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Determination of Activity Coefficients of Wood Smoke Tracer in Artificial and Ambient Organic Semi-Volatile Aerosols Suqi Huang Abstract: Aerosols are known to affect the climate and have been linked to adverse health effects. An effective way of reducing air pollution is to determine and to control the sources of the harmful particles. The chemical mass balance (CMB) receptor model is a method of source apportionment that uses tracer compounds, which should be distinct to certain sources and conserved in the atmosphere. In this experiment, the Integrated Volume Method developed by Saleh and Khlystov (2009) was used to predict the volatility of a wood smoke tracer, levoglucosan, and the interaction of the tracer with artificial and ambient semi-volatile organic aerosols. Artificial mixtures were made with monocarboxylic acids, and ambient particles were collected using a filter and extracted in ethanol. Aerosols were generated from mixtures with different mole fractions of levoglucosan and sent through a heated thermodenuder. The change in volume was calculated by comparing measurements before and after heating and was graphed versus the mole fraction. Aerosol generated from levoglucosan exhibited a change in volume, which suggests that it is semi-volatile. Mixtures of levoglucosan and monocarboxylic acids do not form a solution. Mixtures of levoglucosan and ambient extracts showed interaction and did not display ideal behavior, and the activity coefficients of levoglucosan were determined.
Introduction Organic Semi-Volatile Aerosols: An aerosol is a suspension of small particles in a gas. These particles may be solid, liquid, or a mixture, and a large majority of them are emitted naturally by sources such as volcanoes, forest fires, and plants (Voiland). Those that are produced by human activities are known as anthropogenic and are emitted from sources including biomass burning, food cooking, motor vehicle driving, and cigarette smoking (Voiland). Aerosols affect the climate through the absorption and scattering of solar radiation, which causes temperature changes, and influence cloud formation (Chung and Seinfeld 2002, Voiland). Atmospheric particles have also been associated with increased mortality and adverse health effects, including illnesses, cancer, and other harmful effects when they are inhaled (Lewtas 2007). Ambient aerosols contain organic compounds, inorganic compounds, and water; of the organic compounds, a large portion are semi-volatile (Saleh and Khlystov 2009). Semi-volatile organic aerosols display gas-particle partitioning, which means that the particles change back and forth between the gas phase and the particulate phase (Seinfeld and Pankow 2003, Saleh and Khlystov 2009). The behavior of semi-volatile organic aerosols in the atmosphere is extremely difficult to model because of the large variety of possible chemical reactions and products, all of which affect the volatility of the aerosol (Kroll and Seinfeld 2008). This difficulty comes from the constant partitioning of the particles between the gas phase and the particulate phase. The particles can undergo many potential different reactions in either phase, which makes modeling and predicting organic aerosol very complex and challenging to accomplish accurately.
Modeling with Activity Coefficients: Semi-volatile aerosols are usually modeled under the assumption that the organic particles form an ideal solution, allowing calculations and properties to be described directly using concentrations, partial pressures, and mole fractions (Pankow 1994). However, for nonideal mixtures, the activity coefficient can be included to adjust the model to better fit the behavior of the aerosols. This value can range from 0.3 to 198 and reflects the interaction of the chemical components within the aerosol and reveals any deviation from ideality (Bowman and Melton 2004). If the activity coefficient is greater than one, then a chemical component is dissimilar to the overall aerosol mixture (Bowman and Melton 2004). Models that incorporate activity coefficients agree more with experimental data than do models that assume ideal behavior (Bowman and Melton 2004). If the mixture is ideal and Raoult’s Law is applied, then the activity coefficient equals 1 at any mole fraction. Setting the activity coefficient as 1 for aerosols that display non-ideal behavior causes significant errors in calculations, so it is essential to determine its value in order to achieve more accurate models (Bowman and Melton 2004). Problem Source Apportionment: Air pollution is a challenging problem to resolve because it is difficult to filter the atmosphere of harmful particles, while having every person wear a gas mask is impractical and inconvenient. The best approach to reduce the amount of harmful particles is to find out which sources are emitting particles, to estimate how much these sources are Volume 1 | 2011-2012 | 45
