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## RESEARCH AND DEVELOPMENT OF PLANNED BURNING EMISSION INVENTORIES FOR THE CENTRAL STATES

REGIONAL AIR PLANNING ASSOCIATION

FINAL REPORT STI-902514-2516-FR

By: Stephen B. Reid Steven G. Brown Dana Coe Sullivan Heather L. Arkinson Tami H. Funk Patricia S. Stiefer Sonoma Technology, Inc. 1360 Redwood Way, Suite C

Petaluma, CA 94954-1169

Prepared for: The Central States Air Resource Agencies and The Central Regional Air Planning Association 10005 South Pennsylvania Avenue, Suite C Oklahoma City, Oklahoma 73159

July 30, 2004QUALITY ASSURANCE ST AT E M ENT

This report w a s revie wed and approved by the project Quality Assu rance (QA) Officer o r his delegated representatives, as provide d in th e project QA Plan (Coe, 2003a).

Lyle R. Chink i n Project QA O f f i c e r

iii

## This page is intentionally blank.TABLE OF CONTENTS

Section Page
QUALITY ASSURANCE STATEMENT

.................................................................................... iii LIST OF FI GURES

................................................................................................................ ...... vii LIST OF TABLES

................................................................................................................. ........ ix EXECUTIVE SUMMARY .......................................................................................................ES-1

1. INTRODUCTION

.............................................................................................................. 1- 1 1.1 Background and Key Issues

...................................................................................... 1-1 1.1.1 Fine Particulate Matter Concentrat ions and Im paired Visibility in Class I Areas

................................................................................................. 1-1 1.1.2 Status of Existing P l anned Burning Inventories

........................................... 1-2 1.2 Current Status of the CENRAP Em is sion Inventory

................................................ 1-3

2. SUMMARY AND ASSESSMENT OF THE INVENTORY

............................................ 2-1 2.1 Em issions From Pres cribed Burning

........................................................................ 2-1 2.1.1 Summ ary of Em issions from Prescribed Burning

........................................ 2-1 2.1.2 Assessm ent of Prescribed Burning Em issions

............................................. 2-4 2.2 Em issions from Agricu ltural Burning

...................................................................... 2-5 2.2.1 Summ ary of Em issions from Agricultural Burning

..................................... 2-5 2.2.2 Assessm ent of Em issions from Agricultural Burning

.................................. 2-8 2.3 Miscellaneous Burning Source Categories

............................................................... 2-9

3. SUMMARY AND ASSESSMENT OF THE AIR QUALITY DATA ANALYSIS

......... 3-1 3.1 Objective and Approach

........................................................................................... 3-1 3.2 Summ ary of Findings

............................................................................................... 3-1

4. RECOMME NDATIONS FOR FURT HER RESE ARCH

................................................. 4-1 4.1 Recommendations for Improving Pres cribed Burning Activity Data

...................... 4-1 4.2 Recommendations for Improving Agricu ltural B u rning Activity Data

.................... 4-2 4.3 Recommendations for Improved Fuel Loadings and Em ission Factors

................... 4-2 4.4 Recommendations for A ddition a l A m bient Data Analysis

...................................... 4-3

5. REFERENCES

.................................................................................................................. . 5

- 1

APPENDIX A: EMISSION ESTIM A TION ME THODS FOR THE CENRAP PL ANNED BURNING EMISSION I NVENTORIES

........................................................ A-1 APPENDIX B: ANNUAL EMISSIONS BY STATE AND SOURCE CATEGORY ...............B-1 APPENDIX C: ASSESSME N T OF INF L UENCE F R OM PRE S CRIBED B U RNING ON CLASS I SI TES USING AMBIENT SPECIATE D PM 2. 5 DATA ...................C-1 APPENDIX D: EMISSION INVE NTORY FILE DOC UME N TATION AND ELECTRONIC FILES

............................................................................... ON CD

v

## This page is intentionally blank.LIST OF F I GURES

Figure Page

ES-1. CENRAP a nnual planned bur ning em issions by pollutant ............................................ES-2

ES-2. CENRAP a nnual planned burning P M 2. 5 em issions by source category .......................ES-2

ES-3. Monthly variability in to tal em ission s for the CENRAP region ....................................ES-3

1-1. Com positio ns of annual av erage con centration s of PM 2. 5 observations in urban locations

...................................................................................................................... ..... 1-2

1-2. Total annual PM 2. 5 em issions by type of planned burning for each state of the CENRAP region............................................................................................................... 1-5

1-3. Exam ple m a p of daily em ission de nsities for the CENRAP r e gion

................................ 1-5

2-1. Annual prescribed burni ng em issions by pollutant

.......................................................... 2-2

2-2. Annual prescribed burning PM 2. 5 emissions by state

...................................................... 2-3

2-3. Exam ple m a p of daily PM 2. 5 em issions from prescribed burning

................................... 2-3

2-4. Monthly variation in em issions from prescribed burning

................................................ 2-4

2-5. PM 2. 5 em issions from agricult ural burning by state

......................................................... 2-7

2-6. Percent contribution by crop type to state PM 2. 5 em issions from agricultural burning.

......................................................................................................... 2 -7

2-7. Exam ple m a p of daily agri cultural burning em issions

.................................................... 2-8

2-8. Monthly variation in em issions fr om agricultural burning by state

................................. 2-9

vii

## This page is intentionally blank.LIST OF TABLES

Table Page

1-1. 1999 NEI estim ates of PM 2. 5 em issions in the CE NRAP region

..................................... 1-3

2-1. 2002 acres burned and em issions for pr escribed burning in C E NRAP states

................. 2-1

2-2. 2002 acres burned and em issions for ag ricultural burning in C E NRAP states

............... 2-6

2-3. 2001 logging industry em ploym e nt by state

.................................................................. 2-10

ixEXE CUTI V E SUMM ARY

The Central States Reg i o n al Air Plan ni ng Association (CENRA P) is researching visibility-related issues for its reg i on and is developing a reg i onal haze p l an in respon se to th e U.S. Environm ental Protection Agency’s (EPA) m a nda te to protec t vis i b ility in Cla s s I are a s. Agricultural and prescribed burning activities (“planned burni ng”) contribute to episodes of im paired vis i bility in the CENRAP r e gion-phenom e na that the CENRAP seeks to better understand. Therefore, support of th e CENRAP’s need to develop a regional haze p l an, Sonom a Technology, Inc. (STI) developed planned burni ng em ission inventories for the region.

As detailed in the Methods Docum e nt, pr esented in Appendix A, Em ission Estim ation Methods for the CENRAP Planned Burning Em ission Inventories (M ethods Docum e nt),

em issions estim ates were prepared for prescrib ed and agricultural burning activ ities on f

e deral, state, tribal, and private lands in the CENRAP region. These “bottom up” estim a tes were prepared by using the F i rst-Order Fire Effect s Model (FOFEM), em ission factors and fuel loadings gathered from published literature, ge ographic inform ation system s (GIS) databases of land cover and vegetation, and activity da ta gathered through telephone surveys.

Year-2002 P M 2. 5 em issions of particulate m a tter of less than 2.5 μm aerodynam i c diam eter (P M 2. 5 ) from planned burning activities in the CENRAP states were estim a ted to be 317,000 tons (see Figure ES-1 )—almost 300% higher than the estim a te of 110,000 tons of PM 2. 5 prepared by the EPA for the 1999 National Em issi on Inventory (NEI). In addition, planned burning ac tivities em itte d precurso r s to chem ically f o rm ed PM 2. 5 , including approxim a tely 239,000 tons per year volatile organic com pounds (VOC), 80,000 tons per year nitrogen oxides (NO x ), 47,000 tons per year ammonia (NH 3 ), and 35,000 tons per year sulfur oxides (SO x ). The m o st significant source of thes e em issions was the burning of private rangelands, which accou n ted for 50% of the ann u al plann e d burning PM 2. 5 em issions in the CENRAP region. This source category was especi ally significant in th e states of Texas, Oklahom a, a nd Kansas. Prescribed burning on publicly m a na ged forest and grasslands was the second m o st significan t source of planned burnin g em issions in the region , accounting for 32% of the annual planned burning PM 2. 5 em issions (s ee Figure E S -2 ). This source category was especially important in the stat es of Arkansas, L ouisiana, an d Minnesota. (Em i ssion estim ates by source category and state ar e tabulated in Appendix B.)

Planned burning em issions peak in the spring. More than 25% of annual activity occurs during the month of March. A s m aller peak in em issions occurs during the m onths of Septem ber and October (see Fi gure ES- 3 ). Spring and fall provide the mo st advantageous clim atological and biological conditions for prescribed burning, while agricultural burns tend to occur before spring planting or af ter f a ll ha rves tin g.

ES-10 50

, 0 0 0 100

, 0 0 0 150

, 0 0 0 200

, 0 0 0 250

, 0 0 0 300

, 0 0 0 350

, 0 0 0

PM 2

. 5 V O C N O x N H 3 SO 2

P o llu t a n tEmissions(tons/year)

Figure ES-1. CENRAP annual plan ned burning em issions by pollutant.



## P r es cr i b ed
32%

R a n g el an d
50% C

r opl a nd

18%

Figure ES-2. CENRAP annual planned burning PM 2. 5 em issions by source category.

ES-20% 5% 10 % 15 % 20 % 25 % 30 %

J a n F eb M a r A p r M a y J u n J u l A ug S e p O c t N o v D ec

M ont hPercentageofPlanned Burning

Figure ES-3. Monthly variability in total em issions for the CENRAP region.

The planned burning em ission inventory and speciated PM 2. 5 data from the Interagency Monitoring of Protected Visual Environm ents (I MPROVE) network stations located in Class I areas in the CENRAP region were used to i nvestigate the influence of smoke on am bi ent PM 2. 5 concentrations and whether indi vidual burns can be detected in th e air qua lity d a ta of Class I areas. The em ission inventory and IMPROVE data were utilized to better understand the extent to which prescribed burning aff ects visibility in the CENRAP re gion. This prelim inary analysis showed that, while influence from specific burns could be seen in the m onitoring data on select days when the m e teorology was conducive, am moni um sulfate (a species that does not result f r om burning) was the d o m i nant con s titu ent of th e PM 2. 5 m a s s and visibility reduction, particularly on the 20% worst visibility da ys of the year, for the sites analyzed.

ES-31. INTRODUCTION

The Central States Reg i o n al Air Plan ni ng Association (CENRA P) is researching visibility-related issues for its reg i on, which inclu d es the s t ates of Texas, Oklahom a, Louisiana, Arkansas, Kansas, Missouri, Nebraska, Iowa, an d Minnesota, and is developing a regional haze plan in response to the U.S. Environm ental Protection Agency’s (EPA) m a ndate to protect visibility in Class I a r eas

. 1 In order to develop an effec tive regional haze plan, the CENRAP ultim ately m u st deve lop a conceptu al m odel of the phenom ena that lead to episodes o f low visibility in the CENRAP region. E p isodic co mbustion events (such as agricultural burning, prescribed burning, open burning of wastes, structur al fires, and wildfires) som e tim es contribute to reg i onal o r local haze events in the CENRAP r e gion. Therefore, it is important to develop th e

em issions data necessary to assess the im pacts of these events on visibility in the CE NRAP region.

In support of the CENRAP’s need to deve lop a regional haze plan, Sonom a Technol ogy, Inc. (STI) conducted C E NRAP W o rk Assignm e n t Num b er 02-0214-RP-003-002 “Research and Developm ent of Em ission Inventories for Planne d Burning Activ ities f o r the Centra l State s Regional Air Planning A ssociation” . Consisten t with the p r oject goals presented in the W o rk Plan (Coe, 2003b), em issions were calculated for agricultural and prescrib ed burning on federal, state, tribal, and private lands.

1.1 BACKGROUND AND KEY ISS U ES

1.1.1 Fine Particulate Matter Concentrations a nd Impaired Visibility in Class I Areas

Regional haze is visibility im pair m e nt caused m a inly by particles of less than 2.5 m i crons in diam eter (PM 2. 5 ). PM 2. 5 m a y be directly em itte d f r om em issions sour ce s, such as so urces of f ugitive dus t and com bustion soot, w h ich ar e te r m ed sources of “prim a ry PM 2. 5 ”. Addition a l

m echanism s also occu r allowing PM 2.5 to be formed in the atmosphere, and this phenom e non is

term ed “secondary formation”. Examples includ e condensable organic aerosols which can form from air em i ssions of sem i -volatile and heavy organic com pounds and PM 2. 5 that can for m from photochem i cal reactions of gaseous pr ecurso r s, including su lfur oxides (SO x ), nitrogen oxides (NO x ), volatile organic com pounds (VOC), and amm o nia (NH 3 ).

Analyses of speciated P M 2. 5 sam p les provide an understand ing of the types of e m ission sources that contribute to re gional haze issues in different areas, as depicted in Figure 1-1 . In urban and ammonia-depleted area s of the eastern United States, secondary sulfate contributes a more significant am ount of PM 2. 5 than it does in the wester n United States. Conversely, secondary nitrate is m o re im portant in urban and ammonia-rich areas of the western U n ited States than it is in eas tern areas. In both th e eastern and western United S t ates, the carbonaceous fraction of PM 2. 5 is significant in urban areas. In rura l areas, geologic dust can also be an im portant contributor to PM 2. 5
.

1 Class I la nds i n clude a r eas s u ch as national parks, w ilde r ness areas , a n d national m o numents. T h ese areas have been g r a n t e d s p eci al ai r qual i t y pr ot ect i ons un de r t h e Fe de ral C l ean Ai r Act

.

1-1Figure 1-1. Com positio ns of a nnual average con centration s of PM 2. 5 observations in urban locations (U .S. Environm ental Protection Agency, 1998).

Of particular interest in the CENR AP region is the contribution of PM 2. 5 from wood a nd grassland burning to visibility im pairm e nt in Cl ass I areas. S m oke from t h ese fires em it organic carbon (OC) and elem ental carbon (E C); the latter is som e tim e s referred to as soot or black carbon (BC). OC com e s from m a ny sources, both com bustion and evaporative, while EC only originates from com bustion sources, such as fossil fuel com b usti on (power plants, car exhaust, etc.) o r woodland or grassland burn i ng. Potassiu m (K) is als o em itted during burn i ng of natural

m a terials and can be used as a m a rker for woodland or grassland burning.

1.1.2 Status of E x isting Planned Burning Inventories

Historically, few areas of the CENRAP regi on have experienced si gn if ica n t air quality problem s and, therefore, have not been required to perform air quality m oni toring or develop

em ission inventories. The m o st com p rehensive so urce of em issions es tim ates curren tly available for the region is the EPA’s National Em issions Inve ntory (N EI), which is used as the basis of the EPA’s National Em ission Trends (N ET) docum e n t series and analyses. O n a state lev e l,

em ission inventories of burning activ ities have been prepared by De nnis et al. (2002) f o r Texas. In the NEI, estim ates of PM 2. 5 em issions from planned burning activities in the CENRAP region

am ount to 110,000 tons per year, or about 9% of the total P M 2. 5 inventory for the region (see Table 1-1 ). The NEI indicates that planned burning em issions a r e pa rticula r ly s i g n if icant in the states of Louisiana and Texas.

1-2Table 1-1. 1999 NEI estim ates of PM 2. 5 em issions in the CE NRAP region.

PM 2. 5 (tons)

State All Sources Planned Burning Percent

A r k a n s a s 9 1

, 2 9 4 6,735 7.4% I o w a 1 0 8

, 6 4 1 402 0.4% K a n s a s 1 5 8

, 5 2 1 9,502 6.0% L o u i s i a n a 9 4

, 5 2 2 34,099 36.1% M i n n e s o t a 1 6 3

, 5 4 2 2,874 1.8% M i s s o u r i 1 8 3

, 2 4 5 1,147 0.6% N e b r a s k a 1 3 1

, 4 8 6 2,576 2.0% Oklahom a 1 4 9

, 0 1 5 7,137 4.8% T e x a s 2 2 3

, 4 2 7 45,748 20.5%

T o t a l 1

, 3 0 3

, 6 9 4 110,220 8.5%

As part of its research in to reg i ona l h aze, CENRAP has decided to cond uct

com p rehensive air quality m odeling of visibil ity in 2002. To support this m odeling, a bottom

- up planned burning em ission inventory, which incorporated year-2002-spec ific fire history data and addressed the uncertain ties of the NEI (see below ) is required.

Som e uncertainties are in herent to the NEI:

• Prescr ibed b u rning ac tiv ities f l uctuate dram atically from year to year. F l uctuations are due to policy decisions about the need for wildfire risk m a nagem e nt, current clim ate conditions (drought versus wet conditions), and densities of undergrowth and fuel. Because of these wide fluctuations, em ission inventories of prescrib ed burning are n e arly im possible to predict or project on the basi s of historical inve ntories or trends.

• The NEI is estim ated on an annual average ba sis. Regional haze ha s a seasonal character and is partly driven by photochem i cal proces ses. Adjustm e nts are necessary to develop seasonal, diurnal, and, possibly, day-of-week em i ssion estim ates.

To support modeling sensitiv ity run s , m easures of uncertain ty for all em ission es tim ates are highly valuable for policy decisions and priori tization of future research efforts. To the extent possible, we provide es tim ates of uncertainty for em i ssions associated with planned burning activity data that were gathered for this project.

1.2 CURRE NT STAT US OF THE CE NRAP E M ISSION INVE NTORY

As detailed in the attached Methods Docu m e nt (Appendix A), em issions estim ates were prepared for prescribed and agricu ltu ral burn i ng ac tiv ities on f e dera l, sta t e, trib al, and p r iva t e lands in the CENRAP region. These “bottom up” estim a tes were prepared by using the FirstOrder Fire E ffects Model (FOFEM), em ission fact ors and fuel loadings gathered from published literature, geographic inform ation system s (GIS ) databases of land cover and vegetation, and activity data gathered th rough telephone surveys.

1-3The FOFEM m odel, a com puting tool develo ped through the Joint F i re Science Program (JFSP), was used to generate estim a tes of fuel lo adings and em ission rates for prescribed burns. FOFEM wa s run for local vegetation types usin g fuel m o isture inputs from the W e ather Inform ation Managem e nt System ( W IMS), a data base of daily weather observations gathered from about 1500 fire weather stations throughout the United States. Outputs from FOFEM were then used in conjunction with prescribed burning history inform ation to calculate em issions.

For agricultural burning, em ission factors and fuel-loading factors fo r a variety of crop types are av ailab l e in th e EPA’s guidance docu m ent, “Compila tion of Air Pollu tan t Em ission Factors (AP-42)” (U.S. Environm en tal Protection Agency, 2003) and from Jenkins et al. (1996). From these sources, we identified fuel loading factors and em ission factors for a wide variety crop types and applied these fact ors to county-specifi c agricultural burning activity data to generate emissions estim ates. The activity da ta were obtained through system atic telephone surveys of county agricultural extension services (AES).

For both prescribed and agricultural burning activities, the E P A’s Biogenic Em issions Landcover Database (BELD) Version 3 (U.S. Envi ronm ental Protection Agency, 2001) was used to generate spatial distributions of vegetation types, which in turn were used to select vegetationspecific fuel loading factors output by FOFEM. To do this, cross-walks were established to link the vegetation types in the BELD da tabase with (a) vegetation types in F O FEM and (b) crop types for which em ission s factors and fuel loading s are av ailab l e.

Once a m a p of vegetation and crop types was de veloped, we overlaid hi stories of planned fires, iden tif ied the v e getation types associated with each fire occurren ce, and applied em ission factors generated throug h FOFEM or acquired from literature to produce county-level em ission inventories of agricultural and prescribed burning.

The resulting em ission inve ntory is illustrated in Figures 1-2 and 1-3 and tabulated in Appendix B. In all cases, we applied generally accepted em ission factors and the m o st com p lete and up-to-date activity data se ts th at could be id e n tif ied and acquired. However, we acknowledge that available em ission factors are uncertain and they continue to be the subject of research.

The em ission source type in the inventory that w e qualitatively consider to contribute the greatest deg r ees of unce r ta inty to the tota l estim a ted em issions is prescribed burning on priva t ely

- held lands perf orm e d by the f o restry in dustry. Sin ce new inf o r

m ation will be needed to reduce uncertainties in the future, we have provided the C E NRAP with an inventory and system of da ta files that can be updated with revised em ission factors an d activity data as new inform ation becom e s available (see Appendix D).

1-40 10

, 000 20

, 000 30

, 000 40

, 000 50

, 000 60

, 000 70

, 000 80

, 000 90

, 000

AR I A KS L A M N M O N E O K T XEmissions(tons/year)

P r e scr i b e d C r o p land R angeland

Figure 1-2. Total annual PM 2. 5 em issions by typ e of planned burning for each state in the CENRAP region.

Figure 1-3. Exam ple map of daily em issi on densities for the CENRAP r e gion (for April 10, 2002).

1-52-1 2. SUMMARY AND ASSESSMENT OF THE INVENTORY

STI calculated emissions as detailed in Appendix A, Emission Estimation Methods for the CENRAP Planned Burning Emission Inventorie s, with results tabulated in Appendix B, Tabulation of Planned Burning Emissions Estima tes for the CENRAP Region. In addition, STI carried out quality assurance procedures as provided in the Quality Assurance Plan and as detailed in this section. In summary, the most important sour ce categories are estimated to be rangeland burning and prescribed burning on pub licly managed lands. Total emissions vary seasonally by a factor of three, with peaks o ccurring in the spring and fa ll. Prescribed burning performed on privately held lands by the forestry i ndustry is considered to be the greatest source of uncertainty in the overall inventory.

2.1 EMISSIONS FROM PRESCRIBED BURNING

2.1.1 Summary of Emissions from Prescribed Burning

Emission estimates were generated for prescr ibed burning activities on federal, state, tribal, and private lands. Over one million acres were burned in prescribed fires in 2002 in the CENRAP region, with consequent PM 2.5 emissions of over 100,000 tons and emissions of precursors as shown in Table 2-1 and Figure 2-1

.

Table 2-1. 2002 acres burned a nd emissions (tons) for prescrib ed burning in CENRAP states.

STATE Acres Burned PM 10 PM 2.5 CO NO
x SO 2 NH 3 VOC
Arkansas 244,146 28,130 23,838 302,219 1,961 1,577 2,910 17,444 Iowa 21,449 4,072 3,457 44,542 166 195 257 2,547 Kansas 38,106 1,450 1,226 14,424 228 114 143 881 Louisiana 350,353 45,288 38,376 486,668 3,125 2,531 4,671 28,060 Minnesota 86,642 17,222 14,609 187,853 742 836 1,150 10,740 Missouri 64,781 7,460 6,338 80,019 536 417 756 4,633 Nebraska 6,127 410 347 4,316 36 24 27 254 Oklahoma 104,749 7,322 6,196 76,615 750 479 769 4,507 Texas 137,310 12,669 10,732 134,423 1,071 757 1,427 7,824
Total 1,053,663 124,023 105,119 1,331,080 8,615 6,929 12,111 76,8892-2 0 20,000 40,000 60,000 80,000 100,000 120,000

PM2.5 VOC NOx NH3 SO2

PollutantEmissions (tons/year)

Figure 2-1. Annual prescribed burning emissions by pollutant.

Whenever possible, the exact location, star t date, duration of burn, and size of burn incidents were acquired so that emissions from these incidents could be allocated spatially and temporally. The areas and locations of prescr ibed burn incidents were assigned to their individual centroids. 2 Prescribed burn activities that were reported as incidents (with date, duration, and area) were treated as point sources. Approxima tely 40% of the prescribed burning inventory was allocated spatially and temporally as point sources. Emissions from the remaining prescribed burning activities were treated as ar ea sources. States that were able to provide “incident-level” databases of pr escribed burn activity included Arkansas, Minnesota, and eastern Oklahoma.

The level of prescribed burning activities vari ed from state to state, as illustrated in Figures 2-2 and 2-3 . Land managers in Arkansas and Louisiana conducted the most planned burning, and land managers in Minnesota and Texa s were the second most active; only limited prescribed burning activity occurred in th e states of Iowa, Kansas, and Nebraska.

The seasonal variability of prescribed burni ng emissions follows a bimodal pattern, with a large peak in spring and a smaller peak in fall. Factors that influence th e seasonal variability of burning include weather conditions, fuel moisture content, and the in tended environmental consequences of the burn (Dixon et al., 1989). Anal ysis of fire history r ecords showed that all CENRAP states except Minnesota followed a sim ilar seasonal pattern fo r prescribed burning. The longer winters in Minnesota delay the spring peak from Ma rch to May, while fall-season prescribed burns in Minnesota occur primarily in September rather than being spread evenly over the later summer and fall months as they are in the other states (see Figure 2-4)

.

2 Use of centroids to allocate burns was considered acceptab le because the burn areas ar e typically much smaller in size than the grid resolution of the CENRAP’s modeling grid.2-3 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000

AR IA KS LA MN MO NE OK TX

StateEmissions (tons/year)

Figure 2-2. Annual prescribed burning PM 2.5 emissions by state.

Figure 2-3. Example map of daily PM 2.5 emissions from prescribed burning (for April 10, 2002).2-4 0% 10% 20% 30% 40% 50% 60% 70% 80%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecPercentage of Prescribed Burning

MN Other States

Figure 2-4. Monthly variation in emissions from prescribed burning.

