Morphology, Mobility, and Photoelectric Activity - American Chemical

Switzerland, and Center for Appropriate Technology and. Social Ecology, 4438 Langenbruck, Switzerland. In order to investigate properties of fine part...
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Environ. Sci. Technol. 1997, 31, 3439-3447

Characterization of Wood Combustion Particles: Morphology, Mobility, and Photoelectric Activity

an automatic wood chip burner and several residential wood stoves. The results presented here were obtained from a wood stove with about 15 kW maximum power and a wood chip burner of 60 kW power output.

C H . H U E G L I N , * ,† C H . G A E G A U F , ‡ S. KU ¨ N Z E L , † A N D H . B U R T S C H E R †,§ Laboratory for Solid State Physics, ETH Zu ¨ rich, 8093 Zu ¨ rich, Switzerland, and Center for Appropriate Technology and Social Ecology, 4438 Langenbruck, Switzerland

1. Particle Sampling. A dilution sampling system was used to collect aerosol particles from the stack of wood burners. Figure 1 illustrates the setup of the sampling system. The exhaust gas samples were taken with the use of a probe, which consists of two concentric steel tubes. Exhaust gas samples were collected through the inner tube whereas particle free dilution air was introduced through the outer tube. Toward the tip of the probe, the outer tube is closed. Both tubes are connected through a hole about 1 cm behind the tip. The entrance of the probe was placed perpendicular to the exhaust gas flow. Since the largest part of the probe is placed instack, the dilution air is heated up to stack temperature; therefore, the mixing with the exhaust gas takes place at thermal equilibrium. The dilution factor and the flow of the exhaust gas intake were adjusted by the flow rate of the dilution air and the total flow through the inner tube. Both flow rates were controlled by electronic mass flow controllers. To prevent condensation of water onto the particle surface, the dilution factor was chosen to be high enough so that the dew point of the water vapor was lower than the ambient temperature (typical dilution factors of 5-20 were used). The dilution factor was determined by comparison of the carbon monoxide (CO) concentration in the exhaust gas with the CO concentration in the diluted aerosol. For most measurements, an impactor with a 2-µm cutoff size was used to withdraw the coarse particle fraction. With this sampling method, isokinetic conditions can not be accomplished. The sampling error arising from the probe misalignment and the velocity mismatch has been estimated according to Hinds (7). It can be neglected for particles with diameters smaller than 2 µm. 2. Particle Measurement. The experimental setup for the particle measurement is shown in Figure 1. Particle size distributions and total number concentrations in the range from 0.01 to 0.7 µm were determined by analysis of the particle mobility. The particles were bipolarly charged by a neutralizer (85Kr) and guided into a differential mobility analyzer (DMA, TSI 3071). As a function of the applied DMA voltage, different particle mobilities can be selected. A following condensation particle counter (CPC, TSI 3025) evaluates the number concentration of the monodisperse aerosol particles. The number size distributions are measured by varying the DMA voltage over the given range and subsequent recording of the accompanying particle concentrations with the CPC. This setup was operated computer controlled, using the scanning mobility particle sizer software (SMPS, TSI Inc.). The SMPS system was typically operated with a time resolution that gives a complete size distribution every 4.5 min. For several experiments, an aerodynamic particle sizer (APS, TSI 3300) was additionally used, extending the measured size range up to 15 µm. For the measurements with the APS, the impactor was removed from the sampling line. A photoelectric aerosol sensor (PAS, LQ1 Matter Engineering, Switzerland) was used to obtain information on the particle surface. The photoelectric activity of combustion particles was shown to be strongly related to the amount of particle-bound PAHs (PPAH) in ambient air and in the exhaust of different combustion processes (8-10). In this work, the PAS signal was compared to the mass concentration of PPAHs in wood combustion exhaust as determined by chemical analysis (HPLC). The working principle of a PAS is described in ref 11 and is mentioned here only briefly. It is based on the photoelectric

In order to investigate properties of fine particles emitted by wood burners, different methods were applied. The relationship between emissions of particulates and gaseous compounds was studied; morphology and size distribution of submicron particles were investigated. The particles were found to be compact structures with fractal-like dimensions close to three; they contained low mass fractions of volatile compounds. It was shown that the operating conditions, i.e., the amount of combustion air supply, had a strong impact on the particle size distribution and the emission of particle-bound polycyclic aromatic hydrocarbons (PPAHs). Moreover, the relative coverage of the particle surface with PPAHs was found to increase strongly for operation with reduced combustion air supply.

