We used BEIS3.12 integrated in SMOKE to prep are emissions for simulation Base02a. Most of the preparation for the biogenic emissions pr ocessing was completed during the preliminary 2002 modeling. As the modeling domains did not change from the preliminary 2002 to the Base02a modeling, we re-used the gridded land use data and vegeta tion emissions factors that we prepared for the preliminary simulations. The major difference in the emissions processing between the preliminary 2002 and Base02a modeling was in the integration of improved meteorology in the Base02a simulation.

To QA the biogenic emissions, we used the WR AP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summaries. We us ed tabulated summaries of the input data and SMOKE script settings to document the data a nd configuration of SMOK E used. The graphical QA summaries include, for all emi ssions output species, daily spat ial plots, daily time-series plots, and annual time-series plots. These QA graphics are available at http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.plots/b3/plots/

.



### 2.6.3 Uncertainties and future work

We would like to explore the use of BEIS3.13 in future 2002 simulations.



### 2.7 Fire Emissions

Fire emissions data for SMOKE have traditionally been represented as county-level area-source inventories that were placed in only the first vertical model laye r. We advanced the representation of fire emissions for air quality modeling by preparing the inventory data as point sources with specific latitude-longitude coordinates for each fire centr oid and precomputed plume rise parameters that were derived from individual fire characteristics. These new inventories consist of annual, daily, and hourly IDA-formatted emissi ons inventory files and ancillary data for allocating the inventories in space, in time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This section describes where we obtained these data, how we modeled them, and the types of QA performed to ensure th at SMOKE processed th e data as expected.



### 2.7.1 Data sources

For the fire inventories in simulation Base02a, we used actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadi an inventory, and actual 2002 data for Ontario, Canada. There were no fire emissions in the BRAVO 1999 Mexico inventories, so Mexican fires were not included in the Base02a simulation. The inventories used consisted of both area- and point-source data for the U.S. and Canada. Air Sc iences provided us with the WRAP inventories divided among six different fire categories: wildfires, agricultural fires, wildland fire use, naturalFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

30 prescribed, anthropogenic prescribed, and non-Fede ral rangeland fires (Air Sciences, Inc., 2005). These inventories consisted of annual, daily, a nd hourly IDA-formatted files with information on daily emissions totals and hourly plume character istics for each fire. We received similar pointsource fire inventories for the VISTAS states from Alpine Geophysics (Ste lla, 2005). In addition, we received county-level fire inventories repr esented as area sources for the VISTAS and CENRAP states. With these data we received mo nthly temporal profiles from Alpine Geophysics that we used to distribute these annual inventories thr oughout the year. The area-source inventories for the rest of the RPOs and Canada also contained fire emissions that were not distributed separately. We modeled these source s with the rest of th e stationary-area-source sector. Finally, we received a 2002 fire inventory for Ontario, Ca nada from MANE-VU that is formatted to take advantage of the SMOKE fire plume rise algorithm (Pouliot et al., 2005).



### 2.7.2 Emissions processing

SMOKE is instrumented to distri bute point-source-formatted fire inventories to the vertical model layers either by using a precomputed plume rise approach or by computing the plume rise dynamically using actual 2002 meteorology. We applied both approaches for modeling pointsource fire emissions in simulation Base02a. Fo r the precomputed plume rise approach, SMOKE reads an annual inventory file with information on fire locations, a daily inventory file with daily emission totals for each fire, and an hourly inve ntory file with hourly plume bottom, plume top, and layer 1 fractions for each fire. SMOKE uses this information to locate the fires on the horizontal model grid and to distribute the plum e of each fire vertically to the model layers. Because some of these fires have plumes th at reach the model top, we set the number of emissions layers for processing these inventorie s to the full 19 layers of the meteorology. We applied this approach to the point-sour ce fires for the WRAP and VISTAS regions.

The alternative plume rise approach uses inform ation on fuel loading and the heat flux of the fires to distribute the fires vertically to the mode l layers. The data are provided to SMOKE in the form of an annual inventory with information on fire locations and a daily inventory with daily emission totals for each fire, dail y heat flux, and daily fuel loadi ng. We applied this approach to the point-source fires for Ontario, Canada.

All of the point-source fires used diurnal tem poral profiles and speciation profiles for VOC and PM 2.5 developed by Air Sciences during the pre liminary 2002 modeling (Tonnesen et al., 2005).

We modeled the area-source fires for the non-WRAP U.S. and Canada as standard stationary area sources. We applied monthly temporal profiles provided by VISTAS, flat weekly temporal profiles, and the diurnal profiles developed by Air Sciences. We used the forestland area surrogate to distribute these emi ssions from the county or province level in the inventories to the model grid cells.

To QA the fire emissions, we used the WR AP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summaries. We us ed tabulated summaries of the input data and SMOKE script settings to document the data a nd configuration of SMOK E used. The graphical QA summaries include, for all emi ssions output species, daily spat ial plots, daily time-series plots, annual time-series plot s, and vertical profiles. Thes e QA graphics are available at http://pah.cert.ucr.edu/aqm/308/ QA_base02a36.plots/allf/plots/

.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

31 

### 2.7.3 Uncertainties and future work

The RMC discovered several errors with the WRAP Phase II inventories. Some of these errors were fixed with corrections we made with gui dance from Air Sciences, and others will be addressed by Air Sciences in Phase III of the 200 2 WRAP fire inventories. The errors that we identified, with corrections, were as follows:

• Missing or malformed dates in several agricultural burning ev ents in CA. These events were intended to be dropped and were ultimat ely deleted from the i nventories by the RMC.

• Missing dates in several prescribed burning ev ents in AZ. These records were corrected by Air Sciences and redistributed to the RMC.

• Inconsistencies between the re cords in the hourly and annual inventory files for several agricultural burning events in CA. These re cords were corrected by Air Sciences and redistributed to the RMC.

For the area-source fires outside of the WRAP region, including Canada, we applied monthly temporal profiles developed by VISTAS. While thes e profiles appear to be an improvement over the EPA defaults, they are specific to the VISTAS region and will misrepresent the seasonality of the fires in other regions of the modeling domai n. In addition, we used forestland spatial surrogates to distribute these county-le vel (province-level fo r Canada) data to the model grid. Using spatial surrogates to locate fires is a crude approa ch that results in the artificial smearing of the emissions over too large an area. Both of thes e issues can be remedied by moving to a pointsource approach for representing these fires, similar to the approach used by Air Sciences for preparing the WRAP fire inventories.



### 2.8 Dust Emissions

Dust emissions data for SMOKE have traditionally taken the form of county-level stationaryarea-source inventories. As these emissions are correlated to meteorology, land use, and vegetative cover, we made several changes to how dust emissions are simulated by SMOKE to take these parameters into c onsideration. This section descri bes where we obtained data for windblown, fugitive, and road dust sources, how we modeled them, and the types of QA performed to ensure that SMOKE processed the data as expected.



### 2.8.1 Data sources

For the fugitive dust and road dust inventories in simulation Ba se02a, we used actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the BRAVO 1999 Mexican inventory. We extracted the fugitive dust inventories from the stationary-area inventories for each of the RP Os, Mexico, and Canada. Before modeling these data we further divided them into constructio n/mining sources and agri cultural sources. We defined the fugitive dust sources in the Base 02a modeling based on guidance provided by EPA (2004b). ENVIRON provided WRAP (both CA and non-CA) road dust inventories (Pollack et al., 2006). For the rest of the RPOs and Canada, we extracted the road dust SCCs from theFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

32 stationary-area-source in ventories. The BRAVO 1999 Mexico i nventory did not contain any road dust SCCs. Table 2-6 lists the SCCs for the various fugitive and road dust sources that we modeled in simulation Base02a. We applied near-s ource capture transport factors that are based on county-level vegetative cover to the fugitive a nd road dust inventories to prepare them for input to the air quality models.

For windblown dust, we used gridded emissions prepared outside of SMOKE using a land-useand meteorology-based model developed under funding from the WRAP by ENVIRON and UCRiverside (Mansell, 2005; Mansell et al., 2005).

Table 2-6. Base02a fugitive and road dust SCCs.
Dust Category SCCs Fugitive dust (construction and mining) 2275085000, 2311 010000, 2311010070 , 2311020000, 2311030000, 2325000000 Fugitive dust (agricultural) 2801000003, 2801 000005, 2801000 008, 2805001000 Road dust 2294000000, 2296000000



### 2.8.2 Emissions processing

We modeled the fugitive and road dust inve ntories through SMOKE using an area-source approach. We modeled these data on the assumpti on that they represented weekday, rather than seven-day week, emissions and thus us ed the SMOKE setting WKDAY_NORMALIZE to convert the data to a seven-day average. We configured SMOKE to compute PMc during the processing as (PM 10 - PM 2.5 ). We also assumed that all of th e VOC emissions in the inventories are ROG and used SMOKE to convert the VOC to TOG before converting the emissions into CB-IV speciation for the air quali ty models. We configured SMOKE to create MWSS temporal intermediates rather than daily temporal fi les because the dust sources do not use weekly temporal profiles that vary across the weekdays.

As noted above, we used SMOKE to apply near-source transport factors to the raw fugitive and road dust inventories to prepare them for input to the air quality models. We used U.S. transport factors from work done by Pace (2005) and a 20 01 land use/land cover database to develop a SMOKE input file of county and SCC-based transport factors for the U.S., Canada, and Mexico. We applied these factors to create a new set of i nventories adjusted for these transport factors for all regions except VISTAS; the VISTAS dust sources that we received already had the transport factors applied to them.

We calculated the windblown dust emissions outsi de of SMOKE using an internally developed, process-based model. By “process-based” we re fer to an emissions model that integrates information about the processes that lead to the emissions of interest, in this case windblown dust. The process-based windblown dust model de veloped by the WRAP considers wind speeds, precipitation history, and soil types to derive gridded dust fluxes resulting from windFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

33 disturbances for the modeling domain. More inform ation on this model, its modes of operation, and the configuration used for simulation Base 02a are available in Mansell et al. (2005).

To QA the dust emissions, we used the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summaries. We us ed tabulated summaries of the input data and SMOKE script settings to document the data a nd configuration of SMOK E used. The graphical QA summaries include, for all emi ssions output species, daily spat ial plots, daily time-series plots, and annual time-series plots. These QA graphics are available at http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.plots/fd/plots/ for fugitive dust, http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.plots/rd/plots/ for road dust, and http://pah.cert.ucr.edu/aqm/308/ QA_base02a36.plots/wb_dust/plots/ for windblown dust.



### 2.8.3 Uncertainties and future work

There are several improvements that we will make to the dust emissions modeling in future simulations. We will expand the list of fugitive dus t SCCs that we extract from the stationaryarea-source inventories for appli cation of transport fact ors. This expanded list is based on recent work by EPA (2004b). We will also explore improve ments to the assumptions that we used for generating emissions with the WRAP windblown dust model. Areas of improvement in the windblown dust model include refinements to th e land use data and soil characteristics, additional information about agricultural activit ies in the WRAP and CENRAP regions, detailed model evaluation on targeted wi ndblown dust case studies, and th e application of snow-cover and vegetative transport factors to th ese emissions (Mansell et al., 2005).



### 2.9 Ammonia Emissions

Ammonia (NH 3 ) emissions from agricultural activities are a major source of ammonia and are dependent on many different environmental pa rameters, such as meteorology, crop and soil types, and land use. Traditionally these emissions have been represented in the stationary-areasource inventory as annual, county-level es timates. These estimates did not consider meteorology, and may have used different land use a ssumptions than were us ed in the air quality model simulations to which they were input. Th e WRAP funded development of a process-based agricultural NH 3 emissions model to estimate NH 3 emissions from several different agricultural sources (such as soils, livestock, and fertilizer application) that uses the same meteorology and land use assumptions that are used in CMAQ and CAMx. We combined the WRAP NH 3 model emissions estimates with data provided by the other RPOs to represent agricultural NH 3 emissions in simulation Base02a.



### 2.9.1 Data sources

We developed NH 3 emissions for the WRAP states using the WRAP NH 3 model (Mansell, 2005), generating emissions for the following sectors: domestic sources, wild animals, fertilizers, soils, and livestock. CENRAP and MRPO provide d monthly IDA-formatted inventories to us that they produced from process-based models of their own, along with temporal profiles andFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

34 spatial cross-reference information for these sour ces. The rest of the U.S., Canada, and Mexico had agricultural NH 3 emissions contained within their annual stationary-a rea-source inventories.



### 2.9.2 Emissions processing

We prepared the WRAP NH 3 emissions outside of SMOKE using the WRAP NH 3 model; details of this modeling are available in Mansell (2005) . Due to an incorrect assumption in the soil emission factor used in the model, however, we had to discard the emissions from this sector. CENRAP and MRPO provided monthl y IDA-formatted, county-level NH 3 inventories that they developed with their own process-based models. We modeled these emissions like area sources with SMOKE, applying the temporal profiles and th e spatial cross-referencing that we received from these RPOs. The agricultural NH 3 emissions for the rest of the RPOs, Canada, and Mexico are contained within their stati onary-area inventories. We app lied the SMOKE default temporal profiles and spatial surrogates to all non-process-based NH 3 emissions; details on this processing are provided in Section 2.3.2.

To QA the NH 3 emissions, we used the WRAP emi ssions modeling QA protocol (Adelman, 2004) and a suite of graphical summaries. We us ed tabulated summaries of the input data and SMOKE script settings to document the data a nd configuration of SMOK E used. The graphical QA summaries include, for all emi ssions output species, daily spat ial plots, daily time-series plots, and annual time-series plots. These QA graphics are available at http://pah.cert.ucr.edu/aqm/308/ QA_base02a36.plots/nh3/plots/

.



### 2.9.3 Uncertainties and future work

Like the other emissions categories that have traditionally been represented as stationary area sources, the agricultural NH 3 emissions sector is affected by interregional inconsistencies in the way these emissions are represented. With each region of the domain representing these emissions differently, there are artificial concen tration gradients that follow political boundaries in the modeling domain. Figure 2-1 illustrates th ese gradients in the grid cells between the WRAP, MRPO, and CENRAP states. Because the NH 3 emissions for the MANE-VU, VISTAS, and non-U.S. portions of the domain are containe d in the stationary-area inventories, these emissions do not appear in Figure 2-1. The eastern border of the Dakotas with Minnesota and the border of Colorado and Kansas illustrate these artificial concentration gradients.

During the QA of the Base02a emissions, we discovered a problem with the WRAP soil NH 3 estimates. The emission factor for soil NH 3 that we used in developi ng these data produced too high an emission estimate from this sector. Fo r simulation Base02a we therefore removed the soil NH 3 sector completely from the WRAP domain. In future simulations we will include these emissions with a revised emission factor for NH 3 emissions from soils.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

35 Figure 2-1. Example Base02a NH 3 emissions spatial plot, showing artificial concentration gradients that fo llow some political boundaries.



### 2.10 Oil and Gas Emissions

Simulation Base02a contained a handful of emi ssions sectors that WRAP had never modeled before. Emissions from oil and gas production oper ations have been sporadically reported by some states in their stationary-area-source invent ories, but for the most part were missing from our previous modeling. In simulation Base02a, we represented oil and gas production emissions explicitly as both area and poi nt sources in a handful of states across the WRAP region.



### 2.10.1 Data sources

We received oil and gas production emissions inventories for the WRAP states and for tribal lands in the WRAP region as stationary-area- source and stationary-point-source IDA-formatted inventories. ERG, Inc. provided th e point-source inventories with th e rest of the stationary-point data (ERG, Inc., 2006). ENVIRON provided the area-source oil a nd gas inventories for non-CA WRAP states and for tribal lands in the WR AP region, along with spatial surrogates for allocating these data to the model grid (Polla ck et al., 2006). We extracted CA oil and gas inventories from the stationary-area-source data used in the preliminar y 2002 modeling. Oil andFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

36 gas production emissions data for outside of the WR AP region, if they exist, are contained in the stationary-area inventories that we received from the other RPOs.



### 2.10.2 Emissions processing

We modeled the WRAP point-source oil and gas production emissions in combination with the rest of the stationary-point-source emissions. The SMOKE configuration used to model these inventories is described in Section 2.2.2. We modeled the WR AP area-source oil and gas production emissions explicitly as a separate ca tegory that included WRAP and tribal inventories. These data represent weekly average em issions and did not requ ire any renormalization with SMOKE. We used spatial surrogates ge nerated by ENVIRON to allocate these annual county-level emissions to the model grid. For all oil and gas emissions, we applied flat temporal profiles to create hourly inputs to CMAQ and CAMx.

To QA the oil and gas production emissions, we used the WRAP emissions modeling QA protocol (Adelman, 2004) and a su ite of graphical summaries. We compared the spatial plots that we produced from SMOKE output to spatial pl ots provided by ENVIRON to ensure that we modeled these data correctly. We used tabulated summaries of the input data and SMOKE script settings to document the data and configura tion of SMOKE used. The graphical QA summaries include, for all emissions output species, daily sp atial plots, daily time-series plots, and annual time-series plots. These QA graphics are available at http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.plots/wog/plots/

.



### 2.10.3 Uncertainties and future work

In future 2002 modeling we will replace the CA oil and gas production emissions with revised data provided by the California Air Resources Board (CARB).



### 2.11 Offshore Area and Point Emissions

Offshore-area-source and offshor e-point-source emissions include emissions in the Gulf of Mexico and off the coast of California that are associated with oil and gas drilling platforms.



### 2.11.1 Data sources

We obtained year 2000 IDA-formatted point-source inventories for oil and gas platforms in the Gulf of Mexico from the Minerals Manageme nt Service (MMS) web site (Wilson et al., 2004). We combined these with point-source data for coastal California provided to us by CARB during the preliminary 2002 modeling. We also obtained gr idded area-source emissions for platforms in the Gulf of Mexico from the MMS that we converted to the WR AP 36-km model grid.



### 2.11.2 Emissions processing

We modeled the offshore point and area sources as separate categories. We used SMOKE to locate the offshore point sources on the model grid and to vertically allocate them into 15 modelFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

37 layers. The gridded area-source inventory did not require pro cessing with SMOKE; it required only a conversion from the data grid to the m odeling grid using a program we developed. We applied flat temporal profiles to both the o ffshore area and point emissions, distributing them evenly across all hours, days, and months of the year.

To QA the offshore platform emissions, we us ed the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summarie s. We used tabulated summaries of the input data and SMOKE script settings to document the data and configuration of SMOKE used. The graphical QA summaries include, for all emissions output species, daily sp atial plots, daily timeseries plots, and annual ti me-series plots. These QA gr aphics are available at http://pah.cert.ucr.edu/aqm/ 308/QA_base02a36.p lots/pt/plots/ for the point sources and http://pah.cert.ucr.edu/aqm/308/ QA_base02a36.plots/ofsar/plots/ for the area sources.