2.1.2 Assessment of Prescribed Burning Emissions

The “bottom up” activity data gathered fo r the prescribed burning portion of this inventory improved the reliability of the emissions estimates. Virtually all of the burn records for federal lands (and some state burns) include fire date and location information that allows for the use of day-specific fuel moisture settings in calculating emission factors. Location information also enabled these burns to be tr eated as point sources for spatial allocation
purposes.

As shown in Figure 2-3, emissions from pres cribed burning are most significant in the region from western Arkansas/Louis iana to eastern Texas/Oklahoma. This is to be expected because prescribed burning is more widely practi ced in the southern United States than in other areas (Cleaves et al., 1998). Moreover, the estimate of 137,310 acres burned on wildlands in Texas is within the range of prescribed burning estimates made for that state in 1996 and 1997, when 63,790 acres and 160,890 acres were burne d, respectively (De nnis et al., 2002).

Prescribed burning accounts for about 30% of the annual planned burning PM 2.5 emissions in the CENRAP region. However, emissions from this source category actually exceed those from agricultural burning for five states: Arkansas, Iowa, Louisiana, Minnesota, and Missouri. When only those states are c onsidered, prescribed bur ning accounts for about 80% of the annual planned burning PM 2.5 emissions.

Areas of uncertainty relate d to prescribed burning emissions estimates arise from differences in how fire activity is tracked and repo rted in each state. For example, for Arkansas, Minnesota, and the northeastern por tion of Oklahoma, fire data is available at an “incident2-5 level,” meaning a fire’s date and location were list ed in each fire history record. However, other states did not track this leve l of detail, instead reporting fi re data by region and month, for example. In these cases, individual fire events could not be treated as point sources, and the geographic and temporal resolution of the final inventory was limited as a result.

Differences from state to state are even more pronounced for burns occurring on privately held lands. Such burns are pe rformed by individuals, private comp anies, and organizations such as TNC and the Audubon Society. However, pe rmitting or reporting requirements are not consistent among the nine CENRAP states, and few stable were able to provide us with reliable data on these burns. 3 Persistent attempts were made to contact private companies and organizations, but only TNC was able to provide burn data within the time limits of this project. It should be noted, though, that mo st burns on private lands are likel y to be related to agriculture or waste management (such as the burning of logging residue by forestry companies or pile burns by land developers) (Altman, 2004; Miedtk e, 2004). The former types of burns are covered by the agricultural survey, while the latter burns are not included in the scope of this project.

2.2 EMISSIONS FROM AGRICULTURAL BURNING

2.2.1 Summary of Emissions from Agricultural Burning

Emission estimates were generated for agricu ltural burning activitie s on private rangeland and cropland in each of the CENRAP states. It was determined th at agricultural burning resulted in the burning of about 13 million acres in 2002 in the CENRAP region, with consequent PM 2.5 emissions of over 200,000 tons (see Table 2-2 ).

Table 2-2. 2002 acres burned a nd emissions (tons) for agricultural burning in CENRAP states.

STATE Acres Burned PM 10 PM 2.5 NO
x SO 2 NH 3 VOC
Arkansas 655,307 10,771 10,227 3,692 637 2,100 6,254 Iowa 2,247 44 42 5 1 4 20 Kansas 5,015,790 99,170 75,057 29,094 10,937 11,436 54,884 Louisiana 486,441 8,384 7,888 3,845 609 2,453 7,066 Minnesota 101,925 1,944 1,729 358 69 248 1,155 Missouri 290,978 4,958 4,314 1,907 520 693 2,500 Nebraska 215,526 4,647 3,609 643 244 553 2,950 Oklahoma 2,303,359 45,231 35,228 18,645 6,653 5,124 23,992 Texas 3,798,581 104,709 74,393 13,647 8,725 12,573 63,396
Total 12,870,154 279,858 212,486 71,836 28,395 35,185 162,218

3 Exceptions include the state of Arkansas, which was able to provide a database of virtually all burns in the state larger than 5 acres, including those occurring on private lands. The same was true for a 15-county region of Oklahoma that requires burn permits. The state of Minnesota also requires permits for all prescribed burning activities (including private burns), but does not keep centralized records of these burns.Em issions from agricultural burning cont ribute 70% to total planned burning estim a ted
PM 2. 5 em issions for the CENRAP region, ranging fr om 1% to 99% of total planned burning

em issions from state to s t ate. The m o st im por tant crop/land use types are rangeland (especially in the states of Texas, Oklahom a, and Kansas) and wheat (especially in the states of Kansas, Oklahom a, a nd Arkansas), although sugarcane burning is significant in the state of Louisiana. Figures 2-5 and 2-6 illu stra te th e ov erall em issio n levels by s t ate and the rela tive im portanc e of each crop ty pe in each state, and Fig u re 2-7 shows the geographic a lloca tion of agricu ltur a l burning em issions throughout the C E NRAP region.

0 10, 0 0 0 20, 0 0 0 30, 0 0 0 40, 0 0 0 50, 0 0 0 60, 0 0 0 70, 0 0 0 80, 0 0 0

A R I A K S L A MN MO N E O K T X

Sta t ePM2.5Emissions (tons/year) Ot he r
Ri c e
S o y b e ans
Co r n
S u g a rc ane
Ha y / A l f a l f

a
Wh e a t

R a ng el an d

Figure 2-5. PM 2. 5 em issions from agricultural burning by state.

2-60% 20% 40% 60% 80% 1 00%

A R I A K S L A MN MO N E O K T X

Sta t ePercentageofPM2.5Emissions

Ot he r
Ri c e
S o y bea ns
Co r n
S u ga r c an e
Ha y / A l f a l f

a
Wh e a t

R a n gel an d

Figure 2-6. Percent contribu tion by crop type to state PM 2. 5 em issions from agricultural burning.

Figure 2-7. Exam ple map of daily agricultu ral burning em issions (for A p ril 10, 2002).

2-7Em issions from agricultural burning tend to follow a bim odal pattern of seasonal variab ility, with la rge p eaks in the s p ring and s m aller peaks in the fall (s ee Figure 2-8 ). For most states, the m onth with the highest em issi ons from agricultural burning is March, although northern states like Minnesota and Iowa show a spring peak in May. For Arkansas and Louisiana, the highest em issions occur in September and October, respectively, which is due to the large acreages of winter whea t (A rkansas ) and sugarcan e (Louisiana) b u rned in tho s e states.

2.2.2 Assessment of Emissio n s fr om Agricultural B u rning

The “bottom up” survey data gathered for the agricultural burning portion of this inventory made it possible to generate em issions estim ates that take into account county-level burn practices for each CENRAP state, in cludin g inform ation on the tim i ng of burns and the techniques used to burn individual crops.

This study indicates that agricu ltural burning practices vary wide ly from state to state and even county to county. F o r exam ple, 54 of the 56 counties surveyed in Iowa reported no agricultural burning, as di d 50 of the 77 counties surveyed in Minnesota. Am ong states that do burn extensively, practices vary by crop type. T h e survey indicates that burning is w i dely used to destroy wheat stubble in Arkansas, as over 40 % of that crop is burned each year. By contrast, no other state that grows significant am ounts of wheat burns more than 15% of the crop annually.

0% 5% 10 % 15 % 20 % 25 % 30 % 35 % 40 %

J a n F eb M a r A pr M a y J un J u l A ug S e p O c t N o v D e c

Mo n t hPercentageofAgriculturalBurning AR

IA

KS

LA

MN

MO

NE

OK

TX

Figure 2-8. Monthly varia tion in emissions from ag ricultural burning by state.

2-8It is also important to note that while agricultural burning accounts for about 70% of the annual PM 2. 5 em issions from planned burning activity for the CENRAP region as a whole, alm o st 90% of the agricultural burning em issions occur in thre e states: Tex a s, Oklahoma, and Kansas. Moreover, about 70% of all agricultural burning em issions in the CENRAP states r e sult from the burning of rangeland in these three states.

Uncerta intie s rela ted to a g ricu ltur a l b u rning em issions result largely from an incom p lete understanding of local regulations pertaining to such burning. For example, several states with a significant num b er of counties including Iowa, Minnesota, Nebr aska, and Missouri reported no agricultural burning. Thes e reports of no burning m a y be due to local restricti ons on agricultural burning or o t her facto r s. Also, survey responses for each state were extrapolated to g e nerate a statewide burn profile by crop t ype, and these profiles were used to rep r ese n t all counties f o r which no survey data were available. However, fu rther inves t igation is ne cessary to d e term ine if burn practices vary within individual states enou gh to warrant subdividing certain states into regions. 4

2.3 MISCELL ANEOUS B URNI NG SOURCE CATEGORI E S

Several subcategories of m i scellaneous pr escribed burning occur within the CENRAP region. Most of these burn types relate to the disposal of waste m a terials and, therefore, were not included in the final em issions inventory. Ho wever, som e infor m ation on these burns was gathered during the course of the project and is su mm arized below.

Slash and Site Preparation Burning

Slash burning is typically used to dispose of logging residue produced by the harvesting or trees and, as such, is most often pract iced by private tim b er com p anies. Based on

em ploym e nt estim ates for the logging industr y (U.S. Census Burea u, 2003), states in the CENRAP dom ain that p r oduce sign ificant am ou nts of tim ber are Arkansas, Louisiana, Minnesota, and Texas (see Table 2-3 ).

Table 2-3. 2001 logging indus try employm e nt by state.

State Num b er of employees
A r k a n s a s 2

, 9 1 4
I o w a 1 7 5
K a n s a s 6 5
L o u i s i a n a 3

, 3 2 5
M i n n e s o t a 1

, 0 1 9
M i s s o u r i 3 7 8
N e b r a s k a 6 5 Oklahom a 2 8 1
T e x a s 2

, 2 2 7

4 A s u bre g i o nal app r oach was use d f o r wheat and ra ngeland burning in the stat e of Ka nsas , and suc h a n a p proach may b e app licab le to o t h e r states/cro p typ e s.

2-9To illus t rate the relative s i gnificance of slash burning, Allen & Dennis (2 000) report 55,000 acres of logging-related slash burning in T e xas during 1997, about 3% of the total planned burning acreage for that year. In the fire history data obtained by STI (which mostly pertains to burning on publicly-m anaged lands), very few burns were identified as slash burns—

am ounting to 400 acres in Minnesota and less th an 5 acres in Oklahom a a nd Iowa (no other states identif ied burns as slash).

Additionally, the state of Ar kansas reports 50,000 acres of “site preparation burning,” which are burns largely conducted by the tim b er indus try to prepare lands for reforestation. It is likely that som e of these burns involve slas h fuels, though fuel m odel inform ation was not tracked in the Arkansas database. Sim ilarly, sign ificant num bers of site preparation burns were included in the data we received fro m the st ate o f Minnesota, though these burns were not identified as such (Miedtke, 2004). 5 Note for both Arkansas and Minnesota, these burns were included in the inventory but not as signed the higher fuel loadings that would be associated with slash fuels.

Pile Burning

As the nam e suggests, “pile” burning involve s disposing of waste m a terial by gathering the m a terial into piles and burning it. Types of waste m a terial include leav es and yard waste, logging residue, and brush or trees cleared fr om land for developm ent purposes. W ith the exception of the state of Oklahom a, very few pile burns were included in the data provided to STI. For Oklahom a, a 15-county region in the nort heastern part of the state that requires burn

perm its reported 180 incidents of leaf burning and 570 incidents of brush pile burning for 2002 (750 total). However, no data were provided on the sizes of these burns. The state of Minnesota also requires perm its for private burns, and approxim ately 60,000 such perm its were issued in 2002. It was estim ated that 65% (39,0 00) of these perm its woul d correspond to either pile burns or ditch/fencerow bur ns (covered in the next se ctio n), with th e rem a ining 35% largely represented by burns on open land and rangeland th at would be captured by the agricultural survey (Meadows, 2004).

To roughly estim ate the possible em issions re sulting from pile burns in O k lahom a and Minnesota, a fuel loadin g for a sizeable 6 pile burn published by the California Air Resources Board (2003) and em ission factors published by the U.S. Environm ental Protection A g ency (2003) were applied to the num ber of pile burns in those states. (It was assum e d that half of the 39,000 permits referenced above were for pile burn s, and the 750 pile burns in Oklahom a were multiplied b y 5 to extrap olate from 15 counties to all 77 coun ties in the s t ate). PM 2. 5 em issions were estim a ted as f o llows:

OK: PM 2. 5 = (750 x 5) piles x pile tons 1.36
x ton PM

- lbs 14 2.5 x lbs 2000 ton = 36 tons

5 Pers onnel at t h e Minnes ota Dispatc h of t h e National In teragency Fire Ce nter estim ated that 75% of the site

p r ep aration bu rn ing in Minn eso t a was in cl u d ed in th e d a ta pr ov id ed t o STI

. Site pr ep ar ati o n bur n s no t inclu d e d in

t h e dat a set w o ul d be t h o s e c o nd uct e d by p r i v at e l a n d o w ner s o r c o m p ani e s. 6 Fuel l o adi n gs for a burn 12 feet in diam eter and 8 feet hi gh were use d

.
2-10MN: PM 2. 5 = (39,000 / 2) piles x pile tons 1.36
x ton PM

- lbs 14 2.5 x lbs 2000 ton = 186 tons

For Oklahom a and Minnesota, these pile burns represent only 0.1% and 1.1%,

respec tive l y, of the PM 2. 5 em issions already inclu d ed in th e planned burning inventory for these states. Pile burns in other st ates can not be characterized with the data cu rrently available.

Ditch and F e ncerow Burning

Fires are so m e ti m e s used for weed abatem ent purposes along roadsides and fencerows. In the data obtained by S T I, no individual fires w e re identified as ditch or fencerow burns, and because su ch fires are g e nerally sm all in s cale an d practiced by private p a rties, it is likely that few such burns are inclu d ed.

The only state where so m e assessment of these burns appears to be pos s i ble is Minn esota. As previously stated, app r oxim a tely 3 9 ,000 of the 60,000 burn perm its issu ed in that state each year are for pile burns and ditc h/fencerow burns. To provide a rough estim a te of e m i ssions from this source, it was assumed that half these 39,000 burns were ditch bu rns, and that each burn covered 0.25 acres (Meadows, 2004). Us ing em ission factors published by the U.S. Environm ental Protection Agency (2003), PM 2. 5 em issions w e re estim a ted as f o llow s :

PM 2. 5 = (39,000 / 2) burns x b urn acres 0.25
x acre ton 1
x ton PM

- lbs 15 2.5 x lbs 2000 ton = 37 tons

This estim a te am ounts to only 0.2% of the 16,000 tons of PM 2. 5 already included in the planned burning inventory for Minne sota. Ditch and fencerow burns in other states cannot be characterized with the d a ta cu rren tly available.

2-113. SUMMARY AND ASSESSMENT OF TH E AIR QUAL ITY DATA ANALYSIS

3.1 OBJECTIVE AND AP PROACH

The objectiv e of this analysis was to use am bient speciated P M 2. 5 data fro m Class I areas (from the Interagency M onitoring of Protected Visual Environm ents [IMPROVE] network) in the CENRAP states along with the planned burning em issions estim ated in this study to assess whether am bient data can be used to identify pl anned burning contributions to vis i bility events in Class I areas , and to perf orm a prelim inary a ssessm ent of the im pact of planned burns on PM 2. 5 and visibility at a few monitoring sites. The following approach was employed:

• A ssess the s easonal che m ical com p osition s of PM 2. 5 m a ss and aerosol light extinction to determ ine w h at ind i vidu al spec ies a r e im porta nt to the m a ss and visibility extinc tion in the area.

• Determ ine seasonal con centration s of and ra tios between selected sp ecies, such as O C

, EC, and K, to estab lish a “baseline” average s easo n al com position for comparison to d a ys of poor visibility and days potential ly influenced by prescribed burning.

• A ssess che m ical com p osition s of PM 2. 5 and aerosol light extinction on the 20% best and 20% w o rst v i sibility day s to determ ine w h at species have a large im pact on visibility (i.e., are species from burning typicall y im portant in visibility reduction? )

.

• Analyze IM PROVE data, specifically OC, EC , a nd K concentrations, on dates when extensive burning occurred n ear a m onitoring site in orde r to assess whether wood smoke influences are seen in th e am bient measurem ents and significantly im pact visibility.

• Analyze em issions data o n days when elevated OC, EC, and K concentr ations occurred at IMPROVE sites to determ ine whether days of elevated concentrations corresponded to known burns in the em ission inventory data.

• Analyze air m a ss trajectories on selected days to determ ine whether m e teorology (i.e., transpo r t) exp l ains th e observed effects and to determ ine the extent to which

m e teorology affects haze.

3.2 SUMMARY OF FINDINGS

Details on data, m e thodology, and results from th is analysis are provid ed in Appendix C. This work yielded the following findings:

• Speciated PM 2. 5 data can be used to determ ine in fluence from planned burns when the

m e teorology is conducive to transport from the burn area to a Class I site.

• Sm oke constitu ents, sp e c if ically EC and K

, w e re not a sign if icant f r ac tion of the PM 2. 5

m a ss and light extinction, even on days wh en there was evidence of planned burning influence, at the sites exam in ed in this prelim inary study.

• Ammonium sulfate, which is not generated fr om burning, is the dom inant constituent of the PM 2. 5 mass and light extinction, especially on the 20% w o rst visibility days. This

3-1finding is consistent with other work in the M i dwest and CENRAP regions including studies of Big Bend National Pa rk and Seney W ildlife Refuge.

• On som e days, influences from known prescr ibed burns were seen, though they were generally less than 10% of the PM 2. 5 m a ss and light extinction . Im proved spatially and

tem porally r e solved em ission inven t ories and ad dition a l ca se studies m a y show dif f e rent results.

• The specific influence of sm oke on PM 2. 5 m a ss and light extinction could be better quantified using additional analyses , including source apportionm ent.

3-24. RECOMMENDATIONS FOR F URTHE R RESE ARCH

This study provided an improve d and updated em ission inventor y for planned burning in the CENRAP states for year 2002. Prelim inar y exam ination of a m bient m easure m ents along with the inventory generated in this study suggests that pl anned burning may contribute to visibility im pairm e nt at C l ass I s ites in the C E N R A P states. As noted in prev ious sections of this report, we identified the followi ng significant sources of uncer tainty (roughly in order of im portance): (1) the extent of fires perform e d by t h e USFS on publicly m a naged lands, (2) the extent of prescribed burning on privately held lands perform e d by the forestry industry and organizations such as T N C, (3) a need to better understand count y-level open burning regulations, and (4) the fuel lo adings and em ission factors used for planned burning em issions estim ates— p articularly for prescribed burning in th e state of Minnesota. In this sec tio n, w e provide reco mmendations for im proving each of th ese aspects of the inven t ory and des c ribe additional analyses that could be conducted to better quantify the influence of planned burning on visibility im pair m e nt.

4.1 RECOMMENDATIONS FOR I M PROVING PRESCRIBED B U RNING ACTI V ITY DAT A

As discussed in Section 2.1.2, significant differe n ces exis t in the w a y f i re activ ity da ta is track ed and reported in each state; som e states (such as Arkansas and Minnesota) cap ture a fire’s exact date and location coordina tes, and other states track fi res only by region and m o nth. Encouraging individu al s t ate s to m a intain “ i ncid e n t leve l” da tabases of f

i r e activity w ould allow all p r esc r ibe d f

i res to be trea ted as d i scre te point sources and im prove the geographic and

tem poral resolution of the inventory.

Also, differences from st ate to state are even m o re pronounced for burns perform e d on privately held lands by individua ls, private companies, and orga nization s such as T N C and the Audubon Society. Howe ver, perm itting or reporti ng requirements are not consistent a m ong the ni ne CENR AP st at es, and few st at es wer e able to provide us with reliable data on these burns. 7
Persis tent attem p ts w e re m a de to contact p r ivate com p anies and orga nizations, but only TNC

w a s able to provide bur n data w ith in the tim e lim its of this projec t. I t is recom m ended that further efforts be m a de to survey private partie s regard ing th eir burn activities, espe c i ally in th e Piney W oods region of eastern T e xas, wher e private tim b er com p anies have conducted significant amounts of prescr ibed burning in past year s (Allen & Dennis, 2000) 8

. It should be noted, though, that m o st burns on private lands are likely to be related to agriculture or waste m a nagem e nt (such as the burning of logging residue by forestry com p anies

7 Exce pt i o ns i n cl ude t h e st at e of A r ka nsas , w h i c h was abl e t o pr o v i d e a dat a base of vi rt ua l l y al l bur ns i n t h e st at e l a rger t h a n 5 a c res, --i ncl udi n g t h ose occ u r r i n g o n p r i v at e l a nd s. The sam e was t r ue f o r a 1 5 -c o unt y re gi o n of Okl a hom a t h at re qui re s bu rn perm i t s . The s t at e of M i n n es ot a al so re qui r e s pe rm it s fo r al l presc r i b e d bu r n i n g activ ities (in c lu d i n g priv ate bu rn s), b u t do es no t k e ep cen t ralized reco rd s o f th ese bu rn s.

8 Fo r pu r pos es of t h i s i n ve nt o r y , acres b u r n e d i n 19 9 6 a n d 1 9 9 7 by pri v at e t i m b er com p an i e s i n t h e Pi ney Woo d s regi on we re a v erage d t o pr od uce a n estim ate of 20,000 acres pe r year.

4-1or pile burns by land developers ) (Altm an, 2004; Miedtke, 2004). The form er burns are covered by the agr i c u ltur a l su rve y , and the la tte r burns a r e not inc l ud ed in the sco p e of this pr oject.

Finally, alternative and newly em erging data sources such as satellite data and related products recently developed by the National Oc eanic and Atmospheric Ad m i nistration (NOAA) should be explored to help characterize fire locations and day-specific activity levels.

4.2 RECOMMENDATIONS FOR I M PROV ING AGRICULTURAL B URNI NG ACTI V ITY DAT A

As stated in Section 2.2.2 of this report , unce r ta inties related to agricultural burning

em issions result largely from an incomplete unders tanding of local regulati ons pertaining to such burning. Several states, includ ing Iowa, Minnesota, Nebraska, and Missouri, had significant

num b ers of counties that reporte d no agricultural burning. It is recomm ended that further investigation be undertaken to gain a fulle r understanding of county-level open burning restrictions, as well as an estim ate of how such restrictions ar e enforced. Further d i scussions with county AES, as well as with individual farmers, could be used to acquire this inform ation.

Also, survey responses f o r each state were extrap olated to generate a statewide burn profile by crop type, and these profiles were used to repr esen t all coun tie s f o r w h ich no survey data were av ailab l e. For the state of Kansas, however, subregional burn profiles were developed for wheat and rangeland burning, and further inve stiga tion is n eeded to de term ine if burn practices across other states va ry enough to warrant subdividing these states into regions for certain crop types.

4.3 RECOMMENDATIONS FOR I M PROVE D FUE L L O ADINGS AND EMISSION FACTORS

Em ission factors are often a subject of resear ch, and it is re c o m m e nded that efforts be

m a de to identify and incorporate im proved em ission factors related to p r escrib ed and agricu ltu ral burning that are published in th e future. Also, although the de fault fuel loading values by vegetation type contained in the FOFEM m odel were judged to be sufficiently representative of conditions in the CENRAP region, som e effort should be m a de to study these fuel loadings further. During the course of this project, pers onnel at the U SFS in Minne sota indicated that the default fuel loadings in F O FEM are regularly upd ated during the analysis of burns in that state. STI was provided with adjusted fuel loadings fo r several vegetation and fuel types, m o st of

w h ich w e re rela ted to “b low dow n” burns (the burning of vegetati on after s t orm s to red u ce fire hazard ) . These altered fuel load ings resulted in P M 2. 5 em ission factors up to 70% higher than those ca lculated w ith F O FEM def a ult lo adings. W h en these adjus t ed em ission factors were applied to 3700 acres of burns identified by USFS personnel as blow down, the prescribed burning portion of the PM 2. 5 inventory for Minnesota increased by about 5%.

4-24.4 RECOMMENDATIONS FOR ADDITION AL AMBIE N T DATA ANAL YSIS

In addition to im prove ments to the em ission inventory, additional analyses could be conducted to better quantify the influen ce of burns on visibility im pairment:

• Apply sim ilar analyses to a dditional IMPROVE sites, such as these in Kansas or Minnesota, to investigate whether results of th is task are indicative of the influence of burns throughout the CE NRAP region.

• Utilize continuous PM 2. 5 in conjunc tion w ith m e teorolog ical data to d e te r m ine w h at

m e teorological cond itio ns m a y be responsible fo r changes in PM 2. 5 concentrations.

• Apply source apportionm e nt tools such as UNMIX or Positive Matrix F actorization (PMF) to qu antify influence of specific source types at a site using 24-hr (i.e., IMPROVE, Speciated Tren ds Network [STN], etc.) or continuous speciated data (such as a t Bondville o r St. Louis). These t ools can be used to iden tify individua l so urces such as diesel, wood burning, etc.

4-35-1 5. REFERENCES

Allen D. and Dennis A. (2000) Inventory of air pollutant emissions associated with forest, grassland, and agricultural burning in Texas. February. Available on the Internet at <http://www.utexas.edu/research/ceer/airquality/>.

Altman B. (2004) Personal communication, February 23.

