1

Review of Residential Wood Combustion Data for Mid-Atlantic 

and New England States

David R. Broderick, James E. Houck

OMNI Environmental Services, Inc., 5465 SW Western Avenue, Beaverton, Oregon 97005

John Crouch

HBPA – Hearth Patio & Barbecue Association, 7840 Madison Ave., Suite 185, Fair Oaks, California 

95628

Jack Goldman

HBPA – Hearth Patio & Barbecue Association, 1601 North Kent Street, Suite 1001,

Arlington, Virginia 22209

ABSTRACT

In many of the areas of the eastern United States with elevated PM2.5 levels, residential wood 

combustion (RWC) has been identified as a potential contributor. There are several sources of data 

that have been published on RWC activity levels, emissions, and practices covering the Mid-Atlantic 

and New England states. These include: (1) The MANE-VU Residential Wood Combustion Emission 

Inventory published by the Mid-Atlantic Regional Air Management Association (MARAMA), (2) 

Residential Energy Consumption Surveys published by the Energy Information Administration, (3) the 

National Emission Inventory published by the U.S. Environmental Protection Agency, (4) Simmons 

Marketing Research reports, and (5) American Housing Surveys for the United States published by the 

U.S. Department of Commerce and the U.S. Department of Housing and Urban Development. In 

addition, the results of three RWC surveys at the state-level have been published in the last decade for 

the Mid-Atlantic and New England area, which allow for comparison of data extrapolated from the 

national- and regiona l-scale surveys to the state level for these three states. These are (1) 1995 

Delaware Fuelwood Survey, (2) Residential Fuelwood Use in Maine, Results of 1998/1999 Fuelwood 

Survey, and (3) Vermont Residential Fuel Wood Assessment for 1997-1998. In addition to using the 

states of Delaware, Maine, and Vermont for comparisons of data obtained from the regional and 

national surveys, data was extrapolated from the regional surveys to the five counties making up the 

Pittsburgh, Pennsylvania fine particulate nonattainment area. The Pittsburgh area was targeted for this 

comparison as a woodstove change-out program is being considered for it as part of the actions to 

bring the area into compliance with the PM2.5 standard. The comparability of the results extrapolated 

from the various national- and regional- scale surveys was poor, confirming the need for targeted localscale RWC surveys for specific PM2.5 nonattainment areas. Further, the fact that the surveys were 

designed for purposes other than localized RWC emission inventories, namely, regional haze, energy 

usage, a national multiple source and multiple pollutant inventory, housing characteristics, and 

marketing, lessened their effectiveness in acquiring appropriate RWC information needed to assess 

RWC contributions to airshed PM2.5 and for woodstove change-out programs.
2

INTRODUCTION

With approximately 48 million wood burning appliances in homes in the United States, the 

contribution of residential wood combustion (RWC) needs to be accurately accounted for in emissions 

inventories. This is particularly important for the Mid-Atlantic and New England states, where RWC 

due to the ready availability of fuel, colder climate, and large rural populations, is prevalent. Further, 

an accurate understanding of RWC practices is necessary to make informed regulatory decisions for 

the control of RWC emissions or for the implementation of mitigation programs such as woodstove 

change-outs, particularly in PM2.5 nonattainment areas. There are several sources of regional or

national data that can be applied to the Mid-Atlantic and New England states1-5. Notably, the MidAtlantic Regional Air Management Association (MARAMA) published, “The MANE-VU Residential 

Wood Combustion Emission Inventory” to help address the regional haze issue1. Three statesponsored (Delaware, Vermont, and Maine) RWC surveys6-8

, and local data for the Pittsburgh, 

Pennsylvania metropolitan statistical area (MSA) published by the American Housing Survey9 were 

used to compare the efficacy of scaling regional or national data to the state level (Delaware, Vermont, 

and Maine) or to the local level (Pittsburgh). All data were scaled to the same geographical area and 

the same base year (2002) by proportioning by the number of households. Ancillary data needed in the 

process were obtained from the U.S. Census Bureau county level profiles10, hearth industry surveys11, 

and cordwood species and weight data12-17.

