FINAL REPORT an Analysis of Biomass Burning Impacts on Texas

131 Hartwell Avenue Lexington, Massachusetts 02421-3126 USA Tel: +1 781 761-2288 Fax: +1 781 761-2299 www.aer.com

FINAL REPORT

An Analysis of Biomass Burning Impacts on Texas

TCEQ Contract No. 582-15-50414 Work Order No. 582-16-62311-03 Revision 2.0

Prepared by: Matthew Alvarado, Christopher Brodowski, and Chantelle Lonsdale Atmospheric and Environmental Research, Inc. (AER) 131 Hartwell Ave. Lexington, MA 02466

Correspondence to: [email protected]

Prepared for: Erik Gribbin Texas Commission on Environmental Quality Air Quality Division Building E, Room 342S Austin, TX 78711-3087

June 30, 2016 Work Order No. 582-16-62311-03 Final Report

Document Change Record Revision Revision Date Remarks 0.1 08 June 2016 Internal Version for Review 1.0 10 June 2016 Draft Version Submitted to TCEQ for Review 2.0 30 June 2016 Final Report Submitted to TCEQ

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TABLE OF CONTENTS Executive Summary ...... 8 1. Introduction ...... 10 1.1 Project Objectives ...... 10 1.2 Background ...... 10 1.3 Report Outline ...... 11 2 Task 2: Development of the STILT-ASP Model into a Wildfire Analysis Tool ... 13 2.1 Source Code Updates ...... 13 2.2 GUI and Windows Compatibility ...... 15 3 Task 3: Analysis of 2011 Smoke Events ...... 17 3.1 Austin/Round Rock Sept. 7th and 8th Event ...... 17 3.2 STILT-ASP Simulation of the Houston-Galveston-Brazoria August 26th Event ...... 25 4 Quality Assurance Steps and Reconciliation with User Requirements ...... 31 4.1 Task 2: Development of STILT-ASP Model into a Wildfire Analysis Tool ...... 31 4.2 Task 3: Analysis of 2011 Smoke Events ...... 32 5 Conclusions ...... 33 6 Recommendations for Further Study ...... 34 7 References ...... 35

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List of Figures FIGURE 1. MODIS AEROSOL OPTICAL DEPTH (AOD) OBSERVATIONS ON SEPTEMBER 7TH, 2011 WITH THE STILT BACK- TRAJECTORIES (GENERATED WITH NARR METEOROLOGY) FOR THE 13:00 CST SEPTEMBER 7TH AUSTIN/ROUND ROCK CAMS 614 RUN OVER-PLOTTED AS BLACK CIRCLES FOR EACH 15 MINUTE TIME STEP. THE LOCATION OF BASTROP, TX IS SHOWN AS A BLACK STAR...... 24 FIGURE 2. MODIS AEROSOL OPTICAL DEPTH (AOD) OBSERVATIONS ON SEPTEMBER 8TH, 2011 WITH 25 STILT BACK- TRAJECTORIES (GENERATED WITH NARR METEOROLOGY) FOR THE 13:00 CST SEPTEMBER 7TH AUSTIN/ROUND ROCK CAMS 601 RUN OVER-PLOTTED AS BLACK CIRCLES FOR EACH 15 MINUTE TIME STEP. THE LOCATION OF BASTROP, TX IS SHOWN AS A BLACK STAR...... 25 FIGURE 3. MODIS AEROSOL OPTICAL DEPTH (AOD) OBSERVATIONS ON AUGUST 26, 2011 WITH 25 STILT BACK- TRAJECTORIES (GENERATED WITH NARR METEOROLOGY) FOR THE 13:00 CST AUGUST 26TH HOUSTON CAMS 1 RUN OVER-PLOTTED IN BLACK...... 29 FIGURE 4. MERRA TOTAL COLUMN OZONE OBSERVATIONS ON AUGUST 26, 2011 WITH 25 STILT BACK-TRAJECTORIES (GENERATED WITH NARR METEOROLOGY) FOR THE 13:00 CST AUGUST 26TH HOUSTON CAMS 1 RUN OVER-PLOTTED IN BLACK...... 30

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List of Tables

TABLE 1. PROJECTED SCHEDULE FOR TCEQ WORK ORDER NO. 582-16-62311-03 ...... 10 TH TABLE 2. OBSERVED AND MODELED (ALL EMISSIONS) CONCENTRATIONS OF O3, NOX, AND CO DURING THE SEPTEMBER 7 FIRE EVENT AT THE AUSTIN/ROUND ROCK CAMS 614 SITE...... 20

TABLE 3. OBSERVED AND MODELED (NON-FIRE EMISSIONS ONLY) CONCENTRATIONS OF O3, NOX, AND CO DURING THE SEPTEMBER 7TH FIRE EVENT AT THE AUSTIN/ROUND ROCK CAMS 614 SITE...... 20 TH TABLE 4. MODELED (ALL EMISSIONS) CONCENTRATIONS OF OA, BC, AND PM2.5 DURING THE SEPTEMBER 7 FIRE EVENT AT THE AUSTIN/ROUND ROCK CAMS 614 SITE...... 21 TH TABLE 5. MODELED (NON-FIRE EMISSIONS ONLY) CONCENTRATIONS OF OA, BC, AND PM2.5 DURING THE SEPTEMBER 7 FIRE EVENT AT THE AUSTIN/ROUND ROCK CAMS 614 SITE...... 21 TH TABLE 6. OBSERVED AND MODELED (ALL EMISSIONS) CONCENTRATIONS OF O3, NOX, AND CO DURING THE SEPTEMBER 8 FIRE EVENT AT THE AUSTIN/ROUND ROCK CAMS 601 SITE. NA – NOT AVAILABLE...... 22

TABLE 7. OBSERVED AND MODELED (NON-FIRE EMISSIONS ONLY) CONCENTRATIONS OF O3, NOX, AND CO DURING THE SEPTEMBER 8TH FIRE EVENT AT THE AUSTIN/ROUND ROCK CAMS 601 SITE. NA – NOT AVAILABLE...... 22 TH TABLE 8. MODELED (ALL EMISSIONS) CONCENTRATIONS OF OA, BC, AND PM2.5 DURING THE SEPTEMBER 8 FIRE EVENT AT THE AUSTIN/ROUND ROCK CAMS 601 SITE...... 23 TH TABLE 9. MODELED (NON-FIRE EMISSIONS ONLY) CONCENTRATIONS OF OA, BC, AND PM2.5 DURING THE SEPTEMBER 8 FIRE EVENT AT THE AUSTIN/ROUND ROCK CAMS 601 SITE...... 23 TH TABLE 10. OBSERVED AND MODELED (ALL EMISSIONS) CONCENTRATIONS OF O3, NOX, AND CO DURING THE AUGUST 26 FIRE EVENT AT THE HOUSTON-GALVESTON-BRAZORIA CAMS 1 SITE...... 27 TABLE 11. OBSERVED AND MODELED (NON-FIRE EMISSIONS ONLY) CONCENTRATIONS OF O3, NOX, AND CO DURING THE AUGUST 26TH FIRE EVENT AT THE HOUSTON-GALVESTON-BRAZORIA CAMS 1 SITE...... 27 TABLE 12. OBSERVED AND MODELED (ALL EMISSIONS) CONCENTRATIONS OF OA, BC, AND PM2.5 DURING THE AUGUST 26TH FIRE EVENT AT THE HOUSTON-GALVESTON-BRAZORIA CAMS 1 SITE. QA – DATA FAILED QUALITY CHECKS...... 28 TABLE 13. OBSERVED AND MODELED (NON-FIRE EMISSIONS ONLY) CONCENTRATIONS OF OA, BC, AND PM2 DURING THE AUGUST 26TH FIRE EVENT AT THE HOUSTON-GALVESTON-BRAZORIA CAMS 1 SITE. QA – DATA FAILED QUALITY CHECKS...... 28

