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Aug 16, 2006 - International Laboratory for Air Quality and Health, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, ...
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Environ. Sci. Technol. 2006, 40, 5696-5703

Quantification of Particle Number and Mass Emission Factors from Combustion of Queensland Trees ARINTO Y. P. WARDOYO, LIDIA MORAWSKA,* ZORAN D. RISTOVSKI, AND JACK MARSH International Laboratory for Air Quality and Health, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia

The quantification of particle emission factors under controlled laboratory conditions for burning of the following five common tree species found in South East Queensland forests has been studied: Spotted Gum (Corymbia citriodora), Blue Gum (Eucalyptus tereticornis), Bloodwood (Eucalyptus intermedia), Iron Bark (Eucalyptus crebra), and Stringybark (Eucalyptus umbra). The results of the study show that the particle number emission factors and PM2.5 mass emission factors depend on the type of tree and the burning rate. For fast burning conditions, the average particle number emission factors are in the range of 3.35.7 × 1015 particles/kg for woods and 0.5-6.9 × 1015 particles/kg for leaves and branches, and the PM2.5 emission factors are in the range of 140-210 mg/kg for woods and 450-4700 mg/kg for leaves and branches. For slow burning conditions, the average particle number emission factors are in the range of 2.8-44.8 × 1013 particles/kg for woods and 0.5-9.3 × 1013 particles/kg for leaves and branches, and the PM2.5 emissions factors are in the range of 120-480 mg/kg for woods and 3300-4900 mg/kg for leaves and branches.

Introduction Biomass burning including controlled and uncontrolled forest and savannah fires, as well as various types of residential burning, has been identified as a major contributor to particles and gases in the atmosphere (1-3). These particles and gases impact human health and are linked to morbidity and mortality (4), and play a significant role in affecting atmospheric processes (5) such as radiation balance (6) or acidification of clouds, rain, and fog (7). In particular, forest fires significantly contribute to the particle burden of the atmosphere. More than 1.3 million hectares of forest were burnt in China in 1987. In the same year, forest fires in eastern Asia consumed approximately 14 million hectares (2). In 1994 and 1997, forest burning destroyed more than 50,000 and 20,000 km2, respectively, in Indonesia (7). Other data show that 100,000 km2 of forest in northern latitudes, 400,000 km2 of tropical and subtropical forest, and 5-10 million km2 of open forest and Savannahs are burnt every year (3). In Australia, the state of Queensland recorded its the worst forest fires in 1991 with 37,000 hectares consumed (8). From July 2002 until June 2003, there were 2,618 fires in this states covering one million hectares in this * Corresponding author phone: + 61 7 3864 2616; fax: + 61 7 3864 9079; e-mail: [email protected]. 5696

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state (9). In 2004, the major fires occurring within the southeast corner of Queensland included forest fires at San Fernando and Canugra in July; at Wallaby Hill Mudgeeraba, Gold Coast, Minden, Gilston/Tallai Range, and Tamborine in August; and at Tamborine, Lowry/Hinze Dam, and Nerang in October. Knowledge of particle emission characteristics in terms of size distribution and emission factors, particularly in terms of particle number emissions, has been identified as a very important element in developing quantitative assessment of the impact of the fires. Both these characteristics depend on the type of biomass and the conditions of burning. Studies reported in the literature showed that the majority of particles resulting from biomass burning were less than 2.5 µm in diameter (10-13). The PM2.5 emission factors have been measured in the range of 0.2-12 g/kg (12, 14, 15). In terms of particle number, the reported emission factors ranged from 3 × 1015 to 40 × 1016 particles/cm3 (13). However, the existing data on particle size distribution and number emission factors are still very limited, with data unavailable for many tree species, such as those growing in the frequently fire-ridden state of Queensland. This paper presents the results of a controlled laboratory study aimed at quantifying the particle emission factors from combustion of trees typically found growing in southeast Queensland open forests. A specific emphasis of the study was on developing a better understanding of the size distribution and emission factors from burning conducted under different environmental conditions.