### 2.11.3 Uncertainties and future work

While the MMS data that we used were an improvement over previously modeled Gulf of Mexico platform inventories, the data were de veloped for a different modeling application that covered only the extreme northwestern portion of th e Gulf, so they are missing large areas of the region of the Gulf that contain drilling platforms. The California offshore inventory represents an initial attempt at compiling an emission inventory for this area and contains very few sources. Future simulations will focus on improving these emissions by expanding the coverage of the offshore platform inventories for both the Gulf of Mexico and the Pacific Coast.



### 2.12 Commercial Marine Shipping Emissions

Emission inventory development for regional- a nd continental-scale air quality modeling has historically neglected offshore emissions s ources beyond 25 miles offshore. Concern over the environmental effects of commercial shipping emissi ons in the Pacific on the coastal states in the WRAP region led to the development of a commerci al marine shipping inve ntory for the Pacific. Simulation Base02a is the first WRAP modeling that uses these emissions.



### 2.12.1 Data sources

We obtained gridded annual commercial marine shipping emissions for the Pacific on the WRAP 36-km model grid from ENVIRON for inclusion in simulation Base02a (Pollack et al., 2006). The commercial marine inventory contains all of the criteria pollutants cont ained in the nonroad mobile-source inventory: CO, NO x , VOC, NH 3 , SO 2 , PM 10 , and PM 2.5
.


### 2.12.2 Emissions processing

The commercial marine shipping inventory for simulation Base02a was not processed through SMOKE. ENVIRON delivered the data to the RM C as gridded text files on the 36-km model grid. We reformatted these data to the netCDF CMAQ input format with a utility we developed. We converted the VOC inventory poll utant to CB-IV speciation and the NO x and PM 2.5 inventory pollutants to CMAQ i nput species with SMOKE chemi cal profiles for commercialFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

38 shipping sources. We did not apply any temporal adjustments to these emissions; they use uniform monthly, daily, and diurnal profiles.

To QA the commercial marine shipping emissi ons, we used the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of gra phical summaries. The graphical QA summaries include, for all emissions output species, daily sp atial plots, daily time-series plots, and annual time-series plots. These QA graphics are available at http://pah.cert.ucr.edu/aqm/308/ QA_base02a36.plots/ofsar/plots/

.



### 2.12.3 Uncertainties and future work

As a first attempt at representing shipping emissi ons in the Pacific in international waters, the WRAP 2002 commercial shipping inve ntory is a breakthrough in a historically neglected emissions category. As we evaluate the effects of these emissions on the air quality modeling, we anticipate that there will be refinements to the te mporal profiles and to th e vertical allocation of the emissions. Many of the stacks of large commer cial ships contained in this inventory extend vertically above the firs t model layer. Future versions ofthis inventory should use higherresolution temporal adju stments and should allocate the em issions to the appropriate model layers.



### 2.13 Summary

Simulation Base02a represents a significant improvement to the preliminary 2002 emissions modeling previously performed by the RMC. Wh ile the preliminary 2002 modeling served its purpose to develop the infrastructure at the RM C for modeling large emissions data sets and producing annual emissions simulations, much of the input data (bot h as inventories and ancillary data) were placeholders for actual 2002 data th at were being prepared through calendar year 2005. As these actual 2002 data sets became av ailable to us, we inte grated them into the SMOKE modeling and QA system th at we had developed during the preliminary modeling, to produce a high-quality emissions data set for us e in model performance evaluations of CMAQ and CAMx. The addition of entir ely new inventory categories, lik e the marine shipping and oil and gas production sectors, added complexity to the Base02a modeling. By the end of the data collection phase for simulation Base02a, we had id entified 22 different emissions categories to compose this data set (see Table 2-1).

To facilitate the comparison between the CMAQ and CAMx simulations using the Base02a emissions, we used the CMAQ2CAMx utility developed by ENVIRON to convert the threedimensional emissions categories from CMAQ to CAMx format. For the low-level point and area sources, we used SMOKE to produce the CAMx emissions.

Appendix A contains graphical a nd tabular summaries of the Ba se02a emissions data. Section A.2 contains a tabulated summary of the inventor y files used in simulation Base02a. Section A.3 contains emissions summaries for each major source category. These summaries include tabulated RPO and country totals for the entire modeling domain, pie charts for the entire U.S. and the WRAP states, and spatial plots for th e annual Base02a model-ready emissions. SectionFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

39 A.4 contains tabulated and gra phical emissions summaries by s ource category. For each major source category, this section provides

• Tabulated state/province totals fo r the U.S., Canada, and Mexico

• 3-D bar charts comparing the inventorie s between the RPOs, Canada, and Mexico

• 2-D bar charts comparing simulation Ba se02a with previous simulation Pre02d

• 2-D bar charts comparing selected inventory pollutants between the various WRAP states

• Emissions density charts in the WRAP re gion for selected inventory pollutants.



### 2.14 Effects of Uncertainties and Future Work

As a continually evolving process, emissions mo deling must balance access to the best available data sets with the ability to produce accurate result s in a timely manner. New inventories are continually becoming available that supercede similar data. As the RMC was preparing the Base02a emissions for CMAQ and CAMx modeling, new data sets from the other RPOs and corrections or updates to the WR AP actual 2002 inventories presen ted themselves that we could not integrate into this simulation due to time co nstraints. We made the decision to shelve any new inventory information that became availa ble after July 31, 2005, until the next round of 2002 modeling. Known issues with the Base 02a emissions include the following:

• Missing natural emissions from sea sa lt, lightning, and geogenic sources

• Errors in the CA point-, area-, and m obile-source inventories that lead to underestimations of these emissions

• Errors in the MT on-road mobile-source inventory

• Errors in the WA, ND, OR, and UT area-source and fugitive dust inventories

• Missing Broomfield County, CO, data in the area-source and derivativ e dust inventories

• Errors in the AZ, MT, NM, OR, UT, WA, WY, and WRAP tribal stationary-point inventories

In the next round of 2002 emissions modeling to be conducted by the RMC, we anticipate correcting these inventory issues and any other problems that we find in simulation Base02a as we evaluate the air quality modeli ng results that use these emissions.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

40 

## 3. Meteorology Data

The WRAP RMC conducted meteorological modeli ng of the continental Unites States using MM5 (Anthes and Warner, 1978; Dudhia, 1993; Gr ell et al., 1994). Numer ous MM5 sensitivity tests were conducted that are documented in reports and other documents on the WRAP modeling web site ( http://pah.cert.ucr.edu/aqm/308/mm5.shtml ). Below we summarize our WRAP MM5 modeling, MM5 model performance, and the potential eff ects the MM5 modeling uncertainties might have on vi sibility modeling with CMAQ a nd CAMx. More details can be found in the WRAP MM5 modeling fina l report (Kemball-Cook et al., 2005; http://pah.cert.ucr.edu/aqm/308/reports /mm5/DrftFnl_2002MM5_FinalWRAP_Eval.pdf ).



### 3.1 Summary of Meteorology Evaluation Results

We carried out MM5 simulations for the entirety of 2002 to support visibility modeling for the Section 308 SIPs/TIPs that are due in Decembe r 2007. During the fall of 2003, the RMC made an initial MM5 run for the year 2002 on the RP O Unified 36-km grid. The domain was divided into 11 subdomains for model evaluation, whic h are shown in Figure 3-1. An MM5 modeling protocol was prepared (ENVIRO N and UCR, 2004b), which descri bes in detail the MM5 model and the setup and evaluation methods used in the 2002 modeling effort. The model performance evaluation for the initial 2002 MM5 run on th e RPO Unified domain and results from preliminary MM5 sensitivity tests are discusse d in Morris et al. (2004) , Kemball-Cook et al. (2004a,b), and Emery et al. (2004).

The model configuration for this initial run was based upon a prior 2002 MM5 application undertaken by the Iowa Department of Na tural Resources (IDNR) (Johnson, 2003, personal communication), which was in turn set accordin g to the optimal MM5 physics options that resulted from an in-depth sensitivity project carried out by IDNR and LADCO. While the IDNR simulations used MM5 version 3.5, th e applications carried out for WRAP used the latest version of the model (v3.6.1) available at the end of 2003. Additional modifications to the physics configuration and application methodology were made for the WRAP simulation based on the latest information from EPA, I DNR, LADCO, and others. First, we opted to use the Reisner 2 mixed-phase cloud microphysics package according to suggestions from EPA’s Office of Research and Development (ORD). Second, th e INTERPPX option—which allows for continuous soil moisture initialization from one run segment to the next—was not used, based on poor performance reported by IDNR and LADCO.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

41 Figure 3-1. Definition of subdomains used in MM5 modeling.

The initial 2002 36-km WRAP simu lation (referred to hereafter as WRAP_0) results showed that MM5 performs better in the central and eastern U.S. than in th e West, and performs generally better in winter than in summer(Morris et al., 2004; Kemball-C ook et al., 2004a). In the western U.S., the amplitude of the diurnal temperature cycle was persistently underestimated during the summer, especially in the Southwest. In th e desert Southwest, the humidity was greatly overestimated during the summer as well, and th ere was a pronounced cold bias. Some of these problems appeared to be linked to the exces sive simulated precipi tation generated by MM5 during the summer, especially in the Southwest. This can have serious repercussions for CMAQ and CAMx modeling because too much rain can “wash out” pollutants, while the too cool, humid, and cloudy environment may lead to in correct pollutant chemistry and aerosol thermodynamics. Temperature and humidity problems overshadowed the surface wind performance, which was not particularly good, but was likely affect ed by smaller-scale topographic influences that are not well represented even at th e finer 12-km resolution. Wind performance improved quickly with height above the surface, suggesting that regional transport speeds/directions were reasonably represented.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

42 

### 3.1.1 Preliminary sensitivity tests

Initial sensitivity tests were unde rtaken for a five-day period in July 2002 (July 1-5, 2002) for the RPO Unified 36-km domain in an attempt to fi nd alternative MM5 confi gurations that would improve the poor summertime performance in the western U.S. Further analyses were conducted to compare the WRAP_0 run’s performance ag ainst that of a 36-km MM5 run made for VISTAS, and against operational Eta Data Assim ilation System (EDAS) fiel ds used as input to MM5. From these analyses, we found that using th e Kain-Fritsch II scheme (as was used in the VISTAS 2002 MM5 modeling, compared to the Kain-F ritsch I scheme used in the initial WRAP modeling) improved, but did not entirely solve, the precipitation overprediction problem in the WRAP_0 run. We also found that removing soil moisture nudging improved temperature and humidity performance for the short summertime test s. Evaluation of the EDAS fields used in the MM5 four-dimensional data assimilation (FDDA) revealed that EDAS did not exhibit the summertime cold wet bias in the Southwest. This indicated that the bi as was not introduced by the FDDA. A new model configur ation (termed “WRAP_5”) was identified from these initial tests for new annual 2002 simulations, and the WRAP MM5 modeling protocol was updated to reflect this, as shown in Table 3-1.

Table 3-1. MM5 configurations from original (WRAP_0) and modified (WRAP_5) MM5 runs.*

Run ID LSM PBL Cumulus Microphysics Analysis FDDA
3-D Surface Obs FDDA Soil Moisture Nudging
WRAP_0 PX ACM KF Reisner 2 W/T/H W/T/H None Yes WRAP_5 PX ACM KF II Reisner 2 W/T/H None None No
*Abbreviations: LSM = land-surface model; PBL = planet ary boundary layer; FDDA = four-dimensional data assimilation; obs = observational; PX = Pleim-Xiu; AC M = Asymmetric Convective Model; KF = Kain-Fritsch; W/T/H = wind/temperature/humidity.

Although the WRAP_5 configuratio n did improve humidity, temperature, and precipitation performance over the short July test period (esp ecially where improvement was needed most), the desert Southwest continued to exhibit unsatisfactory levels of precipitation and humidity. As stated above, these issues will li kely play crucial roles in the air quality model performance.



### 3.1.2 Revised sensitivity tests

In an attempt to further improve MM5 performa nce, we carried out additional 36-km MM5 test simulations in 2004. The latest available vers ion of MM5 (v3.6.2) was used. Since the most severe problems for the initial WRAP 2002 MM5 annual simulation were the wet bias and precipitation overestimation in summer, we first i nvestigated these issues for the five-day July test period on the 36-km grid, to maximize comp utational efficiency. We tested four physics options: (1) the cumulus parameterization; (2) the land surface models (LSMs); (3) the PBL models; and (4) the FDDA.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

43 We then undertook a series of tests to determin e the optimal MM5 configuration for the nested 12-km grid of the WRAP modeling domain. In th ese tests, we varied only the physics options that we expected would ameliorate the biases in the WRAP region seen in the 36-km simulation. The overprediction of summer rainfall and associat ed surface humidity bias suggested that the cumulus parameterization might need to be ch anged. Another possible reason for the wet bias was the soil moisture initializati on. Therefore, we focused on sensitivity tests of the soil moisture specification and the cumulus scheme using both the 36- and 12-km grid structures. The results of these sensitivity tests are discussed in Ke mball-Cook et al. (2004b). The optimal model configuration for the 36-km applicati on, referred to as the Final Confi guration, is given in Table 3-2.

Table 3-2. MM5 Final Configuration for the WRAP 2002 36-km MM5 run after revised sensitivity tests were completed.

LSM PBL Cumulus Microphysics Analysis FDDA
3-D Surface Obs FDDA

Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W/T/H Wind

MM5 runs for the test period of July 1-5, 2002, on the 36-km grid made with the physics options indicated in Table 3-2 showed the following characteristics:

• A dramatic reduction in the summertime col d, wet bias in the desert Southwest.

• Surface temperature and humidity performance within benchmarks for all WRAP regions except the desert Southwest for temperature.

• More accurate representation of the diurnal temperature cycl e in the desert Southwest.

• Improvements in the index of agreement for temperature, humidity, and wind speed for all WRAP subdomains.

• A more realistic precipitation pattern over the western U.S.

• Better model performance in the eastern U.S.

• Runs made with these phys ics options generally repres ented an improvement in performance throughout the year, making it unn ecessary to select different physics schemes for different seasons.

In summary, the new configuration of MM5 re presented a significant improvement in model performance relative to the WR AP_0 and WRAP_5 36-km MM5 r uns for the five-day test period in July 2002.

We then conducted an additional series of tests to determine the optimal MM5 configuration for the nested 12-km grid of the WRAP modeling domain. In these tests, we again focused on sensitivity tests of the soil moisture specificati on and the cumulus scheme. The main results from the 12-km runs over the test peri od of July 1-5, 2002, are as follows:Final Report for the WRAP 2002 Visibility Model Performance Evaluation

44 • Use of soil moisture nudging degraded temp erature and humidity model performance.

• Initializing the soil moisture with the 36-km domain MM5 output degraded temperature and humidity model performance.

• Better temperature and humidity performance was obtained by initializing the soil model with the EDAS analysis fields.



### 3.1.3 Final model configurations

Based upon our evaluation of surface variable an d rainfall performance, we selected a configuration that uses the Betts-Miller cumu lus scheme for the 36-km grid and no cumulus parameterization for the 12-km grid. This was among the best performers in terms of surface wind, temperature, and humidity, and performed the best for rainfall. We also chose to remove surface analysis nudging of temperature and humid ity from both grids, based on guidance from reviewers that nudging to these variables can degrade performance al oft. Other MM5 model options remained as given in Table 3-2. Table 3-3 shows the Final Configuration for the WRAP 36/12-km MM5 modeling that was used for the annual 2002 MM5 simulation to support WRAP’s regional haze modeling. The WRAP m odeling protocol (ENVIRON and UCR, 2004b) was updated to reflect this Final Configuration.

Table 3-3. MM5 Final Configuration fo r the WRAP 2002 36/12-km MM5 run.

Grid Resolution LSM PBL Cumulus Microphysics Analysis FDDA
3-D Surface Obs FDDA

36 km Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W Wind
12 km Pleim-Xiu ACM None Reisner 2 W/T/H W Wind



### 3.1.4 Final MM5 model performance

At both the 36-km and 12-km resolutions, the Final Configuration for the WRAP 2002 MM5 simulations produced results that are generally within the range of meteorological model results that have been used in the past for air quality applications. The 36- and 12-km MM5 runs based on the Final Configuration repres ent a significant improvement in performance over the original WRAP_0 run.

In the 36-km WRAP_0 simulation, surface humidity and temperature fields fell within the benchmarks for much of the year for most s ubdomains, but the model had a marked cold, wet bias in the western subdomains, especially duri ng the summer. This was unfortunate because the worst-visibility days at western class I areas that are of high in terest to WRAP generally occur during the summer. The surface wind field failed to meet the performance benchmark for the entire year in subdomains PacificNW, SW , North, and DesertSW (subdomains 1 through 4, respectively, in Figure 4-1). Given that the purpose of this WRAP_0 run was to provide aFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

45 database for a 12-km nested MM5 run and air qu ality modeling studies ce ntered on the western U.S., this was a serious problem.

The Final Configuration 36-km and 12-km MM 5 runs show improvement in the modeled precipitation fields, particularly in the summer in the Southwest. With a reduction in the overprediction of summer convectiv e rainfall in both the 36- and 12-km runs, wet biases in the surface humidity and cold biases in the surface temperature are now smaller in the North and DesertSW subdomains in the summer months. Although the Final Configuration WRAP 36-km and 12-km simulations were optimized for su mmer performance over the WRAP region, the winter performance in the West did not deteri orate significantly. There was a small loss of accuracy, particularly in temperature and humidity, in the East, but this region is not the focus of WRAP. To summarize, the WRAP Fina l Configuration 36-km MM5 run:

• Showed significant improvement in its surf ace wind performance throughout the year and across all subdomains, due to observational nudging of surface winds.

• Showed significant improvement in summer ra infall and surface humidity performance in the WRAP region.

• Did worse than the original WRAP run for hu midity and temperature performance in the East. We can attribute both of these changes in performance to (1) a reduction in the areal coverage of rainfall and (2) an increase in convective rainfall (and therefore surface moisture), both of which happen in going fr om the Kain-Fritsch scheme to the BettsMiller scheme.

• Showed improved temperature performance in summer in the West, and slightly worse performance in winter.

• Showed a small overall degrada tion in performance in the East. Some of this was the result of eliminating the surface analysis nudgi ng of temperature and moisture that was done in WRAP_0.

Based on the results of this study, we conclude that the Final Confi guration 36-km and 12-km WRAP MM5 runs exhibit reasonably good performa nce and are certainly within the bounds of other meteorological databases used for prior ai r quality modeling efforts. It is therefore reasonable to proceed with their use as inputs for visibility modeling.