California Air Resources Board (2003) Smoke Management Program Emission Factors. Available on the Internet at http: //www.arb.ca.gov/smp/techtool/emfac.htm.

Cleaves D., Haines T., and Martinez J. (1998) In fluences on prescribed burning activity in the national forest system. International Fo rest Fire News (19), 43-46. Available on the Internet at <http://www.fire.uni-f reiburg.de/iffn/country/us/us_9.htm>.

Coe D.L. (2003a) Research and development of ammonia emission inventories for the Central States Regional Air Planning Associa tion. Quality Assurance Plan, STI-902504-2331- QAP2, April.

Coe D.L. (2003b) Research and development of emission inventories for planned burning activities for the Central Stat es Regional Air Planning Asso ciation. Final Work Plan, STI902511-2384-FWP, August 7.

Coutant B., Wood B., Scott B., Ma J., Kelly T., and Main H. (2002) Source apportionment analysis of air quality monitoring data: Phase 1. Final report, May.

Coutant B.W., Holloman C.H., Swinton K.E., and Hafner H.R. (2003) Eight-site source apportionment of PM 2.5 speciation trends data. Re vised draft report, April.

Dennis A., Fraser M., Anderson S., and Allen D. (2002) Air pollutant emissions associated with forest, grassland, and agricultural burning in Texas. Atmospheric Environment 36 (no. 23), 3779-3792.

Dixon M., Lunsford J., and Wade D. (1989) A guide for prescribed fire in southern forests. Technical publication R8-TP-11. Available on the Internet at http://www.bugwood.org/pfire. February.

Georgoulias B.A. and Dattner S.L. (2002) Modera ting meteorological fluc tuations on long-term visibility trends at Big Bend National Park in Texas. Paper No. 43561 - 2/11/2002, February.

Jenkins B.M., Turn S.Q., Williams R.B., Goron ea M., Abd-el-Fattah H., Mehlschau J., Raubach N., Chang D.P.Y., Kang M., Teague S.V., Raabe O.G., Campbell D.E., Cahill T.A., Pritchett L., Chow J., and Jones A.D. (1996) Atmospheric pollutant emission factors from open burning of agricultural and forest biom ass by wind tunnel simulations. Final report, California Air Resources Boar d Project No. A932-126, April.5-2 Main H.H. and Roberts P.T. (2001) PM 2.5 data analysis workbook. STI-900242-1988-DWB, February.

Malm W.C. (1999) Introduc tion to visibility. Availa ble on the Internet at <http://www2.nature.nps.gov/ard/vis/intro_to_visibility.pdf>; last accessed October 2, 2000.

Meadows G. (2004) Personal communication: Pl anned burning permits in Minnesota. April 2.

Miedtke D. (2004) Personal communication, February 23.

Pacific Environmental Services (2001) Assessm ent of emission inventory needs for regional haze plans. March.

Sisler J.F. and Malm W.C. (2000) Interpretation of trends of PM 2.5 and reconstructed visibility from the IMPROVE network. Journal of Air and Waste Management Association 50

, 775-789.

U.S. Census Bureau (2003) CenStats databa ses - county business patt erns data. Database. Available on the Internet at <http://censtats.census.gov/>; last accessed April 1, 2003.

U.S. Environmental Protection Agency (1998) Nati onal air quality and emissions trends report, 1997. Report, 454/R-98-016, December.

U.S. Environmental Protection Agency (2001) Bi ogenic emissions landcove r database. Available on the Internet at <ftp://ftp.epa.gov/am d/asmd/beld3/>; last accessed May 10, 2003.

U.S. Environmental Protection Ag ency (2003) Compilation of air po llutant emission factors, AP42. Vol. 1: stationary point and area sour ces. 5th ed., with Supplements A through F and Updates through 2003. Available on the Internet at <http://www.epa.gov/ttn/chief/ap42/index.html>.

## APPENDIX A

EMISSION ESTIMATION METHODS FOR THE CENRAP PLANNED BURNI NG EMISSION INVENTORIES

A- 1

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## EMISSION ESTIMATION METHODS FOR THE CENRAP PLANNED BURNING EMISSION INVENTORIES

Final Methods Document STI-902512-2451-FMD2

By: Stephen B. Reid

Tami H. Funk Patricia S. Stiefer Heather L. Arkinson Dana L. Coe Sonoma Technology, Inc. 1360 Redwood Way, Suite C

Petaluma, CA 94954-1169

Prepared for: The Central States Air Resource Agencies and The Central Regional Air Planning Association 10005 South Pennsylvania Avenue, Suite C Oklahoma City, OK 73159

July 30, 2004QUALITY ASSURANCE STATEMENT

This report was reviewed and approved by the pr oject Quality Assurance (QA) Officer or his delegated representative, as pr ovided in the project QA plan.

Lyle R. Chinkin Project QA OfficerTABLE OF CONTENTS

Section Page

1. INTRODUCTION

.............................................................................................................. 1- 1 1.1 Summ ary of Recommended Methods

...................................................................... 1-1 1.2 Im portant Assum p tions

............................................................................................. 1-2

2. AGRICULTURAL BURNING

.......................................................................................... 2-1 2.1 Overview................................................................................................................... 2-1 2.2 Agricultural Burning Em ission Factors and Fuel Loadings

..................................... 2-1 2.3 Agricultural Burning Activity Data

.......................................................................... 2-3 2.4 Spatial Allocation of Ag ricultural Burning

.............................................................. 2-4 2.5 Tem poral Allocation of Ag ricultural Burning

.......................................................... 2-4 2.6 Che m ical Speciation of Agricultural Burning

.......................................................... 2-4

3. PRESCRIBED BURNI N G

................................................................................................ 3-1 3.1 Overview................................................................................................................... 3-1 3.2 Prescribed Burning Emission Fa ctors and Fuel Loadings

........................................ 3-1 3.3 Prescribed Burning Activity Data

............................................................................. 3-3 3.3.1 Activity Data for Federal Lands

................................................................... 3-3 3.3.2 Activity Data for State, Tr ibal, and Private Lands

....................................... 3-4 3.3.3 Activity Data for Rangelands

....................................................................... 3-6 3.4 Spatial Allocation of Prescribed Burning

................................................................. 3-6 3.5 Tem poral Allocation of Prescribed Burning

............................................................. 3-7 3.6 Che m ical Speciation of Prescribed Burning

............................................................. 3-7

4. AIR QUALITY DATA ANAL YSIS

................................................................................. 4-1

5. PREPARATION OF DIGITAL EMISSI ON INVENTORY FILE SYSTEMS

................ 5-1

6. REFERENCES

.................................................................................................................. . 6

- 1

APPENDIX A: PROPOSED SU RVEY QUESTIONNAIRE

................................................... A-1

APPENDIX B: VEGETATI ON CROSS-WALKS ....................................................................B-1

APPENDIX C: MAPS OF KANSAS AGRICULTURAL SUBREGIONS ..............................C-1

iii

## This page is intentionally blank.LIST OF TABLES

Table Page

1-1. Summ ary of approaches to esti m a te planned-burning em issions

.................................... 1-2

2-1. Fuel loadings and em ission fact ors for agricultural burning

........................................... 2-2

2-2. Che m ical speciation of agricultura l burning: profile inform ation

................................... 2-5

3-1. Che m ical speciation of pr escribed burns: profile infor m ation for grasslands

................. 3-7

3-2. Che m ical speciation of pr escribed burns: profile infor m ation for forestlands

................ 3-8

v1. INTRODUCTION

The Central States Reg i o n al Air Plan ni ng Association (CENRA P) is researching visibility-related issues for its reg i on, which inclu d es the s t ates of Texas, Oklahom a, Louisiana, Arkansas, Kansas, Missouri, Nebraska, Iowa, an d Minnesota, and is developing a regional haze plan in response to the U.S. Environm ental Protection Agency’s (EPA) m a ndate to protect visibility in Class I areas. In order to develop an effectiv e regional haze plan, the CENRAP ultim ately m u st deve lop a conceptu al m odel of the phenom ena that lead to episodes o f low visibility in the CENRAP region. It is recognized that episod ic com bustion events (such as agricultural burning, prescrib ed burning, open burning of wastes, structural fires, and wildfires)

som e ti m e s contribute to regional or local haze ev ents in the C E NRAP region. Therefore, it is im portant to develop the em issions data necessa ry to asses s the im pacts of these events on visibility in the CENRAP region.

In support of the CENRAP’s need to deve lop a regional haze plan, Sonom a Technol ogy, Inc. (STI) developed em ission inventories of epis odic com bustion even ts for the CENRAP region. Consistent with the proj ect goals presented in the W o rk Plan (Coe, 2003), the scope of the inven t ories will b e lim ited to agricultu ral and prescribed b u rning. W ildfires, structural fires and waste burning (such as the “slash” burning of logging residue) were not considered in the developm ent of these inventories.

1.1 SUMMARY OF RECOMMENDED METH ODS

To develop em ission inventories of planne d burning activities for the CENRAP region,

we e m ployed existing models and infor m ation: the First-Ord e r Fire Effec t s Model (F OFEM),

em ission factors gathered from published litera tu re, and Geo g raphic Inform ation System s (GIS) databases of land cover and vegetation. In addition, we gathered new inform ation through telephone and m a il surveys.

FOFEM, a co m puting tool developed through the Joint Fire Science Program (JFSP) , can be used to predict a variety of effects from fi res on forested lands and rangelands, including air pollutant em issions, fuel consum ption, tree m o rt ality, and soil heating (R einhard t et al.

, 2003; Reinhardt et al., 1997). For this project, the FO FEM m odel was used to generate estim a tes of fuel loading s and em ission rates for prescribed b u rns. This data was then used in conjunction with prescribed burning history inform ation (detailing the locati on, land type, season, and size of burn incidents) to calculate em issions from this source. Fire history da ta for prescribed burning on wildlands, publicly m a naged lands , tribal lands, and private lands were gathered f r om federal and state ag encies, as well as som e private organizations.

For agricultural burning, em ission factors and fuel-loading factors fo r a variety of crop types have been published in the EP A’s guida nce docum ent, “Com pilation of Air Pollutant
Em ission Factors (AP-42)” (U.S. Environm ental Protection A g ency, 2003) and by Jenkins et al. (1996). From these sources, we identified fuel loading factors and em ission factors for a wide variety crop types. These factors were applie d to county-specific agricultural burning activity data to generate em issions estim ates. This activity data was obtained through system atic telephone and m a il surveys of county Agri cultural Extension Services (AES).

1-1For both prescribed and agricultural burning activities, the E P A’s Biogenic Em issions Landcover Database (BELD) Version 3 (U.S. Envi ronm ental Protection Agency, 2001) was used to generate spatial distributions of vegetation types, which in turn were used to select vegetationspecific fuel loading factors output by FOFEM. To do this, cross-walks were established to link the vegetation types in the BELD da tabase with (a) vegetation types in F O FEM and (b) crop types for which em ission s factors and fuel loading s are av ailab l e.

Once a m a p of vegetation and crop types was de veloped, we overlaid hi stories of planned fires, iden tif ied the v e getation types associated with each fire occurrence, and applied em ission factors generated through FOFE M or acquired from the sources described above to produce county-level em ission inventories of agricultural and pr escribed burning. Table 1-1 su mma r i z e s sources of em ission factors, activ ity data, and land cover data.

Table 1-1. Summ ary of a pproaches to estim a te planned-burning em issions.

Prescribed Burning Agricult ural and Rangeland Burning

Em ission factors FOFEM Model AP -42; (Jenkins et al., 1996) Fire history data Federal and state agencies; telephone contacts with tribes and private owners of large land tracts Telephone and m a il surveys of County Agricultural Extension Services
Land cover data EPA’s BELD3 da tabase EPA’s BELD3 database

1.2 IMPORTANT AS SUMPTIONS

The m e thods that we selected for use we re based on several important assum p tions:

• Default fuel loading values by vegetation type contained in th e FOFEM model are sufficiently repres entative of conditions in the C E NRAP region 1

.

• The land cover/vegetation types used by th e FOFEM m odel and those in the BELD database are sim ilar enough to allow a reason able cross-walk to be established between the two data sets.

• The crop types in the BELD database are sim ilar enough to crop varieties for which

em ission factors and fuel loadings are availa ble to allow a reasonable cross-walk to be estab lished between the two data sets.

• County AES will be capable of providing respons es that reaso n ably rep r es ent agricultu r al and rangeland burning activities in the CENRAP region.

1 Pers onnel at t h e U.S. Forest Servi ce in Minnes ota provide d up dat e d f

u el l o adi n gs f o r

3

, 70 0 ac res o f gr assl and bu r n s a n d “bl o wd o w n” b u r ns (t he b u r n i n g of ve get a t i o n aft e r st o r m s t o re duce fi re ha zar d) occ u r r i n g i n t h e S u peri or Natio n a l Forest in 200 2. Defau lt fu el lo ad ing s were u s ed in all o t h e r cases.

1-22. AGRICUL TURAL B URNI NG

2.1 OVERVIE W

Agricultural burning is prim arily a means of clearing harvested lands. Because the CENRAP region is largely agricultural, such ac tivity is lik ely to be a source of significant episodic combustion em issions in most countie s. Allen and Dennis (Allen and Dennis, 2000; Dennis et al., 2002) recently com p leted a study of e m issions from fires in Texas, which included agricultural and rangela nd burning in 1996 and 1997. According to their assessm e nts, these types of agricultu ral activities em itted over 66,00 0 tons of particu l ate m a tter of less th an 2.5 μ m aerodynam i c diam eter (PM 2. 5 ) and accounted for 84% of over 3.3 m illion acres of veg e tation burned in Texas during those two years.

2.2 AGRICUL TURAL B URNI NG EMISSI ON FACTORS AND F U E L LOADING S

Em issions from agricultural burning activities are dependent on the types of vegetation burned and the m a nner of com bustion, and can be estim a ted using the following equation:

Emissions (lb) = Fuel loading (ton/acre) * Emission factor (lb/ton) * Acres burned

In its Com p endium of Air Pollu tan t Em ission Fa ctors, (AP-42) (U.S. Environm ental Protection A g ency, 2003), the EPA provides fuel lo adings and em ission factors for particulate

m a tter (PM), carbon m o noxide (CO), m e thane (CH 4 ), and non-m e thane hydrocarbons (NMHC) for a variety of field and orchar d crops. In som e cases, AP-42 em ission factors are provided for two different burning techniques: headfire burning (when a fire is started on the upwind side of a field) and backfire burning (whe n a fire is started downwind). In addition, a m o re recent study at the Univ e r sity of Calif o rnia a t Dav i s der i ved em ission factors for the com bustion of barley straw, corn stover, rice straw, wheat straw, and almond tree prunings (Jenkins et al., 1996). In this study, em ission factors for CO, total hydrocarbons (THC), nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), and PM were based on m easurem ents collected during wind tunnel tests.

Fuel load ing s and em issi on factors are provided in Table 2-1 . For barley, corn, rice, wheat, and alm onds, e m i ssion factors were deri ved entirely from Jenki ns’ (1996) study using

average em i ssion rates and m o isture contents fr om two wind tunnel confi gurations. An e m ission factor for volatile organic com pounds (VOC) wa s derived from Jenkins’ THC values by using the fraction of reactive gases e qual to 0.5698 that was published in a California Air Resources Board (CARB) guidance docum ent (Gaffney, 2000) . For the rem a ining crops, em ission factors for NO x and SO 2 were set equal to Jenkins’s average values for field or orchard crops, and

em issions factors for VOC were calculated from the CH 4 and NMHC values reported in AP-42, again by using the CARB fraction of reactive gases. The em ission factors for CO wer e taken directly from AP-42, and part iculate m a tter of less than 10 μ m aerodynam i c dia m eter (PM 10 ) and PM 2. 5 were calculated from the PM values in AP-42 by using fractions of 0.9835 for PM 10 and 0.9379 for PM 2. 5 for field crops and fractions of 0.9814 for PM 10 and 0.9252 for PM 2. 5 for orchard crops based on CARB’s guidance (Gaffney, 2000). Fuel loadings were taken f r omAP42 for all crop types. (F or grasses and wild reed s, which were not reported in AP-42, the value for wild hay was used.)

2-1Table 2-1. Fuel loadings and em i ssion factors for agricultural burning.

(Pa g e 1 of 2 )

Em i ssi on Fact ors (l bs/ t o n )

Crop Ty p e Fuel Loa d ing ( t o n s / a c r e ) P M 10 P M 2.5 C O V O C NO
x S O 2

Field Cr ops Aspa ra g u s 1

. 5 39

. 3 37

. 5 15 0. 0 49

. 0 4. 5 0. 6
B a rl ey 1

. 7 14

. 3 13

. 8 18 3. 7 15

. 0 5. 1 0. 1
C o r n 4

. 2 11

. 4 10

. 9 70

. 9 6. 6 3. 3 0. 4
C o t t o n 1

. 7 7. 9 7. 5 17 6. 0 3. 6 4. 5 0. 6
Grasse s 1

. 0 15

. 7 15

. 0 10 1. 0 11

. 1 4. 5 0. 6 Pineapple 7. 9 7

. 5 11 2. 0 4. 6 4. 5 0. 6
R i c e 3

. 0 6. 3 5. 9 57

. 4 4. 7 5. 2 1. 1 Saffl owe r 1

. 3 17

. 7 16

. 9 14 4. 0 14

. 8 4. 5 0. 6
Sor g hum 2

. 9 17

. 7 16

. 9 77

. 0 5. 1 4. 5 0. 6
Su gar cane 4. 0 8. 3 7. 9 81 .0 9. 0 4. 5 0. 6 Wheat 1

. 9 10

. 6 10

. 1 12 3. 6 7. 6 4. 3 0. 9
Uns p eci fi e d 2

. 0 20

. 7 19

. 7 11 7. 0 13

. 3 4. 5 0. 6

Al fal f a - Hea d f i re 0. 8 44 .3 42 .2 10 6. 0 20 .8 4. 5 0. 6
Al fal f a - B a c k f i re 0. 8 28 .5 27 .2 11 9. 0 21 .7 4. 5 0. 6
B ean ( r e d ) - H eadfi re 2. 5 42 .3 40 .3 18 6. 0 26 .8 4. 5 0. 6
B ean ( r e d ) - B ackfi re 2. 5 13 .8 13 .1 14 8. 0 14 .2 4. 5 0. 6 Hay ( w i l d ) - H eadfi re 1. 0 31 .5 30 .0 13 9. 0 12 .5 4. 5 0. 6 Hay ( w i l d ) - B ackfi re 1. 0 16 .7 15 .9 15 0. 0 9. 7 4. 5 0. 6
Oat s - Hea d fi r e 1. 6 43 .3 41 .3 13 7. 0 19 .3 4. 5 0. 6
Oat s - B ack fi re 1. 6 20 .7 19 .7 13 6. 0 10 .3 4. 5 0. 6
Pea - Hea d fi re 2. 5 30 .5 29 .1 14 7. 0 21 .7 4. 5 0. 6 Wheat - Hea d f i re 1. 9 21 .6 20 .6 12 8. 0 9. 7 4. 5 0. 6 Wheat - B a c k fi re 1. 9 12 .8 12 .2 10 8. 0 6. 6 4. 5 0. 6

Orchar d Crops
Al m o n d 1

. 6 7. 0 6. 7 52

. 2 5. 2 5. 9 0. 1 Ap pl e 2

. 3 3. 9 3. 7 42

. 0 2. 3 5. 2 0. 1 Ap ri cot 1

. 8 5. 9 5. 6 49

. 0 4. 6 5. 2 0. 1
Av oca d o 1

. 5 20

. 6 19

. 4 11 6. 0 18

. 5 5. 2 0. 1
C h er ry 1

. 0 7. 9 7. 4 44

. 0 6. 0 5. 2 0. 1
C i t r us ( o ran g e, l e m on) 1. 0 5. 9 5. 6 81 .0 6. 8 5. 2 0. 1
Dat e pal m 1. 0 9. 8 9. 3 56 .0 3. 8 5. 2 0. 1
Fi g 2

. 2 6. 9 6. 5 57

. 0 6. 0 5. 2 0. 1
Nect ari n e 2

. 0 3. 9 3. 7 33

. 0 2. 3 5. 2 0. 1 Ol i v e 1

. 2 11

. 8 11

. 1 11 4. 0 10

. 3 5. 2 0. 1
P e a c h 2

. 5 5

. 9 5

. 6 4 2

. 0 3

. 0 5

. 2 0

. 1
P e a r 2

. 6 8. 8 8. 3 57

. 0 5. 1 5. 2 0. 1
Pru n e 1

. 2 2. 9 2. 8 47

. 0 4. 6 5. 2 0. 1 Wal n ut 1

. 2 4. 2 4. 0 67

. 0 4. 8 4. 2 0. 2
Uns p eci fi e d 1

. 6 5. 9 5. 6 52

. 0 6. 0 5. 2 0. 1

2-2Table 2-1. Fuel loadings and em i ssion factors for agricultural burning.

(Pa g e 2 of 2 )

Em i ssi on Fact ors (l bs/ t o n )

Crop Ty p e Fuel Loa d ing ( t o n s / a c r e ) P M 10 P M 2.5 C O V O C NO
x S O 2

Vine Cro p s Uns p eci fi e d 2

. 5 4. 9 4. 7 51

. 0 3. 8 5. 2 0. 1

Weeds
Ru ssian th istle, or

tu m b leweed 0. 1 2 1

. 6 20

. 6 30 9. 0 1. 1 4. 5 0. 6
Tal e s, o r wi l d r eeds 1. 0 4. 9 4. 7 34 .0 15 .7 4. 5 0. 6

Uns p eci fi e d 3

. 2 14

. 8 14

. 1 85

. 0 6. 8 4. 5 0. 6

2.3 AGRICUL TURAL B URNI NG ACTIVIT Y DAT A

To obtain activity data for agricultural burning events in the C E NRAP region, STI’s subcontractor, Population Research System s (P RS), conducted system atic telephone and m a il surveys of county AES offices. PRS attem p ted to contact each AES office in all 969 counties of the CENRAP region in order to recruit AES pe rsonnel to complete a telephone survey. This survey was designed to determ ine th e fraction of each county ’ s acreage ty pically burn e d each year by crop type, the tim i ng of such burn ev ents, and the burn m e thods em ployed. Data collected through the survey was then applied to National Agricultural St atistics Service (NASS) county-level estim ates of acreages grown by crop type for 2002.

This data collection effort ha d a target response rate of 25% to 50%. Ultim a tely, 549

contacts were m a de, for a response rate of 56% (r anging from 36% to 93% from sta t e to state). By including such large proportions of the av ailable respondent pool and the total geographic area of the CENRAP region, the achievable repr esentativeness of the study was m a xim i zed and the potential uncertainties m i ni m i zed. Survey re sponses were used to generate profiles of agricu ltu ral burning practices by geo g raphic region and crop type. In gen e ral, p r ofilin g was done on a statewide basis for each crop : a regional av erage bu rn p r ofile was us ed to rep r esent all counties for which no survey data are available. However, personnel at the Kansas Departm e nt of Health and Environment divided the state of Kansas into three subregions for wheat burning and four subregions for rangeland burning. Sepa rate burn profiles for the burning of wheat and rangeland were produ ced for each of these sub r eg ions and ap plied to cou n ties within those subregions f o r which no survey data were available.

The proposed survey questionnaire is provi ded in Appendix A, and m a ps displaying Kansas subregions for wheat and rangel and burning are provided in Appendix C.

2-32.4 SPATIAL ALLOCATION OF AGRICULT URAL BURNING

Agricultural burning was spatially allocated by using the BE LD GIS database. The BELD database includes spatial distributions ofcrops (by crop type) at the county (and subcounty) level gridded to 1 km 2 . Activity data obtained through the agricultural survey questionnaires about the types of crops burned at the county le vel was spatially allocated by

m a tching the reported crop types from the questionna ire to the crop types in the BELD database by county. The fire activity data was applied to the area (acreage) of crops by county for the purposes of calculating countywide em issions. Gr idded surrogate data, or spatial allocation factors, were developed by gri dding the agricultural burn activ ity data and corresponding crop types to the 12-km × 12-km national Regional Planning Organization (RP O ) grid domain.

2.5 TEMPORAL AL LOCATION OF AGRICUL TURAL B URNI NG

Agricultural burning, like othe r agricultural activities, has a distinct seasonal pattern, although this pattern tends to vary by crop type and region. To identify such seasonal patterns in the CENRAP region, the survey of agricultural e xperts contained questions designed to identify tim es of the year when agricu ltural b u rning takes place for th e various crops grown in each of th e CENRAP states. Survey responses were used to design seasonal profil es that characterize agricu ltu ral burning ac tiviti es by state and crop type.

The survey also con t ain e d questions relate d to w eekly and diurnal variations in agricultural burning ac tivities. These questions were desi gned to identif y the fraction of agricu ltu ral burning th at takes p l ace on weekdays ve rsus weekend days, as well as the fraction of burning th at takes p l ace during day l i ght hours versus nigh ttim e hours.

2.6 CHEMICAL SPE C IATION OF AGRICUL TURAL B URNI NG

PM and VOC em issions were chem ically sp eci ated accord ing to profiles p ublished by the EPA and the CARB. Table 2-2 summ arizes the profile refere nces and the individual com pounds included in the profiles. Using these refere nces, we created speciation profiles and crossreference files acco rding to SMOKE speciation s c hem e s.

