THESIS THE ROLE OF AEROSOLS IN VISIBILITY DEGRADATION DURING TWO FIELD CAMPAIGNS. Submitted by. Ezra J.T. Levin. Department of Atmospheric Science

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1 THESIS THE ROLE OF AEROSOLS IN VISIBILITY DEGRADATION DURING TWO FIELD CAMPAIGNS Submitted by Ezra J.T. Levin Department of Atmospheric Science In partial fulfillment of the requirements For the Degree of Master of Science Colorado State University Fort Collins, Colorado Fall 2008

2 COLORADO STATE UNIVERSITY October 3, 2008 WE HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER OUR SUPERVISION BY EZRA J.T. LEVIN ENTITLED THE ROLE OF AEROSOLS IN VISIBILITY DEGRADATION DURING TWO FIELD CAMPAIGNS BE ACCEPTED AS FULFILLING IN PART REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE. Committee on Graduate Work Jeffrey Collett John Volckens Advisor Sonia M. Kreidenweis Department Chair Richard Johnson ii

3 ABSTRACT OF THESIS THE ROLE OF AEROSOLS IN VISIBILITY DEGRADATION DURING TWO FIELD CAMPAIGNS Visibility degradation in federally protected areas has been a matter of growing concern and even congressional regulation. Aerosols are the primary contributor to visibility degradation and can have large anthropogenic sources. For this work, an aerosol sizing system consisting of three separate instruments was deployed during the Rocky Mountain Atmospheric Nitrogen and Sulfur study (RoMANS) and the second Fire Lab at Missoula Experiment (FLAME 2). These two studies had a common objective of exploring the role of aerosols in visibility reduction. During both campaigns aerosol optical properties were calculated from the sizing rack data as well as chemical data provided by the IMPROVE network. Calculated scattering coefficients were then compared with measured scattering coefficients. The RoMANS campaign was conducted in Rocky Mountain National Park during the spring and summer of During RoMANS total aerosol loading in the park was much higher in the summer than the spring, leading to higher measured and calculated values of the scattering coefficient. Water uptake by hygroscopic aerosols increased light scattering by up to 100% during the spring, but was less important during the summer when aerosols were carbon dominated. During the FLAME 2 campaign, conducted at the Fire Science Lab in Missoula Montana during the summer of 2007, aerosol emissions from a number of small iii

4 controlled fires were measured. High particle concentrations, as well as non-spherical and light absorbing particles, caused several errors in the sizing instruments. For cases with highly light scattering aerosols, however, there was agreement between measured and calculated optical properties. Ezra J.T. Levin Atmospheric Science Department Colorado State University Fort Collins, CO Fall 2008 iv

5 ACKNOWLEDGEMENTS I gratefully thank my advisor, Sonia Kreidenweis, for the immeasurable help and support she has provided me during my master s degree program. I also thank my committee members, Jeff Collett and John Volckens, for their help with this work. I wish to thank Kip Carrico and Gavin McMeeking for their work during both the RoMANS and FLAME 2 campaigns. I also especially thank Gavin for all the time he invested teaching me how to use the sizing rack and alignment program. Logistical support for the RoMANS study was provided by Air Resource Specialists, Inc. as well as Judy Visty and Laura Wheatley at RMNP. I also thank Taehyoung Lee, Amy Sullivan and Suresh Raja for the RoMANS URG data. Funding for RoMANS was provided by the National Park Service through Cooperative Agreement # H and from the State of Colorado. A great number of people were instrumental in the success of FLAME 2. I would like to especially acknowledge the help of Cyle Wold for all of his work during the experiments. I also thank Amy Sullivan and Mandy Holden for the FLAME 2 aerosol composition data. Funding for FLAME 2 was provided by the Joint Fire Science Program under Project JFSP v

6 TABLE OF CONTENTS 1 INTRODUCTION VISIBILITY REGULATION AEROSOLS AND VISIBILITY AEROSOL HYGROSCOPICITY AND VISIBILITY OPTICAL PROPERTIES OF CARBONACEOUS AEROSOLS FIELD STUDIES OF AEROSOLS AND VISIBILITY IMPAIRMENT SIZING INSTRUMENTATION AND ALIGNMENT METHOD OPC APS DMPS RACK SETUP ALIGNMENT METHOD OPC CALIBRATION LAB CALIBRATIONS ADJUSTED OPC CALIBRATION CURVES CALIBRATION CURVES FROM OTHER STUDIES RoMANS METHODS AEROSOL SIZE DISTRIBUTIONS AEROSOL COMPOSITION DATA INTERCOMPARISON MASS/VOLUME COMPARISON SCATTERING COMPARISON CONCLUSIONS FLAME METHODS INSTRUMENTATION vi

7 5.2 SIZING ERRORS SIZE DISTRIBUTIONS RETRIEVED REFRACTIVE INDICES AND DERIVED OPTICAL PROPERTIES AEROSOL COMPOSITION MEASUREMENT INTERCOMPARISONS CONCLUSIONS SUMMARY AND FUTURE WORK SUMMARY RoMANS FLAME FUTURE WORK APPENDIX A AEROSOL INORGANICS MODEL APPENDIX B FLAME 2 COMPOSITION COMPARISON vii