### 3.2 Uncertainties and Effects on Visibility Modeling

Meteorology has a very large effect on the formati on of fine particles, their transport to Class I areas, and their loss rate through deposition; meteor ological conditions consequently have a very strong influence on visibility estimates at Class I areas. There are many scales of meteorological processes, from microscale eddies on the orde r of centimeters to meters up through synopticscale circulations on the order of hundreds of kilometers. Clearly, not all of these scales can, or need to be, addressed in the WRAP 36/12-km MM5 modeling. By the very nature of the MM5 model, it can only resolve flows the size of its gr id resolution (or more accurately, two times the grid resolution). The surface observations, on the other hand, represent point measurements that may or may not be representative of a volumetri c average. Consequently, the evaluation of theFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

46 MM5 model using surface point observations must be interpreted with ca re and acknowledgment of the fundamental differences between the poin t observations and volume-average estimates. In the WRAP MM5 model evaluation, we have attemp ted to overcome this limitation by evaluating the model using many observations and pres enting performance metrics averaged over subdomains.

In complex terrain the MM5 volume-average mete orological estimates may have more uncertainties than in flat terrain, due to terrain forc ing and orographic effects. Given that many of the western Class I areas are located in complex terrain, these enhanced uncertainties may affect the WRAP visibility calculations more than in ot her areas of the country. Uncertainties in the meteorological conditions can affect th e visibility estimates in many ways:

• Meteorological uncertainties can cause incorr ect transport of pollutants from major sources to Class I areas.

• These uncertainties can cause incorr ect dispersion of pollutants.

• These uncertainties can cause too little or too much vertical mixing that affects transport, dispersion, dry deposition, and chemistry.

• Uncertainties in temperature affect emissi ons (biogenic and on-road mobile sources), mixing, chemistry, and aerosol thermodynamics.

• Humidity uncertainties affect chemistr y, aerosol thermodynamics, and, potentially, visibility calculations. *

• Uncertainties in clouds affect mixing, gas-pha se formation processes, and aqueous-phase sulfate formation.

• Precipitation uncertainties affect the loss rate of PM and precursors due to wet deposition.

Probably the largest model performance probl em of the WRAP Final Configuration MM5 simulation is a cold, wet bias in the desert Southwest and Pacific Northwest subdomains during the summer. Although the bias is not as severe as in the initial runs, it is still present. This bias may have the following effects:

• The colder temperatures may inhibit the fo rmation of secondary fine particles.

• The wet bias may cause the presence of ex cessive clouds. These clouds could inhibit gas

phase particle formation, but at the same time could enhance aqueous -phase sulfate formation. Also, the overstated precipitati on from the wet bias could cause the wet deposition of particles and gaseous precurs ors to be overstated, thereby understating visibility impairment.

• The cold, wet bias would also affect the th ermodynamic equilibrium between particulate nitrate and gaseous nitric acid, resulting in an overstatement in the summer nitrate concentrations. Thus, if summer ammonium nitrat e is overstated by the air quality model, it may be due just to meteor ological model uncertainties.

* Note that EPA guidance uses monthly-average relative humidity in the visibility calculations, so uncertainties in the MM5 modeled humidity would not affect those calculations.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

47 

## 4. Air Quality and Visibility Modeling

As discussed in Section 1, the pur pose of the WRAP RMC work de scribed in this final report was to evaluate air quality/visibility modeling sy stems for a historical ep isode—in this case, for calendar year 2002—to demonstrate the suitabilit y of the modeling systems for subsequent planning, sensitivity, and emissions control stra tegy modeling. Model performance evaluation is performed by comparing output from model simula tions with ambient air quality data for the same time period. After creating emissions a nd meteorology inputs for the two air quality models, CMAQ and CAMx (see Sections 2 and 3), th e next step was to perform the visibility modeling and the MPEs, which are described belo w. This section also discusses some model intercomparison work that we performed for the two modelin g systems. We use all this information as the basis for recommending the se lection of CMAQ and/or CAMx to complete the next major work elements in RMC’s support of WRAP:

• Development of visibility planning scenar ios for the regional haze baseline period of 2000-04 and for the initial regional haze future projection period, calendar year 2018.

• Modeling a variety of emissions sensitivity, emissions source apportionment, and emissions control strategies to assess whether pl anned future regional emissions reductions will be sufficient to demonstrate reasonable progress toward achieving visibility goals.

Those two work elements will be discussed in future reports.

Section 4.1 gives summary descriptions of th e CMAQ and CAMx modeling systems. Section 4.2 discusses the ambient data monitoring networks that provided the observations we used in the MPEs, and Section 4.3 describes our general appr oach to conducting the evaluations. Sections 4.4 and 4.5 give details on various aspects of the CMAQ MPE, while Section 4.6 describes a sensitivity exercise we performed to compar e CMAQ 36-km and 12-km simulations. Section 4.7 discusses the CAMx MPE, and compares CAMx results with CMAQ results. Note that although this report discusses MPEs for both CMAQ a nd CAMx, most of Section 4 focuses on CMAQ because most of the WRAP RMC modeling to date has been performed using the CMAQ system. Section 4.8 describes the how various uncer tainties, errors, and biases in the modeling process and results may affect future visibi lity projections. Fina lly, Section 4.9 provides conclusions derived from th e CMAQ and CAMx MPE work.



### 4.1 Description of the CMAQ and CAMx Models

EPA initially developed the CMAQ modeling system in the late 1990s. The model source code and supporting data can be downloaded from th e Community Modeling and Analysis System (CMAS) Center ( http://www.cmascenter.org/ ), which is funded by EPA to distribute and provide limited support for CMAQ users. CMAQ was designed as a “one atmosphere” modeling system. In other words, it was designed to encompass modeling of multiple pollutants and issues, including ozone, PM, visibility, a nd air toxics. This is in contra st to many earlier air quality models that focused on single-pollutant issu es (e.g., ozone modeling by the Urban AirshedFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

48 Model). CMAQ is an Eulerian model—that is, it is a grid-based model in which the frame of reference is a fixed, three-dimensional (3-D) grid with uniformly sized horizontal grid cells and variable vertical laye r thicknesses. The number and size of grid cells and the number and thicknesses of layers are defined by the user, based in part on the size of the modeling domain to be used for each modeling project. The key scie nce processes included in CMAQ are emissions, advection and dispersion, photochemical transf ormation, aerosol thermodynamics and phase transfer, aqueous chemistry, and wet and dry depo sition of trace species. CMAQ offers a variety of choices in the numerical algo rithms for treating many of these processes, and it is designed so that new algorithms can be included in the m odel. CMAQ offers a choice of three photochemical mechanisms for solving gas-phase chemistry: the Regional Acid Deposi tion Mechanism version 2 (RADM2), a fixed-coefficient version of th e SAPRC90 mechanism, and the Carbon Bond IV mechanism (CB-IV).

The Comprehensive Air quality Model with exte nsions (CAMx) was de veloped by ENVIRON in the late 1990s as a nested-grid, gas-phase, Eu lerian photochemical grid model. ENVIRON later revised CAMx to treat PM, visibility, and air toxics. While there are many similarities between the CMAQ and CAMx systems, there are also some significant differences in their treatment of advection, dispersion, aerosol formation, and dry and wet deposition (discussed in Section 4.1.1).



### 4.1.1 Description of model versions

Both EPA and ENVIRON periodicall y update and revise their mode ls as new science or other improvements to the models are developed. Fo r CMAQ, EPA typically provides a new release about once per year. We began work on th e 2002 MPE using CMAQ version 4.4, which was released in October 2004. In October 2005 EPA released CMAQ version 4.5, which includes the following updates and improvements to the modeling system:

• A new vertical advection algorith m with improved mass conservation

• Changes in deposition velocities for some PM species

• A new sea-salt emissions model and inclusion of sea salt in the aerosol thermodynamics

• An option to make vertical mixing paramete rs vary as a function of land use type

We first completed the CMAQ MPE using CM AQ v.4.4. When version 4.5 was released in October, we redid the modeling and compared the performance of the two versions. Both sets of results are presented here. Note that some of the new features in CMAQ v4.5 (e.g., sea salt in the AE4 aerosol dynamics module, and percent urba n minimum vertical diffusivity) require the reprocessing of the MM5 data using the new version of MCIP (MCIP v3.0). However, because such reprocessing could potentially jeopardize the WRAP modeling schedu le, WRAP elected to operate CMAQ v4.5 using the MM5 data processe d with a previous MCIP version, MCIP v2.3, and the AE3 aerosol module that does not include active sea-salt chemistry.

ENVIRON releases updated versi ons of CAMx approximately ev ery two years, or as new features become available. The version we used for the comparison of CMAQ and CAMx was CAMx v4.3beta, which has not yet been posted to the CAMx web site ( www.camx.com ) but is being used by other RPOs (e.g., MRPO, CENRAP) and will be posted in early 2006. There are many similarities between CMAQ and CAMx rega rding the science algorithms and chemicalFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

49 mechanisms used, including the CB-IV gas-phase and RADM aqueous-phase chemistries, ISORROPIA aerosol thermodynamics, and Piec ewise Parabolic Method (PPM) horizontal advection scheme. In the past, th e treatment of vertical advecti on was a major difference between the two models; however, the incorporation of the new mass conservation scheme in CMAQ v4.5 makes its vertical advecti on algorithm much more simila r to that of CAMx. Major differences between the two models that still exist are in the basic model code, in the treatment of horizontal diffusion SOA formation mechanis ms, and in grid nesting (CAMx supports oneway and two-way nesting, whereas CMAQ supports just one-way grid nesting). Both models include process analysis for the gas-phase portions of the model. The publicly released version of CAMx supports ozone and PM source apportionment through its Ozone and PM Source Apportionment Technology (OSAT/PSAT) probing t ools, while for CMAQ there are research versions of the model that include Tagged Species Source Apportionment (TSSA) for some PM species (e.g., sulfate and nitrate). There are al so research versions of CMAQ and CAMx that support the Decoupled Direct Method (DDM) sensitivity tool for PM and ozone.

The CAMx model is computationa lly more efficient than CMAQ. However, CAMx is currently supported for use on only a single central processing unit (CPU) and can perform multiprocessing using Open Multi-Processing (OMP) parallelization (i.e., shared memory multiprocessors). CMAQ parallelization, on the other hand, is implemented using Message Passing Interface (MPI) multiprocessing and theref ore can be run using any number of CPUs. Depending on the number of model simulations to be performed and the manner in which they are set up, there can be a slight advantage e ither to CAMx or to CMAQ in regard to computational efficiency. This difference is unlik ely to be important in selecting one of the models for the future RMC work listed at the beginning of Section 4, however.



### 4.1.2 Selection of model science options

The CMAQ and CAMx modeling systems allow a vari ety of choices in the science algorithms used for creating the numerical solution of the transport proc esses and chemistry. Table 4-1 displays the science options used in the CMAQ v4.4 and v4.5 and CAMx v4.3beta model performance evaluations. Most science options in CMAQ remained the same, with the exception of the vertical advection and dry deposition. In CMAQ v4.5 we used the Yamartino advection scheme, which has improved mass conservation, and revised dry deposition velocities, which have increased rates of deposition for PM species.

Table 4-1. Model configurations for the CMAQ and CAMx simulations.

Model Option CMAQ version 4.4 CMAQ version 4.5 CAMx version 4.3beta

Model version October 2004 October 2005 2006
Horizontal resolution 36 km 36 km and 12 km 36 km Number of vertical layers 19 layers 19 layers 19 layers
Horizontal advection PPM PPM PPM
Vertical advection PPM Yamartino ImplicitFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

50 Model Option CMAQ version 4.4 CMAQ version 4.5 CAMx version 4.3beta Horizontal diffusion Spatially varying Spatially varying Spatially varying
Vertical diffusion K V (eddy diffusion) K V (eddy diffusion) K V (eddy diffusion) Minimum vertical diffusivity 1.0 m 2 /s 1.0 m 2 /s 1.0 m 2 /s MM5 configuration Pleim-Xiu/ACM Pleim-Xiu/ACM Pleim-Xiu/ACM MM5 processing MCIP2.3 pass-through MCIP2.3 pass-through MM5CAMx Gas-phase chemistry CB-IV CB-IV CB-IV Gas-phase chemistry solver MEBI/Hertel MEBI/Hertel CMC Secondary organic aerosol SORGAM SORGAM SOAP Aqueous-phase chemistry RADM RADM RADM Aerosol chemistry AE3/ISORROPIA AE3/ISORROPIA ISORROPIA Dry deposition Pleim-Xiu Revised Pleim-Xiu Wesely Plume-in-grid Off Off Off
Initial concentrations CENRAP 36-km spin-up CENRAP 36-km spin-up CENRAP 36-km spin-up Boundary conditions 2002 3-hourly GEOSChem 2002 3-hourly GEOSChem 2002 3-hourly GEOS-Chem



### 4.1.3 Description of model boundary co nditions and initial conditions

Initial conditions (ICs) are spec ified by the user for the first day of a model simulation. For continental-scale modeling using the RPO Unif ied 36-km domain, the ICs can affect model results for as many as 15 days, although the eff ect typically becomes ve ry small after about 7 days. We included a model spin-up period in each simulation to eliminate any effects from the ICs. For the WRAP modeling, we divided the annual simulation into four quarters, and included a 15-day spin-up period for the quarters beginning in April, July, and October. For the quarter beginning in January 2002, we ran a spin- up period covering December 16-31, 2001, using meteorology and emissions data th at we had developed for CENRAP . The most important aspect of using ICs and spin-up periods is always to tr eat them consistently across all model simulations so that small differences in the ICs or spin- up periods do not affect th e comparison of results across simulations. We considered performi ng new spin-up simulations for December 2001 when we updated the 2002 base A emissions inve ntory; however, the effects of a new spin-up simulation would manifest only in the first few days of January, and this small difference would not justify the extra effort requir ed. We therefore decided to use the same IC file for all model simulations.

Boundary conditions (BCs) specify the concentrati ons of gaseous and PM species at the four lateral boundaries of the mode l domain. BCs determine the amounts of gaseous and PM species that are transported into the model domain wh en wind flow is into the domain. BoundaryFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

51 conditions affect model simulations much more than ICs do. For some areas in the WRAP region and for clean conditions, the BCs can be a substa ntial contributor to vi sibility impairment.

For this study we used BC data generated in an annual simulation of the global-scale GEOS-Chem model that was completed by Jacob et al. ( http://www-as.harvard.edu/chemistry/trop/geos/ ) for calendar year 2002. Additional data processing of the GEOS-Chem data was required before we could use them in CMAQ and CAMx. The data first had to be mapped to the boundaries of the WRAP domain, and the gaseous and PM species had to be remapped to a set of species used in the CMAQ and CAMx models. This work was completed by Byun and coworkers ( http://wwwas.harvard.edu/chemistry/trop/geos/meeti ngs/2005/ppt/Expanding_Model_Capabilities/GEOSCMAQ_april_4_Byun.ppt ).



### 4.2 Summary of Ambient Monitoring Data

We compiled the ground-level model evaluation database for 2002 using several routine and research-grade ambient monitoring databases. The following specialized PM monitoring networks were used as sources of fine-PM data:

• Interagency Monitoring of Protected Vi sual Environments (IMPROVE) network

• Clean Air Status and Trends Network (CASTNet)

• EPA Speciation Trends Network (STN)

• National Atmospheric Deposition Network/ National Trends Network (NADP/NTN)

In addition, we used EPA’s Aerometric Inform ation Retrieval System/Air Quality System (AIRS/AQS) database for routine gas-pha se concentration measurements for O 3 , NO, NO 2 , and CO. Figures 4-1 and 4-2 display the locations of the monitors for the various monitoring networks operating during 2002. T ypically, these networks provide ozone, PM, and visibility measurements. The types of data available from these networks are summarized in Table 4-2, and each of these ambient networks is briefly described below.

IMPROVE. The Interagency Monitoring of Protected Vi sual Environments monitoring network ( http://vista.cira.colostate.edu/improve ) reports detailed chemical species in its measurements of major visibility-reducing aerosol species every th ree days. The PM fine mass species we used in the evaluation are as follows:

• Sulfates (SO 4 ), as sulfate ion

• Nitrates (NO 3 ), as nitrate ion

• Organic carbon (OC), as organic carbon mass

• Elemental carbon (EC), as light-a bsorbing carbon or carbon soot

• Soil (SOIL), as fine soil; this is the sum of multiple inorganic elements, such as Al, Si, Ca, Fe, and Ti.

These species are all measured using a 2.5-micron cut point inlet. The IMPROVE monitors also measure total PM 10 and PM 2.5 mass. These values are reported as the PM 2.5 fine matter (FM)Final Report for the WRAP 2002 Visibility Model Performance Evaluation

52 portion of the mass and the coar se matter (CM) portion (PM 10 - PM 2.5 ). The mapping of the CMAQ and CAMx species to the IMPROVE species c ounterparts is summarized in Tables 4-3a and 4-3b. Noted that for CMAQ and CAMx, water as a fine-particle sp ecies is not included among the mapping of IMPROVE species, because IMPROVE measures only dry particles. In addition, IMPROVE defines SOIL as fine so il concentration, which is the sum of the concentrations of several inorganic species. Al though fine soil is not specifically defined in CMAQ, it is taken as the unspeciated portion of PM 2.5 emitted species. Therefore, the CMAQ model species A25I and A25J are used as surr ogates for IMPROVE’s fine soil concentration. CAMx has the capability of modeling fine and coarse crustal (FCRS and CCRS) species separately from the unidentified fine and coarse other PM spec ies (FPRM and CPRM). However, since the CAMx other fine and coarse PM sp ecies were built from the CMAQ emissions, the separate modeling of the crustal species co uld not be performed in the WRAP modeling.

The IMPROVE data are available both as raw data and as processed data (i.e., with concentrations recalculated based on guidance in the Region al Haze Rule). The sulfate data are available either as elemental sulfur or as sulfate. We use the elemental sulfur in all WRAP MPEs. The IMPROVE data are periodically updated by the Visibi lity Information Exchange Web System (VIEWS). For the MPEs included in this repor t we used the VIEWS release of the IMPROVE data dated May 2004. A new update of the IMPROVE data was released in September 2005; this version will be incorporated in our future CMAQ and CAMx MPEs.

