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Characterization of black carbon: from source to evolution of physical and optical properties in the atmosphere

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Characterization of black carbon: from source to evolution of physical and optical properties in the atmosphere

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Content Characterization of Black Carbon: From Source to Evolution of Physical and Optical Properties in the Atmosphere by Trevor Krasowsky A dissertation presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ENVIRONMENTAL ENGINEERING) December 2017 Copyright 2017 Trevor Krasowsky 2 Dedication: Bo 3 Acknowledgements Above all, I would like to give a special thanks to my PhD advisor, Prof. George Ban- Weiss, for providing me with unwavering support throughout my graduate years. Words will never be able to express my appreciation for everything he has done for me. Throughout my years at the University of Southern California, Dr. Ban-Weiss has gone above and beyond the call of duty of an advisor to guide me through research and life. Thank you for being the ultimate role model, teacher, and friend. I am eternally grateful. Thanks to Prof. Constantinos Sioutas and Prof. Scott Fruin for their advice on research, future career, and life. Thanks to Prof. Kelly Sanders and Ed Avol for reviewing my thesis proposal and providing me with constructive feedback. Thanks to my collaborators Dr. Gavin McMeeking, Dr. Dongbin Wang, Dr. Nancy Daher, Dr. Arian Saffari, and Dr. Sina Hasheminassab for their contribution to this dissertation. Thanks to Prof. Glenn Morrison, Prof. Joel Burken, and Prof. Jianmin Wang for inspiring me to pursue my PhD. Thanks to Mac McGory for helping me build the confidence to pursue engineering school and for making me aware of talents I didn’t know I had. Finally, thanks to my mother and brothers for their persistent love and encouragement. 4 Table of Contents Dedication: ........................................................................................................................ 2 Acknowledgements ........................................................................................................... 3 List of Figures ..................................................................................................................... 6 List of Tables ...................................................................................................................... 9 Abstract ........................................................................................................................... 10 Chapter 1 -‐ Overview ....................................................................................................... 15 1.1. Background ....................................................................................................................................................... 16 1.2. Rationale for completed work .................................................................................................................. 25 Chapter 2 -‐ Measurement of Particulate Matter Emissions from In-‐Use Locomotives ..... 32 2.1. Introduction ...................................................................................................................................................... 33 2.2. Materials and Methods ................................................................................................................................ 34 2.2.1. Location ........................................................................................................................................................... 34 2.2.2. Sampling ......................................................................................................................................................... 34 2.2.3. Meteorology ................................................................................................................................................... 36 2.2.4. Instruments .................................................................................................................................................... 36 2.2.5. Plume Analysis .............................................................................................................................................. 40 2.3. Results and Discussion ................................................................................................................................ 41 2.3.1. Black carbon emissions ............................................................................................................................ 42 2.3.2. Particle number emissions ...................................................................................................................... 46 2.3.3. PM2.5 emissions ............................................................................................................................................. 47 2.3.4. LDSA emissions ............................................................................................................................................. 47 2.3.5. Size-‐resolved particle number emission factors ............................................................................ 48 2.3.6. Black carbon versus particle number emissions ........................................................................... 51 2.3.7. Effects of dilution on emission factors ............................................................................................... 52 2.3.8. Comparing emissions of BC per hauled container for trains versus trucks ....................... 54 2.4. Summary ............................................................................................................................................................ 58 2.5. Appendix 1 ........................................................................................................................................................ 60 2.5.1. Details on the correction factor for the DustTrak ........................................................................ 60 2.5.2. Details on measurements with the DiSCmini .................................................................................. 61 Chapter 3 -‐ Real-‐world measurements of the impact of atmospheric aging on physical and optical properties of ambient black carbon particles ................................................. 63 3.1. Introduction ...................................................................................................................................................... 64 3.2. Materials and Methods ................................................................................................................................ 65 3.2.1. Location .......................................................................................................................................................... 65 3.2.2. Sampling and Instruments ...................................................................................................................... 65 3.2.3. Meteorology ................................................................................................................................................... 70 3.2.4. Analysis of Coating Thickness ................................................................................................................ 71 3.3. Results and Discussion ................................................................................................................................ 73 3.3.1. Black Carbon Time Series Analysis ...................................................................................................... 73 3.3.2. Number Fraction of Black Carbon with Thick Coatings ............................................................ 74 3.3.3. Mass Absorption Cross-‐Section Enhancement from Coatings on Black Carbon Particles ......................................................................................................................................................................................... 76 3.3.4. Particle Age Analysis ................................................................................................................................. 77 3.4. Summary ............................................................................................................................................................ 79 5 3.5. Appendix 2 ........................................................................................................................................................ 81 Chapter 4 -‐ Characterizing the evolution of physical properties and mixing state of black carbon particles: from near a major highway to the broader urban plume in Los Angeles ............................................................................................................................ 84 4.1. Introduction ...................................................................................................................................................... 85 4.2. Materials and methods ................................................................................................................................ 86 4.2.1. Sampling locations ..................................................................................................................................... 86 4.2.1.1. Near-‐road campaign .............................................................................................................................. 86 4.2.1.2. Redlands campaign ................................................................................................................................ 87 4.2.2. Sampling time periods .............................................................................................................................. 87 4.2.3. Instrumentation ........................................................................................................................................... 88 4.2.4. Meteorology ................................................................................................................................................... 89 4.2.4.1. Meteorology near-‐road ......................................................................................................................... 89 4.2.4.2. Redlands ...................................................................................................................................................... 89 4.2.5. Methodology for estimating the number fraction of thickly-‐coated particles (f) ........... 90 4.2.6. Leading-‐edge-‐only fit methodology for quantifying coating thickness on rBC-‐ containing particles ............................................................................................................................................... 91 4.2.7. Estimation of Photochemical Age ........................................................................................................ 91 4.2.8. Weekday and Weekend Analyses ......................................................................................................... 92 4.3. Results and discussion ................................................................................................................................. 92 4.3.1. Near-‐road campaign ................................................................................................................................. 92 4.3.1.1. rBC mass and number concentrations and number fraction of thickly-‐coated particles at different distances from the highway .................................................................................... 92 4.3.1.2. rBC mass and number size distributions at different distances from the highway .... 99 4.3.1.3. Quantifying coating thickness for rBC near the highway using LEO-‐fit ....................... 100 4.3.2. Redlands Campaign ................................................................................................................................ 101 4.3.2.1. Campaign overview ............................................................................................................................. 101 4.3.2.2. Diurnal cycles of rBC mass concentrations and number fraction of thickly-‐coated particles .................................................................................................................................................................... 102 4.3.2.3. Number fraction of thickly-‐coated particles versus photochemical age ...................... 106 4.3.2.4. rBC mass concentration versus number fraction of thickly-‐coated particles ............ 107 4.3.2.5. rBC size distributions and mixing state analysis using LEO-‐fit for different days ... 109 4.3.3. Comparison of near-‐road and Redlands campaigns ................................................................ 114 4.4. Summary .......................................................................................................................................................... 116 4.5. Appendix 3 ...................................................................................................................................................... 119 Chapter 5 -‐ Conclusions ................................................................................................. 121 Bibliography .................................................................................................................. 126 6 List of Figures Figure 2.1. Measured BC, PN, PM 2.5 , LDSA, and CO 2 concentrations in the exhaust plume of a passing freight locomotive. Figure 2.2. Speed distribution for trains measured in the Alameda Corridor (Los Angeles, CA) in this study. Figure 2.3. Cumulative distribution of measured train emission factors for BC, PN, LDSA, and PM 2.5 . The horizontal axis shows the likelihood that a train has an emission factor lower than a given value. Figure 2.4. Cumulative emission factor distributions of particulate matter from individual locomotives, showing the fraction of total PM (vertical axis) corresponding to the highest emitting fraction of trains (indicated by the horizontal axis). For example, 20 to 28% of total emissions of BC, PN, LDSA, and PM 2.5 are emitted from the dirtiest 10% of locomotives. Figure 2.5. Size-resolved particle number emissions measured using a Fast Mobility Particle Sizer Spectrometer (FMPS) for a passing train. Figure 2.6. Size-resolved particle number emissions measured using a Fast Mobility Particle Sizer Spectrometer (FMPS) for two passing trains. Figure 2.7. Black carbon versus particle number emission factors for individual trains. Emission factors are anti-correlated with correlation coefficient r (± standard error) = –0.34 ± 0.21. Figure 3.1. Hourly ambient rBC mass concentration (µg m -3 ), b absorption (Mm -1 ), and the number fraction of thickly-coated particles measured during the campaign. Figure 3.2. A depiction of the sampling configuration for the (a) heated cycle measurement and (b) ambient cycle measurement. Figure 3.3. Mean diurnal cycle of ambient rBC mass concentration (µg m -3 ) averaged over the entire measurement campaign and shown separately for weekdays and weekends. Error bars are 95% confidence representing day-to-day variability in hourly averages. Values shown are for ambient rBC only (i.e. not heated). Figure 3.4. Same as Figure 3.3 but for number fraction of thickly-coated particles (f). Figure 3.5. Number fraction of thickly-coated particles (f) versus photochemical age (PCA) computed using Eq. 1 for the entire campaign. Boxes depict the 25 th and 75 th percentiles, the band in the box is the median, and the whiskers depict the 10 th and 90 th percentiles. 