8 LIST OF FIGURES Figure 1.1: Change in Qscat for a hypothetical population of ammonium sulfate particles as RH increases Figure 1.2: Change in da/dlogd p for a hypothetical population of ammonium sulfate particles as RH increases Figure 1.3: Change in b ext for a hypothetical population of ammonium sulfate particles as RH increases Figure 3.1: Normalized OPC counts versus DMA mobility diameter for ammonium sulfate aerosols from post FLAME 2 calibrations (06/07) Figure 3.2: Normalized OPC counts versus DMA mobility diameter for oleic acid aerosol from post FLAME 2 calibrations (06/07) Figure 3.3: Original FLAME 2 OPC calibration curves Figure 3.4: Adjusted FLAME 2 OPC calibration curves Figure 3.5: BRAVO OPC calibration curves (Hand, 2001) Figure 3.6: RoMANS OPC calibration curves from lab test performed in 06/ Figure 3.7: Ammonium sulfate volume distribution aligned with the original FLAME 2 OPC calibration curves Figure 3.8: Ammonium sulfate data from FLAME 2 aligned with the adjusted FLAME 2 OPC curves Figure 3.9: Alaskan duff core data aligned with the original FLAME 2 OPC curves Figure 3.10: Alaskan duff core data aligned with the adjusted FLAME 2 OPC curves.. 37 Figure 3.11: Black needlerush data aligned with the original FLAME 2 OPC curves Figure 3.12: Black needlerush data aligned with the adjusted FLAME 2 OPC curves Figure 3.13: White spruce data aligned with the original FLAME 2 OPC curves viii

9 Figure 3.14: White spruce aligned with the adjusted FLAME 2 OPC curves Figure 3.15: Ammonium sulfate data from FLAME 2 aligned with the BRAVO OPC curves Figure 3.16: Ammonium sulfate data from FLAME 2 aligned with the RoMANS OPC curves Figure 4.1: Integrated total (a), accumulation mode (b bottom) and coarse mode (c bottom) aerosol number concentrations [cm -3 ] Figure 4.2: Integrated total (a), accumulation mode (b bottom) and coarse mode aerosol (c bottom) volume concentrations [μm 3 cm -3 ] Figure 4.3: Accumulation mode volume fraction Figure 4.4: Mass fraction of PM 2.5 mass components during RoMANS Figure 4.5: Ratio of moles of ammonium to moles of sulfate during RoMANS Figure 4.6: Reconstructed PM 2.5 mass concentration [μg m -3 ] versus PM 2.5 gravimetric mass concentration [μg m -3 ] for (a) spring and (b) summer Figure 4.7: Daily averaged effective refractive index retrieved from alignment method (black) and real (red) and imaginary (blue) refractive indices calculated from aerosol composition Figure 4.8: IMPROVE PM 2.5 gravimetric mass [μg m -3 ] versus PM 2.5 volume [μm 3 cm -3 ] calculated from aligned size distributions Figure 4.9: Measured b sp [Mm -1 ] versus b sp calculated using (a) dry aerosols, (b) humidified aerosols and (c) only accumulation mode humidified aerosols during the spring Figure 4.10: Measured b sp [Mm -1 ] versus b sp calculated using (a) dry aerosols, (b) humidified aerosols and (c) only accumulation mode humidified aerosols during the summer Figure 4.11: Relative humidity in the Optec nephelometer, expressed as a fraction Figure 5.1: Diagram of the FSL facility (McMeeking, 2008) ix

10 Figure 5.2: A volume distribution from the Mississippi palmetto burn demonstrating the OPC s difficulty in measuring absorbing particles Figure 5.3: Sizing rack data for an Alaskan duff core burn Figure 5.4: Sizing rack data for an Alaskan duff core burn Figure 5.5: Sizing rack data for a longleaf pine burn Figure 5.6: Sizing rack data for a dry Douglas fir burn Figure 5.7: Sizing rack data for a rice straw brun Figure 5.8: Sizing rack data for a longleaf pine plus wiregrass burn Figure 5.9: Sizing rack data for an oak plus hickory burn Figure 5.10: Sizing rack data for a dry Douglas fir burn Figure 5.11: Sizing rack data for a black needlerush burn Figure 5.12: Sizing rack data for a black needlerush burn Figure 5.13: Sizing rack data for a white spruce burn Figure 5.14: Sizing rack data for a rhododendron burn Figure 5.15: Sizing rack data for a sugarcane burn Figure 5.16: Sizing rack data for a sagebrush burn Figure 5.17: Sizing rack data for a wiregrass burn Figure 5.18: Sizing rack data for a Mississippi palmetto burn Figure 5.19: Sizing rack data for a Florida palmetto burn Figure 5.20: Sizing rack data for a black spruce burn Figure 5.21: Sizing rack data for a chamise burn x

11 Figure 5.22: Sizing rack data for a gallberry burn Figure 5.23: Sizing rack data for a fresh Douglas fir burn Figure 5.24: Burns ranked by D gn Figure 5.25: Burns ranked by σ gn Figure 5.26: Reconstructed PM 2.5 mass concentration [μg m -3 ] versus PM 2.5 gravimetric mass concentration [μg m -3 ] Figure 5.27: SSA (λ = 532nm) calculated from measured scattering and absorbing coefficients versus SSA (λ = 532nm) calculated from composition Figure 5.28: Measured b sp [Mm -1 ] versus b sp calculated using refractive indices calculated from composition Figure 5.29: Measured b sp [Mm -1 ] versus b sp calculated using retrieved refractive indices Figure 5.30: Alignment retrieved refractive index (black) and real (red) and imaginary (blue) refractive indices calculated from IMPROVE data Figure 5.31: Burns ranked by mass scattering efficiency [m 2 g -1 ] Figure 5.32: Mass scattering efficiency [m 2 g -1 ] versus D gv [μm] Figure 5.33: Mass scattering efficiency [m 2 g -1 ] versus m retrieved Figure A.1: V w /V s as a function of RH for the hygroscopic species H 2 SO 4, NH 4 HSO 4, (NH 4 ) 3 H(SO 4 ) 2, (NH 4 ) 2 SO 4 and NH 4 NO 3 as calculated by AIM Figure B.1: Comparison between SO 4 2- measured by URG and IMPROVE Figure B.2: Comparison between NH 4 + measured by URG and IMPROVE Figure B.3: Comparison between Cl - measured by URG and IMPROVE Figure B.4: Comparison between K + measured by URG and IMPROVE Figure B.5: Comparison between OC measured by HI-VOL and IMPROVE xi