CASTNet. The Clean Air Status and Trends Network (CASTNet) was developed to monitor dry deposition. It includes measurements of ambient concentration and meteorology as well as data on land use, which are then used to calculate dr y deposition rates. A majority of the CASTNet sites measure sulfate, nitrate (including both gaseous [as HNO 3 ] and aerosol phase), and ammonium in seven-day filter samples. Detailed data collection procedures are described at the EPA CASTNet website: http://www.epa.gov/castnet/ . In short, atmospheric concentration data are collected at each site with open-faced, three- stage filter packs. The filter pack contains aTeflon filter for collection of particulate species, a nylon filter for nitric acid, and a baseimpregnated cellulose (Whatman) filter for sulfur dioxide. Filter packs are exposed for one-week intervals, and are later extrac ted and analyzed for certain species. Because CASTNet reports gaseous species (e.g., SO 2 and HNO 3 ) in the μ g/m 3 unit, while the CMAQ and CAMx models have gaseous species in the parts per milli on by volume (ppmv) unit, conversion factors (assumed under standard temperature and pr essure [STP] conditions) of 2617.6 and 2576.7 are applied for the modeled SO 2 and HNO 3 species. It has been suggest ed that total nitrate (NO 3 + HNO 3 ), instead of individual aero sol nitrate and gaseous nitric acid, should be used for observation-modeled comparison, because of the possible volatilization loss from the Teflon filter pack used in CASTNet (Ames and Malm, 2001). Note that a ratio of 0.9841, the molecular weight ratio of NO 3 /HNO 3 , is applied in CASTNet’s total nitrate calculation.

STN. EPA’s Speciation Trends Networ k includes about 215 monitori ng stations nationwide. It appears to us that these 215 site s may include IMPROVE sites or sites from other networks, so the STN network may duplicate some data included in some of the other monitoring networks. However, this needs to be verified. Daily PM 2.5 data are measured for 64 species in the STN network. We obtained some archived STN data files from the web site http://www.epa.gov/ttn/airs/airsaqs /detaildata/requestingaqsdata.htm

.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

53 NADP/NTN. The National Atmospheric Deposition Program/National Trends Network (NADP/NTN) is designed to measure wet depos ition. The network is a cooperative effort involving state Agricultural E xperiment Stations, the U.S. Geological Survey, the U.S. Department of Agriculture, and other government al and private entitie s. It includes over 100 sites in the continental United States, Alaska, Puerto Rico, and the Virgin Islands. The purpose of the network is to collect data on the chemis try of precipitation for monitoring of geographical and temporal long-term trends. The precipitation at each station is collected weekly and analyzed for hydrogen (acidity as pH), sulfate, nitrate, ammonium, chloride, and base cations (such as calcium, magnesium, potassium, and sodium). The NADP/NTN includes a quality assurance program, so we used these data without any additional QA. The NADP/NTN includes the Mercury Deposition Network (MDN) and the Atmo spheric Integrated Research Monitoring Network (AIRMoN), designed to study precipitation chemistry tr ends with greater temporal resolution. Precipitation samples ar e collected daily from a network of nine sites and analyzed for the same constituents as the NADP/NTN samples.

AQS. The Air Quality System database is EPA’s repository of “criteria air pollutants”: CO, NO 2 , SO 2 , O 3 , PM (PM 10 and PM 2.5 ), and Pb; it includes monitoring data from the 1970s forward. It replaced the Aeronomic Information Retrieval System (AIRS) as EPA’s main repository for ambient air monitoring data, incl uding data from the State and Local Air Monitoring Stations (SLAMS), the National Air Monitoring Stati ons (NAMS), the Photochemical Assessment Monitoring Stations (PAMS), and ot her sources of data. A majority of the hourly data for ozone and for several gaseous components (CO, NO 2 , SO 2 , O 3 ) can be retrieved from EPA’s web site, http://www.epa.gov/ttn/airs/airsaqs /detaildata/downloadaqsdata.htm , through data query requests. In this study, only hourly gaseous spec ies from AQS were used for model evaluations. Note that, unlike CASTNet data, gaseous spec ies in AQS are reported in the ppmv unit.

Table 4-2. Summary of ambient database availa ble for the model performance evaluation.

Monitoring Network Chemical Species Measured Sampling Frequency;

Duration Data Availability (Sites)

IMPROVE (Interagency Monitoring of Protected Visual Environments) Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr ~62

CASTNet (Clean Air Status and Trends Network) Speciated PM 2.5 , ozone Weekly; week ~72

STN (Speciation Trends Network) Speciated PM 2.5 Varies; varies ~215

NADP/NTN (National Acid Deposition Network/ National Trends Network) WSO 4 , WNO 3 , WNH 4 Weekly; weekly ~100

AQS (Air Quality System) O 3 , CO, SO 2 , NO, NO y Hourly; 1-hr average ~1,536Final Report for the WRAP 2002 Visibility Model Performance Evaluation

54 -2000 -1500 -1000 -500 0 500 1000 1500 2000 -1500 -1000

-500 0 500 1000

IMPROVE

CASTNet
SEARCH
STN
NADP AQS

Figure 4-1. Locations of sites in each of the PM ambient monitoring networks.

Figure 4-2. Locations of sites in the AQS gas eous species ambient monitoring network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

55 Neither the modeled PM concentr ations nor the measured PM c oncentrations correspond directly with concentrations of actual PM species becau se of different lumping schemes used in the model chemistry and because of artifacts in the measurement methods. Therefore, measured species cannot be compared directly to modele d species, so additional calculations (“mapping schemes”) must be applied to in order to comp are them. Tables 4-3a and 4-3b show the mapping schemes that we used in the CMAQ and CAMx MPEs.Final Report for the WRAP 2002 Visibility Model Performance Evaluation56Table 4-3a. Mapping of gaseous model species to gaseous observation species.Observation SpeciesModel SpeciesCompoundIMPROVECASTNetAQSCMAQCAMxGaseous SpeciesO3O3 O3 O3CO CO CO CONO NO NO NOSO2SO2 SO2 SO2SO2(μg/m3)TOTAL_SO2 2617.6*SO2 2617.6*SO2HNO3HNO3 HNO3HNO3(μg/m3)NHNO3(nylon filter)2576.7*HNO3 2576.7*HNO3NOyNO + NO2 + HONO + NO3 + 2*N2O5 +HNO3 + PAN + XNO2 + TPAN + HNO4NO + NO2 + HONO + NXOY +HNO3 + PAN + NTRFinal Report for the WRAP 2002 Visibility Model Performance Evaluation57Table 4-3b. Mapping of particulate model species to particulate observation species.Observation SpeciesModel SpeciesCompoundIMPROVECASTNetSTNNADP/NTNCMAQCAMxParticulate SpeciesSO43*S TSO4(Teflon filter)SO4 wSO4 ASO4J + ASO4I PSO4NO3NO3 TNO3(Teflon filter)NO3 wNO3 ANO3J + ANO3I PNO3ParticulateNO3+SO40.29*TNO3+0.375*TSO40.29*(ANO3I + ANO3J) +0.375*(ASO4I + ASO4J)0.29*PNO3 + 0.375*PSO4Total NO3(gas+particle)TOTAL_NO3ANO3I + ANO3J +0.9841*2576.7*HNO3PNO3 + 0.9841*2576.7*HNO3NH40.375*SO4 +0.29*NO3TNH4(Teflon filter)NH4 wNH4 ANH4J + ANH4I PNH4OC 1.4*(OC1+OC2 + OC3 +OC4 + OP)OC AORGAJ + AORGAI +AORGPAJ + AORGPAI +AORGBJ + AORGBIPOA + SOA1 + SOA2 + SOA3 +SOA4EC EC1 + EC2 +EC3 - OPEC AECJ + AECI PECTCM OC+ECSOIL 2.2*Al +2.49*Si +1.63*Ca +2.42*Fe +1.94*TiA25I +A25J FCRS + FPRMFinal Report for the WRAP 2002 Visibility Model Performance Evaluation58Observation SpeciesModel SpeciesCompoundIMPROVECASTNetSTNNADP/NTNCMAQCAMxParticulate Species (continued)CM MT - FM ACORS + ASEAS + ASOIL CCRS + CPRMPM2.5FM PM25 ASO4J + ASO4I + ANO3J +ANO3I + ANH4J + ANH4I+AORGAJ + AORGAI +AORGPAJ + AORGPAI +AORGBJ + AORGBI + AECJ +AECI + A25J + A25IPSO4 + PNO3 + PNH4 + POA +SOA1 + SOA2 + SOA3 + SOA4+ PEC + FCRS + FPRMRCFM 1.375*SO4 +1.29*NO3 +EC + OC +SOIL1.375*(ASO4J + ASO4I) +1.29*(ANO3J + ANO3I) +AORGAJ + AORGAI +AORGPAJ + AORGPAI +AORGBJ + AORGBI + AECJ +AECI + A25J + A25I1.375*PSO4 + 1.29*PNO3 +POA + SOA1 + SOA2 + SOA3+ SOA4 + PEC + FCRS + FPRMPM10MTPM25 + CM PM25 + CMFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

59 

### 4.3 Model Performance Evaluation Approach

The objective of an MPE is to compare model-s imulated concentrations with observed data to determine whether the model’s performance is suffi ciently accurate to justify using the model for simulating future conditions. There are a number of challenges in completing an annual MPE for regional haze. The model must be compared to ambient data from several different monitoring networks for both PM and gaseous species, for an annual time period, and for a large number of sites. The model must be evaluated for both the worst visibility conditions and for very clean conditions. Finally, final gui dance on how to perform an MPE fo r fine-particulate models is not yet available from EPA. Therefore, we expe rimented with many different approaches for showing model performance results. The plot types that we recomm end as being the most useful are the following:

• Time-series plots comparing the measured a nd model-predicted species concentrations

• Scatter plots showing model predictions on the y -axis and ambient data on the x -axis

• Spatial analysis plots with ambien t data overlaid on model predictions

• Bar plots comparing the mean fractional bi as (MFB) or mean fractional error (MFE) performance metrics (these a nd other performance metrics ar e described in Section 4.3.4)

• “Bugle plots” showing how model performance varies as a function of the PM species concentration

• Stacked-bar plots of contribut ions to light extinction for the average of the best-20% visibility days or the worst-20% visibili ty days at each site; the higher the light extinction, the lower the visibility

These plots are all described in Section 4.3.1. UCR developed an MPE software package that we used to produce the full set of model evaluation metrics and plots described below. The software is available for download from the RMC web site and is described in a separate report, also available for download on the web site: http://pah.cert.ucr.edu/aqm/308/ cmaq_faq/cmaq_ucr_mpe.shtml

.
Sections 4.3.2 and 4.3.3 describe the subdomains a nd performance time periods we analyzed in the MPEs, while the various performance me trics used are defined in Section 4.3.4.

Each plot type or metric summari zes the MPE in a different way. It is expected that the states and tribes will select from among these plots those that are most useful for conveying the modeling results to stakeholders and for preparing the SIP/TI P technical support documents.



### 4.3.1 The most useful plot types for the MPEs

4.3.1.1 Time-series analysi s at individual sites

We created a set of time-series plots that co mpare the measured and model-predicted species concentrations. For each monitoring site, we genera ted either a single plot for the full year or separate plots by month. Plots for all sites and species are available at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml . Figure 4-3 shows an example plot for sulfateFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

60 ambient data and model concentrations for two different model simulations at the Grand Canyon IMPROVE site.

Because of the large number of plots generate d to cover all sites and all species, we also developed a more comprehensive comparison of model output and observa tions that shows the contribution of each PM component to haze in a singl e, stacked-bar time-series plot for each site. Each plot has two parts: the t op graphic, displaying ambient data , shows the contribution of each PM component to daily average visibility impairment for each day that has ambient data; the bottom graphic, created using model output, shows th e contributions to visibility impairment for each of the 365 modeled days. Figure 4-4 shows an example stacked-bar time-series plot for the Grand Canyon IMPROVE site. The two graphics presen ted together in each plot provide a useful summary of temporal variability in model performa nce; for example, if the model mislocates the high concentration by one day in time, these plot s still show the close temporal association between the modeled and observed data. * The stacked-bar time-series plots use the same fixed y

axis scale for all IMPROVE sites, to facilitate the comparison of variability across sites and also to help establish a characteristic “fingerprint ” for each IMPROVE site. These plots are very useful for presenting model and ambi ent data results to stakeholders.

Figure 4-3. Example time-series plot of su lfate at the Grand Canyon IMPROVE site showing ambient data and two sets of model results for June 2002 (Julian dates 152 to 180).

* Note that this type of information is lost in the animated spatial plots discussed in Section 4.3.1.3.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

61 Figure 4-4. Example stacked-bar time-series plot for the Grand Canyon IMPROVE site showing ambient data (top) and model output (bottom) contributions to light extinction.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

62 4.3.1.2 Scatter plots for each site

We created scatter plots show ing model predictions on the y -axis and ambient data on the x -axis for all data sites, either indivi dually for each month or for the en tire year. This provides a quick summary of the model performance, although it does not include any information about temporal or spatial variability. Figure 4-5 shows an exam ple of a scatter plot for modeled sulfate and IMPROVE data sulfate. The plot-generation softwa re we use also calculates all of the various performance metrics (see Secti on 4.3.4) while producing the sc atter plot, and automatically includes several metrics in the text label on the pl ot. Typically, we prepare a set of these plots for each month of 2002 to show seasonal variability in the model performance, and we usually compare two different model simulations—for example, Figure 4-5 shows model-predicted sulfate for CMAQ v4.4 and CMAQ v4.5 compared to the IMPROVE data. The full set of scatter plots is available at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml

.

Figure 4-5. Example scatter plot comparing CMAQ v4.4 and CMAQ v4.5 model results to ambient data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

63 4.3.1.3 Spatial analysis plots

We developed spatial plots that use the Progr am for the Analysis and Visualization of Environmental data (PAVE) visual ization software to overlay am bient data on top of the model predictions. With one plot for each day during 20 02, there is a set of 365 plots for each species measured at the IMPROVE sites. These are available on the RMC web site ( http://pah.cert.ucr.edu/aqm/308/cmaq.shtml ), where we use a graphi cs tool to conveniently animate the plots. Figure 4-6 shows an exam ple plot. These plots show the IMPROVE data superimposed in colored symbols over the mode l-simulated data. The more often the background color (model output) matches the color of the di amond symbol (ambient data), the better the model performance; a glaring mismatch in color indicates poor model performance at that site. These plots are useful for showi ng spatial distributions of the data as we ll as instances where poor model performance has occurred because the model has mislocated the high concentrations by just a few grid cells from the observed high c oncentrations. These plots are also useful for presenting the model and ambient data to stakeholders.

Figure 4-6. Example PAVE spatial plot with ambient data shown in symbols overlaid on model results shown in ba ckground for the 12-km CMAQ MPE.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

64 4.3.1.4 Bar plots of averag ed model performance metr ics: Fractional bias and

error

We used the MPE software to calculate the aver age of the model performance metrics for each month (see Section 4.3.4), and summ arized these results using bar plots to compare the monthly average mean fractional bias (MFB) or mean fractional error (MFE) for each species. This provides a quick summary of the variability in se asonal model performance. These plots are also useful for quickly comparing model performan ce results for different models and for model sensitivity experiments. Figure 4-7 is an exampl e bar plot showing monthly variability in MFB. Additional plots are available at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml

.

Figure 4-7. Example bar plot sh owing monthly variability in mean fractional bias (MFB) for two model simulations.

4.3.1.5 Bugle plots showing model performance goals and criteria

We produced “bugle plots” to show how model performance varies as a function of the PM species concentration. Monthly av erage error and bias terms are plotted in a bugle plot as a function of the species concentration on the x -axis (see Figure 4-8 for an example). These plots help characterize differences in model performa nce for clean versus polluted conditions. They also include curves showing model performance goals and model performance criteria
, explained below.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

65 Figure 4-8. Example bugle plot showing monthly average sulfate MFB for the CMAQ v4.5 model simulation as a function of the average of the modeled and observed sulfate concentration for the CAS TNet, IMPROVE, and STN data.

Bugle plots were first suggested by James Boyl an of the Georgia Department of Natural Resources. As defined by Boylan and Ru ssell (2005), the performance goals are MFB ≤ ± 30% and MFE ≤ + 50%, and the performance criteria are MFB ≤ ± 60% and MFE ≤ +75%. The performance goals are the more stringent of the two sets of metric s; we believe a goodperforming model would achieve these. The performan ce criteria are less strict . If the criteria are equaled or exceeded, it suggests potential probl ems with the model simulation. The goals and criteria increase at lower concentrat ions according to the following equations:

Performance Goal:

Performance Criteria:

where C o is the observation concentration and C m is the model-predicted concentration.

Note that the performance goal curves in our bugle plots are arbitrarily defined based on ozone performance goals and may represent an unreas onable goal for PM pe rformance. The model 

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66 criteria curves indicate the skill of a wide array of previous PM modeling studies. We are not suggesting that these performance goals/criteria be generally adopted or th at they are the most appropriate to use. Rather, we are using them just to frame and put the PM model performance into context and to facilitate model performa nce intercomparison across episodes, species, models, and sensitivity tests. Additional plots are availa ble on the RMC web site at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml

. 4.3.1.6 Analysis of best-20% and worst-20% visibility days

We prepared stacked-bar plots of contributions to light extinction for the average of the best-20% visibility days or the worst-20% visibility days at each site. Each plot shows one of three types of information:

1. Observed and modeled data matched (paired) in space and time.
2. Observed and modeled data relaxed (also refe rred to as “unpaired”) in time; see Section 4.3.3 for a definition of “relaxed in time.”
3. Observed and modeled data relaxed (unpaired ) in both space and time; see Sections 4.3.2 and 4.3.3 for definitions of “relaxed in sp ace” and “relaxed in time,” respectively.

Figure 4-9 shows an example relaxed-in-time plot. In this plot, the first stacked bar is for the ambient data, and the second stacked bar is for the model prediction for the same grid cell and day of the ambient measurement. For the best-20% average plots, the next three stacked bars are for the best visibility day selected from e xpanding time windows of 3 days (i.e., including one day before and after the measurement), 7 days, and 15 days. For the worst-20% average plots, the worst visibility day is select ed from the expanding time windows.

The purpose of this comparison is to determine wh ether the model can simulate the full range of clean to polluted conditions that we observe in the ambient data at the individual sites. It increases our confidence in the model as a “reas onable version of real ity” if it does well at simulating this range. However, the comparison should not be used as a primary model performance evaluation test because it is likely to obscure errors in the mo del. In particular, we do not want to select the best-fit model resu lts from the relaxed in time and space analysis because this would be an arbitrary use of the mo del results to demonstrate a match to the data without any evidence that the match was achieve d by correctly simulating the air quality. Such a use would also increase the pot ential for compensating errors in the model and give us an exaggerated overconfidence in the model performance. Instead, these plots should be used as a corroborative analysis method designed to gauge mo re generally our confid ence in the reliability of the model for simulating future conditions. Additional plots are available on the RMC web site at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml

.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

67 Figure 4-9. Example stacked-bar pl ot of contributions to light extinction for the average of the worst-20% visibility days at an IM PROVE site, for data relaxed in time.