2-4Table 2-2. Chemical speciation of agricultural burning: profile information.Profile NameProfileNumberProfileSourceSourceCategorySCC CodeSCC DescriptionReferenceProposedClassificationSchemesIndividualCompoundsPMAgriculturalBurning –Field Crops430ARBCARBSCCCode67066202620000(assumed tocorrespond toEPA SCCCode2801500000)Waste Burning –AgriculturalDebris –FieldCrops(Jenkins et al.,1996)Default SMOKEclassificationschemeandindividualcompoundsAluminum, Ammonia, Antimony,Arsenic, Barium, Bromine,Cadmium,Calcium,ElementalCarbon,Organic Carbon, Chlorine,Chromium, Cobalt, Copper,Gallium,Gold,Indium, Iron, Lanthanum,Lead, Manganese, Mercury,Molybdenum,Nickel,Nitrates,Palladium, Phosphorous, Potassium,Rubidium, Selenium, Silicon,Silver,Sodium, Strontium,Sulfur,Thallium,Tin, Titanium, Uranium,Vanadium,Yttrium,Zinc,Zirconium,UnidentifiedVOCMiscellaneousBurning –Forest Fires0307EPA2801500000MiscellaneousAreaSources–AgricultureProduction –Crops –Agricultural FieldBurning – WholeField Seton Fire–Total, all croptypes(Sandberg et al.,1975)Default SMOKEclassificationscheme(CarbonBondIV)andindividualcompoundsAcetylene, 1,3-Butadiene,N-Butane,1-Butene,Isomers ofButene, Ethane,Ethylene, Isobutane,3-Methyl-1-Butene,Propyne,Isomers ofPentane,Propane, Propylene, Unidentified

2-53. PRES CRIB E D B URNI NG

3.1 OVERVIE W The purpose of prescribed burning is commonl y believed to the clearing of undergrowth in tim berlands or grasslands to prevent wildfire s or m a ke various types of land im provem e nts. For exam ple, planned burns are used for tim ber stand im provem e nt (site preparation fires for ref o restation projec ts; re moval of diseased trees), range im provem e nt and wildlife habitat im prove m e nt. The types and am ount s of such burni ng vary regionally both due to local weather and to local forest/land types.

As with agricultural burning, em ission rates are specific to m a terials burned and burn

m a nage m e nt practices. S o m e degree of reporting and record-keeping is required of wildfire prevention efforts by state, federal, and tribal agencies. However, access and interpretation of these records is difficult. Even less infor m ati on is available f o r planned burning of undergrowth for private land im provem e nt. As with agricult ural burning, significant e ffort is necessary to develop activity data sets that can be used for regiona l-scale em issions assessm ents.

3.2 PRES CRIB E D B URNI NG EMISSION FACT ORS AND FUEL LOADINGS

For this project, the FOFEM m odel was used to generate estim a tes of fuel loadings and

em ission rates for prescribed fires w h ich were th en applied to estim ates of acres burned acquired from fire history data. T h is m odel was deve loped based on research findings gathered from peer-reviewed literature sources, internal agency reports, and ot her “g ray lite ra ture” sources. The accuracy and certainty of FOFEM results are cons is tent with the cu rrent status of scien tific

m easure m ents of fuel consum ptions and air em i ssions for prescribed burning and wildfires. Although measurem ent data are lim ited and uncerta in, the FO FEM m odel generally represents a synthesis of the m o st up-to-date inform ation available.

Required inputs to FOFEM 5.0 include the following:

• Vegetation land cover type

• Season of the year (spring, summer, fall, o r winte r)

• Moistu re co nditions (in c luding th e moistu re content of various fuel types)

• Configuration of the burn (natural co nditions, piled fuel, or slash fuel)

• Percent of the tree canopy crown expected to burn (0% for a well- ex ecuted prescribed burn)

• Percent of fallen logs that ar e rotten (default equals 10%)

• Size distribution of fallen logs of 3 in. or greater diam eter

– Even distrib u tion across the size range, from 3 in. to 20+ in.

– A distribution that tends toward the larger logs

– A distribu tio n that tends toward the s m aller logs

– A distribution that tends toward the center of the size range

– A distribution that tends toward the endpoints of the size range.

3-1FOFEM calculates em ission factors for PM 10 , PM 2. 5 , CH 4 , carbon dioxide (CO 2 ), CO, NO x , and SO 2 . For ammonia (NH 3 ) and NMHC, we applied the approxim ations that were

em ployed by Allen and Dennis (200 0), which assum e d NMHC and NH 3 em ission factors that vary as f o llo ws:

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ − × × =

CO CO CO
NO NH EF EF EF 1 14 EF EF

2 2

x 3 (3-1)

(3-2) 1.232) (EF 1.52 EF

4 CH NMHC × + =

Before FOFEM could b e applied to the CENR AP region, it was necessary to determ ine which of the m odel’s vegetation ty pes are found in the region, and what the m o isture contents of various fuel types were at the tim es and places in which prescribed burn i n g events occurred. (For the remaining FOFEM inputs, such as bur n configuration, log-size distributions, and the percentage of fallen logs that are ro tte n, def a ult se tti ngs were used).

FOFEM allows users to choose betw een two m a in vegetation cover classifications: the National Vegetation Classification S y stem (NVCS) and the S o ciety of Am erican Foresters/Society for Range Managem e nt (SAF/ S RM) cover types. (A third option, the Fuel Characteristic Classification [FCC ], does not yet cover all regi ons of the country.) The NVCS uses a classification h i erarchy which em phasizes differences in both vegetation structure and fl ori s ti cs 2 , and the system is periodically updated to include new infor m ation on natural community class i f i catio ns develope d at the sta t e level. Such natura l communities a r e based on all species of vegetation. SAF forest cover types, on the other hand, are based prim arily on dom i nant tree species. While tr ees can be indicators of their environm ents, som e trees are so broadly ad apted that their presence indicates lit tle about th e conditions of the surround ing natu ral community. Thus, SAF cover types are less usef ul than those found in the NVCS (Ne w Ha m p shire Division of Forests and Lands, 2002). To determ ine which of the NVCS or SAF cover types are found in the CENRAP region, a cross-walk w a s developed between the FOFEM and BELD databases. In developing this cro ss-w a lk, BELD vegetation types were m a tched to NVCS cove rage types wherever possible; SAF data was used only when clear m a tches could not be m a de to NVCS cove rages. The cross- walks used are presented in Appendix B.

Fuel m o isture content is the quantity of water in a fuel particle expressed as a percentage of the oven-dry weight of the fuel (National W eather Service, 1998). FOFEM requires settings for three fuel classifications 3 : 10-hour, 1000-hour, and duff 4 . Fuel m o isture data a r e ava ilab l e

2 Floristics is the stud y of th e n u m b e r, d i stri bu tio n, an d rel a t i ons hi ps o f pl a n t s p eci es i n o n e or m o re are a s. 3 The rat e of ch ange o f t

h e m o i s t u re c ont e n t i s de pe nde nt on t h e di am et er of t h e w o ody fu el , va ri o u s di a m et er ran g es are classifie d a ccording t o the i r “tim e lag.” Tim e lag refe rs to th e leng th of tim e i t tak e s a fu el to resp on d to ch an g e s in env i ron m en t a l m o istu re con d ition s : larg er d i am eter fu els g e n e rally h a v e lo ng er tim e la g s

. Th e tim e l a g categories

t y pi cal l y used fo r fi re beha vi or an d fi re da n g er rat i n g a r e s p ecif i ed as 1

- hr (0- 1⁄4 ") , 10 -hr ( 1⁄4 "-1 " ), 1 00-h r (1 "-3 " ), an d 1 000 -h r (3 " o r g r eater)

. 4 Du ff is p a rtially d eco m p o s ed o r g a n i c m a t t er, leaf litte r, o r org a n i c so ils (such as hu m u s or p eat), wh ich

accum u lates in layers on t h e forest floor.

3-2f r om the W ildland Fire Assessm ent System (W FA S)—a database of the National Interagency Fire Center (NIFC) in Boise, Idaho. WFAS is based on daily weather observations taken at about 1500 fire weather stations throughout the U n ited States and ente red into the W e a t her Inform ation Managem e nt System ( W IMS). These weather observations are used to calculate fuel m o isture levels for 1-hr , 10-hr, 100-hr, and 1000-hr fuel types. W I MS data for the CENRAP region was acquired and used to determ ine a range of 10-hr, 1000-hr and duff moisture levels for the CENRAP region for 2002. The 100-hr m o isture values were used as a surrogate for duff moi s ture, following the approach of Harrington (2003).

Once vegeta tion typ e s an d f u el m o isture lev e ls p r esent in the CENRAP region were determ ined, FOFEM wa s run for each unique combin ation of vegetation type-m oisture level to generate emission rates in pounds per acre burned. Outputs from these FOFEM runs were used to produce a look-up table of e m ission factors by vegetation type and m o i s ture condition. For each prescribed burning event, we were ab le to u s e W I MS data from the neares t fire weather station to de term ine f u el moisture co ntents f o r th at event and BELD data to determ ine the type of vegetation burned. This inform ation was used to select and apply an appropriate em ission factor from the FOFEM look-up table.

3.3 PRES CRIB E D B URNI NG ACTIVITY DATA

In summ ary, the pres crib ed burn activity data for state and private lands f r om the CENRAP states will consist of detailed data ob tained from s m oke m a nage m e nt program s, state fire m a rshals, or state forest services; summ ary data obtained from state agencies and allocated by county; summ ary data estim a te d by applying federal surrogates to state lands and allocated by county; and county level data ba sed on the results of the rangeland burning survey questions.

3.3.1 Activity Da ta for Feder a l Lands

The National Interagency Fire Managem e nt Integrated Database (NIFMID) was the source of data used for prescribed fires occurr ing on Department of the Interior (DOI) lands (U.S. Fish and W ildlife Servi ce, National Park Service, a nd Bureau of Indian Affairs [ http://famweb.nwc g.gov/weatherfirecd/ ]). This database contains fi re type (prescribed, wildfire, etc.), start and end dates, extent (acres ), and location (geographic coordinates and township/r a nge/sec tion )

. The National Fire Plan Opera tions and Reporting System (N FPORS), contains year 2002 fire occurrence data for the U.S. Forest Service (USFS). NFPORS data were used to characterize prescribed fires on USFS lands in the six states with land m a naged by that agency: Arkansas, Louisiana, Minnesota, Missouri, Oklahom a, and Texas.

Additional prescribed burn data on federall y m a naged lands were included in data acquired from state sm oke m a nage ment programs (this data was cross-checked against NIFMID and NFPORS data to prevent double-counting). For exam ple, som e DOI data was included

am ong the state reports that did not appear in the NIFMID final report for 2002, and som e USFS burns appeared in these reports as w e ll.

3-33.3.2 Activity Data for State, Tribal, and Private Lands

Each of the CENRAP states has unique regul ations regarding pres cribed burning on state and private lands. Records of prescribed burns are com p iled at different levels within each state. Consequently, several sources of inform ation contributed to the pr escribed burn activity data for state, private, and tribal lands.

In cases where we could not acquire good-quality inform ati on about prescribed burns on state lands, the percentage of federal lands that were burned within the state in the year 2002 was used as a su rrogate for th e percen tage of state lands that were bur ned that year. The to tal acreage of burned state lands were al located accord ing to the proporti on of state lands within each county. In addition, the tem poral profile of the b u rns that occurred on fed e ral lands within th ese states was applied to the burns that were estim a ted for their s t ate lands.

Minnesota, Arkansas and Louisiana have voluntary or m a ndatory sm oke m a nagem e nt
program s for which records of prescribed burns o n state and p r ivate land s are kep t . Records including the scheduled date, ex tent (acres), and location (geographic coordinates or township/range/section) of larg e scale prescribed burns that occurred during the year 2002 on state and private lands in Minnesota and Ar kansas were obtained from the Minnesota Interagency Fire Center and the Arkansas Forestry Comm i ssion, respectively. Also, the Louisiana F o restry Division provided summary data describing the dates and acreages of prescribed burns that occurred on Louisiana’s st ate and private lands during the year 2002. This summary data listed burns by district and had to be allo cated to the coun ty level u s ing the acreage of forested la nd within each county.

A statewide perm itting s y stem exists f o r all oth e r planned bur ns in Minne sota, in cluding

sm all scale r e siden tia l or agricu ltu ral burns. The perm its are issued by local fire wardens, and an estim ated 60 ,000 burn perm its were issued in th e state in 2002 . Records of these perm itted bu rns are not compiled above a county level and are not in electronic form at. Of the 60,000 perm its, roughly 65% are estimated to be issued for “ditch burns” (fires set alongsi de roads or fencerows for weed abatem ent purposes) or “pile burns” (fires used to dispose of piles of waste m a terial). Ditch burns are generally less than one quarter m ile in leng th and were no t consid ered in the inventory due to their sm all size and the lack of sp ecific data. Also, since pile burns are used for

waste m a nagem e nt purposes, they fall beyond the sc ope this inventory. The rem a ining 35% of the perm itte d burns are perf orm e d on open land a nd range an d are likely to be captu re d by the agricultural survey (Meadows, 2004).

In Oklahoma, a 15-county area in the eastern portion of the state has a controlled burn authorization system for open burning on privat e lands and lands m a nage d by the state forest service. Records containing the date, type (grassland, woodland, brus h pile, etc.), extent (acres), and location (address) of prescribed burns that occurred in that region of Oklahom a during 2002 were obtained from the Oklahom a Forestry Service 5 . Oklahom a’s Departm e nt of Wildlife Conservation (DW C ) estim ated the total num be r of acres burned on lands m a naged by the DW C in 2002, which accounted for the rem a inder of the state land s in Oklaho m a that undergo substantial prescribed burning.

5 Ab ou t o n e

- t h i r d of th ese r e co rd s w a s pr ov i d ed in h a r d

- c op y fo r m at an d w e r e no t in cl ud ed in t h e f i n a l in v e n t or y.

3-4The Kansas State Fire M a rshal’s office keeps a database of fire incidents in Kansas as reported by local fire departm e nts (although prescribed burns in Kansas m a y or m a y not be reported to the local fire depa rtm e nts, depending on the specif ic regu lation s within e a c h township). The dates an d locati ons (counties) of the controlled burns that were reported to the local fire departm e nts in Kansas during 2002 were obtained from the Kansas State Fire Marshal’s database.

In Texas, virtually all of the burning on st ate lands is conducted by the Texas Parks and W ildlife De partm e nt (TPW D) in state parks a nd W ildlife Managem e nt Areas (W MA). TPWD was able to provide data on burns occurring in st ate parks, but W M A burns are not tracked in a

centr al da ta base. Attem p ts to gather data from individual WMA m a nagers were not successful, so the num ber of W M A acres bu rned in 2002 was estim ated from data published by Allen and Dennis (2000) for 1996 and 1997.

Missouri, Iowa, and Nebraska have neither sm oke m a nage m e nt program s nor prescribed burn records com p iled above the county level. T h e Forestry Section of the Missouri Departm e nt of Conservation summ arized the number of acres burned by The Nature Conservancy, the Missouri Departm e nt of Natural Resources, a nd the Missouri Departm e nt of Conservation on state and private lands in Miss ouri during the year 2002. In the state of Iowa, the Bureau of W ildlife perfor m s a large portion of the state’s pr escribed burns on public grasslands. However, records of the prescribed burns that occur on Iow a ’s conservation lands are not com p iled by the Bureau of W ildlife above the dispatch level. Ther efore, the percentage of federal lands burned in the state of Iowa during 2002 was used as a surrog ate in order to estim a te the total number of acres bu rned on Iowa’s s t ate lands . In Nebraska, a statewide burn ban requires prescribed burns to be perm itted, but records of prescribed burn pe rm its are not com p iled above the county level. Therefore, the percentage of federal lands burned in the state of Nebraska in 2002 was used as a

surroga te to estim ate the tota l num ber of acres burned on state lands. The estim ated acreage of state lands b u rned in M i s s ouri, Iowa, and Ne brask a will be allocated by co unty using the

percen tage o f state lands in each coun ty within ea ch state.

To ensure that burning on tribal lands wa s captured in the data sources listed above;

contacts were m a de to tribes that collectively hold over 95% of the tribal lands in the CENRAP region. It was confirm e d that th ese tribes report their burns to either the B I A or the Minnesota Interagency Fire Center.

For burning on private land, it was assum e d th at burns by individual parties would be rela ted to ag ricu ltur a l pr actices ( a nd, therefore, captured in the agricultu ra l survey da ta ) or the burning of waste (and, therefore, not considered under the scope of this project). Significant burns on private lands are m o st likely to be cond ucted by the forest industry, or by organizations such as the Nature Cons ervancy (TNC), The Pr airie Plains In stitu te, o r th e Platte Rive r Whooping Crane Maintenance Trust (W hitney, 2003). W e did not obtain specific data from all the aforem entioned organizations due to tim e co nstraints, though the TNC provided a database of all burns conducted by that agency in 2002.

Planned burns by private forestry companie s in Louisiana and Arkansas are largely included in the data received from the Louisian a F o restry Division and the Arkansas Forestry

3-5Comm ission. Forestry com p anies also perform planned burns in the Piney W oods region of east Texas. However, records of the planned bur ns that occurred during 2002 were not available f r om the Texas Forest S e rvic e. Trad ition a lly, the Texas Fores t Service rep o rted p l anne d burning inform ation in the Harvest Trends Report; yet, after 1999, the Harvest Tre nds Report ceased to include inform ation about planned burning becau se the practice of planned burning for forest

m a nage m e nt has dim i nished in recent years due to liability concerns (X u, 2004). In the absence of other inform ation, data reported by Allen & D e nnis (2000) on acres burned by private tim b er

com p anies in the Piney Woods region for 1996 a nd 1997 were averaged to produce an estim ate of 20,000 acres burned per year. These acres were allocated to the county level based on the forested acreage in each county th at m a kes up the Piney W o ods region.

3.3.3 Activity Da ta for Rangelands

Rangeland burning occurs extensively on priv ate lands throughout the CE NRAP states, particularly in Kansas, Nebraska, Oklahom a, and cen tral and west Texas. To obtain activity data for rangeland burning events in the C E NRAP re gion, the agricultural burning survey given to county AES offices included rangeland burning ques tions designed to determ ine the fraction of rangeland acreage typically burned each year and the tim i ng of su ch burn events. The survey results (d isc u ssed in Sec tion 2) y i eld e d activity d a ta f o r pr iva t e land s f o r all of the CE NRAP states. W e obtained additional pr escribed burning inform ation for private lands in some of the CENRAP states, as previously discussed in Section 3.3.2.

3.4 SPATIAL ALLOCATION OF PRESCRI BED BURNING

Fire occu rrence location s for prescribed burns were typically provided as point coordinates (i.e., latitude and l ongitude values), township/range assignm ents, or county nam e

. While the size of the fire was typically provi ded (in acres), th e act ual boundaries of the prescribed burns were not usually provided. To repres ent th e location an d approxim a te size of each burn, the reported location of each burn was a ssum e d to be the cen troid of the bu rn and was

m a pped as a point using the latit ude/longitude coordinates. C ount y-specific vegetation profiles from the BELD data were then m a tched to each fire location to determ ine the vegetation types associated w ith each fire. The vegetati on data (used by the F O FEM m o del), fire size, occurrence date, and ass o ciated fuel moisture data were used to calculate em issions for each fire.

While m a ny of the prescribed burns were large, there were n o fires larger than the 12 -km x 12-km grid cell resolution. Th erefore, when the locations of prescribed burns were known, they were treated as point s ources in the em ission inventor y. Approxim a tely 40% of the prescribed b u rning inv e n t ory was allocated sp atially and temporally as a poi nt source inventory. (States tha t were able to provide “ i n c ident-lev e l” databases of prescrib ed burn activity included Arkansas, Minnesota, and Oklahom a

. ) W h en the lo cations of fires were not reported, a spatial surrogate approach was used to devel op gridded spatial allocation factors.

Spatial allocation factors were used to spat ially distribute em issions at the sub-county level (by grid cell). To devel op gridded surrogate data, a surr ogate data source is used to

repres ent th e locations o f fire activ ity. Prescr ibed burns were spatially distributed on rural grasslands and forested lands, wh ile agricu ltu ral burns were s p atia lly d i str i buted on ag ricu ltur a l

3-6land by crop type based on data obtained from the agricultu ra l burning su r v eys. The s p atia l allocation factors were developed for the 12-km × 12-km Nat i onal RPO grid.

3.5 TEMPORAL ALLOCATION OF PRES CRI BED BURNING

Fire history data collected fo r prescribed burns on federal lands specifies the dates on which the burns began and ended. These data we re used to generate sta t e-specific temporal profiles to allocate em issions from pr escribed burning to the proper m onths of the year and days of the week. Also, by exam ining the num b er of burns com p leted in one day versus those spanning multip le days (and therefore continuin g through th e night), it w a s possib l e to estim ate the fraction of prescribed burni ng that takes place in daylight hours versus nighttim e hours.

In the abs e n ce of date-sp ecific inform ation for prescribed burns on state lands, tem poral profiles derived from fe deral prescribed bur ns were applied to burns on state lands.

3.6 CHEMICAL SPECIATION OF PRESCRIBED BURNING

PM and VOC em issions were chem ically sp eciated accord ing to profiles d e veloped by the EPA and the CARB. Tables 3-1 and 3-2 summ arize the p r ofile refe rences and the individual

com pounds included in two profiles: (1) prescrib ed burning of grasslands and (2) prescribed burning of woodlands. Using these references , we created speciation profiles and crossreference files acco rding to SMOKE speciation s c hem e s.

3-7Table 3-1. Chemical speciation of prescribed burns: profile information for grasslands.Profile NameProfileNumberProfileSourceSourceCategorySCC CodeSCC DescriptionReferenceProposedClassificationSchemesIndividualCompoundsPMRangeImprovementBurning441ARBCARBSCCCode67066402000000(assumed tocorrespond toEPA SCCCode2810020000)Waste Burning –RangeManagement–RangeImprovement(Jenkins et al.,1996)Default SMOKEclassificationschemeandindividualcompoundsAluminum, Ammonia, Antimony,Arsenic, Barium, Bromine,Cadmium,Calcium,ElementalCarbon,Organic Carbon, Chlorine,Chromium, Cobalt, Copper,Gallium,Gold,Indium, Iron, Lanthanum,Lead, Manganese, Mercury,Molybdenum,Nickel,Nitrates,Palladium, Phosphorous, Potassium,Rubidium, Selenium, Silicon,Silver,Sodium, Strontium,Sulfur,Thallium,Tin, Titanium, Uranium,Vanadium,Yttrium,Zinc,Zirconium,UnidentifiedNMHCMiscellaneousBurning –Forest Fires0307EPA2810020000MiscellaneousAreaSources–Other Combustion–PrescribedBurning ofRangeland– Total(Sandberg et al.,1975)Default SMOKEclassificationscheme(CarbonBondIV)andindividualcompoundsAcetylene, 1,3-Butadiene,N-Butane,1-Butene,Isomers ofButene, Ethane,Ethylene, Isobutane,3-Methyl-1-Butene,Propyne,Isomers ofPentane,Propane, Propylene, Unidentified

3-8Table 3-2. Chemical speciation of prescribed burns: profile information for forestlands.Profile NameProfileNumberProfileSourceSourceCategorySCC CodeSCC DescriptionReferenceProposedClassificationSchemesIndividualCompoundsPMForestManagementBurning463ARBCARBSCCCode67066602000000(assumed tocorrespond toEPA SCCCode2810015000)Waste Burning –ForestManagement–ForestManagement(Jenkins et al.,1996)Default SMOKEclassificationschemeandindividualcompoundsAluminum, Ammonia, Antimony,Arsenic, Barium, Bromine,Cadmium,Calcium,ElementalCarbon,Organic Carbon, Chlorine,Chromium, Cobalt, Copper,Gallium,Gold,Indium, Iron, Lanthanum,Lead, Manganese, Mercury,Molybdenum,Nickel,Nitrates,Palladium, Phosphorous, Potassium,Rubidium, Selenium, Silicon,Silver,Sodium, Strontium,Sulfur,Thallium,Tin, Titanium, Uranium,Vanadium,Yttrium,Zinc,Zirconium,UnidentifiedNMHCMiscellaneousBurning –Forest Fires0307EPA2810015000MiscellaneousAreaSources–Other Combustion–PrescribedBurning forForestManagement–Total(Sandberg et al.,1975)Default SMOKEclassificationscheme(CarbonBondIV)andindividualcompoundsAcetylene, 1,3-Butadiene,N-Butane,1-Butene,Isomers ofButene, Ethane,Ethylene, Isobutane,3-Methyl-1-Butene,Propyne,Isomers ofPentane,Propane, Propylene, Unidentified

3-94. AIR QUAL ITY DATA ANALYSIS

The objective of data analysis f o r th is projec t was to pre lim inarily assess w h ether p l ann e d burning appears to contribute to im paired v i sib i lity events in Class I areas . W e used existing

am bient pollutant data from Class I areas in conjunction with the planned burn em ission inventories developed through this project. To m eet this objective, we perform e d the following steps:

• Summ arized 2002 air quality data available for Class I area s in the CENRAP region (e.g., IMPROVE f speciated P M 2. 5 data). Sm oke components th at contribute to visibility im pair m e nt include organic carbon (OC) and elem ental carbon (EC).