Street Broad Scientific contributing to the ambient particles, and to limit or to decrease the amount of particle emission from sources that contribute significant amounts. Different methods of source apportionment are used to identify source contributors, which can include motor vehicle engine exhaust, road dust, food cooking, wood combustion, and others (Schauer et al. 1996). The time and money spent on air pollution control could be used more effectively if there are more data about the many different contributors to airborne particles and how much each source contributes, which can help determine the relative importance of the sources (Cass 1998). Tracer Compounds One well-known method of source apportionment is the chemical mass balance (CMB) receptor model. This method originally used chemical elements as tracer compounds, such as nickel, lead, and aluminum, but air pollution control has now largely eliminated these from emissions (Cass 1998). Instead, organic compounds that are distinctive to certain source classes are being used to help “traceâ€? the particles back to their original sources (Baek et al. 2005, Shrivastava et al. 2007). The CMB technique solves a system of equations in which the concentration of chemical compounds in an ambient sample is set equal to a linear combination of the relative chemical compositions of contributing sources (Schauer et al. 1996). This assumes that the chemical properties of the atmospheric particles are a direct result of the linear accumulation of the emitted particles and requires accurate data concerning the concentration of organic compounds in source emissions and in ambient samples (Schauer et al. 1996). However, this method produces uncertain and possibly inaccurate results if measured concentrations and source profiles are based on limited data (Shrivastava et al. 2007, Baek et al. 2005). In addition, all sources known to contribute the particular tracer compound used must be included in the calculations (Cass 1998). Levoglucosan as a Wood Smoke Tracer A reliable tracer compound must be emitted from specific sources and be stable and conserved from the point of emission to the receptor point where data is collected, i.e. not be changed drastically by volatilization or chemical reactions while in the atmosphere (Schauer et al. 1996). This means that compounds such as polycyclic aromatic hydrocarbons would not make good tracer compounds because they are emitted by almost all combustion sources, react quickly in the atmosphere, and evaporate to the gas phase (Cass 1998). Levoglucosan (1,6-Anhydro-β-Dglucopyranose) is a compound commonly used as an organic tracer for biomass burning and wood smoke (Shrivastava et al. 2007). Jordan et al. (2006) analyzed emission samples from the smoke of woodstoves and compared
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Research the results to ambient samples collected in Launceston, Australia. Levoglucosan was found at expected mass fractions in the ambient samples, while the other compounds tested had much lower mass fractions ( Jordan et al. 2006). However, other studies have suggested that levoglucosan is semi-volatile and experiences changes in volume through evaporation to the gas phase (Oja and Suuberg 1999, Huffman et al. 2009). If levoglucosan is unstable and interacts with other particles in the atmosphere, then its use as a tracer compound becomes questionable. Using the activity coefficient of levoglucosan to correct for its nonideal behavior will make calculations for the apportionment model more accurate. Research Goal The goal of this research was to investigate the volatility of levoglucosan and the interaction of levoglucosan with artificial and ambient organic semi-volatile aerosols. The Integrated Volume Method developed by Saleh and Khlystov (2009) was used to calculate the change in aerosol volume, which is a measure of the aerosol volatility, of mixtures with different amounts of levoglucosan. The value of the activity coefficient of levoglucosan within a semivolatile mixture provides information about the interaction of the tracer compound with the other particles and determines whether those particles have any effect on the volatility of the tracer. Integrated Volume Method Saleh and Khlystov (2009) developed the Integrated Volume Method (IVM) to estimate the activity coefficient of semi-volatile organic aerosols in binary solutions. Figure 1 shows their experimental setup. Compounds based on mole fractions are dissolved in a solvent, usually water or ethanol, and aerosol is generated by spraying the solution with an atomizer. The aerosol then enters a large mixing chamber for dilution and drying. For solutions dissolved in ethanol, the aerosol is drawn through an activated carbon denuder to make sure that the aerosol is dry. After exiting, the aerosol is split into two lines. For one line, reference measurements are taken using the upstream Scanning Mobility Particle sizer (SMPS), while the other passes through a thermodenuder, a stainless steel tube which can be heated up. After the semi-volatile particles evaporate and reestablish equilibrium at a higher temperature in the thermodenuder, the volume is measured again via the downstream SMPS. From these data, the change in volume before and after the aerosol is heated can be calculated by comparing the measurements from the upstream SMPS with those from the downstream SMPS.
