# Appendix B-7

Technical Memorandum: Preparation of Wildland Fire Emissions Inventory fo r 2011

## Technical Memorandum

**August 15, 2012 STI-910321-5446** 

**To: Venkatesh Rao, U.S. Environmental Protection Agen cy** 
**From: ShihMing Huang, Yuan Du, Sean M. Raffuse, and St ephen B. Reid** 

Re: Preparation of Wildland Fire Emissions Inventor y for 2011

**The U.S. Environmental Protection Agency's (EPA) Emissi ons Inventory and Analysis**  **Group (EIAG) compiles the National Emissions Inventory ( NEI) and disseminates inventory**  **data, summaries, model-ready files, and analyses based o n the NEI. To support this program**  **and the analyses conducted by a variety of users, Sonoma T echnology, Inc. (STI) has**  **developed an emissions inventory of wildland fires for th e year 2011, as we did for the years**  **2006 through 2010.**

**This work was done via Work Assignment (WA) 3-21 under C ontract No. EP-D-09-097.**  **This technical memorandum describes the methods used to de velop the 2011 fire emissions**  **inventory, summarizes the 2011 fire activity and emissi ons data, and compares the inventories**  **for the past five years.**



### Technical Approach

Data Sources

**Fire activity data from the following sources were used to develop the 2011 fire**  **inventory:**

· **Inputs to SmartFire 2 (SF2)**

– HMS **data were acquired daily from the National Oceanic and Atmospheric**  **Administration (NOAA) Hazard Mapping System (HMS) via FT P as part of a routine**  **process. Data were acquired in ASCII text format avail able at**  **http://satepsanone.nesdis.noaa.gov/FIRE/fire.html**

**.**

– ICS-209 Reports

**in application (.exe) format were acquired via the Fi re and Aviation**  **Management Web Applications website ( https://fam.nwcg.gov/fam-web/sit/**  **). Upon execution, the application file created a Microsoft Access**  **database containing the fire activity data.** 

– GeoMAC **fire perimeter data were downloaded via the United S tates Geological**  **Survey (USGS) GeoMAC wildland fire support website (**  **http://rmgsc.cr.usgs.gov/outgoing/GeoMAC/ ).** **August 15, 2012 Page 2** 

– Moderate Resolution Imaging Spectroradiometer (MODIS) **satellite data were downloaded via the USDA Forest Service (USFS) Remote Se**  **nsing Applications Center website (**  **http://activefiremaps.fs.fed.us/gisdata.php ). Data were converted**  **from a shapefile to an ASCII text file and used to fi ll in the blank dates from HMS.** 

· Fuel Moistures

–

**Fire weather observation files (fdr_obs.dat) were acqu ired for each**  **analysis day from http://72.32.186.224/archive/www.fs.fed.us/land/wfas/**  **archive . Files**  **were acquired and combined for database ingest using Pyth on scripts.** 

· Fuel Loading

–

**Fuel Characteristic Classification System (FCCS) 1-km fuels shapefile**  **and lookup table for the conterminous United States we re provided by the AirFire Team.**  **The Alaskan FCCS 1-km fuels shapefile and lookup table w ere acquired from the Fire**  and Environmental Research Applications Team’s website **( http://www.fs.fed.us/pnw/fera/fccs/maps.shtml**  **).**

Preparation of Fire Activity Data

**SmartFire 2 was used to process and reconcile fire activity data from HMS, ICS-209**  **Reports, and GeoMAC fire perimeters (Pollard et al., 2011). An extra processing step in**  **SmartFire 2 was taken in developing the 2010 Fire Emissi on Inventory as well as this emission**  **inventory: fire activity data were reconciled twice. D uplicate events that should have been**  **merged were still being found in the fire activity da ta after one round of reconciliation. Late**  **satellite detection of fire reflected in the HMS data set could result in double-counting with MTBS**  **data. This specific issue is resolved through a second reconci liation pass. In future emission**  **inventories, this issue can be minimized by increasing the starting date uncertainty for the HMS**  **dataset. A comparison of the results from single and d ouble reconciliation using 2010 fire**  **activity data showed a decrease of 4% in total acres bur ned by wildfires after the second fire**  **activity reconciliation.**

**In addition, SmartFire 2 was used to generate daily i nput files for emissions processing**  **through the BlueSky Framework for wildland fires. MODI S data were used to gap-fill for dates**  **on which data were missing from the HMS: December 30 a nd December 31, 2011.** 