Experimental Section Quantification of the emission factors and measurements of particle size distribution were conducted by the burning of biomass samples in a stove modified for the purpose of the study. The sampled smoke was diluted in two steps, first with compressed fresh air in an ejector dilutor (Dekati) and then by mixing with filtered air. Particle number emission factors were quantified by measuring the total particle concentration during the combustion process using a condensation particle counter (CPC), while the size distribution of particles was measured using a scanning mobility particle sizer (SMPS). PM2.5 emission factors were approximated using a TSI Dustrak with a 2.5 µm inlet. Experimental Setup. The experiment was designed and carefully controlled to capture the maximum number of parameters, which can be controlled under laboratory conditions. In particular, the flow rate for the experiments was chosen to correspond as closely as possible with the flow rates under natural conditions, represented by wind speed. It has been reported in the literature that most fires occur at a wind speed between 70 and 120 km/h. Bushfires in Australia occur under typical wind speed of about 80 km/h and with a rate of spread of 18-20 km/h (http:// www.ffp.csiro.au). The experiments were set up to simulate burning conditions by injecting air with the speed of 20 m/s (72 km/h) into the stove for fast burning and by keeping the stove unconnected to the blower during slow burning, so as not to force the air supply through the ventilation system (unforced flow rate of the incoming air was in the range between 1.7 and 2.5 m/s). The performance of the measurement system was investigated by adjusting the flow rate of air in the dilution tunnel and the temperature of the heated air in the Dekati diluter. In particular, experiments were conducted for several constant values of temperature of the injected air and for different flow rates of air in the dilution tunnel. The results 10.1021/es0609497 CCC: $33.50

 2006 American Chemical Society Published on Web 08/16/2006

FIGURE 1. Experimental setup consisting of the burning system (modified stove), a dilution and sampling system, and a particle measurement system. have been presented in Figure A in the Supporting Information, which shows that the diameter of particles was found to be most stable at the temperature of 200 °C (for different dilution tunnel flow rate). The second set of experiments was conducted by setting the flow rate of air in the dilution tunnel at 1 m/s and changing the temperature of the Dekati diluter as follows: room temperature, 100 °C, 150 °C, 200 °C, and 250 °C. The results have been plotted in Figure B in the Supporting Information. The diameter of particles was also found to be stable for the temperature between 100 and 200 °C. Based on these experiments, all the measurements were conducted for the air flow rate in the dilution tunnel and the injected air temperature at 1 m/s and 200 °C, respectively. Burning System. Stoves have been used previously as burning systems to characterize biomass burning emissions (13, 14, 16). A modified commercial stove (Figure 1), with dimensions of 66 × 74.5 × 55 cm3, was used as the burning system in this study to simulate the burning rates that would occur in forest fires. Part of the stove, originally used to adjust air flow, was replaced by a ventilation system that enabled the introduction of a controlled amount of air into the stove. In order to obtain a homogeneous rate of air flow, the inlet of the ventilation system was connected to a rectangular hood. The hood was connected to a blower with a maximum capacity of 14 L/s through a pipe 30 mm in diameter. The connection was adjusted by a valve to control the flow rate of the air. Particle Measurement System. The number concentration and size distribution of the particles produced during the burning process was measured using a scanning mobility particle sizer (SMPS). The SMPS consisted of a 3071 TSI electrostatic classifier and a TSI 3010 condensation particle counter (CPC). The SMPS was operated in the window of 10-600 nm. The sheath air flow was selected as 4 L/m and the sample flow was 0.4 L/m. The scanning time and retrace time were set at 120 and 60 s, respectively. Continuous measurement of the total particle number concentration was determined using a CPC TSI 3022, which measures particle concentrations up to more than 106 particles/cm3. The sampling interval was 20 s. Approximation of mass concentration of fine particles (PM2.5) was measured by a TSI 8520 Dustrak using a PM2.5 inlet and the averaging time was 10 s. The Dustrak was calibrated against a 1400a TEOM. The calibration was conducted by burning representative samples of wood in a 3 m3 experimental chamber and conducting simultaneous measurements of concentration with both instruments. The concentration range used for calibration corresponded to the concentrations that were measured in the dilution tunnel. The data were fitted to a linear regression

curve (see Figure D in the Supporting Information) and the following equation was obtained:

PMTEOM ) 0.357 PMDT - 0.276 where PMTEOM is the mass measured by the TEOM and PMDT is the mass measured by the Dustrak. The obtained coefficient of linear regression (R ) 0.997) shows that there is a good linear correlation between the concentrations measured by two instruments. The concentrations and emissions factors of PM2.5 have been recalculated based on the equation. Dilution and Sampling System. The sampling system was a modified version of a system previously used for vehicle emission measurements (17). The samples were taken from the flue through a probe of 1 cm diameter. The sample flow was introduced to an ejector dilutor (Dekati) where it was diluted 10 times with heated, compressed, particle-free air to obtain a dry, diluted sample and to prevent further coagulation. The heated air temperature was set up based on the experimental data as mentioned above. The sample concentration at the inlet of the ejector dilutor was very high and caused frequent blockage of the dilutor inlet, particularly during slow burning. Therefore the dilutor had to be cleaned before each new experiment. The sample flow was next mixed with a constant flow of ambient air filtered by a HEPA filter in a dilution tunnel to further reduce the concentration below 106 per cm3. The flow rate and temperature of the samples in the dilution tunnel were measured using an air velocity meter. The temperature in the dilution tunnel was found to be very stable in the narrow range of 28-30 °C. The stability of the system’s performance was demonstrated by the stability of the dilution ratio measured during the experiments and is presented in Figure C in the Supporting Information. During the experiments the temperature in the dilution tunnel was monitored continuously. The dilution ratio was calculated by measurement of the concentrations of CO2 in the flue, the dilution tunnel, and the ambient air that was used for dilution. The concentration of CO2 in the flue was continuously measured using a flue gas analyzer. At the same time, the concentration of CO2 in the dilution tunnel and ambient air were recorded using a TSI 8554 Q Trak Plus. The dilution ratio was calculated as follows:

DR )

Cf - Cb Cd - Cb

(1)

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tunnel, and background, respectively. Both the flue gas analyzer and the Q-trak were calibrated prior to measurements. The Q-trak was used for the concentration measurement in the range of 430-1000 ppm. And the gas analyzer was used for the measurement in the range of 40 000-100 000 ppm. The dilution ratio was measured to vary from 150 to 200 for fast burning and from 100 to 300 for slow burning, and depended on species of wood burned. The dilution ratio varied from 100 to 250 for the fast and slow burning of leaves and branches. Sample Material and Preparation. The samples consisted of different species of wood, leaves, and branches collected from the trees growing in open forests at Mount Samson, located about 40 km west of Brisbane, Queensland, Australia. Five species of Eucalyptus were selected as samples according to their prevalence in the forest: Spotted Gum (Eucalyptus citriodora), Blue Gum (Eucalyptus tereticornis), Bloodwood (Eucalyptus intermedia), Iron Bark (Eucalyptus crebra), and Stringybark (Eucalyptus umbra). All five species are known as hardwoods. The hardest of these is the Blue Gum, followed in order of decreasing hardness by Bloodwood, Iron Bark, Spotted Gum, and then Stringybark. The logs of wood were placed in an open area of the laboratory for several months to obtain homogeneous moisture contents within the optimum range for burning of 20-30% (18). The wood pieces used for burning were sections of a large block of the trunk. The moisture was not measured directly from the trunk because the sharp part of the moisture meter used to measure resistance of the wood is only one cm long. In order to measure the moisture of all pieces of the wood, the logs were cut into pieces of 15-25 cm long with diameters of 5-12 cm, while branches were selected with diameters less than 2 cm. The measurements of wood moisture were conducted by measuring the dry part (outer part) and wet part (inner part) of the wood several times. For example: the moisture content of 15-26% for Blue Gum means 15% is the measured moisture of the outer part of the wood and 26% is the measured moisture of the inner part of the wood. The measured moisture content of the samples varied from 18-26% for Spotted Gum, 15-26% for Blue Gum, 14-24% for Bloodwood, and 17-25% for Iron Bark. The moisture contents of the branches were 16-18% for Spotted Gum, 18-22% for Iron Bark, and 18-20% for Stringybark. Moisture content of the leaves was measured from the difference between the leaves weight before and after drying in an oven at temperature of 110 °C. The moisture content of the leaves varied from 5-8% for Spotted Gum, 6-10% for Iron Bark, and 6-8% for Stringybark. The wood samples were weighed to about 2 kg, while the mix of leaves and branches was about 0.5 kg for each sample. The unburned fraction samples and ash were weighed after the burning finished. Burning Conditions. The samples were burned in the stove under two different conditions of burning called “fast burning” and “slow burning”. During fast burning the stove was connected to a blower that introduced fresh air at a rate of 14 L/s, while the valve was fully open to let air into the stove with maximum velocity. Under slow burning conditions, the stove was not connected to the blower and the air supply through the ventilation system was not forced during the burning process. The air velocity at the base of the stove for the fast burning condition was measured at several points using an air velocity meter while the door of the stove was closed. The air velocity across horizontal cross section was relatively homogeneous in the region of 15 cm from the middle of the stove base with a speed of 1.8-2.0 m/s. Based on this, the samples for emission characterization during burning were placed in the center of the stove’s base. 5698