7 Figure 4.1. rBC mass concentration (µg m -3 ), rBC number concentration (cm -3 ), and fraction of black carbon that is thickly-coated (f) versus downwind distance from Interstate 405 in Los Angeles, California. Figure 4.2. Fraction of rBC that is thickly-coated (f ) versus (a) rBC mass concentration (µg m -3 ) and (b) rBC number concentration (cm -3 ). Colors depict varying downwind distances from Interstate 405 in Los Angeles, California. The black lines represents a 7 th degree polynomials of best fit. Figure 4.3. (a) Mass and (b) number size distributions of rBC cores versus rBC core diameter. Size distributions were measured at 30 m (100 ft.), 61 m (200 ft.), and 114 m (375 ft.) downwind of Interstate 405 in Los Angeles, California. Figure 4.4. Histogram depicting the frequency of occurrence of specific coating thicknesses on rBC-containing particles as estimated with the Leading-Edge-Only (LEO). Figure 4.5. Mean hourly fraction of rBC that is thickly-coated (f), rBC mass concentration (µg m -3 ), and ozone mixing ratio (ppb) measured in Redlands, California from September 16 to October 10, 2016. Photochemical age (PCA) was computed concurrently using the ratio of NOx to NOy for air masses in Rubidoux, California, a location roughly 30 km (20 miles) to the southwest. Figure 4.6. Mean diurnal cycle of ambient (a) rBC mass concentration (mg m 3 ) and (b) number fraction of particles that are thickly-coated (f) averaged over the entire measurement campaign and shown separately for weekdays and weekends. Error bars are 95% confidence intervals using the Student t-distribution and computed using day- to-day variability in each hourly average. Values for weekend at 23:00 are removed because data for only day was available. Hour 3:00 for rBC weekend has a 95% confidence of 0.347 with mean=0.28547 with an interval of [-0.06153, 0.63247]. Figure 4.7. Mean diurnal cycles of the ozone mixing ratio (ppb) measured in Redlands, California. Weekdays represent Tuesday−Thursdays, and weekends represent Sundays as measured during the campaign from September 16−October 10, 2016. The weekday mean (± 95% confidence interval) was 36.3 ± 2.50 ppb, while the weekend mean (± 95% confidence interval) was 47.1 ± 3.20 ppb. Figure 4.8. Number fraction of rBC particles that are thickly-coated versus photochemical age for the hours of 13:00 to 16:00 Boxes depict the 25 th and 75 th percentiles, whiskers depict the 10 th and 90 th percentile, and the horizontal lines within the boxes show the median. 8 Figure 4.9. Refractory black carbon mass concentration (µg m -3 ) versus the number fraction of rBC particles that are thickly-coated versus for measurements made in Redlands, California from September 16−October 10, 2016. Data here represents 1- min temporal resolution. Boxes depict the 25 th and 75 th percentiles, whiskers depict the 10 th and 90 th percentiles, and the horizontal lines within the boxes show the median. rBC mass concentration and f are anti-correlated with correlation coefficient r = -0.087. Figure 4.10. 12-hour back trajectories from 15:00 local time, 22:00−23:00 UTC, depicted from the HYSPLIT model (NOAA) during four unique meteorological regimes in Redlands, California. Figure 4.11. Investigation of the physical properties of rBC-containing particles through Leading-Edge-Only (LEO) analysis and characterization of size dependence on rBC mass and number concentrations for a mix of meteorological regimes during the Redlands, California measurement campaign. (a and b) depict (a) LEO histogram of coating thickness (nm) and (b) mass/number size distribution of rBC cores, dN/dlogDp MED , (µg m -3 ) for air masses measured on Sunday, September 18, 2016. (c and d) depict (c) LEO histogram of coating thickness (nm) and (d) mass/number size distribution of rBC cores, dN/dlogDp MED , (µg m -3 ) for air masses measured on Sunday, September 25, 2016. (e and f) depict (e) LEO histogram of coating thickness (nm) and (f) mass/number size distribution of rBC cores, dN/dlogDp MED , (µg m -3 ) for air masses measured on Sunday, September 28, 2016. (g and h) depict (g) LEO histogram of coating thickness (nm) and (h) mass/number size distribution of rBC cores, dN/dlogDp MED , (µg m -3 ) for air masses measured on Sunday, October 7, 2016. Appendix 2 - Figure 1. A shematic depicting the thermodenuder used during the sampling campaign. Appendix 2 - Figure 2. Delay times between scattering and incandescence “peak height” signals for individual particles measured in a sample 3.5-hour time period during the campaign. Appendix 2 - Figure 3. Mass absorption cross-section enhancement (MAC E ) versus photochemical age time (PCA) computed using Eq. 1 for the entire campaign. Boxes depict the 25 th and 75 th percentiles, the band in the box is the median, and the whiskers depict the 10 th and 90 th percentiles. 9 List of Tables Table 2.1. Mean, median, maximum, and minimum emission factors per kg of fuel consumed for all 88 measured locomotives. Table 2.2. Parameters used for comparing container-specific emission factors for locomotives versus trucks. Table 3.1. Dates expressing which instruments were operational. Table 3.2. Mean ± standard deviation for ambient rBC mass concentration and number fraction of particles that are thickly-coated (f). Mean and 95% confidence interval for mass absorption cross-section enhancement (MAC E ) throughout the campaign. Standard deviations represent variability in daily averages over the campaign. Table 4.1. Mean rBC mass concentration for the Redlands measurement campaign and mean rBC (and black carbon) mass concentrations corresponding to three measured distances downwind of Interstate 405 in the Los Angeles National Cemetery in 2016 (and 2001). Appendix 1. Table 1. Summary of instruments used in the study. 10 Abstract Black carbon (BC) particles can have deleterious human health consequences and impact regional and global climate. However, to date, global estimates on the impact of BC climate-forcing remain uncertain. Constraining estimates of BC has proved challenging due to the heterogeneous distribution of BC across the globe. Additionally, there is a fundamental lack of knowledge describing how optical properties of BC vary in time and space. This work aims to reduce uncertainty in current climate-forcing estimates of BC by focusing on (1) characterizing understudied sources of anthropogenically emitted BC, (2) evaluating the influence of “mixing state” (describing how black carbon exists with other species) on optical properties of refractory BC (rBC), and (3) confining the spatiotemporal evolution of rBC physical properties and mixing state in distinct environments. In part 1, an important non-road source of BC, locomotives, was characterized. Particulate matter emissions from a large sample (N=88) of in-use line-haul freight locomotives were measured in the Alameda Corridor, located near the ports of Los Angeles and Long Beach. Emission factors for BC, particle number (PN), fine particulate mass (PM 2.5 ), and lung-deposited particle surface area (LDSA) were computed based on 1 Hz measurements of the rise and fall of particulate emissions and CO 2 concentrations as the locomotives passed the sampling location. I include LDSA emission factors as relevant for near-source human exposures. Mean emission factors ± standard deviations were 0.9 ± 0.5 g kg -1 fuel consumed for BC, (2.1 ± 1.5)x10 16 # kg -1 for PN, 1.6 ± 1.3 g kg -1 for PM 2.5 , and (2.2 ± 1.7)x10 13 µm 2 kg -1 for LDSA. Emission factors for individual trains were slightly skewed, with the dirtiest 10% of locomotives responsible for 20%, 11 24%, 28%, and 27% of total BC, PN, PM 2.5 , and LDSA emissions, respectively. The relative importance of high-emitters is therefore lower for these locomotives relative to previously reported diesel truck emissions. BC versus PN emissions from individual locomotives were found to be anti-correlated, suggesting that the highest emitters of particle numbers are the lowest emitters of black carbon. Using results presented here along with previous measurements, I compared for freight trains versus diesel trucks the amount of BC emissions associated with pulling an intermodal freight container over a given distance. This assumption-dependent comparison showed that in most cases locomotives emit less BC per container hauled than diesel trucks. However, continual decreases in diesel truck BC means that unless emissions from locomotives are decreased in the near future, emissions associated with hauling a container could become lower for diesel trucks than locomotives. In part 2, I evaluated how accumulation of soluble coatings on rBC particles influences optical properties of black carbon. Ambient rBC was measured in Rubidoux, California, approximately 90 km (55 miles) downwind of downtown Los Angeles. Collocated NO x and NO y measurements were used to estimate the photochemical age of the sampled air. Sampling was conducted throughout entire days between February 3, 2015 and March 13, 2015 to capture diurnal and daily variations in ambient rBC. Both ambient and thermally-denuded air was sampled in 15-minute cycles to compare the physical and optical properties of coated versus uncoated rBC particles. Physical properties of individual rBC particles including mass and coating thickness were measured using a Single-Particle Soot Photometer (SP2), and rBC optical properties were measured using a Photoacoustic Extinctiometer (PAX) at 870nm. The mean rBC mass 12 concentration (± standard deviation) for the campaign was 0.12 ± 0.08 µg m -3 . rBC mass concentrations were higher on weekdays than weekends, though only differences between 11:00 and 17:00 were statistically distinguishable. The fraction of total rBC particles that were thickly-coated (f) was found to be relatively low, with a mean of 0.05 ± 0.02 over the campaign. Values for f peaked in the afternoon when photochemical pollutant concentrations are also generally at a maximum. Further, f at 15:00-16:00 was found to be statistically higher on weekends than weekdays, potentially due to a higher relative amount of ambient SOA to rBC on weekends versus weekdays, which would enhance SOA coating of primary rBC particles as they age during transport from the western Los Angeles basin to the sampling site on weekends. Differences at other hours during the photochemically active period of the day (10:00 - 14:00) were not statistically different although the weekend values were systematically higher. Comparing f with the photochemical age (PCA) of sampled air showed increases in f as PCA increases; the mean value (± 95% confidence interval) of f for PCA < 1 hour was 0.037 ± 0.004, while that for PCA > 3 hours was 0.12 ± 0.04. This suggests that even in winter, photochemistry in urban environments can lead to increased thickly-coated rBC particles. The mean (± 95% confidence interval) enhancement in mass absorption cross-section (MAC) due to coatings on rBC for the wintertime measurements in urban Los Angeles was found to be 1.03 ± 0.05. Comparisons to other studies that measure enhancement of MAC are presented. In part 3, I investigated how road-to-ambient processing, and longer timescale aging in an urban plume, effect black carbon physical properties. Refractory black carbon was measured during summer 2016 using an SP2 in two distinct environments: near a 13 major freeway and downwind of Los Angeles. The near-road measurements were made in the Los Angeles National Cemetery using a mobile platform at a variety of distances ranging from 30 to 114 m downwind of Interstate 405 in Los Angeles. These results were compared with measurements performed 100 km east of Los Angeles in Redlands, California. Coatings on rBC particles were quantified using both the “Lag-Time” and “Leading-Edge-Only” (LEO) methods. As distance from the highway increased at the near-road site, I observed decreases in rBC mass and number concentrations and increases in f. The latter likely occurred due to rapid processing of the highway plume and entrainment of urban background particles. f was anti-correlated with rBC mass concentrations. Coating thickness histograms suggested that most rBC-containing particles measured near the highway were either uncoated or thinly-coated. In Redlands, I found that rBC mass concentrations on weekdays were similar to those observed at the furthest measured distance from the highway (114 m). However, rBC number concentrations for the smallest measured sizes (~70-100 nm mass equivalent diameter) were an order of magnitude lower in Redlands than all measured distances from the highway. Observations of f indicate that values in Redlands during periods when estimated photochemical age was highest (6-8 hours) were similar to corresponding values at the furthest measured distance from the highway. This suggests that the residence time of air in the Los Angeles basin under typical conditions measured during this campaign may not be sufficient for rBC to acquire thick coatings. However, under certain meteorological conditions, f was observed to be ~0.20, with coating thickness histograms showing a larger contribution of rBC particles with coating thickness > 80 nm. This occurred during a weekend day when local emissions from diesel vehicles were 14 lower (compared to weekdays) and winds brought air from the desert regions to the Northeast of Los Angeles, both of which would increase the relative contribution of remote sources of rBC. Afternoon values of f (and O 3 ) were found to be systematically higher on weekends than weekdays, suggesting that the “weekend effect” can create more thickly-coated rBC particles due to enhanced secondary organic aerosol and reduced available rBC as condensation sites. While this investigation took place during the hottest summer months in Los Angeles when photochemistry is presumably more important than for winter measurements in part 2, reported results corroborate the previous suggestion that the residence time of air in the Los Angeles basin may not be sufficient for rBC to acquire thick coatings even during the summer season. 15 Chapter 1 - Overview 16 1.1. Background The field of environmental engineering employs a variety of methods/sciences to address global problems related to environmental sustainability and public health. Understanding the role that air pollution, and particularly, the chemistry and physics of particulate matter (PM), a large subset of air pollution, is critical for mitigating a large fraction of current global environmental issues related to human health and climate change. PM is known to adversely affect human health by penetrating deeply into the alveolar regions of the lungs where it can diffuse into the circulatory system and accumulate in vital organs such as the liver, brain, or heart (Pope et al., 2004; Campbell et al., 2005; Kennedy et al., 2007). PM also alters regional and global climate through its influence on the radiative balance of Earth, and through additional influences on clouds that indirectly impact climate (Ramanathan et al., 2001; Hansen et al., 2005; Lohmann and Feichter, 2005; IPCC, 2007; Ban-Weiss et al., 2012; Ban-Weiss et al., 2014). A large component of particulate matter, especially in the Los Angeles area, is black carbon (BC), commonly referred to as soot. BC particles are a product of incomplete combustion, and are emitted in high quantities by sources such as diesel engines, coal power plants, and biomass burning (Ban-Weiss et al., 2008; Ramanathan et al., 2011; Krasowsky et al., 2015). Similar to particulate matter in general, the inhalation of black carbon is associated with a myriad of pernicious health effects related to the cardiovascular and respiratory system and can cause cancer and deleterious birth defects (Lloyd and Cackette, 2001; Frampton et al., 2004; Hart et al., 2009; WHO 2012). Black carbon is thought to be the second most important agent of global warming, after CO 2 (Bond et al., 2013). Ambient BC absorbs solar radiation, which leads to overall increases in shortwave radiation being absorbed by the climate system and local atmospheric 17 heating with consequences on atmospheric thermodynamics and surface temperatures (Cooke and Wilson, 1996; Hansen et al., 1997; Hansen et al., 2005; Schwarz et al., 2008a; Schwarz et al., 2008b; Ban-Weiss et al., 2009; Ban-Weiss et al., 2012). As mentioned earlier, BC comes from a variety of sources, but past research has been effective at characterizing one of the more prominent sources of BC, diesel trucks (Ban-Weiss et al., 2009) along with other air pollutants such as nitrogen oxides (NOx) (Ban-Weiss et al., 2008); however, estimates of climate effects of air pollution and black carbon remain tentative (Bond et al., 2013). Effective policy implementation for regulating vehicular emissions of BC (and PM in general) has influenced significant decreases in air pollutant concentrations (Hasheminassab et al., 2014). Past work has specifically demonstrated the effectiveness of diesel particle filters for reducing BC emissions in diesel truck engines (Dallman et al., 2011). As BC emissions from diesel trucks have drastically decreased in recent years, the relative importance of off-road sources (e.g., locomotive diesel engines, coal power plants, and ships) of PM and BC has increased (Krasowsky et al., 2015). As mentioned earlier, reducing uncertainty in the climate-forcing associated with black carbon is not limited to understanding just the sources of emissions. Inconveniently, the unique optical properties (i.e., the ability to absorb shortwave radiation) of black carbon vary in time and space where variation is commonly thought to be dependent on the “mixing state”, a term describing whether other aerosol species exist as separate particles (i.e. externally mixed) or are attached to (or coated on) BC particles (i.e. interally mixed) (Jacobson, 2001; Willis et al., 2016). Black carbon is generally thought to be externally mixed when freshly emitted by fossil fuel combustion