Process Streams

**The BlueSky Framework provides several choices of models at e ach step of the smoke**  **modeling process. The model chain used for the contermin ous United States and Alaska,**  **where FCCS fuel loading data are available, is summariz ed in**  Table 1 **. A new version of Consume (version 4.1) has been implemented in the BlueSky**  **Framework since the development of the 2009 Wildland Fire Emission Invent**  **ory, which was based on Consume version 3.0. The differences between versions 4.1 and 3.0**  **include bug fixes for duff reduction; litter, lichen, and moss reduction; squirrel midden fue**  **l loading; and other minor errors.**

**A different model chain was used for fires in Hawaii an d Puerto Rico, as summarized in**  Table 2 **, because FCCS data are not available in these regions . The Fire Inventory from NCAR**  **(FINN) version 1 is capable of producing global emission estimates for wildland fires, and it**  **therefore was used to develop the emission inventories for Hawaii and Puerto Rico. FINN**  **utilizes satellite-derived land cover data, along with estimated fuel loadings and emission** **August 15, 2012 Page 3** 

**factors, to model smoke emissions (Wiedinmyer et al., 201 1). However, the emission factors of**  **volatile organic compounds (VOCs) and hazardous air pollu tants (HAPs) are not available in**  **FINN.**

**Emission estimates of VOCs and HAPs from wildland fires in Hawaii and Puerto Rico**  **were based on carbon dioxide (CO 2**  **) outputs from FINN. The average ratios of VOCs and HAPs to CO**  **2 for wildland fires in grassland/herbaceous land cover, whi**  **ch is most similar to the vegetation type that burned in Hawaii and Puerto Rico**  **, were calculated for the conterminous United States and applied to the CO**  **2 emissions of Hawaii and Puerto Rico fires to estimate**  **VOC and HAP emissions.**

Table 1. **Model chain for the conterminous U.S. and Alaska port ion of the 2011 Wildland**  **Fire Emissions Inventory development.**

Data Type Model Used Version No.

**Fire a ctivity data**  **SmartFire 2 Version 2.0, Build**  **891 Fuel loading**  **FCCS v2**  **As implemented in Blue**  **Sky Framework 3.**  **3.0, revision 28153 Fuel consumption**  **Consume v 4.1** 
**Emissions Fire Emission**  **Production Simulator (FEPS) v2** 

Table 2. **Model chain for the Hawaii and Puerto Rico portion of the 2011 Wildland Fire**  **Emissions Inventory development.**

Data Type Model Used Version No.

**Fire a ctivity data**  **SmartFire 2 Version 2.0, Build**  **891 Fuel loading**  **FINN v1**  **As implemented in Blue**  **Sky Framework 3.**  **3.0, revision 28153 Fuel consumption**  **FINN v1** 
**Emissions FINN**  **v1**

Emissions Processing

**The following steps were applied to process fire activit y data and estimate emissions for**  **fires in the conterminous United States and Alaska:**

**1.** Assign fuel moistures

– **Individual fire locations from SmartFire 2 prediction points were**  **assigned to the nearest fire weather station reporting on that day using VBA code in**  **AssignFuelMoisture.mxd. This code produced a lookup table (yyyyFandFW.csv) of fire**  **IDs, station IDs, dates, and distances.**

**2.** Create BlueSky input file

– **The daily input files for the BlueSky Framework (fire_locations_yyyymmdd.csv)**  **were created using VBA code in CreateFireLocations.xls. Input tables were stored in the Microsoft Access database C**  **reateFireLocInput.mdb, including the tables FireLocations_yyyy and yyyyFandFW.**  **If the distance between the fire and the nearest fire weather station was greater t**  **han 300 km, default values were August 15, 2012**  **Page 4**

**assigned (fuel_moisture_10hr = 9 and fuel_moisture_1khr = 1 2 for wildfires;**  **fuel_moisture_10hr = 12 and fuel_moisture_1khr = 25 for prescribed fires). Outputs**  **were created in .csv format and saved directly to the B lueSky Framework input directory.** 

**3.** Process through BlueSky Framework

– **A module was customized for the BlueSky Framework to calculate HAP emissions using emission factors pro**  **vided by EPA. The Framework is currently designed to process one day at a t**  **ime. A shell script (batchEmissions) was used to process emissions one year at a t**  **ime. The resulting files are daily BlueSky outputs.** 

