1360 Redwood Way, Suite C 

Petaluma, CA 94954-1169 

707/665-9900 

FAX 707/665-9800 

www.sonomatech.com

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, 2004 

QUALITY ASSURANCE STATEMENT 

This report was reviewed and approved by the project Quality Assurance (QA) Officer or

his delegated representatives, as provided in the project QA Plan (Coe, 2003a). 

 

Lyle R. Chinkin 

 Project QA Officer

iii
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TABLE OF CONTENTS 

Section Page

QUALITY ASSURANCE STATEMENT .................................................................................... iii 

LIST OF FIGURES ...................................................................................................................... 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 Concentrations and Impaired Visibility in 

Class I Areas................................................................................................. 1-1 

1.1.2 Status of Existing Planned Burning Inventories........................................... 1-2 

1.2 Current Status of the CENRAP Emission Inventory................................................ 1-3 

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

2.1 Emissions From Prescribed Burning ........................................................................ 2-1 

2.1.1 Summary of Emissions from Prescribed Burning........................................ 2-1 

2.1.2 Assessment of Prescribed Burning Emissions ............................................. 2-4 

2.2 Emissions from Agricultural Burning ...................................................................... 2-5 

2.2.1 Summary of Emissions from Agricultural Burning ..................................... 2-5 

2.2.2 Assessment of Emissions 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 Summary of Findings ............................................................................................... 3-1 

4. RECOMMENDATIONS FOR FURTHER RESEARCH ................................................. 4-1 

4.1 Recommendations for Improving Prescribed Burning Activity Data ...................... 4-1 

4.2 Recommendations for Improving Agricultural Burning Activity Data.................... 4-2 

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

4.4 Recommendations for Additional Ambient Data Analysis...................................... 4-3 

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

APPENDIX A: EMISSION ESTIMATION METHODS FOR THE CENRAP PLANNED 

BURNING EMISSION INVENTORIES........................................................ A-1 

APPENDIX B: ANNUAL EMISSIONS BY STATE AND SOURCE CATEGORY...............B-1 

APPENDIX C: ASSESSMENT OF INFLUENCE FROM PRESCRIBED BURNING ON 

CLASS I SITES USING AMBIENT SPECIATED PM2.5 DATA...................C-1 

APPENDIX D: EMISSION INVENTORY FILE DOCUMENTATION AND 

ELECTRONIC FILES............................................................................... ON CD 

v
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LIST OF FIGURES 

Figure Page

ES-1. CENRAP annual planned burning emissions by pollutant............................................ES-2 

ES-2. CENRAP annual planned burning PM2.5 emissions by source category.......................ES-2 

ES-3. Monthly variability in total emissions for the CENRAP region....................................ES-3 

1-1. Compositions of annual average concentrations of PM2.5 observations in urban 

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

1-2. Total annual PM2.5 emissions by type of planned burning for each state of the 

CENRAP region............................................................................................................... 1-5

1-3. Example map of daily emission densities for the CENRAP region ................................ 1-5 

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

2-2. Annual prescribed burning PM2.5 emissions by state ...................................................... 2-3 

2-3. Example map of daily PM2.5 emissions from prescribed burning ................................... 2-3 

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

2-5. PM2.5 emissions from agricultural burning by state......................................................... 2-7 

2-6. Percent contribution by crop type to state PM2.5 emissions from 

agricultural burning.......................................................................................................... 2-7 

2-7. Example map of daily agricultural burning emissions .................................................... 2-8 

2-8. Monthly variation in emissions from agricultural burning by state................................. 2-9 

vii
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LIST OF TABLES 

Table Page

1-1. 1999 NEI estimates of PM2.5 emissions in the CENRAP region..................................... 1-3 

2-1. 2002 acres burned and emissions for prescribed burning in CENRAP states................. 2-1 

2-2. 2002 acres burned and emissions for agricultural burning in CENRAP states ............... 2-6 

2-3. 2001 logging industry employment by state.................................................................. 2-10 

ix

EXECUTIVE SUMMARY 

The Central States Regional Air Planning Association (CENRAP) is researching 

visibility-related issues for its region and is developing a regional haze plan in response to the 

U.S. Environmental Protection Agency’s (EPA) mandate to protect visibility in Class I areas. 

Agricultural and prescribed burning activities (“planned burning”) contribute to episodes of 

impaired visibility in the CENRAP region-phenomena that the CENRAP seeks to better 

understand. Therefore, support of the CENRAP’s need to develop a regional haze plan, Sonoma

Technology, Inc. (STI) developed planned burning emission inventories for the region. 

As detailed in the Methods Document, presented in Appendix A, Emission Estimation 

Methods for the CENRAP Planned Burning Emission Inventories (Methods Document), 

emissions estimates were prepared for prescribed and agricultural burning activities on federal, 

state, tribal, and private lands in the CENRAP region. These “bottom up” estimates were 

prepared by using the First-Order Fire Effects Model (FOFEM), emission factors and fuel 

loadings gathered from published literature, geographic information systems (GIS) databases of 

land cover and vegetation, and activity data gathered through telephone surveys. 

Year-2002 PM2.5 emissions of particulate matter of less than 2.5 μm aerodynamic 

diameter (PM2.5) from planned burning activities in the CENRAP states were estimated to be 

317,000 tons (see Figure ES-1)—almost 300% higher than the estimate of 110,000 tons of PM2.5

prepared by the EPA for the 1999 National Emission Inventory (NEI). In addition, planned 

burning activities emitted precursors to chemically formed PM2.5, including approximately 

239,000 tons per year volatile organic compounds (VOC), 80,000 tons per year 

nitrogen oxides (NOx), 47,000 tons per year ammonia (NH3), and 35,000 tons per year sulfur 

oxides (SOx). The most significant source of these emissions was the burning of private 

rangelands, which accounted for 50% of the annual planned burning PM2.5 emissions in the 

CENRAP region. This source category was especially significant in the states of Texas, 

Oklahoma, and Kansas. Prescribed burning on publicly managed forest and grasslands was the 

second most significant source of planned burning emissions in the region, accounting for 32% 

of the annual planned burning PM2.5 emissions (see Figure ES-2). This source category was 

especially important in the states of Arkansas, Louisiana, and Minnesota. (Emission estimates 

by source category and state are tabulated in Appendix B.) 

Planned burning emissions peak in the spring. More than 25% of annual activity occurs 

during the month of March. A smaller peak in emissions occurs during the months of September 

and October (see Figure ES-3). Spring and fall provide the most advantageous climatological 

and biological conditions for prescribed burning, while agricultural burns tend to occur before 

spring planting or after fall harvesting. 

ES-1
0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

PM2.5 VOC NOx NH3 SO2

Pollutant

E

mis

sions (tons/y

e

ar)

Figure ES-1. CENRAP annual planned burning emissions by pollutant. 

Prescribed

32%

Rangeland

50%

Cropland

18%

Figure ES-2. CENRAP annual planned burning PM2.5 emissions by source category. 

ES-2
0%

5%

10%

15%

20%

25%

30%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

P

erc

e

ntage of Pla

nned B

urnin

g

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

The planned burning emission inventory and speciated PM2.5 data from the Interagency 

Monitoring of Protected Visual Environments (IMPROVE) network stations located in Class I 

areas in the CENRAP region were used to investigate the influence of smoke on ambient 

PM2.5 concentrations and whether individual burns can be detected in the air quality data of Class 

I areas. The emission inventory and IMPROVE data were utilized to better understand the extent 

to which prescribed burning affects visibility in the CENRAP region. This preliminary analysis 

showed that, while influence from specific burns could be seen in the monitoring data on select 

days when the meteorology was conducive, ammonium sulfate (a species that does not result 

from burning) was the dominant constituent of the PM2.5 mass and visibility reduction, 

particularly on the 20% worst visibility days of the year, for the sites analyzed. 

ES-3

1. INTRODUCTION 

The Central States Regional Air Planning Association (CENRAP) is researching 

visibility-related issues for its region, which includes the states of Texas, Oklahoma, Louisiana, 

Arkansas, Kansas, Missouri, Nebraska, Iowa, and Minnesota, and is developing a regional haze 

plan in response to the U.S. Environmental Protection Agency’s (EPA) mandate to protect 

visibility in Class I areas.

1

 In order to develop an effective regional haze plan, the CENRAP 

ultimately must develop a conceptual model of the phenomena that lead to episodes of low 

visibility in the CENRAP region. Episodic combustion events (such as agricultural burning, 

prescribed burning, open burning of wastes, structural fires, and wildfires) sometimes contribute 

to regional or local haze events in the CENRAP region. Therefore, it is important to develop the 

emissions data necessary to assess the impacts of these events on visibility in the CENRAP 

region. 

In support of the CENRAP’s need to develop a regional haze plan, Sonoma Technology, 

Inc. (STI) conducted CENRAP Work Assignment Number 02-0214-RP-003-002 “Research and 

Development of Emission Inventories for Planned Burning Activities for the Central States

Regional Air Planning Association”. Consistent with the project goals presented in the Work 

Plan (Coe, 2003b), emissions were calculated for agricultural and prescribed burning on federal, 

state, tribal, and private lands. 

1.1 BACKGROUND AND KEY ISSUES 

1.1.1 Fine Particulate Matter Concentrations and Impaired Visibility in Class I Areas 

Regional haze is visibility impairment caused mainly by particles of less than 2.5 microns 

in diameter (PM2.5). PM2.5 may be directly emitted from emissions sources, such as sources of

fugitive dust and combustion soot, which are termed sources of “primary PM2.5”. Additional 

mechanisms also occur allowing PM2.5 to be formed in the atmosphere, and this phenomenon is 

termed “secondary formation”. Examples include condensable organic aerosols which can form

from air emissions of semi-volatile and heavy organic compounds and PM2.5 that can form from 

photochemical reactions of gaseous precursors, including sulfur oxides (SOx), nitrogen 

oxides (NOx), volatile organic compounds (VOC), and ammonia (NH3). 

Analyses of speciated PM2.5 samples provide an understanding of the types of emission 

sources that contribute to regional haze issues in different areas, as depicted in Figure 1-1. In 

urban and ammonia-depleted areas of the eastern United States, secondary sulfate contributes a 

more significant amount of PM2.5 than it does in the western United States. Conversely, 

secondary nitrate is more important in urban and ammonia-rich areas of the western United 

States than it is in eastern areas. In both the eastern and western United States, the carbonaceous 

fraction of PM2.5 is significant in urban areas. In rural areas, geologic dust can also be an 

important contributor to PM2.5. 

 

1

 Class I lands include areas such as national parks, wilderness areas, and national monuments. These areas have

been granted special air quality protections under the Federal Clean Air Act.

1-1
Figure 1-1. Compositions of annual average concentrations of PM2.5 observations in urban 

locations (U.S. Environmental Protection Agency, 1998). 

Of particular interest in the CENRAP region is the contribution of PM2.5 from wood and 

grassland burning to visibility impairment in Class I areas. Smoke from these fires emit organic 

carbon (OC) and elemental carbon (EC); the latter is sometimes referred to as soot or black 

carbon (BC). OC comes from many sources, both combustion and evaporative, while EC only 

originates from combustion sources, such as fossil fuel combustion (power plants, car exhaust, 

etc.) or woodland or grassland burning. Potassium (K) is also emitted during burning of natural 

materials and can be used as a marker for woodland or grassland burning. 

1.1.2 Status of Existing Planned Burning Inventories 

Historically, few areas of the CENRAP region have experienced significant air quality

problems and, therefore, have not been required to perform air quality monitoring or develop 

emission inventories. The most comprehensive source of emissions estimates currently available 

for the region is the EPA’s National Emissions Inventory (NEI), which is used as the basis of the 

EPA’s National Emission Trends (NET) document series and analyses. On a state level, 

emission inventories of burning activities have been prepared by Dennis et al. (2002) for Texas. 

In the NEI, estimates of PM2.5 emissions from planned burning activities in the CENRAP region 

amount to 110,000 tons per year, or about 9% of the total PM2.5 inventory for the region 

(see Table 1-1). The NEI indicates that planned burning emissions are particularly significant in 

the states of Louisiana and Texas. 

1-2
Table 1-1. 1999 NEI estimates of PM2.5 emissions in the CENRAP region. 

 PM2.5 (tons) 

State All 

Sources 

Planned 

Burning Percent 

Arkansas 91,294 6,735 7.4%

Iowa 108,641 402 0.4%

Kansas 158,521 9,502 6.0%

Louisiana 94,522 34,099 36.1%

Minnesota 163,542 2,874 1.8%

Missouri 183,245 1,147 0.6%

Nebraska 131,486 2,576 2.0%

Oklahoma 149,015 7,137 4.8%

Texas 223,427 45,748 20.5%

Total 1,303,694 110,220 8.5%

As part of its research into regional haze, CENRAP has decided to conduct 

comprehensive air quality modeling of visibility in 2002. To support this modeling, a bottom-up 

planned burning emission inventory, which incorporated year-2002-specific fire history data and 

addressed the uncertainties of the NEI (see below) is required. 

Some uncertainties are inherent to the NEI: 

• Prescribed burning activities fluctuate dramatically from year to year. Fluctuations are 

due to policy decisions about the need for wildfire risk management, current climate 

conditions (drought versus wet conditions), and densities of undergrowth and fuel. 

Because of these wide fluctuations, emission inventories of prescribed burning are nearly 

impossible to predict or project on the basis of historical inventories or trends. 

• The NEI is estimated on an annual average basis. Regional haze has a seasonal character 

and is partly driven by photochemical processes. Adjustments are necessary to develop

seasonal, diurnal, and, possibly, day-of-week emission estimates. 

To support modeling sensitivity runs, measures of uncertainty for all emission estimates 

are highly valuable for policy decisions and prioritization of future research efforts. To the 

extent possible, we provide estimates of uncertainty for emissions associated with planned 

burning activity data that were gathered for this project. 

1.2 CURRENT STATUS OF THE CENRAP EMISSION INVENTORY 

As detailed in the attached Methods Document (Appendix A), emissions estimates were 

prepared for prescribed and agricultural burning activities on federal, state, tribal, and private 

lands in the CENRAP region. These “bottom up” estimates were prepared by using the FirstOrder Fire Effects Model (FOFEM), emission factors and fuel loadings gathered from published 

literature, geographic information systems (GIS) databases of land cover and vegetation, and 

activity data gathered through telephone surveys. 

1-3
The FOFEM model, a computing tool developed through the Joint Fire Science Program

(JFSP), was used to generate estimates of fuel loadings and emission rates for prescribed burns. 

FOFEM was run for local vegetation types using fuel moisture inputs from the Weather 

Information Management System (WIMS), a database 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 information to calculate emissions. 

For agricultural burning, emission factors and fuel-loading factors for a variety of crop 

types are available in the EPA’s guidance document, “Compilation of Air Pollutant Emission 

Factors (AP-42)” (U.S. Environmental Protection Agency, 2003) and from Jenkins et al. (1996). 

From these sources, we identified fuel loading factors and emission factors for a wide variety 

crop types and applied these factors to county-specific agricultural burning activity data to 

generate emissions estimates. The activity data were obtained through systematic telephone 

surveys of county agricultural extension services (AES). 

For both prescribed and agricultural burning activities, the EPA’s Biogenic Emissions 

Landcover Database (BELD) Version 3 (U.S. Environmental 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 database with (a) vegetation types in FOFEM and (b) crop 

types for which emissions factors and fuel loadings are available. 

Once a map of vegetation and crop types was developed, we overlaid histories of planned 

fires, identified the vegetation types associated with each fire occurrence, and applied emission 

factors generated through FOFEM or acquired from literature to produce county-level emission 

inventories of agricultural and prescribed burning. 

The resulting emission inventory is illustrated in Figures 1-2 and 1-3 and tabulated in 

Appendix B. In all cases, we applied generally accepted emission factors and the most complete 

and up-to-date activity data sets that could be identified and acquired. However, we 

acknowledge that available emission factors are uncertain and they continue to be the subject of 

research. 

The emission source type in the inventory that we qualitatively consider to contribute the 

greatest degrees of uncertainty to the total estimated emissions is prescribed burning on 

privately-held lands performed by the forestry industry. Since new information will be needed 

to reduce uncertainties in the future, we have provided the CENRAP with an inventory and 

system of data files that can be updated with revised emission factors and activity data as new 

information becomes available (see Appendix D). 

1-4
0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

AR IA KS LA MN MO NE OK TX

E

missi

o

n

s (to

n

s/year)

Prescribed Cropland Rangeland

Figure 1-2. Total annual PM2.5 emissions by type of planned burning for each state in the 

CENRAP region. 

Figure 1-3. Example map of daily emission densities for the CENRAP region (for 

April 10, 2002). 