Introduction The utilization of wood for residential heating as well as for heat production in wood boilers is gaining renewed interest in Switzerland and many other countries. The reasons for this interest in wood are the present concern about emissions of greenhouse gases and because it is an renewable energy source. However, wood combustion may lead to severe local air pollution and might therefore be responsible for adverse health effects. It was reported by various authors that emissions from wood combustion include a mutagenic fraction (1-4). According to Guenther et al. (5), the contribution of wood combustion to the total atmospheric particle mass in winter urban air (Fairbanks, AK) can be about 65%, and the contribution to the total concentration of polycyclic aromatic hydrocarbons (PAHs) was found to be about 45%. In another study, the contribution to atmospheric PAH level from the combustion of vegetation was determined to be about 30% during winter in Sydney, Australia (6). In this work, properties of particles emitted by wood combustion processes were investigated in detail. The main objective of this study was to evaluate the capability of using a photoelectric aerosol sensor (PAS) as a fast responding instrument for the continuous detection of particle-bound PAHs (PPAHs) in wood combustion exhausts. Moreover, it was our purpose to relate the signal of the PAS to the combustion conditions and to various aspects of the exhaust gas composition. The wood burners under investigation were * Corresponding author telephone: (+41) 1 823 4907; e-mail: [email protected]. Present address: Swiss Federal Laboratories for Material Testing and Research, Ueberlandstrasse 129, CH-8600 Duebendorf, Switzerland. † Laboratory for Solid State Physics. ‡ Center for Appropriate Technology and Social Ecology. § Present address: HTL Brugg Windisch, Klosterzelgstrasse, CH5200 Windisch, Switzerland.

S0013-936X(97)00139-9 CCC: $14.00

 1997 American Chemical Society

Experimental Section

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FIGURE 1. Schematic illustration of the experimental setup. effect applied to submicron particles. This means that electrons can be emitted from the particle surface, when particles are illuminated with photons of energies larger than the work function (typically photons of uv light sources). The particles remain positively charged and are sampled in an electrically isolated fabric filter, from where the total charge is measured as the photoelectric current. Only submicron particles can be charged effectively by this method, because the probability that the emitted electron diffuses back onto the particle surface increases with size (12). In order to use a PAS for measurements at wood burners, the exhaust gas samples had to be further diluted. Therefore, two more dilution units were used, which allowed an aerosol sample to mix with an electronically controlled mass flow of particlefree air. We used a second DMA combined with a low pressure impactor (LPI) (13, 14) to get information on the particle geometry. The impaction of monodisperse particles was measured under varying pressure conditions. From the impaction curves, an equivalent particle density can be calculated, which is for spheres equal to the bulk density of the particle material. With the equivalent particle density and the knowledge of the mobility diameter, the particle mass was obtained. Schmidt-Ott (15, 16) derived scaling laws for fractal-like particles, the relation between particle mass m, mobility diameter dp, and the fractal-like dimension df is therefore given by

m ∝ ddpf In the free molecular regime (Kn ) 2λ/dp . 1, where Kn is the Knudsen number, λ is the mean free path of the gas molecules, and dp is the particle diameter), the relation is limited to df g 2. Using this relation, the fractal-like dimension was determined from the slope of the representation of the logarithm of the particle mass versus the logarithm of the mobility diameter. A thermal denuder consisting of a heating tube followed by an activated charcoal adsorber was placed before the DMA. In order to study the influence of adsorbed/ condensed semivolatile compounds on df, measurements were done with three different temperatures in the denuder heating section (20, 160, and 350 °C). In addition to this technique, diluted exhaust gas samples were collected

