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Cloud Condensation Nuclei (CCN) Analysis of Biogenic Secondary Organic Aerosol Rachel L. Atlas 1 , Celia L. Faiola 2 , Timothy VanReken 2 This work was supported by the National Science Foundation’s REU program under grant number AGS- 1157095 1 University of Chicago, Dept. of Physics and Geophysical Sciences, 2 Washington State University, Dept. of Civil and Environmental Engineering Calibration Motivation Cloud condensation nuclei (CCN) are particles which water vapor condenses onto to form cloud droplets at sufficient supersaturations. CCN can include anthropogenic or biogenic material, the latter often produced from the atmospheric oxidation of biogenic volatile organic compounds (BVOCs) to form secondary organic aerosol (SOA). Trees' BVOC emissions change in response to stress, which may affect how much SOA they form and how effective it is as CCN. Emissions producing more CCN would be expected to have a net cooling effect on climate and those producing fewer CCN, a net warming effect. The relationship is more complex, as stressors, such as increased ozone concentration, temperature and herbivory are linked to climate change. Tree emissions and climate change form a challenging feedback loop, which must be better understood in order to reduce uncertainties in climate models. Figure 1. CCNc with cover removed and labVIEW data collection program. Chamber Experiments An aerosol’s ability to act as a CCN is described by the Kohler Equation (below), which relates the particle’s critical supersaturation, above which the particle will act as a CCN, to its composition and size. This equation is a good approximation for inorganic particles and is used in the calibration of the cloud condensation nuclei counter (CCNc). The CCNc exposes aerosols to a known temperature gradient, in a wetted column, to promote droplet formation. The purpose of the calibration is to find the relationship between the temperature gradient within the CCNc and the effective supersaturation. Four different temperature gradients are used. The scanning mobility particle sizer (SMPS) is used to select ammonium sulfate particles of twenty known sizes. For each temperature gradient, the efficiency spectrum (#CCN/# particles) is plotted against the size of the particles. A sigmoid function is fit to the data and the x-value corresponding to the half max of the fit function is taken as the critical diameter. The raw data must be corrected because the differential mobility analyzer (DMA) does not produce completely monodisperse particles, which causes a discrepancy between the desired particle diameter and the actual mean particle diameter. The mean diameter is calculated from the efficiency spectrum and electrical mobility of the particles. A linear relationship is observed between the CCNc temperature gradients and the effective supersaturations. The supersaturations used to calculate kappa for the chamber experiments were calculated using this linear relationship. RTr v a S sol w w 2 exp Kohler Equation: S is the saturation ratio, a w is the activity of water, v w is the partial molar volume of water, sol is the solution surface tension, R is the gas constant, T is the temperature and r is the particle radius. Figure 4. Linear relationship between the measured CCNc temperature gradients and the calculated effective supersaturations. In this study, trees are kept in a laboratory chamber and their emissions are directed into a separate chamber, where they react with ozone, the oxidizing agent, to form SOA. The experiment is run twice: before and after the trees are stressed by the introduction of ozone or methyl jasmonate (to simulate herbivory) into the plant chamber. Several instruments are used to analyze the plants’ gas -phase emissions and the aerosols they form (figure 6), including a cloud condensation nuclei counter (CCNc) and scanning mobility particle sizer (SMPS). The kohler equation is modified for organic aerosols, using a hygroscopicity parameter, k. Using the data from the SMPS and the CCNc, k is calculated to quantify the relationship between CCN activity and particle size. Figure 7. CCN concentration for five supersaturations. Each point shows six minutes of one-second data. Four minutes have been removed from every supersaturation cycle, to account for the time it takes for the CCNc temperatures to stabilize after switching supersaturations. Figure 8. Image plot of the evolution of particles. A nucleation event is observed at 1:00 PM. The delay between the nucleation event and the spike in CCN concentration is due to the particles’ growth time. References Rose, D., Gunthe, S. S., Mikhailov, E., Frank, G. P., Dusek, U., Andreae, M. O., and Pöschl, U.: Calibration and measurement uncertainties of a continuous-flow cloud condensation nuclei counter (DMT-CCNC): CCN activation of ammonium sulfate and sodium chloride aerosol particles in theory and experiment, Atmos. Chem. Phys., 8, 1153-1179, doi:10.5194/acp-8-1153-2008, 2008. Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity – Part 2: Including solubility, Atmos. Chem. Phys., 8, 6273-6279, doi:10.5194/acp-8-6273-2008, 2008. Seinfeld, John H. ; Pandis, Spyros N. (2006). Atmospheric Chemistry and Physics - From Air Pollution to Climate Change (2nd Edition).. John Wiley & Sons. Baron, Paul A.; Willeke, Klaus (2001). Aerosol Measurement - Principles, Techniques, and Applications (2nd Edition).. John Wiley & Sons. Jacob Oberman, LAR REU student, 2010 Logan Callen, student researcher, 2008 T A D A D D D D S sol w d w d w k 6 3 3 3 3 10 * 69251 . 8 exp ) 1 ( D w and D d are wet and dry particle diameters, sol is the surface tension of the solution, T is the temperature, and k is the hygroscopicity parameter. Further Work Modify CCN analysis program for laboratory measurements, to account for high relative CCN concentrations and seed particles, which are 50 nm ammonium sulfate particles. Produce size-resolved CCN data, by directing the chamber air through the SMPS before the CCNc. Calibration data is consistent with earlier calibrations and literature. Kappa values are low because the CCN analysis program was written for moderate relative CCN concentrations, as are observed in the field, and breaks down at high relative concentrations, as are produced in the laboratory. Conclusions CCN Counter HR-ToF-AMS (Organic Aerosol Characterization) SMPS CPC (Total Particle Count) Ozone Source Ozone Concentration GC-MS (Organic Speciation) PTR-MS (Gas Phase Organics) Biogenic Plant Chamber Zero Air Generator Aerosol Growth Chamber Global Climate Change BVOC Stressors (increased ozone, herbivory) Oxidized BVOC OH, O 3, NO 3, hn SOA CCN Mixing Tube Condensation Particle Counter (CPC) Laboratory Differential Mobility Analyzer (DMA) Cloud Condensation Nuclei Counter (CCNc) Vacuum Pump Neutralizer HEPA Filter Dryer Ammonium Sulfate Solution Atomizer Compressor Flow Meter Flow Controller HEPA Filter Laboratory Indicates Particles Sheath Flow Sample Flow Dilution Flow Figure 2. Feedback loop between climate change and biogenic aerosol. Figure 3. Raw calibration data with sigmoid fit curves and corrected activation curves, with the calculated critical diameters. Figure 5. Laboratory setup for the CCNc calibration Figure 6. Laboratory setup for chamber experiments Figure 9. Total Particle Concentration for comparison with the CCN concentration (above) Figure 10. Kappa values for bristlecone pine biogenic SOA at .38% supersaturation. Modified Kohler Equation