• Identified where and wh en planned, prescribe d, and/or agricultural burns occurred near and/or upwind of Class I areas in 2002 by using the Task 1 em ission inventory.

• Characterized the am bient data for the 20% be st and 20% worst visibility days at the Class I areas , includ ing the averag e com position of the PM 2. 5 and the average contribution of pollutants to light extincti on. Determ ined whether any of these days coincide with burns included in the inventory.

• Investigated the am bient data for days with high concentrations of or contributions from EC and non-soil potassium (associated with biomass burning). Investigated seasonal patterns and whether any of these days coinci de with burns listed in the inventory.

• Analyzed days of interest in m o re detail by perform i ng trajectory analyses, inspecting satellite photos, and inve stigating existing hourly po llutant data (e.g., whether nephelom e ter m easurements ind i ca te the im pact of air parc els with inc r ea sed PM 2. 5 concentratio ns).

The deliverable for this task is a techni cal m e m o randum describing the analyses and summarizing analysis results. A discussion of th e analysis and results is also included in the project Final Report.

f IM PR O V E = Int e ra ge ncy M oni t o ri n g of P r ot ect ed Vi sual En vi ro nm ent s

4-15. PREP ARATION OF DIGITAL EMISSI ON INVE NTORY FIL E S Y STEMS

The following files will be delivered by STI upon com p letion of the planned burning

em ission inventory with accom p anying docum entation:

• Em ission da ta f iles in la test NIF f o rm at

• Em ission data files converted to IDA fo r m at and ready for input to SMOKE 1.5

• Tem poral profile and cross-re ference fi l e s for use by SMOKE

• Spatial surro g ate and cro ss-re ference fi l e s for use by SMOKE

• Che m ical speciation prof iles and cros s-referen ce files for use by SMOKE

5-16-1 6. REFERENCES

Allen D. and Dennis A. (2000) Inventory of air pollutant emissions associated with forest, grassland, and agricultural burning in Texas. February. Available on the Internet at <http://www.utexas.edu/research/ceer/airquality/>.

Dennis A., Fraser M., Anderson S., and Allen D. (2002) Air pollutant emissions associated with forest, grassland, and agricultural burning in Texas. Atmospheric Environment 36 (no. 23), 3779-3792.

Gaffney P. (2000) Emission factors for open burni ng of agricultural residu es. August. Available on the Internet at <h ttp://www.arb.ca.gov/smp/techtool/arbef.pdf>.

Harrington M. (2003) Personal communication with a scientist at the Fire Sciences Lab in Missoula, MT., October 24.

Jenkins B.M., Turn S.Q., Williams R.B., Goron ea M., Abd-el-Fattah H., Mehlschau J., Raubach N., Chang D.P.Y., Kang M., Teague S.V., Raabe O.G., Campbell D.E., Cahill T.A., Pritchett L., Chow J., and Jones A.D. (1996) Atmospheric pollutant emission factors from open burning of agricultural and forest biom ass by wind tunnel simulations. Final report, California Air Resources Boar d Project No. A932-126, April.

Meadows G. (2004) Personal communication: Pl anned burning permits in Minnesota. April 2.

National Weather Service (1998) Fuel moisture . January 31. Available on the Internet at <http://www.seawfo.noaa.gov/fire/fuelmoisture.htm>.

New Hampshire Division of Fo rests and Lands (2002) Introduc tion to Natural Communities. Available on the Internet at <http://www.nhdfl.org/formgt/nhiweb/natural_communities.htm>.

Reinhardt E.D., Keane R.E., and Brown J.K. ( 1997) First Order Fire Effects Model: FOFEM 4.0 user’s guide., Forest Service Ge neral Technical Report INT-GTR-344.

Reinhardt E.D., Keane R.E., and Gangi L. ( 2003) First Order Fire Effects Model: FOFEM 5.0. Available on the Internet at <http://www.nifc.gov/joint_fire _sci/link2.htm>; last accessed April 20, 2003.

Sandberg D.V., et al. (1975) Emissions from sl ash burning and the influence of flame retardant chemicals. J. Air Poll. Cont. Assoc. 25 (3).

U.S. Environmental Protection Agency (2001) Bi ogenic emissions landcove r database. Available on the Internet at <ftp://ftp.epa.gov/am d/asmd/beld3/>; last accessed May 10, 2003.

U.S. Environmental Protection Ag ency (2003) Compilation of air po llutant emission factors, AP42. Vol. 1: stationary point and area sour ces. 5th ed., with Supplements A through F and Updates through 2003. Available on the Internet at <http://www.epa.gov/ttn/chief/ap42/index.html>.6-2 Whitney B. (2003) Personal communication: Planned burning in Nebraska. November 7.

Xu W. (2004) Personal communi cation: The Harvest Trends Re port for the Piney Woods Region of Texas. April 1.

## APPENDIX A

PROPOSED S U RVEY QUES TIONNAIRE

A-1

## This page is intentionally blank.Central State s

Regional Air Planning A ssociation 

### (

CENRAP) Telephone Interview Project #1002

INTRO1 : Hello, my name is _________ _. I’m calling o n

behal f of the Central States Region al Air Planning Association or CENRAP. CENRAP is an organization of states, tribes, federal agencies, and other interested parties that studies and ad dresses regio nal haze and visib ility issu e

s

. Your state is participatin g in CENRAP and as such, your county has been randomly selected to participate in an important air quality study.

Q1a. Our records show that this is a coo perative agric ultural extension office in county in the state of ________. Is that correct?

[1] Yes (Go to Qa2) [2] No (Go to Q1b) [8] DON’T KNOW (Terminate) [9] REFUSED (Terminate)

Q1b. What office have I called?

_________________________ ______________________ (Go to Q2a)

Q2a. I would like to speak with the person who w ould be most knowledgea ble ab out your county’s tilling practices and agricultural burning practic e

s.

[1] I am that p e rson (Go to INTRO3a) [2] I am that person, but I only know tilling (Go to INTRO3a) [3] I am that person, but I only know about burning (Go to Q3a) [4] I’ll get him/her (Go to INTRO2) [5] No one is available no w (Go to CALLBACK) [6] No such person (Terminate) [8] DON’T KNOW (Terminate) [9] REFUSED (Terminate)

INTRO2 : Hello, my name is _________ _. I’m calling o n

behal f of the Central States Region al Air Planning Association or CENRAP. CENRAP is an organization of states, tribes, federal agencies, and other interested parties that studies and ad dresses regio nal haze and visib ility issu e

s

. Your state is participatin g in CENRAP and as such, your county has been randomly selected to parti cipate in an important air quality study (Go to Q2b).

Q2b. Are you the person who is most knowled geab le about your county’s tilling p r actices and agricultural bur ning practices.

[1] I am that person (Go to INTRO3a) [2] I am that person, but I only know tilling (Go to INTRO3a) [3] I am that person, but I only know about burning (Go to Q3a) [8] DON’T KNOW (Terminate) [9] REFUSED (Terminate)

INTRO3a : The interview will take about 10 minutes. Y our responses will be kept confidentia l a nd will not be connected to your name. Can I begi n the interview? <Go to Q3a1>

CENRAP Telephone I n

t e rview P r oject #1 002 12/ 18/

2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 1Q3a. What agency or agencies would ha ve information about tillin g practices in your county? (Probe: Is that a state or county agency?) Can I get their telephone number as well?

1. _______________________________

______________________ ____________ 2. _______________________________

______________________ ____________

777 = NOT APPLICABLE 888 = DON’T KNOW 999 = REFUSED

<Go to INTRO3b>

INTRO3b : The interview will take about 10 minutes. Y our responses will be kept confidentia l a nd will not be connected to your name. Can I begi n the interview? <Go to Q3a1>

CALL BAC K : When would be a good time for us to call back to talk with someone about agricultural burning in your county? Who should we ask for? <Interviewer Note: If told y ou have reached the incorrect agency, as k for correct agency name and telephone number>

TERMINATE : Thank you for your time. Goodbye.

<If Q1a Eq 2, go to Q3a2. If Q1a Eq 1, go to Q3a1>

Q3a1. What i s the name of th is office? ____________

_________________________

88888 = DON’T KNOW 9999 = REFUSED

Q3a2. What i s your name? __________

___________________________

8888 = DON’T KNOW 9999 = REFUSED

Q3a3. What i s your telephone number beginn ing with the area code? ( ) __________

8888888 = DON’T KNOW 9999999 = REFUSED

CENRAP Telephone I n

t e rview P r oject #1 002 12/ 18/

2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 2Q3b. I’m now going to read you a list of crops and I’d lik e you to tell me whether these crops are grown in your county? (Programmer note: 1 dat a output column only; Yes=1, No=2)

1. Corn

_____ 2. Wheat

_____ 3. Sorghum

_____ 4. Rice

_____ 5. Other cereal crops

_____ 6. Soybeans

_____ 7. Sugarcane

_____ 8. Hay or alfalfa

_____ 9. Cotton

_____ 10. Other crops not previ ously mentioned

_____ 11. Grazed rangelands

_____

8 = DON’T KNOW 9 = REFUSED

<If Q2a or Q2b Eq 1 or 2, go to Q4 . If Q2a or Q2b Eq 3, go to Q14>

Agricultural Dust Questions

<Note: Show selected crop names from Q3b for Questions #4 through #12 with the exception of showing Q3b11. Only show Q3b11 for questions #14 through #19>

Q4. How many plantings of each crop type are normally co mpleted during a year? Let ’s start with (name of 1 st

crop). Typically, how m any plantings per year are m ade for (name of 1 st crop)? How about for (name of 2

nd

crop)? For (name of 3 rd

crop)? Read list of remaining selected crops.

a. Corn

_____ b. Wheat

_____ c. Sorghum

_____ d. Rice

_____ e. Other cereal crops

_____ f. Soybeans

_____ g. Sugarcane

_____ h. Hay or alfalfa

_____ i. Cotton

_____ j. Other crops not previously mentioned

_____

88 = DON’T KNOW 99 = REFUSED

CENRAP Telephone I n

t e rview P r oject #1 002 12/ 18/

2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 3Q5. In your county, are tilling passes typically made o n

each crop before planting and after harvesting or are harvesting and planting comp leted in one pass? Let’s start with (name of 1 st crop). Typically, when are tillin g passes made for (name of 1 st crop)? Is it before plant ing and after harvesting or is tilling complet ed in one pass? How about for (name of 2 nd

crop)? Read list of remaining selected crops.

1 = Yes, passes are made befor e planting and after harvesting 2 = Tilling pas ses are completed at the same time 88 = DON’T KNOW 99 = REFUSED

Passes

a. Corn

_____ b. Wheat

_____ c. Sorghum

_____ d. Rice

_____ e. Other cereal crops

_____ f. Soybeans

_____ g. Sugarcane

_____ h. Hay or alfalfa

_____ i. Cotton

_____ j. Other crops not previously mentioned

_____ <If Q5a through Q5j Eq 2, 8, or 9, go to Q6. If Q5a through Q5j Eq 1, go to Q7>

Q6. How many tilling passe s are typically made on each cr op in your county? Let’s start with (name of 1 st crop). Typically, how many tilli ng passes are made for (name of 1 st crop)? How about for (name of 2 nd

crop)? Read list of remaining selec t ed crops.

Passes

a. Corn

_____ b. Wheat

_____ c. Sorghum

_____ d. Rice

_____ e. Other cereal crops

_____ f. Soybeans

_____ g. Sugarcane

_____ h. Hay or alfalfa

_____ i. Cotton

_____ j. Other crops not previously mentioned

_____

88 = DON’T KNOW 99 = REFUSED

<Go to Q8>

CENRAP Telephone I n

t e rview P r oject #1 002 12/ 18/

2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 4Q7. How many tilling passe s are typically made on each crop before planting an d a fter harvesti ng in your county? Let’s start with (name of 1 st crop). Typically, how many passes are made for (name of 1 st crop) before planting? How about for (name of 2 nd

crop)? Read list of rema ining selec t ed crops.

Let’s now turn to harvesting. Typically , how many passes are made for (name of 1 st crop) after harvesting? For (name of 2 rd

crop)? Read list of remaining selected crops.

1. Planting 2. Harvesting

a. Corn

_____ _____ b. Wheat

_____ _____ c. Sorghum

_____ _____ d. Rice

_____ _____ e. Other cereal crops

_____ _____ f. Soybeans

_____ _____ g. Sugarcane

_____ _____ h. Hay or alfalfa

_____ _____ i. Cotton

_____ _____ j. Other crops not previous ly mentioned

_____ _____

88 = DON’T KNOW 99 = REFUSED

Q8. Do farmers use any special tilling pr actices such a no- till, low-till, ridge-till, or mulch-till farming in your county? Let’s start with (name of 1 st crop), are no-till, low-till, ridge-till, or mulch-till tilling practic e

s typically used for this crop?
What about for (name of 2 nd

crop)? Are no-till, lo w-till, ridge-till, or mulch-till pr actices typically u s

ed for this crop? Read list of remaining selected crops.

(Yes=1, No=2)

1. No-till 2. Low-till 3. Ridge-till 4. Mulch-till

a. Corn

_____ _____ _____ _____ b. Wheat

_____ _____ _____ _____ c. Sorghum

_____ _____ _____ _____ d. Rice

_____ _____ _____ _____ e. Other cereal crops

_____ _____ _____ _____ f. Soybeans

_____ _____ _____ _____ g. Sugarcane

_____ _____ _____ _____ h. Hay or alfalfa

_____ _____ _____ _____ i. Cotton

_____ _____ _____ _____ j. Other types of crop not previously mentioned _____

_____ _____ _____

8 = DON’T KNOW 9 = REFUSED

<If Q5a through Q5j Eq 2, 8, or 9, go to Q10. If Q5a through Q5j Eq 1, go to Q9>

CENRAP Telephone I n

t e rview P r oject #1 002 12/ 18/

2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 5Q9. For each crop, please tell me how many days before planting an d after harvesting does tillin g

typically occur in your county. Let ’s start with (name of 1 st crop). Typically, how many days before planting does tilling occur for (name of 1 st crop)? How about for (name of 2 nd

crop)? Read list of rema ining selec t ed crops.

Let’s now turn to harvesting. Typically, how many days after harvesting does tillin g

occur for (name of 1 st crop)? For (name of 2 rd

crop)? Read list of remaining selected crops.

1. Before Planting 2. After Harvesting

a. Corn

_____ _____ b. Wheat

_____ _____ c. Sorghum

_____ _____ d. Rice

_____ _____ e. Other cereal crops

_____ _____ f. Soybeans

_____ _____ g. Sugarcane

_____ _____ h. Hay or alfalfa

_____ _____ i. Cotton

_____ _____ j. Other crops not previous ly mentioned

_____ _____

888 = DON’T KNOW 999 = REFUSED

Q10. For each crop, please tell me whether tilling usu a lly occurs on weekdays, weekends, or both weekdays and weekends? (Programmer note: 1 data output column only; Weekdays=1, Weekends=2, Both=3)

a. Corn

_____ b. Wheat

_____ c. Sorghum

_____ d. Rice

_____ e. Other cereal crops

_____ f. Soybeans

_____ g. Sugarcane

_____ h. Hay or alfalfa

_____ i. Cotton

_____ j. Other crops not previously mentioned

_____

8 = DON’T KNOW 9 = REFUSED

CENRAP Telephone I n

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2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 6Q11. For each crop, please tell me whether tilling usu a lly occurs du ring daytime, nightime, or both daytime and nighttime hours? (Programmer not e: 1 data output column only; Daytime=1, Nightime=2, Both=3)

a. Corn

_____ b. Wheat

_____ c. Sorghum

_____ d. Rice

_____ e. Other cereal crops

_____ f. Soybeans

_____ g. Sugarcane

_____ h. Hay or alfalfa

_____ i. Cotton

_____ j. Other crops not previously mentioned

_____

8 = DON’T KNOW 9 = REFUSED

<If Q11a through Q11j Eq 3 go to Q12. If Q11a through Q11j Eq 1, 2, 8, or, 9 go to Q13>

Q12. For each crop, please tell me what percent of tilling occurs during day time a nd nighttime hours? Let’s start with (name of first cr op). What percent of tilling for this cr op occurs in the daytime and what percent occurs in the nighttime?

1. % Daytime 2. % Nighttime

a. Corn

_____ _____ b. Wheat

_____ _____ c. Sorghum

_____ _____ d. Rice

_____ _____ e. Other cereal crops

_____ _____ f. Soybeans

_____ _____ g. Sugarcane

_____ _____ h. Hay or alfalfa

_____ _____ i. Cotton

_____ _____ j. Other crops not previous ly mentioned

_____ _____

888 = DON’T KNOW 999 = REFUSED

<NOTE: For Q12a1 through Q12j2, daytime % and nigh ttime % must add to 100% or question must be reasked>

<If Q2a or Q2b Eq 2, go to Q13> <If Q2a or Q2b Eq 1, go to Q14>

CENRAP Telephone I n

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2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 7Q13. What agency should I contact concerning agricult u ral burning in your county? (Pr obe: Is that a state or county agenc y?) Can I get the telephone number as well?

a. _______________________________

______________________ ____________ b. _______________________________

______________________ ____________

888 = DON’T KNOW 999 = REFUSED

<Go to THANK YOU>

CENRAP Telephone I n

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2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 8Agricultural Burning Questions

<Note: Show selected crop names fr om Q3b for Questions #14 through #19>

Q14. For each crop, what percent of th e total acreage is typically burned each year in your county? Let’s start with (name of 1 st crop). What percent of (name of 1 st crop) is burned each year? Read list of remaining selected crops.

a. Corn

_____ % b. Wheat

_____ %

c. Sorghum

_____ % d. Rice

_____ %

e. Other cereal crops

_____ % f. Soybeans

_____ % g. Sugarcane

_____ % h. Hay or alfalfa

_____ % i. Cotton

_____ % j. Other crops not previously mentioned _____ % k. Grazed rangelands

_____ %

888 = DON’T KNOW 999 = REFUSED

Q15. For each crop, please tell me how many days bef ore planting and after harvesting does agricultural burning typically occur in your count y. Let’s start with (name of 1 st crop). Typically , how many days before planting does agricultural burning occur for (name of 1 st crop)? How about for (name of 2 nd

crop)? Read list of remaining selected crops.

Let’s now turn to harvesting. Typically, how many days after harvesting does agricultural burning occur for (name of 1 st crop)? For (name of 2 rd

crop)? Read list of remaining selected crops.

1. Before Planting 2. After Harvesting

a. Corn

_____ _____ b. Wheat

_____ _____ c. Sorghum

_____ _____ d. Rice

_____ _____ e. Other cereal crops

_____ _____ f. Soybeans

_____ _____ g. Sugarcane

_____ _____ h. Hay or alfalfa

_____ _____ i. Cotton

_____ _____ j. Other crops not previous ly mentioned

_____ _____ k. Grazed rangelands

_____ _____

888 = DON’T KNOW 999 = REFUSED

CENRAP Telephone I n

t e rview P r oject #1 002 12/ 18/

2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 9Q16. For each crop, please tell me whether agricultu ral burning us ually occurs during weekdays, weekends

, or both weekdays and weekends? (Programmer note: 1 data output column only; Weekdays=1, Weekends=2, Both=3)

a. Corn

_____ b. Wheat

_____ c. Sorghum

_____ d. Rice

_____ e. Other cereal crops

_____ f. Soybeans

_____ g. Sugarcane

_____ h. Hay or alfalfa

_____ i. Cotton

_____ j. Other crops not previously mentioned

_____ k. Grazed rangelands

_____

8 = DON’T KNOW 9 = REFUSED

Q17. For each crop, please tell me whether crop resi due is typically burned in your county during daytime, nightime, or both daytime and nighttime hours? (Progra mmer note: 1 data output co lumn only; Daytime=1, Nightime=2, Both=3)

a. Corn

_____ b. Wheat

_____ c. Sorghum

_____ d. Rice

_____ e. Other cereal crops

_____ f. Soybeans

_____ g. Sugarcane

_____ h. Hay or alfalfa

_____ i. Cotton

_____ j. Other crops not previously mentioned

_____ k. Grazed rangelands

_____

8 = DON’T KNOW 9 = REFUSED

<If Q17 Eq 3, go to Q18. If Q17 Eq 1, 2, 8, or 9, go to Q19>

CENRAP Telephone I n

t e rview P r oject #1 002 12/ 18/

2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 10Q18. For each crop, please tell me what percent of crop residue is burned during daytime and nighttime hours? Let’s start with (name of fi rst crop). What percent of crop residue burning for this crop occurs in the daytime and what percent occurs in the nighttime?

1. % Daytime 2. % Nighttime

a. Corn

_____ _____ b. Wheat

_____ _____ c. Sorghum

_____ _____ d. Rice

_____ _____ e. Other cereal crops

_____ _____ f. Soybeans

_____ _____ g. Sugarcane

_____ _____ h. Hay or alfalfa

_____ _____ i. Cotton

_____ _____ j. Other crops not previous ly mentioned

_____ _____ k. Grazed rangelands

_____ _____

888 = DON’T KNOW 999 = REFUSED

<NOTE: For Q18a1 through Q18k2, daytime % and nigh ttime % must add to 100% or question must be reasked>

Q19. For the following crops, please tell me whether headfires, backf ires, or both types of fires are used. Headfires are burning in the direction of the wind and backfires are burning in a direction oppos ite to the wind. (Programmer note: 1 data output column only; Headfir e=1, Backfire=2, Both=3)

a. Hay or alfalfa

_____ b. Soybeans

_____ c. Wheat

_____

8 = DON’T KNOW 9 = REFUSED

Q20. What agency or agencies regulate agricultural burning in your count y? (Probe: Is that a state or county agency?) Can I get the tel ephone number as well?

a. _______________________________

______________________ ____________ b. _______________________________

______________________ ____________

888 = DON’T KNOW 999 = REFUSED

<Go to THANK YOU>

THAN K YOU : Those are all the questions. Thank you for your time. Goodbye.