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on Nucleopore Teflon filters with a pore size of 300 nm and analyzed by scanning electron microscopy (SEM). 3. Monitoring of Gaseous Emissions. Additional exhaust gas samples were drawn from the stack with the use of a separate sampling line. They were guided through a heated ceramic particle filter to the drier. The dry and particle-free gas entered the CO2, CO, NO (all non-dispersive infrared), and O2 (paramagnetic) analyzer. Upstream of the drier, a portion of the exhaust gas sample was drawn for total hydrocarbon (THC) analysis (flame ionization detector) and H2O measurement. The data points were recorded with a time resolution between 5 and 15 s. 4. Definitions. For non-steady combustion processes as prevailed in wood burners, the amount of air needed for a stoichiometric combustion and the burn rate vary with time. As a consequence, the excess oxygen concentration depends on the state of combustion. In order to be in the position to compare the measured concentration of an exhaust gas compound at any time, the data must be standardized to a constant excess oxygen concentration (in this work, the standardized oxygen concentration [O2]st is 13%). The standardization factor c for every data point was calculated by c ) (21% - [O2]st)/(21% - [O2]), where [O2] is the excess oxygen concentration as received. For the consideration of a wood stove burning cycle, three different combustion phases need to be distinguished: (1) the startup, (2) the intermediate, and (3) the burn-out phase. Typically, the carbon monoxide concentration is used to indicate the state of the combustion process. The startup phase begins with the ignition of the fuel load and is terminated by a state with low and rather constant CO concentrations, which is characterized as the intermediate phase. In our measurements, the starting point of the intermediate phase was set at the time when the standardized CO concentration dropped below 2000 ppm. Toward the end of the burning cycle, charcoal combustion predominates, leading to elevated CO concentrations due to gasification of charcoal. This increase of the CO concentration ([CO]st > 2000 ppm) terminates the intermediate phase and was considered as the beginning of the burn-out phase. The burning cycle was considered to be terminated when the CO2 volume concentration in the exhaust gas was below 2%.

FIGURE 2. Particle size distributions during the different phases of a wood stove burning cycle. The curves were standardized to an excess oxygen concentration of 13%. The photoelectric activity of an aerosol particle is defined in ref 11. In this work, the term photoelectric activity is used as the photoemission signal (PE) measured with the PAS normalized to the total particle concentration as obtained with the SMPS system. Depending on the situation, either particle number concentrations (PNC) or particle surface concentrations (PSC) are considered.

FIGURE 3. Particle size distributions of a wood chip burner operated with varied combustion air supply. The size distributions were averaged over a time period of 45 min and standardized to an excess oxygen concentration of 13%.

Results and Discussion 1. Size and Morphology of Wood Combustion Particles. 1.1. Residential Wood Stove. The emissions of submicron particles during burning cycles of a small residential wood stoves were measured. It was operated with split and dried beech wood logs (water content was 15-18% at dry basis) and an average burn rate of 3.5 kg of fuel/h. Size distributions and number concentrations of submicron particles emitted during a wood stove burning cycle were strongly dependent on the state of the combustion process. Although the mean diameters and number concentrations of combustion particles can vary significantly for similar burning cycles, a general tendency was observed. Figure 2 shows typical number size distributions measured with the SMPS during the three combustion phases. The size distributions were standardized to 13% excess oxygen concentration. Total particle number concentrations for the shown data were 4.4 × 107 cm-3 for the startup phase, 7.8 × 106 cm-3 for the intermediate phase, and 1.5 × 107 cm-3 for the burn-out phase. During the startup phase, the particle concentration and mean diameter of the size distribution were highest. Total particle concentrations and mean diameter were apparently shifted toward lower values in the intermediate and the burn-out phase. The mean particle diameter during the burn-out phase was clearly smaller than during the intermediate phase. However, the total particle concentration was increasing or decreasing in the burn-out phase, depending on the combustion parameters and the stove type. Especially the combustion air supply seemed to have a strong impact on the particle size distribution during the burn-out phase. Large amounts of surplus air (indicated by elevated O2 concentrations in the exhaust gas) tended to result in higher particle number concentrations at lower mean diameters than observed for burn-out phases where lower excess oxygen concentrations were measured. 1.2. Wood Chip Burner. In contrast to residential wood stoves, constant burning conditions can be achieved by an automatically and semicontinuously fed wood chip burner. The operation of the boiler was controlled by a computer, with the excess oxygen concentration in the exhaust gas as the control parameter. Thus, an almost stationary combustion process was realized. Typically, the burner was operated with a burning rate of about 20 kg/h; the water content of the fuel was between 45% and 70% at dry basis.