### 4.3.2 Subdomains analyzed for the MPEs

All MPE results in this report include only thos e monitoring sites that are located within the WRAP region—that is, within the 12-km grid map fo r the western states shown in blue in Figure 1-2. Monitoring sites in other RPO regions are no t included because our analysis is designed to evaluate model performance only for the WRAP region. We included the same set of monitoring sites for both the WRAP 36-km and 12-km domain s so the model results for the two grids of different resolutions are directly comparable. For most of the model evaluation plots described in Section 4.3.1, we used the model concentration from the grid cell in which the monitor site is located. Normally, we do not consider the exact lo cation of the site within the grid cell, and we do not attempt to interpolate from surrounding m odel grid cells. However, in calculating the performance metrics for the average of the be st-20% and worst-20% days (see Section 4.3.1.6), we performed a “relaxed-in-space” comparison in which we compared the model prediction to the highest concentration in th e surrounding nine grid cells for the 20% worst days, and to the lowest concentration in th e surrounding nine grid cells for the 20% best days.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

68 

### 4.3.3 Performance time periods

The CMAQ and CAMx model simulations were performed for calendar year 2002, and the models output the hourly average concentrations for all gaseous and PM species needed for comparing the models to the ambient monitoring data. Because the data collection methods and averaging periods differ across the various ambi ent monitoring networks, for comparing modeled and observed data we always calculate an average of the modeling data that is consistent with the monitoring period used at each of the monitoring networks. This can range from a 1-h average for gaseous species at the AQS s ites, to a 24-h average at the IMPROVE sites, to a 1-wk average at the CASTNet sites. We present the MPE resu lts in a variety of ways, ranging from very detailed analyses at each mon itoring site to more general summaries of the model results for monthly and annual average bias and error over all sites in a given monitoring network. As discussed in Section 4.3.1.6, the model performan ce plots for the average of the best-20% and worst-20% days include a “relaxed-in-time” comp arison of the modeled and observed data in which the highest model concentration is select ed within a narrowly defined time window to compare with the worst-20% visibility days, and the lowest model concentration is selected within a narrowly defined time window to co mpare with the best-20% visibility days.



### 4.3.4 Description of performance metrics

Model performance metrics are statistical measur es used to quantify the model’s performance relative to the observed data in terms of bias a nd error. These statistics can be computed in a variety of different ways, as shown in Tabl e 4-4. Traditionally, ozone model performance evaluations were performed using the mean nor malized error (MNE) and mean normalized bias (MNB) performance metrics. However, this a pproach overemphasized model overpredictions because the minimum and maximum bounds on the mean normalized bias ranged from 0 to infinity. For ozone modeling, an observed concentra tions cutoff is used (typically 60 ppb) that keeps the MNB and MNE performance metric s from “blowing up.” For the WRAP model performance evaluation this was especially pr oblematic because we must evaluate model performance on the cleanest days when ambient da ta concentrations are low, leading to very large computed values of MNB and MNE.

An alternate approach is to use mean fractional bias (MFB) and mean fractional error (MFE). For the MFB metric, the minimum and maximum rang e in bias is symmetric about zero and can extend to + 200%; thus, it does not ov eremphasize the importance of model overpredictions. Similarly, the MFE is bounded from 0 to +200% and cannot “blow up” to inordinately high numbers like the MNB and MNE can. In our mode l performance evaluation, we computed and saved all of the bias and error metrics shown in Table 4-4. However, we usually present only the mean fractional bias and gross erro r metrics, as they have been found to provide the most useful descriptions of model performa nce, especially when combined with the graphical displays discussed in Section 4.3.1.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

69 Table 4-4. Recommended statistical measur es for model performance evaluation.

Measure Mathematical Expression Notation Accuracy of unpaired peak ( A u )

peak peak u peak 

### O O P

− O peak = peak observation;

u peak P = unpaired peak prediction within certain surrounding grid cells of peak observation Accuracy of paired peak ( A

p )

peak peak 

### O O P

− P peak = paired (in both time and space) peak prediction

Coefficient of determination ( r

2 )


## ∑ ∑ ∑

= =

=

− − ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− −

N

i N

i i i N

i i i

O O P P O O P P

1 1 2 2 2

1 ) ( ) ( ) )( ( P i = prediction at time and location i ; O i = observation at time and location i ;
P = arithmetic average of P i , i=1,2,..., N;
O = arithmetic average of O i , i=1,2,...,N

Normalized Mean Error ( NME ) 

# ∑ ∑

=

= 

### −

N

i i N

i i i



### O O P

1 1 Reported as %

Root Mean Squared Error ( RMSE ) 

## ( ) 2 1

1 2 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− 

## ∑

= N

i i i O P N

Mean Fractional Error ( MFE ) 

# ∑

= 

### + − N

i i i i i 

### O P O P
N 1 

### 2 Reported as %

Mean Absolute Gross Error ( MAGE ) 

# ∑

= 

### − N

i i i 

### O P N 1 

### 1

Mean Normalized Gross Error ( MNGE ) 

# ∑

= 

## − N

i i i i 

## O O P
N 1 

## 1 Reported as %

Mean Bias ( MB ) 

# ( ) 

# ∑

= 

### − N

i i i 

### O P N 1 

### 1

Mean Normalized Bias ( MNB ) 

## ( ) 

# ∑

= 

### − N

i i i i 

### O O P
N 1 

### 1 Reported as %

Mean Fractional Bias ( MFB ) 

# ∑

= 

### ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ − N

i i i i i 

### O P O P
N 1 

### 2 Reported as %Final Report for the WRAP 2002 Visibility Model Performance Evaluation

70 Measure Mathematical Expression Notation

Normalized Mean Bias ( NMB ) 

## ∑ ∑

= = −

N

i i N

i i i

O O P

1 1 )

( Reported as %

Bias Factor ( BF ) 

# ∑

= N

i i i 

### O P
N 1 

### 1 Bias factor = 1 + MNB; reported as ratio notation (prediction : observation)



### 4.4 CMAQ Model Performance E valuation in the WRAP States for Calendar Year 2002

In the CMAQ MPE discussions below, for each sp ecies we present three types of plots that summarize the results as monthly averages for al l sites in each monitoring network. These plots include:

• A bar plot showing the CMAQ version 4.4 and CMAQ version 4.5 MFBs compared to the IMPROVE data

• A bar plot of MFB for those species with data from multiple monitoring networks

• Bugle plots of MFB and MFE for each species

This is the minimum set of plots needed to evaluate model performance. Additional plots reflecting model performance at the individual IMPROVE monitori ng sites are included in the appendices to this report and on the RMC web site. We focus here on MPE results for the IMPROVE sites because IMPROVE data are measur ed in Class I areas, making these data the most pertinent to the Regional Haze Rule and the ultimate purpose of the model performance evaluation; however, results for the other mon itoring networks are also included on the RMC web site.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

71 

### 4.4.1 Sulfate (SO 4 

### )

Figure 4-10 shows the CMAQ v4.4 and v4.5 modeled su lfate compared to the IMPROVE sulfate data. CMAQ v4.4 overpredicted sulfate in the wi nter and underpredicted it in the summer. CMAQ v4.5 predicted consistently lower sulf ate than did CMAQ v4.4, which meant that CMAQ v4.5 had improved performance (i.e., lower positiv e bias) in the winter months and poorer performance (i.e., increased negative bias) in the summer months. The reduced sulfate in CMAQ v4.5 may be caused in part by increased depos ition rates for particles in CMAQ v4.5.

Figure 4-11 compares the CMAQ v4.5 sulfate to the IMPROVE, CASTNet, and STN networks. The model performance is similar for all three netw orks, although negative bias is slightly larger for the STN sites, which are more influenced by urban conditions than either CASTNet or IMPROVE sites.

Figure 4-12 shows the bugle plots of MFB and MF E for sulfate for the same three monitoring networks. There is a slight tendency for CMAQ v4.5 to overpredict the lowest sulfate concentrations (i.e., the winter sulfate at th e IMPROVE sites), and to underpredict the higher concentrations. The model’s sulf ate performance is quite good, with all of the IMPROVE and CASTNet monthly average MFBs and MFEs falli ng within the model performance goals, and the STN results close to the model performance goals.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

72 Figure 4-10. Sulfate monthly MFB fo r the 36-km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data.

Figure 4-11. Monthly average sulfate MFB for CMAQ version 4.5 36-km results compared to the CASTNet, IMPROVE, and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

73 Figure 4-12. Bugle plot of monthly average sulfate MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the CA STNet, IMPROVE, and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

74 

### 4.4.2 Nitrate (NO 3 

### )

Figure 4-13 shows the CMAQ v4.4 and v4.5 modeled ni trate compared to the IMPROVE nitrate data. As for sulfate, CMAQ v4.4 overpredicted nitr ate in the winter and underpredicted it in the summer. Again as for sulfate, CMAQ v4.5 predicte d consistently lower nitrate than did CMAQ v4.4, which meant that CMAQ v4.5 had improved performance (i.e., lower positive bias) in the winter months and poorer performance (i.e., increas ed negative bias) in the summer months. The reduced nitrate in CMAQ v4.5 may also be caused by increased de position rates for particles in CMAQ v4.5.

Figure 4-14 compares the CMAQ v4.5 nitrate to the IMPROVE, CASTNet, and STN networks. The model performance is generally similar for al l three networks, with the exception of larger negative bias for the STN sites in the winter months.

Figure 4-15 shows the nitrate bugle plots for th e three monitoring networks. For the IMPROVE and CASTNet data, CMAQ v4.5 has the largest unde rpredictions for nitrate in the summer months, when the nitrate concentrations are lowest , and there is a small tendency to overpredict nitrate in the winter months at the CASTNet and IMPROVE sites, when the nitrate concentrations are greatest. The model meets the perfor mance goals for both the CASTNet and IMPROVE data, but it fails to meet both the performance goa ls and the less stringent performance criteria for most months for the STN data. A possible expl anation is that the STN sites are located near urban areas with high NO x emissions and high nitrate concentr ations, and that the coarse grid resolution of the 36-km model causes artificial dilution and underpredictions for these sites. However, results from the 12-km CMAQ simulati ons (discussed in S ection 4.6) do not support this hypothesis; the 12-km CMAQ model has very similar performan ce to the 36-km model, with large underpredictions of nitrate for the STN sites.

If we focus primarily on the results at the IM PROVE and CASTNet sites, the model performance for nitrate is reasonably good. Although there are la rge underpredictions fo r the summer months, the summer also has high temperatures and low nitrate concentrations, so even large negative bias errors in nitrate in the summer months will have a relatively small effect on the error in total light extinction and visibility.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

75 Figure 4-13. Nitrate monthly MFB for the 36-km results for CMAQ for versions 4.4 and 4.5 compared to the IMPROVE data.

Figure 4-14. Monthly average nitrate MFB for CMAQ version 4.5 36-km results compared to the CASTNet, IMPROVE, and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

76 Figure 4-15. Bugle plot of monthly average nitrate MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the CA STNet, IMPROVE, and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

77 

### 4.4.3 Organic carbon (OC)

Figure 4-16 shows the CMAQ v4.4 and v4.5 modeled OC compared to the IMPROVE OC data. CMAQ v4.4 overpredicted OC during all 12 months, with the positive MFB varying from 15% to 40%. CMAQ v4.5 had significantly improved perf ormance, with lower OC predictions and MFB close to zero for several months and a maximum MFB of about 20%.

Figure 4-17 compares the CMAQ v4.5 OC to the IMPROVE and STN networks. The CMAQ results had uniformly large negati ve biases for the STN sites.

Figure 4-18 shows the OC bugle plots for the two monitoring networks. For IMPROVE, CMAQ v4.5 meets the performance goals for MFB for all months, and it meets the performance goals for MFE for all but two months. In contrast, the model fails to meet the performance goals for OC at the STN sites for all 12 months, and it fails to m eet the less stringent performance criteria for most months. A couple comments on the OC model performance should be made:

• The STN OC measurements are believed to be less reliable than the IMPROVE data, since the former are not blank corrected.

• The OC measurements were adjusted to or ganic matter carbon (OMC ) using a 1.4 factor. This value is likely too low; OMC/OC ra tios between 1.6 and 2.2 have been suggested, with an average value of 1.8. Using a 1.8 OM C/OC ratio factor would reduce the OC overprediction bias.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

78 Figure 4-16. OC monthly MFB fo r the 36-km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data.

Figure 4-17. Monthly average OC MFB for CMAQ version 4.5 36-km results compared to the IMPROVE and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

79 Figure 4-18. Bugle plot of monthly average OC MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the IMPROVE and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

80 

### 4.4.4 Elemental carbon (EC)

Figure 4-19 shows the CMAQ v4.4 and v4.5 modeled EC compared to the IMPROVE EC data. CMAQ v4.4 underpredicted EC dur ing the winter and overpredi cted it during the summer. CMAQ v4.5 predicted lower EC concentrations th an did CMAQ v4.4; this resulted in larger negative bias during the winter months, but im proved performance during the summer months, with MFB close to zero in July and August.

Figure 4-20 compares the CMAQ v4.5 EC to the IMPROVE and STN networks. CMAQ had better performance for EC at the STN sites, with smaller negative bias in most months and small overpredictions of EC in summer months.

Figure 4-21 shows the EC bugle plots for both monitoring networks. CMAQ v4.5 met the performance goals for EC for both the IMPROV E and STN data. Interestingly, the model had opposite trends in performance for the two netw orks. For IMPROVE the modeled negative bias increased for the months with the lowest meas ured EC, while for STN the model tended toward negative bias for the those months w ith the highest EC concentrations.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

81 Figure 4-19. EC monthly MFB fo r the 36-km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data.

Figure 4-20. Monthly average EC MFB for CMAQ version 4.5 36-km results compared to the IMPROVE and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

82 Figure 4-21. Bugle plot of monthly average EC MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results a nd the IMPROVE and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

83 

### 4.4.5 Other fine PM (soil)

Figure 4-22 shows the CMAQ v4.4 and v4.5 modeled soil compared to th e IMPROVE soil data. CMAQ v4.4 overpredicted soil during the wint er months and underpredicted it during the summer months. CMAQ v4.5 predicted lower soil c oncentrations than did CMAQ v4.4; this resulted in larger negative bias during the wi nter months, but improved performance during the summer months. (Note that a plot compari ng CMAQ performance across multiple ambient monitoring networks is not included for soil be cause these data are measured only at the IMPROVE network.)

Figure 4-23 shows the soil bugle plots for the IMPROVE network. CMAQ v4.5 met the performance goals for soil for most months, and wh en it failed to meet the performance goals it did meet the performance criteria.

Figure 4-22. Soil monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

84 Figure 4-23. Bugle plot of monthly average soil MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results and the IMPROVE network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

85 

### 4.4.6 Coarse mass (CM)

Figure 4-24 shows the CMAQ v4.4 and v4.5 modeled CM compared to the IMPROVE CM data. CMAQ v4.4 underpredicted CM dur ing most months, and the unde rpredictions became worse with CMAQ v4.5. (Note that a plot compari ng CMAQ performance across multiple ambient monitoring networks is not included for CM b ecause these data are measured only at the IMPROVE network.)

Figure 4-25 shows the CM bugle plots for the IMPROVE network. CMAQ v4.5 failed to meet both the performance goals and the performa nce criteria for CM for most months.

Figure 4-24. CM monthly MFB fo r the 36-km results for CMAQ versions 4.4 and 4.5 compared to the IMPROVE data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

86 Figure 4-25. Bugle plot of monthly average CM MFB (top) and MFE (bottom) for CMAQ version 4.5 36-km results for the IMPROVE network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

87 

### 4.4.7 Ozone

Although ozone does not directly affect visibility, it is an important oxidant that affects the production of nitrate, sulfate, and OC from s econdary organic aerosols. Thus, the model must accurately simulate ozone to correctly represen t the formation of several SOA species. Figure 4-26 compares the ozone performance for CMAQ v4.4 and v4.5 for the AQS data. Following traditional ozone model performance guidance, we excluded all ambient data for which the measured concentration was less than 60 ppb. As a result, the comparison was weighted toward those days with high ambient ozone, and this te nded to cause the model to underpredict the data, especially for the winter months during which th ere were few days with measured ozone greater than 60 ppb. We also performed this analysis wit hout the filter on the ambient data so that all ozone data were included in the comparison. In that comparison (not shown here) the model tended to overpredict ozone, with larger overpredictions occu rring during the winter months. Overall, we expect CMAQ to overpredict total ox idant and to slightly ov erpredict formation of SOA for low ambient ozone conditions, and to sli ghtly underpredict formation of SOA for high ambient ozone conditions.

Figure 4-26. Ozone monthly MFB for the 36- km results for CMAQ versions 4.4 and 4.5 compared to the AQS hourly average ozone data, using a filter to exclude all hours with data values less than 60 ppb.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

88 

### 4.5 Evaluation of CMAQ at Class I Areas for the Best-20% and Worst-20% Visibility Days

Plots of the model performance av eraged for the best-20% and wors t-20% visibility days at each IMPROVE site in the Class I areas of the WRAP region are included in the following appendices:

Appendix B: Stacked-Bar Plots for CMAQ—Annual Time Series of Contributions to Light Extinction Appendix C: Stacked-Bar Plots for CMAQ—Contribu tions to Light Extinction for the Average of Best-20% Visibility Da ys, with Data Relaxed in Time Appendix D: Stacked-Bar Plots for CMAQ—Contribu tions to Light Extinction for the Average of Worst-20% Visibility Da ys, with Data Relaxed in Time Appendix E: Stacked-Bar Plots for CMAQ—Contribu tions to Light Extinction for the Average of Best-20% Visibility Days, with Data Relaxed in Time and Space Appendix F: Stacked-Bar Plots for CMAQ—Contribu tions to Light Extinction for the Average of Worst-20% Visibility Days, with Data Relaxed in Time and Space

These plots were prepared for the CMAQ v4.4 si mulation using the 2004 release of the VIEWS IMPROVE data. For all plots we used the default, monthly average relative humidity at each site to calculate the light extinction for each PM speci es. Each appendix contains a total of 88 plots, one plot for each of the WRAP IMPROVE site s. As discussed in Section 4.3.1, the annual stacked-bar time-series plots are useful for co mparing the model results for model-simulated total light extinction to the obs erved light extinction calcula ted for each IMPROVE site. An example of these plots was shown in Figure 4-4. Comparing the plots acro ss the 88 sites, it is apparent that many of the sites ha ve a unique pattern or “fingerpri nt” of seasonal variation in the contributions of different PM species to total light extinction. In general the model provides a reasonably good approximation of the fingerprint of the ambient data at many of the IMPROVE sites. An advantage of these plots is that th ey indicate good performance in a qualitative sense when the model predicts peak PM concentrations within a day or two of the ambient data peaks. Because the data are sparse relative to the m odel results (i.e., sampling occurs only every third day), a strictly quantitative ev aluation using error and bias me trics would indicate very poor model performance on some days, when for those same days the stacked-bar time-series plot indicates relatively good performance. These plot s are useful for indica ting the ability of the model to simulate observed conditions reasonably well, and they provide a sense of increased confidence in the model’s skill compared to si mply relying on the ta bulated error and bias metrics.