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The experiment is repeated multiple times with different mole fractions. To check the ideality of the aerosol, the change in volume is graphed along the y-axis with the mole fraction along the xaxis. If the resulting graph does not display a straight line, the conclusion is that the mixture is not ideal and that the evaporation does not follow Raoult’s Law. If the aerosol had shown ideal behavior, the change in volumewould be linearly proportional with the mole fraction and the activity coefficient would equal 1.Determination of Activity Coefficients: The method of determining the value of the activity coefficient involves repeating an algorithm until convergence is reached, which is described in Saleh and Khlystov (2009). First, the change in volume of component i in an aerosol with more than one component is represented by: Δvp,i = (Mi)/(ρρ,iRT0) * (xi, yi,1Psat,i,0) where T0 is the initial equilibrium temperature, xi is the mole fraction of the component, yi is the activity coefficient, Psat,i is the saturation pressure of the pure component, and the subscript 1 indicates the conditions at the new higher temperature. Therefore, the total change in volume of the aerosol is the sum of the change in volumes of the individual components: Δvp,total = ∑ Δvp,i Assuming that the aerosol is binary and only has two components A and B, the following set of equations apply: C A,0 + γA, xA,0Csat, A, 0 = C A,1 + γA, xA,1Csat, A, 1 CB,0 + γB( 1 - xA,0 ) Csat, B,0 = CB,1 + γB( 1 - xA,1 ) Csat, B,1 Where C is the molarity, Csat is the saturation molar concentration, and the subscripts 0 and 1 indicate
conditions at the initial temperature T0 and the at the higher temperature, respectively. Using the following equation, one can solve for CB :
xA =
CA CA + CB
CB = 1−XXAA CA Using this value of CB and combining the previous equations forms a quadratic equation in terms of mole fractions:
[gγBCsat , B , 1 − γg ACsat , A , 1]x A2 ,1 +[CA , 0 + CB , 0 + γg A , xA , 0Csat , A , 0 + γgB(1 − xB , 0)Csat , B , O + gγ ACsat , A , 1 − gγBCsat , B , 1]xA , 1 + [−CA , 0 − gγ A , xA , 0Csat , A , 0] =0 The Van Laar equation (Smith and Van Ness 1987), shown below, is substituted into the formula used to calculate the total change in volume of the aerosol:
lnγg1 =
A (1+ BAxx21 )2
lnγg 2 =
B (1+ BAxx21 )2
where A and B are constants determined by graphing the total volume change of the aerosol against the mole fraction x. The activity coefficients are calculated using the Van Laar equation above. Because the mole fractions change depending on the volatility of the components, the quadratic equation must be solved again for the mole fractions using the values of the activity coefficients. The process is then repeated until an agreement is reached between the mole fractions and the activity coefficients. Volume 1 | 2011-2012 | 47
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Procedure For artificial aerosols, mixtures of six monocarboxylic acids were made in ethanol. The monocarboxylic acid mixtures included pentadecanoic acid, palmitic acid, heptadecanoic acid, stearic acid, nonadecanoic acid, and eicosanoic acid, which are C15-C20 monocarboxylic acids. The tracer compound tested was levoglucosan, which is a wood smoke tracer. Solutions of pure monocarboxylic acids, pure levoglucosan, and several different ratios of levoglucosan to acid mixtures were made at least a day in advance before running the experiments. Changing the ratios subsequently changed the mole fractions. For ambient aerosols, samples were collected on glass-fiber filters using a HighVolume air sampler for three weeks on the rooftop of one of the university buildings. The filters were extracted in ethanol and then filtered with a syringe to separate out particles smaller than 0.2 micrometers. Aerosol was generated from a solution made from the filter extract and the mass of the ambient aerosol was estimated from the SMPS measurements. Using the estimated mass, it was possible to calculate the amount of levoglucosan needed to create mixtures with ratios of 0.5:1, 1:1, 1:2, and 1:3. After the aerosol was generated and stabilized at equilibrium, the thermodenuder was heated up to 60°C for five measurements. It was then unplugged and allowed to cool back down to room temperature, or about 25°C. The change in volume, Δv, was calculated by comparing the reference SMPS measurements with the heated SMPS measurements. By using interpolation, Δv was determined at a specific temperature for each mixture. Levoglucosan was defined as component A, while the monocarboxylic acids or ambient extract were treated as component B. Therefore, the mole fractions were calculated by dividing the number of moles of levoglucosan by the total number of moles in solution. Results