FIGURE 4. Particle volume concentration versus particle size measured at the stack of the wood chip burner using a SMPS and an APS. For the SMPS, particle size is given as the mobility diameter, whereas for the APS aerodynamic diameters are plotted. Particulate emissions were found to depend to a major part on the operating conditions. The strongest effect on particulate emissions was due to variation of the combustion air supply. The geometric mean diameter of the size distribution and total number concentration are lowest for an appropriate combustion air supply indicated by the corresponding excess oxygen concentration (about [O2] ) 6.2% for the investigated boiler). Figure 3 shows particle size distributions in the SMPS size range for varied air supply leading to different values of [O2] in the exhaust gas. The amount of wood supply was kept constant. The geometric mean diameters and total number concentrations were varying between 68.6 nm and 3.11 × 107 cm-3 for [O2] ) 6.2% and between 95.7 nm and 1.01 × 108 cm-3 for [O2] ) 11.6%, respectively. All size distributions were averaged over a time period of 45 min and standardized to [O2] ) 13%. Reduction of the air supply as well as too much surplus air were leading to an increase in particle size and total number concentration. The increase of the particle concentration was more distinct toward enhanced air supply than toward shortage of combustion air. Measurements using the APS showed a further decrease of the particle number concentrations toward larger diameters. The number concentrations of particles with diameters larger than 1 µm were found to be negligible. The situation was different when particle volume concentrations were considered. Figure 4 illustrates the particle volume concentration as a function of particle diameter up to 15 µm. Note, that the SMPS data are shown as a function of mobility diameter (dp), whereas the APS data are presented versus aerodynamic diameter (da). Both quantities are related by da ) dp[FpCc(dp)/F0Cc(da)]1/2, where Fp is the particle density, F0 is the unit density (F0 ) 1g/cm3), and Cc is the Cunningham

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the particle concentration at the boundaries of the measuring range. Particles with diameters larger than 1 µm made up at least 25% of the total particle volume. Because of the anisokinetic sampling procedure, which leads to a loss of larger particles, only this lower boundary value could be determined.

FIGURE 5. Determination of the fractal-like particle dimension from the slope of the logarithm of the particle mass versus the logarithm of the particle mobility diameter. Shown are the results of measurements with different temperatures in the heating section of a thermal denuder (see text). slip correction factor. In the region where the measuring range of both instruments comes close or is overlapping (depending on the values of Fp and Cc), the correspondence of the size distributions is not very good. Measuring errors of concentrations of the larger particle fraction have a dramatic effect on the size distribution when volume concentrations are considered. The increase of the SMPS spectra might be erroneous as the instrument tends to slightly overestimate

Figure 5 shows the logarithm of the particle mass as a function of the logarithm of the mobility diameter in the range from 40 to 133 nm. Results of measurements with three different temperatures in the heating section of the thermal denuder are shown (20, 160, and 350 °C). The fractallike dimension df was found to be three (experimental values were between 2.93 and 3.05). The temperature of the thermal denuder had no measurable influence on df. For the equivalent particle density, average values between 1.74 g/cm3 (standard deviation σ ) 0.11 g/cm3) for T ) 20 °C and 1.90 g/cm3 (σ ) 0.04 g/cm3) for T ) 350 °C were determined, indicating a slight loss of semivolatile material through thermal desorption. In addition, particle size distributions were measured for the three temperatures. The geometric mean diameter and the standard deviation of the size distributions were identical, which proves the previous indication that fine particles from the wood chip burner contain low amounts of semivolatile compounds, typically considered as organic carbon. In contrast, similar measurements with particles from a spark ignition engine showed a

FIGURE 6. Scanning electron micrographs of particles emitted by a wood chip burner. Two particle classes can be distinguished. Submicron particles resulting from incomplete combustion (a) and those of the larger particle fraction considered as fly ash (b and c).