12 Figure B.6: Comparison between EC measured by HI-VOL and IMPROVE Figure B.7: Comparison between TC measured by HI-VOL and IMPROVE xii

13 LIST OF TABLES Table 3.1: OPC D 50 s from ammonium sulfate and oleic acid lab calibrations performed after FLAME 2 (06/07) Table 3.2: Constants for the OPC scaling polynomials from the original FLAME 2 calibration curves Table 3.3: Constants for the OPC scaling polynomials from the adjusted FLAME 2 calibration curves Table 4.1: Mean and standard deviations for number and volume aerosol concentrations and mode statistics Table 4.2: Mean and standard deviations for aerosol composition components during the two RoMANS periods as well as historical means and standard deviations for the same months from Table 4.3: Densities and refractive indices for species used in reconstructed fine mass calculations (Hand and Kreidenweis, 2002) Table 5.1: Fuels burned during FLAME 2 chamber burns. IMPROVE composition data for each burn with refractive index and SSA calculated from composition data. Burns are ranked from highest to lowest SSA Table 5.2: Densities and refractive indices for species used in reconstructed fine mass calculations Table A1: V w /V s as a function of RH for the hygroscopic species H 2 SO 4, NH 4 HSO 4, (NH 4 ) 3 H(SO 4 ) 2, (NH 4 ) 2 SO 4 and NH 4 NO 3 as calculated by AIM xiii

14 LIST OF ACRONYMS AND SYMBOLS AIM... Aerosol Inorganics Model AN... Ammonium Nitrate APS... Aerodynamic Particle Sizer AS... Ammonium Sulfate CPC... Condensation Particle Counter DMA. Differential Mobility Analyzer DMPS Differential Mobility Particle Sizer EC. Elemental Carbon FSL Fire Science Lab IMPROVE Intergovernmental Monitoring of Protected Visual Environments IPA Isopropyl Alcohol FLAME. Fire Lab at Missoula Experiment GM Gravimetric Mass LAC.. Light Absorbing Carbon MSE.. Mass Scattering Efficiency OC. Organic Carbon OMC. Organic Mass from Carbon OPC.. Optical Particle Counter PAS... Photoacoustic Spectrometer PSL Polystyrene Latex RCFM... Reconstructed Fine Mass RH. Relative Humidity RMNP... Rocky Mountain National Park RoMANS.. Rocky Mountain Atmospheric Nitrogen and Sulfur study SSA... Single Scattering Albedo TOA.. Thermal Optical Analysis XRF... X-Ray Fluorescence b ext. Extinction coefficient b sp.. Scattering by particles coefficient D 50. OPC bin limit diameter D a.. Aerodynamic diameter D e.. Spherical equivalent diameter D g.. Geometric mean diameter D p.. Physical diameter k. Imaginary refractive index λ. Wavelength m Complex refractive index n Real refractive index xiv

15 Q abs... Light absorption efficiency Q scat... Light scattering efficiency ρ. Density σ g... Geometric standard deviation Z p... Electrical mobility xv

16 1 INTRODUCTION This work presents results from the Rocky Mountain Airborne Nitrogen and Sulfur (RoMANS) study and the second Fire Lab at Missoula Experiment (FLAME 2). RoMANS was conducted in Rocky Mountain National Park (RMNP) during the spring and summer of 2006 while FLAME 2 was conducted at the United States Forest Service s (USFS) Fire Science Lab (FSL) in Missoula, Montana during May and June of One common goal of these two studies was to examine the effects of atmospheric aerosols from various sources on visibility. To achieve this goal, an aerosol sizing system consisting of three separate instruments was deployed during both studies. An alignment method was employed to reconcile the output from these three instruments and provide a continuous aerosol size distribution. In this thesis, I examine the data collected by this sizing system and present results from the two studies. Also, I will discuss the effectiveness of the sizing system in meeting the study objectives under the highly different conditions presented by the two studies. 1.1 VISIBILITY REGULATION A major motivation behind both studies discussed here is the federal laws regulating visibility impairment in federally protected areas. Although visibility impairment is one of the most obvious manifestations of air pollution, it was not directly addressed as an air quality issue until 1977 when Congress amended the 1970 Clean Air 1

17 Act. This amendment set forth as a national goal the improvement of visibility in federally mandated Class I areas (national parks and wilderness areas) as well as the prevention of future visibility degradation in these areas (Watson, 2002). As a result of the 1977 amendment, in 1988 the Interagency Monitoring of Protected Visual Environments (IMPROVE) network was established to measure visibility in Class I areas as well as the pollutants responsible for visibility reduction (Malm et al., 1994). Improving visibility in Class I areas was given even greater importance in 1999 with the passing of the Regional Haze Rule (RHR). The RHR mandates that visibility in these federally protected areas has to be returned to natural levels within 60 years (Watson, 2002). 1.2 AEROSOLS AND VISIBILITY In order to achieve the goals set forth by the RHR we need to understand the mechanisms behind visibility degradation. Light traveling through the atmosphere can be absorbed or scattered by aerosol particles and gas molecules. Scattering changes the direction of the photon s propagation while absorption removes the photon from the beam by conversion to thermal or electronic energy in the particle or molecule. These two processes, collectively known as light extinction, both act to remove light from a beam and thus lead to a decreased visible range. Maximum visible range is defined as the furthest distance at which a dark object can be distinguished from the horizon. In a pristine environment at sea level this range is about 300 km, for λ=520 nm, (Seinfeld and Pandis, 2006). Atmospheric light extinction is characterized by the extinction coefficient (b ext ) which has units of inverse length (typically Mm -1 ) and describes the reduction in the 2