Change in Aerosol Volume of LevoglucosanMonocarboxylic Acid Mixtures at 35°C
∆ν
�
µm3 cm3
�
Change in Aerosol Volume of LevoglucosanMonocarboxylic Acid Mixtures at 40 °C
∆ν
Levoglucosan Mole Fraction Figure 1
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Figures 1 and 2 show the change in aerosol volume of levoglucosan-monocarboxylic acid mixtures at 35°C and 40°C for different mole fractions of levoglucosan. The mole fraction of 0 corresponds with an equimolar mixture of the six monocarboxylic acids listed previously in he procedure, and the mole fraction of 1 corresponds with a pure levoglucosan mixture. If the levoglucosan and monocarboxylic acids had no interaction with each other, then the total change in volume for the aerosol would be equal to the sum of the changes in volume of each of the components in the mixture, i.e., the sum of the change in volume of pure levoglucosan and the change in volume of pure monocarboxylic acids. With no interaction among the particles, the change in volume is not dependent on the mole fraction of the individual components and does not need to be corrected with activity coefficients. This can be seen in the graph because the change in volume for three different mole fractions is almost the same. The data collected at 35°C supports the conclusion that there is no interaction among the components in the aerosol. The change in volume of the pure levoglucosan mixture was measured at 17.6µm³/cm³, and the change in volume of the pure monocarboxylic acid mixture was measured at 40.5µm³/cm³, making the sum, or total change in volume of the aerosol, approximately 58.1µm³/cm³ with no interaction. The total change in volume of the aerosol for all three mole fractions is around 55µm³/cm³. These values are close enough to conclude that the components did not interact with each other and did not affect each other’s volatilities within the aerosol at 35°C. At 40°C, the changes in volume are greater than they were at 35°C, which is expected because the temperature is higher and more particles evaporated to the gas phase. If there was no interaction among the particles, which was the conclusion reached from the previous data set, then the total change in aerosol volume would be the sum of the change in volume of pure levoglucosan, 42.6µm³/cm³, and the
�
µm3 cm3
�
Levoglucosan Mole Fraction Figure 2
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∆ν
�
µm3 cm3
�
Activity Coefficients of Levoglucosan in Mixture with Ambient Particles at 35 °C
γ
Levoglucosan Mole Fraction
Levoglucosan Mole Fraction Figure 3
change in volume of the pure monocarboxylic acid mixture, 92.6µm³/cm³. This sum is equal to approximately 135.2µm³/cm³if there was no interaction. Because the data at 35°C shows that there is no interaction between levoglucosan and monocarboxylic acids, it can be said that these compounds do not form a solution. Although both levoglucosan and monocarboxylic acids are polar solutes and ethanol is a polar solvent, it is expected that these compounds would dissolve. Further work could be done to examine the interaction between levoglucosan and other polar compounds, such as dicarboxylic acids. Figure 3 shows the change in aerosol volume of levoglucosan-ambient extract mixtures at 35°C for different mole fractions of levoglucosan. Unlike the previous graph of levoglucosan-monocarboxylic acid mixture, these components do form a solution and interact with each other because the change in volume is less than the sum of the changes in volume of the individual compounds. In this aerosol, the sum of the change in volume of the pure levoglucosan mixture, 17.6µm³/cm³ , and the change in volume of the pure ambient particle mixture, 13.8µm³/cm³ , is approximately 31.4µm³/cm³ . All of the data points for the mole fractions lie below 20µm³/cm³ , so the levoglucosan and ambient particles must interact with each other. According to the data, when the