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Burning of the wood samples was repeated in sequences 4 times for fast burning and 3 times for slow burning conditions. Some of the sequences were repeated several times. This was done to confirm the reproducibility of the results for the same wood species. Initially two experiments were conducted on different days to check the repeatability of the results. Experiment I was conducted when the temperature and humidity of the ambient air were 20.5 °C and 48.7% and experiment II was conducted at a temperature and humidity of 22.3 °C and 55.6%. The particle number and mass emission factors were found to be (4.3 ( 0.4) × 1015 particles/cm3 and 107 ( 8 mg/kg for experiment I and (4.7 ( 0.3) × 1015 particles/cm3 and 96 ( 7 mg/kg for experiment II. These results showed that the measurements conducted on different days gave consistent results. However, since ambient conditions vary between different days, a sequence of experiments was conducted to derive the emission factors for each of the samples during 1 day, rather than during measurements conducted on different days. To start the burning, newspaper and small pieces of the same wood types were used as a starter and kindling for the burning of the first sample. Pieces of the same wood were used as a starter and kindling for burning the following samples. The second and subsequent samples were placed in the stove just before burning of the previous sample finished. The same method was applied for burning leaves and branches. The processes of burning (such as igniting, flaming, and smoldering (19)) were observed through the window glass of the stove and recorded every 3 min to identify the relationships among the processes and the particle size distribution, concentration, and emission factors. The flue temperatures were measured by attaching a K-type thermocouple in the flue that was positioned 50 cm from the top of the stove. The thermocouple was connected to a data logger through an analogue-to-digital converter for temperature recordings. The temperature was recorded every 30 s during the burning process. The same method was used for fast and slow burning of the leaves and branches. The burning procedure was repeated 3 times for fast and slow burning.

Results and Discussion Sampling System Performance. To identify the optimum sampling conditions a series of tests was conducted for different dilution ratios and different temperatures of the air injected at the first stage of dilution. The experimental data show that the count median diameter (CMD) of the samples did not depend on the dilution ratio for the temperatures of the first stage dilution greater than 200 °C. For lower temperature the dilution ratio influenced the measured CMD (see Supporting Information, Figure A). Based on these experimental data, the sampling measurements were conducted at a temperature of 200 °C for both fast and slow burning. Temperature in the second stage dilution tunnel was measured and kept in a small range between 28 and 30 °C. Particle Size Distribution. Figure 2 shows examples of typical particle size distributions for fast burning of the wood samples during ignition, flaming, and smoldering processes. In general, the distributions are unimodal, with most of the particle numbers centered between 30 and 40 nm. The diameter of the particles emitted is larger during the ignition process and varies from 50 to 70 nm depending on the species of the wood, with the smallest diameters for Blue Gum and Bloodwood and the largest for Stringybark samples. Particle size distribution for slow burning was also unimodal with most of the particle numbers centered around 50-60 nm. Particles of larger diameters, in the range from 110 to 150 nm, were emitted during the ignition process particularly from burning of Blue Gum and Bloodwood. The

FIGURE 2. Representative size distribution of particles from fast burning of woods.