18 intensity of light in a beam over a path length. The extinction coefficient can be broken down into four components: absorption by gasses (b ag ), scattering by gasses (b sg ), absorption by particles (b ap ) and scattering by particles (b sp ). b ext = b ag + b sg + b ap + b sp (1.2.1) Nitrogen dioxide (NO 2 ) is the only atmospherically relevant gas which absorbs in the visual range. Scattering by gasses is almost entirely a function of air density, and thus elevation, and is not significantly affected by anthropogenic emissions. Scattering and absorption by aerosols, however, are strongly correlated with anthropogenic emissions, and are the main contributors to extinction in most visibility impaired areas (Seinfeld and Pandis, 2006). Therefore, an objective of this work is to examine the contributions of aerosols from various sources to visibility reduction. How much light an aerosol particle scatters depends on its cross sectional area (A) and how effectively it scatters light, its scattering cross section (C scat ). Depending on the optical properties of the particle and the wavelength of light being considered, C scat can be significantly larger or smaller than A. Normalizing C scat by A results in the dimensionless scattering efficiency (Q scat ). An analogous term can be derived for the absorption efficiency (Q abs ). The equations for both Q scat and Q abs are formally derived by Mie theory and are functions of the wavelength of incident light (λ), the diameter of the particle (D p ) and the particle s complex refractive index (m = n - ki) (Bohren and Huffman, 1983). Mie theory only strictly applies to spherical particles, however, it is commonly used to estimate extinction from atmospheric particles of unknown shape (Bohren and Huffman, 1983; Eldering et al., 1994). If Q scat and the aerosol size 3

19 distribution are known, b sp can be estimated using the following equation (Seinfeld and Pandis, 2006) 2 p, i b sp N iqscat, i = i πd 4 (1.2.2) where D p,i is the particle diameter in μm, N i is particle number concentration at that diameter in cm -3, and the sum is over all diameters. Again, b ap can be derived in similar fashion. According to Mie theory, aerosols with a diameter similar to the wavelength of incident light are more effective scatterers than those with wavelengths much larger or smaller. This means that aerosols in the accumulation mode, roughly between 0.1 and 1 μm, scatter visible light more effectively than those in the nucleation or coarse modes. It is therefore important to know the aerosol size distribution in order to understand the effect of the aerosols on visibility (Seinfeld and Pandis, 2006). The discussion above shows that four parameters are relevant in the relationship between aerosols and visibility. The particle s complex refractive index and diameter are necessary to calculate Q scat and Q abs, which describe how effectively a single particle absorbs and scatters light. The aerosol size distribution is relevant in determining the effectiveness of an aerosol population at scattering light. The total number concentration of particles dictates how many absorption or scattering events a beam of light will encounter in its path length. These parameters, therefore, must be known to understand the effect of aerosols on visibility in an area. The sizing rack and alignment method (described in detail in Chapter 2) provide information on all of these. 4

20 1.2.1 AEROSOL HYGROSCOPICITY AND VISIBILITY Aerosol hygroscopicity, how much water a particle takes up as humidity increases, can also have a huge effect on visibility. Water uptake by an aerosol particle changes the particle s composition, and thus its refractive index. This in turn affects the scattering efficiency of the particle. Figure 1.1 shows the changes in Q scat calculated from Mie theory for a hypothetical population of ammonium sulfate particles as humidity increases. As can be seen, the peak scattering efficiency moves towards larger diameters and decreases slightly as RH increases. This is due partly to the changes in composition as the particles become more dilute. More important than the small changes in Q scat caused by water uptake, however, is the change in particle size as RH increases. Inorganic salts, such as sulfates and nitrates, are the most common hygroscopic aerosol species. At high humidity these species can take up large amounts of water and greatly increase the total mass in the aerosol phase. Thus, scattering by these species can increase by a factor of five or more for RH = 90% (Malm and Day, 2001). Figure 1.2 shows the increase in the aerosol surface area size distribution (da/dlogd p ) for the same ammonium sulfate population shown before as RH increases. Total aerosol surface area increases rapidly, and non-linearly, as RH increases and this greatly influences total scattering (Figure 1.3). Because of this, it is very important to know the relative humidity and aerosol hygroscopic properties when estimating the contribution of aerosols to visibility impairment OPTICAL PROPERTIES OF CARBONACEOUS AEROSOLS While the optical properties of common inorganic salts are well known, those of carbonaceous aerosols, both organic (OC) and elemental (EC), are less well understood 5

21 (Schkolnik et al., 2007). Aerosols from biomass burning are typically carbon dominated and can significantly contribute to total aerosol loading, especially during the wildfire and prescribed burning seasons in the western and southeastern United States (Malm et al., 2004). These carbon containing aerosols can both scatter and absorb light and thus contribute to significant visibility reduction (Reid et al., 2005). We, therefore, need to improve our understanding of the optical properties of these aerosols. Part of the problem in developing a database of expected properties lies in the wide range of possible carbonaceous aerosol compounds. There are hundreds, or possibly thousands, of organic compounds which could be present in the aerosol phase and only a small number of these have been studied in any detail (Brown et al., 2002; McMeeking et al., 2005b). Because the technology to measure every organic compound in the aerosol phase is not currently available, bulk properties, such as density and refractive index, are applied to the entire OC fraction (Turpin and Lim, 2001). Because of the wide range of possible values for these variables, however, this can lead to errors. Elemental carbon also presents many challenges. These particles often are nonspherical which can significantly alter both absorption and scattering. Recall that Mie theory is only valid for spherical aerosols. Elemental carbon aerosols can form highly non-spherical chain aggregates leading to complex absorption and scattering characteristics quite different from Mie theory predictions. Chakrabarty et al. (2007) found that measured scattering decreased compared to that expected from Mie theory as the number of monomers in a fractal chain (N) increased, up to a value of N 200. Bond and Bergstrom (2006) report that as the void fraction (the void volume in an aerosol divided by its total volume) increases, both density and the real and imaginary parts of 6