majority of the mixture is ambient particles, the total change in volume drastically decreases. When the majority of the mixture is levoglucosan, the total change in volume nearly matches the change in volume of the pure levoglucosan mixture. The behavior of these mixtures is non-ideal and does not follow Raoult’s law. The data points clearly cannot be fitted to a line. If the behavior had been ideal, then the change in volume would be linearly proportional to the mole fraction, and the data points would lie in a line that connects the points that represent change in volume for the pure mixtures. Since this is not the case, activity coefficients for levoglucosan need to be determined and included in models to correct for the non-ideal behavior. Figure 4 shows the activity coefficients for levoglucosan when in a mixture with par-
Figure 4
ticles from ambient extracts as a function of levoglucosan mole fraction. As established earlier, the behavior of these mixtures is non-ideal, and the activity coefficients are not equal to 1. Discussion Levoglucosan-Monocarboxylic Acid Mixtures Two sets of data were graphed for the total change in aerosol volume fotr levoglucosanmonocarboxylic acid mixtures, one at 35°C and one at 40°C. It was clear from the data collected at 35°C that levoglucosan and monocarboxylic acids do not interact with each other because they do not form a solution. The aerosol generated from mixtures with different mole fractions of levoglucosan displayed volume changes approximately equal to the sum of the volume changes of levoglucosan and monocarboxylic acids, indicating that there is no interaction among these particles. All individual components partitioned to the gas phase as they normally would in a pure mixture. There was no need to calculate activity coefficients because the volatilities of the compounds were not affected. The mixture of levoglucosan and monocarboxylic acids is an example of a combination of a tracer compound and a group of semi-volatile organic aerosols that do not interact with each other. This suggests the possibility of other tracer compounds and semi-volatile organic aerosol 14 mixtures that also do not affect each other. When completing models and calculations for source apportionment, one should be aware ofone should be aware of the organic aerosols in the atmosphere surrounding the tracer compound and account for their relationships, i.e., whether or not they form a solution and incorporating activity coefficients when applicable. The fact that monocarboxylic acids do not change the volatility of levoglucosan could help support and encourage the use of levoglucosan as a tracer compound because this specific group of organic aerosols does not affect its partitioning. Levoglucosan-Ambient Extract Mixtures The mixtures of levoglucosan and particles from ambient extracts were discovered to be non-ideal, meaning that the change in aerosol volume could not be graphed as a linear function of the levoglucosan mole fraction. The values of the activity coefficients Volume 1 | 2011-2012 | 49
Street Broad Scientific of levoglucosan determined with these experimental methods and algorithm were not equal to 1, which is expected for nonideal mixtures. The compounds in the ambient extracts do affect the volatility of levoglucosan, so the activity coefficients should be included in source apportionment calculations to account for the effect of the ambient particles on the volume change of thelevoglucosan and to more accurately determine the amount of particles emitted from sources. Other Studies The data showed that levoglucosan is not completely nonvolatile, since it displayed differences in volume between the reference measurements and heated measurements, which indicate that a portion of it evaporated atthe higher temperature. At 35°C, the change in aerosol volume of the pure levoglucosan mixture was 17.6µm³/cm³ , and the change in volume increased to 42.6µm³/cm³ at 40°C. This conclusion is consistent with previous studies that suggest that levoglucosan is semi-volatile (Oja and Suuberg 1999, Huffman et al. 2009). Conclusions and Future Work The goal of this research project was to examine the volatility of a wood smoke tracer, levoglucosan, and the interaction of levoglucosan with artificial and ambient organic semivolatile aerosols with the Integrated Volume Method developed