CENRAP Telephone I n

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2003 Populat ion Research Syst em

s, LLC, San Francisco, CA 11

## APPENDIX B

VEGETATION CROSS-WALKS

B-1

## This page is intentionally blank.List of Crops included in the BELD Database for each CENRAP State

STATE ST_NAME INDEX GENUS Unique Crops 05 ARKANSAS 24 CORN ALFALFA 05 ARKANSAS 25 COTTON BARLEY 05 ARKANSAS 27 HAY CORN 05 ARKANSAS 28 MISC_CROP COTTON 05 ARKANSAS 29 OATS HAY 05 ARKANSAS 31 PEANUTS MISC_CROP 05 ARKANSAS 32 POTATOES OATS 05 ARKANSAS 33 RICE PEANUTS 05 ARKANSAS 35 SORGHUM POTATOES 05 ARKANSAS 36 SOYBEANS RICE 05 ARKANSAS 38 WHEAT RYE 19 IOWA 23 BARLEY SORGHUM 19 IOWA 24 CORN SOYBEANS 19 IOWA 27 HAY TOBACCO 19 IOWA 28 MISC_CROP WHEAT 19 IOWA 29 OATS 19 IOWA 32 POTATOES 19 IOWA 34 RYE 19 IOWA 35 SORGHUM 19 IOWA 36 SOYBEANS 19 IOWA 38 WHEAT 20 KANSAS 22 ALFALFA 20 KANSAS 23 BARLEY 20 KANSAS 24 CORN 20 KANSAS 27 HAY 20 KANSAS 28 MISC_CROP 20 KANSAS 29 OATS 20 KANSAS 34 RYE 20 KANSAS 35 SORGHUM 20 KANSAS 36 SOYBEANS 20 KANSAS 37 TOBACCO 20 KANSAS 38 WHEAT 22 LOUISIANA 24 CORN 22 LOUISIANA 25 COTTON 22 LOUISIANA 27 HAY 22 LOUISIANA 28 MISC_CROP 22 LOUISIANA 29 OATS 22 LOUISIANA 31 PEANUTS 22 LOUISIANA 32 POTATOES 22 LOUISIANA 33 RICE 22 LOUISIANA 35 SORGHUM 22 LOUISIANA 36 SOYBEANS 22 LOUISIANA 38 WHEAT 27 MINNESOTA 22 ALFALFA 27 MINNESOTA 23 BARLEY 27 MINNESOTA 24 CORN 27 MINNESOTA 27 HAY 27 MINNESOTA 28 MISC_CROP 27 MINNESOTA 29 OATS 27 MINNESOTA 32 POTATOES 27 MINNESOTA 33 RICE 27 MINNESOTA 34 RYE 27 MINNESOTA 36 SOYBEANS 27 MINNESOTA 38 WHEATList of Crops included in the BELD Database for each CENRAP State

STATE ST_NAME INDEX GENUS Unique Crops 29 MISSOURI 23 BARLEY 29 MISSOURI 24 CORN 29 MISSOURI 25 COTTON 29 MISSOURI 27 HAY 29 MISSOURI 28 MISC_CROP 29 MISSOURI 29 OATS 29 MISSOURI 31 PEANUTS 29 MISSOURI 32 POTATOES 29 MISSOURI 33 RICE 29 MISSOURI 34 RYE 29 MISSOURI 35 SORGHUM 29 MISSOURI 36 SOYBEANS 29 MISSOURI 37 TOBACCO 29 MISSOURI 38 WHEAT 31 NEBRASKA 22 ALFALFA 31 NEBRASKA 23 BARLEY 31 NEBRASKA 24 CORN 31 NEBRASKA 27 HAY 31 NEBRASKA 28 MISC_CROP 31 NEBRASKA 29 OATS 31 NEBRASKA 32 POTATOES 31 NEBRASKA 34 RYE 31 NEBRASKA 35 SORGHUM 31 NEBRASKA 36 SOYBEANS 31 NEBRASKA 38 WHEAT 40 OKLAHOMA 22 ALFALFA 40 OKLAHOMA 23 BARLEY 40 OKLAHOMA 24 CORN 40 OKLAHOMA 25 COTTON 40 OKLAHOMA 27 HAY 40 OKLAHOMA 28 MISC_CROP 40 OKLAHOMA 29 OATS 40 OKLAHOMA 31 PEANUTS 40 OKLAHOMA 32 POTATOES 40 OKLAHOMA 34 RYE 40 OKLAHOMA 35 SORGHUM 40 OKLAHOMA 36 SOYBEANS 40 OKLAHOMA 38 WHEAT 48 TEXAS 22 ALFALFA 48 TEXAS 23 BARLEY 48 TEXAS 24 CORN 48 TEXAS 25 COTTON 48 TEXAS 27 HAY 48 TEXAS 28 MISC_CROP 48 TEXAS 29 OATS 48 TEXAS 31 PEANUTS 48 TEXAS 32 POTATOES 48 TEXAS 33 RICE 48 TEXAS 34 RYE 48 TEXAS 35 SORGHUM 48 TEXAS 36 SOYBEANS 48 TEXAS 38 WHEATCross-walk of BELD Vegetation Types to FOFEM Forest Classification TypesBELD Genus Species FOFEM ID FOFEM Cover Description26 GRASSLAND ---- SRM 215 Valley Grassland (Annual Grassland)30 RANGELAND ---- SRM 215 Valley Grassland (Annual Grassland)39 ACACIA ---- SAF 70 Longleaf Pine - rough age 340 AILANTHUS ALTISSIMA SAF 70 Longleaf Pine - rough age 341 ALNUS RUBRA NVCS 1371 Alnus rubra Forest42 MALUS ---- SAF 70 Longleaf Pine - rough age 343 FRAXINUS AMERICANA NVCS 2030 Acer saccharinum - Fraxinus americana - Tilia americana Forest44 TILIA AMERICANA NVCS 1360 Acer saccharum - Tilia americana - (Quercus rubra) Forest45 FAGUS GRANDIFOLIA NVCS 1420 Fagus grandifolia - Acer saccharum - (Liriodendron tulipifera) Forest46 BETULA NIGRA SAF 70 Longleaf Pine - rough age 347 BUMELIA LANUGINOSA SAF 70 Longleaf Pine - rough age 348 MELALEUCA QUINQUENERVIA SAF 70 Longleaf Pine - rough age 349 UMBELLULARIA CALIFORNICA SAF 70 Longleaf Pine - rough age 351 CASTANEA DENTATA SAF 70 Longleaf Pine - rough age 352 CATALPA ---- SAF 70 Longleaf Pine - rough age 353 CHAMAECYPARIS NOOTKATENSIS NVCS 660 Chamaecyparis nootkatensis Forest54 THUJA OCCIDENTALIS SAF 83 Longleaf Pine - Slash Pine - rough age 355 AESCULUS OCTANDRA SAF 70 Longleaf Pine - rough age 356 MELIA AZEDARACH SAF 70 Longleaf Pine - rough age 358 TAXODIUM MUCRONATUM SAF 70 Longleaf Pine - rough age 359 CORNUS FLORIDA SAF 70 Longleaf Pine - rough age 360 PSEUSOTSUGA MENZIESII SAF 70 Longleaf Pine - rough age 361 OSTRYA VIRGINIANA NVCS 5370 Juniperus virginiana - (Fraxinus americana, ostrya virginiana) Woodland63 ULMUS AMERICANA NVCS 2310 Quercus texana,Celtis laevigata,Ulmus(americana,crassifolia),Gleditsia tricanthos)64 EUCALYPTUS ---- SAF 70 Longleaf Pine - rough age 365 ABIES BALSAMEA SAF 51 White Pine - Chestnut Oak66 ABIES MAGNIFICA NVCS 631 Abies magnifica Forest67 ABIES LASIOCARPA NVCS 881 Abies lasiocarpa Forest69 ABIES GRANDIS NVCS 621 Abies grandis Forest70 ABIES PROCERA SAF 70 Longleaf Pine - rough age 371 ABIES AMABILIS NVCS 570 Abies amabilis - Abies concolor Forest73 ABIES MAGNIFICA SHASTENSIS SAF 70 Longleaf Pine - rough age 374 ABIES ---- SAF 70 Longleaf Pine - rough age 375 ABIES LASIOCARPA ARIZONICA SAF 70 Longleaf Pine - rough age 376 ABIES CONCOLOR NVCS 570 Abies amabilis - Abies concolor Forest77 GLEDITSIA TRIACANTHOS SAF 70 Longleaf Pine - rough age 378 CELTIS OCCIDENTALIS SRM 604 Bluestem - Grama Prairie79 CRATAEGUS ---- SAF 70 Longleaf Pine - rough age 3Cross-walk of BELD Vegetation Types to FOFEM Forest Classification TypesBELD Genus Species FOFEM ID FOFEM Cover Description80 TSUGA ---- SAF 70 Longleaf Pine - rough age 381 CARYA OVATA NVCS 1380 Carya (glabra, ovata) - Fraxinus americana - Quercus (alba, rubra) Forest82 ILEX OPACO SAF 70 Longleaf Pine - rough age 383 CARPINUS CAROLINIANA SAF 70 Longleaf Pine - rough age 384 CALOCEDRUS DECURRENS NVCS 650 Calocedrus decurrens - Pseudotsuga menziesii Forest85 JUNIPERUS VIRGINIANA NVCS 5370 Juniperus virginiana - (Fraxinus americana, ostrya virginiana) Woodland86 GYMNOCLADUS DIOICUS SAF 70 Longleaf Pine - rough age 387 LARIX LARICINA SAF 98 Pond Pine88 GORDONIA LASIANTHUS SAF 70 Longleaf Pine - rough age 389 ARBUTUS MENZIESII NVCS 790 Pseudotsuga menziesii - Arbutus menziesii Forest90 MAGNOLIA GRANDIFLORA SRM 910 Hairgrass91 CERCOCARPUS LEDIFOLIUS NVCS 6461 Cercocarpus ledifolius Shrubland92 ACER GRANDIDENTATUM NVCS 1301 Acer grandidentatum Lowland Forest93 ACER MACROPHYLLUM NVCS 3350 Pseudotsuga menziesii - Acer macrophyllum Forest94 ACER NIGRUM SAF 27 Sugar Maple95 ACER NEGUNDO SRM 404 Threetip Sagebrush96 ACER BARBATUM SAF 70 Longleaf Pine - rough age 397 ACER SPICATUM SAF 70 Longleaf Pine - rough age 398 ACER PLATANOIDES SAF 70 Longleaf Pine - rough age 399 ACER RUBRUM NVCS 5270 Acer rubrum Saturated Woodland100 ACER GLABRUM SAF 70 Longleaf Pine - rough age 3101 ACER SACCHARINUM NVCS 2030 Acer saccharinum - Fraxinus americana - Tilia americana Forest103 ACER PENSYLVANICUM SAF 70 Longleaf Pine - rough age 3104 ACER SACCHARUM NVCS 1320 Acer saccharum - Betula alleghaniensis - (Fagus grandifolia) Forest105 PROSOPIS ---- SAF 70 Longleaf Pine - rough age 3106 MISCELLANEOUS HD SPP SAF 70 Longleaf Pine - rough age 3108 SORBUS AMERICANA SAF 70 Longleaf Pine - rough age 3109 MORUS RUBRA SAF 70 Longleaf Pine - rough age 3110 NYSSA SYLVATICA SAF 70 Longleaf Pine - rough age 3111 QUERCUS ARIZONICA SAF 70 Longleaf Pine - rough age 3112 QUERCUS ILICIFOLIA NVCS 7200 Quercus ilicifolia Shrubland113 QUERCUS VELUTINA NVCS 3320 Pinus virginiana - Quercus (alba, stellata, falcata, velutina) Forest114 QUERCUS MARILANDICA NVCS 3000 Juniperus virginiana - Quercus (stellata, velutina, marilandica) Forest115 QUERCUS DOUGLASII SAF 70 Longleaf Pine - rough age 3116 QUERCUS INCANA NVCS 5320 Quercus hemisphaerica - Quercus margarettiae - Quercus incana Woodland117 QUERCUS MACROCARPA NVCS 11951 Quercus macrocarpa - (Quercus alba) Wooded Herbland118 QUERCUS KELLOGGII NVCS 5130 Quercus kelloggii Temporarily Flooded Woodland119 QUERCUS AGRIFOLIA SAF 70 Longleaf Pine - rough age 3Cross-walk of BELD Vegetation Types to FOFEM Forest Classification TypesBELD Genus Species FOFEM ID FOFEM Cover Description120 QUERCUS LOBATA NVCS 4940 Quercus lobata Woodland121 QUERCUS CHRYSOLEPIS SAF 59 Yellow Poplar - White Oak - Northern Red Oak122 QUERCUS PRINUS NVCS 3330 Pinus virginiana - Quercus (coccinea, prinus) Forest123 QUERCUS MUEHLENBERGII NVCS 2990 Juniperus virginiana - Quercus (muehlenbergii, stellata) Forest124 QUERCUS STELLATA NVCS 2990 Juniperus virginiana - Quercus (muehlenbergii, stellata) Forest125 QUERCUS DURANDII SAF 70 Longleaf Pine - rough age 3126 QUERCUS EMORYI SAF 70 Longleaf Pine - rough age 3127 QUERCUS ENGELMANNII SAF 70 Longleaf Pine - rough age 3128 QUERCUS ---- SAF 70 Longleaf Pine - rough age 3129 QUERCUS GAMBELII NVCS 7181 Quercus gambelii Shrubland130 QUERCUS WISLIZENNI SAF 70 Longleaf Pine - rough age 3131 QUERCUS LAURIFOLIA NVCS 3460 Pinus taeda - Quercus (phellos, nigra, laurifolia)132 QUERCUS VIRGINIANA SAF 22 White Pine - Hemlock133 QUERCUS OBLONGIFOLIA SAF 70 Longleaf Pine - rough age 3134 QUERCUS ELLIPSOIDALIS NVCS 11960 Quercus velutina - (Quercus ellipsoidalis) Wooded Herbland135 QUERCUS RUBRA NVCS 1360 Acer saccharum - Tilia americana - (Quercus rubra) Forest136 QUERCUS NUTTALLII SAF 70 Longleaf Pine - rough age 3137 QUERCUS GARRYANA NVCS 5510 Pseudotsuga menziesii - Quercus garryana Woodland138 QUERCUS LYRATA NVCS 2550 Quercus lyrata - (Carya aquatica) Seasonally Flooded Forest139 QUERCUS PALUSTRIS NVCS 2560 Quercus palustris - (Quercus bicolor) Seasonally Flooded Forest140 QUERCUS STELLATA NVCS 1570 Quercus alba - Quercus (falcata, stellata) Forest141 QUERCUS COCCINEA NVCS 1590 Quercus coccinea - Quercus falcata Forest142 QUERCUS ---- SAF 70 Longleaf Pine - rough age 3143 QUERCUS IMBRICARIA SAF 70 Longleaf Pine - rough age 3144 QUERCUS SHUMARDII NVCS 3450 Pinus taeda - Quercus (pagoda, michauxii, shumardii)145 QUERCUS HYPOLEUCOIDES SAF 70 Longleaf Pine - rough age 3146 QUERCUS FALCATA NVCS 3280 Pinus taeda - Quercus (alba, falcata, stellata) Forest147 QUERCUS ---- SAF 70 Longleaf Pine - rough age 3148 QUERCUS MICHAUXII NVCS 2760 Quercus michauxii - Quercus pagoda Saturated Forest149 QUERCUS FALCATA NVCS 3200 Pinus palustris,Pinus(echinata,taeda),Quercus(incana,margarettiae,falcata,laevis150 QUERCUS BICOLOR NVCS 2260 Quercus macrocarpa - Quercus bicolor - (Carya laciniosa)151 QUERCUS LAEVIS NVCS 1610 Quercus laevis Forest152 QUERCUS NIGRA NVCS 3460 Pinus taeda - Quercus (phellos, nigra, laurifolia)153 QUERCUS ALBA NVCS 1540 Quercus alba - (Quercus nigra) Forest154 QUERCUS PHELLOS NVCS 2300 Quercus phellos - Ulmus crassifolia155 MACLURA POMIFERA SAF 70 Longleaf Pine - rough age 3156 PAULOWNIA TOMENTOSA SAF 70 Longleaf Pine - rough age 3157 ASIMINA TRILOBA SAF 70 Longleaf Pine - rough age 3Cross-walk of BELD Vegetation Types to FOFEM Forest Classification TypesBELD Genus Species FOFEM ID FOFEM Cover Description158 DIASPYROS VIRGINIANA SAF 70 Longleaf Pine - rough age 3162 PINUS MURICATA SAF 70 Longleaf Pine - rough age 3164 PINUS ARISTATA NVCS 3931 Pinus aristata Woodland165 PINUS LEIOPHYLLA SAF 70 Longleaf Pine - rough age 3166 PINUS COULTERI SAF 70 Longleaf Pine - rough age 3167 PINUS SABINIANA SAF 70 Longleaf Pine - rough age 3168 PINUS STROBUS NVCS 530 Pinus strobus Forest169 PINUS BALFOURIANA SAF 70 Longleaf Pine - rough age 3170 PINUS BANKSIANA NVCS 390 Pinus banksiana Forest171 PINUS JEFFERYI SAF 70 Longleaf Pine - rough age 3172 PINUS ATTENUATA NVCS 3940 Pinus attenuata Woodland173 PINUS FLEXILIS NVCS 4051 Pinus flexilis Woodland174 PINUS TAEDA NVCS 550 Pinus taeda Forest175 PINUS CONTORTA NVCS 411 Pinus contorta Forest176 PINUS PALUSTRIS NVCS 3200 Pinus palustris,Pinus(echinata,taeda),Quercus(incana,margarettiae,falcata,laevis177 PINUS RADIATA SAF 84 Slash Pine - rough age 3178 PINUS MONOPHYLLA NVCS 4091 Pinus monophylla Woodland179 PINUS DISCOLOR NVCS 4011 Pinus discolor Woodland180 PINUS EDULIS NVCS 431 Pinus edulis Forest181 PINUS RIGIDA NVCS 4230 Pinus virginiana - Pinus rigida Woodland182 PINUS SEROTINA NVCS 4620 Pinus palustris - Pinus serotina Saturated Woodland183 PINUS PONDEROSA NVCS 481 Pinus ponderosa - Pseudotsuga menziesii Forest184 PINUS RESINOSA NVCS 510 Pinus resinosa Forest185 PINUS CLAUSA NVCS 400 Pinus clausa Forest186 PINUS SYLVESTRIS SAF 70 Longleaf Pine - rough age 3187 PINUS ECHINATA NVCS 420 Pinus echinata Forest188 PINUS ELLIOTTII NVCS 250 Pinus elliottii Tropical Forest189 PINUS GLABRA SAF 70 Longleaf Pine - rough age 3190 PINUS LAMBERTIANA SAF 70 Longleaf Pine - rough age 3191 PINUS STROBIFORMIS SAF 70 Longleaf Pine - rough age 3192 PINUS PUNGENS SAF 70 Longleaf Pine - rough age 3193 PINUS VIRGINIANA NVCS 3310 Pinus virginiana - Liquidambar styraciflua - Liriodendron tulipifera Forest195 PINUS ALBICAULIS NVCS 381 Pinus albicaulis Forest196 PINUS MONTICOLA NVCS 451 Pinus monticola Forest198 POPULUS GRANDIDENTATA SAF 70 Longleaf Pine - rough age 3199 PRUNUS ---- SAF 70 Longleaf Pine - rough age 3200 CERCIS CANADENSIS SAF 70 Longleaf Pine - rough age 3201 ROBINIA PSEUDOACACIA SAF 70 Longleaf Pine - rough age 3Cross-walk of BELD Vegetation Types to FOFEM Forest Classification TypesBELD Genus Species FOFEM ID FOFEM Cover Description202 SASSAFRAS ALBIDUM SAF 70 Longleaf Pine - rough age 3203 SEQUOIA SEMPERVIRENS NVCS 310 Sequoia sempervirens - Tsuga heterophylla Forest204 AMELANCHIER ARBOREA SAF 70 Longleaf Pine - rough age 3205 HALESIA ---- SAF 70 Longleaf Pine - rough age 3206 COTINUS OBOVATUS SAF 70 Longleaf Pine - rough age 3207 SAPINDUS DRUMMONDII SAF 70 Longleaf Pine - rough age 3208 OXYDENDRUM ARBORETUM SAF 70 Longleaf Pine - rough age 3209 VACCINIUM ARBOREUM SAF 70 Longleaf Pine - rough age 3210 PICEA MARIANNA SAF 70 Longleaf Pine - rough age 3211 PICEA PUNGENS NVCS 731 Picea pungens Forest212 PICEA BREWERIANA SAF 70 Longleaf Pine - rough age 3213 PICEA ENGELMANNII NVCS 691 Picea engelmannii Forest214 PICEA ABIES SAF 70 Longleaf Pine - rough age 3215 PICEA RUBENS SAF 84 Slash Pine - rough age 3216 PICEA SITCHENSIS NVCS 760 Picea sitchensis Forest217 PICEA ---- SAF 70 Longleaf Pine - rough age 3218 PICEA GLAUCA NVCS 711 Picea glauca Forest219 LIQUIDAMBAR STYRACIFLUA NVCS 3270 Pinus taeda - (Liquidambar styraciflua - Liriodendron tulipifera) Forest220 PLATANUS OCCIDENTALIS NVCS 3440 Pinus taeda - Platanus occidentalis - Acer negundo Temporarily Flooded Forest221 SAPIUM SEBIFERUM SAF 70 Longleaf Pine - rough age 3222 TAMARIX CHINENSIS SAF 70 Longleaf Pine - rough age 3223 LITHOCARPUS DENSIFLORUS SAF 70 Longleaf Pine - rough age 3224 TORREYA CALIFORNICA SAF 70 Longleaf Pine - rough age 3225 ALEURITES FORDII SAF 70 Longleaf Pine - rough age 3226 UNKNOWN ---- SAF 70 Longleaf Pine - rough age 3227 JUGLANS NIGRA SAF 70 Longleaf Pine - rough age 3228 PLANERA AQUATICA SRM 723 Sea Oats229 SALIX NIGRA SAF 104 Sweetbay - Swamp Tupelo - Redbay230 LIRIODENDRON TULIPIFERA NVCS 1420 Tamarack231 CLADRASTIS LUTEA SAF 70 Longleaf Pine - rough age 3

## APPENDIX C

MAPS OF KANS AS AGRICULTURAL SUBREGIONS

C-1CHEYENNE

SHERMAN

WALLACE

GREELY

HAMILTON

STANTON

MORTON RAWLINS

THOMAS

LOGAN

WICHITA

KEARNY

GRANT DECATUR

STEVENS SHERIDAN

GOVE

SCOTT

GRAY

HASKELL FINNEY

SEWARD NORTON

GRAHAM

TREGO

NESS

HODGEMAN LANE

FORD

MEADE PHILLIPS

ROOKS

ELLIS

RUSH

PAWNEE

EDWARDS

KIOWA

COMANCHE CLARK SMITH

OSBORNE

RUSSELL

STAFFORD BARTON

PRATT

BARBER JEWELL

MITCHELL

LINCOLN

ELLSWORTH

RICE

RENO

KINGMAN

HARPER REPUBLIC

CLOUD

OTTAWA

SALINE

MCPHERSON

HARVEY

SEDGWICK

SUMNER

WASHINGTON

CL AY

DICKINSON

MARION

BUTLER

MARSHALL

POTTAWATOMIE RILEY

GEARY

CHASE MORRIS

LYON

GREENWOOD

ELK

CHAU TAUQUA NEMAHA

JACKSON

SHAWNEE

OSAGE

COFFEY

WILSON WOODSON

MONTGOMERY BROWN

DONIPHAN

ATCHISON

JEFFERSON

DOUGLAS

FRANKLIN WYANDOTTE

JOHNSON

MIAMI

LINN

BOURBON ANDERSON

ALLEN

NEOSHO

LABETTE CRAWFORD

CHEROKEE 197
WABAUNSEE

40,800ac. 0ac. 59,500ac. 0 ac. 35,100ac. 1,755ac. 102,400ac. 20,480ac. 368,400ac. 184,200ac. 5,500ac. 1,650ac. 41,000ac.

14,350ac. 70,500ac. 21,150ac. 34,100ac. 3,410ac. 43,600ac. 13,080ac. 5,600ac. 0ac. 11,200ac. 1,120ac. 35,400ac. 8,850ac. 25 % burned 10 % burned 58,000ac. 5,800ac.
10 % burned 186,200ac. 1,862ac.
1 % burned 47,100ac. 0ac. 0 % burned 85,000ac. 8,500ac. 10 % burned 69,400ac. 0ac. 0 % burned 83,900ac.

12,585ac.
15 % burned 87,700ac. 1,75 4 ac. 2 % burned

71,800ac. 3,590ac. 5 % burned 127,600ac. 63,800ac.
50 % burned 32,200ac. 322ac.
1 % burned 10,300ac. 2,575ac. 25 % burned 122,200ac. 30,550ac. 25 % burned 200,900ac. 10,045ac. 5 % burned 142,800ac. 14,280ac. 10 % burned 152,700ac. 7,635ac.
5 % burned 119,300ac. 1,19 3 ac. 1 % burned 150,100ac. 1,50 1 ac. 1 % burned 107,200ac. 1,07 2 ac. 1 % burned 117,200ac. 1,172ac. 1 % burned 104,900ac. 1,049ac.
1 % burned 92,900ac. 0ac. 0 % burned

91,800ac. 9,180ac. 10 % burned 133,500ac. 66,750ac. 50 % burned 9,800ac. 980ac. 10 % burned 200ac.

0 ac. 0 % burned 4,900ac. 0ac. 0 % burned

4,200ac. 0ac. 0 % burned

4,500ac. 0ac. 0 % burned 13,900ac. 2,780ac. 20 % burned 24,200ac. 0ac.
0 % burned 7,100ac. 142ac. 2 % burned 6,900ac.

0ac. 0 % burned 900ac. 0ac. 0 % burned 6,600ac. 0ac. 0 % burned 61,300ac. 15,325ac. 25 % burned 79,800ac. 3,990ac.
5 % burned 86,400ac. 34,560ac. 40 % burned

113,300ac. 7,931ac. 7 % burned 98,600ac. 19,720ac. 20 % burned 160,500ac. 32,100ac. 20 % burned 111,500ac. 11,150ac. 10 % burned 110,300ac.
0ac.

0 % burned 76,500ac. 0 ac. 0 % burning 141,900ac. 0 ac. 0 % burned

0 % burned 0 % burned 5 % burned 20 % burned 50 % burned 30 % burned 0 % burned 30 % burned

35 % burned 10 % burned

30 % burned 

# KANSAS

Acre s o f Whea t Burned/ Year

MORTON
40,800ac. 0ac. 0 % burned Count y Name

T ota l Whea t i n Count y (acres)

T ota l Whea t i n Count y Burne d (acres)

% Burned 118,900ac. 0ac. 0 % burned

COWLEY

22,625ac. 90,500ac.