sources, becoming internally mixed in the atmosphere after acquiring soluble coatings such as sulfates, 18 nitrates, and organics, in a process known as often referred to as aging (Weingartner el al., 1997; Riemer et al., 2010; Bond et al., 2013; Zhang et al. 2015). Through its ability to act as cloud condensation nuclei, BC can also influence cloud microphysics (IPCC, 2007). From an engineering standpoint, BC is a unique due to its short atmospheric lifetime (days to weeks) compared with other air pollutants that often have much longer atmospheric lifetimes (decades to centuries) (Cooke and Wilson, 1996). Because the majority of emitted BC comes from anthropogenic practices and emissions fall out of the atmosphere quickly, mitigating the sources of these emissions may be a cost effective engineering approach to benefit the climate and rapidly reduce near-source human exposure to environmental toxins. Because the atmospheric lifetime of BC is short and past policy has been effective at reducing emissions of BC and other air pollutants, BC has become even more spatially heterogeneous (Sardar et al., 2005; Krasowsky et al., 2015). Unique variation in morphology and spatial distribution leads to uncertain estimates of emissions and climate consequences of BC compared to other climate forcing agents like greenhouse gases (Bond et al., 2013). Improving the understanding of spatiotemporal variation (from pollutant source to the remote atmosphere) in BC concentrations and physical properties is critical for reducing uncertainties in quantifying its climate and health impacts. Though characterizing the mixing state of ambient black carbon through observations is challenging, there have been recent advances in instrumentation and data analysis methods capable of determining coating thickness in addition to BC mass and number concentrations and BC size distributions (e.g. Gao et al., 2007; Moteki and Kondo, 2007). Uncertainties remain in part due to variability in the structure of BC- 19 containing particles (Sedlacek et al., 2012). Past research has suggested that the mixing state of black carbon at the emissions source can influence the aging of BC even after time for significant atmospheric processing (Willis et al., 2016). Coatings on BC are thought to enhance its mass absorption cross-section (MAC), a metric describing absorption of radiation normalized by the mass of the particle (Fuller et al., 1999; Lack et al., 2009; Subramanian et al., 2010; Healy et al., 2015); furthermore, coatings influence the hygroscopicity of BC with significant effects on climate (Laborde et al., 2013; Schwarz et al., 2014) in addition to possible but largely unexplored effects on particle health impacts. Uncoated BC has been shown to be hydrophobic while coated BC has a higher affinity for water (i.e. hydrophilic) (Dahlkötter et al., 2014). This change in hygroscopicity has an influence on BC’s ability to act as cloud condensation nuclei with subsequent effects on wet deposition rates, but this phenomenon still has associated uncertainties. The high relative humidity in the human lungs (99.5%) (Ferron et al., 1988; Anselm et al., 1990) suggests a relationship between BC hygroscopicity and lung deposition probability. As BC becomes hydrophilic with acquired coatings, health impacts may be modified due to competition between (1) the decrease in deposition probability as particles in the ultrafine mode (particle diameter < 100 nm) grow to the accumulation mode (0.1 µm < particle diameter < 2.5 µm), and (2) the potential for coated BC to be more toxic than uncoated BC. The mixing state of BC is sensitive to a variety of factors and varies in time and space. Composition of coatings can depend on season and location. As an example of seasonal dependence, one modeling study focusing on southwestern Germany showed that condensation of sulfuric acid dominates aging of BC during the summer season 20 while the relative importance of ammonium nitrate coatings increases during the winter season (Riemer et al., 2004). A more recent observational study in urban Los Angeles found that BC coatings are mostly comprised of secondary organic aerosol (Lee et al., 2017). Previous observations have also concluded that the mixing state of BC in urban areas can vary for weekdays versus weekends. The substantial decrease in heavy-duty diesel traffic on weekends compared to weekdays has been shown to increase secondary organic aerosol (SOA) formation. This would lead to a higher ratio of organic aerosol to refractory BC, leading to a higher fraction of BC with thick coatings on weekends (Metcalf et al., 2012; Krasowsky et al., 2016). Time of day can also influence the mechanisms that create coatings on BC. For example, a recent modeling study suggested that BC aging over central-eastern China is dominated by condensation of photochemical pollutants while coatings at night occur at slower time scales dominated by coagulation aging (Chen et al., 2017). The physical properties and mixing state of ambient black carbon can be determined using the Single-Particle Soot Photometer (SP2). This instrument uses laser- induced incandescence and scattering to determine (a) refractory black carbon (rBC) mass and number concentrations and size distributions and (b) physical properties of rBC-containing particles including coating thickness (Laborde et al., 2012; Dahlkötter et al., 2014). Two different data analysis techniques can be used with SP2 measurements to quantify the mixing state of individual rBC-containing particles. The first is the lag-time method, which takes advantage of the time delay between peak scattering and incandescence signal responses to stratify particles as those that are (1) uncoated or “thinly” coated, versus (2) “thickly” coated, based on a selected time delay threshold. 21 rBC-containing particles with time delays greater than the set threshold are deemed as thickly-coated and vice versa. More information on the lag-time method can be found in previous studies (e.g. Moteki et al., 2007; McMeeking et al., 2011; Wang et al., 2014; Krasowsky et al., 2016). The second technique, called the leading-edge-only (LEO) method (Gao et al., 2007), is capable of quantifying coating thickness. Coatings vaporize as rBC-containing particles traverse the laser beam in the SP2. Thus, the LEO method works by reconstructing the Gaussian scattering function of scattering signal response curves (i.e. the scattering signal prior to coating vaporization) using the initial 1 to 5% of the measured signal. The LEO method employs measurements from the two-element avalanche photodiode in the SP2 to determine particle position as it traverses the laser beam at a near-fixed velocity. After Mie theory modeling with assumed refractive indices, the reconstructed scattering signal is used to quantify coating thickness for internally mixed rBC-containing particles (Gao et al., 2007). Several studies have used the LEO method or related analyses to quantify coating thickness for internally mixed rBC- containing particles. The next paragraph summarizes these studies. Note that there have been important variations in how the LEO method has been applied in previous literature (e.g. variations in rBC core size ranges for which coating thicknesses are analyzed), making synthesizing previous investigations difficult. The aim for the following section is to provide sufficient detail on each study to aid in interpreting differences in analysis techniques and conclusions. Taylor et al. (2014) investigated rBC wet removal in biomass burning plumes to help constrain rBC radiative forcing estimates. Along with measurements of rBC core size and removal efficiency, the mixing state for 130−230 nm rBC cores was measured 22 using the LEO method with parameters similar to many other studies reported here. They used the scattering signal up to 5% of peak intensity to reconstruct the Gaussian scattering signal. Coating thickness was quantified using Mie calculations with an assumed core refractive index of RI=2.26+1.26i at 1064 nm based on previous work by Moteki et al. (2010), and an assumed shell (or coating) refractive index of RI=1.5+0i. Results from this study demonstrated more efficient scavenging of larger rBC cores with thicker coatings. Liu et al. (2014) measured size distributions, performed a source apportionment analysis, and characterized the mixing state of rBC-containing aerosols in London as part of the Clean Air for London project. To perform the mixing state analysis, Liu et al. (2014) used the LEO method with 1 to 5% peak laser intensity for rBC cores ranging from 100−200 nm with the same refractive indices as Taylor et al. (2014). Results showed that traffic-related rBC-containing particles exhibited very thin coatings with remarkably similar rBC core size distributions for summer and winter measurements (Liu et al., 2014). Laborde et al. (2013) investigated the relationship between hygroscopicity and black carbon mixing state during the wintertime in suburban Paris for rBC core diameters in the range of 180−220 nm and 240−280 nm. The LEO method was applied using up to 1% of peak laser intensity to reduce the chance of including the vaporization signal when performing the Gaussian fitting, effectively using much less of the initial scattering signal than Taylor et al. (2014). Results from Laborde et al. suggest that particles emitted by traffic have essentially no coatings (<10nm), and that as coatings increase in size, the rBC cores become more spherical, demonstrating the influence of coatings on rBC morphology. Schwarz et al. (2008a) measured mass, mixing state, and optical size of individual rBC-containing particles. The LEO method was used to 23 compare the mixing state of urban and biomass burning emissions of rBC cores in the size range of 190−210 nm volume-equivalent-diameter (VED). Results indicated that urban rBC-containing particles consistently had smaller rBC core sizes and thinner non- rBC coatings than those in biomass burning plumes. Schwarz et al. (2008b) assessed coatings using the LEO method for measurements in a NASA research aircraft in the tropics over Costa Rica. They found that 200 nm VED rBC cores had mean coating thicknesses of 30 nm. Schwarz et al. (2014) developed a compact humidification system to quantify the hygroscopicity of rBC in relation to coating thickness quantified by the LEO method. This study introduced a unique way to bridge SP2-measured light scattering to aerosol water uptake properties based on Mie and κ-Köhler theory. Dahlkötter et al. (2014) evaluated aerosol properties and rBC mixing state after long- range transport to the upper troposphere using an aircraft during the CONCERT 2011 field experiment. They found coatings to be much thicker than many other studies (i.e. median thickness ranged from 105 to 136 nm depending on the flight). The SP2 settings used by Dahlkötter et al. made measurements sensitive to scattering material in the optical diameter range of 140 to 290 nm. This study showed that, assuming a homogenous sphere with refractive index of 1.59 + 0.00i versus the previous literature value of 1.50 + 0.00i, produced uncertainty in the range of 5% for a 200 nm particle. Thus, the assumed RI did not vastly impact the computed coating thickness. Their reported LEO coating thickness histogram showed values ranging from 20 to 180 nm for rBC cores in the range of 140 to 160 nm and 180 to 220 nm. Metcalf et al. (2012) performed a study over the Los Angeles basin during the CalNex campaign and evaluated mixing state using both the LEO and lag-time methods. They presented evidence that 24 shifts in the vehicle fleet on weekends can induce more SOA formation and consequently more thickly-coated rBC particles during the weekends. Shiraiwa et al. (2008) assessed the radiative impacts of rBC mixing state in the Asian outflow at Fukue, a Japanese island. They showed that coating thicknesses ranged from 25 to 400 nm with dependence on source region. Measurements made for Asian continental and maritime air masses exhibited a greater shell to core diameter (1.6) than for Japanese and free troposphere air masses (1.3−1.4). For the LEO method, they elected to set the last point of the leading edge of the scattering signal to a triple Gaussian width away from the center position of the Gaussian. As part of the MIRAGE campaign, Subramanian et al. (2010) performed an analysis over Mexico to investigate rBC mixing state and light interactions as it relates to atmospheric transport. Results from this study show relatively more thinly-coated rBC for measurements made within the urban Mexico City area relative to rBC-containing particles measured at locations away from the city where air masses are more aged. 25 1.2. Rationale for completed work Emissions of PM from motor vehicles have been relatively well characterized, while emissions from many non-road sources including locomotives are uncertain due to a lack of “real-world” measurements of in-use engines (Dallmann and Harley, 2010; Cahill et al., 2011; Krasowsky et al., 2015). As on-road emissions have decreased over past decades (Ban-Weiss et al., 2008; Dallmann et al. 2012), the fraction of PM emitted by non-road sources in California has increased and is out of proportion with its numerical population (Sawant et al., 2007). Locomotives contribute to human exposure of diesel pollutants near ports, railyards, and rail lines. Reducing uncertainty in current estimates of locomotive emissions is needed for enhancing the accuracy of emission inventories with corresponding improvements in health risk, air pollution, and climate assessments. Completed work presented here consists of a detailed characterization locomotive studies prior to the publication of Krasowsky et al. (2015). Popp et al. (1999) measured NO emissions from locomotives, but the remote sensing methods employed in their approach were not capable of measuring relevant metrics for PM such as fine particle mass (PM 2.5 ) and particle number (PN) concentrations. A series of studies (Fritz, 1993; Fritz, 1995; Fritz et al., 1994; Fritz et al., 1995; Hedrick and Fritz, 2008) measured locomotive emissions in detail, but the number of locomotives investigated was generally small and therefore may not be representative of the in-use fleet. Yanowitz (2003) found that notch changes in line-haul and switcher locomotives account for approximately 40% of locomotive PM emissions. Johnson et al. (2013) measured emission factors of particle number, PM 2.5 , SO 2 , and NO x from a large sample (N=56) of trains from an Australian shipping port. Another study (Galvis et al., 2013) estimated railyard BC and PM 2.5 26 emission factors using a downwind/upwind difference approach in Atlanta, Georgia. Their emission factors were representative of both line-haul and switcher locomotives, and included possible other local sources of emissions. Hricko et al. (2014) completed one of the few studies on health risks associated with 18 railyards in California and found that the higher cancer risk zones surrounding railyards were represented primarily by low-income populations. Freight locomotives are powered by large two- or four-stroke diesel engines. Emissions regulations set by the Environmental Protection Agency for new and remanufactured locomotives have become modestly more stringent over the last four decades (EPA, 2009a). Tier 0 and Tier 1 standards, which apply to locomotives manufactured from 1973-92 and 1993-2004, respectively, require particulate matter emissions to be below 0.22 grams per brake horsepower-hour (g/bhp-hr) for line-haul locomotives. More stringent PM regulations of 0.10 g/bhp-hr were imposed for “Tier 2” (2005-11) and “Tier 3” (2012-14) line-haul locomotives. These standards were met using modifications to engine design and operation. “Tier 4” locomotives (2015 and newer) will be required to reduce particulate matter emissions substantially (0.03 g/bhp-hr), requiring the use of exhaust after-treatment devices including diesel particle filters (EPA, 2009b). While regulatory control has been effective at reducing emissions from on-road sources (e.g. Ban-Weiss et al., 2008; Dallmann et al., 2011), locomotive emissions of PM have remained relatively constant (Dallmann and Harley, 2010). The locomotive study completed by Krasowsky et al. (2015) demonstrates fuel- based emission factors of black carbon, particle number, particle mass, and lung- deposited surface area for real-world locomotives representative of typical locomotives 27 used in line-haul transport for the United States to address the knowledge gap described in the above paragraphs. While this study provided insight to locomotive engine efficiency in relation to the efficiency of well-studied diesel truck engines, it remains necessary to evaluate the behavior of black carbon well beyond the tail pipe to assess health and climate characteristics of the species. The