Appendices C and D present stacked-bar plots of c ontributions to light extinction for the average of the best-20% and worst-20% visibility days, respectively, with the mode led and observed data relaxed in time. The motivation for these plots, as discussed in Section 4.3.1.6, is to determine whether the model can simulate the extremes of clean and polluted conditions at the IMPROVE sites. An example plot was shown in Figure 4-9.

Paging through the PDF file of these plots shows that at most sites the model overpredicts the observed data on the cleanest days and underpredi cts the observed data on the most polluted days when matched precisely in space and time. Howeve r, comparing the bar plot for the 3-day timeFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

89 window to the observed data, it is also apparent that the model can match the extremes of clean and polluted conditions at many sites simply by expanding the comparison by one day before and after the measured data. For the 7-day and 15-day time windows, there is some additional decrease in modeled extinction for the cleanest days (and some additional increase in modeled extinction for the most polluted days), but most of the change occurs within the 3-day time window. These results indicate that the model can simulate the extremes of clean and polluted conditions, or that the model is able to simulate the dynamic range of visibility conditions observed at the IMPROVE sites. As noted earlie r, however, this should not be construed to represent a validation of the model, because th e “improvements” in model performance may be an artifact of compensating errors in the mode l input data. Instead, these plots are useful as supporting evidence that the model can predict th e extremes of the clean and polluted condition, and they show that the model is not unresponsive to combinations of conditions of input data that could predict clean or polluted extremes.

Appendices E and F are analogous to Appendices C and D, except th at the comparisons of model results and observed data are relaxed in both time and space. Given the known errors in wind direction and wind speed, it is plausible that, at least in some cases, the model may mislocate the extreme clean and polluted events by one day, or may spatially offset the extremes by one or more grid cells.



### 4.6 Grid Resolution Sensitivity Analysis: CMAQ 12-km Visibility Modeling for the WRAP Region

Using CMAQ, we performed a model sensitivity simulation using a 12-km horizontal resolution grid for the WRAP nested subdomain shown in blue in Figure 1-4. First, a 12-km simulation of the MM5 model and emissions processing at the 12-km resolution were completed in order to develop the high-resoluti on input data required for this sens itivity simulation. Compared with a 36-km grid, a 12-km grid requires a factor of 9 increase in the number of grid cells needed to represent the same geographic area. The ar ea in each 12-km grid cell is only 144 km 2 compared to 1,296 km 2 in each 36-km grid cell. Possible benefits of using a finer-resolution 12-km grid instead of a 36-km grid include the following:

• Better resolved and possibly more accurate meteorology and emissions input data.

• Less numerical dispersion, resulting from redu ced artificial dilution compared to point sources being averaged over the coarse 36-km grid.

• Less numerical dispersion in the advection algorithms.

• Improved accuracy resulting from higher topographical resolution.

• More accurate treatment of nonlinear photochemical reactions.

• More precise location of the ambient monitoring sites relati ve to topographical features and emissions sources.

The primary disadvantage of running a 12-km mode l is the increased time and compute resource costs: (1) the additional cost of modeling the much larger number of grid cells required to represent the nested subregion, and (2) the addi tional cost of having to perform a coarse-gridFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

90 model simulation as well as the 12-km simulatio n, because the 36-km simulation is needed for developing boundary conditions for the 12-km grid.

Figures 4-27 and 4-28 show the CMAQ 12-km and 36-km model MFB compared to the IMPROVE, CASTNet, NADP/NTN, and STN data fo r sulfate and nitrate, respectively. Figure 4-29 displays the CMAQ 12-km and 36-km mode l MFB compared to the IMPROVE OC, EC, SOIL, and CM. Finally, Figure 4-30 shows the CMAQ 12-km and 36-km model MFB compared to the AQS gas-phase ambient data for ozone an d CO, where the ozone MFB is calculated using the ambient data filter to exclude measured ozon e values less than 60 ppb, as described earlier. Each of these plots shows the same general re sult: although there are sm all differences between the MFBs calculated for the 12-km and the 36-km models, their performance is quite similar. It is not apparent that one version of the model is in any way superior to the other. These results indicate that there would be no advantage in terms of our MPE to running the more resourceintensive 12-km model inst ead of the 36-km model.

We also examined the spatial resolution di fferences between the CMAQ 12-km and 36-km results by preparing PAVE plots that have ambient IMPROVE data overlaid on the model results, using the same spatial domains for bot h versions of the model—in other words, we extracted data from the 36-km domain results to make the plots directly comparable to the 12-km model domain. The daily spatial plots can be viewed on the web site using the animation tool at this link:

http://pah.cert.ucr.edu/aq m/308/cmaq.shtml#base02aV4512kvs36k

. To compare the 12-km and 36-km plots via the web site, it is necessary to open two browsers so that both a 12-km and a 36-km plot can be view ed at the same time. Note that the IMPROVE data are measured only every third day, so only one-third of these plots will show both model and ambient data.

To illustrate this comparison here, we selected a pair of plots for a single day for the IMPROVE sulfate, nitrate, OC, EC, and so il; these results are shown, resp ectively, in Figures 4-31 through Figure 4-35. Each pair of plots shows the same ge neral result. The spatial features or plumes in the concentrations are more detailed in the 12 -km plot than in the 36-km plot, and the peak model value in the domain is usually considerably greater in the 12-km model than in the 36-km model. This is a result of th e reduced numerical dispersion in the 12-km model, as discussed above. (Note that these differences in the domain p eak concentration can be read only in the text at the bottom of each plot, where the grid ce lls with the domain-wide minimum and maximum are identified in each plot. The differences in pe ak values between the 12-km and 36-km results are not apparent in the plots themselves because the color scale was chosen at the low range of the modeled concentration, in order to distinguish concentration gradient s in the areas of the domain that have generally cl ean conditions with low PM con centrations. Choosing a greater value in the color range would primarily identi fy urban areas and woul d not distinguish PM concentration gradients in or near Class I areas.)

Next, we note that in each pair of plots, th e increased spatial detail and increased peak concentration do not significantly change the gene ral appearance of the pl ots nor do they change the regions that experience high or low PM concen trations. Thus, the spatial plots are consistentFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

91 with the results of the model performance metric comparison for the 12-km and 36-km model results discussed above: there appears to be no significant benefit in using the finer-resolution grid for modeling the lower concentrations of PM that typically occur at Class I areas.

There remains a possibility that the 12-km model might have a different response to emissions controls than the 36-km model does, even though there is no substantial difference in the model performance at the Class I sites. However, in a se parate study we investigat ed this possibility in previous model emissions reduction sensitivity simulations using the VISTAS 12-km and 36-km model scenarios, and we found that the model grid resolution had only small effects on the model response to emissions reductions.

Bearing out the results of the cu rrent sensitivity study described in this subsection, past model sensitivity experiments performed using high- resolution grids have shown little or no improvement in MPEs for finer-resolution grids. These studies include 12-km and 4-km model simulations performed in parts of the WRAP do main, and sensitivity simulations using a 12-km nested subregion in the VISTAS 36-km coarse gr id. Similar results have also been observed by National Park Service and EPA st aff when modeling PM for the s outhwestern U.S. and Big Bend regions. Thus, the results described here are gene rally consistent with ou r expectations based on previous modeling studies. A possible explanation for the lack of notable improvement in the 12-km model simulation is that the effects on mo del performance resultin g from uncertainty and errors in model inputs and in model formula tion (see Section 4.8) have a significantly greater effect on model performance than does any perfor mance benefit resulting from the better spatial resolution. In fact, the use of a higher resolution may actually degrade model performance if there are slight biases in the model inputs. For ex ample, a slight error in the wind direction might cause a plume to miss a monitor when a high-resolution grid is used, whereas a coarse-resolution grid allows the plume to impact the monitor due to enhanced numerical disp ersion. If it is indeed the case that the effects of thes e uncertainties and errors far out weigh any performance benefit of using a higher resolution, we will need to achie ve substantial improvements in the ability to model meteorology, emissions, and chemistry before we can expect to realize benefits from using a finer spatial resolution. However, it is important to note that th is general finding is offered only within the context of regional haze m odeling in the western U. S., where the receptor areas of interest (Class I areas ) are primarily in remote areas away from most major source regions. For modeling elevated ozone and PM 2.5 concentrations that prim arily occur in or near major urban areas or near high-density source re gions, the model performance benefits of higher resolution grids may be more apparent.

In summary, because of the substantial increas e in the time and compute resource costs of performing high-resolution modeling, we do not recommend the routine application of additional 12-km modeling as part of the WRAP planning effort. However, this conclusion applies only to the clean conditions experienced at Class I areas . It is likely that th e finer-resolution model would provide some benefit for modeling higher ozone and PM 2.5 concentrations relevant to health-based air quality standard s in urban or suburban areas.Final Report for the WRAP 2002 Visibility Model Performance Evaluation92IMPROVEsulfate-50.0-40.0-30.0-20.0-10.00.010.020.0

Fractional Gross Error12km36km12km-0.4 -5.4 -40.8 -33.3 3.236km1.0 -0.8 -38.1 -23.1 10.8January February June July NovemberCASTNET sulfate-70-60-50-40-30-20-10010

Fraction Gross Erro r12km36km12km-20.5 -21.7 -56.8 -35.2 -2.236km-24.6 -19.4 -61.1 -35.1 2.3January February June July NovemberNADP sulfate-40-30-20-10010203040

Fraction Gross Erro r12km36km12km27.8 4.6 -4.1 -14.5 17.736km-8 -14.9 -15.3 -34 6.9January February June July NovemberSTN sulfate-45-40-35-30-25-20-15-10-505

Fraction Gross Erro r12km36km12km0 1.9 -25.2 -37.8 -2.436km-32.7 -29.3 -42.7 -39.5 -23.5January February June July NovemberFigure 4-27. MFB for the 12-km and 36-km CMAQ sulfate compared to each monitoring network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation93IMPROVEnitrate-200.0-150.0-100.0-50.00.050.0

Fractional Gross Error12km36km12km-24.0 -12.1 -152.0 -168.0 -44.336km-4.5 10.7 -137.0 -156.0 -15.1January February June July NovemberCASTNETnitrate-30-20-100102030

Fractional Gross Error12km36km12km6.4 14.9 -22.9 -25.2 8.836km10.6 23.2 -25.2 -23.9 17.5January February June July NovemberNADPnitrate-70-60-50-40-30-20-10010

Fractional Gross Error12km36km12km0.6 -8.9 -17.9 -48.2 -8.336km-30.7 -27.5 -30.5 -62.3 -15.4January February June July NovemberSTNnitrate-160-140-120-100-80-60-40-200

Fractional Gross Error12km36km12km-56 -56.9 -124 -145 -6536km-60 -64.4 -125 -137 -69January February June July NovemberFigure 4-28. MFB for the 12-km and 36-km CMAQ nitrate compared to each monitoring network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation94IMPROVEOC-50.0-40.0-30.0-20.0-10.00.010.020.030.0

Fractional Gross Error12km36km12km-0.4 -6.3 -29.1 -38.5 -26.836km9.7 20.5 -0.2 -2.4 -3.0January February June July NovemberIMPROVEEC-60.0-50.0-40.0-30.0-20.0-10.00.0

Fractional Gross Error12km36km12km-48.4 -43.4 -18.4 -13.8 -37.736km-47.5 -41.4 -7.9 -0.3 -30.1January February June July NovemberIMPROVESOIL-80.0-60.0-40.0-20.00.020.040.060.0

Fractional Gross Error12km36km12km45.9 10.6 -55.9 -50.6 -12.736km43.5 11.9 -39.8 -21.7 4.8January February June July NovemberIMPROVECM-160.0-140.0-120.0-100.0-80.0-60.0-40.0-20.00.0

Fractional Gross Error12km36km12km-52.8 -88.8 -138.0 -151.0 -57.836km-49.9 -84.5 -138.4 -148.0 -56.7January February June July NovemberFigure 4-29. MFB for the 12-km and 36-km CMAQ compared to IMPROVE data for OC, EC, soil, and CM.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

95 AQS

O3

-45 -40 -35 -30 -25 -20 -15 -10

-5 0Fractional Gross Error

12km
36km

12km -37.7 -19.5 -12.1 -23.2 -36.5

36km -38.1 -16.4 -7.5 -17.5 -30.9 January February June July November

AQS

CO

-100

-80 -60 -40 -20

0Fractional Gross Error

12km
36km

12km -76.8 -78.1 -58.5 -55.3 -72.7

36km -90.4 -90 -62.3 -58 -83.3 January February June July November

Figure 4-30. Mean fractional bias for the 12-km and 36-km CMAQ compared to AQS gas-phase ozone and CO data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

96 Figure 4-31. Comparison of spatia l plots for the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE sulfate data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

97 Figure 4-32. Comparison of spatia l plots for the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE nitrate data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

98 Figure 4-33. Comparison of spatia l plots for the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE OC data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

99 Figure 4-34. Comparison of spatia l plots for the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE EC data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

100 Figure 4-35. Comparison of spatia l plots for the CMAQ 36-km (top) and 12-km (bottom) runs with IMPROVE soil data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

101 

### 4.7 CAMx Model Performance Eval uation and Comparison to CMAQ

As noted in Section 4.1, CAMx is a three-dimens ional photochemical grid model that supports aerosols, mercury, air toxics a nd other reactive and nonreactive compounds. The model currently contains two main aerosol treatments: (1) the CMU multisectional approach, where aerosols can grow and shrink among, say, 10 size sections; and (2 ) a two-section coarse /fine approach that assumes all secondary PM species are in the fi ne mode. CAMx contains several of the same modules as CMAQ (e.g., CB-IV gas-phase ch emistry, RADM aqueous-phase chemistry, ISORROPIA aerosol thermodynamics, and PPM adv ection solver), but there are significant differences in the core model formulati on and some of the major processes.

For our MPE study, we performed an annual CAMx simulation on the same 36-km grid that was used for the CMAQ modeling. Emissions data were consistent with the CMAQ data and were either processed using SMOKE with the same em issions input data or converted from CMAQ binary files to CAMx binary files using cust om software. The same horizontal (36 km) and vertical (19 layers) grid structure was used in the CAMx and CMAQ simulations.

In this section we present summary results for the CAMx monthly averaged MFBs compared to the ambient monitoring data and to the CMAQ m odel results. More detailed results are included on the project web site: http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#CAMxvsCMAQV4536k and in the following appe ndices to this report:

Appendix G: Stacked-Bar Plots for CAMx—Annual Time Series of Contributions to Light Extinction Appendix H: Stacked-Bar Plots for CAMx—Contribut ions to Light Extinction for the Average of Best-20% Visibility Da ys, with Data Relaxed in Time Appendix I: Stacked-Bar Plots for CAMx—Contribut ions to Light Extinction for the Average of Worst-20% Visibility Da ys, with Data Relaxed in Time

These are analogous to Appendices B, C, and D for the CMAQ results. A useful approach for comparing the CMAQ and CAMx model results at individual IMPROVE sites is to open the corresponding CMAQ and CAMx appendices and comp are the results side by side for each site. In the discussion below, we focus on the monthly average bias for each model in our comparisons of results.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

102 

### 4.7.1 Sulfate (SO 4 

### )

When the model predictions were averaged ove r all IMPROVE sites, CAMx predicted greater sulfate concentrations than did CMAQ. Figur e 4-36 shows the MFB for CAMx compared to each of the monitoring networks. CAMx overpre dicted sulfate at the IMPROVE and CASTNet sites during the winter months, but during th e summer months had good agreement with the observed sulfate, with small bias. CAMx had small negative (underprediction) bias throughout the year at the more urban STN sites.

Figure 4-37 shows the MFB for both CAMx a nd CMAQ sulfate predictions using IMPROVE data at sites in the WRAP states. For the wi nter months when CMAQ had small positive bias, CAMx had larger positive bias . The winter performance was poorest for both models in December, for which CMAQ and CAMx had 38% and 80% positive biases, respectively. The sign of the model bias was reversed for th e summer months, when CMAQ had negative bias of -40% while CAMx had close to zero bias.

Figure 4-38 shows the bugle plots for the CAMx m onthly averaged bias and error for all three ambient data networks in the WRAP states. Co mparing Figure 4-38 to the CMAQ bugle plots in Figure 4-12 shows very similar performance for th e two models, with larger sulfate overpredictions and larger error at the lower sulfate le vels during winter months, and improved CAMx performance at the STN sites throughout the year.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

103 

### CAMx SO4

-80 -60 -40 -20

0 20 40 60 80 100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%) CASTNET IMPROVE STN

Figure 4-36. Sulfate monthly MFB for CAMx compared to ambient data at the CASTNet, IMPROVE, and STN sites.

IMPROVE SO4

-80 -60 -40 -20

0 20 40 60 80 100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%)

CAMx CMAQ V45

Figure 4-37. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE sulfate data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

104 Figure 4-38. Bugle plot of monthly average sulfate MFB (top) and MFE (bottom) for CAMx 36-km results and the CASTNet, IMPROVE, and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

105 

### 4.7.2 Nitrate (NO 3 

### )

Figure 4-39 shows the nitrate MFB for CAMx compar ed to each of the monitoring networks in the WRAP States. CAMx had a large positive bi as for nitrate at the IMPROVE and CASTNet sites during the winter months and a large nega tive bias during the summer months. The model had a small negative bias during th e winter at the STN sites.

Figure 4-40 shows the nitrate MFB for both CAMx and CMAQ compared to the IMPROVE data. For the winter months, CMAQ had good mode l performance with small bias, while CAMx had large positive bias. The performance was similar for both models during the summer months, both having large negative bias. However, underpredi ction of nitrate during the summer is not of concern at most IMPROVE sites because nitrate typically has very low observed concentrations and only small contributions to visibili ty impairment during summer months.