Pittsburgh was selected for the local-level comparison because it is a PM2.5 nonattainment area and a 

woodstove change-out is planned for it. Five counties make-up the Pittsburgh nonattainment area –

Westmoreland, Washington, Beaver, Butler, and Alleghany counties. The Pittsburgh MSA is 

comprised of these five counties plus one more – Fayette County. The data for the Pittsburgh MSA 

was adjusted to be consistent with the Pittsburgh nonattainment area by removing the Fayette County 

contribution by using U.S. Census Bureau county level profiles. Delaware, Vermont, and Maine were 

selected for the state-level comparisons simply because they were the only northeastern states where 

surveys could be identified as having been conducted within the last decade.

Regardless of the exact calculation or the statistical manipulations methods used, an emission 

inventory for RWC is fundamentally the sum of the products of activity levels for the various RWC 

appliance types and their respective emission factors. For RWC, since wood use is generally reported 

by volume (cords) and emission factors are generally in the units of mass of pollutant per mass of dry 

wood (g pollutant/kg dry wood), an additional fundamental step is needed – the conversion of wood 

volume to dry wood mass. This paper focuses on activity levels and the volume-to-mass conversion. 

There is a paucity of quality emission factors for most RWC pollutants and a review of the limitations 

these pose to RWC emission inventories is outside the scope of this review.
3

APPLICABILITY OF THE MANE-VU SURVEY TO STATE AND LOCAL LEVELS

The MANE-VU survey is notable as it is recent, it specifically targeted RWC, it has gained wide 

exposure through an internet posting and through presentations at national conferences, and it is 

especially illustrative of the problems associated with using regional data to apply to state or local 

areas. Its primary objective (regional haze) is inconsistent with the need for state and local data, which 

has become a topical issue in part due to PM2.5 nonattainment areas. The MANE-VU survey was 

conducted without regard to state boundaries or important sociodemographic factors, i.e., the 11 

MANE-VU states (plus Washington DC) were divided into 24 test matrix cells based on solely 

urban/suburban, forested/unforested, single-family/multiple-family, and heating-degree day (HDD) 

characteristics. State and local differences in social and economic factors, regulations, forest 

management practices, availability of woodburning appliance dealers, prevalence and availability of 

other fuels, e.g., coal, fuel oil, and natural gas, all effect the number and usage of RWC appliances 

beyond the simplistic test matrix cell breakdown. Again, while the MANE-VU survey may provide 

useful insight for its primary objective – regional-scale haze, its application to specific state and local 

areas is inappropriate as RWC practices change with political boundaries and with many local factors.

The “n” values, i.e., the number of survey responses and the number of respondents indicating that

they use RWC are illustrated in Table 1 for the MANE-VU survey. Complex and sophisticated 

statistical analyses are not needed to understand the limitations of applying the MANE-VU data to the 

state or local level. Simply and succinctly put, the “n” values are far too small. For example, for the 

three states (Delaware, Vermont, and Maine) and for the sum of the five counties making the 

Pittsburgh nonattainment area, which are specifically being evaluated here, the number of respondents 

indicating that they use RWC were: 2, 12, 45, and 11 as compared to 342,437, 293,708, 650,090, and 

906,531 total households in each geographic area, respectively.
4

Table 1

Mane-Vu RWC EI – Number of Responses and Households

for Each State and for the Pittsburgh Nonattainment Area

Geographical 

Area

Total Number

of Households 

in Geographical 

Area

Number

of Survey

Responses

Number

of RWC 

Households that 

were Surveyed

Connecticut 1,385,361 74 12

Delaware 342,437 35 2

Washington, DC 274,561 4 2

Maine 650,090 186 45

Maryland 2,144,554 201 45

Massachusetts 2,621,366 127 23

New Hampshire 546,239 83 15

New Jersey 3,309,488 115 21

New York 7,673,327 550 79

Pennsylvania 5,245,052 449 85

Rhode Island 439,713 24 7

Vermont 293,708 57 12

Total 24,925,896 1905 348

Pittsburgh NAA 906,531 47 11

As noted, the factor to convert the volume of wood burned (cords) into the dry mass of wood burned is 

very significant in the development of activity levels used to produce a RWC emission inventory 

because typically survey respondents report the amount of wood that is burned in the units of cords 

whereas emission factors are typically in the units of mass pollutant per dry mass of wood burned. The 

mass of a cord of wood varies with tree species, with hardwoods (wood from deciduous trees) 

generally weighing more than softwoods (wood from coniferous trees). The correlation between cords 

and mass is not a true density correlation because of the spacing associated with stacking of fuelwood 

pieces. Table 2 illustrates the typical range in conversion factors, with the lower values associated 

with areas that burn more softwood and higher values associated with areas that burn more hardwood. 