first part of this dissertation focused on characterizing a highly understudied emissions source of black carbon in California. However, there still exists a fundamental lack of knowledge on how the light-absorption properties (i.e. optical properties) of black carbon vary in time and space, which causes uncertainty of air pollution and climate effects associated with ambient black carbon. There are three approaches to assessing the influence of coating accumulation on black carbon optical properties, including theoretical optical modeling, laboratory experiments, and real-world measurements. Theoretical optical modeling may over predict the impact of coatings on BC optical properties because formulations assume that coatings fully engulf black carbon particles, and usually assume the BC core is spherical (Fuller et al., 1999). The state of ambient BC particles is far more complex; BC is often shaped irregularly and coated non-uniformly (Sedlacek et al., 2012), making model calculations of BC optical properties uncertain (Krieger et al., 2012). Laboratory experiments are often considered a more robust method for assessing the mixing state of BC and examining specific aging processes, but laboratory experiments can be an imperfect representation of the complex compound chemical and photolytic reactions that cause changes in surface kinetics, bulk kinetics, hygroscopic growth, volatile and phase partitioning, and oxidative aging that occurs in the real atmosphere (Jacobson, 2001; Krieger et al., 2012; Sedlacek et al., 2012). Real- 28 world measurements may therefore be the most effective way to assess absorption enhancement associated with BC coatings affected by conditions such as humidity, time, oxidant concentration, composition and concentration, and light irradiance (Cappa et al., 2012; Dahlkötter et al., 2014; Krieger et al., 2012; Sedlacek et al., 2012). Limited real-world measurements exist that focus on MAC enhancement (MAC E ), which describes the relative increase in MAC associated with a change in particle morphology from coatings on BC particles. The MAC E is derived from comparing ambient (coated) BC particle measurements with thermally-denuded (uncoated) BC particles to determine the relative increase, or “enhancement”, in the mass absorption cross-section from the coatings on the BC. The second part of this dissertation presents MAC E as the ratio MAC ambient to MAC thermally-denuded . Past field studies focusing on measurements that assess MAC E from BC coatings prior to the completion of Krasowsky et al. (2016) are summarized here. Cappa et al. (2012) reported measurements at two locations in California (i.e. Sacramento and west of Los Angeles, CA over the Pacific Ocean), with results suggesting weak MAC E (1.06). Laborde et al. (2013) completed a study at a suburban location in Paris with results suggesting that MAC is enhanced by a factor of 1.20 when fresh traffic BC is compared with aged BC. Wang et al. (2014) investigated a polluted area of western China and found that MAC E was about 1.8 due to internal mixing. A study in Ontario, Canada, which employed a photoacoustic spectrometer and laser-induced incandescence instrument system, showed that MAC is largely dependent on non-refractory material coatings, and varies in the range of 9 ± 2 and 43 ± 4 m 2 g -1 , influenced by the time and location of measurements (Chan et al., 2011). A recent study by Healy et al. (2015) 29 indicated that absorption enhancement is weak in downtown Toronto, Canada, where BC levels reflect a mixture of urban influences and long-range transport events. Lack et al. (2012) performed measurements from biomass burning near Boulder, Colorado and found a 1.7 factor enhancement in black carbon absorption from internal mixing. Nakayama et al. (2014) investigated the “lensing effect” on urban BC in Japan and obtained a small enhancement factor of 1.10. Krasowsky et al. (2016), assesses absorption enhancement associated with coatings on individual BC particles using real-world ambient measurements in an area representative of presumably “longer” aged emissions from the downtown Los Angeles area. This study confines uncertainty related to how coatings influence BC optical properties in the real-world. The first two studies presented in this dissertation address complex knowledge gaps relating to the characterization an understudied non-road BC in addition to quantifying how coatings on individual ambient rBC particles influence MAC E (or black carbon optical properties). The last chapter focuses on understanding the spatiotemporal scale of how coatings develop on rBC particles. Many people live near roadways, and there is little work demonstrating the dynamics of the way primary coatings accumulate on ambient BC near a roadway. These coatings may substantially change the health impacts of ambient BC for those near a roadway. Most previous research on the mixing state of rBC-containing particles focuses on time scales of hours or longer as urban (biomass burning) plumes advect away from cities (fires). There is limited prior work assessing the evolution of mixing state on more rapid timescales as pollutants are transported away from sources. However, previous work has 30 investigated the evolution of particle size distributions (i.e. including all species) during “road-to-ambient” processing (i.e. where highly concentrated aerosols from highway emissions dilute to ambient urban background concentrations). Zhang et al. (2004) showed that condensation, evaporation, and dilution dominate the evolution of aerosol physical properties associated with road-to-ambient processing. Changes in aerosol size on these rapid timescales near sources can be described through the competition of partial pressure and saturation vapor pressure, where particle growth through condensation has been shown to occur beyond 90 m from a major highway (Zhang et al., 2004). In theory, these processes could also impact the mixing state of rBC. Note that during these more rapid timescales there is likely insufficient time for complex photochemical reactions or coagulation of rBC with non-refractory material to occur. Coagulation becomes more significant when particle number concentrations are high and/or aging time scales are greater than 10 hours (Riemer et al., 2004), substantiating assertions that particle growth of fresh emissions near a major highway is attributed primarily to condensation and evaporation with coagulation playing a supporting role (Zhang et al., 2004). One previous study (Willis et al., 2016) used a soot-particle aerosol mass spectrometer to measure traffic emissions in an urban environment and found that BC at the origin is mixed with and abundance of hydrocarbon-like aerosols (HOA) leading to internally mixed BC particles that are either “rBC-rich” or “HOA-rich” with the majority of measured BC mass associated with rBC-rich particles. Another study (Lee et al., 2017) investigated the evolution of rBC-containing particles near major highways in the Los Angeles Basin using the ratio of NOx to total reactive nitrogen (NOy) as a surrogate for photochemical age of the aerosol. They found that SOA was responsible for substantial coatings on rBC 31 during the day when photochemistry is most important. Note that measurements were made roughly 3 km (2 miles) from the nearest highway, meaning that measured rBC- containing particles included a mix of fresh vehicular emissions along with the greater urban plume. An earlier study by Massoli et al. (2012) reported that under stable atmospheric conditions, vehicular air pollution becomes relatively well mixed with background air within 150 m of the Long Island Expressway (Interstate 495) in Queens, New York. This assertion alludes to the difficulty of attributing specific atmospheric processing mechanisms to describe changes in the mixing state of aerosols at locations greater than 150 m downwind of highways where air masses are heavily influenced from vehicular traffic emissions but not independent of the broader city’s emissions. Additional measurements are needed at a variety of locations and over a range of aging timescales to develop a comprehensive understanding for how morphology of rBC- containing particles varies from source to urban, continental, and global scale. 32 Chapter 2 - Measurement of Particulate Matter Emissions from In-Use Locomotives This chapter is based on the following publication: Krasowsky, T. S., Daher, N., Sioutas, C., and Ban-Weiss, G. A.: Measurement of emission factors from in-use locomotives, Atmospheric Environment., 113, 187-196, doi:10.1016/j.atmosenv.2015.04.046, 2015. 33 2.1. Introduction The first study described in this dissertation aims to addresses the knowledge gap associated with quantifying a highly understudied non-road source of BC emissions, locomotives. For this study, I performed measurements of particulate matter emissions from a large sample (N=88) of in-use freight locomotives traveling through the Alameda corridor. The Alameda Corridor is a 32 km freight rail expressway within urban Los Angeles that connects the two largest ports in the United States, the Ports of Los Angeles and Long Beach, to the national rail system (ACTA, 2014). Emission factors were derived for individual trains using concurrent 1 Hz measurements of CO 2 and particulate matter including BC, PN, PM 2.5 , and particle lung-deposited surface area (LDSA). Distributions of emissions from individual trains along with relationships between different PM metrics (i.e. BC vs. PN) for the measured fleet are presented. Since trains and trucks are the two main modes of transporting freight containers on land, emission results derived here are compared to those for diesel trucks, including a comparison of BC emitted per freight container hauled for trains versus heavy-duty diesel trucks. Emission factors for locomotives presented here establish a baseline prior to reductions that are anticipated as a result of Federal regulation and state control efforts in 2015 (EPA, 2009b). 34 2.2. Materials and Methods 2.2.1. Location Particulate emissions were measured from freight locomotives traveling through the Alameda Corridor to and from the Ports of Los Angeles and Long Beach. Roughly 40% of trains traveling through the Alameda Corridor have destinations outside of California, and the remaining 60% travel to locations within California (ACTA, 2015). Thus, the freight locomotives measured here are reasonably representative of line-haul freight locomotives in California and the United States. Portions of the corridor are trenched with a depth of about 10m (ACTA, 2014), allowing measurement sample line inlets to be placed directly over the railroad tracks at approximately 1 m above locomotive exhaust stack heights. The sampling location was about 16 km north of the Port of Los Angeles and Long Beach at the intersection of E. Greenleaf Blvd. and S. Alameda Street. 2.2.2. Sampling Sampling was completed on eight days during the period of November 2013 to January 2014. Measurements were conducted during either 08:00-12:00 or 13:00-17:00. Emission factors were derived from the measured rise and fall of pollutant concentrations as the locomotive passed the measurement site (Figure 2.1) and are reported per kg fuel consumed. This technique has been used for deriving emission factors from motor vehicles (e.g. Hansen and Rosen, 1990; Ban-Weiss et al., 2009; Hak et al., 2009; Dallmann et al., 2011; Ban-Weiss et al., 2012). 35 Figure 2.1. Measured BC, PN, PM 2.5 , LDSA, and CO 2 concentrations in the exhaust plume of a passing freight locomotive. 36 2.2.3. Meteorology Based on historical weather data in Compton, California, the mean high temperature at the beginning of November 2013 was 22° C (72° F), and at the end of January 2014 was 19.5° C (67° F) (Weather Underground, 2015a). Although measurements were performed in the winter season, the temperatures in Los Angeles remained remarkably moderate. I note that the particle number concentrations measured here may be dependent on ambient temperature since semi-volatile species are more likely to be found in the particle phase in colder conditions (Turpin et al., 1994). 2.2.4. Instruments All instruments (see Appendix 1, Table 1) were portable and battery-powered with internal pumps, allowing for expeditious set-up during each sampling day. The instruments were operated at 1 Hz to allow for characterizing quickly changing pollutant concentrations as the locomotives passed. BC was measured using a MicroAeth (AethLabs, San Francisco, CA, model AE51) and corrected using Eq. 1 for an instrument artifact in which BC concentrations diminish as the filter within the instrument becomes increasingly loaded (Kirchstetter and Novakov, 2007). 𝐵𝐶= !" ! !.!!!"!!.!" (1) Tr represents the filter transmission from the MicroAeth, and BC and BC 0 are the adjusted and raw data respectively (µg m -3 ) (Kirchstetter and Novakov, 2007). As was done in previous studies that measured emissions plumes from diesel trucks (Ban-Weiss et al., 2009; Dallmann et al., 2011; Dallman et al., 2012), I use the manufacturer 37 attenuation coefficient and thus leave out the factor of 0.6 in the denominator of Eq. (2) in Kirchstetter and Novakov (2007). This decision is based on a previous study (Ban- Weiss et al., 2008) comparing BC from an Aethalometer versus thermal-optical analysis of quartz filters for fresh engine emissions. CO 2 was measured using a non-dispersive infrared analyzer (Licor, Lincoln, NE, model LI-840 CO 2 /H 2 O analyzer). A DustTrak Aerosol Monitor Model 8520 (TSI Inc., Shoreview, MN, USA) was used to measure PM 2.5 mass concentrations. The DustTrak monitor, which is a light-scattering laser photometer, was factory-calibrated using the International Organization for Standardization (ISO) 12103-1, A1 dust (Arizona Test Dust). Mass measurements using the DustTrak are subject to error if the physical characteristics (e.g. density and index of refraction) of the sampled aerosol differ from those of the reference aerosol (Gorner et al., 1995). A correction factor was therefore applied to adjust the DustTrak readings to co-located gravimetric filter measurements (see Appendix 1, section 2.5.1 for more details). The mean correction factor, calculated as the ratio of the mass concentrations from the DustTrak versus filters, was 1.09±0.23 (mean ± standard deviation). Particle number and lung-deposited surface area concentrations were measured using a diffusion size classifier (DiSCmini, Matter Aerosol, Switzerland) that was calibrated to measure particles with diameters ranging from 10 to 300 nm. The instrument was preceded by an impactor with a 0.7 µm cutpoint. The DiSCmini (DM) first charges particles using a positive unipolar diffusion charger, which introduces an average charge that is proportional to particle diameter, and then detects particles using two electrometers (Fierz et al., 2011). The first electrometer is connected to a stack of 38 stainless steel screens designed to capture small particles via diffusion. The captured particles generate a current that is measured by the electrometer. Remaining particles are captured downstream by a HEPA filter where the resulting current is measured using the second electrometer. The ratio of the current measured in the filter stage to the diffusion stage is an indicator of the average particle size, and the particle number concentration can be computed (Fierz et al., 2011). LDSA, the particle surface area concentration weighted by the size-dependent deposition probability of particles in the lung, can also be quantified using the DiSCmini. This metric may be more relevant to health impacts than particle number or mass as it relates to particle surface area available in the lung (Oberdörster et al., 2005). Past work has shown that the signal from a diffusion charger is well correlated with LDSA for particles with Dp < 300 nm (Asbach et al., 2009). Since lung deposition fraction in the lower airways is proportional to D 1 for particles with Dp < 300 nm, the DiSCmini can be used directly to estimate LDSA (Fierz et al., 2011). Please note that LDSA reported here is only an estimation of lung-deposited PM and does not include intricacies that can occur due to variations in breathing parameters (e.g. breathing rate and tidal volume) and particle morphology and chemistry (Naneos, 2012), which can affect deposition probability. Past studies (Fierz et al., 2011; Meier et al., 2013; Mills et al., 2013) have evaluated PN and LDSA measured by the DiSCmini by comparing to widely used reference instruments such as the Scanning Mobility Particle Sizer (SMPS) and Condensation Particle Counter (CPC). Fierz et al. (2011) found that near-road measurements of PN using the DiSCmini, CPC, and SMPS, agreed to within 20%. Mills et al. (2013) found that PN measurements of polydispersed laboratory aerosols using the 39 DiSCmini agreed to within 17% and 21% of those using an SMPS and CPC (both with lower size cuts of 10 nm), respectively. Differences between LDSA concentrations measured using the DiSCmini versus computed using SMPS-measured size distributions ranged from 4% to 55% (Mills et al., 2013). Meier et al. (2013) compared near-roadway PN concentrations measured using the DiSCmini versus an SMPS and found correlations of r = 0.98 and differences of up to 30%, which were mostly attributed to discrepancies in the lower cut point of each instrument. Meier et al. concluded that the DiSCmini accurately measures particle number concentrations at traffic-influenced locations. On December 6 and 9 the DiSCmini was inoperable and therefore trains measured on these days have no corresponding PN and LDSA emission factors. See Appendix 2 section 2.5.2 for more details on the DiSCmini measurements. Particle size distributions (5.6<Dp<560 nm) were measured at 1 Hz using a Fast Mobility Particle Sizer Spectrometer (FMPS) (TSI, Shoreview, MN, model 3091). Please note that particles exist outside the size range measured. As always, particle number concentrations need to be taken in the context of the size range measured. In Section 2.3.5, number concentrations measured by the DiSCmini were compared to those from the FMPS for two passing trains. In addition to emissions measurements, information was collected for each locomotive including passing time, identification number, direction of travel, speed, and number of freight cars being pulled. The speed was calculated based on the measured time to travel a known distance of 158.5 m. The results demonstrated here do not report details on the engine age and type for each locomotive or throttle “notch” setting as the locomotives passed the sampling location. 