**4.** Process BlueSky Outputs

– **The BlueSky Framework produces three output files for each day. For this project, we only required fire_loca**  **tions_yyyymmdd.csv, which contains the same data as the input file, but with add**  **itional calculated fields (fuel loadings, fuel consumptions, and emissions) appended to eac**  **h fire record. The daily files were concatenated using a Python script, ConcatFire**  **Loc.py, into yearly files (yyyyFireLoc.csv) for ingest into the emissionsyyyy.mdb data**  **base and analysis.**

**5.** Post-process emissions

– **There is a known issue existing in the Consume model. T he**  **algorithm does not behave properly for prescribed burns (RX) in areas with large duff**  **depths. In such a case, Consume will overestimate the duff consumptions, resulting in**  **considerably high consumptions and emissions. To solve the problem in a timely way**  **before a new Consume version is released with the issue fixe d, a post-processing**  **procedure was applied to each RX to scale down phase-specifi c consumptions and daily**  **total emissions. The scaling procedure was implemented in t he development of the**  **2010 and 2011 Fire Emission Inventories, as well as the revised 2008 and 2009 Fire**  **Emission Inventory. The scaling factor for each RX was de rived according to its location**  **and duff consumption. The step by step process was as follow s:** 

· **New duff consumption of each prescribed burn was re-** calculated by setting a “cap” **value for the duff consumption. For burns in western states (all states west of Texas,**  **plus the Dakotas), the duff consumption cap was set to 2 0 tons per acre. For**  **eastern states, the duff consumption cap was set to 5 tons per acre. These caps**  **were developed via expert judgment in consultation with USFS and U.S. Department**  **of Interior experts. For each fire, the exceedance in duff consumption was calculated**  **by subtracting capped duff consumption from the original duff consumption.** 

· **The new total consumption of each prescribed burn was cal culated by removing the**  **exceedance in duff consumption from the original total consumption.** 

· **A scaling factor for each prescribed burn was calculated as the ratio of the new total**  **consumption over the original total consumption.**

· **Finally, the burn-specific scaling factor was applied to phase-specific consumption**  **(flaming, smoldering, and residual) and daily emissions o f all pollutants to compute**  **new fuel consumption and emissions.**

**6.** Prepare agricultural data

– **BlueSky does not currently provide methods for estima ting**  **emissions from agricultural burns, and this inventory exclu des agricultural fires. Fire**  **activity data in the conterminous United States from Sm artFire 2 were intersected with**  **the U.S. Geological Survey 2006 30-m National Land Co ver Dataset (NLCD), and fire**  **activity data in Alaska, Hawaii, and Puerto Rico were in tersected with the 2001 30-m** **August 15, 2012 Page 5** 

**NLCD. NLCD classifies all area in the United States int o one of 19 land cover types**  **(** Figure 1 **). Fires that fell within land cover types 81 (Pasture /Hay) or 82 (Cultivated**  **Crops) were assumed to be agricultural burns and removed f rom the yyyyFireLoc table**  **to make a yearly agricultural fire table (AgActivityCl ean) in the emissionsyyyy.mdb**  **database.**

**7.** Preparing wildland fire data

– **yyyyFireLoc table in the emissions.mdb database was formatted into the final emission inventory table (WF_**  **locations_All) after filtering out the agricultural burn data.** 

**The emissions processing steps 1 to 5 were omitted for Hawa ii and Puerto Rico wildland**  **fires because the model chain used in the BlueSky Framework was different. FINN does not**  **require fuel moisture data, nor does it need daily fi re activity input files. The activity data of fires**  **in Hawaii and Puerto Rico were input into the BlueSky Framework in one single file, and the**  **output file, fire_locations.csv, contained the fuel consu mptions and emissions data for all fires in**  **the input file. The outputs were first post-processed t o estimate VOC and HAP emissions as**  **described previously in the** “ **Process Streams** ” **section, appended to the yyyyFireLoc table in the emissionsyyyy.mdb database, and finalized for this emissions i**  **nventory through steps 6 and 7.**

Figure 1. **NLCD map used to identify and segregate agricultural burns. Puerto Rico data**  **are not shown in this figure. August 15, 2012**  **Page 6**



### Emissions QA/QC

**For the 2011 fire inventory produced with SmartFire 2 , several steps were taken to QC**  **the data and confirm that the algorithms and datasets i ncorporated into SmartFire 2 worked**  **appropriately.**

Before Running SmartFire 2:

· **We reviewed input datasets and identified any data gaps that needed to be filled with**  **alternative data (e.g., gaps in HMS data were filled w ith MODIS data).** 