Composite estimates of conversion factors from all of the species that were identified from the various 

surveys were made. National values and values for several Mid-Atlantic states, for which independent 

data were available (New Jersey, Maryland and New York)12 are shown in Table 2. The National 

values are lower (approximately 1.2 tons/dry cord) due to more softwood being burned in the western 

United States. The three Mid-Atlantic states for which data were available and the state of Minnesota 

predominately burned hardwoods and their conversion factors were higher (approximately 1.4 tons/dry 

cord). (Minnesota is shown because a quality statewide RWC survey was recently completed there18.) 

Conversion factors are also shown for West Virginia and Virginia due to the availability of data for 

them14, their proximity to the Mid-Atlantic states, and the fact they show the effect that the varying 

hardwood/softwood mix has on conversion factors. West Virginia burns about 75% hardwood and 
5

25% softwood and its conversion factor is 1.29 tons/dry cord. Virginia burns about 65% hardwood 

and 35% softwood and its conversion factor is 1.23 tons/dry cord. The conversion factors for West 

Virginia and Virginia compare favorably to the three Mid-Atlantic states shown in Table 2 when the 

facts that the three Mid-Atlantic states burn 93% to 97% hardwood and their conversion factors range 

from 1.37 to 1.44 tons/dry cord are taken into consideration. The authors calculated a conversion 

factor from the MANE-VU raw survey data (predominately hardwood is burned in the states covered 

by MANE-VU) as being 1.45 tons/dry cord. An unexplainably high conversion factor of 1.8 tons/dry 

cord was used in the Mane-VU emission inventory calculations1. It needs to be emphasized that an 

accurate (particularly accurate locally) conversion factor needs to be used because the conversion 

factor has a direct one-to-one effect on the final emission inventory results.

Table 2

Cord Wood Conversion Factors

Geographic Area Source Tons/Dry 

Cord*

National EIA/NEI 1.16

National Caltech 1.23

MANE-VU MANE-VU Survey, 

MARAMA Calculated

1.8

MANE-VU MANE-VU Survey, 

OMNI Calculated

1.45

New Jersey U.S. Forest Service 1.44

Maryland U.S. Forest Service 1.44

New York U.S. Forest Service 1.37

Minnesota State Survey 1.41

West Virginia Southern States Energy 

Board

1.29

Virginia Southern States Energy 

Board

1.23

 *tons are English (short) tons, i.e., 2000 lbs. 

PREDICTION OF WOOD USE AT THE STATE AND LOCAL LEVELS

While activity levels (wood usage) are needed for each individual RWC appliance type (i.e., wood 

heaters, fireplaces, centralized heating systems, etc.) for an emission inventory since emission factors 

are different for different appliance types, the comparison of the overall total amount of wood used for 

residential heating predicted from the various databases allows for a direct and perhaps the most 

fundamental assessment of the level of agreement among them. Table 3 shows total wood usage 

calculated by six different methods utilizing different survey data and/or calculation methods. The 

Pittsburgh nonattainment area and the three states for which there are state surveys available are 

included in the table. The state of Pennsylvania was also included since the five counties that make up 
6

the Pittsburgh nonattainement area are, in terms of the number of households, a significant fraction of 

the state (17%).

Table 3

Wood Use By Example States and the Pittsburgh Nonattainment Area,

Dry Tons/Year

Geographical 

Area

MANE-VU

(back 

calculated 

from final 

report*)

OMNI 

(calculated 

from 

Mane-Vu 

EI raw 

data, 1.45 

tons per 

cord 

conversion 

factor)

EIA 2002

(adjusted 

from 2000 

survey, 

1.163 

tons/cord 

conversion 

factor)

NEI 2002

(from NEI 

database)

State 

Survey 

(adjusted 

to 2002)

AHS/Census/ 

EIA

Delaware 74,900 72,900 60,500 75,400 67,500 29,000

Maine 820,600 533,000 150,500 285,800 580,200 91,900

Pennsylvania 2,628,800 1,944,600 588,500 889,800 - 1,718,300

Vermont 387,300 242,200 69,800 126,500 276,700 42,900

Pittsburgh 323,800 235,900 111,800 152,400 - 169,800

*Wood use was not presented; these data were back calculated from final emission inventory results.