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decrease of the fractal-like dimension from df ) 3.0 at T ) 20 °C to df ) 2.2 at T ) 350 °C (17) accompanied by a significant shift of the particle number distribution toward smaller diameters. The composition of submicron particles in wood combustion exhaust is known to depend strongly on the combustion conditions. For a wood stove, the contribution of organic carbon to the total particle mass was found to be 14% for burning conditions with surplus air supply and 57% under conditions with air supply shortage (18). Figure 6 shows a typical scanning electron micrograph of particles found in the exhaust of the wood chip burner. Two particle classes can be distinguished. The first was a class of submicron particles that were by far dominating in number (Figure 6a). These particles look similar to those shown by Muhlbaier Dasch (19). They were found to be compact structures, which is in agreement with the determined fractallike dimension. The mean diameter of those particles as sampled on the Nucleopore filter appeared larger than the values measured with the SMPS setup. The fact that the particles were mainly arranged near the filter pores indicates that a loss of smaller particles due to penetration through the filter occurred. Second, few particles with different size and structure were found on the filter, belonging to a different particle class (Figure 6b,c). Those particles were either of perfectly spherical structure (Figure 6b) and similar to coal fly ash particles (7) or irregularly shaped ensembles built of crystalline platelets (Figure 6c). The submicron particle fraction was assumed to result from incomplete combustion, whereas the larger particles were considered as fly ash, consisting of unburnable material. 2. Photoelectric Activity of Wood Combustion Particles. 2.1. Comparison of PAS Signal and Chemical PAH Analysis (HPLC). The response of the photoelectric aerosol sensor was compared with the chemical analysis of PPAHs. The setup for this comparative evaluation is described in the following. An exhaust gas flow of 2.0 L/min was drawn from the stack and immediately diluted by a factor of 4, using the previously described probe. The exhaust gas sample was passed through an aerosol diluter of the type published in ref 20. In the diluter, a small fraction (20 cm3/min) was extracted and mixed with particle-free air, which leads to a further dilution by a factor of 100. This highly diluted part of the exhaust gas sample was analyzed with the PAS; the remaining low diluted part was guided through a quartz fiber filter (Pallflex Products Corp., 47mm). To remove the coarse particle fraction, an impactor with a cutoff size of 3.5 µm was placed prior to the filter holder. Sampling line and filter holder were kept at a constant temperature of 45 °C. This arrangement allowed continuous recording of the photoemission signal and, in parallel, collecting the combustion particles on quartz fiber filters. The extractable fractions of the collected particles were then analyzed by high pressure liquid chromatography (HPLC) with UV absorption and fluorescence detection techniques. The mass concentration of 11 parental PAH compounds from fluoranthene to dibenz[a,h]anthracene were determined. The total PPAH concentration of the exhaust gas was obtained by summing the concentrations of these compounds. The total PPAH concentration determined by HPLC was compared with the average of the photoemission signal over the time period of the particle sampling. Exhaust gas samples were collected on a total of eight filters; the sampling time for each filter was about 1 h. In order to achieve a wide variation of the PPAH mass concentration, the wood chip burner was operated at different load and with varied combustion air supply. Figure 7 shows the photoemission signal in terms of charge density concentration versus the total PPAH concentration determined by HPLC. Both signals were linearly related; the evaluated correlation coefficient was r ) 0.875. The linear regression line did not pass through the graph origin, which indicates that compounds contributed

FIGURE 7. Comparison between the signal of the photoelectric aerosol sensor (PE) and the total mass concentration of particlebound PAHs (PPAHs) obtained by chemical analysis (HPLC).

FIGURE 8. Photoemission signal (PE), total hydrocarbon concentration (THC), CO concentration, and photoelectric activity in terms of PE/PSC over a complete wood stove burning cycle. The horizontal bars indicate the time interval of the size distribution measurements. The data is standardized to [O2] ) 13%. to the photoemission signal which were not measured by the HPLC analysis. However, the obtained correlation between PAS signal and chemical analysis differs widely from the calibration curve of the PAS that was determined from ambient air measurements. Compared to the calibration, the PAS signal was approximately 100 times higher with respect to the HPLC analysis. A similar effect was observed by emission measurements of a diesel engine exhaust where the identical sampling setup was used (21). 2.2. Residential Wood Stove. Figure 8 illustrates the photoemission signal over a complete wood stove burning cycle. In addition, the exhaust gas concentrations of the products of incomplete combustion CO and total hydrocarbons (THC) as well as the photoelectric activity in terms of the photoemission signal divided by the total particle surface concentration (PE/PSC) are shown. The wood load (3 kg) was added to a hot stove and placed onto a bed of embers. The wood stove was operated with high burn rate, resulting in rather high excess oxygen concentrations. The PE signal and concentrations of CO and THC showed a reasonable correlation up to the burn-out phase. This observation is quantified in Table 1, where the linear correlation coefficients between the concentrations of the different exhaust gas components are displayed. Shown are the correlation coefficients for a complete burning cycle and for the time period from ignition to the beginning of the burn out phase (in parentheses). The entries were determined from the unstandardized data; they are average values for four burn cycles carried out under identical conditions that were the same as described above. While the photoemission signal (PE) and the concentrations of the gaseous compounds were recorded with a time resolution of 5 s, the particle number