· **We identified fire incidents that appeared to be doub le-counted in individual data sets**  **and removed duplicate records.**

· **We examined fires with long durations or conflicts bet ween start date and report date to**  **identify fires that may have erroneous start dates. S tart dates later than report dates**  **were replaced with the report dates.**

· **We spot-checked reported location coordinates for selec ted fires.** 

After Running SmartFire 2:

· **We checked the location, duration, underlying fire a ctivity input data, final shape, and**  **final size for large fire events (i.e., area burned > 10,000 acres) to ensure that the results**  **were reasonable.**

· **We checked large fire events (i.e., area burned > 10, 000 acres) by state and by name**  **and removed duplicate events.**

· **We produced and reviewed summary tables and plots of th e 2011 fire inventory data.** 

· **We compared acres burned by state to National Interag ency Fire Center**  **( http://www.nifc.gov/fireInfo/fireInfo_statistics.html**  **) data to make sure the summary values are within reasonable range.** 



### Summary of 2011 Wildland Fire Emission Inventory

**We estimated that, in 2011, wildland fires burned o ver 23 million acres in the United**  **States and emitted more than 2.6 million tons of PM 2.5**  **. The area burned by wild and prescribed fires make up 43% and 57% of the total area burned,**  **respectively. Wildfire PM 2.5**  **emissions account for 67% of the total emissions and prescribed bur**  **ns account for 33%.**

**In the 2011 Wildland Fire Emission Inventory, the bulk of emissions originate from two**  **regions: the West and the Southeast. This concentrat ion of emissions is consistent with**  **previous national fire inventories prepared by STI for E PA and can be seen in the emissions**  **density plot shown in** Figure 2

**.** Figure 3 **depicts the total monthly PM 2.5**  **emissions for each state. The springtime emissions are mostly from the southea**  **stern states, where prescribed burning is a common land management practice in spring.**  **The summer/fall emissions occur primarily in the West and Southwest, particularly in Cal**  **ifornia, Oregon, Arizona, and New Mexico.**  Figures 4 and 5 **depict the area burned per square mile by county for wildland fires**  **and total area burned by county for agricultural fires , as calculated by SmartFire 2.** **August 15, 2012 Page 7** 

Figure 2. **2011 wildland fire PM 2.5**  **emission density. Puerto Rico data are not shown in**  **this figure.**

Figure 3. **2011 total monthly PM 2.5**  **emissions (tons) from wildland fires by state. Pu erto**  **Rico data are not shown in this figure. August 15, 2012**  **Page 8**

Figure 4. **2011 wildland fire area burned (acres per square mi le) by county. Puerto Rico**  **data are not shown in this figure.**

Figure 5. **2011 agricultural fire total area burned (acres) by county. Puerto Rico data are**  **not shown in this figure.**

**Area burned and PM 2.5**  **results by state and fire type are presented in** Figure 6 **. Large totals of area burned are present throughout the Southeast**  **, the Southern Plains, Southwest, August 15, 2012**  **Page 9**

**and West. State totals of area burned are dominated by prescribed burning in most of the**  **states on the eastern half of the country. Conversely, the emissions pattern is dominated by**  **wildfires in Minnesota, North Carolina, and most of the states in the western half of the country.** 

Figure 6. **State totals of area burned (top) and PM 2.5**  **emitted (bottom) by fire type. Pie sizes are proportional to state totals. Each pie cons**  **ists of two components: prescribed fire (green) and wildfire (red). Alaska, Hawaii, and**  **Puerto Rico data are not shown in this figure.** **August 15, 2012 Page 10** 

**The monthly patterns of area burned and PM 2.5**  **emissions are shown in** Figures 7 and 8 **. In this inventory, the peak season for area burned is late winter/early spring, but the**  **peaks of PM 2.5**  **emissions are in early summer and fall. This difference i s a result of the offset**  **between the prescribed and wildfire burning seasons and the larger relative emissions per area**  **burned for wildfires. In 2011, substantial wildfire activities in the southwest (particularly Arizona**  **and New Mexico) and Alaska resulted in the peak of PM 2.5**  **emissions in June. The Pains Bay fire and Juniper Road fire in North Carolina also cont**  **ributed significant emissions in June from the smoldering of organic soil. The Pagami Creek fire**  **in Minnesota burned from late August through October, consumed an extensive amount of duff**  **fuels, and in turn resulted in the emission peak in September.** 