1-5

2-1

2. SUMMARY AND ASSESSMENT OF THE INVENTORY 

STI calculated emissions as detailed in Appendix A, Emission Estimation Methods for 

the CENRAP Planned Burning Emission Inventories, with results tabulated in Appendix B, 

Tabulation of Planned Burning Emissions Estimates 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 source categories are estimated to be 

rangeland burning and prescribed burning on publicly managed lands. Total emissions vary 

seasonally by a factor of three, with peaks occurring in the spring and fall. Prescribed burning 

performed on privately held lands by the forestry industry 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 prescribed 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 PM2.5 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 and emissions (tons) for prescribed burning in CENRAP states. 

STATE 

Acres 

Burned PM10 PM2.5 CO NOx SO2 NH3 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 76,889
2-2

0

20,000

40,000

60,000

80,000

100,000

120,000

PM2.5 VOC NOx NH3 SO2

Pollutant

Emissions (tons/year)

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

Whenever possible, the exact location, start 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 prescribed 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. Approximately 40% of the prescribed burning 

inventory was allocated spatially and temporally as point sources. Emissions from the remaining 

prescribed burning activities were treated as area sources. States that were able to provide 

“incident-level” databases of prescribed burn activity included Arkansas, Minnesota, and eastern 

Oklahoma. 

The level of prescribed burning activities varied 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 Texas were the second most active; only limited 

prescribed burning activity occurred in the states of Iowa, Kansas, and Nebraska. 

The seasonal variability of prescribed burning emissions follows a bimodal pattern, with 

a large peak in spring and a smaller peak in fall. Factors that influence the seasonal variability of 

burning include weather conditions, fuel moisture content, and the intended environmental 

consequences of the burn (Dixon et al., 1989). Analysis of fire history records showed that all 

CENRAP states except Minnesota followed a similar seasonal pattern for prescribed burning. 

The longer winters in Minnesota delay the spring peak from March 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 acceptable because the burn areas are 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

State

Emissions (tons/year)

Figure 2-2. Annual prescribed burning PM2.5 emissions by state. 

Figure 2-3. Example map of daily PM2.5 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 Dec

Percentage 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 for 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 treated as point sources for spatial allocation 

purposes. 

As shown in Figure 2-3, emissions from prescribed burning are most significant in the 

region from western Arkansas/Louisiana to eastern Texas/Oklahoma. This is to be expected 

because prescribed burning is more widely practiced 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 burned, respectively (Dennis et al., 2002). 

Prescribed burning accounts for about 30% of the annual planned burning PM2.5

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 considered, prescribed burning accounts for about 

80% of the annual planned burning PM2.5 emissions. 

Areas of uncertainty related to prescribed burning emissions estimates arise from 

differences in how fire activity is tracked and reported in each state. For example, for Arkansas, 

Minnesota, and the northeastern portion of Oklahoma, fire data is available at an “incident 
2-5

level,” meaning a fire’s date and location were listed in each fire history record. However, other 

states did not track this level of detail, instead reporting fire 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 performed by individuals, private companies, and organizations such 

as TNC and the Audubon Society. However, permitting 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 most burns on private lands are likely 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; Miedtke, 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 agricultural burning activities on private rangeland 

and cropland in each of the CENRAP states. It was determined that agricultural burning resulted 

in the burning of about 13 million acres in 2002 in the CENRAP region, with consequent PM2.5

emissions of over 200,000 tons (see Table 2-2). 

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

STATE 

Acres 

Burned PM10 PM2.5 NOx SO2 NH3 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. 
 Emissions from agricultural burning contribute 70% to total planned burning estimated 

PM2.5 emissions for the CENRAP region, ranging from 1% to 99% of total planned burning 

emissions from state to state. The most important crop/land use types are rangeland (especially 

in the states of Texas, Oklahoma, and Kansas) and wheat (especially in the states of Kansas, 

Oklahoma, and Arkansas), although sugarcane burning is significant in the state of Louisiana. 

Figures 2-5 and 2-6 illustrate the overall emission levels by state and the relative importance of

each crop type in each state, and Figure 2-7 shows the geographic allocation of agricultural 

burning emissions throughout the CENRAP region. 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

AR IA KS LA MN MO NE OK TX

State

P

M2.

5 E

missi

o

ns (to

n

s/y

e

ar)

Other

Rice

Soybeans

Corn

Sugarcane

Hay/Alfalfa

Wheat

Rangeland

Figure 2-5. PM2.5 emissions from agricultural burning by state. 

2-6
0%

20%

40%

60%

80%

100%

AR IA KS LA MN MO NE OK TX

State

P

erc

e

ntage of P

M

2.5 E

mis

sions

Other

Rice

Soybeans

Corn

Sugarcane

Hay/Alfalfa

Wheat

Rangeland

Figure 2-6. Percent contribution by crop type to state PM2.5 emissions from agricultural 

burning. 

Figure 2-7. Example map of daily agricultural burning emissions (for April 10, 2002). 

2-7
Emissions from agricultural burning tend to follow a bimodal pattern of seasonal 

variability, with large peaks in the spring and smaller peaks in the fall (see Figure 2-8). For 

most states, the month with the highest emissions from agricultural burning is March, although 

northern states like Minnesota and Iowa show a spring peak in May. For Arkansas and 

Louisiana, the highest emissions occur in September and October, respectively, which is due to 

the large acreages of winter wheat (Arkansas) and sugarcane (Louisiana) burned in those states. 

2.2.2 Assessment of Emissions from Agricultural Burning 

The “bottom up” survey data gathered for the agricultural burning portion of this 

inventory made it possible to generate emissions estimates that take into account county-level 

burn practices for each CENRAP state, including information on the timing of burns and the 

techniques used to burn individual crops. 

This study indicates that agricultural burning practices vary widely from state to state and 

even county to county. For example, 54 of the 56 counties surveyed in Iowa reported no 

agricultural burning, as did 50 of the 77 counties surveyed in Minnesota. Among states that do 

burn extensively, practices vary by crop type. The survey indicates that burning is widely 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 amounts of wheat burns more than 15% of the crop 

annually. 

0%

5%

10%

15%

20%

25%

30%

35%

40%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Perc

e

nta

g

e of A

gric

ult

ural B

urnin

g

AR

IA

KS

LA

MN

MO

NE

OK

TX

Figure 2-8. Monthly variation in emissions from agricultural burning by state. 

2-8
It is also important to note that while agricultural burning accounts for about 70% of the 

annual PM2.5 emissions from planned burning activity for the CENRAP region as a whole, 

almost 90% of the agricultural burning emissions occur in three states: Texas, Oklahoma, and 

Kansas. Moreover, about 70% of all agricultural burning emissions in the CENRAP states result 

from the burning of rangeland in these three states. 

Uncertainties related to agricultural burning emissions result largely from an incomplete 

understanding of local regulations pertaining to such burning. For example, several states with a 

significant number of counties including Iowa, Minnesota, Nebraska, and Missouri reported no 

agricultural burning. These reports of no burning may be due to local restrictions on agricultural 

burning or other factors. Also, survey responses for each state were extrapolated to generate a 

statewide burn profile by crop type, and these profiles were used to represent all counties for 

which no survey data were available. However, further investigation is necessary to determine if

burn practices vary within individual states enough to warrant subdividing certain states into 

regions.4

2.3 MISCELLANEOUS BURNING SOURCE CATEGORIES 

Several subcategories of miscellaneous prescribed burning occur within the CENRAP 

region. Most of these burn types relate to the disposal of waste materials and, therefore, were not 

included in the final emissions inventory. However, some information on these burns was 

gathered during the course of the project and is summarized 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 practiced by private timber companies. Based on 

employment estimates for the logging industry (U.S. Census Bureau, 2003), states in the 

CENRAP domain that produce significant amounts of timber are Arkansas, Louisiana, 

Minnesota, and Texas (see Table 2-3). 

Table 2-3. 2001 logging industry employment by state. 

State Number of employees 

Arkansas 2,914

Iowa 175

Kansas 65

Louisiana 3,325

Minnesota 1,019

Missouri 378

Nebraska 65

Oklahoma 281

Texas 2,227

 

4

 A subregional approach was used for wheat and rangeland burning in the state of Kansas, and such an approach 

may be applicable to other states/crop types. 

2-9
To illustrate the relative significance of slash burning, Allen & Dennis (2000) report 

55,000 acres of logging-related slash burning in Texas 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-managed lands), very few burns were identified as slash burns—

amounting to 400 acres in Minnesota and less than 5 acres in Oklahoma and Iowa (no other 

states identified burns as slash). 

Additionally, the state of Arkansas reports 50,000 acres of “site preparation burning,” 

which are burns largely conducted by the timber industry to prepare lands for reforestation. It is 

likely that some of these burns involve slash fuels, though fuel model information was not 

tracked in the Arkansas database. Similarly, significant numbers of site preparation burns were 

included in the data we received from the state of 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 assigned the higher fuel loadings that would be associated with 

slash fuels. 

Pile Burning

As the name suggests, “pile” burning involves disposing of waste material by gathering 

the material into piles and burning it. Types of waste material include leaves and yard waste, 

logging residue, and brush or trees cleared from land for development purposes. With the 

exception of the state of Oklahoma, very few pile burns were included in the data provided to 

STI. For Oklahoma, a 15-county region in the northeastern part of the state that requires burn 

permits 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 permits for private burns, and approximately 60,000 such permits were 

issued in 2002. It was estimated that 65% (39,000) of these permits would correspond to either 

pile burns or ditch/fencerow burns (covered in the next section), with the remaining 35% largely 

represented by burns on open land and rangeland that would be captured by the agricultural 

survey (Meadows, 2004). 

To roughly estimate the possible emissions resulting from pile burns in Oklahoma and 

Minnesota, a fuel loading for a sizeable6

 pile burn published by the California Air Resources 

Board (2003) and emission factors published by the U.S. Environmental Protection Agency 

(2003) were applied to the number of pile burns in those states. (It was assumed that half of the 

39,000 permits referenced above were for pile burns, and the 750 pile burns in Oklahoma were 

multiplied by 5 to extrapolate from 15 counties to all 77 counties in the state). PM2.5 emissions 

were estimated as follows: 

OK: PM2.5 = (750 x 5) piles x pile

1.36 tons

 x 

ton

14 lbs- PM2.5

 x 

2000 lbs

ton = 36 tons 

 5

 Personnel at the Minnesota Dispatch of the National Interagency Fire Center estimated that 75% of the site 

preparation burning in Minnesota was included in the data provided to STI. Site preparation burns not included in 

the data set would be those conducted by private landowners or companies. 6

 Fuel loadings for a burn 12 feet in diameter and 8 feet high were used. 

2-10
MN: PM2.5 = (39,000 / 2) piles x pile

1.36 tons

 x 

ton

14 lbs- PM2.5

 x 

2000 lbs

ton = 186 tons 

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

respectively, of the PM2.5 emissions already included in the planned burning inventory for these 

states. Pile burns in other states cannot be characterized with the data currently available. 

Ditch and Fencerow Burning

Fires are sometimes used for weed abatement purposes along roadsides and fencerows. 

In the data obtained by STI, no individual fires were identified as ditch or fencerow burns, and 

because such fires are generally small in scale and practiced by private parties, it is likely that 

few such burns are included. 

The only state where some assessment of these burns appears to be possible is Minnesota. 

As previously stated, approximately 39,000 of the 60,000 burn permits issued in that state each 

year are for pile burns and ditch/fencerow burns. To provide a rough estimate of emissions from

this source, it was assumed that half these 39,000 burns were ditch burns, and that each burn 

covered 0.25 acres (Meadows, 2004). Using emission factors published by the 

U.S. Environmental Protection Agency (2003), PM2.5 emissions were estimated as follows: 

PM2.5 = (39,000 / 2) burns x burn

0.25 acres

 x 

acre

1 ton

 x 

ton

15 lbs- PM2.5

 x

2000 lbs

ton = 37 tons 

This estimate amounts to only 0.2% of the 16,000 tons of PM2.5 already included in the 

planned burning inventory for Minnesota. Ditch and fencerow burns in other states cannot be 

characterized with the data currently available.

2-11

3. SUMMARY AND ASSESSMENT OF THE AIR QUALITY DATA ANALYSIS

3.1 OBJECTIVE AND APPROACH 

The objective of this analysis was to use ambient speciated PM2.5 data from Class I areas 

(from the Interagency Monitoring of Protected Visual Environments [IMPROVE] network) in 

the CENRAP states 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 PM2.5

and visibility at a few monitoring sites. The following approach was employed: 

• Assess the seasonal chemical compositions of PM2.5 mass and aerosol light extinction to 

determine what individual species are important to the mass and visibility extinction in

the area. 

• Determine seasonal concentrations of and ratios 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 potentially influenced by prescribed burning. 

• Assess chemical compositions of PM2.5 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 typically important in visibility reduction?). 

• Analyze IMPROVE data, specifically OC, EC, and K concentrations, on dates when 

extensive burning occurred near a monitoring site in order to assess whether wood smoke 

influences are seen in the ambient measurements and significantly impact visibility. 

• Analyze emissions data on days when elevated OC, EC, and K concentrations occurred at 

IMPROVE sites to determine whether 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 the observed effects and to determine the extent to which 

meteorology affects haze. 

3.2 SUMMARY OF FINDINGS 

Details on data, methodology, and results from this analysis are provided in Appendix C. 

This work yielded the following findings: 

• Speciated PM2.5 data can be used to determine influence from planned burns when the 

meteorology is conducive to transport from the burn area to a Class I site. 

• Smoke constituents, specifically EC and K, were not a significant fraction of the PM2.5 

mass and light extinction, even on days when there was evidence of planned burning 

influence, at the sites examined in this preliminary study. 

• Ammonium sulfate, which is not generated from burning, is the dominant constituent of

the PM2.5 mass and light extinction, especially on the 20% worst visibility days. This 

3-1
finding is consistent with other work in the Midwest and CENRAP regions including 

studies of Big Bend National Park and Seney Wildlife Refuge. 

• On some days, influences from known prescribed burns were seen, though they were 

generally less than 10% of the PM2.5 mass and light extinction. Improved spatially and 

temporally resolved emission inventories and additional case studies may show different 

results. 

• The specific influence of smoke on PM2.5 mass and light extinction could be better 

quantified using additional analyses, including source apportionment. 

3-2
4. RECOMMENDATIONS FOR FURTHER RESEARCH 

This study provided an improved and updated emission inventory for planned burning in 

the CENRAP states for year 2002. Preliminary examination of ambient measurements along 

with the inventory generated in this study suggests that planned burning may contribute to 

visibility impairment at Class I sites in the CENRAP states. As noted in previous sections of this 

report, we identified the following significant sources of uncertainty (roughly in order of 

importance): (1) the extent of fires performed by the USFS on publicly managed lands, (2) the 

extent of prescribed burning on privately held lands performed by the forestry industry and 

organizations such as TNC, (3) a need to better understand county-level open burning 

regulations, and (4) the fuel loadings and emission factors used for planned burning emissions 

estimates—particularly for prescribed burning in the state of Minnesota. In this section, we

provide recommendations for improving each of these aspects of the inventory and describe 

additional analyses that could be conducted to better quantify the influence of planned burning 

on visibility impairment. 

4.1 RECOMMENDATIONS FOR IMPROVING PRESCRIBED BURNING 

ACTIVITY DATA 

As discussed in Section 2.1.2, significant differences exist in the way fire activity data is 

tracked and reported in each state; some states (such as Arkansas and Minnesota) capture a fire’s 

exact date and location coordinates, and other states track fires only by region and month. 

Encouraging individual states to maintain “incident level” databases of fire activity would allow

all prescribed fires to be treated as discrete point sources and improve the geographic and 

temporal resolution of the inventory. 

Also, differences from state to state are even more pronounced for burns performed on 

privately held lands by individuals, private companies, and organizations such as TNC and the 

Audubon Society. However, permitting or reporting requirements are not consistent among the 

nine CENRAP states, and few states were able to provide us with reliable data on these burns.7

 

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 is recommended that 

further efforts be made to survey private parties regarding their burn activities, especially in the 

Piney Woods region of eastern Texas, where private timber companies have conducted 

significant amounts of prescribed burning in past years (Allen & Dennis, 2000)8

. 

It should be noted, though, that most burns on private lands are likely to be related to 

agriculture or waste management (such as the burning of logging residue by forestry companies 

 7

 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. 

8

 For purposes of this inventory, acres burned in 1996 and 1997 by private timber companies in the Piney Woods

region were averaged to produce an estimate of 20,000 acres per year. 

4-1
or pile burns by land developers) (Altman, 2004; Miedtke, 2004). The former burns are covered 

by the agricultural survey, and the latter burns are not included in the scope of this project. 