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TABLE 1. Linear Correlation Coefficients between Concentrations of Exhaust Gas Compounds Measured during Operation of a Residential Wood Stove (15 kW at Maximum Load)a PE PE CO THC

CO

THC

CO2

H2O

NO

PNC

PSC

0.402 (0.698)

0.710 (0.656) 0.628 (0.827)

0.339 (0.044) -0.236 (-0.107) -0.039 (-0.363)

0.497 (0.187) -0.144 (0.079) 0.223 (-0.068) 0.928 (0.890)

0.190 (-0.224) -0.472 (-0.450) -0.166 (-0.584) 0.938 (0.886) 0.866 (0.754)

0.612 (0.415) 0.086 (0.436) 0.521 (0.414) 0.710 (0.443) 0.805 (0.615) 0.602 (0.166)

0.873 (0.803) 0.065 (0.314) 0.453 (0.369) 0.418 (0.073) 0.464 (0.150) 0.357 (-0.097) 0.574 (0.475)

CO2 H2O NO PNC PSC a

Shown are correlation coefficients for a complete burning cycle and correlation coefficients determined for the time period from ignition to the beginning of the burn-out phase (in parentheses) (see text).

concentration was measured with a time resolution of 4.5 min. Therefore, PE and gaseous concentrations were averaged over the time interval of the particle measurement in order to calculate the correlation coefficients. The photoemission signal was linearly correlated to the concentrations of THC (r ) 0.710) and the particle surface concentration (PSC, r ) 0.873), which was determined with the assumption of spherical particle shape. The correlation coefficient between PE and the particle number concentration was clearly lower (PNC, r ) 0.612). This result proves that photoelectric charging of particles is a surface-sensitive method. For the time period up to the burn-out phase, the correlation coefficient between PE and the CO concentration was determined to be 0.698, whereas the correlation vanished when complete burning cycles were considered (r ) 0.402). For wood stove burning cycles that were characterized by a very low temperature rise in the startup phase, the development of THC concentration and photoemission signal as a function of time was different from what is shown in Figure 8. The maximum of the THC concentration and the photoemission signal were clearly separated. The photoemission signal was highest when the THC concentration was already decreased to a lower level. A possible explanation could be given by the thermal degradation of the different major wood compounds. This is discussed in section 2.4 in more detail. The highest correlations were observed between CO2, H2O, and NO. This is not surprising because all of these compounds are combustion products. For wood burning processes, the combustion temperatures are too low to dissociate the N2 molecules of the air. The nitrogen oxides in the exhaust gas are formed by oxidation of nitrogen compounds coming from the wood fuel. The photoemission signal was not correlated to these combustion products. For the four identical wood stove burning cycles, the photoelectric activity in terms of PE/PSC was found to be mainly constant during the startup and the intermediate phase and was decreasing during the burn-out phase. In contrast, PE/PNC tended to decrease continuously with time. 2.3. Wood Chip Burner. The photoemission signal of exhaust gas samples from the wood chip burner was examined for varied excess oxygen concentrations. Figure 9 illustrates the results of these measurements together with the concentrations of the other measured products of incomplete combustion (PICs), THC, CO, PNC, and PSC as well as the photoelectric activity PE/PSC. The data points were standardized to 13% [O2] and are average values over a time period of 45 min. The photoemission signal and therefore the PPAH mass concentration was minimal for a certain excess oxygen concentration ([O2] ∼ 6.2%). Higher and lower values of [O2]