by Saleh and Khlystov (2009). The results showed that levoglucosan partitioned to the gas phase and evaporated at 35°C and 40°C, suggesting the possibility that it is semi-volatile, which has also been suggested by previous work. The data collected at 40°C for levoglucosan-monocarboxylic mixtures is inconclusive, and further experiments are needed to confirm or reject the conclusion drawn from the data collected at 35°C that levoglucosan and monocarboxylic acids do not form a mixture and do not interact with each other. Future work could also include finding the interaction of levoglucosan with other semi-volatile aerosols, such as dicarboxylic acids. Mixtures of levoglucosan and ambient particles did form a solution, and the generated aerosol displayed nonideal behavior. Activity coefficients corresponding with the mole fractions of levoglucosan were calculated. As expected for non-ideal mixtures, none of the activity coefficients were equal to 1. Levoglucosan is only one of many tracer compounds. The Integrated Volume Method can be applied to many other possible mixtures to test the volatility of the tracer compounds and the interaction between components in a variety of mixtures. This project only looked at binary mixtures, so the relationship between levoglucosan and multiple different components is still unknown. References Baek, J., Park, S. K., Hu, Y., Russell, A. G., 2005: Source apportionment of fine organic aerosol using CMAQ tracers. 2005. Bowman, F. M., Melton, J. A., 2004: Effect of activity coefficient models on predictions of secondary organic aerosol partitioning. Aerosol Science, 35, 1415-1438. Cass, G.R., 1998: Organic molecular tracers for particulate air pollution sources. Trends in Analytical Chemistry, 17, 356-366. Chung, S. H., Seinfeld, J. H., 2002: Global distribution and climate forcing of carbonaceous aerosols. J. Geophys. Research, 107, 4407. 50 | 2011-2012 | Volume 1
Research Hennigan, C. J., Sullivan, A. P., Collett, J. L., Robinson, A. L., 2010: Levoglucosan stability in biomass burning particles exposed to hydroxyl radicals. Geophysical Research Letters, 37, L09806. Hoffmann, D., Tilgner, A., Iinuma, Y., Herrmann, H., 2009: Atmospheric stability of levoglucosan: a detailed laboratory and modeling study. Environmental Science and Technology, 44(2), 694-699. Huffman, J. A., Docherty, K. S., Mohr, C., Ulbrich, I. M., Ziemann, P. J., Onasch, T.B., and Jimenez, J. L., 2009: Chemicallyresolved volatility measurements of organic aerosol from different sources. Environmental Science and Technology, 43(1), 5351–5357. Lewtas, J., 2007: Air pollution combustion emissions: Characterization of causative agents and mechanisms associated with cancer, reproductive, and cardiovascular effects. Mutation Research, 636, 95-133. Oja, V., Suuberg, E. M., 1999: Vapor pressures and enthalpies of sublimation of D-glucose, Dxylose, cellobiose, and levoglucosan. Journal of Chemical and Engineering Data, 44, 26–29. Pankow, J. F., 1994: An absorption model of gas/particle partitioning of organic compounds in the atmosphere. Atmospheric Environment, 28, 185-188. Saleh, R., Khlystov, A., 2009: Determination of activity coefficients of semi-volatile organic aerosols using the integrated volume method. Aerosol Science and Technology, 43:8, 838-846. Schauer, J. J., Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R., 1996: Source apportionment of airborne particulate matter using organic compounds as tracers. Atmospheric Environment, 30, 3837-3855. 17 Seinfeld, J. H., Pankow, J. F., 2003: Organic atmospheric particulate material. Annual Review of Physical Chemistry, 54, 121-140. Simoneit, B. R. T., 2002: Biomass burning – a review of organic tracers for smoke from incomplete combustion. Applied Geochemistry, 17(3), 129-162. Smith, J. M., Van Ness, H. C. (1987). Introduction to Chemical Engineering Thermodynamics, Chemical Engineering Series. New York: McGraw-Hill. Shrivastava, M. K., Subramanian, R., Rogge, W. F., Robinson, A. L., 2007: Sources of organic aerosol: Positive matrix factorization of molecular marker data and comparison of results from different source apportionment models. Atmospheric Environment, 41, 9353-9369. Voiland, Adam. “Aerosols: Tiny Particles, Big Impact.” NASA Earth Observatory. NASA, 02 Nov. 2010. Web. 21 June 2011. <http://earthobservatory.nasa.gov/Features/Aerosols/page1.php>.