22 the refractive index decrease. The shape and morphology of fresh carbonaceous aerosols from biomass burning depend on many factors especially combustion temperature (Bond and Bergstrom, 2006) which is often unknown. However, even highly non-spherical particles typically compact and become more spherical with time (Chakrabarty et al., 2007). Martins et al. (1998) reported that even after just one hour of aging in the atmosphere, most emissions from biomass burning can be considered spherical and thus Mie theory is applicable. 1.3 FIELD STUDIES OF AEROSOLS AND VISIBILITY IMPAIRMENT Much previous work has examined the effects of aerosols on visibility and the role of various aerosol species on visibility impairment in different regions of the United States. Two reports by Malm et al (1994; 2004) present data collected by the IMPROVE network showing the spatial and temporal patterns of aerosols important in visibility reduction in the United States. Malm showed that sulfates, organics and dust are the largest contributors to visibility impairment in most areas across the US, with nitrates also contributing significantly in southern California. Sisler and Malm (1994) examined the same IMPROVE data but focused specifically on the role of soluble aerosols on visibility reduction. They showed that visibility impairment is greater in the eastern United States because of the combination of high humidity and the abundance of hygroscopic sulfate aerosols in this region. The above reports focused on general trends in aerosols across the country. More in-depth and localized studies have also been conducted to improve understanding of which species are important in degrading visibility in specific areas. Some of these studies include the 1995 Southeastern Aerosol and Visibility Study (SEAVS) (Malm et 7

23 al., 2000a; Malm et al., 2000b), the Big Bend Regional Aerosol and Visibility Observational Study (BRAVO) conducted in 1999 (Hand et al., 2002; Lee et al., 2004) and the Yosemite Aerosol Characterization Study (YACS) conducted in 2002 (Carrico et al., 2005; McMeeking et al., 2005a; McMeeking et al., 2005b). Two studies were also conducted in Grand Canyon National Park in 1991 (Malm et al., 1996) and 1998 (Malm and Day, 2000) to determine the roles of various aerosol sources and aerosol properties on visibility reduction in the park. A major goal of both YACS and BRAVO was to examine the role of aerosol size distributions on scattering. The sizing system used for the work presented here was also deployed during these two studies. Hand et al. (2002) found that, averaged over the entire BRAVO study period, accumulation mode aerosols accounted for 80% of the total dry scattering coefficient. The coarse mode scattering coefficient was found to be larger than that for the fine mode during several long range dust transport events when the coarse mode accounted for more than half of the total aerosol volume. During these times, however, the coarse mode geometric mean diameter (D gv ) was slightly smaller than the study average coarse mode D gv. During other episodes with large coarse mode volume fractions and larger coarse mode D gv, scattering was dominated by accumulation mode aerosols. McMeeking et al (2005a) found that during several smoke transport events during YACS, the accumulation mode was shifted to larger sizes. During these times the mass scattering efficiency, the Mie calculated scattering coefficient normalized by total mass, also increased by up to a factor of 1.5. Many studies have also examined the hygroscopic nature of aerosols and the role of water uptake on visibility. Tang (1996) modeled the changes in scattering coefficients 8

24 for a number of hygroscopic species as a function of humidity. As discussed above (section 1.2.1) he found that the changes in aerosol size far outweighed the changes in composition in determining the effect of water uptake on scattering. A main goal of the field studies mentioned above was also to look at aerosol hygroscopicity and its role in regional haze formation and visibility impairment. Malm et al. (2000a) examined the ability of several different aerosol scattering models to reconstruct scattering coefficients measured during SEAVS as a function of RH. They found that the best agreement was achieved when water uptake was allowed to vary based on the acidity of the sulfate species. Day and Malm (2001) examined aerosol growth, and its effects on light scattering, at three different national parks: Great Smoky Mountains, Grand Canyon and Big Bend. They found that growth was largest when the aerosols were dominated by inorganic salts. Malm and Day (2001) also examined water uptake by organic aerosols by comparing growth curves measured at the Grand Canyon and Great Smoky Mountains with theoretical growth curves for inorganic salts. The differences between the two curves were attributed to organics. They found that organic aerosols were very weakly hygroscopic, or completely non-hygroscopic. Yu et al. (2005a), however, showed that water soluble organics, such as malonic acid, were present during the SEAVS study. These species can affect total water uptake by the aerosol and therefore may have important impacts on visibility. However, the mass fraction of these water soluble organics is typically very small. Carrico et al. (2005) also showed that organic aerosols, sampled during YACS, can exhibit some hygroscopic growth, although much less than that of inorganic salts. 9

25 Similar to the previous studies which focused on visibility reduction in specific areas, the RoMANS campaign examined the aerosols contributing to light extinction in RMNP. The FLAME campaigns, instead, examined the effects of biomass burning on atmospheric aerosols and visibility which has potential application in many regions (Chen et al., 2007). Some results and conclusions from the RoMANS and FLAME 2 campaigns will be presented in Chapters 4 and 5 respectively. First, however, I will present the methods used for this work in the following two chapters. 10

26 6 5 4 RH Q sca D [μm] Figure 1.1: Change in Q scat for a hypothetical population of ammonium sulfate particles as RH increases002e RH da/dlogd p D [μm] Figure 1.2: Change in da/dlogd p for a hypothetical population of ammonium sulfate particles as RH increases. 11

27 b ext [Mm -1 ] RH Figure 1.3: Change in b ext for a hypothetical population of ammonium sulfate particles as RH increases. 12