Figure 4-41 shows the bugle plots for the CAMx m onthly averaged bias and error for all three ambient data networks. Comparing Figure 4-41 to the CMAQ nitrate bugl e plots in Figure 4-15 shows that CMAQ generally met the performan ce goals at the IMPROVE and CASTNet sites while CAMx failed to meet the performance goals and criteria during th e months with higher nitrate concentration (i.e., the wi nter months). Both models faile d to meet performance criteria for some months at the STN sites, although CAMx had better performance than CMAQ for that network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

106 CAMx NO3

-200 -150 -100

-50

0 50 100 150

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%) CASTNET IMPROVE STN

Figure 4-39. Nitrate monthly MFB for CAMx compared to ambient data at the CASTNet, IMPROVE, and STN sites.



### IMPROVE NO3

-200 -150 -100

-50 0 50 100 150

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%)

CAMx CMAQ v45

Figure 4-40. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE nitrate data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

107 Figure 4-41. Bugle plot of monthly average nitrate MFB (top) and MFE (bottom) for CAMx 36-km results versus the CASTNet, IMPROVE, and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

108 

### 4.7.3 Organic carbon (OC)

Figure 4-42 shows the OC MFB for CAMx compared to each of the monitoring networks with OC data. CAMx had negative bias of about -20% to -40% for OC throughout the year at the IMPROVE sites, and larger negative bias at the STN sites.

Figure 4-43 shows the OC MFB for both CAMx and CMAQ compared to the IMPROVE data. CMAQ had generally better performance than CA Mx for OC when considering the monthly average over all IMPROVE sites. CMAQ had a sma ll positive bias of about 0% to -20% with no seasonal pattern, while CAMx had a negative bi as throughout the year. These results use an OMC/OC ratio of 1.4. If we were to use a ra tio of 1.8, as discussed in Section 4.4.3, the CMAQ positive bias would be reduced and the CAMx negative bias would be increased.

Figure 4-44 shows the OC bugle plots for the CAMx monthly averaged bias and error. Comparing Figure 4-44 to the CM AQ OC bugle plots in Figure 4-18 shows that CMAQ met the performance goals and criteria for IMPROVE whil e CAMx failed to meet the performance goals and marginally met the criteria. Both models faile d to meet performance goals at the STN sites, although CMAQ met the performance criteria for some months for that network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

109 CAMx OC

-120 -100

-80 -60 -40 -20

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%) IMPROVE STN

Figure 4-42. OC monthly MFB for CAM x compared to ambient data at the IMPROVE and STN sites.



### IMPROVE OC

-60 -50 -40 -30 -20 -10 0 10 20 30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%)

CAMx CMAQ V45

Figure 4-43. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE OC data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

110 Figure 4-44. Bugle plot of monthly average OC MFB (top) and MFE (bottom) for CAMx 36-km results versus the IMPROVE and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

111 

### 4.7.4 Elemental carbon (EC)

Figure 4-45 shows the EC MFB for CAMx compared to each of the monitoring networks. CAMx had nearly zero bias in winter, and relatively small positive bias in spring and summer at the IMPROVE sites and larger negative bias at the STN sites.

Figure 4-46 shows the EC MFB for both CAMx and CMAQ compared to the IMPROVE data. CMAQ predicted lower EC concentrations than did CAMx, and CMAQ had a relatively large negative bias of about -15% to -30% in the wi nter months and a small negative bias in the summer months, while CAMx had good performance in the winter and a re latively large positive bias during the spring and summer.

Figure 4-47 shows the EC bugle plots for the CAMx monthly averaged bias and error. Comparing Figure 4-47 to the CM AQ EC bugle plots in Figure 4- 21 shows that both models met the performance goals. CAMx might have slightly better overall pe rformance in terms of bias for EC because its summer overpredictions ar e slightly smaller than the CMAQ winter underpredictions.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

112 CAMx EC

-60 -50 -40 -30 -20 -10

0 10 20 30 40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%) IMPROVE STN

Figure 4-45. EC monthly MFB for CAMx compared to ambient data at the IMPROVE and STN sites.

IMPROVE EC

-60 -50 -40 -30 -20 -10 0 10 20 30 40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%)

CAMx CMAQ V45

Figure 4-46. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE EC data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

113

Figure 4-47. Bugle plot of monthly average EC MFB (top) and error (bottom) for CAMx 36-km results versus the IMPROVE and STN networks.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

114 

### 4.7.5 Other fine PM (soil)

Figure 4-48 shows the soil MFB for CAMx a nd CMAQ compared to IMPROVE data. CAMx overpredicted soil throughout the y ear, with a large positive bias in winter months and a smaller positive bias in summer months, while CMAQ had a relatively small positive bias in winter months with a larger negative bias in summer months. (Note th at a plot showing the soil MFB for CAMx compared across multiple monitoring networks is not in cluded because soil data are measured only at the IMPROVE network.)

Figure 4-49 shows the soil bugle plots for the CA Mx monthly averaged bias and error. Comparing Figure 4-49 to the CMAQ soil bugle plots in Figure 4-23 shows that both models met the performance criteria but each model failed to meet the performance goals for a few months, with CAMx erring on the side of overprediction a nd CMAQ erring on the side of underprediction.

Note that CAMx has two separate species for “other fine PM,” one for crustal and one for “other.” When developing emission inventories for CAMx, all fine PM from dust sources (e.g., road dust, agricultural dust, construction dust, and windblown dust) are assigned to the fine crustal species that is typically compared to the IMPROVE soil measurement. However, because in our MPE study the CAMx emissions were obt ained from the CMAQ emissions that do not make this distinction, the fine crustal species could not be separately modeled and compared with the IMPROVE soil measurements. This like ly explains the difference in the CAMx soil performance in this study compared to other studies (e.g., for MRPO a nd CENRAP) where fine crustal was modeled as a se parate species in CAMx.



### IMPROVE SOIL

-80 -60 -40 -20 0 20 40 60 80 100 120

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%)

CAMx CMAQ V45

Figure 4-48. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE soil data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

115

Figure 4-49. Bugle plot of monthly average soil MFB (top) and MFE (bottom) for CAMx 36-km results versus the IMPROVE network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

116 

### 4.7.6 Coarse mass (CM)

Figure 4-50 shows the CM MFB for CAMx and CMAQ compared to the IMPROVE data. The models have very similar performance for CM, showing a large negative bias throughout the year, with the highest underpre diction bias occurring during th e summer months. (Note that a plot showing the CM MFB for CAMx compared across multiple monitoring networks is not included because CM data are measur ed only at the IMPROVE network.)

Figure 4-51 shows the CM bugle plots for the CAMx monthly averaged bias and error. Comparing Figure 4-51 to the CM AQ CM bugle plots in Figure 4- 25 shows that both models fail to meet both the performance goals and the performance criteria.

The poor model performance for coarse mass is cons istent with our results in previous modeling studies. It may result in part from the difficulty of accurately modeling sources and transport of windblown dust or missing sources of CM emissi ons in the emissions inventory data. The measured CM concentrations may also be due to local sources that cannot be accounted for in the coarse (36-km) grid resolution used in the CMAQ and CAMx modeling.



### IMPROVE CM

-160 -140 -120 -100

-80 -60 -40 -20 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias(%)

CAMx CMAQ V45

Figure 4-50. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE CM data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

117

Figure 4-51. Bugle plot of monthly average CM MFB (top) and MFE (bottom) for CAMx 36-km results versus the IMPROVE network.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

118 

### 4.8 Uncertainties, Errors, and Biases and Their Effects on Future Visibility Projection

The 2002 base case version A model results discusse d in this report have large uncertainties and known errors and bias in the inpu t data. The levels of uncertainty in the emissions inventories depend on the source category and ra nge from about ±30% for sources with the least uncertainty to more than a factor of 3 uncertainty for thos e sources with the greatest uncertainty, such as NH 3 , biogenic emissions, and fires. It is also known that the gas-phase photochemical mechanism is incomplete and uncertain, and that ther e are error and bias in the meteorology modeling. Moreover, there remains uncertainty in the tr eatment of aqueous chemistry, aerosol thermodynamics, and rates of removal of PM and gaseous species by wet and dry deposition. The magnitude and effects of errors in input data ar e likely to vary depending on the location in the modeling domain. It is possible that the effects of errors in emissions and chemistry on modeled visibility at Class I areas may be mitigated somewhat because Class I areas tend to be distant from the largest emissions source areas. This mean s that the effects of errors may be reduced by near-field removal of pollutants, and that errors may be cancell ed out by compensating errors in input data. The effects of errors in meteorology, however, may increase with greater transport distances because of biases in wind speed and direction. There are also errors and biases in the monitoring data that were used in the MPE, which in some cases may be as large as the errors in some of the input data. Because of the nonlinear ity of the chemical reactions and the complex interactions of errors in the i nput data, it is not possible to quan titatively estimate the aggregate uncertainty in the model predictions. However, we can conclude that we should expect the model to exhibit large errors and bias when compared to the ambient data. It is likely that major advances would be required in our ability to model the emissions and meteorology data before we can hope to substantially improve model pe rformance for long-term visibility modeling beyond the levels of error and bias described in the results above.

It is also likely that the effects of errors in the base case model performance will be mitigated somewhat because the model predictions are being used in a relative sense to project future-year fine-particle and visibility leve ls. That is, relative reduction fact ors (RRFs) are developed as the ratio of future-year to current- year modeling results and are th en applied to the current-year visibility observations to project future-year visibility, so some uncertainties in the modeling results may cancel each other. When used in a rela tive sense, it is most important that the model correctly predict the direction of the change in visibility in res ponse to changes in emissions. In previous model sensitivity simulations that we have performed for VISTAS, we found that the model response to changes in emissions was not very sensitive to ch anges in the base case modeling, including changes in gr id resolution, changes in phot ochemical mechanism used, and changes in the base case NH 3 emissions inventory. We expect that the model responsiveness for the WRAP scenarios used here will also be not ve ry sensitive to similar changes in model inputs and choice of science options.

The key issue with respect to model uncertainty is whether the model correctly simulates the direction of change in visibility in response to emissions changes. It is likely that for sulfate, nitrate, and ammonium species, this will depe nd on acid-to-base ratios. There are only very limited NH 3 and ammonium ambient monitoring data ava ilable, and it is not possible to evaluate the model’s ability to distinguish accurately acidic versus basic conditions. Collection ofFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

119 additional NH 3 and ammonium data should be a high priority for future monitoring efforts so that this can be better evalua ted in future modeling studies.

In spite of these uncertainties, the model performance is quite good for sulfate, and the formation of sulfate and responsiv eness of sulfate to SO x controls is relatively insensitive to errors in modeled ozone and NH 3 concentrations. This is true because there are multiple oxidation pathways to convert SO x to sulfuric acid, and because sulfur ic acid can form sulfate even at low NH 3 levels. Therefore, we expect the model to be most reliable for pr edicting the effects of changes in SO x on changes in visibility. The same reas oning holds true for several other PM compounds, including OC, EC, soil, and CM. The sp ecies that have the la rgest uncertainty are nitrate and ammonium, bu t these species tend to represent onl y a small fraction of total PM on many of the worst visibility days, particularly in summer months.



### 4.9 Conclusions of Mode l Performance Evaluation

The results presented above for the CMAQ and CAMx 36-km base case version A model performance evaluations for calendar year 2002 show th at the models have some skill in simulating several of the PM species that contribute to regional haze. Als o, these model results are significantly improved compared to the Section 309 mode ling that was performe d using calendar year 1996 (see http://wrapair.org/ 309/031215Final309TSD.pdf ). The improved model performance may result from a combination of improveme nts in the 2002 meteorology modeling, new data sets and emissions models developed for th e 2002 emissions inventories, and updates and improvements to the CMAQ and CAMx models.

The CAMx model performance evaluation, a nd the 2002 base case version A CMAQ 12-km simulation, did not show clear-c ut improvement in model performance over the CMAQ 36-km simulation. Neither CMAQ nor CAMx exhibited im proved model performan ce across all species and seasons. Whereas CMAQ exhib ited better sulfate performan ce in the winter, the CAMx summer sulfate performance appeared better than CMAQ’s. CMAQ nitrate performance was better in the winter, while both models exhibite d large summer nitrate un derprediction biases. CMAQ exhibited better OC model performan ce than CAMx, but uncertainties with the OMC/OC factor and known missing processe s in both the CMAQ and CAMx SOA modules (e.g., missing SOA from biogenic se squiterpenes and isoprene) make these comparisons difficult to interpret. The models exhibited similar fa irly good performance for EC, while both models showed fairly poor performance for soil and CM. Despite the significant in crease in computer resources needed for the 12-km simulation, CM AQ model performance for the 36-km and 12-km grids were nearly identical, with neither model configuration being superior to the other when examining across all species and seasons. These results are consistent with previous WRAP model comparisons using the 1996 modeling year and the preliminary 2002 modeling, which showed that there is no clear im provement in model performance when using alternate models or a higher grid resolution. Typicall y, model performance improves s lightly for some species and degrades slightly for other species.

In summary, the CMAQ and CAMx models both met many of the performance goals for most months of the year and for most of the ambient monitoring networks for su lfate, nitrate, OC, EC, and soil. Both models failed to meet performance goals for coarse mass and also for most PMFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

120 species measured at the STN network, includi ng soil, EC, OC, and nitrate. The STN data, however, are located in areas influenced by urba n emissions. Thus, poor performance at the STN sites may not be indicative of the models’ performance at Class I areas.

We conclude from our results that the CMAQ and/or CAMx 36-km modeling can be used in combination with the RRF approach to evaluate th e benefits of emissions reduction strategies for all PM species other than CM to project visibility changes at Cla ss I areas. We also conclude that there is little benefit in using the 12-km gr id resolution and many detriments, due to the increased computer run times and disk storage, and consequently incr eased likelihood for the introduction of errors in the analysis.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

121 

## 5. Computational Aspects of the Modeling

5.1 Description of Hardware and Software Used

5.1.1 Hardware

At a minimum, a basic computer hardware syst em for visibility modeling should include the components described in this section. Several di fferent options describe d here range from building your own computer systems (least expensive, but most effort) to the purchase of turnkey systems from computer vendors (most expensive, but least effort).
5.1.1.1 Compute nodes

We recommend PC/Linux computer systems based on x86 and x86-64 architecture, i.e., Intel or AMD CPUs. Given their relatively low cost and hi gh performance, these PC/Linux systems offer the best performance-to-cost ratio of any co mputing platforms. The one disadvantage of PC/Linux systems is that they tend to be less reliable, having more down time than the more expensive compute hardware alte rnatives. More expensive and more reliable systems include workstations from IBM, SUN, and SGI. PC/Linux systems can be purchased from a variety of leading vendors. A more economical option for states and tribes with some hardware expertise is to purchase components and build your own co mpute nodes. At UCR we purchase parts and build our own nodes. This option is about one-hal f to one-third the cost of purchasing prebuilt compute nodes from vendors. An advantage of this approach is that when these compute systems fail, you will have the experience and knowledge n eeded to rapidly repair them. With the cost savings in building our own PCs at UCR, we can maintain a large i nventory of spare parts so that dead computers can be brought back online the same day.

There is a significant cost benef it in the purchase of the dual-CP U servers. We have also found the best price-to-performance ratio using the AMD Athlon systems. The AMD Opteron systems may have a slight advantage when performing si mulations using parallel code with 8 or more CPUs. We have found the Intel Xeon CPUs to be mo re tolerant of high temperatures than are the AMD CPUs, so the Intel CPUs might be a better choice for environments that do not have excellent temperature control. AMD now offers (as of July 2005) a new dual-core Opteron CPU. Intel now offers a 64-bit, 2-GB L2 Cache 90nm dual- core CPU. AMD also offers energy-efficient versions of the Opteron CPUs that may func tion better in rooms with limited air conditioning control.

For large-scale modeling, we recommend that seve ral dual-CPU systems be installed, along with a high-speed gigabit network switch and a separate file server with a RAID5 disk storage system.
5.1.1.2 Options for RAM memory

At a minimum, all systems should be configured with at least 1 GB of RAM. System cost increases significantly with 2 GB or more of RAM. We have found that 1 GB of RAM is sufficientFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

122 for modeling on the RPO Unified 36-km domain. Th ere may be some benefit in installing 2 GB of RAM on compute nodes used for 12-km modeli ng domains. All file se rvers should include 2 GB of RAM.

5.1.1.3 Data storage systems

For large-scale modeling, we typically require the ab ility to store data sets as large as several terabytes (TB). Large disk storage systems are built using redundant arrays of independent disks ( RAID ) (also referred to as redundant arrays of inexpensive disks). RAID systems should be configured as RAID5 or RAID6, which reserve part of the disk space for recovering data when one of the disk drives fails. Disks in the RA ID systems should be “hot-swappable,” that is, it should be possible to replace a failed drive wit hout shutting down the RAID system. Ideally, the RAID system should be configured with a stand by spare drive so that the RAID can rebuild the data system immediately when a disk fails. At UCR we have experienced four catastrophic failures of RAID5 systems during the past five year s, in which we lost a ll data on the RAID even though we used RAID5 with standby spares. Two of these failures were due to unreliable IBM Deskstar hard drives, and the others were due to failures of the RAID5 hardware itself. Thus, even a RAID5 or RAID6 system should not be cons idered 100% reliable, and all critical data should be backed up to a second RAID5 system and also archived to tape or backup disk drives.

For all large primary data storage systems, we r ecommend that the disk systems be hard mounted (soft mounting will result in corrupted writes of output data).

5.1.1.4 Network equipment

Gigabit Ethernet switches and adapters are rela tively inexpensive, so we strongly recommend the use of Gigabit network equipment. The older 10/100 network switches have significant performance problems. There are other, higher-speed options availa ble—for example, Myrinet—but these would probably be cost prohibitive. We have obtained excellent performance on our Gigabit Ethernet, and we do not reco mmend the more expensive Myrinet.

5.1.1.5 Backup, archiving, and data transfer

Because of the large numbers and sizes of f iles involved in our modeling projects, we use (1) Firewire/USB disk storage for archiving and backup, and (2) redundant RAID5 systems for critical data backup.

Transfer of files over the Internet is not feasible for large files due to very slow transfer times. However, transfer by ftp can be used for small files (typically less than 1 GB).

5.1.1.6 Server room

A key aspect of computer hardware systems is an adequate room to house the computers. When more than two or three compute nodes and server s are used together, th ey have high noise and heat loads. A server room should have the follo wing attributes: (1) secu re, locked room with limited access; (2) good air conditioning and humid ity control; (3) automated monitoring and alarm systems in case climate control fails; (4) a clean, dust-free space; and (5) high-speed Internet access for electronic transfer of data.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

123 

### 5.1.2 Software

Software required for visibility modeling in cludes the components described below.