FIGURE 3. CMD characteristics of samples burned: (a) Beech wood with moisture content between 15-18% (20); (b) Birch wood (11), the diameter of particles for igniting was found to be 500 nm; (c) Birch wood (21); (d) Ponderosa Pine and Western Hemlock (12). smallest particles were released during the smoldering process with particle CMD ranging from 30 to 40 nm. Likewise, size distributions of particles from fast and slow burning of the leaves and branches were unimodal, with the exception of the ignition phase of the slow burning of Spotted Gum samples. In this case, all three experiments showed bimodal size distribution, with the diameters of the first and the second modes centered between 60 and 100 nm, and 110 and 150 nm, respectively. In general, during the ignition and flaming processes, most particles released by slow burning of the Spotted Gum samples were of diameters around 150

nm, while smaller particles with a diameter of 50 nm were emitted during the smoldering process. For fast burning the largest particles with diameters of about 60 nm were emitted at the beginning of the burning, decreasing to approximately 50 nm during the flaming phase, and to 30 nm toward the end of the burning process. Figure 3 shows the average CMD and its standard deviation for particles emitted during ignition, flaming, and smoldering processes for fast and slow burning of all the wood samples from this work, and for comparison, of CMDs reported in the literature. Generally, fast burning of wood VOL. 40, NO. 18, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. Total number concentration for fast burning of Blue Gum. samples produces particles with a small diameter, approximately 50-70 nm during the ignition process and 3040 nm during the rest of the burning process. Larger particles are released during slow burning, with diameters of around 110-150 nm at the beginning, 50-60 nm during the flaming phase, and 30-40 nm during smoldering. The characteristics of the particles released during fast and slow burning of leaves and branches are similar to those arising from the burning of wood samples, as can be seen from Figure 3. For fast burning, the largest particles (with diameters of about 60 nm) are found during the ignition process. Most of the particles produced during flaming are smaller, in the range of 50 nm, and the particles resulting from smouldering are even smaller, of the order of 30 nm. Under slow burning conditions, most of the particles released during ignition and flaming processes are larger, with diameters ranging from 100 to 200 nm, while few of them with smaller diameter of 50 nm were produced during the smoldering process. The diameter characteristics of the particles from the burning of the samples in this study are generally comparable with those reported in literature. The differences in results are likely to be caused by the variation in the species investigated and the method of burning. Particle Number Concentrations. Figure 4 shows an example of the change in particle number concentration for four repeated runs during fast burning of Blue Gum. The average particle concentration was calculated by integrating the total concentration during one run and dividing by the burning time. The standard deviation was derived from 4 repeated runs for fast burning and 3 repeated runs for slow burning. The results show that fast burning produces higher average particle concentrations than slow burning. For fast burning, harder woods result in emissions of higher particle concentrations than softer woods. Blue Gum, Bloodwood, and Iron Bark, having relatively the same hardness, produced particle number concentrations of (1.1 ( 0.2) × 108 particles/cm3, (1.2 ( 0.2) × 108 particles/cm3, and (1.4 ( 0.2) × 108 particles/ cm3, respectively. Fast burning of the softer woods produced lower particle number concentrations of (8.6 ( 2.0) × 107 particles/cm3 for Spotted Gum and (9.8 ( 2.0) × 107 particles/ cm3 for Stringybark. 5700