28 2 SIZING INSTRUMENTATION AND ALIGNMENT METHOD Many different instruments have been developed to measure aerosol size distributions. These instruments employ different measurement techniques and are thus sensitive to different aerosol properties and measure over different aerosol diameter ranges. Combining various types of instruments can, therefore, greatly increase the measured aerosol size range (Wilson et al., 1988; Eldering et al., 1994; Shen et al., 2002). Also, using different sizing methods can provide information on other unknown variables such as density, refractive index and shape factor (Stolzenburg et al., 1998; DeCarlo et al., 2004; Murphy et al., 2004). The aerosol sizing system used for this work consists of an optical particle counter (OPC; PMS LASAIR 1002 or 1003), a differential mobility particle sizer (DMPS; TSI 3085) and an aerodynamic particle sizer (APS; TSI 3321). Combining these three instruments, with their different measurement techniques, yields size distributions between and 20 μm, a much larger range than any one of these instruments could provide alone. Also, by reconciling the differences in the measurement overlap regions of the different instruments, real refractive index and aerosol density can be estimated. This method for reconciling the data is discussed below. First, however, I will give some background on the operating principles behind each instrument. 13

29 2.1 OPC Optical Particle Counters (OPC) use light scattered by an aerosol passing through a laser beam (or some other light source) to determine particle size and concentration. The Particle Measurement Systems (PMS) LASAIR 1002 and 1003 use a nm laser and size aerosol particles into one of eight bins. These bins have manufacturer calibrated lower bin limit diameters of 0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1.0 and 2.0 μm. Aerosol particles enter through the top of the instrument and pass through the laser beam in the optical chamber where a parabolic detector collects the scattered light between 35 and 105. The intensity of the signal from the scattered light is then used to determine particle size based on manufacturer calibrations (PMS LASAIR 1003 Instrument Manual). The LASAIR 1002 operates at a sample flow rate of lpm and the LASAIR 1003 at lpm. These very low flow rates insure that only one particle at a time enters the optical chamber at concentrations below 4000 and cm -3 respectively (Hand and Kreidenweis, 2002). At aerosol concentrations above these levels, the OPC is saturated and the data can not be used. OPC saturation was not an issue during RoMANS. However, during FLAME 2 aerosol concentrations frequently surpassed these levels. Although the OPC contains an internal flow meter which constantly monitors the sample flow rate, additional flow checks were routinely performed using a low flow Gilibrator flow cell. Knowing the sample flow accurately is very important as this is used to calculate aerosol concentrations in each bin. Because the OPC utilizes light scattering to size particles the instrument is sensitive to the aerosol s refractive index. Two particles of the same size but different refractive indices will scatter different amounts of light, thus causing the OPC to size 14

30 them into different diameter bins. The OPC therefore measures optical diameter. Optical diameter can easily be converted to a physical diameter if the aerosol refractive index and the OPC response are known, and if the particle is spherical. Refractive index is often not known when measuring ambient aerosols, although it can be calculated from aerosol chemical data if these are available (Hasan and Dzubay, 1983). The change in OPC response as a function of refractive index was determined via laboratory calibrations described in detail in Chapter APS The APS estimates aerodynamic diameter using a time of flight method. Internal pumps draw 5.0 lpm into the instrument. Of this flow, 4.0 lpm are filtered, and then sent back into the inlet nozzle as sheath flow. The remaining 1.0 lpm, the sample flow, is injected into the inlet nozzle in the center of the sheath flow. As the sample flow enters the nozzle it is accelerated by the sheath flow. Smaller particles, with less inertial lag, are accelerated quickly with the flow while larger particles take longer to accelerate. The APS exploits this variation in particle acceleration to size the aerosols into one of 52 bins, between 0.5 and 20 μm with a base 10 logarithmic bin width of 0.03 μm, based on particle velocity at the end of the inlet nozzle (TSI 3321 Instrument Manual). After being accelerated in the inlet nozzle, particles pass into the optical chamber and through two laser beams, separated by 100 μm. As the particles pass through the lasers the side scattered light is detected. The APS converts the time between detected scattering events to an aerosol velocity (v p ) using the equation: v p vgtmin = (2.2.1) t 15

31 where v g is the velocity of the gas at the exit of the acceleration nozzle and t and t min are the measured time of flight of the particle and the minimum time of flight for the smallest particle measurable by the instrument respectively. Using ambient temperature (T) and pressure (P) as well as the pressure drop across the nozzle (ΔP), v g can be calculated as follows (Chen et al., 1985): 1 2 2RT P v g = ln (2.2.2) M P ΔP where R is the gas constant and M is the molecular weight of air. From v p, an aerodynamic diameter (D a ) is calculated based on manufacturer calibrations. Aerodynamic diameter, as reported by the APS, can then be converted to equivalent spherical diameter (D e ) via the following equation (Hinds, 1999): D a = D e Ce ρ ec a 1 2 (2.2.3) In the above equation, ρ e is the equivalent density which takes into account the particle shape via the dynamic shape factor, χ (Hinds, 1999): ρ ρ = p e χ (2.2.4) where ρ p is the true particle density. The dynamic shape factor is defined as the ratio of a particle s aerodynamic mobility to that of a spherical particle with the same volume and velocity (Hinds, 1999). The Cunningham correction factors, C a and C e, account for the deviation of small particles from Stokes theory which assumes an air velocity of zero at the particle surface. The Cunningham correction factor is described by the equitation (Hinds, 1999): 16