The data in Table 3 illustrate two things. With the exception of the state of Delaware, the wood usage 

predicted from the MANE-VU data is markedly the highest. This is in large part due to the high 1.8 

tons/dry cord conversion factor used. Secondly, for each geographical area the range of predicted 

wood usage is large, for example, in the worst case (Vermont), there is almost an order of magnitude 

difference between the lowest and highest predicted wood usage. Because there are a number of 

complex factors that contribute to the amount of wood predicted to be consumed by RWC it is beyond 

the scope of this paper to address all the problems in detail. There are, however, several obvious 

causes for the large variations; (1) As noted, the differences in the tons to dry cords conversion factors 

can make a significant difference. (2) The confusion over the term dry wood has potentially caused 

some errors in the prediction of wood usage. The dry wood usage values shown in Table 3 are tons of 

wood adjusted to 0% moisture because emission factors are in the units of mass pollutant per tons of 

dry (0%) wood. On the other hand, dry wood to a home user or a fuelwood supplier often simply 

means wood with a moisture content of about 20% or less. Additionally, the lack of awareness in the 

difference or confusion between the terms moisture on a “dry basis” or on a “wet basis” can cause 

considerable differences in wood use numbers. Finally, confusion and errors can be caused by the fact 

that natural resource or energy related wood use values often reflect a mass number that is different 

than the “dry” mass number since the average moisture of cordwood in the U.S. is 24.1%19(dry basis) 

making actual wood mass numbers higher than those reported as dry wood. (3) Differences in the 

regional state groupings unrealistically influence the results of wood use calculations. For example, 

Delaware is grouped into the South U.S. Census region and the South Atlantic Census division. 
7

Because these include states with very mild climates, such as Florida, the mean wood usage from 

which the Delaware state values are derived based on these grouping are unrealistically low. (4) 

Virtually all data were obtained from surveys that were not primarily designed for emission inventory 

purposes; therefore considerable assumptions and extrapolations are needed to apply the data for wood 

use calculations.

In summary, the wide variation in the wood use values predicted by the various surveys, confirms that 

the existing database for the Mid-Atlantic and New England states is of limited value when applied to 

the state or local levels. Local surveys designed specifically for emission inventories are needed to 

provide acceptable wood use data (activity levels) for state or local emission inventories in the region.

PREDICTION OF RWC APPLIANCE TYPES AT THE STATE AND LOCAL LEVELS

Knowing the number of appliances by type that are used is important for two reasons. First, it is 

essential in the development of emission inventories as different appliance types have different 

emission factors. Second, the efficacy of regulatory control options or mitigation strategies can only 

be judged when the magnitude of their effect can be estimated by the number of appliances involved. 

For example, the magnitude of the effect of a woodstove change-out program is directly related to the 

number of older conventional stoves that are available for replacement with low-emitting certified 

stoves. Tables 4-6 show the number of RWC appliances by type in use for the three states for which 

there are RWC surveys available that were conducted in the last decade (Delaware, Maine, and 

Vermont)6-8. Table 7 shows the number of RWC appliances in use by type for the Pittsburgh 

nonattainment area, which has as a local source of data, the 1995 American Housing Survey conducted 

in the Pittsburgh MSA9. It needs to be remembered that there are more appliances that are owned than 

the number in use. For example, roughly one-quarter to one-third of fireplaces are not used in any 

given heating season and about 20% of woodstoves are not used. To be consistent with emission 

inventory, regulatory decision-making, and planning needs, four primary RWC appliance categories 

are included in Tables 4-7. These are (1) fireplaces, (2) central heating units, (3) all wood heaters (sum 

of woodstoves and fireplace inserts), and (4) pellet. The “all wood heaters” category was further 

broken down into certified wood heaters (woodstoves and inserts) and conventional wood heaters 

(woodstoves and inserts). (There are also minor RWC categories such as masonry heaters, firepits, 

cookstoves, and water heaters but their contribution to RWC emission inventories is insignificant.)
8

Table 4

Number of Appliances Used by Geographic Area – Delaware*

MANE-VU State 

Survey**

AHS Simmons 

Research

Fireplaces 26,900 67,500 25,600 -

Central 

Heating Units

0 11,800 - -

Pellet 1,970 1,890 - -

All Wood 

Heaters 

(Woodstoves 

and Inserts)

12,000 20,400 52,000 34,800

Certified 

Woodstoves 

or Inserts

4,570 8,390 - -

Conventional 

Woodstoves 

or Inserts

7,470 12,000 - -

Note: Simmons Research reports number of wood heaters owned, so that number was adjusted to 90% to 

reflect those households that do not use their appliances.