Finally, alternative and newly emerging data sources such as satellite data and related 

products recently developed by the National Oceanic and Atmospheric Administration (NOAA) 

should be explored to help characterize fire locations and day-specific activity levels. 

4.2 RECOMMENDATIONS FOR IMPROVING AGRICULTURAL BURNING 

ACTIVITY DATA 

As stated in Section 2.2.2 of this report, uncertainties related to agricultural burning 

emissions result largely from an incomplete understanding of local regulations pertaining to such 

burning. Several states, including Iowa, Minnesota, Nebraska, and Missouri, had significant 

numbers of counties that reported no agricultural burning. It is recommended that further 

investigation be undertaken to gain a fuller understanding of county-level open burning 

restrictions, as well as an estimate of how such restrictions are enforced. Further discussions 

with county AES, as well as with individual farmers, could be used to acquire this information. 

Also, survey responses for each state were extrapolated to generate a statewide burn 

profile by crop type, and these profiles were used to represent all counties for which no survey 

data were available. For the state of Kansas, however, subregional burn profiles were developed 

for wheat and rangeland burning, and further investigation is needed to determine if burn 

practices across other states vary enough to warrant subdividing these states into regions for 

certain crop types. 

4.3 RECOMMENDATIONS FOR IMPROVED FUEL LOADINGS AND EMISSION 

FACTORS

Emission factors are often a subject of research, and it is recommended that efforts be 

made to identify and incorporate improved emission factors related to prescribed and agricultural 

burning that are published in the future. Also, although the default fuel loading values by 

vegetation type contained in the FOFEM model were judged to be sufficiently representative of 

conditions in the CENRAP region, some effort should be made to study these fuel loadings 

further. During the course of this project, personnel at the USFS in Minnesota indicated that the 

default fuel loadings in FOFEM are regularly updated during the analysis of burns in that state. 

STI was provided with adjusted fuel loadings for several vegetation and fuel types, most of 

which were related to “blowdown” burns (the burning of vegetation after storms to reduce fire 

hazard). These altered fuel loadings resulted in PM2.5 emission factors up to 70% higher than 

those calculated with FOFEM default loadings. When these adjusted emission factors were 

applied to 3700 acres of burns identified by USFS personnel as blowdown, the prescribed 

burning portion of the PM2.5 inventory for Minnesota increased by about 5%. 

4-2
4.4 RECOMMENDATIONS FOR ADDITIONAL AMBIENT DATA ANALYSIS 

In addition to improvements to the emission inventory, additional analyses could be 

conducted to better quantify the influence of burns on visibility impairment: 

• Apply similar analyses to additional IMPROVE sites, such as these in Kansas or 

Minnesota, to investigate whether results of this task are indicative of the influence of 

burns throughout the CENRAP region. 

• Utilize continuous PM2.5 in conjunction with meteorological data to determine what 

meteorological conditions may be responsible for changes in PM2.5 concentrations. 

• Apply source apportionment tools such as UNMIX or Positive Matrix Factorization 

(PMF) to quantify influence of specific source types at a site using 24-hr 

(i.e., IMPROVE, Speciated Trends Network [STN], etc.) or continuous speciated data 

(such as at Bondville or St. Louis). These tools can be used to identify individual sources 

such as diesel, wood burning, etc. 

4-3

5-1

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) Influences on prescribed burning activity in the 

national forest system. International Forest Fire News (19), 43-46. Available on the 

Internet at <http://www.fire.uni-freiburg.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 Association. 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 States Regional Air Planning Association. 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 PM2.5 speciation trends data. Revised 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) Moderating meteorological fluctuations 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., Goronea 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 biomass by wind tunnel simulations. Final report, 

California Air Resources Board Project No. A932-126, April. 
5-2

Main H.H. and Roberts P.T. (2001) PM2.5 data analysis workbook. STI-900242-1988-DWB, 

February. 

Malm W.C. (1999) Introduction to visibility. Available 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: Planned burning permits in Minnesota. April 2. 

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

Pacific Environmental Services (2001) Assessment of emission inventory needs for regional 

haze plans. March. 

Sisler J.F. and Malm W.C. (2000) Interpretation of trends of PM2.5 and reconstructed visibility 

from the IMPROVE network. Journal of Air and Waste Management Association 50, 

775-789. 

U.S. Census Bureau (2003) CenStats databases - county business patterns data. Database. 

Available on the Internet at <http://censtats.census.gov/>; last accessed April 1, 2003. 

U.S. Environmental Protection Agency (1998) National air quality and emissions trends report, 

1997. Report, 454/R-98-016, December. 

U.S. Environmental Protection Agency (2001) Biogenic emissions landcover database. Available 

on the Internet at <ftp://ftp.epa.gov/amd/asmd/beld3/>; last accessed May 10, 2003. 

U.S. Environmental Protection Agency (2003) Compilation of air pollutant emission factors, AP42. Vol. 1: stationary point and area sources. 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 BURNING EMISSION INVENTORIES 

A-1
This page is intentionally blank.
1360 Redwood Way, Suite C 

Petaluma, CA 94954-1169 

707/665-9900 

FAX 707/665-9800 

www.sonomatech.com

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, 2004 

Appendix A of Final Report (STI-902514-2516-FR)
QUALITY ASSURANCE STATEMENT 

This report was reviewed and approved by the project Quality Assurance (QA) Officer or his 

delegated representative, as provided in the project QA plan. 

 

 Lyle R. Chinkin 

 Project QA Officer 

Appendix A of Final Report (STI-902514-2516-FR)
TABLE OF CONTENTS 

Section Page

1. INTRODUCTION.............................................................................................................. 1-1 

1.1 Summary of Recommended Methods ...................................................................... 1-1 

1.2 Important Assumptions............................................................................................. 1-2 

2. AGRICULTURAL BURNING.......................................................................................... 2-1 

2.1 Overview................................................................................................................... 2-1 

2.2 Agricultural Burning Emission Factors and Fuel Loadings..................................... 2-1 

2.3 Agricultural Burning Activity Data .......................................................................... 2-3 

2.4 Spatial Allocation of Agricultural Burning .............................................................. 2-4 

2.5 Temporal Allocation of Agricultural Burning.......................................................... 2-4 

2.6 Chemical Speciation of Agricultural Burning .......................................................... 2-4 

3. PRESCRIBED BURNING ................................................................................................ 3-1 

3.1 Overview................................................................................................................... 3-1 

3.2 Prescribed Burning Emission Factors 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, Tribal, 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 Temporal Allocation of Prescribed Burning............................................................. 3-7 

3.6 Chemical Speciation of Prescribed Burning............................................................. 3-7 

4. AIR QUALITY DATA ANALYSIS ................................................................................. 4-1 

5. PREPARATION OF DIGITAL EMISSION INVENTORY FILE SYSTEMS ................ 5-1 

6. REFERENCES................................................................................................................... 6-1

APPENDIX A: PROPOSED SURVEY QUESTIONNAIRE................................................... A-1 

 

APPENDIX B: VEGETATION CROSS-WALKS....................................................................B-1 

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

iii

Appendix A of Final Report (STI-902514-2516-FR)
This page is intentionally blank.
LIST OF TABLES 

Table Page

1-1. Summary of approaches to estimate planned-burning emissions.................................... 1-2 

2-1. Fuel loadings and emission factors for agricultural burning ........................................... 2-2 

2-2. Chemical speciation of agricultural burning: profile information ................................... 2-5 

3-1. Chemical speciation of prescribed burns: profile information for grasslands................. 3-7 

3-2. Chemical speciation of prescribed burns: profile information for forestlands................ 3-8

v

Appendix A of Final Report (STI-902514-2516-FR)

1. INTRODUCTION 

The Central States Regional Air Planning Association (CENRAP) is researching 

visibility-related issues for its region, which includes the states of Texas, Oklahoma, Louisiana, 

Arkansas, Kansas, Missouri, Nebraska, Iowa, and Minnesota, and is developing a regional haze 

plan in response to the U.S. Environmental Protection Agency’s (EPA) mandate to protect 

visibility in Class I areas. In order to develop an effective regional haze plan, the CENRAP 

ultimately must develop a conceptual model of the phenomena that lead to episodes of low 

visibility in the CENRAP region. It is recognized that episodic combustion events (such as 

agricultural burning, prescribed burning, open burning of wastes, structural fires, and wildfires) 

sometimes contribute to regional or local haze events in the CENRAP region. Therefore, it is 

important to develop the emissions data necessary to assess the impacts of these events on 

visibility in the CENRAP region. 

In support of the CENRAP’s need to develop a regional haze plan, Sonoma Technology, 

Inc. (STI) developed emission inventories of episodic combustion events for the CENRAP 

region. Consistent with the project goals presented in the Work Plan (Coe, 2003), the scope of 

the inventories will be limited to agricultural and prescribed burning. Wildfires, structural fires 

and waste burning (such as the “slash” burning of logging residue) were not considered in the 

development of these inventories. 

1.1 SUMMARY OF RECOMMENDED METHODS 

To develop emission inventories of planned burning activities for the CENRAP region, 

we employed existing models and information: the First-Order Fire Effects Model (FOFEM), 

emission factors gathered from published literature, and Geographic Information Systems (GIS) 

databases of land cover and vegetation. In addition, we gathered new information through 

telephone and mail surveys. 

FOFEM, a computing tool developed through the Joint Fire Science Program (JFSP), can 

be used to predict a variety of effects from fires on forested lands and rangelands, including air 

pollutant emissions, fuel consumption, tree mortality, and soil heating (Reinhardt et al., 2003; 

Reinhardt et al., 1997). For this project, the FOFEM model was used to generate estimates of 

fuel loadings and emission rates for prescribed burns. This data was then used in conjunction 

with prescribed burning history information (detailing the location, land type, season, and size of 

burn incidents) to calculate emissions from this source. Fire history data for prescribed burning 

on wildlands, publicly managed lands, tribal lands, and private lands were gathered from federal 

and state agencies, as well as some private organizations. 

For agricultural burning, emission factors and fuel-loading factors for a variety of crop 

types have been published in the EPA’s guidance document, “Compilation of Air Pollutant 

Emission Factors (AP-42)” (U.S. Environmental Protection Agency, 2003) and by Jenkins et al. 

(1996). From these sources, we identified fuel loading factors and emission factors for a wide 

variety crop types. These factors were applied to county-specific agricultural burning activity 

data to generate emissions estimates. This activity data was obtained through systematic 

telephone and mail surveys of county Agricultural Extension Services (AES). 

1-1

Appendix A of Final Report (STI-902514-2516-FR)
For both prescribed and agricultural burning activities, the EPA’s Biogenic Emissions 

Landcover Database (BELD) Version 3 (U.S. Environmental 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 database with (a) vegetation types in FOFEM and (b) crop 

types for which emissions factors and fuel loadings are available. 

Once a map of vegetation and crop types was developed, we overlaid histories of planned 

fires, identified the vegetation types associated with each fire occurrence, and applied emission 

factors generated through FOFEM or acquired from the sources described above to produce 

county-level emission inventories of agricultural and prescribed burning. Table 1-1 summarizes

sources of emission factors, activity data, and land cover data. 

Table 1-1. Summary of approaches to estimate planned-burning emissions. 

Prescribed Burning Agricultural and Rangeland Burning 

Emission 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 mail surveys of 

County Agricultural Extension 

Services 

Land cover data EPA’s BELD3 database EPA’s BELD3 database 

1.2 IMPORTANT ASSUMPTIONS 

The methods that we selected for use were based on several important assumptions: 

• Default fuel loading values by vegetation type contained in the FOFEM model are 

sufficiently representative of conditions in the CENRAP region1

. 

• The land cover/vegetation types used by the FOFEM model and those in the BELD 

database are similar enough to allow a reasonable cross-walk to be established between 

the two data sets. 

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

emission factors and fuel loadings are available to allow a reasonable cross-walk to be 

established between the two data sets. 

• County AES will be capable of providing responses that reasonably represent agricultural 

and rangeland burning activities in the CENRAP region. 

 

1

 Personnel at the U.S. Forest Service in Minnesota provided updated fuel loadings for 3,700 acres of grassland

burns and “blowdown” burns (the burning of vegetation after storms to reduce fire hazard) occurring in the Superior 

National Forest in 2002. Default fuel loadings were used in all other cases. 

1-2

Appendix A of Final Report (STI-902514-2516-FR)
2. AGRICULTURAL BURNING 

2.1 OVERVIEW

Agricultural burning is primarily a means of clearing harvested lands. Because the 

CENRAP region is largely agricultural, such activity is likely to be a source of significant 

episodic combustion emissions in most counties. Allen and Dennis (Allen and Dennis, 2000; 

Dennis et al., 2002) recently completed a study of emissions from fires in Texas, which included 

agricultural and rangeland burning in 1996 and 1997. According to their assessments, these 

types of agricultural activities emitted over 66,000 tons of particulate matter of less than 2.5 μm 

aerodynamic diameter (PM2.5) and accounted for 84% of over 3.3 million acres of vegetation 

burned in Texas during those two years. 

2.2 AGRICULTURAL BURNING EMISSION FACTORS AND FUEL LOADINGS 

Emissions from agricultural burning activities are dependent on the types of vegetation 

burned and the manner of combustion, and can be estimated using the following equation: 

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

In its Compendium of Air Pollutant Emission Factors, (AP-42) (U.S. Environmental 

Protection Agency, 2003), the EPA provides fuel loadings and emission factors for particulate 

matter (PM), carbon monoxide (CO), methane (CH4), and non-methane hydrocarbons (NMHC) 

for a variety of field and orchard crops. In some cases, AP-42 emission 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 (when a fire is started downwind). In addition, a more recent study 

at the University of California at Davis derived emission factors for the combustion of barley 

straw, corn stover, rice straw, wheat straw, and almond tree prunings (Jenkins et al., 1996). In 

this study, emission factors for CO, total hydrocarbons (THC), nitrogen oxides (NOx), sulfur 

dioxide (SO2), and PM were based on measurements collected during wind tunnel tests. 

Fuel loadings and emission factors are provided in Table 2-1. For barley, corn, rice, 

wheat, and almonds, emission factors were derived entirely from Jenkins’ (1996) study using 

average emission rates and moisture contents from two wind tunnel configurations. An emission 

factor for volatile organic compounds (VOC) was derived from Jenkins’ THC values by using 

the fraction of reactive gases equal to 0.5698 that was published in a California Air Resources 

Board (CARB) guidance document (Gaffney, 2000). For the remaining crops, emission factors 

for NOx and SO2 were set equal to Jenkins’s average values for field or orchard crops, and 

emissions factors for VOC were calculated from the CH4 and NMHC values reported in AP-42, 

again by using the CARB fraction of reactive gases. The emission factors for CO were taken 

directly from AP-42, and particulate matter of less than 10 μm aerodynamic diameter (PM10) and

PM2.5 were calculated from the PM values in AP-42 by using fractions of 0.9835 for PM10 and 

0.9379 for PM2.5 for field crops and fractions of 0.9814 for PM10 and 0.9252 for PM2.5 for 

orchard crops based on CARB’s guidance (Gaffney, 2000). Fuel loadings were taken from AP42 for all crop types. (For grasses and wild reeds, which were not reported in AP-42, the value 

for wild hay was used.) 

2-1

Appendix A of Final Report (STI-902514-2516-FR)
Table 2-1. Fuel loadings and emission factors for agricultural burning. 