32 2 Ca = 1+ [ exp( Pd a )] (2.2.5) PD a As can be seen from the above equations, the sizing method used by the APS is affected by both particle shape and density. Denser particles, with a higher ρ e, will accelerate more slowly in the nozzle and will thus be sized into a larger bin. Nonspherical particles, with a larger χ and thus smaller ρ e, will be sized into smaller bins. Because these particles have a larger surface area than a spherical particle of the same volume, they have a greater drag and are thus accelerated faster by the sheath flow around them. This effect can potentially produce large errors when sampling nonspherical particles of unknown shape. 2.3 DMPS The DMPS consists of a differential mobility analyzer (DMA, TSI 3081) in series with a condensation particle counter (CPC, TSI 3785). The DMA selects particles over a diameter range of roughly μm, depending on ambient pressure, which are then counted by the CPC. The DMA was run at a flow ratio of 10:1 with a sheath flow of 3.0 lpm and stepped through the diameter range every 10 or 15 minutes, depending on the user specified sampling time. Differential mobility analyzers utilize the monotonic relationship between the motion of a singly charged particle in an electric field, the electrical mobility, and particle diameter to select particles of a specified diameter. Particles first pass through a 210 Po neutralizer which imparts an equilibrium bipolar electrical charge distribution to the aerosols. After passing through the neutralizer, the charged particles are injected with the sheath flow into the DMA column which consists of two concentric cylinders. The outer 17

33 cylinder is grounded while a specified negative charge is applied to the inner cylinder, thus creating an electric field in the annular region between the cylinders. The charged, polydisperse particles enter the column near the outer wall and as they move down the column positively charged particles move inward toward the negatively charged inner cylinder. Particles with high mobilities move horizontally across the column faster than the sheath flow is carrying them down the column and are thus deposited onto the inner cylinder. Lower mobility particles move more slowly across the column and exit at the base of the column with the excess air. Only particles with a very specific mobility exit with the monodisperse flow through a small slit near the bottom of the inner cylinder and are sent to the CPC to be counted. By adjusting the voltage on the inner cylinder, a range of particle mobilities, and hence particle sizes, can be selected. Electrical mobility (Z p ) describes a particle s motion in an electric field (E) and is defined as the particle s terminal velocity (v t ) in a given electric field: vt Z p = (2.3.1) E At equilibrium, the equations describing this motion can be derived by assuming a force balance between the electrostatic force (left side of equation 2.3.2) and the Stokes drag force (right side of equation 2.3.2) 3πηvt χd p NeE = (2.3.2) C p where N is the number of elemental charges, e, on the particle, η is the viscosity of air, χ is the dynamic shape factor, D p is the particle diameter and C p is the Cunningham correction factor (see equation 2.2.5). Equation can then be solved for electrical mobility: 18

34 Z p v NeC t p = = (2.3.3) E 3πηχD p The specific range of aerosol diameters that can be sized by a DMA is a function of column geometry, sheath and sample flow rates and aerosol particle mobility. The equation describing the electrical mobility for a given instrument was developed by Knutson and Whitby (1975): Z p = [ Q 0.5( Q + Q )] t m 2πVL p r ln r out in (2.3.4) The mobility bandwidth, the range of mobilities that can exit with the monodisperse flow at a given voltage, is given by equation (Knutson, 1975): ΔZ p = ( Q + Q ) m 2πVL s r ln r out in (2.3.5) In equations and 2.3.5, Q t, Q m, Q p and Q s are the total, monodisperse, polydisperse and sheath flows, V is the voltage on the inner cylinder, and L, r out and r in are the length, outer radius and inner radius of the column. For the TSI 3081, these parameters are, L = cm, r out = cm and r in = cm (TSI 3081 Instrument Manual). The total flow is equal to the sheath flow plus the polydisperse flow, Q t = Q s + Q p, or the excess flow plus the monodisperse flow, Q t = Q e + Q m. The TSI 3081 recirculates the excess air and uses this as the sheath flow thus causing Q e = Q s. This forces Q p = Q m. Therefore, the numerator in equation (excluding the natural log) can be replaced simply with Q s. The relationship between voltage and particle diameter can now be derived from equations and 2.3.4: 19

35 V 3χηD pqs = 2NeC L p r ln r out in (2.3.6) Equation is used by the DMA software to select a voltage based on the desired diameter. Note that the relationship between V and D p also has a T and P dependence through the η and C p terms respectively. These parameters must therefore be supplied to the program running the DMA to ensure accurate size selection. Although it is assumed that N = 1 when choosing the DMA voltage corresponding to a desired monodisperse output, particles with more than one charge do exist, especially at larger diameters. Multiply charged particles will have a higher mobility than singly charged particles of the same size, thus larger particles than desired can exit the DMA with the monodisperse flow. The presence of multiply charged particles is corrected for following the methods of Wiedensohler (1988) and Fuchs (1964). To close these equations the largest diameter particles sampled by the DMA must be known. This gives an upper limit for the sizes of multiply charged particles which must be accounted for. Typically an impactor is attached to the DMA inlet to give a known upper diameter cutoff. Instead, we use OPC data to infer concentrations of multiply charged particles above the upper size range of the DMA. After being size selected by the DMA, the monodisperse aerosol is sent through the CPC which counts particle number concentration. The CPC passes the particles through a tube supersaturated with respect to water. This causes the particles to grow rapidly so that they can easily be detected by an optical particle counter at the end of the growth tube. Because of this growth tube, a CPC on its own can not provide any information on particle size. Combining this instrument with a DMA, however, provides very accurate data on the number of particles in each DMA size bin. 20