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List of Acronyms AER – Atmospheric and Environmental Research AOD – Aerosol Optical Depth ARL – Air Resources Laboratory ASP – Aerosol Simulation Program BEIS – Biogenic Emission Inventory System CAMS – Continuous Ambient Monitoring Station CB05 – Carbon Bond mechanism version 5 CST – Central Standard Time CSV – Comma Separated Value DISCOVER-AQ – Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality FINN – Fire INventory from NCAR GAM – Generalized Additive Model GB – GigaByte GFED – Global Fire Emissions Database GUI – Graphical User Interface HTAP – Hemispheric Transport of Air Pollution HYSPLIT – Hybrid Single Particle Lagrangian Integrated Trajectory Model IOAPI – Input/Output Applications Programming Interface MDA8 – Maximum Daily 8-hour Average MEGAN – Model of Emissions of Gases and Aerosols from Nature MERRA – Modern-Era Retrospective analysis for Research and Applications MODIS – Moderate Resolution Imaging Spectroradiometer MOZART – Model for OZone and Related chemical Tracers NAAQS – National Ambient Air Quality Standards NAM – North American Mesoscale forecast system NARR – North American Regional Reanalysis NASA – National Aeronautics and Space Administration NCAR – National Center for Atmospheric Research NCEP – National Centers for Environmental Prediction NetCDF – Network Common Data Form NOAA – National Oceanic and Atmospheric Administration

PM2.5 – Particulate Matter with a diameter below 2.5 µm ppb – parts per billion QAPP – Quality Assurance Project Plan RAM – Random Access Memory

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RMS – Root Mean Square SAPRC07 – Statewide Air Pollution Research Center chemical mechanism, 2007 version SIP – State Implementation Plan STILT – Stochastic Time Inverted Lagrangian Transport SMOKE –Sparse Matrix Operator Kernel Emissions TCEQ – Texas Commission on Environmental Quality UTC – Coordinated Universal Time VBS – Volatility Basis Set WRF – Weather Research and Forecasting

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Executive Summary In this project, AER furthered the development of a trajectory-based modeling tool that will allow the Air Modeling and Data Analysis Section of the TCEQ to assess the impact of certain wildfire events on ozone or fine particulate matter (PM2.5) measurements in the metropolitan areas of Texas. This modeling tool is comprised of the Stochastic Time Inverted Lagrangian Transport (STILT) model with an integrated Aerosol Simulation Program (ASP). The coupled modeling tool, called STILT-ASP, was also used to model wildfire impacts in two test cases:  The impact of wildfires in Bastrop, Texas on the Austin-Round Rock metropolitan area in September 2011; and  The impact of wildfires in the Pacific Northwest and Lower Mississippi River areas on the Houston metropolitan area in late August 2011. The majority of our work in this project focused on the development and testing of the STILT-ASP model. We coupled ASP to STILT and made several changes to the software and model inputs, including (a) modifications to the code to allow it to run on Windows 7 64-bit hardware, (b) adding a method to estimate the relative contributions of boundary conditions, non-fire emissions, and fire emissions on the modeled O3 and PM2.5 at the receptor (i.e., the location and time that the measurement was made), and (c) additional approaches to modeling the secondary organic aerosol from fires. We also developed an emission preprocessor for STILT-ASP that combines publicly available default anthropogenic, biogenic, and fire emissions to prepare the needed emission files for STILT-ASP without requiring a full SMOKE run. This work included adding the capability to use fire emissions data from the Fire INventory from NCAR (FINN), anthropogenic emission data from the Hemispheric Transport of Air Pollution inventory (HTAP v2), and biogenic emissions from previously generated monthly average output of the Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1). In addition, we developed a simple Java-based Graphical User Interface (GUI) that allows a non-expert user to easily set up and run the coupled model using publically available inputs. We also documented the STILT-ASP model in a Technical Memo that describes the equations and processing steps of the model, and prepared a User’s Guide that documents how to install and run the combined model from the GUI. We then used the combined model to investigate the two 2011 wildfire cases discussed above. One of the key challenges in this work was the long computation time for the STILT-ASP simulations. To save time, 32 km resolution meteorological fields from the National Centers for Environmental Prediction (NCEP) National Center for Atmospheric Research Reanalysis (NARR) were used in our evaluations. We also limited our focus on analyzing the hours leading to the measured maximum daily 8-hour average (MDA8) on September 7th and 8th, 2011 at Austin (as measured at the CAMS 614 and 601 sites, respectively) and on August 26th, 2011 event at Houston as measured at the CAMS 1 site. The Austin event was simulated using 5-day back-trajectories, with each model run (i.e., for a single location and time) taking about 3 hours of wall-clock time. For the Houston case, as we were interested in the potential influence of fires from the Pacific Northwest, 7-day back-trajectories were used, which took about 5.5 hours of wall-clock time for each hour simulated. The back-trajectories calculated for these cases were compared to satellite observations of aerosol optical depth (AOD) to determine if they were consistent with long-range transport of fire emissions to the receptors.

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Our results for the Austin case show that STILT-ASP v1.0 underestimates the MDA8 O3 on September 7th by 14.3 ppbv (a modeled average value of 72.2 ppbv versus a measured value of 86.5 ppbv) while it overestimates the average NOx concentration by a factor of 2.5 (a modeled average value of 5.34 ppbv versus a measured value of 2.04 ppbv). However, the dependence of these concentrations on hour is very different between the model and the measurements. The measurements show the O3 peaking at a value of 96 ppbv on 13:00 CST (19:00 UTC) while the model predicts the maximum O3 at a value of 76.1 ppbv at 17:00 CST (23:00 UTC). The results for September 8th show a similar underestimate, with a mean underestimate of 16.7 ppbv, but both the model and measurements show fairly constant O3 during the MDA8 window.

Comparing model runs with and without fire emissions gives estimates of ΔO3/ΔCO of 14.3% and 25%, consistent with the average value for extratropical fire plumes more than 2 days old (20%). Thus, we estimate the impact of fires on O3 on these two days as 0.8 ppbv and 0.4 ppbv. These low impacts are consistent with the calculated back-trajectories, which come from the north rather than from the fires in the southeast, and with an examination of the residuals of statistical model fits between meteorology and MDA8 O3 in the Austin/Round Rock area.

Our results for the Houston case show a large overestimate of O3, with the model predicting an MDA8 O3 concentration of 140.5 ppbv compared to the measured MDA8 of 98 ppbv. NOx is overestimated, but the relative overestimate is not as severe as in the Austin case (a modeled average concentration of 12.5 ppbv versus a measured value of 9.4 ppbv). The predicted impact of fire emissions on CO is larger than in the Austin case (12.6 ppbv), but the estimated impact of fire emissions on NOx is similar (0.14 ppbv). As with the Austin case, the model predictions of O3 are much more constant throughout the day than in the observations. STILT-ASP estimates the impact of fires on MDA8 O3 on this day as 3.5 ppbv, but the large overestimate in total O3 makes it difficult to draw solid conclusions.

STILT-ASP v1.0 simulations of PM2.5 give significant underestimates when the default emissions are used, likely due to the neglect of natural sources of PM2.5, like wind-blown dust and sea salt, in these emissions. However, the estimated fire impacts on PM2.5 appear reasonable given the impact on CO. We have thus developed a wildfire analysis tool that can be easily run on Windows-based computers through a simple GUI interface using publically available meteorological and emission inputs. However, the model simulations for the two 2011 fire cases show the need for further research to improve the performance of the tool before it is sufficiently validated to use for air quality regulatory and policy purposes. We thus recommend that future development work of the STILT-ASP wildfire analysis tool focus on: 1) Simulating cases where more data on the gas and aerosol composition of the atmosphere is available, such as during the 2013 Houston DISCOVER-AQ campaign, in order to thoroughly test the STILT-ASP model predictions of multiple species and use these findings to improve the model. 2) Further development of the emissions preprocessor to get more accurate estimates of the emissions, such as by running the biogenic emissions models BEIS and MEGAN on-line in the tool. 3) Exploring methods to improve the speed of the STILT-ASP model through code parallelization and other techniques. This would both make the tool easier to use and would allow more sensitivity cases to be evaluated, allowing improvements to the model to be tested more quickly than is currently possible.

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1. Introduction 1.1 Project Objectives AER performed a research project titled “An Analysis of Biomass Burning Impacts on Texas” for the Texas Commission on Environmental Quality (TCEQ). The purpose of this project was to further the development of a trajectory-based modeling tool that will allow the Air Modeling and Data Analysis Section to assess the impact of certain wildfire events on ozone and fine particulate matter (PM2.5) measurements in Texas. This modeling tool is comprised of the Stochastic Time Inverted Lagrangian Transport (STILT) model with an integrated Aerosol Simulation Program (ASP). The modeling tool was also used to model wildfire impacts in two test cases:  The impact of wildfires in Bastrop, Texas on the Austin-Round Rock metropolitan area in September 2011; and  The impact of wildfires in the Pacific Northwest and Lower Mississippi River areas on the Houston metropolitan area in late August 2011.