(Page 1 of 2)

Emission Factors (lbs/ton)

Crop Type

Fuel 

Loading 

(tons/acre) PM10 PM2.5 CO VOC NOx SO2

 

Field Crops 

Asparagus 1.5 39.3 37.5 150.0 49.0 4.5 0.6

Barley 1.7 14.3 13.8 183.7 15.0 5.1 0.1

Corn 4.2 11.4 10.9 70.9 6.6 3.3 0.4

Cotton 1.7 7.9 7.5 176.0 3.6 4.5 0.6

Grasses 1.0 15.7 15.0 101.0 11.1 4.5 0.6

Pineapple 7.9 7.5 112.0 4.6 4.5 0.6

Rice 3.0 6.3 5.9 57.4 4.7 5.2 1.1

Safflower 1.3 17.7 16.9 144.0 14.8 4.5 0.6

Sorghum 2.9 17.7 16.9 77.0 5.1 4.5 0.6

Sugar cane 4.0 8.3 7.9 81.0 9.0 4.5 0.6 

Wheat 1.9 10.6 10.1 123.6 7.6 4.3 0.9

Unspecified 2.0 20.7 19.7 117.0 13.3 4.5 0.6

 

Alfalfa - Headfire 0.8 44.3 42.2 106.0 20.8 4.5 0.6 

Alfalfa - Backfire 0.8 28.5 27.2 119.0 21.7 4.5 0.6 

Bean (red) - Headfire 2.5 42.3 40.3 186.0 26.8 4.5 0.6 

Bean (red) - Backfire 2.5 13.8 13.1 148.0 14.2 4.5 0.6 

Hay (wild) - Headfire 1.0 31.5 30.0 139.0 12.5 4.5 0.6 

Hay (wild) - Backfire 1.0 16.7 15.9 150.0 9.7 4.5 0.6 

Oats - Headfire 1.6 43.3 41.3 137.0 19.3 4.5 0.6 

Oats - Backfire 1.6 20.7 19.7 136.0 10.3 4.5 0.6 

Pea - Headfire 2.5 30.5 29.1 147.0 21.7 4.5 0.6 

Wheat - Headfire 1.9 21.6 20.6 128.0 9.7 4.5 0.6 

Wheat - Backfire 1.9 12.8 12.2 108.0 6.6 4.5 0.6 

 

Orchard Crops 

Almond 1.6 7.0 6.7 52.2 5.2 5.9 0.1

Apple 2.3 3.9 3.7 42.0 2.3 5.2 0.1

Apricot 1.8 5.9 5.6 49.0 4.6 5.2 0.1

Avocado 1.5 20.6 19.4 116.0 18.5 5.2 0.1

Cherry 1.0 7.9 7.4 44.0 6.0 5.2 0.1

Citrus (orange, lemon) 1.0 5.9 5.6 81.0 6.8 5.2 0.1 

Date palm 1.0 9.8 9.3 56.0 3.8 5.2 0.1 

Fig 2.2 6.9 6.5 57.0 6.0 5.2 0.1

Nectarine 2.0 3.9 3.7 33.0 2.3 5.2 0.1

Olive 1.2 11.8 11.1 114.0 10.3 5.2 0.1

Peach 2.5 5.9 5.6 42.0 3.0 5.2 0.1

Pear 2.6 8.8 8.3 57.0 5.1 5.2 0.1

Prune 1.2 2.9 2.8 47.0 4.6 5.2 0.1

Walnut 1.2 4.2 4.0 67.0 4.8 4.2 0.2

Unspecified 1.6 5.9 5.6 52.0 6.0 5.2 0.1

2-2

Appendix A of Final Report (STI-902514-2516-FR)
Table 2-1. Fuel loadings and emission factors for agricultural burning. 

(Page 2 of 2)

Emission Factors (lbs/ton)

Crop Type

Fuel 

Loading 

(tons/acre) PM10 PM2.5 CO VOC NOx SO2

 

Vine Crops 

Unspecified 2.5 4.9 4.7 51.0 3.8 5.2 0.1

 

Weeds 

Russian thistle, or 

tumbleweed 0.1 21.6 20.6 309.0 1.1 4.5 0.6

Tales, or wild reeds 1.0 4.9 4.7 34.0 15.7 4.5 0.6 

Unspecified 3.2 14.8 14.1 85.0 6.8 4.5 0.6

2.3 AGRICULTURAL BURNING ACTIVITY DATA 

To obtain activity data for agricultural burning events in the CENRAP region, STI’s 

subcontractor, Population Research Systems (PRS), conducted systematic telephone and mail 

surveys of county AES offices. PRS attempted to contact each AES office in all 969 counties of 

the CENRAP region in order to recruit AES personnel to complete a telephone survey. This 

survey was designed to determine the fraction of each county’s acreage typically burned each 

year by crop type, the timing of such burn events, and the burn methods employed. Data 

collected through the survey was then applied to National Agricultural Statistics Service (NASS) 

county-level estimates of acreages grown by crop type for 2002. 

This data collection effort had a target response rate of 25% to 50%. Ultimately, 549 

contacts were made, for a response rate of 56% (ranging from 36% to 93% from state to state). 

By including such large proportions of the available respondent pool and the total geographic 

area of the CENRAP region, the achievable representativeness of the study was maximized and 

the potential uncertainties minimized. Survey responses were used to generate profiles of 

agricultural burning practices by geographic region and crop type. In general, profiling was done 

on a statewide basis for each crop: a regional average burn profile was used to represent all 

counties for which no survey data are available. However, personnel at the Kansas Department 

of Health and Environment divided the state of Kansas into three subregions for wheat burning 

and four subregions for rangeland burning. Separate burn profiles for the burning of wheat and 

rangeland were produced for each of these subregions and applied to counties within those 

subregions for which no survey data were available. 

The proposed survey questionnaire is provided in Appendix A, and maps displaying 

Kansas subregions for wheat and rangeland burning are provided in Appendix C. 

2-3

Appendix A of Final Report (STI-902514-2516-FR)
2.4 SPATIAL ALLOCATION OF AGRICULTURAL BURNING 

Agricultural burning was spatially allocated by using the BELD GIS database. The 

BELD database includes spatial distributions of crops (by crop type) at the county (and subcounty) level gridded to 1 km2

. Activity data obtained through the agricultural survey 

questionnaires about the types of crops burned at the county level was spatially allocated by 

matching the reported crop types from the questionnaire 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 emissions. Gridded surrogate data, or spatial allocation 

factors, were developed by gridding the agricultural burn activity data and corresponding crop 

types to the 12-km × 12-km national Regional Planning Organization (RPO) grid domain. 

2.5 TEMPORAL ALLOCATION OF AGRICULTURAL BURNING 

Agricultural burning, like other 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 experts contained questions designed to identify 

times of the year when agricultural burning takes place for the various crops grown in each of the 

CENRAP states. Survey responses were used to design seasonal profiles that characterize 

agricultural burning activities by state and crop type. 

The survey also contained questions related to weekly and diurnal variations in 

agricultural burning activities. These questions were designed to identify the fraction of 

agricultural burning that takes place on weekdays versus weekend days, as well as the fraction of 

burning that takes place during daylight hours versus nighttime hours. 

2.6 CHEMICAL SPECIATION OF AGRICULTURAL BURNING 

PM and VOC emissions were chemically speciated according to profiles published by the 

EPA and the CARB. Table 2-2 summarizes the profile references and the individual compounds 

included in the profiles. Using these references, we created speciation profiles and crossreference files according to SMOKE speciation schemes. 

2-4

Appendix A of Final Report (STI-902514-2516-FR)
Table 2-2. Chemical speciation of agricultural burning: profile information. 

Profile Name Profile Number Profile Source Source Category SCC Code SCC Description Reference Proposed Classification Schemes Indi

PM 

Agricultural Burning – Field Crops 

430 ARB CARB SCC

Code67066202620000 (assumed to correspond to EPA SCC Code 2801500000)

Waste Burning –Agricultural Debris – Field Crops 

(Jenkins et al., 1996)

Default SMOKE classification scheme and individualcompounds 

Aluminum, Arsenic, BaCadmium, Carbon, OrChromium, Gold, IndiuLead, MangMolybdenuPalladium, Rubidium, Sodium, StTin, TitaniuYttrium, ZiUnidentifie

VOC 

MiscellaneousBurning – Forest Fires 

0307 EPA 2801500000 Miscellaneous

Area Sources – Agriculture Production – Crops –Agricultural FieldBurning – Whole Field Set on Fire – Total, all croptypes 

(Sandberg et al., 1975)

Default SMOKE classification scheme (CarbonBond IV) and individualcompounds 

Acetylene, 1-Butene,IsEthylene, IButene, ProPropane, Pr

2-5

Appendix A of Final Report (STI-902514-2516-FR)

3. PRESCRIBED BURNING 

3.1 OVERVIEW

The purpose of prescribed burning is commonly believed to the clearing of undergrowth 

in timberlands or grasslands to prevent wildfires or make various types of land improvements. 

For example, planned burns are used for timber stand improvement (site preparation fires for 

reforestation projects; removal of diseased trees), range improvement and wildlife habitat 

improvement. The types and amounts of such burning vary regionally both due to local weather 

and to local forest/land types. 

As with agricultural burning, emission rates are specific to materials burned and burn 

management practices. Some 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 information is available for planned burning of undergrowth 

for private land improvement. As with agricultural burning, significant effort is necessary to 

develop activity data sets that can be used for regional-scale emissions assessments. 

3.2 PRESCRIBED BURNING EMISSION FACTORS AND FUEL LOADINGS 

For this project, the FOFEM model was used to generate estimates of fuel loadings and 

emission rates for prescribed fires which were then applied to estimates of acres burned acquired 

from fire history data. This model was developed based on research findings gathered from

peer-reviewed literature sources, internal agency reports, and other “gray literature” sources. 

The accuracy and certainty of FOFEM results are consistent with the current status of scientific 

measurements of fuel consumptions and air emissions for prescribed burning and wildfires. 

Although measurement data are limited and uncertain, the FOFEM model generally represents a 

synthesis of the most up-to-date information available. 

Required inputs to FOFEM 5.0 include the following: 

• Vegetation land cover type 

• Season of the year (spring, summer, fall, or winter) 

• Moisture conditions (including the moisture content of various fuel types) 

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

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

burn) 

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

• Size distribution of fallen logs of 3 in. or greater diameter 

– Even distribution across the size range, from 3 in. to 20+ in. 

– A distribution that tends toward the larger logs 

– A distribution that tends toward the smaller logs 

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

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

3-1

Appendix A of Final Report (STI-902514-2516-FR)
FOFEM calculates emission factors for PM10, PM2.5, CH4, carbon dioxide (CO2), CO, 

NOx, and SO2. For ammonia (NH3) and NMHC, we applied the approximations that were 

employed by Allen and Dennis (2000), which assumed NMHC and NH3 emission factors that 

vary as follows: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ = × × −

CO CO

CO

NH NO EF EF

EF

EF EF 14 1

2

2

3 x (3-1)

EF 1.52 (EF 1.232) (3-2) NMHC CH4 = + ×

Before FOFEM could be applied to the CENRAP region, it was necessary to determine 

which of the model’s vegetation types are found in the region, and what the moisture contents of 

various fuel types were at the times and places in which prescribed burning events occurred. 

(For the remaining FOFEM inputs, such as burn configuration, log-size distributions, and the 

percentage of fallen logs that are rotten, default settings were used). 

FOFEM allows users to choose between two main vegetation cover classifications: the 

National Vegetation Classification System (NVCS) and the Society of American 

Foresters/Society for Range Management (SAF/SRM) cover types. (A third option, the Fuel 

Characteristic Classification [FCC], does not yet cover all regions of the country.) The NVCS 

uses a classification hierarchy which emphasizes differences in both vegetation structure and 

floristics2

, and the system is periodically updated to include new information on natural 

community classifications developed at the state level. Such natural communities are based on 

all species of vegetation. SAF forest cover types, on the other hand, are based primarily on 

dominant tree species. While trees can be indicators of their environments, some trees are so 

broadly adapted that their presence indicates little about the conditions of the surrounding natural 

community. Thus, SAF cover types are less useful than those found in the NVCS (New

Hampshire Division of Forests and Lands, 2002). To determine which of the NVCS or SAF 

cover types are found in the CENRAP region, a cross-walk was developed between the FOFEM 

and BELD databases. In developing this cross-walk, BELD vegetation types were matched to 

NVCS coverage types wherever possible; SAF data was used only when clear matches could not 

be made to NVCS coverages. The cross-walks used are presented in Appendix B. 

Fuel moisture content is the quantity of water in a fuel particle expressed as a percentage 

of the oven-dry weight of the fuel (National Weather Service, 1998). FOFEM requires settings 

for three fuel classifications3

: 10-hour, 1000-hour, and duff4. Fuel moisture data are available 

 

2

 Floristics is the study of the number, distribution, and relationships of plant species in one or more areas. 3

 The rate of change of the moisture content is dependent on the diameter of the woody fuel, various diameter ranges 

are classified according to their “time lag.” Time lag refers to the length of time it takes a fuel to respond to changes 

in environmental moisture conditions: larger diameter fuels generally have longer time lags. The time lag categories 

typically used for fire behavior and fire danger rating are specified as 1-hr (0-1⁄4"), 10-hr (1⁄4"-1"), 100-hr (1"-3"), and 

1000-hr (3" or greater). 4

 Duff is partially decomposed organic matter, leaf litter, or organic soils (such as humus or peat), which

accumulates in layers on the forest floor. 

3-2

Appendix A of Final Report (STI-902514-2516-FR)
from the Wildland Fire Assessment System (WFAS)—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 United States and entered into the Weather 

Information Management System (WIMS). These weather observations are used to calculate 

fuel moisture levels for 1-hr, 10-hr, 100-hr, and 1000-hr fuel types. WIMS data for the 

CENRAP region was acquired and used to determine a range of 10-hr, 1000-hr and duff moisture 

levels for the CENRAP region for 2002. The 100-hr moisture values were used as a surrogate 

for duff moisture, following the approach of Harrington (2003). 

Once vegetation types and fuel moisture levels present in the CENRAP region were 

determined, FOFEM was run for each unique combination of vegetation type-moisture level to 

generate emission rates in pounds per acre burned. Outputs from these FOFEM runs were used 

to produce a look-up table of emission factors by vegetation type and moisture condition. For 

each prescribed burning event, we were able to use WIMS data from the nearest fire weather 

station to determine fuel moisture contents for that event and BELD data to determine the type of 

vegetation burned. This information was used to select and apply an appropriate emission factor 

from the FOFEM look-up table. 

3.3 PRESCRIBED BURNING ACTIVITY DATA 

In summary, the prescribed burn activity data for state and private lands from the 

CENRAP states will consist of detailed data obtained from smoke management programs, state 

fire marshals, or state forest services; summary data obtained from state agencies and allocated 

by county; summary data estimated by applying federal surrogates to state lands and allocated by 

county; and county level data based on the results of the rangeland burning survey questions. 

3.3.1 Activity Data for Federal Lands 

The National Interagency Fire Management Integrated Database (NIFMID) was the 

source of data used for prescribed fires occurring on Department of the Interior (DOI) lands 

(U.S. Fish and Wildlife Service, National Park Service, and Bureau of Indian Affairs 

[http://famweb.nwcg.gov/weatherfirecd/]). This database contains fire type (prescribed, wildfire, 

etc.), start and end dates, extent (acres), and location (geographic coordinates and 

township/range/section). 

The National Fire Plan Operations and Reporting System (NFPORS), 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 managed by that agency: Arkansas, 

Louisiana, Minnesota, Missouri, Oklahoma, and Texas. 

Additional prescribed burn data on federally managed lands were included in data 

acquired from state smoke management programs (this data was cross-checked against NIFMID

and NFPORS data to prevent double-counting). For example, some DOI data was included 

among the state reports that did not appear in the NIFMID final report for 2002, and some USFS 

burns appeared in these reports as well. 

3-3

Appendix A of Final Report (STI-902514-2516-FR)
3.3.2 Activity Data for State, Tribal, and Private Lands 

Each of the CENRAP states has unique regulations regarding prescribed burning on state 

and private lands. Records of prescribed burns are compiled at different levels within each state. 

Consequently, several sources of information contributed to the prescribed burn activity data for 

state, private, and tribal lands. 

In cases where we could not acquire good-quality information 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 surrogate for the percentage of state lands that were burned that year. The total acreage 

of burned state lands were allocated according to the proportion of state lands within each 

county. In addition, the temporal profile of the burns that occurred on federal lands within these 

states was applied to the burns that were estimated for their state lands. 

Minnesota, Arkansas and Louisiana have voluntary or mandatory smoke management 

programs for which records of prescribed burns on state and private lands are kept. Records 

including the scheduled date, extent (acres), and location (geographic coordinates or 

township/range/section) of large scale prescribed burns that occurred during the year 2002 on 

state and private lands in Minnesota and Arkansas were obtained from the Minnesota 

Interagency Fire Center and the Arkansas Forestry Commission, respectively. Also, the 

Louisiana Forestry Division provided summary data describing the dates and acreages of 

prescribed burns that occurred on Louisiana’s state and private lands during the year 2002. This 

summary data listed burns by district and had to be allocated to the county level using the 

acreage of forested land within each county. 

A statewide permitting system exists for all other planned burns in Minnesota, including 

small scale residential or agricultural burns. The permits are issued by local fire wardens, and an 

estimated 60,000 burn permits were issued in the state in 2002. Records of these permitted burns 

are not compiled above a county level and are not in electronic format. Of the 60,000 permits, 

roughly 65% are estimated to be issued for “ditch burns” (fires set alongside roads or fencerows 

for weed abatement purposes) or “pile burns” (fires used to dispose of piles of waste material). 