*There were 74 responses within the state of Delaware in the MANE-VU survey of which two were 

wood appliance users. The state survey was based on 879 responses.

**The Delaware survey only reported appliances owned, so that data was adjusted using best professional 

judgment to reflect the number of households that use their appliances (fireplaces 73%, central heating 

units 93%, pellet and certified appliances 96%, and conventional woodstoves and inserts 80%)
9

Table 5

Number of Appliances Used by Geographic Area – Maine*

MANE-VU State Survey AHS Simmons 

Research

Fireplaces 46,300 - 18,800 -

Central 

Heating Units

18,900 17,800 - -

Pellet 4,090 3,820** - -

All Wood 

Heaters 

(Woodstoves 

and Inserts)

78,600 112,040 97,900 85,600

Certified 

Woodstoves 

or Inserts

32,200 - - -

Conventional 

Woodstoves 

or Inserts

46,400 - - -

*There were 186 responses within the state of Maine in the MANE-VU survey of which 45 were wood 

appliance users. The state survey was based on 407 responses.

**Includes other minor RWC appliance types in addition to pellet-fuel appliances

Note: Simmons Research reports number of wood heaters owned, so that number was adjusted to 90% to 

reflect those households that do not use their appliances.
10

Table 6

Number of Appliances Used by Geographic Area – Vermont*

MANE-VU State Survey AHS Simmons 

Research

Fireplaces 21,400 13,700 8,760 -

Central 

Heating Units

9,700 17,800 - -

Pellet 2,120 - - -

All Wood 

Heaters 

(Woodstoves 

and Inserts)

36,900 61,500 45,700 39,800

Certified 

Woodstoves 

or Inserts

15,200 18,800 - -

Conventional 

Woodstoves 

or Inserts

21,700 42,700 - -

*There were 57 responses in the state of Vermont in the MANE-VU survey of which 12 were wood 

appliance users. The state survey was based on 482 responses.

Note: Simmons Research reports number of wood heaters owned, so that number was adjusted to 90% to 

reflect those households that do not use their appliances.
11

Table 7

Number of Appliances Used by Geographic Area – Pittsburgh Nonattainment Area*

MANE-VU Pittsburgh 

AHS

Simmons 

Research

Fireplaces 91,600 82,500 -

Central 

Heating Units

1,810 - -

Pellet 5,440 - -

All Wood 

Heaters 

(Woodstoves 

and Inserts)

40,800 50,000 113,000

Certified 

Woodstoves 

or Inserts

13,600 - -

Conventional 

Woodstoves 

or Inserts

27,200 - -

*There were 47 responses in the Pittsburgh nonattainment area in the MANE-VU survey of which 11 

were wood appliance users. The AHS survey for the Pittsburgh MSA was based on 4153 responses. 

When reduced in proportion to population to remove Fayette County and make it consistent with the 

Pittsburgh nonattainment area this corresponds to 3895.

Note: Simmons Research reports number of wood heaters owned, so that number was adjusted to 90% to 

reflect those households that do not use their appliances.

The estimation of the number of appliances by type using the various databases showed poor 

correlation in all four geographical areas. As with the prediction of wood usage from surveys, there 

are a number of complex factors that can effect the prediction of the number of appliance types from 

the survey data. Again it is beyond the scope of this paper to address them in detail, however there are 

several obvious causes. These include: (1) Fireplaces with and without inserts are often confused 

and/or double counted. (2) The term fireplace insert is often confused with manufactured built-in 

fireplaces as they are “inserted” into the wall. (3) The recognition of EPA certified appliances by 

home occupants is often inaccurate since many have safety labels that are not associated with EPA low 

emission certification. (4) Home occupants are often not the original purchaser of a wood heater and 

the date of purchase is not known (certified wood heaters were phased into use almost 15 years ago). 