25 % burned

*Not e - Dat a supplie d b y Sonom a T echnolog y , Inc. Apri l 2004 Regio n #1 Regio n #2 Regio n #3

*Not e - Whea t region s verifie d b y Bil l Hargrov e at Kansa s Stat e Universit y Agricultur e Extension OfficeCHEYENNE

SHERMAN

WALLACE

GREELY

HAMILTON

STANTON

MORTON RAWLINS

THOMAS

LOGAN

WICHITA

KEARNY

GRANT DECATUR

STEVENS SHERIDAN

GOVE

SCOTT

GRAY

HASKELL FINNEY

SEWARD NORTON

GRAHAM

TREGO

NESS

HODGEMAN LANE

FORD

MEADE PHILLIPS

ROOKS

ELLIS

RUSH

EDWARDS

KIOWA

COMANCHE CLARK SMITH

OSBORNE

RUSSELL

STAFFORD BARTON

PRATT

BARBER JEWELL

MITCHELL

LINCOLN

ELLSWORTH

RICE

RENO

KINGMAN

HARPER REPUBLIC

CLOUD

OTTAWA

SALINE

MCPHERSON

HARVEY

SEDGWICK

SUMNER

WASHINGTON

CL AY

DICKINSON

MARION

BUTLER

COWLEY MARSHALL

POTTAWATOMIE RILEY

GEARY

CHASE MORRIS

LYON

GREENWOOD

ELK

CHAU TAUQUA NEMAHA

JACKSON

SHAWNEE

OSAGE

COFFEY

WILSON WOODSON

MONTGOMERY BROWN

DONIPHAN

ATCHISON

JEFFERSON

DOUGLAS

FRANKLIN WYANDOTTE

JOHNSON

MIAMI

LINN

BOURBON ANDERSON

ALLEN

NEOSHO

LABETTE CRAWFORD

CHEROKEE 197
WABAUNSEE

147,63 8 ac. 0 ac. 193,20 9 ac. 0 ac. 325,77 3 ac. 48,86 6 ac. 393,49 1 ac. 39,34 9 ac. 88,42 7 ac. 8,84 3 ac. 340,54 3 ac. 255,40 7 ac. 302,34 6 ac. 136,05 6 ac. 116,46 7 ac. 34,94 0 ac. 35,47 0 ac. 7,09 4 ac. 96,27 0 ac. 9,62 7 ac. 102,49 9 ac. 76,87 4 ac. 238,72 1 ac. 119,36 1 ac. 140,67 5 ac. 105,50 6 ac. 69,35 0 ac. 52,01 3 ac. 75 % burned 75 % burned 413,02 4 ac. 330,41 9 ac. 80 % burned 100,81 7 ac. 20,16 3 ac. 20 % burned 151,82 5 ac. 0 ac. 0 % burned 74,95 0 ac. 0 ac. 0 % burned 1 15,93 0 ac. 0 ac. 0 % burned 137,41 5 ac. 0 ac. 0 % burned 133,23 6 ac. 0 ac. 0 % burned

66,90 6 ac. 13,38 1 ac. 20 % burned 73,53 5 ac. 0 ac. 0 % burned 93,53 5 ac. 18,70 7 ac. 20 % burned 306,37 9 ac. 245,10 3 ac. 80 % burned 174,44 9 ac. 104,66 9 ac. 60 % burned 91,48 6 ac. 91 5 ac. 1 % burned 98,40 1 ac. 19,68 0 ac. 20 % burned 103,73 3 ac. 0 ac. 0 % burned 74,92 0 ac. 0 ac. 0 % burned 198,23 6 ac. 0 ac. 0 % burned 109,70 5 ac. 0 ac. 0 % burned 260,63 7 ac. 2,60 6 ac. 1 % burned 180,56 8 ac. 1,80 6 ac. 1 % burned 151,20 3 ac.

3,02 4 ac. 2 % burned

130,17 9 ac.

39,05 4 ac. 30 % burned 105,87 9 ac. 21,17 6 ac. 20 % burned 67,65 7 ac. 13,53 1 ac. 20 % burned 3,31 1 ac.

0 ac. 0 % burned 46,45 5 ac. 4,64 6 ac. 10 % burned

59,60 0 ac. 2,98 0 ac. 5 % burned

49,55 9 ac. 14,86 8 ac. 30 % burned 68,68 4 ac. 51,51 3 ac. 75 % burned 105,31 6 ac. 31,59 5 ac.
30 % burned 101,33 7 ac. 25,33 4 ac. 25 % burned 36,34 8 ac.

1,81 7 ac. 5 % burned 15,25 0 ac. 30 5 ac. 2 % burned 33,49 3 ac. 67 0 ac. 2 % burned 115,43 5 ac. 28,85 9 ac. 25 % burned 153,95 8 ac. 76,97 9 ac. 50 % burned 84,78 5 ac. 33,91 4 ac. 40 % burned

143,02 7 ac. 28,60 5 ac. 20 % burned 162,57 6 ac. 8,12 9 ac. 5 % burned 87,26 0 ac. 4,36 3 ac. 5 % burned 182,03 3 ac. 0 ac. 0 % burned 128,56 7 ac. 12,85 7 ac.
10 % burned 170,99 5 ac. 0 ac. 0 % burning 218,24 7 ac 0 ac. 0 % burned

0 % burned 0 % burned 15 % burned 10 % burned 10 % burned 75 % burned
45 % burned 50 % burned 75 % burned

30 % burned 10 % burned

20 % burned 

# KANSAS

Acre s o f Rangelan d Burned/ Year

MORTON

147,63 8 ac. 0 ac. 0 % burned Count y Name

T ota l Rangelan d i n Count y (acres)

T ota l Rangelan d i n Count y Burne d (acres)

% Burned Regio n #1 Regio n #2 Regio n #3 Regio n #4

*Not e - Dat a supplie d b y Sonom a T echnolog y , Inc. Apri l 2004 PAWNEE 80,59 7 ac. 24,17 9 ac. 30 % burned

## APPENDIX B

TABULATION OF EMIS SIONS ESTI MATES FOR THE CENR AP REGION

B-1

## This page is intentionally blank.Table B-1. Annual em issions by state and source category.
Page 1 of 3

Acres Em issions (to n s /y ear)

S t a t e B u rn Ty p e B u r n e d PM 10 P M 2.5 C O N O
x S O 2 N H 3 V O C

Ar ka n s a s P r e s c r i b e d B u r n i n g 24 4, 1 4 6 2 8 ,1 3 0 2 3 ,8 3 8 30 2, 2 1 9 1, 96 1 1, 57 7 2

, 91 0 1 7 ,4 4 4
R a ngel a nd B u r n i n g 3, 06 1 6 2 5 2 30 7 4 4 1 5 7 2 9
C r o p l a n d B u r n i n g 65 2, 2 4 6 1 0 ,7 0 9 1 0 ,1 7 5 74 ,2 2 3 3, 64 8 62 2 2

, 09 4 6, 22 5 Whe a t 35 4, 2 09 5, 96 8 5, 69 1 40 ,1 1 6 1, 51 4 20 2 1, 07 7 2, 79 8
H

a

y/

Al

f

a l

f

a 8, 05 0 7 3 7 0 59 9 1 8 2 1 3 4 0
S

u g

a rc a n e 0 0 0 0 0 0 0 0
C o r

n 0 0 0 0 0 0 0 0
S

o ybe a ns 67 ,3 9 8 2, 56 4 2, 44 5 14 ,3 4 2 37 9 51 27 0 1, 81 8
Ri ce 22 2, 5 89 2, 10 4 1, 97 0 19 ,1 6 5 1, 73 6 36 7 73 5 1, 56 9
Ot

he r 0 0 0 0 0 0 0 0

Tot a l 89 9, 4 5 3 3 8 ,9 0 1 3 4 ,0 6 5 37 6, 7 4 9 5, 65 3 2, 21 4 5

, 01 1 2 3 ,6 9 8

Io w a Presc r i b e d B u r n i n g 21 ,4 4 9 4, 07 2 3, 45 7 4 4 ,5 4 2 1 6 6 1 9 5 25 7 2, 54 7
R a ngel a nd B u r n i n g 0 0 0 0 0 0 0 0
C r o p l a n d B u r n i n g 2, 24 7 4 4 4 2 14 5 5 1 4 2 0 Whe a t 0 0 0 0 0 0 0 0
H

a

y/

Al

f

a l

f

a 1, 66 0 2 9 2 7 8 1 3 0 2 1 3
S

u g

a rc a n e 0 0 0 0 0 0 0
C o r

n 0 0 0 0 0 0 0
S

o ybe a ns 0 0 0 0 0 0 0
Ri ce 0 0 0 0 0 0 0
Ot

he r 58 7 1 5 1 4 6 4 2 0 2 7

Tot a l 23 ,6 9 6 4, 11 6 3, 49 8 4 4 ,6 8 8 1 7 1 1 9 5 26 1 2, 56 7

Kans a s Presc r i b e d B u r n i n g 38 ,1 0 6 1, 45 0 1, 22 6 1 4 ,4 2 4 2 2 8 1 1 4 14 3 88 1
Ran g e lan d Burn ing 3

, 6 2 5

, 27 0 7 5 ,943 5 2 ,901 6 5 2

, 25 0 2 3

, 1 8 5 1 0

, 1 6 0 7

, 4 8 7 4 3 ,483
C r o p l a n d B u r n i n g 1, 39 0, 5 2 0 2 3 ,2 2 7 2 2 ,1 5 6 15 3, 3 1 3 5, 90 9 77 7 3

, 95 0 1 1 ,4 0 1 Whe a t 1, 05 8, 0 14 17 ,4 2 0 16 ,6 1 0 11 8, 9 02 4, 52 3 60 3 3, 21 6 8, 19 4
H

a

y/

Al

f

a l

f

a 18 9, 0 85 2, 25 2 2, 14 8 12 ,7 0 1 40 8 54 29 0 1, 14 3
S

u g

a rc a n e 0 0 0 0 0 0 0 0
C o r

n 12 6, 9 56 3, 03 9 2, 90 6 18 ,9 0 2 88 0 10 7 37 3 1, 76 0
S

o ybe a n s 9, 99 6 2 1 0 2 0 0 1, 25 2 3 4 5 2 4 1 5 4
Ri ce 0 0 0 0 0 0 0 0
Ot

he r 6, 46 9 3 0 6 2 9 2 1, 55 7 6 5 9 4 6 1 5 0

To t a l 5

, 0 5 3

, 89 6 1 0 0

, 62 0 7 6 ,283 8 1 9

, 98 7 2 9

, 3 2 2 1 1

, 0 5 2 1 1

, 5 7 9 5 5 ,765

Lo ui si a n a Presc r i b e d B u r n i n g 35 0, 3 5 3 4 5 ,2 8 8 3 8 ,3 7 6 48 6, 6 6 8 3, 12 5 2, 53 1 4

, 67 1 2 8 ,0 6 0
R a ngel a nd B u r n i n g 29 ,5 4 0 6 1 3 4 9 1 3, 59 7 37 2 12 8 6 5 3 0 5
C r o p l a n d B u r n i n g 45 6, 9 0 1 7

, 77 1 7

, 39 7 66 ,2 0 3 3, 47 4 48 2 2, 38 8 6

, 76 2 Whe a t 11 4, 6 61 2, 18 9 2, 08 7 13 ,5 7 0 49 0 65 34 9 99 8
H

a

y/

Al

f

a l

f

a 5, 76 3 9 0 8 5 40 1 1 3 2 9 3 6
S

u g

a rc a n e 29 6, 9 94 4, 93 0 4, 69 3 48 ,1 1 3 2, 67 3 35 6 1, 90 1 5, 34 6
C o r

n 5, 81 7 13 9 13 3 86 6 40 5 17 81

B-3Table B-1. Annual em issions by state and source category.
Page 2 of 3

Acres Em issions (to n s /y ear)

S t a t e B u rn Ty p e B u r n e d PM10 PM2.5 C O NO
x SO2 NH3 V O C

S

o ybe a ns 2, 41 8 12 8 12 2 56 2 14 2 10 81
Ri ce 31 ,2 4 8 29 5 27 7 2, 69 1 24 4 52 10 3 22 0
Ot

he r 0 0 0 0 0 0 0 0

Tot a l 83 6, 7 9 4 5 3 ,6 7 2 4 6 ,2 6 4 55 6, 4 6 8 6, 97 0 3, 14 0 7

, 12 4 3 5 ,1 2 6

M i nnes o t a Presc r i b e d B u r n i n g 86 ,6 4 2 1 7 ,2 2 2 1 4 ,6 0 9 18 7, 8 5 3 74 2 83 6 1

, 15 0 1 0 ,7 4 0
R a ngel a nd B u r n i n g 17 ,3 1 4 3 5 8 2 1 6 3, 90 4 1 6 2 5 3 3 2 2 8
C r o p l a n d B u r n i ng 84 ,6 1 1 1, 58 7 1, 51 3 8, 62 1 34 1 44 21 5 92 8 Whe a t 7, 96 2 13 2 12 6 89 7 34 5 24 62
H

a

y/

Al

f

a l

f

a 28 ,5 0 3 4 0 2 3 8 3 1, 56 5 5 6 8 4 0 2 1 1
S

u g

a rc a n e 0 0 0 0 0 0 0 0
C o r

n 14 ,2 2 3 34 1 32 6 2, 11 8 99 12 42 19 7
S

o ybe a ns 0 0 0 0 0 0 0 0
Ri ce 0 0 0 0 0 0 0 0
Ot

he r 33 ,9 2 3 71 2 67 8 4, 04 1 15 3 20 10 9 45 8

Tot a l 18 8, 5 6 7 1 9 ,1 6 7 1 6 ,3 3 8 20 0, 3 7 8 1, 10 0 90 5 1

, 39 8 1 1 ,8 9 5

M i ssou r i Presc r i b e d B u r n i n g 64 ,7 8 1 7, 46 0 6, 33 8 8 0 ,0 1 9 5 3 6 4 1 7 75 6 4, 63 3
R a ngel a nd B u r n i n g 10 9, 1 6 0 2

, 28 1 1

, 76 3 15 ,2 4 4 1, 18 2 41 5 22 8 1

, 18 2
C r o p l a n d B u r n i n g 18 1, 8 1 8 2, 67 7 2, 55 1 1 7 ,8 4 5 7 2 5 1 0 5 46 5 1, 31 7 Whe a t 94 ,2 7 9 1, 54 6 1, 47 4 10 ,5 8 1 40 3 54 28 7 72 8
H

a

y/

Al

f

a l

f

a 63 ,5 4 5 76 7 73 2 4, 59 0 14 3 19 10 2 35 3
S

u g

a rc a n e 0 0 0 0 0 0 0 0
C o r

n 8, 83 7 21 2 20 2 1, 31 6 61 7 26 12 3
S

o ybe a n s 45 8 1 3 1 2 9 2 3 0 2 1 0
Ri c e 14 ,6 7 3 1 3 9 1 3 0 1, 26 3 11 4 2 4 4 8 1 0 3
Ot

he r 26 1 1 3 0 0 0 0

Tot a l 35 5, 7 5 9 1 2 ,4 1 9 1 0 ,6 5 2 11 3, 1 0 7 2, 44 3 93 7 1

, 44 8 7, 13 2

Neb r as k a Presc r i b e d B u r n i n g 6, 12 7 4 1 0 3 4 7 4, 31 6 3 6 2 4 2 7 2 5 4
R a ngel a nd B u r n i n g 11 4, 8 0 7 2, 40 3 1, 46 8 2 5 ,8 6 3 1 5 2 1 7 9 22 3 1, 52 0
C r o p l a n d B u r n i n g 10 0, 7 1 9 2

, 24 4 2

, 14 0 14 ,4 3 9 49 1 6 5 33 0 1

, 43 0 Whe a t 47 ,3 3 6 65 6 62 5 5, 03 9 20 2 27 14 4 32 4
H

a

y/

Al

f

a l

f

a 5, 43 0 7 2 6 8 32 3 1 1 1 8 3 8
S

u g

a rc a n e 0 0 0 0 0 0 0 0
C o r

n 9, 43 0 22 6 21 6 1, 40 4 65 8 28 13 1
S

o ybe a ns 0 0 0 0 0 0 0 0
Ri ce 0 0 0 0 0 0 0 0 Othe r 38 ,5 2 3 1

, 29 1 1

, 23 1 7, 67 3 21 2 2 8 15 1 93 8

Tot a l 22 1, 6 5 3 5, 05 7 3, 95 6 4 4 ,6 1 9 6 7 9 2 6 8 58 0 3, 20 5

Okl a hom a Presc r i b e d B u r n i n g 10 4, 7 4 9 7, 32 2 6, 19 6 7 6 ,6 1 5 7 5 0 4 7 9 76 9 4, 50 7
Ran g e lan d Burn ing 1

, 8 3 0

, 01 7 3 8 ,117 2 8 ,443 2 8 0

, 78 0 1 6

, 8 8 5 6

, 4 1 9 3

, 8 9 0 2 0 ,578

B-4Table B-1. Annual em issions by state and source category.
Page 3 of 3

Acres Em issions (to n s /y ear)

S t a t e B u rn Ty p e B u r n e d PM10 PM2.5 C O NO
x SO2 NH3 V O C

C r o p l a n d B u r n i n g 47 3, 3 4 2 7

, 11 4 6

, 78 5 47 ,1 5 7 1, 76 0 23 4 1, 23 4 3

, 41 4 Whe a t 32 5, 8 38 5, 19 7 4, 95 5 36 ,2 3 8 1, 39 3 18 6 99 1 2, 46 5
H

a

y/

Al

f

a l

f

a 13 7, 7 07 1, 69 0 1, 61 2 9, 46 4 30 2 40 21 4 81 5
S

u g

a rc a n e 0 0 0 0 0 0 0 0
C o r

n 8, 87 9 21 3 20 3 1, 32 2 62 7 26 12 3
S

o ybe a ns 0 0 0 0 0 0 0 0
Ri ce 0 0 0 0 0 0 0 0
Ot

he r 91 8 1 5 1 4 13 3 4 0 3 1 1

To t a l 2

, 4 0 8

, 10 8 5 2 ,552 4 1 ,424 4 0 4

, 55 1 1 9

, 3 9 5 7

, 1 3 1 5

, 8 9 3 2 8 ,499

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, 42 7 7, 82 4
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B-5

## APPENDIX C

ASSESSMENT OF INFLUENCE F R OM PRESCRI BED BURNIN G ON CLASS I SITES USING AMBIENT SPECIATED PM 2. 5 

### DATA

C-1C-3 C.1 OBJECTIVE AND APPROACH

The objective of this task was to use ambient speciated PM 2.5 data from Class I areas (from the IMPROVE network) in the CENRAP st ates along with the planned burning emissions estimated in this study to assess whether ambient data can be used to identify planned burning contributions to visibility events in Class I areas , and to perform a preliminary assessment of the impact of planned burns on PM 2.5 and visibility The followi ng approach was employed:

• Assess the seasonal chemical compositions of PM 2.5 mass and aerosol light extinction in order to determine what individual species are important to the mass and visibility extinction in the area.

• Determine seasonal concentrations of and ra tios between selected species, such as OC, EC and K, to establish a “baseline” average seasonal composition for comparison to days of poor visibility and days potential ly influenced by prescribed burning.

• Assess chemical compositions of PM 2.5 and aerosol light extinction on the 20% best and 20% worst visibility days to determine what species have a large impact on visibility (i.e., are species from burning typicall y important in visibility reduction?).

• Analyze IMPROVE data, specifically OC, EC , and K concentrations, on dates when extensive burning occurred nearby a monitoring site in order to assess whether wood smoke influences are seen in the ambien t measurements and significantly impacts visibility.

• Analyze emissions data on days when elevated OC, EC and K concentrations occurred at IMPROVE sites in order to determine whet her days of elevated concentrations corresponded to known burns in the emission inventory data.

• Analyze air mass trajectories on selected days to determine whether meteorology (i.e., transport) explains th e observed effects and to determine the extent to which meteorology affects haze

C.2 AMBIENT MONITORING DATA

We analyzed ambient monitoring data from IMPROVE stations in order to assess the potential effect of prescribed burning emissions on visibility in the CENRAP region. We used ambient data from two IMPROVE stations lo cated in Arkansas, Caney Creek (CACR1) and Upper Buffalo Wilderness (UPBU1). At the time of analysis, these sites we re located in the area with the highest resolved fire histories, whic h would allow the best chance of showing direct influence between prescribed burni ng and ambient Class 1 data. Figure C-1 shows the locations of IMPROVE stations in the CENRAP region, alo ng with the point locations of prescribed burns that were available from the 2002 emissions inventory.Figure C-1. IMPROVE st ation and fire locations.

We acquired data from the two am bi ent m onitoring stations from the online IMPROVE database ( http://v ista.cira

. colostate.ed u/im prove/ ). Specifically, we obtain e d values of all available param e ters for the years 2000, 2001, and 2002, during which years the IMP R OVE network collected 24-hr sam p le s once every three days. A lthough the emissions inventory included fires only from 2002, IMPROVE data from all three years were used to ensure a robust

C-4C-5 statistical analysis of seas onal and annual aerosol compos itions and species ratios. Table C-1 summarizes the number of complete samples that we obtained from the IMPROVE database for 2000 through 2002 and for 2002 alone. The comple te samples were cases in which all key species in our analysis were available: elemental carbon (EC), organic carbon (OC), potassium (K), ammonium nitrate (NH 4 NO 3 ), and ammonium sulfate ((NH 4 ) 2 SO 4 ).

Table C-1. Number of complete sample s available from 2000–2002 and from 2002 at Caney Creek and Upper Buffalo Wilderness.

Site N samples (2000 – 2002) N samples (2002) Caney Creek 254 110 Upper Buffalo Wilderness 318 117

In analyzing the ambient monitoring data with respect to fire activity data, we focused on species that generally characte rize fine particulate aerosols a nd species that derive from wood smoke: elemental carbon (EC), organic ca rbon mass (OCM), amm onium nitrate (NH 4 NO 3 ), ammonium sulfate ((NH 4 ) 2 SO 4 ), potassium (K), non-soil potassi um (KNS), and a composite of species that derive from soils (G EO). Several of the parameters were calculated from measured values according to IMPROVE protocol, as summarized in Table C-2

.

Table C-2. IMPROVE algorithms for mass c oncentrations of fine aerosol species.

Species Abbreviation IMPROVE Calculation Organic Carbon Mass OCM 1.4*[organic carbon] Ammonium Nitrate NH 4 NO 3 1.29*[nitrate] Ammonium Sulfate (NH 4 ) 2 SO 4 4.125*[sulfur] Non-soil Potassium KNS [potassium]-0.6*[iron]

Soil Elements Soil 2.20*[aluminum]+2.49*[silicon]+1.63*[calcium] +2.42*[iron]+1.94*[titanium]

The IMPROVE algorithm for OCM adjusts the measured OC value for other elements associated with carbon molecules, such as oxygen and hydrogen, and it relies on the assumption that the average organic molecu le contains 70% carbon. The a mmonium nitrate and ammonium sulfate algorithms assume that nitrate a nd sulfate ions are fully neutralized by NH 4 + . The ammonium sulfate algorithm also assu mes that all elemental sulfur is in the form of sulfate, and it converts the mass of elemental sulfur to ammoni um sulfate using 4.125, which is the ratio of the molecular weight of ammonium sulfate (132 g/mol) to the molecular weight of elemental sulfur (32 g/mol). Similarly, the ammonium n itrate algorithm multiplies the nitrate concentration by the ratio (1.29) of the molecu lar weight of ammonium nitrat e (80 g/mol) to the molecular weight of nitrate (62 g/mol). The non-soil potassium (KNS) al gorithm results from the observed ratio (0.6) of potassium to iron in soils. The residual, non-soil potassium (KNS) is assumed to derive from smoke. Lastly, the soil algorithm includes the sum of soil-derived elements, adjusted by coefficients that a ccount for their normal oxides.C-6 The IMPROVE network utilizes the measur ed mass concentrations of OCM, EC, (NH 4 ) 2 SO 4 , NH 4 NO 3 , and soil components to estimate the light extinction resulting from each species. Light extinction values associated with the individual species are summed to reconstruct an overall aerosol extinction parameter (b ext ). The IMPROVE extincti on calculations account for scattering, absorption, and the effects of relative humidity, as illustrated by equations listed in Table C-3 . The coefficients represent the dry sca ttering efficiencies of the compounds, except the coefficient for the EC algorithm, which represents the light absorbing efficiency of EC.
F T (RH) equals an empirically determined relative humidity correction factor that accounts for the hygroscopic nature of the ionic aerosol species.

Table C-3. IMPROVE algorithms for light extinctions of fine aerosol species.

Species Abbreviation IMPROVE Calculation Organic Carbon Mass OCM Extinction 4*[organic carbon] Ammonium Nitrate NH 4 NO 3 Extinction 3*F T (RH)*[nitrate] Ammonium Sulfate (NH 4 ) 2 SO 4 Extinction 3*F T (RH)*[sulfur] Elemental Carbon EC Extinction 10*[elemental carbon] Soil Elements Soil Extinction 1*[Soil]

C.3 DATA VALIDATION

Data validation helps to prevent serious errors in data analysis and modeling results by identifying erroneous indivi dual data values. The PM 2.5 Data Analysis Wo rkbook contains the guidelines that we employ for PM data valida tion (Main and Roberts, 2001). The validation incorporates internal consistency checks of ambi ent monitoring data, such as the comparison of species concentrations using sca tter plots, the calculat ion of reconstructed particulate mass, and the preparation of material balan ces. Scatter plots that illustra te the relationships between well characterized species enable data analysts to quickly inspect da ta and identify any suspect points that may require further attention. Scatter plots also provide a general over view of a data set and preliminary data analysis. Plots that compare species from common sources, such as soil, or from different analytic al techniques, such as ion chro matography (IC) or x-ray fluorescence (XRF), can target outlying data points that may indicate an un usual event or an equipment problem. Plots between reconstr ucted mass and measured mass or between cations and anions help the analyst to visually assess data complete ness and to validate data resulting from different measurement techniques. We generated a numbe r of scatter plots using SYSTAT statistical software in an effort to validate the IMPROVE da ta before performing the comparative analysis. Table C-4 summarizes the species we inspected using scatter plots, along with their expected relationships and typical sources.

The data quality was good, as IMPROVE data is quality controlled prior to being incorporated into the database; thus, minimal e ffort was required. The data validation plots explored include 2000 through 2002 da ta for both Caney Creek and Upper Buffalo Wilderness. The data from both sites exhibit similar relationships between measured species. Figure C-2 illustrates the comparison between sulfur (S) and sulfate (SO 4 -2 ) for the data set from Upper Buffalo Wilderness. A relatively tight correl ation and a slope of r oughly three indicate a goodTable C-4. Scatter plo t speci es and expected relationships.