The schedule of deliverables for this project is given in Table 1. Table 1. Projected Schedule for TCEQ Work Order No. 582-16-62311-03

Milestones Planned Date

Task 1 - Work Plan 1.1: TCEQ-approved Work Plan Feb. 4, 2016 1.2: TCEQ-approved QAPP Feb. 4, 2016 Task 2 - Development of STILT-ASP Model into a wildfire analysis tool 2.1: A Draft Technical Memo describing and documenting the initial May 13, 2016 version of the wildfire analysis tool and a Draft User’s Guide for using the tool. 2.2: The final code, Technical Memo, and User’s Guide for the June 30, 2016 wildfire analysis tool, including a description of revisions made to the initial tool as a result of the analysis of the 2011 smoke events in Task 3 below. Task 3 – Analysis of 2011 Smoke Events Task 4 – Draft and Final Reports 4.1: Draft Report June 10, 2016 4.2: Final Report June 30, 2016

1.2 Background With the recent establishment of 70 parts per billion ozone National Ambient Air Quality Standard (NAAQS) the State of Texas has become more susceptible to NAAQS exceedances

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caused by the transport of ozone, PM2.5, and their precursors from wildfires outside of Texas. The development of an analysis tool that can utilize meteorological and photochemical methods to assess wildfire impacts without a State Implementation Plan (SIP) level modeling effort would be a significant benefit to TCEQ analysts documenting exceptional events. The STILT model (http://www.stilt-model.org; Lin et.al., 2003) is a Lagrangian particle dispersion model derived from HYSPLIT but includes modifications that improve the mass- conservation of the simulations and allow the use of customized WRF meteorological fields (Nehrkorn et al., 2010), which have been shown to improve the model performance when compared with tracer-release studies (e.g., Hegarty et al., 2013). STILT has been extensively used at AER in inverse modeling to improve emission estimates for greenhouse gases (e.g., McKain et al., 2012, 2015; Miller et al., 2013; Henderson et al., 2015). ASP v2.1 (Alvarado et al., 2015a) simulates the gas-phase, aerosol-phase, and heterogeneous chemistry of young biomass-burning smoke plumes, including the formation of O3 and secondary inorganic and organic aerosol. All gas-phase chemistry for organic compounds containing 4 carbons or less is explicitly resolved following the Leeds Master Chemical Mechanism (MCM) v3.2 (e.g., Jenkin et al., 2003). The lumped chemistry for all other organic compounds in ASP v2.1 has been updated to follow the Regional Atmospheric Chemistry Mechanism (RACM) v2 (Goliff et al., 2013). ASP uses a sectional aerosol size distribution and includes modules to calculate aerosol thermodynamics, gas-to-aerosol mass transfer (condensation/evaporation), coagulation of aerosols, and aerosol optical properties. Alvarado et al. (2015a) recently evaluated ASP simulations for a fire in California (Williams fire, Akagi et al., 2012). This study showed that ASP v2.1 could simulate most of the observations (e.g., O3, NOx, OH, OA, PM2.5) using reasonable assumptions about the chemistry of the unidentified organic compounds. Recently, Prof. John Lin of the University of Utah developed an extension of the STILT model to include gas-phase chemistry (Wen et al., 2012, 2013, 2014). While previous versions of STILT-Chem have used the CB4 chemical mechanism, in this project AER developed a version of STILT-Chem that is coupled with the gas and aerosol chemistry calculations of ASP (STILT- ASP) to better account for the impacts of biomass burning emissions on O3 at urban receptors. 1.3 Report Outline This Final Report highlights major activities and key findings, provides pertinent analysis, describes encountered problems and associated corrective actions, and details relevant statistics including data, parameter, or model completeness, accuracy and precision. It satisfies Deliverable 4.2 of the Work Plan for Work Order No. 582-16-62311-03: Deliverable 4.2: Final Report delivered electronically via file transfer protocol or e- mail in Microsoft Word format and PDF format Deliverable 4.2 Due Date: June 30, 2016 This report contains two sections that describe our efforts under Task 2 (Development of STILT- ASP Model into a wildfire analysis tool, Section 2) and Task 3 (Analysis of 2011 Smoke Events, Section 3). A Technical Memo describing and documenting the initial version of the wildfire analysis tool is included as a companion document to this report, along with a User’s Guide for using the tool are also supplied with this report as separate documents.

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Section 4 discusses the Quality Assurance performed for the project, including answers to the assessment questions from the Quality Assurance Project Plan (QAPP). Section 5 summarizes the conclusions of our study and our recommendations for further research.

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2 Task 2: Development of the STILT-ASP Model into a Wildfire Analysis Tool In this task, we coupled the STILT and ASP models and then used the combined model to develop a wildfire analysis tool. The tool (STILT-ASP) is documented in the Technical Memo (STILT-ASP_v1.0_Tech_Memo_D2_2_R2_0.pdf) that is included along with this Final Report. The Technical Memo describes:  The conceptual model for the system (Section 2),  The model equations and assumptions (Section 3),  The inputs, processing, and outputs of the model software routines (Section 4),  Our efforts in model calibration, validation, and evaluation to date, focusing on the Houston and Austin 2011 fire events (Section 5), and  The current limitations on the use of the model based on the evaluations (Section 6). The STILT-ASP User’s Guide (STILT-ASP_v1.0_UsersGuide_D2_2_R2_0.pdf) describes how to run the combined model from the GUI. It includes:  The software and hardware requirements of the model (Section 2),  Explanations for the GUI input options (Section 3),  A diagram of the processing flow for the combined wildfire analysis tool (Section 4), and  A description of the model outputs (Section 5). Rather than repeat this documentation within the final report, here we focus on providing an overview of the development activities we performed under this task. To parallel the description of this task in the Work Plan, we divide up our discussion between source code updates (Section 2.1) and GUI and Windows compatibility tasks (Section 2.2). Note that the drafting of the Technical Memo and User’s Guide were also performed under this task. 2.1 Source Code Updates In this task, we made several updates to the source code of STILT-ASP:  We made code changes to STILT-ASP to allow the model to determine and keep track of which of the simulated Lagrangian parcels were influenced by fire emissions. We used this to develop a “single-run” approach to estimating the impact of fires on O3 and PM2.5 measured at a receptor (see Section 3.4.2 of the Technical Memo). However, our initial evaluations of this method (see Section 5.3 of the Technical Memo) suggest that this method overestimates the impact of fires on the concentrations at the receptors, and thus a more sophisticated single-run method needs to be developed.  We made many changes to STILT-ASP to improve the use, readability, and maintainability of the code. o We separated the back-trajectory calculation from the chemistry calculation, so that the back-trajectory section of STILT-ASP could more easily be kept consistent with the WRF-STILT modeling system in the future (see Sections 4.1 and 4.2 of the Technical Memo). o We developed routines to print the back-trajectory data out to a NetCDF file, so that this data would not have to be stored in memory during the forward chemistry calculation of STILT-ASP (see Section 4.1 of the Technical Memo). This dramatically reduced the memory requirements of the model,

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allowing it to be run on a desktop machine. It also provided a mechanism for restarting STILT-ASP from an intermediate step if desired. o We wrote new output routines for STILT-ASP that produce NetCDF files that allow us to better understand the model output and diagnose potential problems. We also re-wrote existing CSV output routines in order to produce output files more compatible for import into Microsoft Excel. o We developed a mapping routine (Section 3.4.1 of the Technical Memo) that allows us to flexibly connect the species in the MOZART-based boundary condition and FINN fire emission input files of STILT-ASP to the more detailed speciation of ASP. This algorithm replaced the myriad uses of hard- coded array indices to refer to species identification with a set of configuration files that may be modified by the user without the need to rebuild the model from source code. o We have added functionality to allow the operator to select a particular combustion threshold concentration required to activate the fire setting for a given parcel. o We developed a utility routine that takes the ASP chemical speciation files and uses them to produce the deposition parameter files needed for the STILT deposition routines. o We developed a way to propagate the entire time/parcel history of nearly 800 gas and aerosol species without storing everything in memory, which would have rendered the tool unusable by a standard Windows workstation. o We removed a great deal of unneeded, commented out code from the main model routines to improve the readability of the code.  We updated STILT-ASP to allow sources of emissions data from sources other than IOAPI files1 produced by the SMOKE model. The source code of STILT-ASP was updated to allow the input of two 3D (x, y, time) emission files in NetCDF format, one for emissions from fires (currently assumed to follow the MOZART-4 speciation) and one for emissions from non-fire sources (e.g., biogenic and anthropogenic sources, currently assumed to be in ASP speciation). We then built an Emission Preprocessor (Section 3.5 of the Technical Memo) to prepare these NetCDF files from publically available “default” data sources, including: o The Fire INventory from NCAR (FINN, Wiedinmyer et al., 2011), a daily fire emission inventory that is publically available online in near-real-time (downloaded from http://www.acom.ucar.edu/acresp/dc3/AMADEUS/finn/emis/ on March 24, 2016). o Anthropogenic emission files from the HTAP v2 inventory (downloaded from http://edgar.jrc.ec.europa.eu/htap_v2/index.php on March 20, 2016). o Monthly emissions estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2 (Guenther et al., 2006) model for the years 2000 to 2010 at a horizontal resolution of 0.5o x 0.5o (downloaded from http://lar.wsu.edu/megan/docs/05degree_MEGAN/ on March 20, 2016).