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On the other hand, slow burning of harder woods yielded lower particle number concentrations than softer woods. The particle number concentrations of Blue Gum, Bloodwood, and Iron Bark were (5.3 ( 1.0) × 106 particles/cm3, (3.1 ( 0.9) × 106 particles/cm3, and (5.0 ( 2.0) × 106 particles/ cm3, respectively. Spotted Gum and Stringybark (the softer woods) emitted particles with average number concentrations of (3.3 ( 1.0) × 107 particles/cm3 and (5.6 ( 2.8) × 107 particles/cm3. Stringybark, the softest wood, released the highest concentration of particles during slow burning. Number concentrations of the particles emitted during fast and slow burning of wood samples in this study are comparable with the results from the literature. Total particle number concentration resulting from the burning of Beech wood using a residential wood stove was reported in the range of 7.8 × 106 to 4.4 × 107 particles/cm3 (20) while results from the burning of Birch wood in a commercial soapstone stove have been documented in the range between 1.5 and 8.0 × 107 particles/cm3 respectively (11). The concentrations from burning of logs from the same wood sample in different combustion systems have been shown to vary from 0.4 × 104 to 1.5 × 107 particles/cm3 (13). The particle number concentration emitted from leaves and branches taken from the trees having harder wood produce higher particle number concentration for fast and slow burning compared to those from trees with softer wood. The average particle concentrations from fast burning of the samples are (1.5 ( 0.8) × 108 particles/cm3 for Iron Bark, (4.4 ( 3.0) × 107 particles/cm3 for Spotted Gum, and (1.3 ( 0.6) × 107 particles/cm3 for Stringybark. The same pattern has been found for slow burning of leaves and branches: Iron Bark produces the highest particle number concentration of (1.9 ( 1.0) × 107 particles/cm3, followed by Spotted Gum with (0.6 ( 0.2) 107 particles/cm3, and by Stringybark producing (0.6 ( 0.2) × 107 particles/cm3. PM2.5 Concentrations. The average PM2.5 concentrations show no statistically significant differences between different wood types for fast burning. The concentrations are 1.3 ( 0.2 mg/m3 for Blue Gum, 1.6 ( 0.3 mg/m3 for Bloodwood, 0.8 ( 0.1 mg/m3 for Iron Bark, 1.5 ( 0.2 mg/m3 for Spotted Gum, and 2.1 ( 0.4 mg/m3 for Stringybark. However, significant differences in PM2.5 concentrations can be seen during slow burning. Stringybark and Spotted Gum emit the highest particle concentrations of 42.3 ( 14.0 mg/m3 and

FIGURE 5. Average particle number emission factors of burned samples for fast burning and slow burning.

FIGURE 6. PM2.5 emission factors of burned samples. 24.3 ( 15.5 mg/m3, respectively, while harder woods produce concentrations of 8.0 ( 4.3 mg/m3 for Blue Gum, 2.3 ( 1.4 mg/m3 for Bloodwood, and 4.3 ( 1.9 mg/m3 for Iron Bark. The trend in PM2.5 concentration emissions for leaf and branch samples shows that the samples taken from trees with harder wood produced higher PM2.5 concentrations for fast burning than samples from trees with softer woods. The concentrations for Iron Bark, Spotted Gum, and Stringybark are 130 ( 60, 29 ( 9, and 8 ( 6 mg/m3, respectively. PM2.5 emissions during slow burning were significantly higher, and the concentrations recorded were 700 ( 250 mg/m3 for Iron

Bark, 1000 ( 300 mg/m3 for Spotted Gum, and 440 ( 130 g/m3 for Stringybark. It was observed that heavy smoke was produced during slow burning of the samples, indicating emissions of large particles and thus higher emissions of particle mass. Particle Number Emission Factors. Emission factors were calculated using the following equation:

EF ) Cavqt/m

(2)