Ditch burns are generally less than one quarter mile in length and were not considered in the 

inventory due to their small size and the lack of specific data. Also, since pile burns are used for 

waste management purposes, they fall beyond the scope this inventory. The remaining 35% of 

the permitted burns are performed on open land and range and are likely to be captured 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 private lands and lands managed by the state forest 

service. Records containing the date, type (grassland, woodland, brush pile, etc.), extent (acres), 

and location (address) of prescribed burns that occurred in that region of Oklahoma during 2002 

were obtained from the Oklahoma Forestry Service5

. Oklahoma’s Department of Wildlife 

Conservation (DWC) estimated the total number of acres burned on lands managed by the DWC 

in 2002, which accounted for the remainder of the state lands in Oklahoma that undergo 

substantial prescribed burning. 

 

5

 About one-third of these records was provided in hard-copy format and were not included in the final inventory. 

3-4

Appendix A of Final Report (STI-902514-2516-FR)
The Kansas State Fire Marshal’s office keeps a database of fire incidents in Kansas as 

reported by local fire departments (although prescribed burns in Kansas may or may not be 

reported to the local fire departments, depending on the specific regulations within each

township). The dates and locations (counties) of the controlled burns that were reported to the 

local fire departments in Kansas during 2002 were obtained from the Kansas State Fire 

Marshal’s database. 

In Texas, virtually all of the burning on state lands is conducted by the Texas Parks and 

Wildlife Department (TPWD) in state parks and Wildlife Management Areas (WMA). TPWD 

was able to provide data on burns occurring in state parks, but WMA burns are not tracked in a 

central database. Attempts to gather data from individual WMA managers were not successful, 

so the number of WMA acres burned in 2002 was estimated from data published by Allen and 

Dennis (2000) for 1996 and 1997. 

Missouri, Iowa, and Nebraska have neither smoke management programs nor prescribed 

burn records compiled above the county level. The Forestry Section of the Missouri Department 

of Conservation summarized the number of acres burned by The Nature Conservancy, the 

Missouri Department of Natural Resources, and the Missouri Department of Conservation on 

state and private lands in Missouri during the year 2002. In the state of Iowa, the Bureau of 

Wildlife performs a large portion of the state’s prescribed burns on public grasslands. However, 

records of the prescribed burns that occur on Iowa’s conservation lands are not compiled by the 

Bureau of Wildlife above the dispatch level. Therefore, the percentage of federal lands burned in 

the state of Iowa during 2002 was used as a surrogate in order to estimate the total number of 

acres burned on Iowa’s state lands. In Nebraska, a statewide burn ban requires prescribed burns 

to be permitted, but records of prescribed burn permits are not compiled above the county level. 

Therefore, the percentage of federal lands burned in the state of Nebraska in 2002 was used as a 

surrogate to estimate the total number of acres burned on state lands. The estimated acreage of 

state lands burned in Missouri, Iowa, and Nebraska will be allocated by county using the 

percentage of state lands in each county within each state. 

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

contacts were made to tribes that collectively hold over 95% of the tribal lands in the CENRAP 

region. It was confirmed that these tribes report their burns to either the BIA or the Minnesota 

Interagency Fire Center. 

For burning on private land, it was assumed that burns by individual parties would be 

related to agricultural practices (and, therefore, captured in the agricultural survey data) or the 

burning of waste (and, therefore, not considered under the scope of this project). Significant 

burns on private lands are most likely to be conducted by the forest industry, or by organizations 

such as the Nature Conservancy (TNC), The Prairie Plains Institute, or the Platte River 

Whooping Crane Maintenance Trust (Whitney, 2003). We did not obtain specific data from all 

the aforementioned organizations due to time constraints, though the TNC provided a database of 

all burns conducted by that agency in 2002. 

Planned burns by private forestry companies in Louisiana and Arkansas are largely 

included in the data received from the Louisiana Forestry Division and the Arkansas Forestry 

3-5

Appendix A of Final Report (STI-902514-2516-FR)
Commission. Forestry companies also perform planned burns in the Piney Woods region of east 

Texas. However, records of the planned burns that occurred during 2002 were not available 

from the Texas Forest Service. Traditionally, the Texas Forest Service reported planned burning 

information in the Harvest Trends Report; yet, after 1999, the Harvest Trends Report ceased to 

include information about planned burning because the practice of planned burning for forest 

management has diminished in recent years due to liability concerns (Xu, 2004). In the absence 

of other information, data reported by Allen & Dennis (2000) on acres burned by private timber 

companies in the Piney Woods region for 1996 and 1997 were averaged to produce an estimate 

of 20,000 acres burned per year. These acres were allocated to the county level based on the 

forested acreage in each county that makes up the Piney Woods region. 

3.3.3 Activity Data for Rangelands 

Rangeland burning occurs extensively on private lands throughout the CENRAP states, 

particularly in Kansas, Nebraska, Oklahoma, and central and west Texas. To obtain activity data 

for rangeland burning events in the CENRAP region, the agricultural burning survey given to 

county AES offices included rangeland burning questions designed to determine the fraction of 

rangeland acreage typically burned each year and the timing of such burn events. The survey 

results (discussed in Section 2) yielded activity data for private lands for all of the CENRAP 

states. We obtained additional prescribed burning information for private lands in some of the 

CENRAP states, as previously discussed in Section 3.3.2. 

3.4 SPATIAL ALLOCATION OF PRESCRIBED BURNING 

Fire occurrence locations for prescribed burns were typically provided as point 

coordinates (i.e., latitude and longitude values), township/range assignments, or county name. 

While the size of the fire was typically provided (in acres), the actual boundaries of the 

prescribed burns were not usually provided. To represent the location and approximate size of 

each burn, the reported location of each burn was assumed to be the centroid of the burn and was 

mapped as a point using the latitude/longitude coordinates. County-specific vegetation profiles 

from the BELD data were then matched to each fire location to determine the vegetation types 

associated with each fire. The vegetation data (used by the FOFEM model), fire size, 

occurrence date, and associated fuel moisture data were used to calculate emissions for each fire. 

While many of the prescribed burns were large, there were no fires larger than the 12-km

x 12-km grid cell resolution. Therefore, when the locations of prescribed burns were known, 

they were treated as point sources in the emission inventory. Approximately 40% of the 

prescribed burning inventory was allocated spatially and temporally as a point source inventory. 

(States that were able to provide “incident-level” databases of prescribed burn activity included 

Arkansas, Minnesota, and Oklahoma.) When the locations of fires were not reported, a spatial 

surrogate approach was used to develop gridded spatial allocation factors. 

Spatial allocation factors were used to spatially distribute emissions at the sub-county 

level (by grid cell). To develop gridded surrogate data, a surrogate data source is used to 

represent the locations of fire activity. Prescribed burns were spatially distributed on rural 

grasslands and forested lands, while agricultural burns were spatially distributed on agricultural 

3-6

Appendix A of Final Report (STI-902514-2516-FR)
land by crop type based on data obtained from the agricultural burning surveys. The spatial 

allocation factors were developed for the 12-km × 12-km National RPO grid. 

3.5 TEMPORAL ALLOCATION OF PRESCRIBED BURNING 

Fire history data collected for prescribed burns on federal lands specifies the dates on 

which the burns began and ended. These data were used to generate state-specific temporal 

profiles to allocate emissions from prescribed burning to the proper months of the year and days 

of the week. Also, by examining the number of burns completed in one day versus those 

spanning multiple days (and therefore continuing through the night), it was possible to estimate 

the fraction of prescribed burning that takes place in daylight hours versus nighttime hours. 

In the absence of date-specific information for prescribed burns on state lands, temporal 

profiles derived from federal prescribed burns were applied to burns on state lands. 

3.6 CHEMICAL SPECIATION OF PRESCRIBED BURNING 

PM and VOC emissions were chemically speciated according to profiles developed by 

the EPA and the CARB. Tables 3-1 and 3-2 summarize the profile references and the individual 

compounds included in two profiles: (1) prescribed burning of grasslands and (2) prescribed 

burning of woodlands. Using these references, we created speciation profiles and crossreference files according to SMOKE speciation schemes.

3-7

Appendix A of Final Report (STI-902514-2516-FR)
Table 3-1. Chemical speciation of prescribed burns: profile information for grasslands. 

Profile Name Profile Number Profile Source Source Category SCC Code SCC Description Reference Proposed Classification Schemes Indi

PM 

RangeImprovementBurning

441 ARB CARB SCC

Code67066402000000 (assumed to correspond to EPA SCC Code 2810020000)

Waste Burning –RangeManagement –RangeImprovement

(Jenkins et al., 1996)

Default SMOKE classification scheme and individualcompounds 

Aluminum, Arsenic, BaCadmium, Carbon, OrChromium, Gold, IndiuLead, MangMolybdenuPalladium, Rubidium, Sodium, StTin, TitaniuYttrium, ZiUnidentifie

NMHC 

MiscellaneousBurning – Forest Fires 

0307 EPA 2810020000 Miscellaneous

Area Sources – Other Combustion – PrescribedBurning ofRangeland – Total 

(Sandberg et al., 1975)

Default SMOKE classification scheme (CarbonBond IV) and individualcompounds 

Acetylene, 1-Butene,IsEthylene, IButene, ProPropane, Pr

3-8

Appendix A of Final Report (STI-902514-2516-FR)
Table 3-2. Chemical speciation of prescribed burns: profile information for forestlands. 

Profile Name Profile Number Profile Source Source Category SCC Code SCC Description Reference Proposed Classification Schemes Indi

PM 

Forest ManagementBurning

463 ARB CARB SCC

Code67066602000000 (assumed to correspond to EPA SCC Code 2810015000)

Waste Burning –Forest Management –Forest Management

(Jenkins et al., 1996)

Default SMOKE classification scheme and individualcompounds 

Aluminum, Arsenic, BaCadmium, Carbon, OrChromium, Gold, IndiuLead, MangMolybdenuPalladium, Rubidium, Sodium, StTin, TitaniuYttrium, ZiUnidentifie

NMHC 

MiscellaneousBurning – Forest Fires 

0307 EPA 2810015000 Miscellaneous

Area Sources – Other Combustion – PrescribedBurning for Forest Management –Total 

(Sandberg et al., 1975)

Default SMOKE classification scheme (CarbonBond IV) and individualcompounds 

Acetylene, 1-Butene,IsEthylene, IButene, ProPropane, Pr

3-9

Appendix A of Final Report (STI-902514-2516-FR)

4. AIR QUALITY DATA ANALYSIS

The objective of data analysis for this project was to preliminarily assess whether planned 

burning appears to contribute to impaired visibility events in Class I areas. We used existing 

ambient pollutant data from Class I areas in conjunction with the planned burn emission 

inventories developed through this project. To meet this objective, we performed the following 

steps: 

• Summarized 2002 air quality data available for Class I areas in the CENRAP region (e.g., 

IMPROVEf

 speciated PM2.5 data). Smoke components that contribute to visibility 

impairment include organic carbon (OC) and elemental carbon (EC). 

• Identified where and when planned, prescribed, and/or agricultural burns occurred near 

and/or upwind of Class I areas in 2002 by using the Task 1 emission inventory. 

• Characterized the ambient data for the 20% best and 20% worst visibility days at the 

Class I areas, including the average composition of the PM2.5 and the average 

contribution of pollutants to light extinction. Determined whether any of these days 

coincide with burns included in the inventory. 

• Investigated the ambient 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 coincide with burns listed in the inventory. 

• Analyzed days of interest in more detail by performing trajectory analyses, inspecting 

satellite photos, and investigating existing hourly pollutant data (e.g., whether 

nephelometer measurements indicate the impact of air parcels with increased PM2.5

concentrations). 

The deliverable for this task is a technical memorandum describing the analyses and 

summarizing analysis results. A discussion of the analysis and results is also included in the 

project Final Report. 

 

f

 IMPROVE = Interagency Monitoring of Protected Visual Environments

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Appendix A of Final Report (STI-902514-2516-FR)

5. PREPARATION OF DIGITAL EMISSION INVENTORY FILE SYSTEMS 

The following files will be delivered by STI upon completion of the planned burning 

emission inventory with accompanying documentation: 

• Emission data files in latest NIF format 

• Emission data files converted to IDA format and ready for input to SMOKE 1.5 

• Temporal profile and cross-reference files for use by SMOKE

• Spatial surrogate and cross-reference files for use by SMOKE

• Chemical speciation profiles and cross-reference files for use by SMOKE 

5-1

Appendix A of Final Report (STI-902514-2516-FR)

6-1

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 burning of agricultural residues. August. Available 

on the Internet at <http://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., Goronea 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 biomass by wind tunnel simulations. Final report, 

California Air Resources Board Project No. A932-126, April. 

Meadows G. (2004) Personal communication: Planned 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 Forests and Lands (2002) Introduction 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 General 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 slash burning and the influence of flame retardant 

chemicals. J. Air Poll. Cont. Assoc. 25 (3). 

U.S. Environmental Protection Agency (2001) Biogenic emissions landcover database. Available 

on the Internet at <ftp://ftp.epa.gov/amd/asmd/beld3/>; last accessed May 10, 2003. 

U.S. Environmental Protection Agency (2003) Compilation of air pollutant emission factors, AP42. Vol. 1: stationary point and area sources. 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 of Final Report (STI-902514-2516-FR)
6-2

Whitney B. (2003) Personal communication: Planned burning in Nebraska. November 7. 

Xu W. (2004) Personal communication: The Harvest Trends Report for the Piney Woods Region 

of Texas. April 1. 

Appendix A of Final Report (STI-902514-2516-FR)
APPENDIX A 

PROPOSED SURVEY QUESTIONNAIRE 

A-1

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Appendix A of Final Report (STI-902514-2516-FR)
Central States Regional Air Planning Association (CENRAP) 

Telephone Interview 

Project #1002 

INTRO1: Hello, my name is __________. I’m calling on behalf of the Central States Regional Air Planning 

Association or CENRAP. CENRAP is an organization of states, tribes, federal agencies, and other interested 

parties that studies and addresses regional haze and visibility issues. Your state is participating 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 cooperative agricultural 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 would be most knowledgeable about your county’s tilling 

practices and agricultural burning 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) 

[4] I’ll get him/her (Go to INTRO2) 

[5] No one is available now (Go to CALLBACK) 

[6] No such person (Terminate) 

[8] DON’T KNOW (Terminate) 

[9] REFUSED (Terminate) 

INTRO2: Hello, my name is __________. I’m calling on behalf of the Central States Regional Air Planning 

Association or CENRAP. CENRAP is an organization of states, tribes, federal agencies, and other interested 

parties that studies and addresses regional haze and visibility issues. Your state is participating in CENRAP and 

as such, your county has been randomly selected to participate in an important air quality study (Go to Q2b). 

Q2b. Are you the person who is most knowledgeable about your county’s tilling practices and agricultural burning 

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. Your responses will be kept confidential and will not be 

connected to your name. Can I begin the interview? <Go to Q3a1> 

CENRAP Telephone Interview Project #1002 12/18/2003 

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Appendix A of Final Report (STI-902514-2516-FR)
Q3a. What agency or agencies would have information about tilling 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. Your responses will be kept confidential and will not be 

connected to your name. Can I begin the interview? <Go to Q3a1> 

CALLBACK: 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 you have reached the incorrect agency, ask

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 is the name of this office? _____________________________________ 

88888 = DON’T KNOW 

9999 = REFUSED 

Q3a2. What is your name? _____________________________________ 

8888 = DON’T KNOW 

9999 = REFUSED 

Q3a3. What is your telephone number beginning with the area code? ( ) __________ 

8888888 = DON’T KNOW 

9999999 = REFUSED 

CENRAP Telephone Interview Project #1002 12/18/2003 

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Appendix A of Final Report (STI-902514-2516-FR)
Q3b. I’m now going to read you a list of crops and I’d like you to tell me whether these crops are grown in your

county? (Programmer note: 1 data 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 previously 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 completed during a year? Let’s start with (name of 

1st crop). Typically, how many plantings per year are made for (name of 1st crop)? How about for (name of 

2nd crop)? For (name of 3rd 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 Interview Project #1002 12/18/2003 

Population Research Systems, LLC, San Francisco, CA 

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Appendix A of Final Report (STI-902514-2516-FR)
Q5. In your county, are tilling passes typically made on each crop before planting and after harvesting or are 

harvesting and planting completed in one pass? Let’s start with (name of 1st crop). Typically, when are tilling

passes made for (name of 1st crop)? Is it before planting and after harvesting or is tilling completed in one 

pass? How about for (name of 2nd crop)? Read list of remaining selected crops. 

1 = Yes, passes are made before planting and after harvesting 

2 = Tilling passes 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 passes are typically made on each crop in your county? Let’s start with (name of 1st

crop). Typically, how many tilling passes are made for (name of 1st crop)? How about for (name of 2nd crop)? 