1 https://www.cmascenter.org/ioapi/

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 We developed new input files for STILT-ASP that allow the use of two different approaches for estimating the production of secondary organic aerosols from biomass burning emissions: o The standard ASP Volatility Basis Set (VBS) scheme of Alvarado et al. (2015a) (Section 3.2.3 of the Technical Memo), and o The simplified approach of Hodzic and Jimenez (2011) (Section 3.2.4 of the Technical Memo). 2.2 GUI and Windows Compatibility In this task, we modified the STILT-ASP source code to run on Windows 7 64-bit hardware via a Java-based Graphical User Interface (GUI), as described in the STILT-ASP User’s Guide. As called for in the work plan, the GUI has been designed to allow STILT-ASP users to choose between input files stored locally, input files generated by utility routines such as the Emission Preprocessor, and input files available from on-line sources (currently only available for meteorological files). Our activities on this task included:  Designing, coding, and debugging the GUI.  Developing a system of shell scripts that uses the inputs gathered from the GUI to link or produce the needed input and configuration files for STILT-ASP, set up the model configuration, run the model executable, and tag and archive the output.  Developing makefiles and other utilities that allow advanced users to build the STILT-ASP executable directly from the source code on the Windows deployment environment using Cygwin, an open-source Linux-to-Windows command-line mechanism.  Making adjustments to the STILT-ASP source code and run scripts to allow the use of a variety of publically available sources of input data for STILT-ASP, including: o The FINN, HTAP, and MEGAN emissions files discussed in Section 2.1. o Chemical boundary and initial conditions from the MOZART-4/GEOS-5 output from the NCAR Chemical Forecasts (http://www.acom.ucar.edu/acresp/forecast/). o Meteorological inputs from the National Centers for Environmental Prediction (NCEP) National Center for Atmospheric Research Reanalysis (NARR) 32 km and North American Mesoscale (NAM) 12 km wind fields supplied on the NOAA Air Resources Laboratory (ARL) website (e.g., http://ready.arl.noaa.gov/archives.php).  Making several other source code changes to ensure that STILT-ASP worked properly on a Windows environment, including: o Changes to the string processing routines of ASP and the format of the text input files. o IOAPI and NetCDF libraries were built from source on Cygwin. These will be supplied with the source code delivery since the build process for each is not trivial. o Correcting various conditional statements that try to determine if a given variable equals a floating-point zero. These statements generally do not cause problems on the Linux environment used to originally build STILT and ASP, but can cause odd behavior on Windows platforms. It is expected that the

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proposed migration of STILT-ASP from Cygwin to a native Windows environment will eliminate/reduce these inconsistencies.

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3 Task 3: Analysis of 2011 Smoke Events We simulated the two 2011 fire events discussed in the Introduction using STILT-ASP to evaluate the model performance and to estimate the impact of fires on the MDA8 O3 values measured during these events. Generally the only data available co-located with the hourly O3 measurements were hourly measurements of NOx from TAMIS, which is what we use in our analysis. The STILT-ASP simulations for these events are described in Sections 5.3, 5.4, and 5.5 of the Technical Memo, and that discussion is briefly summarized here. We also used observations of Aerosol Optical Depth (AOD) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), total ozone column from the NASA Modern Era-Retrospective Analysis For Research And Applications (MERRA), and the statistical model results of Alvarado et al. (2015b) to put our results into a broader context. 3.1 Austin/Round Rock Sept. 7th and 8th Event Here we evaluate a STILT-ASP simulation of the hours leading to the measured maximum daily 8-hour average (MDA8) on September 7th, 2011 at Austin (as measured at the CAMS 614 site). These simulations were performed using the 32 km NARR meteorological fields to perform 500 5-day back-trajectories from the CAMS 614 site. Our results show that STILT-ASP v1.0 using the default non-fire emissions from the emission pre-processor th underestimates the MDA8 O3 on September 7 by 14.3 ppbv while it overestimates the average NOx concentration by a factor of 2.5 (Table 2). The time dependence of the gas and particle- phase species (not shown) over the simulation time appears reasonable. However, the dependence of these concentrations on receptor hour is very different between the model and the measurements. The measurements show the O3 peaking at a value of 96 ppbv on 13:00 CST (19:00 UTC) while the model predicts the maximum O3 at a value of 76.1 ppbv at 17:00 CST (23:00 UTC). However, the maximum in the estimate of the fire contribution to O3, CO, and NOx occurs at 13:00 CST (19:00 UTC), close to the measured peak of O3 for this day. Reducing the number of days in the simulation to 3 or 2 does not significantly affect the final results. When a threshold of 0.02 ppmv hr-1 of fire CO emissions is used to identify parcels influenced by fire, the single-run method for estimating fire impacts gives an estimate of 1.0 ppbv for the impact of fires on the MDA8 O3, and an estimate of 2.2 ppbv for the impact of fires on the 8-hour average for CO in this same period (Table 2). This gives an estimate of the fire enhancement ratio (ΔO3/ΔCO, see Akagi et al., 2011) of 45.5% (mol/mol). While this level of enhancement of O3 due to fires has been observed in a few cases, it is a fairly high value (e.g., Wigder and Jaffe, 2012), suggesting that the single-run method is overestimating the impact of fires on the MDA8 O3. This is also suggested by a comparison of two runs of the STILT-ASP model, the first as above with all emission sources included (Table 2) and the second with only the non-fire emission sources included (Table 3). This comparison suggests that the single-run method with a -1 threshold of 0.02 ppmv hr overestimates the impact of fire emissions on O3 and NOx but underestimates the impact on CO. Comparing model runs with and without fire emissions gives a slightly lower estimate of the impact of fires on the MDA8 O3 of 0.8 ppbv (Table 3). The estimated ΔO3/ΔCO from this method (14.3%) is close to the average value for extratropical fire plumes more than 2 days old (20%, Jaffe and Wigder, 2012). Thus, while the single run method with a threshold of 0.02 ppmv hr-1 can be used in screening runs to get a reasonable upper-limit on the impact of fires on O3, the two-run, zero-out method should be used for the final analysis of any events that the screening runs suggest had a significant impact on total O3.

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The STILT-ASP model predictions for PM2.5, organic aerosol (OA) and black carbon (BC) for the September 7th event are shown in Table 4. No observations of these species were available at the Austin/Round Rock CAMS 614 site for comparison. We can see that the model -3 predicts an average PM2.5 concentration of 5.53 µg m over this 8-hour period. However, this is likely an underestimate of ambient PM2.5, as it only includes the components from anthropogenic emissions, fire emissions, and the secondary aerosol formed by anthropogenic, biogenic, and fire gas emissions. Thus the contributions from dust, sea salt, and other natural sources are not included in the model estimate.