40 2.2.5. Plume Analysis We characterized emissions from a sample (N=88) of passing freight locomotives. The large sample size is important for measuring emissions from a representative in-use fleet, including potentially less common “high-emitter” engines (Ban-Weiss et al., 2009). Freight trains often have multiple locomotives that are connected in series. Because the plumes from these consecutively passing locomotives were not distinguishable, average emission factors for each group of connected locomotives per train were derived. The mean emission factors averaged over all 88 measured locomotives do not depend on this locomotive versus train distinction. However, when assessing individual emission factors, the process is done on a “per train” rather than “per locomotive” basis. BC, PN, PM 2.5 , and LDSA emission factors (E) for individual trains were derived by carbon balance from the 1 Hz plume data by normalizing PM to CO 2 as a surrogate for fuel consumed and were expressed in terms of amount of pollutant emitted per mass of diesel fuel consumed. Figure 2.1 demonstrates the rise and fall of pollutant concentrations for a passing locomotive. E P , the emission factor (g kg -1 fuel consumed) of an individual locomotive for each pollutant P (i.e. BC, PN, PM 2.5 , and LDSA) was calculated as 𝐸 ! = ([!] ! ! ! ! ! ) ! ! ! ! !" ([!" ! ] ! ! !" ! ! ! ) ! ! ! ! !" 𝑤 ! 𝛼 ! (2) where w c = 0.87 is the mass fraction of carbon in diesel fuel, α P is a unit conversion factor applied for each pollutant P, and [P] t is the time varying (1 Hz) concentration of P. For BC and PM 2.5 , [P] has units of µg m -3 and α P = 1, with resulting E BC and E PM25 reported in g kg -1 fuel consumed. For PN and LDSA, [P] has units of # cm -3 and µm 2 cm -3 , respectively, and α P = 10 12 . Resulting units for E PN and E LDSA are # kg -1 and µm 2 kg -1 fuel 41 burned, respectively. All four pollutants P are normalized by time varying (1 Hz) CO 2 mass concentrations [CO 2 ] t with units of mg C m -3 . For each plume, t 1 and t 2 are start- and end-times. For each recorded locomotive passing time, a corresponding plume was identified in the CO 2 data, and t 1 and t 2 were chosen by manual inspection of the plume data (Ban-Weiss et al., 2009). The start-time (t 1 ) corresponded to the inflection point to the left of the concentration peak, while the end-time (t 2 ) corresponded to the inflection point to the right of the concentration peak (see Figure 2.1). Measured values of pollutant concentrations at time t 1 were used as a background subtraction to ensure that measurements were attributed only to passing locomotives. Locomotive passing times were recorded and plumes were deemed successfully captured when the corresponding CO 2 increased by 25 ppm. 2.3. Results and Discussion Emissions were measured for trains under a range of speeds and loads. Train speeds ranged from 24 to 93 km h -1 , with a mean ± standard deviation of 56 ±18 km h -1 (Figure 2.2). The number of freight cars per locomotive ranged from 11 to 86, with a mean for all trains of 34. 42 Figure 2.2. Speed distribution for trains measured in the Alameda Corridor (Los Angeles, CA) in this study. 2.3.1. Black carbon emissions The arithmetic mean (± standard deviation) emission factor for black carbon was 0.9 ± 0.5 g kg -1 . Median, minimum, and maximum values were 0.8, 0.2, and 1.9 g kg -1 , respectively (Table 2.1). The mean presented here is similar to a previous study (Galvis et al., 2013) that reported mean BC emission factors of 1.0 ± 0.06 and 0.8 ± 0.06 g kg -1 for locomotives at two different railyards. Since their emission factors were derived using measured concentrations downwind of railyards, results are representative of both switcher and line-haul locomotives, and may include contributions from non-locomotive sources. Since measurements reported here were in 2013 and 2014 prior to Tier 4 standards planned for 2015, one can assume that none of the locomotives measured have exhaust after-treatment devices such as diesel particle filters. 43 Table 2.1. Mean, median, maximum, and minimum emission factors per kg of fuel consumed for all 88 measured locomotives. Pollutant Mean ± Standard Deviation Median Maximum Minimum Black carbon, BC (g kg -1 ) 0.9 ± 0.5 0.8 1.9 0.2 Particle number, PN (# kg -1 ) (2.1 ± 1.5)x10 16 1.7x10 15 5.6x10 16 2.6x10 15 PM 2.5 (g kg -1 ) 1.6 ± 1.3 1.4 7.2 0.3 Lung-deposited particle surface area, LDSA (µm 2 kg -1 ) (2.2 ±1.7)x10 13 1.7x10 13 7.6x10 13 5.0x10 12 BC emission factors reported here are comparable to those from diesel trucks. Past measurements in a roadway tunnel reported mean (±95% confidence interval) BC emission factors for medium/heavy-duty diesel trucks of 1.4 ± 0.6, 0.92 ± 0.07, and 0.54 ± 0.07 g kg -1 , corresponding to fleet averages in 1997, 2006, and 2010 (Ban-Weiss et al., 2008; Dallmann et al., 2012). Note that the newest of the 2010 fleet of trucks (model year 2007 to 2010) would have had diesel particle filters installed. Another study (Geller et al., 2005) estimated mean (±standard deviation) emission factors of elemental carbon (EC) for the fleet of trucks in 2004 to be 0.78 ± 0.06 g kg -1 . Thus, locomotive emission factors for black carbon reported here are similar to those for the fleet of trucks from about a decade ago (Geller et al., 2005; Ban-Weiss et al., 2008), prior to the deployment of diesel particle filters in new trucks since 2007. The cumulative distribution of BC emission factors from each train is presented in Figure 2.3. Other than the four highest emitting trains, emissions are nearly linearly distributed. This contrasts past work for on-road heavy-duty diesel trucks, which showed 44 that BC emission factors are log-normally distributed (Ban-Weiss et al., 2009; Dallmann et al., 2012). The nearly linear distribution reported here may be a consequence of the limited past regulations for locomotives relative to diesel trucks. Variation in emission factor values are likely dominated by differences in engine age, model, and condition. In addition, variation in the number of freight cars per locomotive may have led to differences in engine load for each train, potentially contributing to the variation in emission factors reported here. While particulate matter emissions from diesel engines are load-dependent, the dependence is minimized by focusing on fuel-based emission factors in this study. Figure 2.3. Cumulative distribution of measured train emission factors for BC, PN, LDSA, and PM 2.5 . The horizontal axis shows the likelihood that a train has an emission factor lower than a given value. BC emissions are slightly skewed, with the highest emitting 10% of trains responsible for 20% of emissions (Figure 2.4). On-road diesel trucks are more skewed with the highest emitting 10% of trucks responsible for 42% and 47% of total emissions 45 in 2006 and 2010 (Ban-Weiss et al., 2009; Dallmann et al., 2012), a likely consequence of increasingly stringent emissions standards for new trucks (Kozawa et al., 2014). The importance of high emitting locomotives is expected to increase in the future as locomotives that meet Tier 4 regulations make up a larger fraction of the fleet. Heavy- duty diesel trucks have faced more stringent emissions regulations than locomotives, and thus older and poorly maintained trucks are responsible for a higher percentage of total emissions in the fleet. Note that the skewness of emissions from individual trains is likely lower than would be reported for individual locomotives; the importance of high-emitting locomotives is underplayed by averaging all connected locomotives per train. Figure 2.4. Cumulative emission factor distributions of particulate matter from individual locomotives, showing the fraction of total PM (vertical axis) corresponding to the highest emitting fraction of trains (indicated by the horizontal axis). For example, 20 to 28% of total emissions of BC, PN, LDSA, and PM 2.5 are emitted from the dirtiest 10% of locomotives. 46 2.3.2. Particle number emissions The arithmetic mean of particle number emission factors calculated using the DiSCmini measurements was (2.1 ± 1.5)x10 16 # kg -1 (Table 2.1). As a comparison, the mean particle number emission factor from 56 trains servicing an Australian shipping port was (1.7 ± 1)x10 16 # kg -1 (Johnson et al., 2013). Thus, the PN emission factors from the two studies are not statistically distinguishable. It should be noted that the lower cut point for the particle number measurements was 10 nm in my study versus 7 nm in Johnson et al. The PN emission factor reported in my study was higher even though my instrument measured over a smaller range of particle sizes. Locomotive emission factors reported here are higher than the arithmetic mean for a fleet of on-road diesel trucks measured in 2006 (4.7 x 10 15 # kg -1 ) (Ban-Weiss et al., 2009). Locomotive emission factors for PN were higher even though the PN emission factor for trucks was measured for Dp > 3 nm (versus Dp > 10 nm for locomotives). Similarly, the locomotive emission factors exceeded arithmetic mean (± standard deviation) values for diesel trucks of (8.2 ± 2.5)x10 15 # kg -1 and (9.3 ± 1.1)x10 15 # kg -1 , determined from measurements (Dp > 7 nm) in a roadway tunnel in 2004 (Geller et al., 2005) and on a roadside in 2004 and 2005 (Ning et al., 2008), respectively. Note that the mean from roadside measurements (Ning et al., 2008) reported here is computed using results presented in their Table 2.1 and Eq. (3). Similar to BC, all but the six highest PN emitting trains show nearly linearly distributed emission factors (Figure 2.3). This contrasts past work for on-road heavy-duty diesel trucks, which showed that PN emission factors are log-normally distributed (Ban- Weiss et al., 2009). PN emissions are slightly skewed with the highest emitting 10% of 47 trains responsible for 24% of emissions (Figure 2.4), which is less skewed than on-road diesel trucks where the highest emitting 10% of trucks were responsible for 41% of total emissions in 2006 (Ban-Weiss et al., 2009). 2.3.3. PM 2.5 emissions The arithmetic mean emission factor for PM 2.5 was 1.6 ± 1.3 g kg -1 , with minimum and maximum values of 0.3 and 7.2 g kg -1 , respectively. As a comparison, Johnson et al. (2013) reported a mean PM 2.5 emission factor of 1.1 ± 0.5 g kg -1 for 56 trains. The mean PM 2.5 emission factor of 1.4 ± 0.3 g kg -1 for a fleet of heavy-duty diesel trucks from 2006 (Ban-Weiss et al., 2008) was similar to trains reported here. Other than the four highest emitting trains, emissions are nearly linearly distributed (Figure 2.3). PM 2.5 emissions are slightly skewed, with the highest emitting 10% of trains responsible for 28% of emissions (Figure 2.4). PM 2.5 was positively correlated (r=0.29) with train speed. Comparisons to other studies using different measurement techniques for PM 2.5 should be interpreted with caution because of previously reported uncertainties in measuring vehicle emissions using the DustTrak (Moosemuller et al., 2001; Dallmann et al., 2012). Uncertainties are reduced by calibrating the measured PM 2.5 to gravimetric measurements of co-located Teflon filters (see Section 2.2.3 and Appendix 1). 2.3.4. LDSA emissions The arithmetic mean emission factor for LDSA was (2.2 ± 1.7)x10 13 µm 2 kg -1 . Other than the three highest emitting trains, emissions are nearly linearly distributed (Figure 2.3), with the highest emitting 10% of trains are responsible for 27% of LDSA 48 emissions (Figure 2.4). LDSA emission factors have never been previously reported in the literature, even though it may be a more relevant metric for assessing health relevant emissions. While this is a first attempt at reporting LDSA emission factors, they should be interpreted with caution since previous comparisons of LDSA derived using a DiSCmini versus SMPS have shown differences ranging from 4 to 55% (see Section 2.2.3). I nonetheless believe that future emissions studies should report particle surface area or LDSA emission factors as a potentially more health-relevant metric for near- source exposures (Oberdörster et al., 2005). 2.3.5. Size-resolved particle number emission factors The time evolution of particle size distributions measured by the FMPS at 1 Hz for a passing train is shown in Figure 2.5. Maximum particle concentrations are observed in the range of 10 < Dp < 50 nm as the train passes under the sample line and as the plume dissipates in the following ~100 seconds. 49 Figure 2.5. Size-resolved particle number emissions measured using a Fast Mobility Particle Sizer Spectrometer (FMPS) for a passing train. Particle size distributions were used to calculate size-resolved PN emission factors for two trains (Figure 2.6). The particle size with the highest emission factor for the two trains was Dp = 26 and 34 nm, respectively. 50 Figure 2.6. Size-resolved particle number emissions measured using a Fast Mobility Particle Sizer Spectrometer (FMPS) for two passing trains. 51 Size-resolved PN emission factors reported here are slightly higher than measured in 2006 for on-road heavy-duty diesel trucks (Ban-Weiss et al., 2010). The particle sizes with maximum emission factors are similar for the two locomotives relative to the measured on-road diesel trucks. Total PN emission factors for the two locomotives measured by the FMPS were computed by integrating the size-resolved (5.6 < Dp < 560 nm) emission factors shown in Figure 2.6. For these two trains, emission factors were 7.5x10 15 and 1.6x10 16 # kg -1 , respectively. Integrated over 10 < Dp < 560 nm to match the lower cut point of the DiSCmini, PN emission factors from the FMPS were 7.3x10 15 and 1.5x10 16 # kg -1 , which is about 6% lower than those measured using the DiSCmini of 7.7x10 15 and 1.9x10 16 # kg -1 , respectively, thereby corroborating the emission factors derived by means of the DiSCmini. It should be noted that neither instrument measures particle number directly. 2.3.6. Black carbon versus particle number emissions BC and PN emission factors are anti-correlated (correlation coefficient r ± standard error = −0.34 ± 0.21), implying that trains with the highest emissions of black carbon generally have the lowest emissions of PN and vice versa (Figure 2.7). This is likely a consequence of the fact that BC particles can act as condensation sites for particulate precursors. Thus, as has been observed in past studies on diesel trucks, BC in the exhaust can inhibit particle nucleation that creates large numbers of small particles (Kittelson et al., 2006; Ban-Weiss et al., 2009). Only one train is in the highest ~25% of both BC and PN emitters (Figure 2.7). 52 Figure 2.7. Black carbon versus particle number emission factors for individual trains depicting anti-correlated with correlation coefficient r (± std. error) = - 0.34 ± 0.21. 