Figure 7. **Monthly area burned in the United States in 2011 by fire type. Red indicates**  **wildfires and green indicates prescribed burns.**

Figure 8. **Monthly PM 2.5**  **emitted from wildland fires in the United States i n 2011 by fire**  **type. Red indicates wildfires and green indicates prescribed burns.** 

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

1 2 3 4 5 6 7 8 9 10 11 12**Area Burned (million acres)**

**Month** WF

RX

0 50 100 150 200 250 300 350 400 450 500

1 2 3 4 5 6 7 8 9 10 11 12**PM 2.5** **Emissions (1,000 tons)**

**Month** WF

RX**August 15, 2012 Page 11** 



### Comparison with Previous Years

**The 2011 Wi** ldland Fire Emission Inventory was compared with prior yea rs’ inventories **developed by STI for EPA.** Figure 9 **displays the temporal variability in PM 2.5**  **emissions from wildland fires in the contiguous United States in th**  **e past five years (from 2007 to 2011). The total wildland fire PM**  **2.5 emissions in 2011 are within the range shown in recent ye**  **ars. An apparent contrast in the temporal variability between e**  **missions from wildfires and from prescribed burns demonstrates that wildfire smoke emissions**  **vary significantly from year to year, whereas the differences in prescribed burn emissions ove**  **r time change at a much smaller magnitude. The trends acknowledge the unpredictable**  **nature of wildfire occurrence and indicate that the managed practice of prescribed burns ta**  **kes place more or less regularly and consistently over the years.** 

Figure 9. **Total PM 2.5**  **emissions from wildland fires in the contiguous Un ited States from**  **2007 to 2011, separated by fire type (WF = wildfires ; RX = prescribed fires).** 

**The spatial distributions and densities of PM 2.5**  **emissions from wildland fires in the contiguous United States from 2007 to 2011 are illustra**  **ted in** Figure 10 **. The emissions are consistently concentrated in the western and southeastern s**  **tates over the five-year period. As discussed previously, prescribed burning is practiced commonly**  **in spring in the southeastern United States, and western states are prone to wildfir**  **es under dry conditions in summer. The locations of large wildfires are represented by dark red**  **spots on the maps where high amounts of PM**  **2.5 were emitted, and the locations are different every yea**  **r.** 0 500 1,000 1,500 2,000 2,500 3,000

2007 2008 2009 2010 2011**PM 2.5** **Emissions (1,000 tons)**

**Year** WF

RX**August 15, 2012 Page 12** 

**(a) 2007**

**(b) 2008 (c) 2009** 

**(d) 2010 (e) 2011** 

Figure 10. **Annual wildland fire PM 2.5**  **emission density in the contiguous United States estimated for year (a) 2007, (b) 2008, (c) 2009, (d) 201**  **0, and (e) 2011. August 15, 2012**  **Page 13**



### Deliverables

**STI is providing the 2011 Wildland Fire Emission Inven tory in the following formats:** 

· **Microsoft Access files formatted identically to those pre pared for the previous efforts**  **(WA 5-17 under Contract No. EP-D-05-004 and WA 2-21 under Contract No. EP-D-09-**  **097).**

· **EIS Events format, which consists of Microsoft Access-** based “staging tables” that can **be converted to XML format by EP** A’s Consolidated Emissions Reporting Schema **(CERS) XML file generator and uploaded to EIS. STI populated the Access staging**  **tables with 2011 fire emissions data and produced separate Access databases for each**  **state.**
**In addition, STI is providing all relevant daily and aggregated data and metadata in**  **Microsoft Access or Excel tables.**



### References

**Pollard E.K., Du Y., Raffuse S.M., and Reid S.B. (2011 ) Preparation of wildland and agricultural**  **fire emissions inventories for 2009. Technical memorandum p repared for the U.S.**  **Environmental Protection Agency, Research Triangle Park, NC, by Sonoma Technology,**  **Inc., Petaluma, CA, STI-910221-4231, October 6.**
**Wiedinmyer C., Akagi S.K., Yokelson R.J., Emmons L.K., Al-Saadi J.A., Orlando J.J., and Soja**  **A.J. (2011) The Fire INventory from NCAR (FINN): a hi gh resolution global model to**  **estimate the emissions from open burning.** Geosci. Model Dev. Discuss., 3 **, 2439-2476 (doi:10.5194/gmd-4-625-2011). Available on the Inte**  **rnet at http://www.geosci-model**  **dev.net/4/625/2011/gmd-4-625-2011.pdf**

**.**