The predicted impact of fire emissions on PM2.5 concentrations during this period, calculated with the single-run method for estimating fire impacts and a threshold of 0.02 ppmv hr-1 is 0.17 µg m-3, with OA accounting for most of this impact (0.15 µg m-3). However, comparisons with model runs without fire emissions suggest much larger impacts on PM2.5 and OA (0.64 µg m-3 and 0.59 µg m-3, respectively), suggesting that the single-run method underestimates the impact of fires on these species, and that the more accurate two-run, zero-out method should be used to estimate these impacts. This two-run method gives an estimate of ΔPM2.5/ΔCO of 10% (g/g), roughly consistent with the ratio observed for grassland files (11.4%) but lower than the estimate for temperate forest fires (14.3%) and higher than that for crop residue burns (6.1%, Akagi et al., 2011). For the event on September 8th, 2011 at Austin (as measured at the CAMS 601 site), our results show that the coupled model underestimates the MDA8 O3 by 16.3 ppbv (Table 6). Also, in this case both the model and the measurements show fairly steady O3 levels over the MDA8 window. The single-run method predicts a negligible impact of the fires on O3 (<0.01 ppbv). In contrast, comparing model runs with and without fire emissions gives an estimate of the impact of fires on the MDA8 O3 of 0.4 ppbv (Table 7). The estimated ΔO3/ΔCO via the two-run method is 25%, consistent with the average value for extratropical fire plumes more than 2 days old (20%, Jaffe and Wigder, 2012).

For this event, hourly PM2.5 observations are available (Table 8). With the default emissions, STILT-ASP v1.0 substantially underestimates the PM2.5 concentrations (measured 8- hour average of 13.78 µg m-3 versus modeled concentration of 3.58 µg m-3) likely due to neglecting dust, sea salt, and other natural sources of aerosol as discussed above. As before, -3 estimating the impact of fires on PM2.5 with the single-run method (0.01 µg m ) underestimates the impact relative to that calculated by comparing runs with and without fire emissions (0.12 µg -3 m , see Table 9). The two-run method gives an estimate of ΔPM2.5/ΔCO of 6.7% (g/g), roughly consistent with the ratio observed for crop residue burns (6.1%, Akagi et al., 2011).

The low calculated impact of fires on O3 in Austin/Round Rock can be explained by examining the model calculated back-trajectories, which are shown in Figure 1 for CAMS 614 on Sept. 7th and Figure 2 CAMS 601 on Sept. 8th plotted over observations of Aerosol Optical Depth (AOD) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS). We can see that the air masses affecting these sites at 13:00 CST were generally from the north to northeast, while Bastrop, TX, the location of the wildfires, is southeast of Austin/Round Rock (consistent with the light blue band of elevated AOD southeast of Austin in Figure 2). Thus the back-trajectories suggest that the fires near Bastrop, TX had little impact on the O3 measured at Austin/Round Rock – in fact, the transport pattern is more consistent with transport of emission from the Dallas/Fort Worth region to the Austin/Round Rock one.

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Another way to look for the influence of wildfires on O3 is to look for large residuals in statistical models relating meteorological predictors to measured O3 (e.g., Jaffe et al., 2013). We thus examined the residuals of the Austin generalized additive model fits from Alvarado et al. (2015b) for these two days. The residuals show that the GAM underestimates MDA8 O3 on Sept. 7th and 8th by 3.3 and 4.5 ppb, respectively, which could be considered an upper-limit for the impact of fires on the MDA8 O3 on these days. However, these residuals are well within the root-mean-square errors of the GAM fits (7 ppb for Austin/Round Rock), and thus these residual values cannot be considered exceptional. Thus the analysis of the GAMs also suggests a small influence of biomass burning, consistent with the STILT back trajectories and the STILT-ASP model results.

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Table 2. Observed and modeled (all emissions) concentrations of O3, NOx, and CO during the September 7th fire event at the Austin/Round Rock CAMS 614 site.

Time O3 (ppbv) NOx (ppbv) CO (ppbv) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Obs. Model Model Impacta Impacta Impacta 11:00 76.6 67.6 1.5 2.0 5.74 0.25 140.9 3.0 12:00 81.6 70.0 2.1 2.1 5.82 0.30 141.9 4.1 13:00 96.4 70.2 1.1 3.2 5.73 0.24 144.4 2.9 14:00 92.2 70.7 0.5 3.0 5.51 0.08 142.5 1.1 15:00 88.9 73.2 0.6 1.9 5.61 0.09 150.9 2.0 16:00 88.5 75.6 1.5 1.6 5.04 0.16 151.0 3.2 17:00 87.1 76.1 0.4 1.4 4.68 0.08 151.2 1.2 18:00 81.0 74.3 0.2 1.1 4.56 0.04 146.2 0.4 Mean 86.5 72.2 1.0 2.04 5.34 0.16 146.1 2.2 a Estimated using the single-run method and a threshold of 0.02 ppmv hr-1 of CO.

Table 3. Observed and modeled (non-fire emissions only) concentrations of O3, NOx, and CO during the September 7th fire event at the Austin/Round Rock CAMS 614 site.

Time O3 (ppbv) NOx (ppbv) CO (ppbv) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Obs. Model Model Impactb Impactb Impactb 11:00 76.6 67.0 0.6 2.0 5.65 0.09 137.0 3.9 12:00 81.6 69.1 0.9 2.1 5.67 0.15 136.2 5.7 13:00 96.4 69.6 0.6 3.2 5.59 0.14 138.9 5.5 14:00 92.2 70.2 0.5 3.0 5.39 0.12 138.2 4.3 15:00 88.9 72.5 0.7 1.9 5.47 0.14 145.9 5.0 16:00 88.5 74.6 1.0 1.6 4.85 0.19 144.1 5.9 17:00 87.1 74.9 1.2 1.4 4.49 0.19 144.6 6.6 18:00 81.0 73.1 1.2 1.1 4.33 0.23 138.6 7.6 Mean 86.5 71.4 0.8 2.04 5.18 0.16 140.4 5.6 b Estimated as the difference between the all emissions runs in Table 2 and this non-fire emissions only run.

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th Table 4. Modeled (all emissions) concentrations of OA, BC, and PM2.5 during the September 7 fire event at the Austin/Round Rock CAMS 614 site. -3 -3 -3 Time PM2.5 (µg m ) BC (µg m ) OA (µg m ) (CST) Est. Fire Est. Fire Est. Fire Model Model Model Impacta Impacta Impacta 11:00 4.67 0.19 0.30 0.01 3.60 0.17 12:00 4.82 0.26 0.29 0.01 3.80 0.23 13:00 5.07 0.20 0.31 0.01 3.98 0.18 14:00 5.00 0.09 0.31 < 0.01 3.95 0.08 15:00 5.93 0.19 0.35 < 0.01 4.63 0.18 16:00 6.25 0.26 0.34 0.01 4.89 0.24 17:00 6.70 0.10 0.36 < 0.01 5.09 0.09 18:00 5.79 0.03 0.30 < 0.01 4.60 0.03 Mean 5.53 0.17 0.32 ~0.01 4.32 0.15 a Estimated using the single-run method and a threshold of 0.02 ppmv hr-1 of CO.

Table 5. Modeled (non-fire emissions only) concentrations of OA, BC, and PM2.5 during the September 7th fire event at the Austin/Round Rock CAMS 614 site. -3 -3 -3 Time PM2.5 (µg m ) BC (µg m ) OA (µg m ) (CST) Est. Fire Est. Fire Est. Fire Model Model Model Impactb Impactb Impactb 11:00 4.31 0.36 0.28 0.02 3.25 0.35 12:00 4.23 0.59 0.27 0.02 3.25 0.55 13:00 4.41 0.66 0.28 0.03 3.37 0.61 14:00 4.40 0.60 0.28 0.03 3.40 0.55 15:00 5.30 0.63 0.33 0.02 4.04 0.59 16:00 5.44 0.81 0.31 0.03 4.14 0.75 17:00 5.99 0.71 0.33 0.03 4.44 0.65 18:00 5.04 0.75 0.26 0.04 3.92 0.68 Mean 4.89 0.64 0.29 0.03 3.73 0.59 b Estimated as the difference between the all emissions runs in Table 4 and this non-fire emissions only run.

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Table 6. Observed and modeled (all emissions) concentrations of O3, NOx, and CO during the September 8th fire event at the Austin/Round Rock CAMS 601 site. NA – not available. Time O3 (ppbv) NOx (ppbv) CO (ppbv) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Obs. Model Model Impacta Impacta Impacta 11:00 77 57.6 <0.1 NA 4.42 0.01 124.6 0.1 12:00 76 58.3 0.3 NA 4.30 0.04 124.9 0.7 13:00 77 58.5 ~0.0 NA 4.26 ~0.00 122.1 ~0.0 14:00 74 58.9 <0.1 NA 4.35 0.01 123.1 0.2 15:00 75 59.4 ~0.0 NA 4.36 0.03 121.8 0.1 16:00 76 61.6 ~0.0 NA 5.59 0.01 127.3 0.1 17:00 76 59.4 <0.1 NA 5.02 0.01 120.0 0.2 18:00 72 59.1 ~0.0 NA 6.06 ~0.00 118.9 ~0.0 Mean 75.4 59.1 <0.1 NA 4.80 0.01 122.8 0.2 a Estimated using the single-run method and a threshold of 0.02 ppmv hr-1 of CO.