2.3.7. Effects of dilution on emission factors For the locomotives measured here, BC comprised 56% of PM 2.5 , indicating that a significant fraction of particulate mass was made up of non-BC species including semi- volatile organics. Measurements of particulate mass and number emissions from sources with significant semi-volatile contributions are dependent on the amount of dilution that has taken place as the aerosol mixes with ambient air before it is sampled. This occurs for two reasons. First, engine exhaust cools as it mixes with ambient air, leading to reductions in saturation vapor pressure and resulting gas-to-particle conversion of semi- volatile species. Once dilution ratios are roughly 100:1 and exhaust is cooled to near ambient conditions (Zhang and Wexler, 2004), further dilution occurs at constant temperature, lowering concentrations of semi-volatile species, and leading to their preferential partitioning to the gas phase to maintain phase equilibrium (e.g. Robinson et 53 al., 2007). In an effort to understand the importance of dilution on the results presented here, the dilution ratios (DR) of sampled exhaust plumes from the locomotives are estimated as DR= !" ! !"#$%&' !!" ! !"#$%&'()* !" ! !"#$%&' !"#$% !!" ! !"#$%&'()* (3) where CO 2,exhaust is the mixing ratio of CO 2 in the exhaust gas, 7.1% or 71000 ppm, estimated based on the chemical balance for the complete combustion of diesel fuel (Ntziachristos et al., 2007). The global background CO 2 mixing ratio CO 2,background was assumed to be 400 ppm. The CO 2 in the sampled plume was CO 2,sampled plume determined based on the peak CO 2 mixing ratios of passing locomotives sampled in the study; the mean ± standard deviation peak CO 2 for all sampled locomotives was 870 ± 430 ppm. The resulting mean DR was found to be 150:1, with a corresponding range of about 80:1 to 1750:1 (based on the standard deviation of measured peak CO 2 mixing ratios). The mean DR is similar to those found for sampling vehicle emissions in roadway tunnels and/or using mobile laboratory units that capture emissions plumes by chasing vehicles (Ntziachristos et al., 2007). The dilution ratios estimated here suggest that the measured exhaust remained relatively undiluted compared to the median dilution ratio of exhaust in an urban atmosphere of 10,000:1. Therefore it should be acknowledged that the PM 2.5 and PN emission factors might be lower if measured at higher dilution levels. BC emission factors are expected to be insensitive to dilution (e.g. Lipskey and Robinson). While emission factors of semi volatile species are sensitive to dilution, prioritization was made 54 to ensure measurements were at low dilution ratios to ensure that the sample was dominated by locomotive exhaust and not contaminated by other sources. 2.3.8. Comparing emissions of BC per hauled container for trains versus trucks In the previous sections I compared fuel-based emission factors from trains to those from heavy-duty diesel trucks. However, since a locomotive is capable of pulling more containers than a single truck-tractor (while also consuming more fuel per distance traveled), it is of interest to develop a metric that characterizes the amount of particulate matter associated with pulling a container for a given distance. In this analysis, I focus on BC emissions and express the metric, the container-specific emission factor (CSEF), as g BC per container-km. “Containers” here are assumed to be six meter (20 foot) standard intermodal freight containers. While intermodal container size can range from six to 16 meters (20 to 53 feet), the six meter version is often used to express the capacity of freight transport using the “twenty-foot equivalent unit”. CSEF is qualitatively similar to reporting emission factors in units of grams per ton-mile. It can be computed from the previously reported fuel-based emission factors (E BC ) as follows, 𝐶𝑆𝐸𝐹 !",!"#$% = (𝐸 !",!"#$% )𝜌𝑈 !"#$% 100 ⋅𝑛 !"# (4a) 𝐶𝑆𝐸𝐹 !",!"#"$"%&'( = (𝐸 !",!"#"$"%&'( )𝜌𝑈 !"#"$"%&'( 100 ⋅𝑛 !"# ⋅𝑛 !"! (4b) where 𝜌 = 0.83 kg L -1 is the density of diesel fuel (Fritz, 2000), U truck (Table 2.2) is the mean fuel consumption (L 100 km -1 ) for trucks obtained from national estimates of total fuel use and distance traveled (DOT, 2013a), U locomotive (Table 2.2) is similarly obtained 55 for locomotives (DOT 2013b), 𝐸 !",!"#$% is derived from on-road measurements from 2010 of over 500 trucks (Dallmann et al., 2012), 𝐸 !",!"#"$"%&'( is from this study, and n CPT , n CPL , and n CPC represent the number of containers per truck-tractor, number of freight cars per locomotive, and number containers per freight car, respectively (Table 2.2). There is some associated uncertainty in estimating values for n. Therefore, I use estimated ranges for n to compute upper- and lower-bound estimates of CSEF depending on assumptions. The number of containers hauled per truck-tractor (n CPT ) is assumed to be one or two, where two six-meter containers are for simplicity equivalent to one 12- meter (40 foot) intermodal container. The number of freight cars pulled per locomotive (n CPL ) was estimated to range from 25 (DOT, 2013b) to 34 (mean value for trains measured in this study). The number of containers per freight car (n CPC ) was assumed to range from one to four (Table 2.2). (Freight cars can generally carry up to four six-meter (20 foot) containers or two 12-meter (40 foot) containers.) Container-specific emission factors of BC for locomotives was found to be 0.03 to 0.17 g BC per container-km. Corresponding values for trucks were 0.09 to 0.19 g BC per container-km (Table 2.2). Assuming a standard configuration of n CPC = 4 (i.e. double- stacking containers on freight cars) and n CPT = 2, CSEF BC is 2-3 times higher for trucks than trains. However, assuming instead n CPC = 2 (e.g. equivalent to single-stacking containers on freight cars) leads to similar values of CSEF BC for trucks versus trains. It should be cautioned that these values should be interpreted as first estimates of CSEF BC for locomotives versus trucks in part because both fuel consumption and E BC can be dependent on the ranges of n presented in Table 2.2. On the other hand, fuel-based emission factors such as E BC are expected to have relatively little load dependence as 56 discussed in Ban-Weiss et al. (2009). Further, properly comparing locomotives versus trucks depends on accurately characterizing the relative differences in fuel consumption rather than absolute values. Nonetheless, particulate matter emissions from diesel trucks are continuing to decrease as more of the on-road fleet adopts diesel particle filters (e.g. Ban-Weiss et al., 2008; Dallmann et al., 2011; Dallmann et al., 2012). This suggests that unless particulate matter emissions from locomotives are decreased in the near future, BC emissions associated with hauling a container for a given distance could become lower for diesel trucks than locomotives. 57 Table 2.2. Parameters used for comparing container-specific emission factors for locomotives versus trucks. Parameter Locomotive Truck Fuel Consumption U (L 100km -1 ) 575 a 40 b Number of containers per truck tractor n CPT N/A 1 to 2 Number of freight cars per locomotive n CPL 25 a to 34 c N/A Number of containers per freight car n CPC 1 to 4 N/A Fuel-based emission factor for BC E BC (g kg -1 ) 0.9 d 0.56 e Container-specific emission factor for BC CSEF (g BC per container-km) 0.03 to 0.17 0.09 to 0.19 a Bureau of Transportation Statistics, U.S. Department of Transportation b Bureau of Transportation Statistics, U.S. Department of Transportation c Measured results of cars per locomotive in the Alameda Corridor d This study e Mean BC emission factor for >500 on-road trucks measured in 2010. Value here is the mean derived using two independent measurement methods (Aethalometer and photoacoustic instruments). 58 2.4. Summary In this study, measurements of a large sample (N=88) of line-haul freight locomotives traveling through the Alameda Corridor, which connects the Ports of Los Angeles and Long Beach to the national rail system, were performed. Measuring a large sample of locomotives allows for characterizing a more representative sample of the in- use fleet compared to previous studies with lower sample sizes. Sample lines were placed directly over railroad tracks allowing for measuring the rise and fall of pollutant concentrations as the locomotives passed. Emission factors (g of pollutant emitted per kg fuel consumed) were computed for black carbon (BC), particle number (PN), fine particulate mass (PM 2.5 ), and lung-deposited particle surface area (LDSA), by normalizing to measured CO 2 emissions. The mean emission factor for BC reported here was found to be similar to a previous study that measured locomotive emissions from two railyards in Atlanta, Georgia (Galvis et al., 2013). It was also similar to mean BC emission factors for fleets of diesel trucks measured about a decade ago, prior to the deployment of diesel particle filters in new trucks since 2007 (Geller et al., 2005; Ban-Weiss et al., 2008). The mean emission factor for PN reported here was statistically indistinguishable from that of 56 trains servicing an Australian shipping port (Johnson et al., 2013). However, it is higher than that of a fleet of on-road diesel trucks measured almost a decade ago in 2006 (Ban-Weiss et al., 2009). The emission factor was higher for locomotives even though the lower size cutpoint for the measurements of diesel trucks was lower. In this study, PN measurements using the diffusion size classifier were corroborated by comparing to results obtained using an FMPS, and found agreement to within 6%. 59 The mean emission factor for PM 2.5 reported here was statistically indistinguishable from that of the 56 trains in Australia reported in Johnson et al. (2013), and from previous measurements of diesel trucks from 2006 (Ban-Weiss et al., 2008). In this study, I reported LDSA emission factors for the first time to my knowledge and suggested it as a potentially important health-relevant metric for near-source exposures (Oberdörster et al., 2005). I suggest that future studies on engine emissions should consider reporting particle surface area or LDSA emission factors. Emission factors for individual trains were slightly skewed with the dirtiest 10% of locomotives responsible for 20-28% of total emissions, depending on pollutant. This is less skewed than indicated by previous measurements of diesel trucks from 2006 and 2010 (Ban-Weiss et al., 2009; Dallmann et al., 2012), a likely consequence of the less stringent PM emissions regulations for locomotives versus diesel trucks. BC and PN emission factors were found to be anti-correlated indicating that the highest emitters of BC were among the lowest emitters of PN, consistent with previous measurements of diesel trucks (Ban-Weiss et al., 2009). BC comprised 56% of PM 2.5 , suggesting that an appreciable fraction of PM was from non-BC species including semi-volatile organics. I estimated that the mean dilution ratio of sampled exhaust was 150:1, indicating that the average measured plume had cooled to near-ambient conditions, but remained relatively undiluted compared to typical dilution ratios of urban atmospheres (10,000:1). Thus, as dilution beyond ~100:1 is thought to cause preferential partitioning of semi-volatile species to the gas phase, PN and PM 2.5 emission factors may have been lower had they been measured at higher dilution ratios. 60 2.5. Appendix 1 2.5.1. Details on the correction factor for the DustTrak A DustTrak Aerosol Monitor Model 8520 (TSI Inc., Shoreview, MN, USA) was used to measure PM 2.5 mass concentration. The DustTrak monitor, which is a light- scattering laser photometer, was factory-calibrated using the International Organization for Standardization (ISO) 12103-1, A1 Test Dust. However, mass measurements using the DustTrak may be subject to error if the physical characteristics (e.g. density and index of refraction) of the sampled aerosol differ from those of the reference aerosol (Gӧrner et al., 1995). A correction factor was therefore applied to adjust the DustTrak readings to actual concentrations. A Sioutas Personal Cascade Impactor Sampler (Sioutas PCIS, SKC Inc., Eighty Four, PA, USA) (Misra et al., 2002; Singh et al., 2003), operating at 9 lpm and side-by-side with the DustTrak, was used for this purpose. Both samplers were operating continuously over each sampling period. The PCIS was loaded with 37 mm Teflon filters (Pall Life Sciences, Ann Arbor, MI) for PM 2.5 collection. Mass concentration of the fine PM samples was determined by pre- and post-weighing the filters using a Microbalance (Mettler Toledo Inc., Columbus, OH, USA), following equilibration under controlled temperature and relative humidity (RH) conditions (22– 24 °C and RH = 40–50%). For a given sampling period, the DustTrak data, which was logged at 1 s intervals, was averaged over the entire sampling period for comparison with the co-located gravimetric filter measurement. The DustTrak correction factor was then calculated as the ratio of the mass concentration from the DustTrak to that from the filter. The mean correction factor was 1.09±0.23 (arithmetic mean ± standard deviation) across sampling periods. Noteworthy is that the correction factor derived in this study is similar 61 to that reported for PM 2.5 in the Los Angeles ground-level rail system (Kam et al., 2011) and the Barcelona metro system (Querol et al., 2012). 2.5.2. Details on measurements with the DiSCmini Due to high particle number concentrations, the sampled aerosol was diluted with particle-free air at a dilution ratio of 7. The diluted aerosol stream was then split into two fractions, with one fraction drawn into the DiSCmini at 1 lpm, and the remaining sample drawn into a separate line at 6 lpm. Even with this dilution, some measurements exceeded the maximum recommended concentration of the DiSCmini. However, all technical parameters (e.g. corona voltage, temperature, diffusion, and filter stage currents) were stable and within the recommended range, indicating that the instrument was operating under optimal measurement conditions. 62 Appendix 1. Table 1. Summary of instruments used in the study. Pollutant Measurement Method Averaging Time Carbon dioxide (CO 2 ) CO 2 gas analyzer (Licor, Lincoln, NE, model LI-840 CO 2 /H 2 O analyzer) 1-s Black carbon (BC) MicroAeth (AethLabs, San Francisco, Ca, model AE51) 1-s Fine particulate matter (PM 2.5 ) DustTrak (TSI, Shorview, MN, model 8520) 1-s Particle number concentration (PN) and lung-deposited surface area (LDSA) DISCmini (Matter- Engineering, Switzerland, Discmini) 1-s Size-resolved particle number concentration Fast Mobility Particle Sizer Spectrometer (TSI, Shoreview, MN, model 3091) 1-s 63 Chapter 3 - Real-world measurements of the impact of atmospheric aging on physical and optical properties of ambient black carbon particles This chapter is based on the following publication: Krasowsky, T. S., Wang, D., McMeeking, G., Sioutas, C., and Ban-Weiss, G. A.: Real-world measurements of the impact of atmospheric aging on physical and optical properties of ambient black carbon particles, Atmospheric Environment, 142, 496-504, 2016. 64 3.1. Introduction In this study, measurements were performed of ambient refractory black carbon during winter in Rubidoux, California, a region in which carbonaceous PM has been advected from the western portions of the Los Angeles basin. I investigated physical and optical properties of ambient and thermally-denuded refractory black carbon. Number fraction of particles that were thickly-coated (f), MAC, and MAC E were subsequently computed. I qualitatively investigated diurnal variations for weekday versus weekend differences during the campaign. Comparing weekdays to weekends allows for sampling black carbon with distinctly different photochemical ages (see next section), given the substantially lower rBC emissions and concentrations of other gaseous and particulate species on the weekend. As previously described in section 1.1, the work presented here and the following section contrasts with the previous study that characterized locomotive black carbon emissions through the use of a handheld MicroAeth, a portable aethelometer capable of recording filter-based measurements of BC in real-time (De Nazelle et al., 2012; Krasowsky et al., 2015). The following studies demonstrate the use of an SP2 to measure refractory BC. As such, the following sections will refer to BC as “rBC” or “rBC- containing” when discussing black carbon measured using the SP2 to provide consistency with previous work (e.g., Laborde et al., 2013). 65 3.2. Materials and Methods 3.2.1. Location Ambient rBC was measured at the South Coast Air Quality Management District’s (SCAQMD) Rubidoux air monitoring site, a location approximately 90 km (55 miles) east and typically downwind of the downtown Los Angeles area. rBC at this site represents a mix of particles: (a) rBC-containing particles emitted from the western Los Angeles basin that have been advected and aged, a process that presumably increases the number of rBC particles that have become internally mixed with other particulate species, and (b) relatively fresh rBC that has been emitted by nearby sources. 