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FIGURE 9. Average values of the different measured products of incomplete combustion (PICs) at varied excess oxygen concentrations [O2]. For [O2] ) 2.7%, the CO concentration is missing because the CO analyzer was in saturation. The data points are average values over 45 min and standardized to [O2] ) 13%. were leading to a steeply increasing photoemission signal. The behavior of the particle concentration (PNC, PSC) was similar, although as mentioned before, the increase was more pronounced toward higher values of [O2]. In contrast, the CO and THC concentrations and also the ratio of PE and PSC were increasing drastically for combustion air shortage, whereas surplus air supply led to mainly constant concentrations of these compounds. The fact that the CO concentration was minimal at [O2] ∼ 7.5% indicates that the operating condition with lowest emissions of PICs were between 6.2% and 7.5% [O2]. Similar to our studies, the influence of varied excess oxygen concentration on particulate emission was observed for oil boilers (22, 23). The fine particle emission rate was minimal when the boilers were operated with an optimized value of [O2]. As another combustion parameter, the ratio of overfire to underfire air was varied from 0.6 to 2.4 at constant total combustion air supply. The variation of this combustion parameter did not significantly influence the photoemission signal and the submicron particle concentration. In contrast, Hubbard (24) observed an evident dependence of the PAH emissions from the ratio of overfire to underfire air at a wood boiler with 4 MW output. PAH emissions were minimized by operation with a balanced ratio of overfire to underfire air. The results in this study are assumed to be due to the fact that in the investigated wood burner the zone of fuel gasification and the combustion zone were spatially not well separated. It should be noted that the photoelectric activity PE/PSC of the particles from the wood chip burner operated at ideal

FIGURE 10. Photoelectric activity of size-selected particles from the exhaust of a wood chip burner as a function of the square of the particle mobility diameter. The linear relationship indicates that the PAH coverage of the particles is due to an adsorption or condensation process. combustion conditions was by a factor of about 1620 lower than the photoelectric activity of the particles emitted by the wood stove (PE/PSC averaged over four complete burning cycles); for PE/PNC the factor was about 480. The THC concentration was found to be about 100 times lower than the average THC concentration of the wood stove burn cycles; for CO this factor was about 15. Moreover, the photoelectric activity (PE/PNC) was investigated for size-selected particles with mobility diameters ranging between 43 and 253 nm. A linear relationship between the photoelectric activity and the square of the particle mobility diameter was found (Figure 10). This observed relation indicates that the PAHs are adsorbing/ condensing onto the particle surface when the exhaust gas cools along the stack. The diffusive adsorption or condensation rate of gas molecules onto the surface of particles is described by the so-called attachment coefficient. This quantity is defined as the ratio of the stationary total molecular flux to the particle and the volume concentration of the surrounding gas molecules (25). Schmidt-Ott et al. (26) derived scaling laws for aerosol particles. They showed that the attachment coefficient is proportional to the square of the particle mobility diameter in the molecular regime. Although the conditions for the investigated particle sizes must be considered to the transition regime, the observed relationship is consistent with linearity. As a consequence, the PAH coverage is suspected to be resulting from an adsorption or condensation process. Similar to the measurements presented here, Eggenberger et al. (27) obtained a linear relationship for submicron particles emitted by diesel engines. 2.4. Photoemission and Thermal Decomposition of Wood. It was previously stated that, for wood stove burning cycles that were characterized by a very low temperature rise in the startup phase, the development of the total hydrocarbon concentration (THC) and the photoemission signal as a function of time were different from what can be considered as normal operation. This is illustrated in Figure 11. The startup phase and a part of the intermediate phase is shown for three burning cycles with different combustion temperature time courses. The given temperature was the exhaust gas temperature and is suspected to be directly related to the combustion temperature. The upper part of the graph shows the temperature rise of a wood stove operated at normal load. The THC concentration and the photoemission signal were increasing simultaneously and also decreasing at the same time when a sufficient high combustion temperature was reached. The situation was much the same for a lower temperature rise due to operation of the stove with a partial