Table 7. Observed and modeled (non-fire emissions only) concentrations of O3, NOx, and CO during the September 8th fire event at the Austin/Round Rock CAMS 601 site. NA – not available.

Time O3 (ppbv) NOx (ppbv) CO (ppbv) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Obs. Model Model Impactb Impactb Impactb 11:00 77 56.9 0.7 NA 4.35 0.07 121.6 3.0 12:00 76 57.6 0.7 NA 4.23 0.07 121.9 3.0 13:00 77 58.1 0.4 NA 4.23 0.03 120.6 1.5 14:00 74 58.5 0.4 NA 4.32 0.03 121.7 1.4 15:00 75 59.1 0.3 NA 4.34 0.02 120.9 0.9 16:00 76 61.2 0.4 NA 5.56 0.03 126.1 1.2 17:00 76 59.1 0.3 NA 5.00 0.02 119.1 0.9 18:00 72 58.9 0.2 NA 6.05 0.01 118.4 0.5 Mean 75.4 58.7 0.4 NA 4.76 0.04 121.3 1.6 b Estimated as the difference between the all emissions runs in Table 6 and this non-fire emissions only run.

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th Table 8. Modeled (all emissions) concentrations of OA, BC, and PM2.5 during the September 8 fire event at the Austin/Round Rock CAMS 601 site. -3 -3 -3 Time PM2.5 (µg m ) BC (µg m ) OA (µg m ) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Model Model Impacta Impacta Impacta 11:00 38.6 3.14 0.01 0.21 <0.01 2.21 0.01 12:00 23.9 3.41 0.05 0.23 <0.01 2.38 0.05 13:00 13.0 3.39 ~0.00 0.23 ~0.00 2.35 ~0.00 14:00 6.5 3.74 0.01 0.25 <0.01 2.62 0.01 15:00 6.2 3.54 <0.01 0.23 <0.01 2.50 <0.01 16:00 6.6 3.82 0.01 0.28 <0.01 2.67 0.01 17:00 6.8 3.66 0.02 0.26 <0.01 2.52 0.01 18:00 8.6 3.92 ~0.00 0.28 ~0.00 2.68 ~0.00 Mean 13.78 3.58 0.01 0.25 <0.01 2.49 0.01 a Estimated using the single-run method and a threshold of 0.02 ppmv hr-1 of CO.

Table 9. Modeled (non-fire emissions only) concentrations of OA, BC, and PM2.5 during the September 8th fire event at the Austin/Round Rock CAMS 601 site. -3 -3 -3 Time PM2.5 (µg m ) BC (µg m ) OA (µg m ) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Model Model Impactb Impactb Impactb 11:00 38.6 2.90 0.24 0.19 0.02 1.99 0.22 12:00 23.9 3.18 0.23 0.22 0.01 2.17 0.21 13:00 13.0 3.28 0.11 0.23 <0.01 2.25 0.10 14:00 6.5 3.65 0.09 0.25 <0.01 2.53 0.09 15:00 6.2 3.47 0.07 0.23 <0.01 2.44 0.06 16:00 6.6 3.75 0.09 0.27 0.01 2.60 0.07 17:00 6.8 3.59 0.07 0.26 <0.01 2.46 0.06 18:00 8.6 3.87 0.05 0.27 0.01 2.65 0.03 Mean 13.78 3.46 0.12 0.24 0.01 2.39 0.11 b Estimated as the difference between the all emissions runs in Table 8 and this non-fire emissions only run.

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Figure 1. MODIS Aerosol Optical Depth (AOD) observations on September 7th, 2011 with the STILT back-trajectories (generated with NARR meteorology) for the 13:00 CST September 7th Austin/Round Rock CAMS 614 run over-plotted as black circles for each 15 minute time step. The location of Bastrop, TX is shown as a black star.

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Figure 2. MODIS Aerosol Optical Depth (AOD) observations on September 8th, 2011 with 25 STILT back-trajectories (generated with NARR meteorology) for the 13:00 CST September 7th Austin/Round Rock CAMS 601 run over-plotted as black circles for each 15 minute time step. The location of Bastrop, TX is shown as a black star.

3.2 STILT-ASP Simulation of the Houston-Galveston-Brazoria August 26th Event

We have also simulated the eight hours of the MDA8 O3 measured during the August 26th, 2011 event as measured at the CAMS 1 site. These simulations were performed using the 32 km NARR meteorological fields and 500 7-day back-trajectories to allow us to simulate the potential impact of long-range transport of pollution from fires in the Pacific Northwest on this event. As in the Austin events above, these simulations used the default emissions and included the effects of clouds on photolysis rates, N2O5 hydrolysis (uptake coefficient of 0.02) and aerosol processes.

The results in Table 10 show a large overestimate of O3, with the model predicting and average MDA8 concentration of 140.5 ppbv compared to the measurement average of 98 ppbv. While STILT-ASP v1.0 does a reasonable job of simulating the hour of maximum measured O3 (modeled value of 134.7 ppbv versus measured value of 128 ppbv), the model predicts fairly constant values of O3 through the day, in contrast to the rapid increase and decrease seen in the measurements. Comparison with runs without fire emissions (Table 11) and with runs without

25 Work Order No. 582-16-62311-03 Final Report any emissions (not shown) suggest that this overestimate is due to the default non-fire emissions estimate, but it’s unclear how we can solve this overestimate without making the underestimate seen in the Austin cases (Section 3.1) worse. Changing the number of days in the simulation from 7 to 5 doesn’t appear to make a significant difference to the simulation. NOx is overestimated, but the relative overestimate is not as severe as in the Austin cases (modeled average concentration of 12.55 ppbv versus a measured value of 9.4 ppbv). In addition, the threshold of 0.02 ppmv hr-1 of CO emissions to identify fire parcels for the single run method does not work well for this event. The impact of the fires on O3 is overestimated at 22.4 ppbv, which gives an estimate of the fire enhancement ratio for O3 (ΔO3/ΔCO) of 86%, which is unreasonably high (Jaffe and Wigder, 2012). In contrast, the STILT-ASP results for model simulations with only non-fire emissions in Table 11 give an estimate of the fire impact on O3 of 3.5 ppbv, with a value for ΔO3/ΔCO (27.8%) that is reasonable when compared with measurements from the literature (Jaffe and Wigder, 2012). As in the Sept. 8 Austin case (Section 5.4), STILT-ASP underestimates the total concentration of PM2.5 relative to observations (Table 12). The fire impact estimates on PM2.5 are higher than those from the two-run method (1.93 µg m-3 versus 1.26 µg m-3), but are relatively closer than the O3 estimates. The two-run method gives an estimate of ΔPM2.5/ΔCO of 9% (g/g), roughly between the ratios observed for grassland files (11.4%) and for crop residue burns (6.1%, Akagi et al., 2011). We can also examine the satellite data and statistical models for insight into the impact of fires on the MDA8 O3, as in Section 3.1. Figure 3 and Figure 4 show plots of the MODIS AOD and MERRA Total O3 column, respectively, for 13:00 CST (19:00 UTC) on August 26, 2011. 25 of the 500 STILT back-trajectories are plotted as well. MODIS shows a hot spot in AOD in southern Louisiana consistent with the fire burning in that location during the event, and the STILT back-trajectories (calculated with the NAM12 meteorology) pass directly over this hot spot. This suggests that emissions from these fires could have impacted the O3 measured at Houston at this time. The MERRA total O3 column (Figure 4) doesn’t suggest any role of stratospheric intrusions along the back-trajectories, but this plot does show that the STILT back- trajectories also travel back to the Pacific Northwest and may be influenced by fire emissions in that location as well. Examining the statistical model of Alvarado et al. (2015b) for the Houston area th maximum MDA8 O3 shows a model underestimate of 2.3 ppbv on August 26 , 2011, consistent with the STILT-ASP estimate of 2 ppbv but well within the RMS error of the fit (~9 ppbv). Thus neither STILT-ASP nor the statistical model are able to give any convincing evidence of a large impact of biomass burning on the MDA8 O3 measured during this event. However, the large bias in the STILT-ASP modeling for this event makes it hard to say anything definitive at this time.