3.2.2. Sampling and Instruments Sampling was completed between February 3, 2015 and March 13, 2015. However, due to instrument complications and days with precipitation, which were removed from the analysis, 20 days from this time period were available for analysis (Fig. 3.1). See table 3.1 for an expression of which instruments were operational for the days in the analysis. 66 Figure 3.1. Hourly ambient rBC mass concentration (µg m -3 ), b absorption (Mm -1 ), and the number fraction of thickly-coated particles measured during the campaign. Table 3.1. Dates expressing which instruments were operational. Date Single-Particle Soot Photometer Photoacoustic Extinctiometer 2/3/15 X 2/4/15 X 2/5/15 X 2/6/15 X X 2/10/15 X X 2/12/15 X 2/13/15 X X 2/14/15 X X 2/26/15 X X 2/27/15 X X 3/3/15 X X 3/4/15 X X 3/5/15 X X 3/6/15 X X 3/7/15 X X 3/8/15 X X 3/9/15 X X 3/10/15 X X 3/11/15 X 3/12/15 X 0.8 0.6 0.4 0.2 0.0 2/26/15 3/1/15 3/4/15 3/7/15 3/10/15 5.0 4.0 3.0 2.0 1.0 0.0 0.25 0.20 0.15 0.10 0.05 0.00 2/8/15 2/11/15 2/14/15 67 Measurements were made throughout the entire day to capture diurnal variation in conditions that impact aging of rBC, including temperature dependent phase partitioning of semi-volatile species, and SOA formation. This is important because large variations in photochemical activity, concentrations of semi-volatile species, and SOA formation impact coating thickness on rBC particles, species apportionment, and optical properties of rBC directly. Ambient air was sampled into two parallel streams (Fig. 3.2a and b)). In the first stream, the sampled air was unaltered and therefore representative of real-world ambient black carbon. In the second stream, coatings on black carbon were vaporized by heating the sampled air to 230°C (450°F) for about 13 seconds in a thermodenuder (TD). This study’s TD was built to similar specifications described in a study by Huffman et al. (2008) where particle losses were experimentally determined to be comparable to the losses detailed in Wehner et al. (2002). The TD was heated by two 4-ft. 312 Watt, 120 VAC Extreme-Temperature Heat Cables (McMaster-Carr, Santa Fe Springs, CA) that were wrapped around a fabricated stainless steel tube measuring 61 cm (24 inches) in length with a 2.2 cm (0.875 in.) inner diameter and 2.54 cm (1 inch) outer diameter. Rigid High-Temperature Fiberglass Pipe Insulation 3.81 cm (1.5 inches) thick with a 2.54 cm (1 inch) inner diameter (McMaster-Carr, Santa Fe Springs, CA) was wrapped around the tube and heat cables to reduce heat conduction outside of the TD. A type-K thermocouple (Omega Engineering, Inc., Stamford, CT) housed inside the TD was used in conjunction with a 5A variable AC power transformer 0-130 VAC (Parts Express, Springboro, OH), and a temperature controller (Model CN701, Omega Engineering, Stamford CN), to maintain an approximate temperature of 230°C (450°F) inside the TD 68 (Appendix 2 – Figure 1). Immediately downstream of the TD, a diffusion dryer (DD) (Model 3062,TSI Inc., Shoreview, MN) adsorbed the resulting gas-phase species that could have otherwise condensed back onto particle surfaces downstream of the heater. Black carbon cores remained unperturbed because their vaporization temperature is approximately 4000K (Bond et al., 2013). Coated versus uncoated black carbon was compared by alternately sampling from the two parallel streams every 15 minutes using automated valves (Model SS-62XTS4-41ACX, Swagelok, Torrance, CA). Each stream entered a manifold designed to send isokinetically-sampled air to each particle-measuring instrument. a.) b.) Figure 3.2. A depiction of the sampling configuration for the (a) heated cycle measurement and (b) ambient cycle measurement. Physical properties, including black carbon mass, size, and coating thickness, of individual black carbon particles were measured in real-time using an SP2 (Droplet Measurement Technologies, Boulder, CO) (Figure 3.2a and b)). The SP2 is widely used to measure rBC-containing particles and has been described at length in past work (e.g. Gao et al., 2007; Moteki and Kondo 2007). In short, using laser-induced incandescence combined with a light-scattering measurement, the SP2 measures the mass and mixing THERMODENUDER* DIFFUSION*DRYER* MANIFOLD* SP2* PAX* INLET* HEATED*AIR* AMBIENT*AIR* >* >* >* >* >* >* >* >* > >* >* 12# Exhaust* >* >* PUMP* 3=way*valve* 3=way*valve* THERMODENUDER* DIFFUSION*DRYER* MANIFOLD* SP2* PAX* INLET* HEATED*AIR* AMBIENT*AIR* >* >* >* >* >* >* >* > >* >* 12# PUMP* 37way*valve* 37way*valve* >* >* >* Exhaust* 69 state of individual refractory black carbon particles in real time. An Nd:YAG laser (λ=1.064 mm) heats individual black carbon particles to the point that coatings vaporize and refractory black carbon begins to incandesce. Two photomultiplier tubes measure incandescence, one measuring narrowband light (λ ~600-700 nm) and the other measuring broadband light (λ=400-700 nm). For the analysis, the lower threshold limits were set to 70 nm and 170 nm for the volume equivalent diameters of incandescence and scattering signals, respectively. However, for determining coating thickness using the lag-time method, the lower incandescence threshold was restricted to 170 nm for direct comparison to the scattering signal. See section 2.2.4 for more information on the coating thickness analysis. A standard Aquadag calibration sent from the manufacturer was used to create linear proportionality between incandescence signal peaks and the associated black carbon mass. Scattered light in the SP2 is detected by two avalanche photodiodes and is detected in the size range of 170 nm to 750 nm based on the polystyrene latex sphere calibration. Light absorption and scattering coefficients at 870 nm were measured using a Photoacoustic Extinctiometer (PAX) (Droplet Measurement Technologies, Boulder, CO) (Nakayama et al., 2015). The PAX splits sampled air into two measurement cells where reciprocal nephelometry is used to determine the light scattering coefficient, and the photoacoustic technique is used to determine the light absorption coefficient. In the photoacoustic cell, incident light from a diode laser is modulated at the resonance frequency of the acoustic cavity and any particles and gases that absorb at the wavelength of the light (870 nm) heat the surrounding air, creating a pressure wave that is measured using a microphone and converted to a light absorption signal. The PAX subtracts 15- 70 minute average background absorption and scattering coefficients measured for filtered ambient air to provide coefficients for the aerosol component only. Large variations in the background absorption and scattering within 15 minutes can cause small artifacts in the aerosol measurements if they are large relative to the aerosol signal, so only the first 150 seconds of data in each cycle before the actual background changed significantly was used. The PAX recorded data at 1-second resolution, but was averaged over the first 150 seconds of each 15-minute cycle. The manufacturer’s calibration was used, which is based on an extinction measurement made at high concentrations using measured laser power and Beer’s Law for a purely scattering and partially absorbing aerosol. See Nakayama et al. (2015) for details regarding the calibration and uncertainties associated with the technique. The above flow network in conjunction with outlined instruments was used to quantify MAC E , a real-world scaling factor that expresses the absorption enhancement at 870 nm from coatings that acquire on rBC particles. MAC E is determined from comparing unperturbed ambient (coated) rBC to thermally-denuded (uncoated) rBC (Figure 3.2a and b) and is quantified in subsequent text. 3.2.3. Meteorology Based on historical weather data measured at the Riverside Municipal Airport (KRAL), approximately 11 km (7 miles) from the sampling location in Rubidoux, the average daily maximum temperature for February 2015 was 24.4°C (76°F), while the average daily minimum was 8.9°C (48°F). The mean 24-hour average temperature was 16.7°C (62°F). For March 2015, average daily max, min, and mean temperatures were 27.8°C (82°F), 11.1°C (52°F) and 19.4°C (67°F) (Weather Underground, 2015b). 71 Observed temperatures were consistent with Southern California’s typical temperate winter weather patterns. Wind speeds were moderate and varied in the range of 0-5 m s -1 during the late winter months of February and March 2015, and were undetectably calm (<0.5 m s -1 ) for most of the campaign (CARB, 2015). Daher et al. (2013) found similar results for their winter campaign in Riverside (adjacent to Rubidoux). Winds were predominately westerly and northerly when detectable, consistent with the dominant on-shore flow patterns in this region. Past work has also shown that Rubidoux is typically subject to aged particles advected from the Los Angeles area (Sardar et al., 2005; Daher et al., 2013; Hasheminassab et al., 2014). 3.2.4. Analysis of Coating Thickness We used the lag-time method to analyze black carbon coating thickness (Moteki and Kondo, 2007; McMeeking et al., 2011; Wang et al., 2014). As a rBC-containing particle enters the YAG laser beam in the SP2, initial detection is usually from scattering (or coating) material on the rBC-containing particle depending on the orientation of the scattering material on the particle with respect to the laser in the cavity (Sedlacek et al., 2012). As the scattering material begins to vaporize within the YAG laser a signal is produced proportional to the volume equivalent diameter of the coating and buried absorbing material. As coatings vaporize off, refractive BC material is exposed to the laser. The rBC core begins to incandesce producing a signal proportional to the mass of the given particle. Typically, there is a time between signal peaks for scattering and incandescence. The time between the peaks of the responses to determine the lag-time as a metric for distinguishing between thinly and thickly-coated rBC particles was used. 72 When analyzing lag-times for each rBC-containing particle measured, two distinct time delay modes became apparent, representing thinly and thickly-coated rBC particles. The cutoff for thinly versus thickly-coated rBC particles was chosen to be 3 µs based on the bimodal distribution of lag times (see Appendix 2 – Figure 2). A similar cutoff was used by Wang et al. (2014). It should be noted that a small fraction of the rBC-containing particles in the study produce negative lag-times. These lag-times may be a result of coatings on rBC particle that do not fully engulf the bulk mass of the rBC core (Sedlacek et al., 2012). Depending on the orientation of the particle in relation to the laser and detectors, an incandescence signal from rBC exposure to the laser may be produced before the typically preceding scattering signal. For these unique cases, a latent scattering signal is produced when coating material becomes exposed to the laser after the rBC core mass has begun to incandesce. Because of the small fraction of the particles that fall into this category, the results reported here may slightly under-estimate the fraction of particles with thick coatings or agglomerations of scattering material (Table 3.1). The lag-time approach has been implemented in past studies (e.g. Wang et al., 2014). For the lag-time analysis, it was crucial that the lower threshold of black carbon detection is restricted to a 170 nm mass equivalent diameters. Because the lowest scattering size accepted is also 170 nm volume equivalent diameters it can be certain that a particle of 170 nm total volume equivalent diameter would have no scattering material present. 73 3.3. Results and Discussion 3.3.1. Black Carbon Time Series Analysis The mean mass concentration (± standard deviation) for ambient black carbon over the entire campaign was 0.12 ± 0.08 µg m -3 . rBC mass concentrations on weekdays appeared to be higher when compared to weekends (Figure 3.3 and Table 3.2). This is presumably due to higher weekday diesel truck emissions relative to weekends (Marr and Harley, 2002). Further, as fewer diesel trucks transport goods on the weekends, the relative proportion of light-duty vehicle (LDV) traffic increases during the weekend period. Black carbon mass concentrations exhibit a peak in the early morning hours on weekdays (Figure 3.3) when commuter traffic increases and the atmospheric mixing height is low. Black carbon mass concentrations are generally increased during nighttime hours relative to daytime hours on both weekdays and weekends (Figure 3.3). (We refer to “daytime” as 6:00 to 18:00 and “nighttime” as 18:00 to 6:00.) Though emissions are expected to be higher during the day than night, vastly lower atmospheric mixing heights at night relative to the day lead to higher observed concentrations. In addition, rBC mass concentrations are relatively low in the afternoon despite commuter traffic since the atmospheric mixing height is high relative to the early morning hours (Figure 3.3). 74 Figure 3.3. Mean diurnal cycle of ambient rBC mass concentration (µg m -3 ) averaged over the entire measurement campaign and shown separately for weekdays and weekends. Error bars are 95% confidence representing day-to-day variability in hourly averages. Values shown are for ambient rBC only (i.e. not heated). Table 3.2. Mean ± standard deviation for ambient rBC mass concentration and number fraction of particles that are thickly-coated (f). Mean and 95% confidence interval for mass absorption cross-section enhancement (MAC E ) throughout the campaign. Standard deviations represent variability in daily averages over the campaign. rBC mass concentration (µg m -3 ) f MAC E 0.12 ± 0.08 0.052 ± 0.020 1.03, 95% CI= [0.98, 1.08] 3.3.2. Number Fraction of Black Carbon with Thick Coatings The number fraction of rBC particles that were thickly-coated (f) was evaluated throughout the campaign, and was found to be relatively low with a mean (± standard deviation) of 0.052 ±0.020 (Table 1). The hourly mean diurnal cycle of f for weekdays and weekends during the campaign is presented in Figure 3.4. 75 Figure 3.4. Same as Figure 3.3 but for number fraction of thickly-coated particles (f). The f generally peaks during the afternoon for weekdays and weekends, which is when rBC mass concentrations are lowest (Figure 3.3) and photochemical activity is presumed to be at its highest level. The fraction of thickly-coated particles appears to be slightly higher on the weekend during the day than during weekdays for similar time periods (Figure 3.4). The analysis lacked the detailed chemical composition data and coating information to fully explore reasons for the differences in day versus night and weekday versus weekend f. However, I present a few hypotheses here. First, elevated weekend versus weekday f is likely due in part to the relative decrease during the weekend in fresh diesel truck emissions along with a higher relative proportion of light- duty vehicles (Marr and Harley, 2002). The relative decrease of freshly emitted rBC on weekends implies that the average measured rBC-containing particle is expected to have aged longer in the atmosphere, with a corresponding increase in coating material. In 76 addition, changes in the vehicle fleet, i.e. fewer diesel truck emissions on weekends, will lead to changes in atmospheric photochemistry. Previous studies in the LA basin have shown that photochemical production of ambient ozone, which is indicative of photochemical production of secondary organic aerosol, exhibits higher concentrations on weekends than on weekdays. This is due mainly to a larger decrease in NO x emissions from motor vehicles than in non-methane volatile organic compounds (NMVOCs) emissions on weekends, leading to more rapid ozone production and less ozone destruction by NO x titration (Turpin and Huntzicker, 1991; Turpin et al., 1994; Pollack et al., 2012; Warneke et al., 2013; Heo et al., 2015). Thus, increased production of SOA during weekend daytimes versus weekdays could lead to consequent increases in SOA- coated rBC leading to increased f during that time period. 3.3.3. Mass Absorption Cross-Section Enhancement from Coatings on Black Carbon Particles Comparing MAC for ambient versus thermally-denuded rBC suggests a weak mean enhancement factor of 1.03 with a 95% confidence interval of [0.98, 1.08]. This overall MAC E factor is comparable with the value of 1.06 found in Cappa et al. (2012). I believe that enhancement is low due primarily to the lack of coatings present on rBC particles measured in this analysis (section 3.2). The results reported here are consistent with what would be expected for wintertime measurements when photochemistry is less important than during the summer months. Furthermore, instrument complications on some weekends with PAX data (a large component of developing enhancement factors) have prevented us from robustly comparing MAC E on weekends to the observed differences reported for values of f (section 3.2). I found no discernible trends in MAC E at various values for PCA (Appendix 2 – Figure 3). Diurnal cycles of f show periods of 77 elevated coatings, which does not appear to be reflected in the overall average of MAC E . It is possible that coating thickness may not fully describe changes in optical properties of rBC during aging. 