FIGURE 11. Time courses of exhaust gas temperature, total hydrocarbon concentration (THC), and photoemission signal (PE) for the first 20 min of three different wood stove burning cycles. THC concentration and PE signal were standardized to [O2] ) 13%. fuel load (middle part of the graph). For an operation of the wood stove with a further reduced fuel load, the situation shown in the lower part of the graph was observed. The THC concentration and the photoemission signal increased at about the same time. Both signals stayed mainly constant. At about 8 min, the THC concentration showed a distinct maximum and subsequently began to decrease. In contrast, the photoemission signal was maximal when the THC concentration was already on a lower level. The maximum of the photoemission signal was in all three cases at about the same combustion temperature. A possible explanation of this behavior might be found in the different thermal stability of the major wood compounds, the celluloses (cellulose and hemicellulose) and lignin. The celluloses are polymerized polysaccharids; lignin is a macromolecule composed of aromatic basic units. It makes up about 25-30% of the weight. Lignin is thermally more stable than the celluloses (28). As a consequence, the pyrolysis of wood takes place in two stages: first the decomposition of the celluloses and second the pyrolysis of lignin. In the situation shown in the lower part of Figure 11, the thermal decomposition of both compounds might temporally be separated. Different aromatic compounds are products of thermal decomposition of lignin (29). The presence of aromatic compounds increases the formation rate of PAHs and should therefore cause a higher photoemission signal. It is suspected that the high photoemission signal at about 15 min corresponded to the decomposition of lignin. To achieve more information on the photoemission signal during the process of wood decomposition, a series of laboratory experiments have been carried out. A small amount of beech wood (∼0.1 g) was placed into a quartz tube and heated with the use of a tube furnace. The temperature of the furnace was measured with a thermocouple. It does

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FIGURE 12. Thermal decomposition of wood in a tube furnace and measurement of photoemission signal, particle number concentration (CPC), and carbon monoxide concentration (CO). Experiments in air (a) and argon (b) atmosphere have been carried out. Graphs that shows the temperature increase with time are included. The temperature given is the furnace temperature and does not necessarily represent the temperature of the wood sample. The signals of the lower part of the graph were more noisy than those in the upper part because a higher dilution of the aerosol sample was performed. not necessarily represent the temperature of the wood sample. The temperature was varied linearly with time up to about 1000 °C (heating rates between 0.05 and 0.1 °C/s), and the photoemission signal, total particle number concentration, and CO concentration were recorded continuously. The experiments were done under a steady gas flow of 1.0 L/min, alternatively using air or argon as the carrier gas. Figure 12 illustrates two measurements with different gas atmospheres. The upper part of the graph represents the measurement in an air atmosphere. The photoemission signal showed two peaks where the second peak was identified to be due to the onset of the combustion process. This can be seen from the disturbance at about 320 °C illustrated in the included temperature versus time graph. In addition, the first peak showed a shoulder toward lower temperatures. In the lower part of the graph, a measurement of wood decomposition in an argon atmosphere is shown. Here, the peak corresponding to the onset of wood combustion was missing. However, the photoemission signal also showed two peaks. It seemed that the shoulder that was present in the upper part of the graph was temporally separated from the main peak. This can also be due to the fact that the heating rate was lower in this measurement (0.05 °C/s as compared to 0.1 °C/s). The first and small peak might be related to the decomposition of the celluloses, whereas the larger peak at higher temperatures might be due to the decomposition of lignin. With this interpretation, the observations are in agreement with the findings for the different temperature time courses in wood stove burning cycles as illustrated in Figure 11.

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Acknowledgments We would like to express our gratitude to H. C. Siegmann for his helpful suggestions and to P. Cohn and L. Scherrer for their technical assistance. We also want to thank P. Walter from the Institute of Cell Biology, ETH Zu ¨ rich, and P. Wa¨gli from the Laboratory for Solid State Physics, ETH Zu¨rich, for carrying out the electron microscopic analyses and the Institute for Organic Chemistry at the EMPA in Du ¨ bendorf for performing the HPLC analysis of the filter samples. This work was supported by the Swiss National Energy Foundation (NEFF).

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Received for review February 18, 1997. Revised manuscript received September 4, 1997. Accepted September 15, 1997.X ES970139I X

Abstract published in Advance ACS Abstracts, October 15, 1997.

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