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Table 10. Observed and modeled (all emissions) concentrations of O3, NOx, and CO during the August 26th fire event at the Houston-Galveston-Brazoria CAMS 1 site.

Time O3 (ppbv) NOx (ppbv) CO (ppbv) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Obs. Model Model Impacta Impacta Impacta 11:00 77 135.5 53.6 8.8 10.60 5.95 184.8 56.8 12:00 112 138.6 52.6 9.7 10.63 5.33 180.1 51.2 13:00 128 134.7 13.1 11.0 11.35 1.34 174.8 16.0 14:00 109 136.5 12.1 13.3 12.00 1.55 182.2 16.1 15:00 100 146.7 9.9 11.3 12.09 1.07 189.7 12.4 16:00 92 148.7 13.5 7.2 13.07 1.34 192.9 16.7 17:00 87 144.7 13.2 7.2 15.23 1.81 202.4 22.5 18:00 77 138.7 10.8 6.6 15.41 1.53 201.0 15.6 Mean 98 140.5 22.4 9.4 12.5 2.5 188.5 25.9 a Estimated using the single-run method and a CO emission threshold of 0.02 ppmv/hr.

Table 11. Observed and modeled (non-fire emissions only) concentrations of O3, NOx, and CO during the August 26th fire event at the Houston-Galveston-Brazoria CAMS 1 site.

Time O3 (ppbv) NOx (ppbv) CO (ppbv) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Obs. Model Model Impactb Impactb Impactb 11:00 77 131.7 3.8 8.8 10.52 0.08 171.9 12.9 12:00 112 135.1 3.5 9.7 10.51 0.12 168.3 11.8 13:00 128 131.2 3.5 11.0 11.22 0.13 162.7 12.1 14:00 109 133.0 3.5 13.3 11.85 0.15 169.6 12.6 15:00 100 142.9 3.8 11.3 11.91 0.18 176.6 13.1 16:00 92 145.5 3.2 7.2 12.92 0.15 180.8 12.1 17:00 87 141.0 3.6 7.2 15.05 0.18 187.5 14.9 18:00 77 135.5 3.2 6.6 15.25 0.16 189.7 11.3 Mean 98 137.0 3.5 9.4 12.40 0.14 175.9 12.6 b Estimated as the difference between the all emissions runs in Table 10 and this non-fire emissions only run.

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Table 12. Observed and modeled (all emissions) concentrations of OA, BC, and PM2.5 during the August 26th fire event at the Houston-Galveston-Brazoria CAMS 1 site. QA – data failed quality checks. -3 -3 -3 Time PM2.5 (µg m ) BC (µg m ) OA (µg m ) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Model Model Impacta Impacta Impacta 11:00 QA 9.37 6.61 0.84 0.29 6.81 4.84 12:00 QA 8.37 2.51 0.77 0.25 5.94 1.77 13:00 QA 7.98 1.10 0.73 0.08 5.60 0.89 14:00 QA 8.46 1.11 0.80 0.08 5.80 0.87 15:00 QA 8.60 0.83 0.82 0.06 6.03 0.67 16:00 22.1 8.49 0.94 0.85 0.07 5.87 0.75 17:00 24.7 9.21 1.35 0.95 0.12 6.33 1.02 18:00 23.7 9.29 0.98 1.01 0.08 6.10 0.75 Mean 23.5 8.72 1.93 0.85 0.13 6.06 1.45 a Estimated using the single-run method a CO emission threshold of 0.02 ppmv/hr.

Table 13. Observed and modeled (non-fire emissions only) concentrations of OA, BC, and PM2 during the August 26th fire event at the Houston-Galveston-Brazoria CAMS 1 site. QA – data failed quality checks. -3 -3 -3 Time PM2.5 (µg m ) BC (µg m ) OA (µg m ) (CST) Est. Fire Est. Fire Est. Fire Obs. Model Model Model Impactb Impactb Impactb 11:00 QA 7.98 1.39 0.79 0.05 5.52 1.29 12:00 QA 7.13 1.24 0.72 0.05 4.80 1.14 13:00 QA 6.77 1.21 0.69 0.04 4.49 1.11 14:00 QA 7.23 1.23 0.75 0.05 4.67 1.13 15:00 QA 7.33 1.27 0.76 0.06 4.88 1.15 16:00 22.1 7.32 1.17 0.81 0.04 4.81 1.06 17:00 24.7 7.76 1.45 0.88 0.07 5.02 1.31 18:00 23.7 8.16 1.13 0.96 0.05 5.07 1.03 Mean 23.50 7.46 1.26 0.80 0.05 4.91 1.15 b Estimated as the difference between the all emissions runs in Table 12 and this non-fire emissions only run.

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Figure 3. MODIS Aerosol Optical Depth (AOD) observations on August 26, 2011 with 25 STILT back-trajectories (generated with NARR meteorology) for the 13:00 CST August 26th Houston CAMS 1 run over-plotted in black.

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Figure 4. MERRA total column ozone observations on August 26, 2011 with 25 STILT back- trajectories (generated with NARR meteorology) for the 13:00 CST August 26th Houston CAMS 1 run over-plotted in black.

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4 Quality Assurance Steps and Reconciliation with User Requirements All work on the project was done in accordance with the Quality Assurance Project Plan (QAPP). The source code, data files, and model output produced in this project were inspected by team members different from the original author to ensure they were correct, and any errors noted in early versions were fixed. Other required evaluations are contained within this report (for example, see Section 0) or in the attached Technical Memo and User’s Guide. In order to ensure that the combined model is working as expected, we have performed several checks of the model performance based on simulations of the Houston and Austin 2011 fire events. Every time a new component was added to the STILT-ASP model, several tests were performed to confirm that the new routine was giving correct results. In addition, our initial simulations of the 2011 fire cases uncovered several bugs in the treatment of emissions, chemistry, and other processes in the coupled STILT-ASP model, which we corrected in the course of this project. However, our evaluations have uncovered a couple of important limitations on the model due to currently unresolved errors, as discussed below. We are continuing to work on these errors in the remaining time on this project. In addition, the QAPP listed several questions that needed to be addressed for each project task. These questions are addressed below. 4.1 Task 2: Development of STILT-ASP Model into a Wildfire Analysis Tool  What are the software and hardware requirements for the updated STILT-ASP tool? How long should a reference model run take?

The hardware and software requirements of STILT-ASP are discussed in Section 2 of the STILT-ASP User’s Guide, while the timing of reference runs for the September 7th, 2011 Austin fire event is described in Section 5.3 of the Technical memo. In summary, STILT-ASP v1.0 should only be run on a Windows 7 Professional 64-bit workstation with at least 16 GB of RAM. A 2-day back- trajectory model run with 500 particles and no aerosol processes takes about 1 hour of wall-clock time Windows 7 Professional 64-bit workstation with a 4-core AMD Opterontm 6176SE processor running at 2.30 GHz and 16 GB of RAM.

 Are the back-trajectories and surface influence functions (i.e., “footprints”) generated by the updated STILT-ASP tool reasonable? Are they consistent with the original STILT model?

The back-trajectories for the STILT-ASP tool have been validated against the original STILT model, as described in Section 5.2 of the Technical Memo and are consistent with the original STILT model.

 Is the simulated chemical formation of O3, PM2.5, and other chemical species along the particle trajectories in STILT-ASP reasonable? Are these predictions consistent with the original ASP model? Are these predictions consistent with the scientific literature on the impacts of wildfires on O3 and PM2.5?

As discussed in Section 3 of this report and Section 5.3 and 5.4 of the Technical Memo, the simulated formation of O3 and other species along the particle

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trajectories are reasonable, but can result in significant over or underestimates. These predictions appear to be consistent with the original ASP model. Furthermore, the predicted impacts of fires on O3 during these events, relative to their impacts of CO, are consistent with literature estimates and the original ASP model IF estimated by comparing two STILT-ASP runs with and without fire emissions. The single-run method, however, can give unreasonable results thus we recommend using the two-run, zero-out method.

 Under what conditions is the model expected to be valid?