Read list of remaining selected 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 Interview Project #1002 12/18/2003 

Population Research Systems, LLC, San Francisco, CA 

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Appendix A of Final Report (STI-902514-2516-FR)
Q7. How many tilling passes are typically made on each crop before planting and after harvesting in your 

county? Let’s start with (name of 1st crop). Typically, how many passes are made for (name of 1st crop) 

before planting? How about for (name of 2nd crop)? Read list of remaining selected crops. 

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

For (name of 2rd 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 previously mentioned _____ _____ 

88 = DON’T KNOW 

99 = REFUSED 

Q8. Do farmers use any special tilling practices such a no-till, low-till, ridge-till, or mulch-till farming in your 

county? Let’s start with (name of 1st crop), are no-till, low-till, ridge-till, or mulch-till tilling practices typically 

used for this crop? 

What about for (name of 2nd crop)? Are no-till, low-till, ridge-till, or mulch-till practices typically used 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 Interview Project #1002 12/18/2003 

Population Research Systems, LLC, San Francisco, CA 

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Appendix A of Final Report (STI-902514-2516-FR)
Q9. For each crop, please tell me how many days before planting and after harvesting does tilling typically 

occur in your county. Let’s start with (name of 1st crop). Typically, how many days before planting does tilling 

occur for (name of 1st crop)? How about for (name of 2nd crop)? Read list of remaining selected crops. 

Let’s now turn to harvesting. Typically, how many days after harvesting does tilling occur for (name of 1st

crop)? For (name of 2rd 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 previously mentioned _____ _____ 

888 = DON’T KNOW 

999 = REFUSED 

Q10. For each crop, please tell me whether tilling usually 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 Interview Project #1002 12/18/2003 

Population Research Systems, LLC, San Francisco, CA 

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Appendix A of Final Report (STI-902514-2516-FR)
Q11. For each crop, please tell me whether tilling usually occurs during daytime, nightime, or both daytime 

and nighttime hours? (Programmer note: 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 daytime and nighttime hours? Let’s 

start with (name of first crop). What percent of tilling 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 previously mentioned _____ _____ 

888 = DON’T KNOW 

999 = REFUSED 

<NOTE: For Q12a1 through Q12j2, daytime % and nighttime % 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 Interview Project #1002 12/18/2003 

Population Research Systems, LLC, San Francisco, CA 

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Appendix A of Final Report (STI-902514-2516-FR)
Q13. What agency should I contact concerning agricultural burning in your county? (Probe: Is that a state or 

county agency?) Can I get the telephone number as well? 

a. _________________________________________________________________ 

b. _________________________________________________________________ 

888 = DON’T KNOW 

999 = REFUSED 

<Go to THANK YOU> 

CENRAP Telephone Interview Project #1002 12/18/2003 

Population Research Systems, LLC, San Francisco, CA 

8

Appendix A of Final Report (STI-902514-2516-FR)
Agricultural Burning Questions

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

Q14. For each crop, what percent of the total acreage is typically burned each year in your county? Let’s start 

with (name of 1st crop). What percent of (name of 1st 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 before planting and after harvesting does agricultural 

burning typically occur in your county. Let’s start with (name of 1st crop). Typically, how many days before 

planting does agricultural burning occur for (name of 1st crop)? How about for (name of 2nd 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 1st crop)? For (name of 2rd 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 previously mentioned _____ _____ 

k. Grazed rangelands _____ _____ 

888 = DON’T KNOW 

999 = REFUSED 

CENRAP Telephone Interview Project #1002 12/18/2003 

Population Research Systems, LLC, San Francisco, CA 

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Appendix A of Final Report (STI-902514-2516-FR)
Q16. For each crop, please tell me whether agricultural burning usually 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 residue is typically burned in your county during daytime, 

nightime, or both daytime and nighttime hours? (Programmer note: 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 _____ 

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 Interview Project #1002 12/18/2003 

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Appendix A of Final Report (STI-902514-2516-FR)
Q18. For each crop, please tell me what percent of crop residue is burned during daytime and nighttime 

hours? Let’s start with (name of first 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 previously mentioned _____ _____ 

k. Grazed rangelands _____ _____ 

888 = DON’T KNOW 

999 = REFUSED 

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

Q19. For the following crops, please tell me whether headfires, backfires, or both types of fires are used. 

Headfires are burning in the direction of the wind and backfires are burning in a direction opposite to the wind. 

(Programmer note: 1 data output column only; Headfire=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 county? (Probe: Is that a state or county 

agency?) Can I get the telephone number as well? 

a. _________________________________________________________________ 

b. _________________________________________________________________ 

888 = DON’T KNOW 

999 = REFUSED 

<Go to THANK YOU> 

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

CENRAP Telephone Interview Project #1002 12/18/2003 

Population Research Systems, LLC, San Francisco, CA 

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Appendix A of Final Report (STI-902514-2516-FR)

APPENDIX B 

VEGETATION CROSS-WALKS 

B-1

Appendix A of Final Report (STI-902514-2516-FR)
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Appendix A of Final Report (STI-902514-2516-FR)
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 WHEAT

Appendix A of Final Report (STI-902514-2516-FR)
List 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 WHEAT

Appendix A of Final Report (STI-902514-2516-FR)
Cross-walk of BELD Vegetation Types to FOFEM Forest Classification Types

BELD Genus Species FOFEM ID FOFEM Cover Description

26 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 44 TILIA AMERICANA NVCS 1360 Acer saccharum - Tilia americana - (Quercus rubra) Fores45 FAGUS GRANDIFOLIA NVCS 1420 Fagus grandifolia - Acer saccharum - (Liriodendron tulipife46 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 virginia63 ULMUS AMERICANA NVCS 2310 Quercus texana,Celtis laevigata,Ulmus(americana,crassif64 EUCALYPTUS ---- SAF 70 Longleaf Pine - rough age 365 ABIES BALSAMEA SAF 51 White Pine - Chestnut Oak66 ABIES MAGNIFICA NVCS 631 Abies magnifica Forest 67 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 3

Appen
Cross-walk of BELD Vegetation Types to FOFEM Forest Classification Types

BELD Genus Species FOFEM ID FOFEM Cover Description

80 TSUGA ---- SAF 70 Longleaf Pine - rough age 381 CARYA OVATA NVCS 1380 Carya (glabra, ovata) - Fraxinus americana - Quercus (alb82 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 Forest 85 JUNIPERUS VIRGINIANA NVCS 5370 Juniperus virginiana - (Fraxinus americana, ostrya virginia86 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 Forest 90 MAGNOLIA GRANDIFLORA SRM 910 Hairgrass91 CERCOCARPUS LEDIFOLIUS NVCS 6461 Cercocarpus ledifolius Shrubland 92 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 Woodland

100 ACER GLABRUM SAF 70 Longleaf Pine - rough age 3101 ACER SACCHARINUM NVCS 2030 Acer saccharinum - Fraxinus americana - Tilia americana 103 ACER PENSYLVANICUM SAF 70 Longleaf Pine - rough age 3104 ACER SACCHARUM NVCS 1320 Acer saccharum - Betula alleghaniensis - (Fagus grandifo105 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, velutina114 QUERCUS MARILANDICA NVCS 3000 Juniperus virginiana - Quercus (stellata, velutina, mariland115 QUERCUS DOUGLASII SAF 70 Longleaf Pine - rough age 3116 QUERCUS INCANA NVCS 5320 Quercus hemisphaerica - Quercus margarettiae - Quercus 117 QUERCUS MACROCARPA NVCS 11951 Quercus macrocarpa - (Quercus alba) Wooded Herbland 118 QUERCUS KELLOGGII NVCS 5130 Quercus kelloggii Temporarily Flooded Woodland119 QUERCUS AGRIFOLIA SAF 70 Longleaf Pine - rough age 3

Appen
Cross-walk of BELD Vegetation Types to FOFEM Forest Classification Types

BELD Genus Species FOFEM ID FOFEM Cover Description

120 QUERCUS LOBATA NVCS 4940 Quercus lobata Woodland121 QUERCUS CHRYSOLEPIS SAF 59 Yellow Poplar - White Oak - Northern Red Oak 122 QUERCUS PRINUS NVCS 3330 Pinus virginiana - Quercus (coccinea, prinus) Forest123 QUERCUS MUEHLENBERGII NVCS 2990 Juniperus virginiana - Quercus (muehlenbergii, stellata) F124 QUERCUS STELLATA NVCS 2990 Juniperus virginiana - Quercus (muehlenbergii, stellata) F125 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 Shrubland 130 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 - Hemlock 133 QUERCUS OBLONGIFOLIA SAF 70 Longleaf Pine - rough age 3134 QUERCUS ELLIPSOIDALIS NVCS 11960 Quercus velutina - (Quercus ellipsoidalis) Wooded Herbla135 QUERCUS RUBRA NVCS 1360 Acer saccharum - Tilia americana - (Quercus rubra) Fores136 QUERCUS NUTTALLII SAF 70 Longleaf Pine - rough age 3137 QUERCUS GARRYANA NVCS 5510 Pseudotsuga menziesii - Quercus garryana Woodland 138 QUERCUS LYRATA NVCS 2550 Quercus lyrata - (Carya aquatica) Seasonally Flooded Fo139 QUERCUS PALUSTRIS NVCS 2560 Quercus palustris - (Quercus bicolor) Seasonally Flooded 140 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) Forest 147 QUERCUS ---- SAF 70 Longleaf Pine - rough age 3148 QUERCUS MICHAUXII NVCS 2760 Quercus michauxii - Quercus pagoda Saturated Forest 149 QUERCUS FALCATA NVCS 3200 Pinus palustris,Pinus(echinata,taeda),Quercus(incana,ma150 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) Forest 154 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 3

Appen
Cross-walk of BELD Vegetation Types to FOFEM Forest Classification Types

BELD Genus Species FOFEM ID FOFEM Cover Description

158 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 Forest 175 PINUS CONTORTA NVCS 411 Pinus contorta Forest176 PINUS PALUSTRIS NVCS 3200 Pinus palustris,Pinus(echinata,taeda),Quercus(incana,ma177 PINUS RADIATA SAF 84 Slash Pine - rough age 3178 PINUS MONOPHYLLA NVCS 4091 Pinus monophylla Woodland179 PINUS DISCOLOR NVCS 4011 Pinus discolor Woodland 180 PINUS EDULIS NVCS 431 Pinus edulis Forest181 PINUS RIGIDA NVCS 4230 Pinus virginiana - Pinus rigida Woodland 182 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 Forest 186 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 tu195 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 3

Appen
Cross-walk of BELD Vegetation Types to FOFEM Forest Classification Types

BELD Genus Species FOFEM ID FOFEM Cover Description

202 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 Forest 212 PICEA BREWERIANA SAF 70 Longleaf Pine - rough age 3213 PICEA ENGELMANNII NVCS 691 Picea engelmannii Forest 214 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 tulip220 PLATANUS OCCIDENTALIS NVCS 3440 Pinus taeda - Platanus occidentalis - Acer negundo Temp221 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

Appen

APPENDIX C 

MAPS OF KANSAS AGRICULTURAL SUBREGIONS 

C-1

Appendix A of Final Report (STI-902514-2516-FR)

CHEYENNE

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

CLAY

DICKINSON

MARION

BUTLER

MARSHALL

RILEY POTTAWATOMIE

GEARY

CHASE

MORRIS

LYON

GREENWOOD

ELK

CHAUTAUQUA

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.

47,100ac. 1% burned

0ac.

0% burned

85,000ac.

8,500ac.

10% burned

69,400ac.

0ac.

0% burned

83,900ac.

12,585ac.

15% burned

87,700ac.

1,754 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.

1,193 ac.

1% burned

150,100ac.

1,501 ac.

1% burned

107,200ac.

1,072 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.

13,900ac. 0% burned 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.

98,600ac. 7% burned

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

Acres of Wheat Burned/Year

MORTON

40,800ac.

0ac.

0% burned

County Name

Total Wheat in County (acres)

Total Wheat in County Burned (acres)

% Burned

118,900ac.

0ac.

0% burned

COWLEY

22,625ac.

90,500ac.

25% burned

*Note - Data supplied by Sonoma Technology, Inc.

April 2004

Region #1 Region #2 Region #3

*Note - Wheat regions verified by Bill Hargrove at

Kansas State University Agriculture Extension

OfficeAppendix A of Final Report (STI-902514-2516-FR)
CHEYENNE

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

CLAY

DICKINSON

MARION

BUTLER

COWLEY

MARSHALL

RILEY POTTAWATOMIE

GEARY

CHASE

MORRIS

LYON

GREENWOOD

ELK

CHAUTAUQUA

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

147,638 ac.

0 ac.

193,209 ac.

0 ac.

325,773 ac.

48,866 ac.

393,491 ac.

39,349 ac.

88,427 ac.

8,843 ac.

340,543 ac.

255,407 ac. 302,346 ac.

136,056 ac.

116,467 ac.

34,940 ac.

35,470 ac.

7,094 ac.

96,270 ac.

9,627 ac.

102,499 ac.

76,874 ac.

238,721 ac.

119,361 ac.

140,675 ac.

105,506 ac.

69,350 ac.

52,013 ac.

75% burned 75% burned 413,024 ac.

330,419 ac.

80% burned 100,817 ac.

20,163 ac.

20% burned 151,825 ac.

0 ac.

0% burned

74,950 ac.

0 ac.

0% burned

115,930 ac.

0 ac.

0% burned

137,415 ac.

0 ac.

0% burned

133,236 ac.

0 ac.

0% burned

66,906 ac.

13,381 ac.

20% burned

73,535 ac.

0 ac.

0% burned

93,535 ac.

18,707 ac.

20% burned

306,379 ac.

245,103 ac.

80% burned

174,449 ac.

104,669 ac.

60% burned

91,486 ac.

915 ac.

1% burned

98,401 ac.

19,680 ac.

20% burned

103,733 ac.

0 ac.

0% burned

74,920 ac.

0 ac.

0% burned

198,236 ac.

0 ac.

0% burned

109,705 ac.

0 ac.

0% burned

260,637 ac.

2,606 ac.

1% burned

180,568 ac.

1,806 ac.

1% burned

151,203 ac.

3,024 ac.

2% burned

130,179 ac.

39,054 ac.

30% burned

105,879 ac.

21,176 ac.

20% burned

67,657 ac.

13,531 ac.

20% burned

3,311 ac.

0 ac.

0% burned

46,455 ac.

4,646 ac.

10% burned

59,600 ac.

2,980 ac.

5% burned

49,559 ac.

14,868 ac.

68,684 ac. 30% burned

51,513 ac.

75% burned

105,316 ac.

31,595 ac.

30% burned

101,337 ac.

25,334 ac.

25% burned

36,348 ac.

1,817 ac.

5% burned

15,250 ac.

305 ac.

2% burned

33,493 ac.

670 ac.

2% burned

115,435 ac.

28,859 ac.

25% burned

153,958 ac.

76,979 ac.

50% burned

84,785 ac.

33,914 ac.

40% burned

143,027 ac.

28,605 ac.

162,576 ac. 20% burned

8,129 ac.

5% burned

87,260 ac.

4,363 ac.

5% burned

182,033 ac.

0 ac.

0% burned

128,567 ac.

12,857 ac.

10% burned

170,995 ac.

0 ac.

0% burning

218,247 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

Acres of Rangeland Burned/Year

MORTON

147,638 ac.

0 ac.

0% burned

County Name

Total Rangeland in County (acres)

Total Rangeland in County Burned (acres)

% Burned

Region #1 Region #2 Region #3 Region #4

*Note - Data supplied by Sonoma Technology, Inc.

April 2004

PAWNEE

80,597 ac.

24,179 ac.

30% burned

Appendix A of Final Report (STI-902514-2516-FR)
APPENDIX B 

TABULATION OF EMISSIONS ESTIMATES 

FOR THE CENRAP REGION 

B-1
This page is intentionally blank.
Table B-1. Annual emissions by state and source category. 