(5) Large woodstoves are frequently incorrectly referred to as furnaces. (6) Ten to twenty percent of 

households have multiple woodburning appliances that may not be adequately counted. (7) Gas-fired 

fireplaces are common and can easily be confused with woodburning fireplaces or inserts.

Not only are there these aforementioned inherent issues associated with estimating appliance numbers, 

most surveys have not been primarily designed for emission inventory development and simply do not 

include appliance-specific questions. As with predicting wood usage, accurate numbers of appliances 
12

by type can only be obtained by emission-inventory focused local surveys. For the Mid-Atlantic and 

New England states these surveys currently do not exist. 

CONCLUSIONS

The application of regional surveys, such as the MANE-VU survey, to local areas does not provide the 

quality data needed for RWC emission inventories, air quality planning, or mitigation programs. In 

addition, even state and local surveys not designed with emission inventory or RWC mitigation 

programs as their primary objective often do not provide the database that is needed. Local or state 

surveys however can be designed, as evidenced by the recent Minnesota survey18, to provide 

appropriate RWC information. To the author’s knowledge, such information is not currently available 

for any of the relevant PM2.5 nonattainment areas in the Mid-Atlantic and New England states.

REFERENCES

1. Final Report: MANE-VU Residential Wood Combustion Emission Inventory, E.H. Pechan & 

Associates, Inc. prepared for MARAMA, June 22, 2004.

2. Residential Energy Consumption Surveys, 2001 Consumption and Expenditures Table s, 

Energy Information Administration, Washington, DC, 2001.

3. American Housing Survey for the United States: 2001, American Housing Survey, October 

2002.

4. 2002 National Emission Inventory, U.S. Environmental Protection Agency: Research Triangle 

Park, NC, March 2005. 

5. National Consumer Study, Spring 2003: Adult Full Year (June 2002-May 2003), Simmons 

Market Research Bureau, New York, NY, 2003.

6. Report on the 1995 Delaware Fuelwood Survey, Delaware Forest Service, May 1995.

7. Vermont Residential Fuel Wood Assessment 1997-1998, Vermont Department of Public 

Service, December 2000.

8. Residential Fuelwood Use in Maine, Results of 1998/1999 Fuelwood Survey, Maine State 

Planning Office, November 1999.

9. American Housing Survey for the Pittsburgh Metropolitan Area in 1995, American Housing 

Survey, 1995.
13

10. Profile of General Demographic Characteristics: 2000, data for Pennsylvania counties, U.S. 

Census Bureau, Census 2000.

11. Fireplace and Freestanding Stove Usage and Attitude Study 2002, Hearth, Patio & Barbecue 

Association, 2002.

12. Residential Fuelwood Demand Assessment, United States Forest Service, Northeastern Area, 

October 1981.

13. Extension Circular 1023, Hansen, H.J., Oregon State University, Corvallis, Oregon, Sept. 1980.

14. Residential Fuelwood Consumption in the Southeastern United States, The Gallup 

Organization, prepared for the Southern States Energy Board, August 1991. 

15. Chemical Characterization of Fine Particle Emissions from Fireplace Combustion of Woods 

Grown in the Northeastern United States, Fine, P.M., California Institute of Technology, 

published in Environmental Science and Technology, Vol. 35, No. 13, 2001.

16. Estimates of U.S. Biomass Energy Consumption 1992, Energy Information Administration, 

Washington, DC, May 1994. 

17. Documentation for the 2002 Draft Nonpoint Area Source National Emission Inventory for 

Criteria and Hazardous Air Pollutants, U.S. Environmental Protection Agency: Research 

Triangle Park, NC, March 2005.

18. Emissions Inventory Oriented Residential Wood Combustion Survey, Minnesota State Survey,

Wu, C.Y., Piva, R., Broderick, D.R., Houck, J.E., and Crouch, J., 14th Annual Emission 

Inventory Conference, March 2005.

19. Updated Emissions Data for Revision on AP-42, section 1.9, Residential Fireplaces, Houck, 

J.E., Crouch, J., report prepared by OMNI Consulting Services, Inc. and Hearth, Patio and 

Barbecue Association, December 18, 2002.