Species Species Expected Relationship Source or R eason

S S O 4 -2 3*S ~ SO 4 -2 IC vs. XRF Cl ion Cl ~ 1:1 IC vs. XRF Na ion Na ~ 1:1 IC vs. XRF K ion K ~ 1:1 IC vs. XRF
N a C l Correl a t i o n S e a salt
C a S i C o rrela t i o n S o i l
A l S i C o rrela t i o n S o i l
F e S i C o rrela t i o n S o i l
F e K C o rrela t i o n S o i l
O C Total Carbo n (TC) Correla tion O C large pa rt of TC
EC TC So m e Correlation EC part of TC
S e S O 4 -2 Som e Correlation Coal Em issions
Fe Zn Som e C o rrelation Sm elter Em issions
Ni V Som e Correlation Oil Com bustion
K EC Som e Correlation Wood Smoke

b abs EC C o rrela t i o n EC absorbs most light
C a t i o n s A n i o n s N e a r 1 : 1 N e u t r a l i z e d A e r o s o l
PM 2. 5 Reconstructed Mass Good Correlation Should be equal

Figure C-2. Concentrations of XR F sulfur (S) ve rsus IC sulfa t e (SO 4 -2 ) fro m t h e Upper Buffal o W ilderness IMPROVE st ation ( μ g/m 3 ). The lin e has a sl ope of one third, representing the expected 1:3 ra tio b e tw een sulf u r

and sulf ate

. C-7com p arison between the data obtained from the XRF and IC analyses. T h e slope equ a ls 3 because the molecular m a ss of su lfate (96 g/m o l) is three times the m o lecular m a ss of sulfur (32 g/m o l). Figure C-3 highlights the good correlation betwee n the m easured fine particulate

m a ss (PM 2. 5 ) and the reconstructed fi ne particulate m a ss (RFM). A ccord ing to IMPR O V E protocol, RFM equals the sum of SO 4 -2 , NO 3 - , EC, OCM, and soil com ponents. The good correlation b e tween PM 2. 5 and RFM indicates the overall re lia bility of the data se ts and

m easurem ent techn i ques. The correlation exh i bi ted between iron (Fe) and potassium (K) in Figure C-4 is confounded by several data points of high K and low Fe, which suggests an additional source of K, possibly wood sm oke, si n ce both species comm only derive from soils. Overall this indicates that m o st K is from soil, which suggests influence from the non-soil sources is infrequent and contributes only a sm all am ount of t h e

K.

Figure C-3. Concentrations of reconstructed fine m a ss (RFM ) versus fine particulate m a ss (PM 2. 5 ) from the Caney Creek IMP R OVE station ( μ g/m 3 ). The line has a slope of one, representing a one to one ratio between RFM and PM 2. 5
.

C-8Fe

Figure C-4. Concentrations of iron (Fe) versus potassium (K) from the Upper Buffalo W ilderness IMPROVE st ation ( μ g/m 3 ). Points tha t exhib it hig h er than no r m al K to Fe ratios are highlighted.

C.4 CHARACT ERI Z ING PM 2. 5 DAT A

It is im portant to firs t ch aracterize th e typical seas onal concentrations of and ratios between spe c ies to understand what c o m p rises th e “norm a l” com position of PM 2. 5 before identifying whether specific source influences su ch as pres cribed burn i n g can be determ ined. Figure C-5 depicts seasonal proportions of the m e dian m a s s concentrations of OCM, EC, NH 4 NO 3 , (NH 4 ) 2 SO 4 , an d soil influ e nces for Caney Creek ; U pper Buffalo W ilderness showed sim ilar resu lts. Summ ar y statistics are given in Appendix A. At both sites, (NH 4 ) 2 SO 4 and OCM com p r i se th e dom i n ant fraction s of PM 2. 5 in all s easons except win t er, when NH 4 NO 3 also contributes a significa nt fraction. T h e larger fraction of NH 4 NO 3 in w i nter is con s is tent w ith nitrate formation m echanism s which favor cold, wet conditions, and the dom i nant fractions of (N H 4 ) 2 SO 4 are consistent with observations m a de at othe r eas tern IMPRO V E sites (M alm

, 1999). EC is a sm all component of the m a ss in all seasons.

Figure C-6 illus t rates th e proportion s of light ex tinction attributed to OCM, EC, NH 4 NO 3 , (NH 4 ) 2 SO 4 , an d soil for each season at Cane y Creek; Upper Buffalo W ilderness showed sim i lar results. Summ ary statistics ar e given in Appendix A. The dom inant portion of light extinction derives from (NH 4 ) 2 SO 4 in all seasons except winter, when NH 4 NO 3 als o contributes significantly. This is cons isten t w ith o t her an alyse s of PM 2. 5 aerosol in the Midwest and CENRAP region ( http://www2.nature.nps.gov/air/st udies/bravo/bravo2003factsheet.htm )

C-9Figure C-5. Median m a ss and com p osition of P M 2. 5 by season (spring is March to M a y, summer is June to August, fall is Se ptem ber to Nove m b er, and winter is Decem ber to February) at Caney Creek for 2000 through 200 2.

(Coutant et al., 2003; C outant et al., 2002; Georgoulias and Dattn er, 2002; Sisler and Malm

, 2000; Malm , 1999). PM 2. 5 com position at othe r C l ass 1 are a s in the C E N R A P region w ill likely be sim ilar. The light extinc tion proportions resemble the m a ss concentration proportions, because the extinction calculation s d i re ctly dep e nd on m a ss concentratio ns. (NH 4 ) 2 SO 4 has a large effect on visibility due to its extrem ely hygroscopic nature and large contribution to the overall m a ss. The effect of EC on vis i bility is m o st pronounced during th e winter m onths when the effect of (NH 4 ) 2 SO 4 is at a m i nimum

, though it only accounts for about 5% of the total extinc tion.

C-10Figure C-6. Median extincti on and com position of extinction by season (spring is March to May, summer is June to August, fall is Septem ber to Novem b er, and winter is Decem ber to February) at Caney Creek for 2000 through 200 2.

C.5 CHARACT ERI Z ATION OF VISIBILITY

In order to determ ine which species are m o st responsible for poor vi sibility, we isolated the top and bottom 20% visibility days in 2000 through 2002 by aerosol extin ction at each site. Sum m ary statis tic s of the best v i sib i lity da ta, w o r s t vis i bility data, and ov erall data f o r C a ney Creek and Upper Buffalo W ilderness were calcula ted. The m e dian m a ss com positions for the best and w o r s t vis i bility days, as w e ll as th e a nnu al m e dian f r om C a ney Creek a r e dep i cted in Figure C-7 ; results for Upper Buffa lo W ilderness are si m ilar to those for Caney Creek. At both sites, days with poor visi bility are dom i nated by (NH 4 ) 2 SO 4 and show a decrease in th e fraction s of the other species, esp ecially OCM and NH 4 NO 3 . The fr actions of EC and Soil com ponents vary to a lesser extent between the good visi bility and poor visibil ity days and are m i nor contributors to m a ss and extinction.

C-11A n nua l
PM 2. 5 = 8. 76

So i l 5% OC M
31 %

(N H 4 ) 2 SO 4 53 % EC 4% NH 4 NO 3 7% B est
PM 2. 5 = 2. 98

So i l 8% OC M 36%

(N H 4 ) 2 SO 4 37 % EC 6% NH 4 NO 3 13% Wo r s t
PM 2. 5 = 17. 31

OC M 26%

So i l 5% EC 3% NH 4 NO 3 3%

(N H 4 ) 2 SO 4 63%

Figure C-7. Median PM 2. 5 com position for the 20% best and worst aerosol extinction days and the m e dian annual com p osition at Ca ney Creek from 2000 to 2002.

C.6 FIRE HIST ORY DAT A

In order to evaluate the effect of prescribed fires on visibility, we analyzed fire history data with the 2002 IMP R OVE data for Caney Creek and Upper Buffalo W ilderness. W e isolated the dates with IMPROVE data that corresponded to the day of or the day after prescribed burning occurred within a specific radius (i.e., range of in fluence) of each site. The range of influence around each site was established by u s ing data fr om nearby meteoro l ogical stations: th e radius around each site was calculated as the sum of th e 24 hourly av eraged wind speeds for each date, which represented an estim a te of the distan ce that a parcel of air cou l d have traveled on a given day. Theore tica lly, em issions f r om f

i res loc a ted w ithin th e ra nge of inf l uence could have been detected by the IMPROVE station if transport co nditions were conducive. We then analyzed dates when the m o st extensive burn i n g (with resp ect to acreag e ) occu rred.

D u e to the p r oxim ity of the tw o IMP R O V E sites, severa l of the dates s e le cted f o r eac h site overlap. The OCM, EC , K and KNS m a ss c oncent r at i o ns from overlapping dates that correspond to the day of or the da y after extensive burning within the vicinity of both sites are

com p ared to the springtim e and annual m ean c oncentration s from 2000 to 2002 for each site in Figures C-8 and C-9 . Error bars rep r esenting th e 95% confidence lim its for the m ean concentrations of EC and KNS fo r the springtim e and annual data sets are also plotted. In

C-12Appendix A, m a ss concentrations of EC, KNS and the other key sp ecies for the selected dates and whether the EC and KNS concentrations sign ificantly exceed the sp rin g tim e are presented.

Figure C-8. EC, OCM, K a nd KNS m a ss concentrations ( μ g/m 3 ) for select dates com p ared to the spring and annual means for Caney Creek.

C-13Figure C-9. EC and KN S m a ss concentrations ( μ g/m 3 ) for select dates co mpared to th e spring and annual m eans for Upper Buffalo W ilderness.

Extensive burns occurred on the days before March 6, 15, 24, and April 5. On these dates, the measured EC significan tly (at a 95 % confidence level) ex ceeds the springtim e and annual m eans for both sites. On March 15 and 24, the contributions of EC in relation to OC are significantly higher than the spring tim e and annual average E C contributions for both sites. The elevated EC em issions observed on March 6, 15, 24, and April 5 could derive from the extensive

C-14burning that occurred on the prev ious days, if transport condi tions were correct. Ex tens ive burning occurred on February 28, but the EC m e as urem ents fell below the springtim e and annual

m eans at both sites, although not statistically significant at a 95% confidence level. On March 6, 15, and 24, the KNS m a s s concentrations exceeded the m ean springtim e and annual KNS concentrations for both sites, a nd the relative contribution of KNS in com p arison to K is also higher on these dates. T h e elev ated KNS also suggests influe nce from wood burning on these days. Air mass trajectories were run to further in vestig ate the potential influence of prescrib ed burns on ambient m easurem ents, as discussed in Section C.9.

Figure C-10 com p ares the m a ss concentration ratios of EC to OCM and of KNS to K for the selected dates to the annual m e dian ratios. T h e KNS to K ratios from March 6, 15, and 24 clearly exceed the annual ratio, indicating a rela tively large contributi on of KNS during these dates. Since KNS largely derives from w ood s m oke, em issions from nearby burns likely influenced the IMPROVE sites. The EC to OC M ratio from March 15 also clearly ex ceeds th e annual ratio, further suggesting fire influence on this day.

C.7 DO HIGH CONCE NT RAT IONS OCCUR O N DAYS OF PRES CRIBED B U RNS?

In addition to isolating the dates associated w ith extens ive b u rning f r om the f i re h i sto r y data and analyzing corresponding ambient m easurem ents, we also isolated the dates with high

m a ss concentrations from the am bie n t m easurem ents and analyzed corresponding fire history data. For each site, we ranked the 2002 IMPR OVE data by the m a ss concentration s of EC, OCM, KNS, and K. W e summarized the selected dates, ranks of each compound, whether a fire

occurr ed, an d the to tal a c res burn e d w ithin th e s phere of inf l uence of eac h site in Tables C-5 an d C-6

. At both sites, the dates of higher EC and OCM m a ss concentrations ove rla p m o re w ith each other than with the dates of hig h er K m a ss concentratio ns, as EC and OCM both commonly derive from com bustion sources and K derives larg ely f r om soils. A t U p per B u f f a lo W ilderness, three of the five highest EC m a ss concentration s were m easured on the d a tes that we had isolated during th e p r evious an alysis of fire o ccurrence, nam e ly March 6, 24, and April 5. Since EC partly derives from wood sm oke and extensive burns occurred on the day of and the day before these dates, the elevated EC e m issions could derive from n earby prescribed burns. W e further analyze the potential co nnection b e tween elevated em issions of key species and fire occurrence in the nex t s ection, u t iliz ing air m a ss tra j ecto r ie s f o r sele ct da tes.

C-15Figure C-10. EC to OCM and KNS to K m a ss concentr ation ratios for select d a tes co mpared to the annual m e dian ratios for both sites

C-16Table C-5. Dates with high m easured EC, OCM, KNS and K m a ss conc e n tra tions an d tota l acres bu rned within th e s phere of influe nce of Caney Creek. The ranks order the days acco rding to the fiv e highes t m e asured m a ss concentratio ns of each species.

Date EC Rank OCM Rank KNS Rank K Rank Day of or before fi res? Total Acr e s 0 1 / 1 7 / 0 2 2 Of an d Be fo re 4,618 0 3 / 0 6 / 0 2 5 Of an d Be fo re 19,509 0 5 / 0 2 / 0 2 3 No Fires 0 0 5 / 0 8 / 0 2 2 4 Of an d Be fo re N/ A 0 6 / 2 2 / 0 2 5 5 Day Bef o re 107 0 7 / 0 1 / 0 2 1 Day O f 41 0 7 / 0 4 / 0 2 5 No Fires 0 0 7 / 3 1 / 0 2 2 Of an d Be fo re 1,157 0 8 / 0 6 / 0 2 3 Of an d Be fo re 1,727 0 8 / 0 9 / 0 2 3 4 Of an d Be fo re 388 0 8 / 3 0 / 0 2 4 Of an d Be fo re 476 0 9 / 0 5 / 0 2 2 4 Of an d Be fo re 1,973 0 9 / 1 4 / 0 2 1 1 1 3 Of an d Be fo re 135

Table C-6. Dates with high m easured EC, OCM, KNS, and K m a ss con centra tions a nd tota l acres bu rned within th e s phere of influe nce of Caney Creek. The ranks order the days acco rding to the fiv e highes t m e asured m a ss concentratio ns of each species.

Dat e EC Rank OCM Rank KNS Rank K Rank Day of or before fires? Total Acres

0 3 / 0 6 / 0 2 5 Of a n d Be f o r e 2 0 7 7 1 0 3 / 2 4 / 0 2 2 Of a n d Be f o r e 2 8 5 6 7 0 4 / 0 5 / 0 2 4 Of a n d Be f o r e 8 1 9 0 05/08/02 2 4 Of and Be fore N/A 06/19/02 3 3 3 Of and Be fore 661 0 6 / 2 2 / 0 2 1 4 4 Day B e f o r e 3 5 6 0 7 / 0 1 / 0 2 1 D a y O f 4 1 07/10/02 2 Of and Be fore 2114 07/31/02 3 Of and Be fore 927 08/03/02 5 Of and Be fore 189 08/06/02 5 Of and Be fore 1729 0 9 / 1 4 / 0 2 1 1 2 Of a n d Be f o r e 2 5 3
1 1 / 2 5 / 0 2 5 Of a n d Be f o r e 2 0 8

C-17C.8 AIR MASS TRAJ ECT O RIES

Back trajectories of air m a sses for the sele cted dates were created using the Nation a l Oceanic and At m o spheric Adm i nist ration (NOAA) Hybrid Single-Particle Lagrangian Integrated
Tr aj ect o r y (HYSPLI T ) model

. The NOAA HYSPL IT m o del is a thr ee- dim e nsional air m a ss t r aj ect or y model based on weat her m odel data and can be obtai ned fr om t h e NOAA web si te at ht t p :/ / www.ar l

. noaa.gov/ r eady/ hyspli t 4 .ht m l . The final (FNL) product of the Global Data Assim ilation System (GDAS) that uses the Gl ob al Spectral Medium Range Forecas t (MRF) model provides the weather data for the HYSPLIT m odel. The HYSPLI T m odel uses National Weather Service soundings and other diagnostic param e ters such as tem p erature, relative hum i dity and radiative and m o m e ntum fluxes. It uses a 129 x 129 polar stereograph (three-dim e n sional) grid, with approxim ately 190 km resolution and 12 vertical layers, and is run at 6-hour increm ents. Back trajectories we re run from 1800 CST with ending heights of 1000 and 500 m e t e rs in order to capture short-ra nge transport in the lower boundary layer.

We ran trajectories for March 6, 15, and 24 and plotted them along with f i res that occurred the day of and the day before the selected dates. Figures C-11 through C-13 show the

m a ps for March 6, 15, an d 24. On these date s, th e EC and KNS m a ss concentration s exceeded the annual m eans for each site, as su mmarized in Figure C-9.

Air m a ss trajectories demonstr ated no influence from known burns at the Caney Creek site on M a rch 6. However, the inventory does not include detailed fire history data for the southeastern corner of Oklahom a or for the ea stern portion of Texas, over which the air m a ss advected b e fore reaching the site. On approach to the U pper B u ff alo W ilderness s ite, the air did pass directly over extensive fires th at occurred on March 5. T h erefore, th e elevated EC and KNS

em issions measured at U pper Buffalo W ilder ness on March 6 could derive from wood sm oke

em issions from the previous day that influen ced the site over a 24 hour period. Additionally, there were num e rous nearby fires to Caney Creek on March 5 and 6 that would have affected the site via flow below 500 m e ters.

Sim ilar to th e situ ation o n March 6, the air f l ow e d direc tly ov er burns rep o rted in the

em issions inventory before reach ing Upper Buffa lo W ilderness on March 15, but the air did not flow over the reported burns when approachi ng Caney Creek. The elev ated EC and KNS

em issions measured at U pper Buffalo W ilderness on March 15 could be attributed to the wood

sm oke e m is sions from t h e extensive March 14 bu rns, and detailed fire history data from neighboring states would allow m o re definitiv e co nclusions to be drawn ab out the m easurem ents f r om C a ney C r eek. A l so , the sim ilar i ty in PM 2. 5 c o m position at the tw o sites on th is d a y indica tes the y w e re inf l u e nced f r om sim ilar sources, again suggesting local low level flow advecting smoke to Caney Creek that is not shown by the trajectories.

Finally, on March 24, the air approaching Cane y Creek circum vented th e extens ive fires reported by the inventory, while the air approaching Upper Buffalo W ilderness passed directly over them . Once again, the higher th an averag e EC and KNS m a ss concentrations observed on March 24 at Upper Buffa lo W ilderness could have orig inated from prescr ibed burning em issions, while m o re infor m ation would support definitive c onclusions as to the Caney Creek em issions. O v erall, the PM 2. 5 com p osition and air m a ss trajectories show that fire influence from l a rge-s cale burns can be seen in the am bient data at Ca ney Creek and Upper Buffalo W ilderness. More

C-18extensive em ission inventory data is needed to b e tter ass e ss the im pact of prescribed burn

em issions in the CENRAP region.

Figure C-11. Air m a ss trajectories and associ ated fires for M a rch 6. Squares along each trajecto r y are placed every 6 hours.

C-19Figure C-12. Air m a ss trajectories and asso ciated fires for M a rch 15. Sq uares along each trajecto r y are placed every 6 hours.

C-20Figure C-13. Air m a ss trajectories and asso ciated fires for M a rch 15. Sq uares along each trajecto r y are placed every 6 hours.

C.9 EFFECTS ON VISIBI LITY

We have demonstrated a poten tial connection between pres cribed bu rn occurrence and elevated EC and KNS em issions at Upper Buffa lo W ilderness via com p arative analyses of

am bient data and fire history data and air m a ss traj ectories. In order to as sess the im pact th at th e elevated em issions have on visibi lity, w e plotted the m e dian PM 2. 5 m a ss com positions of the annual, best visibility, and worst visibility data sets from Upper Buffalo W ilderness in order to

C-21com p are them to the m a ss and vis i bility com positions m easur ed on the se lect da tes Ma rch 6, 15, and 24, as illustrated in Figure C

- 14 . The PM 2. 5 m a ss com positions consist of the m easured NH 4 NO 3 , (NH 4 ) 2 SO 4 , soil elem ents, OCM, and EC m a ss c oncentrations. The worst visibility data se t is c h arac terized by high (NH 4 ) 2 SO 4 and OCM m easurem ents, wh ile the bes t visibility data se t is c h arac ter i zed by rela tive l y low conce n tra tions of all the spec ies. Since (N H 4 ) 2 SO 4 does not derive from wood sm oke, a nd OCM can de rive fro m other sources, the sp ecies th at dom i nate poor visibility condi tions are not necessarily connect ed with em issions from wood

sm oke.

Figure C-14. PM 2. 5 m a s s com positions for select dates a nd for the annual, best visibility, and

w o rst vis i bility da ta se ts.

C.10 CONCLUSIONS

Speciated PM 2. 5 data collected at IM PROVE sites in Class 1 areas in Ark a nsas were u s ed to dete rm ine w h ether suc h data can h e lp to ex am i n e the influence of prescribed burn i n g and determ ine if burns in th e em ission inventory signif i cantly im pact the PM 2. 5 com position and visibility reduction. Over all conclusions include:

• Speciated PM 2. 5 data at IMPROVE sites are us eful for characterizing so urces im pacting
PM 2. 5 and visibility reduc tion, including burns.

• Influence from specific known burns (as seen by elevated concen trations of EC or K) can be seen on select days w h en the m e teorology is conducive for transport.

• Days when high OC or EC concentrations are observed at the sites do not always coincide with known burns; however, the em issi on inventory is not com p lete and m a y be

C-22m i ssing burns in the areas of influence on th ese days, such as southern O k lahom a and eastern Texas.

• Meteorology plays an important ro le in determ ining the areas im pacted by prescribed burns.

• EC, the prim ary m a rke r of s m oke, is a relatively sm all part of both the PM 2. 5 m a ss and light extinction.

• Ammonium sulfate is generally th e largest contributor to the PM 2. 5 m a ss and ligh t extinction; this com ponent does not originate fr om burns. This finding is consistent w ith other work in the Midwest and CENRAP re gion including Big Bend National Park and Seney W ildlife Refuge.

C.11 RECOMMENDATIONS FOR FUTURE WORK

Additional analyses could be conducted to better quantify the influence of burns on visibility im pairm e nt. Such analyses could include:

• Apply analyses conducted in this task to addi tional IMPROVE sites, such as in Kansas or Minnesota to investigate whether results in th is task are indicative of trends throughout the CENRAP region

• Utilize continuous PM 2. 5 in conjunc tion w ith m e teorolog ical data to d e te r m ine w h at

m e teorological cond itio ns m a y be responsible fo r changes in PM 2. 5 concentrations.

• Apply source apportionm e nt tools such as UNMIX or Positive Matrix F actorization (PMF) to quantify influen ce of specific source types at a site using 24-hour (i.e., IMPROVE, STN, etc) or continuous speciated data (such as at Bondville or St. Louis). These tools can be used to identify individual s ources such as diesel, wood burning, etc.

• Develop a better conceptual m odel of PM 2. 5 in th e C E N R A P region :

− Are there d i fferences in PM 2. 5 composition an d meteorology among different locations in the CENRAP region? Significant differences in PM 2. 5 concentrations and

com position am ong sites in different geogra phic locations within the CENRAP region

m a y provide insight into PM 2. 5 tran sp ort and formation. For exam ple, a surface high

pressur e sys t em located over the U p per Midw es t w ill of ten d r ive sou t hea s ter l y w i nds across the CENRAP region, which ca n transport higher levels of PM 2. 5 from upwind sources within m a jor population centers.

− How are P M 2. 5 concentrations and visibility depe ndent on large-scale meteorological patterns? T h e effect of large-s cale synoptic patterns on PM 2. 5 concentrations and regional haze is a critical issue because synopt ic patterns affect tran sport, vertical and horizontal dispersion, form ation, and the im pact of local em issions on an area. For

exam ple, tra n sport of w a rm , m o ist air f r om the Gulf of Mexico m a y result in secondary particle form ation within th e CENRAP region, reducing visibility.

C-23− What are the compositional and meteorologi cal differences betw een days of high and low PM 2. 5 c oncentra tion s ? Di fferences i n PM 2. 5 com position m a y indicate different transpo r t r e g i m e s, and m i ght id entif y which species are dom inant on high PM 2. 5 days, both of which would assist forecas ters. One useful way of exam ining the

m e teorology on these days is to perform se veral case study analyses of high and low
PM 2. 5 concentration episodes. A typical case stud y analys is would exam ine:

• Upper-air and surface synop tic patterns for each day . These patterns as sist

m e teorologists in de te rm ining th e extent to w h ich particle s m a y be allow e d to

m i x, or disperse. For exam ple, an upper-l evel high pressure system is typically associa t ed w ith sink ing a i r, w h ich w ill help to tr ap particle s ne ar the su rf ac e.

• Vertica l tem p erature sou ndings whenever ava ilab l e . Vertical tem p erature soundings give m e teorologists the ability to assess the vertical structure of the

atm o sphere, in particular, how m u ch ve rtical m i xing can occur. Typically, a strong su rface-based inv e rsion will trap pa rticles near th e surf ace, allowin g PM 2. 5 levels to be high.

• Back-tra j ectories fo r each day . Back-trajectories provide m e teorolog ists w ith a tool for assessing whether transport of pa rtic les could have o ccurr ed w ith in a region or from another region.

C-24