Page 1 of 3 

 Acres Emissions (tons/year) 

State Burn Type Burned PM10 PM2.5 CO NOx SO2 NH3 VOC

Arkansas Prescribed Burning 244,146 28,130 23,838 302,219 1,961 1,577 2,910 17,444

Rangeland Burning 3,061 62 52 307 44 15 7 29

Cropland Burning 652,246 10,709 10,175 74,223 3,648 622 2,094 6,225

 Wheat 354,209 5,968 5,691 40,116 1,514 202 1,077 2,798

 Hay/Alfalfa 8,050 73 70 599 18 2 13 40

 Sugarcane 0 0 0 0 0 0 0 0

 Corn 0 0 0 0 0 0 0 0

 Soybeans 67,398 2,564 2,445 14,342 379 51 270 1,818

 Rice 222,589 2,104 1,970 19,165 1,736 367 735 1,569

 Other 0 0 0 0 0 0 0 0

Total 899,453 38,901 34,065 376,749 5,653 2,214 5,011 23,698

 

Iowa Prescribed Burning 21,449 4,072 3,457 44,542 166 195 257 2,547

Rangeland Burning 0 0 0 0 0 0 0 0

Cropland Burning 2,247 44 42 145 5 1 4 20

 Wheat 0 0 0 0 0 0 0 0

 Hay/Alfalfa 1,660 29 27 81 3 0 2 13

 Sugarcane 0 0 0 0 0 0 0

 Corn 0 0 0 0 0 0 0

 Soybeans 0 0 0 0 0 0 0

 Rice 0 0 0 0 0 0 0

 Other 587 15 14 64 2 0 2 7

Total 23,696 4,116 3,498 44,688 171 195 261 2,567

 

Kansas Prescribed Burning 38,106 1,450 1,226 14,424 228 114 143 881

Rangeland Burning 3,625,270 75,943 52,901 652,250 23,185 10,160 7,487 43,483

Cropland Burning 1,390,520 23,227 22,156 153,313 5,909 777 3,950 11,401

 Wheat 1,058,014 17,420 16,610 118,902 4,523 603 3,216 8,194

 Hay/Alfalfa 189,085 2,252 2,148 12,701 408 54 290 1,143

 Sugarcane 0 0 0 0 0 0 0 0

 Corn 126,956 3,039 2,906 18,902 880 107 373 1,760

 Soybeans 9,996 210 200 1,252 34 5 24 154

 Rice 0 0 0 0 0 0 0 0

 Other 6,469 306 292 1,557 65 9 46 150

Total 5,053,896 100,620 76,283 819,987 29,322 11,052 11,579 55,765

 

Louisiana Prescribed Burning 350,353 45,288 38,376 486,668 3,125 2,531 4,671 28,060

Rangeland Burning 29,540 613 491 3,597 372 128 65 305

Cropland Burning 456,901 7,771 7,397 66,203 3,474 482 2,388 6,762

 Wheat 114,661 2,189 2,087 13,570 490 65 349 998

 Hay/Alfalfa 5,763 90 85 401 13 2 9 36

 Sugarcane 296,994 4,930 4,693 48,113 2,673 356 1,901 5,346

 Corn 5,817 139 133 866 40 5 17 81

B-3
Table B-1. Annual emissions by state and source category. 

Page 2 of 3 

 Acres Emissions (tons/year) 

State Burn Type Burned PM10 PM2.5 CO NOx SO2 NH3 VOC

 Soybeans 2,418 128 122 562 14 2 10 81

 Rice 31,248 295 277 2,691 244 52 103 220

 Other 0 0 0 0 0 0 0 0

Total 836,794 53,672 46,264 556,468 6,970 3,140 7,124 35,126

 

Minnesota Prescribed Burning 86,642 17,222 14,609 187,853 742 836 1,150 10,740

Rangeland Burning 17,314 358 216 3,904 16 25 33 228

 Cropland Burning 84,611 1,587 1,513 8,621 341 44 215 928

 Wheat 7,962 132 126 897 34 5 24 62

 Hay/Alfalfa 28,503 402 383 1,565 56 8 40 211

 Sugarcane 0 0 0 0 0 0 0 0

 Corn 14,223 341 326 2,118 99 12 42 197

 Soybeans 0 0 0 0 0 0 0 0

 Rice 0 0 0 0 0 0 0 0

 Other 33,923 712 678 4,041 153 20 109 458

Total 188,567 19,167 16,338 200,378 1,100 905 1,398 11,895

 

Missouri Prescribed Burning 64,781 7,460 6,338 80,019 536 417 756 4,633

Rangeland Burning 109,160 2,281 1,763 15,244 1,182 415 228 1,182

Cropland Burning 181,818 2,677 2,551 17,845 725 105 465 1,317

 Wheat 94,279 1,546 1,474 10,581 403 54 287 728

 Hay/Alfalfa 63,545 767 732 4,590 143 19 102 353

 Sugarcane 0 0 0 0 0 0 0 0

 Corn 8,837 212 202 1,316 61 7 26 123

 Soybeans 458 13 12 92 3 0 2 10

 Rice 14,673 139 130 1,263 114 24 48 103

 Other 26 1 1 3 0 0 0 0

Total 355,759 12,419 10,652 113,107 2,443 937 1,448 7,132

 

Nebraska Prescribed Burning 6,127 410 347 4,316 36 24 27 254

Rangeland Burning 114,807 2,403 1,468 25,863 152 179 223 1,520

Cropland Burning 100,719 2,244 2,140 14,439 491 65 330 1,430

 Wheat 47,336 656 625 5,039 202 27 144 324

 Hay/Alfalfa 5,430 72 68 323 11 1 8 38

 Sugarcane 0 0 0 0 0 0 0 0

 Corn 9,430 226 216 1,404 65 8 28 131

 Soybeans 0 0 0 0 0 0 0 0

 Rice 0 0 0 0 0 0 0 0

 Other 38,523 1,291 1,231 7,673 212 28 151 938

Total 221,653 5,057 3,956 44,619 679 268 580 3,205

 

Oklahoma Prescribed Burning 104,749 7,322 6,196 76,615 750 479 769 4,507

Rangeland Burning 1,830,017 38,117 28,443 280,780 16,885 6,419 3,890 20,578

B-4
Table B-1. Annual emissions by state and source category. 

Page 3 of 3 

 Acres Emissions (tons/year) 

State Burn Type Burned PM10 PM2.5 CO NOx SO2 NH3 VOC

Cropland Burning 473,342 7,114 6,785 47,157 1,760 234 1,234 3,414

 Wheat 325,838 5,197 4,955 36,238 1,393 186 991 2,465

 Hay/Alfalfa 137,707 1,690 1,612 9,464 302 40 214 815

 Sugarcane 0 0 0 0 0 0 0 0

 Corn 8,879 213 203 1,322 62 7 26 123

 Soybeans 0 0 0 0 0 0 0 0

 Rice 0 0 0 0 0 0 0 0

 Other 918 15 14 133 4 0 3 11

Total 2,408,108 52,552 41,424 404,551 19,395 7,131 5,893 28,499

 

Texas Prescribed Burning 137,310 12,669 10,732 134,423 1,071 757 1,427 7,824

 Rangeland Burning 3,576,810 101,580 71,407 1,033,500 12,979 8,637 12,114 61,961

Cropland Burning 221,771 3,129 2,986 18,929 668 89 459 1,435

 Wheat 39,472 729 695 4,615 169 22 120 334

 Hay/Alfalfa 161,566 1,895 1,808 11,711 364 49 258 887

 Sugarcane 501 8 8 81 5 1 3 9

 Corn 7,481 179 171 1,114 52 6 22 104

 Soybeans 0 0 0 0 0 0 0 0

 Rice 640 6 6 55 5 1 2 5

 Other 12,111 312 298 1,352 75 10 53 97

Total 3,935,891 117,378 85,125 1,186,851 14,718 9,482 14,000 71,220

 

All States Prescribed Burning 1,053,663 124,023 105,119 1,331,080 8,615 6,929 12,111 76,889

Rangeland Burning 9,305,979 221,357 156,742 2,015,445 54,815 25,977 24,046 129,287

Cropland Burning 3,564,175 58,501 55,744 400,874 17,021 2,418 11,139 32,931

 Wheat 2,041,771 33,836 32,263 229,956 8,729 1,164 6,207 15,903

 Hay/Alfalfa 601,309 7,269 6,934 41,436 1,318 176 937 3,535

 Sugarcane 297,495 4,938 4,700 48,194 2,678 357 1,904 5,355

 Corn 181,623 4,348 4,157 27,041 1,259 152 534 2,517

 Soybeans 80,270 2,915 2,779 16,248 429 57 305 2,062

 Rice 269,150 2,544 2,382 23,174 2,099 444 888 1,898

 Other 92,557 2,652 2,528 14,824 510 67 364 1,661

Total 13,923,817 403,882 317,605 3,747,399 80,451 35,324 47,295 239,107

B-5

APPENDIX C 

ASSESSMENT OF INFLUENCE FROM PRESCRIBED BURNING ON CLASS I 

SITES USING AMBIENT SPECIATED PM2.5 DATA 

C-1

C-3

C.1 OBJECTIVE AND APPROACH 

The objective of this task was to use ambient speciated PM2.5 data from Class I areas 

(from the IMPROVE network) in the CENRAP states 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 PM2.5 and visibility The following approach was employed: 

• Assess the seasonal chemical compositions of PM2.5 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 ratios 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 potentially influenced by prescribed burning. 

• Assess chemical compositions of PM2.5 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 typically 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 ambient 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 whether 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 the 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 located in Arkansas, Caney Creek (CACR1) and 

Upper Buffalo Wilderness (UPBU1). At the time of analysis, these sites were located in the area 

with the highest resolved fire histories, which would allow the best chance of showing direct 

influence between prescribed burning and ambient Class 1 data. Figure C-1 shows the locations 

of IMPROVE stations in the CENRAP region, along with the point locations of prescribed burns 

that were available from the 2002 emissions inventory. 
Figure C-1. IMPROVE station and fire locations. 

We acquired data from the two ambient monitoring stations from the online IMPROVE 

database (http://vista.cira.colostate.edu/improve/). Specifically, we obtained values of all 

available parameters for the years 2000, 2001, and 2002, during which years the IMPROVE 

network collected 24-hr samples once every three days. Although the emissions inventory 

included fires only from 2002, IMPROVE data from all three years were used to ensure a robust 

C-4
C-5

statistical analysis of seasonal and annual aerosol compositions 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 complete samples were cases in which all key 

species in our analysis were available: elemental carbon (EC), organic carbon (OC), 

potassium (K), ammonium nitrate (NH4NO3), and ammonium sulfate ((NH4)2SO4). 

Table C-1. Number of complete samples 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 characterize fine particulate aerosols and species that derive from wood 

smoke: elemental carbon (EC), organic carbon mass (OCM), ammonium nitrate (NH4NO3), 

ammonium sulfate ((NH4)2SO4), potassium (K), non-soil potassium (KNS), and a composite of 

species that derive from soils (GEO). 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 concentrations of fine aerosol species. 

Species Abbreviation IMPROVE Calculation 

Organic Carbon Mass OCM 1.4*[organic carbon] 

Ammonium Nitrate NH4NO3 1.29*[nitrate] 

Ammonium Sulfate (NH4)2SO4 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 molecule contains 70% carbon. The ammonium nitrate and ammonium 

sulfate algorithms assume that nitrate and sulfate ions are fully neutralized by NH4

+

. The 

ammonium sulfate algorithm also assumes that all elemental sulfur is in the form of sulfate, and 

it converts the mass of elemental sulfur to ammonium 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 nitrate algorithm multiplies the nitrate concentration 

by the ratio (1.29) of the molecular weight of ammonium nitrate (80 g/mol) to the molecular 

weight of nitrate (62 g/mol). The non-soil potassium (KNS) algorithm 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 account for their normal oxides. 
C-6

The IMPROVE network utilizes the measured mass concentrations of OCM, EC, 

(NH4)2SO4, NH4NO3, 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 (bext). The IMPROVE extinction 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 scattering efficiencies of the compounds, except 

the coefficient for the EC algorithm, which represents the light absorbing efficiency of EC. 

FT(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 NH4NO3 Extinction 3*FT(RH)*[nitrate] 

Ammonium Sulfate (NH4)2SO4 Extinction 3*FT(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 individual data values. The PM2.5 Data Analysis Workbook contains the 

guidelines that we employ for PM data validation (Main and Roberts, 2001). The validation 

incorporates internal consistency checks of ambient monitoring data, such as the comparison of 

species concentrations using scatter plots, the calculation of reconstructed particulate mass, and 

the preparation of material balances. Scatter plots that illustrate the relationships between well 

characterized species enable data analysts to quickly inspect data and identify any suspect points 

that may require further attention. Scatter plots also provide a general overview of a data set and 

preliminary data analysis. Plots that compare species from common sources, such as soil, or 

from different analytical techniques, such as ion chromatography (IC) or x-ray fluorescence 

(XRF), can target outlying data points that may indicate an unusual event or an equipment 

problem. Plots between reconstructed mass and measured mass or between cations and anions 

help the analyst to visually assess data completeness and to validate data resulting from different 

measurement techniques. We generated a number of scatter plots using SYSTAT statistical 

software in an effort to validate the IMPROVE data 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 effort was required. The data validation plots 

explored include 2000 through 2002 data 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 (SO4

-2) for the data set from Upper 

Buffalo Wilderness. A relatively tight correlation and a slope of roughly three indicate a good 
Table C-4. Scatter plot species and expected relationships. 

Species Species Expected Relationship Source or Reason 

S SO4

-2 3*S ~ SO4-2 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 

Na Cl Correlation Sea salt

Ca Si Correlation Soil

Al Si Correlation Soil

Fe Si Correlation Soil

Fe K Correlation Soil

OC Total Carbon (TC) Correlation OC large part of TC 

EC TC Some Correlation EC part of TC 

Se SO4

-2 Some Correlation Coal Emissions 

Fe Zn Some Correlation Smelter Emissions 

Ni V Some Correlation Oil Combustion 

K EC Some Correlation Wood Smoke 

babs EC Correlation

EC absorbs most 

light 

Cations Anions Near 1:1 Neutralized Aerosol

PM2.5 Reconstructed Mass Good Correlation Should be equal 

Figure C-2. Concentrations of XRF sulfur (S) versus IC sulfate (SO4

-2) from the Upper Buffalo

Wilderness IMPROVE station (μg/m3

). The line has a slope of one third, 

representing the expected 1:3 ratio between sulfur and sulfate.

C-7
comparison between the data obtained from the XRF and IC analyses. The slope equals 3 

because the molecular mass of sulfate (96 g/mol) is three times the molecular mass of sulfur 

(32 g/mol). Figure C-3 highlights the good correlation between the measured fine particulate 

mass (PM2.5) and the reconstructed fine particulate mass (RFM). According to IMPROVE

protocol, RFM equals the sum of SO4

-2, NO3-

, EC, OCM, and soil components. The good 

correlation between PM2.5 and RFM indicates the overall reliability of the data sets and

measurement techniques. The correlation exhibited 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 smoke, since both species commonly derive from soils. 

Overall this indicates that most K is from soil, which suggests influence from the non-soil 

sources is infrequent and contributes only a small amount of the 

K.

Figure C-3. Concentrations of reconstructed fine mass (RFM) versus fine particulate mass 

(PM2.5) from the Caney Creek IMPROVE station (μg/m3

). The line has a slope 

of one, representing a one to one ratio between RFM and PM2.5. 

C-8
Fe 

Figure C-4. Concentrations of iron (Fe) versus potassium (K) from the Upper Buffalo 

Wilderness IMPROVE station (μg/m3

). Points that exhibit higher than normal 

K to Fe ratios are highlighted. 

C.4 CHARACTERIZING PM2.5 DATA 

It is important to first characterize the typical seasonal concentrations of and ratios 

between species to understand what comprises the “normal” composition of PM2.5 before 

identifying whether specific source influences such as prescribed burning can be determined. 

Figure C-5 depicts seasonal proportions of the median mass concentrations of OCM, EC, 

NH4NO3, (NH4)2SO4, and soil influences for Caney Creek; Upper Buffalo Wilderness showed 

similar results. Summary statistics are given in Appendix A. At both sites, (NH4)2SO4 and 

OCM comprise the dominant fractions of PM2.5 in all seasons except winter, when NH4NO3 also 

contributes a significant fraction. The larger fraction of NH4NO3 in winter is consistent with 

nitrate formation mechanisms which favor cold, wet conditions, and the dominant fractions of 

(NH4)2SO4 are consistent with observations made at other eastern IMPROVE sites (Malm,

1999). EC is a small component of the mass in all seasons. 

Figure C-6 illustrates the proportions of light extinction attributed to OCM, EC, 

NH4NO3, (NH4)2SO4, and soil for each season at Caney Creek; Upper Buffalo Wilderness 

showed similar results. Summary statistics are given in Appendix A. The dominant portion of 

light extinction derives from (NH4)2SO4 in all seasons except winter, when NH4NO3 also

contributes significantly. This is consistent with other analyses of PM2.5 aerosol in the Midwest 

and CENRAP region (http://www2.nature.nps.gov/air/studies/bravo/bravo2003factsheet.htm) 

C-9
Figure C-5. Median mass and composition of PM2.5 by season (spring is March to May, 

summer is June to August, fall is September to November, and winter is 

December to February) at Caney Creek for 2000 through 2002. 