STILT-ASP v1.0 is considered suitable for simulating O3 and PM2.5, but needs further work to be accurate enough for regulatory purposes. STILT-ASP v1.0 can also be used to estimate the impact of fires on urban O3; however, this MUST be done by comparing two STILT-ASP runs, one with and one without fire emissions. The “single run” method can give unrealistically large estimates of the impact of fires on O3 relative to their impact on CO.

4.2 Task 3: Analysis of 2011 Smoke Events

 Do the model predictions of the impact of fires and other sources on O3 and PM2.5 for the two 2011 smoke events make physical sense given our conceptual models of O3 and PM2.5 emissions, chemistry, and transport during these events? Are these predictions consistent with the scientific literature on the impacts of wildfires on O3 and PM2.5? If not, is there a reasonable explanation for the differences?

The estimated impact of fires on MDA8 O3 and 8-hour average PM2.5 during these events, calculated by comparing two runs, is fairly small, but is consistent with our conceptual models of the impact of fires during these events given the small predicted impacts on CO and NOx. However, we cannot rule out that the model may be currently underestimating the impact of fire emissions on both O3 and CO.

 What are the uncertainties in the model estimates of the impacts of fires during these events? How sensitive are the results to errors in the meteorological inputs, emission estimates, or other model inputs?

We have focused our efforts on ensuring that the calculations of STILT-ASP are being performed correctly, and thus we have not performed an extensive set of sensitivity studies with the model. Thus we cannot yet comment on the uncertainties in the model estimates of the impact of fires during these events.

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5 Conclusions Here we summarize the conclusions of our project, with reference to the corresponding report section.

 STILT-ASP v1.0 is considered suitable for simulating O3 and PM2.5, but the default non-fire emissions can lead to large under or overestimates of these species, as shown in Section 3.  STILT-ASP v1.0 can also be used to estimate the impact of fires on urban O3; however, this should be done by comparing two STILT-ASP runs, one with and one without fire emissions. The “single run” method described in Section 3.4.2 of the Technical Memo can lead to unrealistically large estimates of the impact of fires on O3 relative to their impact on CO.  The impact of the fires in Bastrop, TX on MDA8 O3 levels in the Austin/Round Rock area on September 7 and 8, 2011, are likely small. STILT-ASP predicted a fire impact of only 0.8 ppbv and 0.4 ppbv on the MDA8 O3 on these days due to the fact that the model back-trajectories came from the north rather than from Bastrop in the southeast. However, it should be noted that the estimated ΔO3/ΔCO from this method is very reasonable (14.3% and 25%), and is close to the average value for extratropical fire plumes more than 2 days old (20%, Jaffe and Wigder, 2012). An examination of the statistical models of Alvarado et al. (2015b) also suggests that fires had a small impact on this event, with an upper limit impact of 3-4 ppbv (Section 3.1). th  STILT-ASP currently overestimates MDA8 O3 for the Houston August 26 event by about 40 ppbv, and thus it is difficult to draw significant conclusions about this event (Section 3.2). However, the cause of this overestimate appears to be in the default- non-fire emissions, and the estimated impact of fires on the MDA8 O3 (3.5 ppbv, with a ΔO3/ΔCO of 27.8%) appears reasonable.

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6 Recommendations for Further Study We recommend that future development work of the STILT-ASP wildfire analysis tool focus on:  Simulating cases where more data on the gas and aerosol composition of the atmosphere is available, such as during the 2013 Houston DISCOVER-AQ campaign, in order to thoroughly test the STILT-ASP model predictions of multiple species and use these findings to improve the model.  Further development of the emissions preprocessor to get more accurate estimates of the non-fire emissions. This work should include: o Running the biogenic emissions models BEIS and MEGAN on-line in the tool. o Applying annual scale factors to previous, validated SMOKE model runs for anthropogenic emissions, to include weekday-weekend effects. o Improving the speciation of the anthropogenic emissions to better match the ASP chemical mechanism. o Adding natural PM2.5 sources such as wind-blown dust and sea salt.  Further development of the scientific routines of STILT-ASP, including: o Using WRF cloud fields to calculate photolysis rates, instead of the approximate RH-profile based approach described in Section 3.2.2 of the technical memo. o Integrating TUV or FAST-JX photolysis models into STILT-ASP to calculate photolysis rates online. o Improving the depositions routines to make them consistent with those in CMAQ and CAMx. o Linking the ASP aerosol scheme with EPA AERO6 model inputs. o Adding irreversible aqueous organic and inorganic chemistry to ASP. o Exploring the impact of including grid-scale mixing between Lagrangian parcels on model results and determine appropriate mixing timescales.  Exploring methods to improve the speed of the STILT-ASP model through code parallelization and other techniques. This would both make the tool easier to use and would allow more sensitivity cases to be evaluated, allowing improvements to the model to be tested more quickly than is currently possible.  Develop a platform-independent build system for STILT-ASP to allow for better validation, versatility and accuracy.  Modify STILT-ASP to accept GEOS-Chem and CMAQ boundary condition inputs.  Allow the user to select the type of chemical speciation used by an input file (CB05, SAPRC07, etc.)

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7 References Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, doi:10.5194/acp-11-4039-2011, 2011. Akagi, S. K., Craven, J. S., Taylor, J. W., McMeeking, G. R., Yokelson, R. J., Burling, I. R., Urbanski, S. P., Wold, C. E., Seinfeld, J. H., Coe, H., Alvarado, M. J., and Weise, D. R.: Evolution of trace gases and particles emitted by a chaparral fire in California, Atmos. Chem. Phys., 12, 1397–1421, 2012. Alvarado, M. J., Lonsdale, C. R., Yokelson, R. J., Akagi, S. K., Coe, H., Craven, J. S., Fischer, E. V., McMeeking, G. R., Seinfeld, J. H., Soni, T., Taylor, J. W., Weise, D. R., and Wold, C. E.: Investigating the links between ozone and organic aerosol chemistry in a biomass burning plume from a prescribed fire in California chaparral, Atmos. Chem. Phys., 15, 6667–6688, doi:10.5194/acp-15-6667-2015, 2015a. Alvarado, M. J., Lonsdale, C. R., Mountain, M. E., and Hegarty, J. D.: Investigating the Impact of Meteorology on O3 and PM2.5 Trends, Background Levels, and NAAQS Exceedances, Final Report to Texas Commission on Environmental Quality (TCEQ) for Work Order No. 582-15-54118-01 under TCEQ Contract No. No. 582-15-50415, August 31, 2015b. Goliff, W. S., Stockwell, W. R., and Lawson, C. V.: The regional atmospheric chemistry mechanism, version 2, Atmos. Environ., 68, 174–185, 2013. Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 3181–3210, doi:10.5194/acp-6-3181-2006, 2006. Hegarty, J., Draxler, R. R., Stein, F., Brioude, J., Mountain, M., Eluszkiewicz, J., Nehrkorn, T., Ngan, F., and Andrews, A.: Evaluation of Lagrangian Particle Dispersion Models with Measurements from Controlled Tracer Releases, J. Appl. Meteor. Climatol., 52, 2623–2637, doi: 10.1175/JAMC-D-13-0125.1, 2013. Henderson, J. M., Eluszkiewicz, J., Mountain, M. E., Nehrkorn, T., Chang, R. Y.-W., Karion, A., Miller, J. B., Sweeney, C., Steiner, N., Wofsy, S. C., and Miller, C. E.: Atmospheric transport simulations in support of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), Atmos. Chem. Phys., 15, 4093–4116, doi:10.5194/acp-15-4093-2015, 2015. Hodzic, A., and Jimenez, J. L.: Modeling anthropogenically controlled secondary organic aerosols in a megacity: a simplified framework for global and climate models, Geosci. Model Dev., 4, 901–917, doi:10.5194/gmd-4-901-2011, 2011. Jaffe, D. A., and Wigder N. L.: Ozone production from wildfires: A critical review, Atmospheric Environment, 51, 1–10, 2012. Jaffe, D. A., Wigder, N., Downey, N., Pfister, G., Boynard, A., and Reid. S. B.: Impact of wildfires on ozone exceptional events in the western US, Environ. Sci. Technol., 47(19), 11065–11072, 2013. Jenkin, M. E., Saunders, S. M., Wagner, V., and Pilling, M. J.: Protocol for the development of the Master Chemical Mechanism, MCM v3 (Part B): tropospheric degradation of aromatic

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