36 The CPC has an internal pump which draws in a sample flow of 1 lpm. The monodisperse flow out of the DMA, however, was only 0.3 lpm. To balance this, 0.7 lpm of filtered, dry air was added to the sample line between the DMA and the CPC. Instrument operators routinely monitored this flow to ensure the proper flow ratios in the DMA. 2.4 RACK SETUP The three aerosol sizing instruments, as well as sample lines, flow controllers, and temperature and relative humidity sensors, are all housed in a mobile rack. The OPC and DMPS sample through a common inlet which can be pre-dried by passing the sample through a Perma Pure dryer (Perma Pure Inc., Toms River, NJ). The Perma Pure drier uses dried sheath air to dry the sample to RH < 10%. The flow through the OPC/DMPS inlet is 0.6 lpm which is split isokinetically into two 0.3 lpm flows, using a TSI flow splitter, and sent to the two the instruments. Because the OPC only draws in a sample flow of or lpm, depending on the model, the extra flow is siphoned off before the OPC inlet using a critical orifice to control the flow. Temperature and relative humidity sensors are located on the OPC/DMPS sample line before and after the Perma Pure drier as well as on the DMA sheath flow line. The APS draws in 5.0 lpm though its own inlet which can also be dried, RH < 15%, using an insulated heating tape wrapped around the inlet tube. This method is used instead of a Perma Pure drier to reduce losses of large particles. The APS inlet also has a temperature and relative humidity sensor located downstream of the heating tape. 21

37 2.5 ALIGNMENT METHOD Because the three aerosol sizing instruments all measure over different size ranges and exploit different aerosol characteristics to make their measurements, the output from the instruments has to be reconciled to produce one continuous size distribution. This is done following the alignment method developed by Hand and Kreidenweis (2002). The data from the DMPS, OPC and APS are first fit to the same diameter grid using a Twomey fit (Twomey, 1975; Markowski, 1987; Winklmayr et al., 1990). The alignment method then reconciles DMPS, OPC and APS data in two separate steps. The first step fits the OPC data to the DMPS data by adjusting real refractive index, since the OPC output is a function of aerosol refractive index. The response of the OPC output to changes in refractive index is discussed in the next chapter. The alignment process scans through real refractive index, from in increments of and adjusts the OPC measured size distribution based on previously calculated calibration curves (discussed in Chapter 3) for each refractive index. The DMPS data are also inverted in this step of the alignment. Although the DMPS output does not depend on refractive index, the OPC data are used in the inversion to correct for multiply charged particles (Hand and Kreidenweis, 2002). Therefore, the DMA data must be reinverted during each refractive index loop iteration. The fit between the refractive index adjusted OPC and inverted DMPS distributions is tested using the χ 2 statistic. The second step of the alignment fits the APS data to the aligned OPC data in much the same way by scanning through equivalent density (equation 2.2.4) from g cm -3 in 0.05 g cm -3 increments. Output from the APS, in aerodynamic diameter, is converted to equivalent spherical diameter using the equations in section 2.2 and the 22

38 density specified by the iteration loop. The density-adjusted APS distribution is then compared to the already aligned OPC distribution, again using the χ 2 statistic. For both steps of the alignment, the best-fit size distribution is determined by the minimum χ 2 value. The final output from the alignment technique is a dry aerosol number distribution between 0.04 and 20 μm expressed as dn/dlog 10 D p evaluated at 96 diameters with a base 10 logarithmic bin width of The alignment also records the real refractive index and the particle density resulting in the best fit at each measurement point. 23

39 3 OPC CALIBRATION As described in Chapter 2, the OPC measures light scattered by a particle passing through a laser beam and uses this signal to size the particle into one of eight bins. Because light scattering is a function of the particle s refractive index, and shape, the OPC output is also affected by these parameters. The OPC may size particles with different refractive indices into different bins even if they are both spherical and have the same physical diameter. The OPC, therefore, reports an optical diameter. The optical diameter can be converted to a physical diameter if both the aerosol refractive index and OPC response are known, assuming spherical particles. However, since the OPC utilizes scattered light, if a particle is highly light absorbing, the OPC response can not accurately be converted to physical diameter. This will be discussed much more in Chapter 5. From the alignment technique we get an estimate of the refractive index which can be used to adjust the OPC output; however, the changes in OPC response with refractive index must be determined from laboratory measurements. The OPC is calibrated by the manufacturer using spherical polystyrene latex (PSL) particles which have a known real refractive index of 1.588, and no absorption at 632 nm. These calibrations reset the lower bin limits for the eight bins to 0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1.0 and 2.0 μm. At these limits, the bin D 50, 50% of the particles will be sized into the bins on either side. For example, if PSL particles with a physical diameter of 0.3 μm 24

40 are sent into the OPC, 50% of them will be sized into bin 2 and 50% into bin 3, if the instrument is correctly calibrated. Because the OPC is calibrated by the manufacturer at a real refractive index of 1.588, nonabsorbing spherical particles with a real refractive index close to this will be sized correctly, that is the optical diameter reported by the OPC will be the same as the particle s physical diameter. The real refractive index of PSL, however, is at the upper range of those typically observed in the atmosphere (Seinfeld and Pandis, 2006). Particles with a real refractive index different from that of PSL can have large errors in sizing (Hinds, 1999). To correct for this, lab tests are done to determine the OPC response to particles with lower real refractive indices. The OPC used for this work was last calibrated by the manufacturer in December 2004 before the ROMANS study and May 2007 before the FLAME 2 study. Because there is some time between manufacturer calibrations and the studies, these calibration points have more uncertainty than the calibration points performed in our own lab. Lab calibrations were performed immediately before and after each study. 3.1 LAB CALIBRATIONS To determine the OPC response to refractive indices lower than that of PSL, lab calibrations were performed with ammonium sulfate and oleic acid aerosols. These compounds have known real refractive indices of 1.53 and 1.46 respectively, and are nonabsorbing at 632 nm. Aerosols were generated by atomizing an ammonium sulfate or oleic acid solution with a TSI atomizer (TSI 3076). The ammonium sulfate solution used in the atomizer was approximately 1% ammonium sulfate by weight dissolved in deionized water and the oleic acid solution was approximately 0.5% oleic acid by volume 25