(Coutant et al., 2003; Coutant et al., 2002; Georgoulias and Dattner, 2002; Sisler and Malm, 

2000; Malm, 1999). PM2.5 composition at other Class 1 areas in the CENRAP region will likely 

be similar. The light extinction proportions resemble the mass concentration proportions, 

because the extinction calculations directly depend on mass concentrations. (NH4)2SO4 has a 

large effect on visibility due to its extremely hygroscopic nature and large contribution to the 

overall mass. The effect of EC on visibility is most pronounced during the winter months when 

the effect of (NH4)2SO4 is at a minimum, though it only accounts for about 5% of the total 

extinction.

C-10
Figure C-6. Median extinction and composition of extinction by season (spring is March to 

May, summer is June to August, fall is September to November, and winter is 

December to February) at Caney Creek for 2000 through 2002. 

C.5 CHARACTERIZATION OF VISIBILITY 

In order to determine which species are most responsible for poor visibility, we isolated 

the top and bottom 20% visibility days in 2000 through 2002 by aerosol extinction at each site. 

Summary statistics of the best visibility data, worst visibility data, and overall data for Caney 

Creek and Upper Buffalo Wilderness were calculated. The median mass compositions for the 

best and worst visibility days, as well as the annual median from Caney Creek are depicted in 

Figure C-7; results for Upper Buffalo Wilderness are similar to those for Caney Creek. At both 

sites, days with poor visibility are dominated by (NH4)2SO4 and show a decrease in the fractions

of the other species, especially OCM and NH4NO3. The fractions of EC and Soil components 

vary to a lesser extent between the good visibility and poor visibility days and are minor 

contributors to mass and extinction. 

C-11
Annual

PM2.5 = 8.76

Soil

5%

OCM

31%

(NH4)2SO4

53%

EC

4%

NH4NO3

7%

Best

PM2.5 = 2.98

Soil

8%

OCM

36%

(NH4)2SO4

37%

EC

6% NH4NO3

13%

Worst

PM2.5 = 17.31

OCM

26%

Soil

5%

EC

3%

NH4NO3

3%

(NH4)2SO4

63%

Figure C-7. Median PM2.5 composition for the 20% best and worst aerosol extinction days and 

the median annual composition at Caney Creek from 2000 to 2002. 

C.6 FIRE HISTORY DATA

In order to evaluate the effect of prescribed fires on visibility, we analyzed fire history 

data with the 2002 IMPROVE data for Caney Creek and Upper Buffalo Wilderness. We 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 influence) of each site. The range of influence 

around each site was established by using data from nearby meteorological stations: the radius 

around each site was calculated as the sum of the 24 hourly averaged wind speeds for each date, 

which represented an estimate of the distance that a parcel of air could have traveled on a given 

day. Theoretically, emissions from fires located within the range of influence could have been 

detected by the IMPROVE station if transport conditions were conducive. We then analyzed 

dates when the most extensive burning (with respect to acreage) occurred.

Due to the proximity of the two IMPROVE sites, several of the dates selected for each 

site overlap. The OCM, EC, K and KNS mass concentrations from overlapping dates that 

correspond to the day of or the day after extensive burning within the vicinity of both sites are 

compared to the springtime and annual mean concentrations from 2000 to 2002 for each site in 

Figures C-8 and C-9. Error bars representing the 95% confidence limits for the mean 

concentrations of EC and KNS for the springtime and annual data sets are also plotted. In 

C-12
Appendix A, mass concentrations of EC, KNS and the other key species for the selected dates 

and whether the EC and KNS concentrations significantly exceed the springtime are presented. 

Figure C-8. EC, OCM, K and KNS mass concentrations (μg/m3

) for select dates compared to 

the spring and annual means for Caney Creek. 

C-13
Figure C-9. EC and KNS mass concentrations (μg/m3

) for select dates compared to the spring 

and annual means for Upper Buffalo Wilderness. 

Extensive burns occurred on the days before March 6, 15, 24, and April 5. On these 

dates, the measured EC significantly (at a 95% confidence level) exceeds the springtime and 

annual means for both sites. On March 15 and 24, the contributions of EC in relation to OC are 

significantly higher than the springtime and annual average EC contributions for both sites. The 

elevated EC emissions observed on March 6, 15, 24, and April 5 could derive from the extensive 

C-14
burning that occurred on the previous days, if transport conditions were correct. Extensive 

burning occurred on February 28, but the EC measurements fell below the springtime and annual 

means at both sites, although not statistically significant at a 95% confidence level. On March 6, 

15, and 24, the KNS mass concentrations exceeded the mean springtime and annual KNS 

concentrations for both sites, and the relative contribution of KNS in comparison to K is also 

higher on these dates. The elevated KNS also suggests influence from wood burning on these 

days. Air mass trajectories were run to further investigate the potential influence of prescribed 

burns on ambient measurements, as discussed in Section C.9. 

Figure C-10 compares the mass concentration ratios of EC to OCM and of KNS to K for 

the selected dates to the annual median ratios. The KNS to K ratios from March 6, 15, and 24 

clearly exceed the annual ratio, indicating a relatively large contribution of KNS during these 

dates. Since KNS largely derives from wood smoke, emissions from nearby burns likely 

influenced the IMPROVE sites. The EC to OCM ratio from March 15 also clearly exceeds the 

annual ratio, further suggesting fire influence on this day. 

C.7 DO HIGH CONCENTRATIONS OCCUR ON DAYS OF PRESCRIBED BURNS? 

In addition to isolating the dates associated with extensive burning from the fire history 

data and analyzing corresponding ambient measurements, we also isolated the dates with high 

mass concentrations from the ambient measurements and analyzed corresponding fire history 

data. For each site, we ranked the 2002 IMPROVE data by the mass concentrations of EC, 

OCM, KNS, and K. We summarized the selected dates, ranks of each compound, whether a fire 

occurred, and the total acres burned within the sphere of influence of each site in Tables C-5 and

C-6. 

At both sites, the dates of higher EC and OCM mass concentrations overlap more with 

each other than with the dates of higher K mass concentrations, as EC and OCM both commonly 

derive from combustion sources and K derives largely from soils. At Upper Buffalo Wilderness, 

three of the five highest EC mass concentrations were measured on the dates that we had isolated 

during the previous analysis of fire occurrence, namely March 6, 24, and April 5. Since EC 

partly derives from wood smoke and extensive burns occurred on the day of and the day before 

these dates, the elevated EC emissions could derive from nearby prescribed burns. We further 

analyze the potential connection between elevated emissions of key species and fire occurrence 

in the next section, utilizing air mass trajectories for select dates. 

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Figure C-10. EC to OCM and KNS to K mass concentration ratios for select dates compared to 

the annual median ratios for both sites 

C-16
Table C-5. Dates with high measured EC, OCM, KNS and K mass concentrations and total 

acres burned within the sphere of influence of Caney Creek. The ranks order the 

days according to the five highest measured mass concentrations of each species. 

Date EC Rank OCM Rank KNS Rank K Rank Day of or 

before fires? 

Total 

Acres 

01/17/02 2 Of and Before 4,618 

03/06/02 5 Of and Before 19,509 

05/02/02 3 No Fires 0 

05/08/02 2 4 Of and Before N/A 

06/22/02 5 5 Day Before 107 

07/01/02 1 Day Of 41 

07/04/02 5 No Fires 0 

07/31/02 2 Of and Before 1,157 

08/06/02 3 Of and Before 1,727 

08/09/02 3 4 Of and Before 388 

08/30/02 4 Of and Before 476 

09/05/02 2 4 Of and Before 1,973 

09/14/02 1 1 1 3 Of and Before 135 

Table C-6. Dates with high measured EC, OCM, KNS, and K mass concentrations and total 

acres burned within the sphere of influence of Caney Creek. The ranks order the 

days according to the five highest measured mass concentrations of each species. 

Date

EC 

Rank 

OCM 

Rank 

KNS 

Rank K Rank Day of or before fires? Total Acres

03/06/02 5 Of and Before 20771

03/24/02 2 Of and Before 28567

04/05/02 4 Of and Before 8190

05/08/02 2 4 Of and Before N/A 

06/19/02 3 3 3 Of and Before 661 

06/22/02 1 4 4 Day Before 356

07/01/02 1 Day Of 41

07/10/02 2 Of and Before 2114 

07/31/02 3 Of and Before 927 

08/03/02 5 Of and Before 189 

08/06/02 5 Of and Before 1729 

09/14/02 1 1 2 Of and Before 253

11/25/02 5 Of and Before 208

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C.8 AIR MASS TRAJECTORIES 

Back trajectories of air masses for the selected dates were created using the National 

Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated 

Trajectory (HYSPLIT) model. The NOAA HYSPLIT model is a three-dimensional air mass 

trajectory model based on weather model data and can be obtained from the NOAA web site at

http://www.arl.noaa.gov/ready/hysplit4.html. The final (FNL) product of the Global Data 

Assimilation System (GDAS) that uses the Global Spectral Medium Range Forecast (MRF) 

model provides the weather data for the HYSPLIT model. The HYSPLIT model uses National 

Weather Service soundings and other diagnostic parameters such as temperature, relative 

humidity and radiative and momentum fluxes. It uses a 129 x 129 polar stereograph 

(three-dimensional) grid, with approximately 190 km resolution and 12 vertical layers, and is run 

at 6-hour increments. Back trajectories were run from 1800 CST with ending heights of 1000 

and 500 meters in order to capture short-range transport in the lower boundary layer. 

We ran trajectories for March 6, 15, and 24 and plotted them along with fires that 

occurred the day of and the day before the selected dates. Figures C-11 through C-13 show the 

maps for March 6, 15, and 24. On these dates, the EC and KNS mass concentrations exceeded 

the annual means for each site, as summarized in Figure C-9. 

Air mass trajectories demonstrated no influence from known burns at the Caney Creek 

site on March 6. However, the inventory does not include detailed fire history data for the 

southeastern corner of Oklahoma or for the eastern portion of Texas, over which the air mass 

advected before reaching the site. On approach to the Upper Buffalo Wilderness site, the air did 

pass directly over extensive fires that occurred on March 5. Therefore, the elevated EC and KNS 

emissions measured at Upper Buffalo Wilderness on March 6 could derive from wood smoke 

emissions from the previous day that influenced the site over a 24 hour period. Additionally, 

there were numerous nearby fires to Caney Creek on March 5 and 6 that would have affected the 

site via flow below 500 meters. 

Similar to the situation on March 6, the air flowed directly over burns reported in the 

emissions inventory before reaching Upper Buffalo Wilderness on March 15, but the air did not 

flow over the reported burns when approaching Caney Creek. The elevated EC and KNS 

emissions measured at Upper Buffalo Wilderness on March 15 could be attributed to the wood 

smoke emissions from the extensive March 14 burns, and detailed fire history data from

neighboring states would allow more definitive conclusions to be drawn about the measurements 

from Caney Creek. Also, the similarity in PM2.5 composition at the two sites on this day 

indicates they were influenced from similar 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 Caney Creek circumvented the extensive fires 

reported by the inventory, while the air approaching Upper Buffalo Wilderness passed directly 

over them. Once again, the higher than average EC and KNS mass concentrations observed on 

March 24 at Upper Buffalo Wilderness could have originated from prescribed burning emissions, 

while more information would support definitive conclusions as to the Caney Creek emissions. 

Overall, the PM2.5 composition and air mass trajectories show that fire influence from large-scale 

burns can be seen in the ambient data at Caney Creek and Upper Buffalo Wilderness. More 

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extensive emission inventory data is needed to better assess the impact of prescribed burn 

emissions in the CENRAP region. 

Figure C-11. Air mass trajectories and associated fires for March 6. Squares along each 

trajectory are placed every 6 hours. 

C-19
Figure C-12. Air mass trajectories and associated fires for March 15. Squares along each 

trajectory are placed every 6 hours. 

C-20
Figure C-13. Air mass trajectories and associated fires for March 15. Squares along each 

trajectory are placed every 6 hours. 

C.9 EFFECTS ON VISIBILITY 

We have demonstrated a potential connection between prescribed burn occurrence and 

elevated EC and KNS emissions at Upper Buffalo Wilderness via comparative analyses of 

ambient data and fire history data and air mass trajectories. In order to assess the impact that the 

elevated emissions have on visibility, we plotted the median PM2.5 mass compositions of the 

annual, best visibility, and worst visibility data sets from Upper Buffalo Wilderness in order to 

C-21
compare them to the mass and visibility compositions measured on the select dates March 6, 

15, and 24, as illustrated in Figure C-14. The PM2.5 mass compositions consist of the measured 

NH4NO3, (NH4)2SO4, soil elements, OCM, and EC mass concentrations. The worst visibility 

data set is characterized by high (NH4)2SO4 and OCM measurements, while the best visibility 

data set is characterized by relatively low concentrations of all the species. Since (NH4)2SO4

does not derive from wood smoke, and OCM can derive from other sources, the species that 

dominate poor visibility conditions are not necessarily connected with emissions from wood 

smoke. 

Figure C-14. PM2.5 mass compositions for select dates and for the annual, best visibility, and 

worst visibility data sets.

C.10 CONCLUSIONS 

Speciated PM2.5 data collected at IMPROVE sites in Class 1 areas in Arkansas were used 

to determine whether such data can help to examine the influence of prescribed burning and 

determine if burns in the emission inventory significantly impact the PM2.5 composition and 

visibility reduction. Overall conclusions include: 

• Speciated PM2.5 data at IMPROVE sites are useful for characterizing sources impacting 

PM2.5 and visibility reduction, including burns. 

• Influence from specific known burns (as seen by elevated concentrations of EC or K) can 

be seen on select days when the meteorology 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 emission inventory is not complete and may be 

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missing burns in the areas of influence on these days, such as southern Oklahoma and 

eastern Texas. 

• Meteorology plays an important role in determining the areas impacted by prescribed 

burns. 

• EC, the primary marker of smoke, is a relatively small part of both the PM2.5 mass and 

light extinction. 

• Ammonium sulfate is generally the largest contributor to the PM2.5 mass and light

extinction; this component does not originate from burns. This finding is consistent with 

other work in the Midwest and CENRAP region including Big Bend National Park and 

Seney Wildlife Refuge. 

C.11 RECOMMENDATIONS FOR FUTURE WORK 

Additional analyses could be conducted to better quantify the influence of burns on 

visibility impairment. Such analyses could include: 

• Apply analyses conducted in this task to additional IMPROVE sites, such as in Kansas or 

Minnesota to investigate whether results in this task are indicative of trends throughout 

the CENRAP region 

• Utilize continuous PM2.5 in conjunction with meteorological data to determine what 

meteorological conditions may be responsible for changes in PM2.5 concentrations. 

• Apply source apportionment tools such as UNMIX or Positive Matrix Factorization 

(PMF) to quantify influence 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 sources such as diesel, wood 

burning, etc. 

• Develop a better conceptual model of PM2.5 in the CENRAP region: 

− Are there differences in PM2.5 composition and meteorology among different 

locations in the CENRAP region? Significant differences in PM2.5 concentrations and 

composition among sites in different geographic locations within the CENRAP region 

may provide insight into PM2.5 transport and formation. For example, a surface high 

pressure system located over the Upper Midwest will often drive southeasterly winds 

across the CENRAP region, which can transport higher levels of PM2.5 from upwind 

sources within major population centers. 

− How are PM2.5 concentrations and visibility dependent on large-scale meteorological 

patterns? The effect of large-scale synoptic patterns on PM2.5 concentrations and 

regional haze is a critical issue because synoptic patterns affect transport, vertical and 

horizontal dispersion, formation, and the impact of local emissions on an area. For 

example, transport of warm, moist air from the Gulf of Mexico may result in 

secondary particle formation within the CENRAP region, reducing visibility. 

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− What are the compositional and meteorological differences between days of high and 

low PM2.5 concentrations? Differences in PM2.5 composition may indicate different 

transport regimes, and might identify which species are dominant on high PM2.5 days, 

both of which would assist forecasters. One useful way of examining the 

meteorology on these days is to perform several case study analyses of high and low 

PM2.5 concentration episodes. A typical case study analysis would examine: 

• Upper-air and surface synoptic patterns for each day. These patterns assist 

meteorologists in determining the extent to which particles may be allowed to 

mix, or disperse. For example, an upper-level high pressure system is typically 

associated with sinking air, which will help to trap particles near the surface. 

• Vertical temperature soundings whenever available. Vertical temperature 

soundings give meteorologists the ability to assess the vertical structure of the 

atmosphere, in particular, how much vertical mixing can occur. Typically, a 

strong surface-based inversion will trap particles near the surface, allowing PM2.5

levels to be high. 

• Back-trajectories for each day. Back-trajectories provide meteorologists with a 

tool for assessing whether transport of particles could have occurred within a 

region or from another region. 

C-24