5.1.2.1 The operating system

Red Hat Enterprise Linux is currently our operating system (OS) of choice. A disadvantage is that it must be purchased from Red Hat, but the purchase includes support from Red Hat. A less expensive option is CentOS, which is a free repack aging of Red Hat's Enterprise server without Red Hat support. Until recently we used and recommended free Linux distributions such as Fedora and CentOS. However, the lack of support can result in c onsiderable staff time spent in installing and debugging hardware installed on these systems; al so, we do not recommend use of Fedora because it is no longer being updated. If y ou need OS support or have complex hardware configurations, th en the use of Red Hat Enterprise Linux or other commercia l Linux distributions is strongly recommended. SuSE is another very well supporte d commercial Linux distribution. The basic installation of a Linux operating system should be rather straightforward and sufficient for air quality modeling. For a relatively simple ha rdware configuration and staff with experience in maintaining the OS, the free distribution of CentOS could be a good choice.

5.1.2.2 The Fortran compiler

A Fortran compiler is an integral part of the software. Both Portland Group Fortran Compiler and Intel Fortran Compiler can be used to compile CMAQ, CAMx, and all relevant software programs. At UCR we use the Portland Group Fortran compiler.

5.1.2.3 NetCDF

The netCDF libraries were developed to support large, 3-D data sets using a binary, machineindependent, self-describing file structure. The netCDF libraries are required to compile the CMAQ model. The netCDF package can be downloaded from the following web site: http://www.unidata.ucar.edu/software/netcdf

. 5.1.2.4 Input/Output Applications Programming Interface (I/O API)

The Input/Output Applications Programming Interface (I/O API) libra ries are built on the netCDF libraries and were developed specifi cally for use in SMOKE and CMAQ. You can download the I/O API library from http://www.baronams.com/products/ioapi . To build the I/O API library, first edit the configuration file for the appropriate architecture and compiler, then build the library by typing “make”.

In the past we have had problems when th e I/O API libraries and the CMAQ model were compiled with different compiler versions. We therefore recommend that the same compiler be used to compile all of CMAQ and all of its supp orting libraries.

5.1.2.5 MPICH

MPICH is a freely available, portable implementation of MPI , which is the standard for messagepassing libraries. MPICH can be downloaded from http://www-unix.mcs.anl.gov/mpi/mpich

.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

124 Either MPICH1 or MPICH2 will work with the CMAQ code. For CAMx users, the current CAMx release uses only OMP and not MPICH, so there is no need to compile the MPICH library.

5.1.2.6 Parallel applications

To run CMAQ on more than one machine via MPICH, three things need to be configured.

First, the users’ home directories have to be av ailable on each machine. T ypically this would be done via NFS (network file system). Here at U CR, in addition to the home directories, the software that does not come standard with th e operating system is placed on an NFS server, along with all of our data. Our various NFS sh ares are mounted on all of our compute nodes, making all of our data and applic ations available everywhere th ey are needed. To help reduce network overhead, we use the Linux automounter daemon so that NFS shares are mounted only when they are accessed, and then automatically unmounted again after being inactive for a set period of time. We also discovered that the “har d” mount option is necessary to eliminate data corruption problems.

The second thing to set up is “authentication.” Authentication means a mechanism that allows the users to log in with the same user name and password on any of the machines on your network. Two common ways to achieve auth entication are with NIS or OpenLDAP.

The third thing is passwordless logi ns for the MPICH jobs. This can be achieved either via an rsh server or an ssh server. With rsh, you simply creat e a .rhosts file in your home directory with the names or IPs of the machines you want to be able to log in to without a password. Using ssh is somewhat more complicated. The RMC staff can pr ovide a UNIX script that can be run inside the .ssh directory in your home directory if the simpler rsh option is not available on your system.



### 5.2 Summary of Procedures for Modeling

The CMAQ modeling system includes interface pro cessors to incorporate the outputs of the meteorology and emissions processors and to prep are the requisite input information for initial and boundary conditions and photolysis rates to the CMAQ Chemistry Transport Model (CCTM).

The most current CMAQ model a nd data are available from the CMAS download center . The download center includes the CMAS Model Clearinghouse and Data Clearinghouse . Additional information is available from the Community Modeling and Analysis System (CMAS) help desk. Although one could run CMAQ on a Wi ndows platform, we recommend running it on UNIX/Linux platforms.

This section briefly summarizes the proce dures for air quality modeling with CMAQ.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

125 

### 5.2.1 The Meteorology-Chemistr y Interface Processor (MCIP)

The CMAQ model requires inputs of three-dime nsional gridded wind, temperature, humidity, cloud/precipitation, and boundary layer parameters. The current version of CMAQ can utilize output fields from the PSU/NCAR MM5 meteorological model and the Weather Research and Forecast (WRF) Modeling System.

MM5 is a state-of-the science atmosphere model that has proven useful for air quality applications and has been used extensiv ely in past local, state, region al, and national modeling efforts. The MM5 modeling system software is freely pr ovided and supported by the Mesoscale Prediction Group in the Mesoscale and Micros cale Meteorology Division of NCAR.

The first step is to run MM5 to generate the meteorological fields for CMAQ. Detailed information on how we set up and run MM5 is at WRAP MM5 Webpage

. After running MM5, the next step is to run the Meteorology-Chemistry Interface Processor (MCIP) to provide the complete set of meteorological data in ne tCDF binary format required by the CCTM.



### 5.2.2 Initial condition (ICON) and b oundary condition (BCON) processors

Initial conditions (ICONs) and boundary conditions (BCONs) are necessary to solve the partial and ordinary differential equatio ns in air quality models such as CMAQ. The CMAQ ICON and BCON processors typically gene rate IC and BC files from two sources. The first is a timeinvariant set of vertical concentration profile s. The second source is from existing CCTM 3-D concentration files; this source is usua lly used in one-way nested simulations.



### 5.2.3 CMAQ photolysis rates processor (JPROC)

The CMAQ photolysis rates processor (JPROC) estim ates a table of clear-sky photolysis rates for a specific date. Many chemical reactions in the atmosphere are initiated by the photodissociation of numerous trace gases (e.g., ozone, HCHO, carbonyls, H 2 O 2 , HONO). These photodissociative reactions are responsible fo r most of the smog buildup. Accurate photodissociation reaction rate (or phot olysis rate) estimates must be made to accurately model and predict the effects of air pollution.

The Total Ozone Mapping Spectrometer (TOMS) total ozone column data could also be used in the JPROC processor to bette r estimate the photolysis rate s for the specific date.



### 5.2.4 CMAQ emissions processing

CMAQ requires 3-D gridded emissions input files. The current em issions files are generated by the Sparse Matrix Operator Kernel Emi ssions (SMOKE) system. The SMOKE emissions processor can be downloaded from the CMAS home page.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

126 Reports and documents on emissions pro cessing with SMOKE for the WRAP CMAQ simulations are available on the following web site: http://pah.cert.ucr.edu/aqm/308/docs.shtml

.



### 5.2.5 CMAQ Chemical Tr ansport Model (CCTM)

The CCTM simulates the relevant and major at mospheric chemistry, transport, and deposition processes involved throughout the modeling domains.

Key science processes in CCTM include (1) adve ction and diffusion; (2) gas-phase chemistry (CB-IV and SAPRC); (3) aerosols; (4) cloud dynamics and chemistr y; (5) photolysis rates; and (6) plume-in-grid. The two steps to set up and r un CCTM involve (1) using the “bldit” script to create the CCTM executable; and (2) using th e run scripts to execute the CCTM processor

Reports and documents on the WRAP CMAQ 36-km and 12-km simulations are available on the following web site: http://pah.cert.ucr.edu/aqm/308/docs.shtml

.



### 5.3 Computational Performance Benchmarks

CMAQ Suite: The most current CMAQ model (v4.5) is used for the benchmark.

Fortran/C Compiler: Portland Group Fortran 90 compiler (pgf90 v6.0-5) and GNU C compiler (gcc version 3.4.4 20050721 [Red Hat 3.4.4-2]) are used for the benchmark.

Computing Platform: Dual-CPU AMD Opteron 246 systems, each running Red Hat Enterprise Linux . Each system has 2 GB of RAM, an 80-GB system disk, and gigabit Ethernet. The 64-bit Opteron CPUs are backwardly compatible w ith the x86 instruction set, although a 64-bit operating system is required to benefit from the 64-bit extensions. Communication for these machines is provided by a high-sp eed gigabit Ethernet switch.

CMAQ Run Setup: The CMAQ annual runs are divided into four seasons with 15 days of spinup period back-dating from the first day of the season. The last hour from the previous day is the initial condition for the subsequent day. Parallel processing (MPI; see ab ove) is used in these simulations.

Run time: The run time (wall-clock time) ranges fr om 2.25 hours with two CPUs to 1-1.5 hours with four CPUs for the WRAP 36-km simulations. Of the wall-clock time, approximately 65% of the time is used in computation and 35% by the system to handle I/O. The run time for a 24-hour WRAP 12-km simulation is approximately 3.5 hours with six CPUs.

Run-time Memory Requirement: For each CMAQ job, the total run-time memory requirement (dependent on the WRAP domain and model phys ics) is approximately 422 Mb for the 36-km domain and 970 Mb for the 12-km domain.

Disk Storage: CMAQ I/O files are stored on IDE-SCSI RA ID5 array units that use inexpensive IDE disks but interface with our file servers via an Ultra 160 SCSI port. Total disk storage requirements for a WRAP 36-km simulation, for each day, are 1.4 GB for input files and 2.6 GBFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

127 for output files. For the 12-km domain, the di sk requirements for input and output files are 3.3 GB and 5.9 GB, respectively.



### 5.4 Technology Transfer

Technology transfer involves transf erring the models, model evaluation tools, and data sets to the Nation Park Services, states, and tribes so that they can perfor m additional studies of emissions reduction strategies. It also includes support in the setup and operation of the models. A relatively small budget is included in the current WRAP RMC work plan for technology transfer activities.

Because of the large numbers and sizes of files, the two main options we commonly use include:

• Transfer by ftp: Transfer of files over the Internet is not feasible for large files due to very slow transfer times. However, ftp a nd e-mail and can be used for small files (typically less than 1 GB).

• Firewire/USB drives: For large numbers and sizes of CM AQ files, Firewire/USB drives with capacities of 250 GB or higher are used.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

128 

## 6. Conclusions

The primary objectives of this report were

• to document the model performance evaluations using the CMAQ and CAMx models for the WRAP calendar year 2002 visibility modeling,

• to make recommendations on the configur ation of the modeling systems, and

• to determine whether the modeling systems are sufficiently reliable to be used as one of several tools in the process of de veloping visibility SIPs and TIPs.

Concerning model performance for visibility-related species, both the CMAQ and CAMx models met many of the performance goals for most mont hs of the year and for most of the ambient monitoring networks for sulfate, nitrate, OC, EC, and soil. A key component of the modeling effort was the development of performance me trics and approaches for presenting complex modeling results in a way that effectively comm unicates the model performance. Among the new approaches that we developed in this effort, the annual stacked-bar time-series plots and the stacked-bar plots for the average of the best-20% and worst-20% visibility days at each individual IMPROVE site are especially useful for summarizing the model performance. These plots are included in Appendices B through I.

Both models failed to meet performance goals when compared against data from the Speciation Trends Network (STN) for coarse mass and also for most PM species measured, including soil, EC, OC, and nitrate. The STN data, however, are located in areas influenced by urban emissions. Thus, poor performance at the STN sites may not be indicative of the quality of model performance at Class I areas.

As discussed in this report, ther e remain substantial uncertainties in the data used in the modeling systems as well as in the systems themselves. These include

• Uncertainties in the model formulation, incl uding the gas-phase photochemical reactions and aerosol formation

• Known error, bias, and uncertainty in some of the meteorology input data

• Large uncertainties in some componen ts of the emissions inventory data

• Errors and uncertainty in the obser vational data used in the MPE

Even with these uncertainties, the model vi sibility predictions for calendar year 2002 are generally consistent with the ambient data at mo st sites within the WRAP region. The models are reasonably successful in reproduci ng both the seasonal trends and th e relative contributions of most PM 2.5 components to visibility impairment at th e individual IMPROVE sites, as illustrated in the stacked-bar time-series plots. The models are able to simulate most PM 2.5 components reasonably well because the fundamental processes that contribute to emissions of primary PM, formation of secondary PM, and transport and removal of PM 2.5 are generally well understood. For example, the oxidation of SO x , NO x , and VOC to form sulfate, nitrate, and SOA are wellFinal Report for the WRAP 2002 Visibility Model Performance Evaluation

129 understood in general terms. It is considerably more difficult, however, to simulate these processes precisely, and the addi tional error and uncertainty in the emissions and meteorology data make it very difficult for the models to accurately simulate the concentrations of each component of PM 2.5 at a particular location on any give n day. Therefore, the model results should be thought of as providing verisimilitude of ambient visibility conditions in general, rather than an accurate prediction of visibility on any particular day.

The key issue in considering th e usefulness of the CMAQ and CAMx models is whether they can be used to accurately predict the changes in future visibility at Class I ar eas as a result of reductions in emissions of PM 2.5 and its precursors. Given the unc ertainties discussed above, it is difficult to judge the accuracy of model predic tions in absolute terms for predicting future visibility changes. However, it is likely that the models will accurately predict the direction of change in visibility impa cts for all components of PM 2.5 . Moreover, when used in combination with the relative reduction factor approach, the models may also predict the magnitude of change in visibility impacts for most components of PM 2.5 . This conclusion is based on the relative insensitivity of the models’ res ponse to changes in their input data. We have considerably less confidence in the reliability of the models for pr edicting changes in visibility impacts for coarse mass because of large uncertainties in our ability to model the emissions, dispersion, transport, and deposition of coarse mass.

Finally, we note that the model results for cal endar year 2002 are si gnificantly improved compared to the Section 309 modeling that was performed using calendar year 1996 (see http://pah.cert.ucr.edu/rmc/index.shtml and http://wrapair.org /309/031215Final309TSD.pdf ). The improved model performance may result fr om a combination of improvements in the 2002 meteorology modeling, new data sets and emissi ons models developed for the 2002 emissions inventories, and updates and improveme nts to the CMAQ and CAMx models.

Based on our comparisons of the 36-km and 12-kmmodeling results presented here for CMAQ, and also based on the results of previous m odeling studies, we concl ude that there is no significant benefit in using the finer-resolution grid for modeling the concentrations of PM 2.5 that typically occur at the Class I areas. It is possible that fine -resolution modeling would provide more benefit for ozone and PM 2.5 modeling closer to urban areas or in regions with higher PM 2.5 concentrations. However, for the relatively clean PM 2.5 conditions at the WRAP Class I IMPROVE sites and the larger source-receptor rela tionships in the West, the effects of grid resolution are generally small. Therefore, we do not recommend the ro utine appli cation of additional 12-km modeling as part of the WR AP regional haze planni ng effort, especially because of the significantly increased cost that would be involved in doing so.

Based on all of our results, we conclude that either the CMAQ or the CAMx 36-km modeling can be used in combination with the RRF appr oach to evaluate the benefits of emissions reduction strategies for all PM sp ecies other than coarse mass to project visibility changes at Class I areas for regional haze planning purposes. The choice of one model or the other should be based on factors such as the amount of effort , resources, and time required to complete the model simulations. To the extent that resources ar e available, it may be useful to perform some simulations with each model because obtaining sim ilar results from two different models would add to the weight of evidence supporti ng the visibility modeling results.Final Report for the WRAP 2002 Visibility Model Performance Evaluation

130 

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133 Wang, Z. and G. Tonnesen, Effects of Vertical Laye r Averaging on Air Quality Modeling of Ozone and Fine Particulates, manuscript submitted to Geophys. Research Letters, 7/26/2005. WGA (Western Governors’ Association). 2003. Strategic Plan 2003- 2008 of the Western Regional Air Partnership. Western Governors Associa tion, Denver, Colorado. September 29. ( http://wrapair.org/WRAP/meetings/031014b oard/Tab_4_Strategic_Plan_Final.pdf ) Wilson, D., J. Fanjoy, and R. Billings, 2004: Fina l Report: Gulfwide Emission Inventory Study for the Regional Haze and Ozone Modeling Effort, Prepared fo r the U.S. Department of the Interior by ERG, Inc., Morrisville, NC. http://www.gomr.mms.gov/homepg/regulate/environ/studies/2004/2004- 072.pdf WRAP (Western Regional Air Partnership). 2003. Regional Technical Support Document for the Requirements of Section 309 of the Regional Haze Rule (64 Federal Regist er 35714 – July 1, 1999). Western Regional Air Partnership. December 15. ( http://wrapair.org/309/031215Final309TSD.pdf )Final Report for the WRAP 2002 Visibility Model Performance Evaluation

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## Appendices to the Report

This report references nine appendices. Their combined size makes them too large to include within this document file. They can be downl oaded from the RMC web site. Their titles and locations are as follows:

A: Emissions Modeling and Quality Assurance (referenced in Section 2) http://pah.cert.ucr.edu/aqm/308/reports /final/2002_MPE_report_Appendix_A.pdf

B: Stacked-Bar Plots for CMAQ—Annual Time Series of Contributions to Light Extinction (referenced in Section 4.5) 2002_MPE_report_Appendices_B-I.zip

C: Stacked-Bar Plots for CMAQ—Contributio ns to Light Extinction for the Average of Best-20% Visibility Days, with Data Relaxed in Time (referenced in Section 4.5) 2002_MPE_report_Appendices_B-I.zip

D: Stacked-Bar Plots for CMAQ—Contributio ns to Light Extinction for the Average of Worst-20% Visibility Days, with Data Relaxed in Time (referenced in Section 4.5) 2002_MPE_report_Appendices_B-I.zip

E: Stacked-Bar Plots for CMAQ—Contributio ns to Light Extinction for the Average of Best-20% Visibility Days, with Data Relaxed in Time and Space (referenced in Section 4.5) 2002_MPE_report_Appendices_B-I.zip

F: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average of Worst-20% Visibility Days, with Data Relaxed in Time and Space (referenced in Section 4.5) 2002_MPE_report_Appendices_B-I.zip

G: Stacked-Bar Plots for CAMx—Annual Time Series of Contributions to Light Extinction (referenced in Section 4.7) 2002_MPE_report_Appendices_B-I.zip

H: Stacked-Bar Plots for CAMx—Contributions to Light Extinction for the Average of Best-20% Visibility Days, with Data Relaxed in Time (referenced in Section 4.7) 2002_MPE_report_Appendices_B-I.zip

I: Stacked-Bar Plots for CAMx—Contributions to Light Extinction for the Average of Worst-20% Visibility Days, with Data Relaxed in Time (referenced in Section 4.7) 2002_MPE_report_Appendices_B-I.zip