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particle number concentration; g/kg of sample for PM2.5 concentration), Cav is the average concentration (particles/ cm3 for particle number concentration; g/m3 for PM2.5 concentration), q is the flow rate (m3/s), t is the period of burning, and m is the burned mass (kg). Figure 5 presents the average particle number emission factors and the standard deviations for fast burning of the different species of wood as well as leaves and branches. It can be seen that harder woods have higher particle number emission factors during fast burning than softer woods. On the other hand, softer woods produce particles with higher particle number emission factors. Under fast burning condition, the particle number emission factors of Blue Gum, Bloodwood, and Iron Bark (harder woods) are (5.7 ( 0.7) × 1015 particles/kg, (5.1 ( 0.6) × 1015 particles/kg, and (5.7 ( 0.4) × 1015 particles/kg, respectively, while the emission factors of Spotted Gum and Stringybark (softer woods) are (3.2 ( 0.4) × 1015 particles/kg and (4.5 ( 0.3) × 1015 particles/ kg. For slow burning, the particle number emission factor is (6.6 ( 0.7) × 1013 particles/kg for Blue Gum, (3.4 ( 0.2) × 1013 particles/kg for Bloodwood, and (5.5 ( 0.7) × 1013 particles/ kg while particle number emission factors for Spotted gum and Stringybark are (2.8 ( 0.7) × 1014 and (4.4 ( 0.3) × 1014 particles/kg. In general, fast burning produces significantly more particles than slow burning with particle number emission factors about ten times higher. There have been no studies reporting particle emission factors for these specific wood species; however, emission factors for unspecified wood logs burned in different combustion systems have been measured in the range of 1.43-39.5 × 1016 particles/kg depending on the combustion system used (13). The trend in particle number emission factor from fast burning for leaf and branch samples is similar to that found for wood samples (see Figure 5). The samples from the trees of harder wood produce particle numbers with higher emission factors, with Iron Bark, Spotted Gum, and Stringybark emitting (6.9 ( 0.4) × 1015 particles/kg, (2.2 ( 0.1) × 1015 particles/kg, and (0.5 ( 0.2) × 1015 particles/kg, respectively. The particle number emission factors from slow burning show no significant difference and are (8.4 ( 2.3) × 1013, (9.2 ( 2.0) × 1013, and (5.7 ( 2.0) × 1013 particles/kg for Iron Bark, Spotted Gum, and Stringybark, respectively. PM2.5 Emission Factors. PM2.5 emission factors for fast and slow burning of all the samples are presented in Figure 6. Under fast burning conditions, the emission factors for the wood samples are 68 ( 21 mg/kg for Blue Gum, 67 ( 25 mg/kg for Bloodwood, 40 ( 7 mg/kg for Iron Bark, 53 ( 17 mg/kg for Spotted Gum, and 96 ( 8 mg/kg for Stringybark. During slow burning the emission factors are 94 ( 43 mg/kg for Blue Gum, 25 ( 13 mg/kg for Bloodwood, 43 ( 20 mg/kg for Iron Bark, 269 ( 183 mg/kg for Spotted Gum, and 180 ( 103 mg/kg for Stringybark. It can be seen that Blue Gum, Bloodwood, and Iron Bark show no significant difference in emission factors for fast and slow burning. However, Spotted Gum and Stringybark have higher PM2.5 emission factors during slow burning. In general the PM2.5 emission factors for leaf and branch samples are much higher than the emission factors of the wood samples, with the highest for Iron Bark of 1750 ( 1000 mg/kg, followed by Spotted Gum of 550 ( 200 mg/kg and Stringybark of 160 ( 110 mg/kg for fast burning. For slow burning the emission factors of Iron Bark, Spotted Gum, and Stringybark are 1800 ( 700, 2300 ( 1200, and 1200 ( 100 mg/kg, respectively. The trends in the emission factors of these samples are similar to those displayed in the emission factors of the wood samples from the same species of trees. Iron bark has relatively the same emission factor for fast and slow burning, and Spotted Gum and Stringybark have higher emission factors for slow burning. 5702

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There have been several studies reporting PM2.5 emission factors for different species of tree. A study of an open burning of mixed hardwood forest foliage in the United States showed that the PM2.5 emission factors were 10.8 ( 3.9 g/kg (12). PM2.5 emission factors from burning of wood grown in the northeastern United States were measured in the range of 2.7-5.7 g/kg for hard woods and 3.7-11.4 g/kg for soft woods (22), while a similar study of the wood grown in the southern United States yielded emission factors in the range of 3.36.8 g/kg for hard woods and 1.6-3.7 g/kg for soft woods (15). A study aimed at characterization of emissions from burning of wood in a fireplace found that the emission factors were 2.9-9 g/kg for softwoods and 2.3-8.3 g/kg for hardwoods (14). The burning of Birch wood in a stove produced particles with the emission factor of 0.1-2.6 g/kg (11). The emission factors from burning of wood logs in several combustion systems were reported in the range of 0.13-1.68 g/kg (13). The values obtained in our study aligned most closely with those of the last study. Variation in the species of wood, burning system, and burning conditions gives rise to the differences between the measured emission factors and those reported in other studies.

Supporting Information Available Figures illustrating system performance, dilution ratio, Dustrak and TEOM calibration, and air flow rate. This material is available free of charge via the Internet at http:// pubs.acs.org.

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Received for review April 19, 2006. Accepted July 11, 2006. ES0609497

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