3.3.4. Particle Age Analysis Collocated NO x and NO y measurements were made available by the SCAQMD through the Environmental Protection Agency Speciation Trends Network. The photochemical age (PCA) of the sampled air was subsequently computed in Eq. 1 following Cappa et al. (2012) as 𝑃𝐶𝐴= ln [!" ! ] ! [!" ! ] ! /𝑘 !"# [𝑂𝐻]≈ ln [!" ! ] ! [!" ! ] /𝑘 !"# [𝑂𝐻] (1) where PCA represents the time rBC has been in the atmosphere undergoing aging processes. For this estimation, it is assumed that NO x is the source of all NO y and by the same method as Cappa et al. (2012), [NO x ] 0 =[NO y ]. The reaction rate, k rxn was assumed to be 7.9x10 -12 cm 3 molecules -1 s -1 and [OH] was assumed to be 4x10 6 molecules cm -3 (Cappa et al., 2012). PCA was found to be low in the campaign, typically less than 3 hours (Figure 5). The PCA times presented here are expected with wintertime measurements when photochemistry is low. Cappa et al. (2012), who sampled in two locations in California: one in the Sacramento area, and the other off the west coast of Los Angeles over the Pacific Ocean experienced much longer photochemical age times as high 20 hours due to the seclusion of the off shore site and increased photolytic activity in the summer months. 78 Figure 3.5. Number fraction of thickly-coated particles (f) versus photochemical age (PCA) computed using Eq. 1 for the entire campaign. Boxes depict the 25 th and 75 th percentiles, the band in the box is the median, and the whiskers depict the 10 th and 90 th percentiles. Number fraction of thickly-coated particles versus photochemical age computed using Eq. 1 for the entire campaign is presented in Figure 4. The results indicated that f and PCA are well correlated. Even for low PCA times presented here, results show substantial increases in coating thickness beyond PCA times of 2 hours. Although I lacked information on coating type, past work has shown that there may be interesting distinctions in MAC E from coatings associated with SOA formation and possibly some aqueous phase SOA production at night (Venkatachari et al., 2005; Lim et al., 2010; Ervens et al., 2011; Hersey et al., 2011), which could be interesting to assess in future studies. 79 3.4. Summary In this study, I performed wintertime measurements of ambient black carbon in Rubidoux, California, 90 km (55 miles) east of downtown Los Angeles, California. The objective was to assess light absorption enhancement from coatings on rBC particles acquired through atmospheric aging. Mean (± standard deviation) black carbon mass concentrations observed through the campaign were of 0.12 ± 0.08 µg m -3 . rBC mass concentrations were higher during the weekdays when traffic emissions were expected to be higher. The number fraction of total particles that were thickly-coated (f) was evaluated throughout the campaign. The diurnal cycle in f was found to peak in the afternoon, which is when BC concentrations were lowest, and when photochemical production of secondary organic aerosols is expected to be at a maximum. The highest f values appear to be on weekends during the daytime. This may be a consequence of increased SOA production during the day on weekends, and consequently, enhanced SOA condensation and coating of primary rBC particles as they age during transport from the western Los Angeles basin to the sampling site. We assessed enhancement in mass absorption cross-section (MAC E ) from coatings on black carbon by comparing MAC for ambient versus thermally-denuded rBC. Results suggested weak MAC E of 1.03 with a 95% confidence interval of [0.98, 1.08]. These results are similar to an important study (Cappa et al., 2012). I suspect that greater enhancements may have occurred during the weekend daytime hours when f appeared to peak. However, due to a series of instrumental and weather complications the analysis lacked enough data to assess this comparison at a statistically significant level. I 80 investigated f as a function of photochemical age (PCA) and found values f and PCA to be well correlated with substantial increases in f for PCA times greater than 2 hours. 81 3.5. Appendix 2 Appendix 2 - Figure 1. A shematic depicting the thermodenuder used during the sampling campaign. Two$4&'.$312$Wa.$120$VAC$Extreme&Temperature$ Heat$Cables$(McMaster&Carr,$Santa$Fe$Springs,$CA)$ Type&K$thermocouple$(Omega$Engineering,$Inc.,$Stamford,$CT)$ Fabricated$Steel$Rectangular$Prism$Enclosure$ Length=55$cm$Width=10.2$cm$Height=10.2$cm$ Inlet$ Fabricated$Steel$Tube$$ Length=61$cm,$Outer$Diameter=2.5$cm,$Inner$Diameter=2.2$cm$ 1$cm$thick$MACOR$cap&machined$glass$ceramic$ Rigid$High&Temperature$Fiberglass$Pipe$InsulaXon$ (McMaster&Carr,$Santa$Fe$Springs,$CA)$ Thickness=3.8$cm,$Inner$Diameter=2.5$cm$ 82 Appendix 2 - Figure 2. Delay times between scattering and incandescence “peak height” signals for individual particles measured in a sample 3.5-hour time period during the campaign. 10 9 8 7 6 5 4 3 2 1 0 -1 -2 50x10 3 40 30 20 10 83 Appendix 2 - Figure 3. Mass absorption cross-section enhancement (MAC E ) versus photochemical age time (PCA) computed using Eq. 1 for the entire campaign. Boxes depict the 25 th and 75 th percentiles, the band in the box is the median, and the whiskers depict the 10 th and 90 th percentiles. 84 Chapter 4 - Characterizing the evolution of physical properties and mixing state of black carbon particles: from near a major highway to the broader urban plume in Los Angeles ! 85 4.1. Introduction In this study, I systematically compare the evolution of physical properties and mixing state for rBC-containing particles at two distinct spatiotemporal scales: rapid timescales during road-to-ambient processing near a major highway, and longer timescales after urban emissions have aged during transport to a measurement site downwind of urban Los Angeles. The evolution of physical properties and mixing state at rapid timescales is investigated by making measurements of rBC-containing particles from 30 to 114 m from a major highway on the west side of Los Angeles near the Pacific Ocean. This location was chosen to minimize the contribution of the broader urban plume on measurements. Longer time scales are investigated here by measuring rBC-containing particles in Redlands, CA, roughly 100 km downwind (assuming prevailing westerly winds) of downtown Los Angeles. Mass and number concentrations of rBC-containing particles, rBC size distributions, the fraction of rBC-containing particles that are thickly- coated (i.e. using the lag-time method), and coating thickness histograms (i.e. using the LEO method) are reported. Besides comparing the morphology of rBC-containing particles at these two aging timescales, the results reported here investigate the influence of meteorology and vehicle fleet (i.e. weekdays versus weekends) on the mixing state of rBC-containing particles at the Redlands site. A detailed procedure for how I performed the LEO method is also described. 86 4.2. Materials and methods 4.2.1. Sampling locations Two measurement campaigns were completed in 2016 during the hottest season in southern California. Ambient rBC-containing particles were measured in two distinct environments: the first campaign was conducted near a major highway in Los Angeles, California (i.e., Interstate 405), while the second campaign was conducted ~100 km east and generally downwind of downtown Los Angeles (i.e., Redlands, California) in an area where rBC is presumably more aged relative to locations closer to downtown. 4.2.1.1. Near-road campaign The near-road campaign was carried out at the Los Angeles National Cemetery, which is adjacent to Interstate 405. This site is on the west side of the Los Angeles basin, ~7km (~4 miles) from the Pacific Ocean, and upwind (assuming the dominant westerly on-shore flow) of most of the basin including downtown. A previous study by Zhu et al. (2002) demonstrated that the winds at this site are generally westerly and perpendicular to Interstate 405. See section 2.4.1 for a summary of observed meteorology during the campaign for this study. The Los Angeles National Cemetery is therefore an ideal location for investigating the evolution of rBC mixing state from road to ambient environments (Zhang et al., 2004). Adjacent to the cemetery, Interstate 405 runs along a 330-degree path or virtually north/south (Zhu et al., 2002). The western (eastern) edge of the Los Angeles National Cemetery is 30 m (730 m) from Interstate 405 (Zhu et al., 2002). Measurements were recorded in increments of about 8 m (25 ft.) ranging from 30 m (100 ft.) to 114 m (375 ft.) from the highway for each day using a mobile platform. As described in Zhu et al. (2002), there was not a true “0 m” measurement location given (a) 87 the difficulty of approaching the highway with the mobile platform, and (b) the width of the highway itself (i.e. even if I could have sampled at the edge of the highway, I would have been measuring a mix of particles emitted from the nearest lane to the farthest lane). 4.2.1.2. Redlands campaign Measurements were made at the South Coast Air Quality Management District’s Redlands Site (500 N. Dearborn St. Redlands, CA 92374). The location is approximately 100 km east of downtown Los Angeles in a neighborhood ~1.5 km (~1 mile) from a major highway (Interstate 10). Therefore, aerosols measured at this location are dominated by a mix of sources: (a) vehicular emissions from the nearby highway, and (b) aerosols advected from the greater Los Angeles basin when winds are westerly, or presumably more aged aerosols from the east when winds are easterly. Instruments were housed in an air-conditioned trailer kept at roughly 24°C (75°F) throughout the campaign. 4.2.2. Sampling time periods The near-road measurement campaign was completed on four days from 12:00−14:00 local time. Morning and afternoon rush hour on Interstate 405 causes traffic to slow and even halt. Therefore, I chose a time period between these rush hour episodes when traffic flow was uncongested and speeds were steady at roughly 105−120 kph (65−75 mph) (estimated, not measured). Because the goal of this campaign was to assess rBC mixing state with respect to distance from the highway, short sampling time periods of 5 minutes (per distance from the highway) were used to reduce the influence of other confounding factors such as changes in traffic flow, wind speed and direction, atmospheric stability, and solar irradiance, that would shift following a typical diurnal cycle. Sampling dates were August 4, August 5, September 12, and September 14, 2016. 88 The Redlands measurement campaign was completed during the late summer from September 16−October 10, 2016 using stationary instrumentation with measurements recorded 24 hours per day to capture diurnal changes in rBC-containing particles. 4.2.3. Instrumentation During both sampling campaigns, an SP2 (Droplet Measurement Technologies, Boulder, CO) was used to quantify the physical characteristics rBC-containing particles. Briefly, the SP2 measures physical properties of rBC-containing particles by focusing a flow of sample air across a high-intensity intra-cavity Nd:YAG laser (λ = 1064 nm). As an individual rBC-containing particle traverses the cross-section of the laser beam, the temperature of the particle increases to the point that any coatings on the rBC vaporize and rBC core incandesces. The SP2 is capable of detecting rBC-containing particles to a lower detection limit of approximately 0.5 fg (Gao et al., 2007; Moteki and Kondo, 2007; Dahlkötter et al., 2014; Krasowsky et al., 2016). A MicroAeth (MA) model AE51 (Aeth Labs, San Francisco, CA) was positioned at a fixed location ~35 m from Interstate 405 to ensure the black carbon mass concentration remained consistent (±20%) throughout each 2-hour measurement period. The MA is a handheld aethelometer capable of measuring black carbon mass concentrations in real time. A study completed by De Nazelle et al. (2012) shows good agreement for measurements from the MA when compared to traditional filter-based black carbon measurements. For the near-road measurement campaign, a standard gasoline powered vehicle was used to house and transport instrumentation, and measurements were taken when the 89 engine was turned off. The SP2 was powered by a 12-volt deep cycle battery along with a DC to AC power inverter. On each day of the near-road measurement campaign (before and after each day’s sampling period), and at the beginning and end of the Redlands measurement campaign, an Aerosol Generator AG-100 (Droplet Measurement Technologies, Boulder, CO) was used to suspend 269 nm polystyrene-latex spheres (PSLs) (Thermo Scientific, formerly Duke Scientific) in particle free air. The purely scattering PSLs were measured by the SP2 and used in the LEO analysis to aid in understanding its performance and to verify the position of optical components after transit to a new location. Sampling a known size of purely scattering particles can provide detailed information on where the notch in the split detector occurs as described in Gao et al. (2007). Also see Laborde et al. (2013) for more information. The LEO analysis is further described in section 2.6. 4.2.4. Meteorology 4.2.4.1. Meteorology near-road Temperatures for the near-road measurement campaign were remarkably moderate with maximum daily temperatures of 25.6°C (78°F), 23.9°C (75°), 21.7°C (71°F), and 21.7°C (71°F) for August 4, August 5, September 12 and September 14, respectively (Weather Underground, 2016a). Winds were westerly, causing pollutants to advect across the cemetery perpendicular to Interstate 405. Similar wind patterns were reported at the same location in Zhu et al. (2002). 4.2.4.2. Redlands Temperatures for the Redlands measurement campaign ranged from 10.6−40.6°C (51−105°F) (Weather Underground, 2016b). For most days, winds were westerly during 90 the day, with speeds increasing in the afternoon as is typical for the sea breeze in this region. For a few sampling days winds were variable in both speed and direction. 4.2.5. Methodology for estimating the number fraction of thickly-coated particles (f) As previously mentioned, the mixing state of rBC can be identified using the lag- time method to bin rBC-containing particles in two categories: thinly and thickly-coated. It should be noted that thinly-coated rBC includes particles with no detectable coatings. The lag-time method has been used in numerous studies and takes advantage of the connection between coating thickness and time delay between measured pseudo- Gaussian scattering and incandescence signal peaks for a given rBC-containing particle (Moteki and Kondo, 2007; McMeeking et al., 2011; Metcalf et al., 2012; Wang et al., 2014; Krasowsky et al., 2016). Generally, both incandescence and scattering signals will increase as an rBC-containing particle begins to traverse the cross-section of the SP2 laser beam. However, thickly-coated rBC-containing particles will have a discernable peak in scattering as the coating vaporizes prior to the measured peak in incandescence, which occurs when the particle reaches the center of the laser beam. This method assumes that rBC-containing particles have a core-shell morphology, and that coatings of differing species evaporate at the same rate (Metcalf et al., 2012). A small fraction of rBC-containing particles have an incandescence signal that precedes the scattering signal due to non-core-shell structure (Sedlacek et al., 2012). Measurements of ambient air usually show a bimodal distribution of lag-times where the cluster of longer (shorter) lag- times corresponds to rBC-containing particles with thicker (thinner) coatings. To stratify rBC-containing particles as thinly or thickly-coated, the user selects a fixed lag-time cutoff based on this measured bimodal distribution, and particles with lag-times greater 91 than the set cutoff are binned as thickly-coated. For my study, I chose a time cutoff (1 µs) based on the near-road site and applied this to both sampling locations for consistency. After classifying each measured particle as thinly or thickly-coated, I computed the number fraction of rBC-containing particles that are thickly-coated (f) as the ratio of particles with lag-times greater than 1 µs to the number of all rBC-containing particles. Note, an important limitation of reporting f is that the metric gives insight as to whether or not thick coatings are present without attempting to quantify the coating thickness. 4.2.6. Leading-edge-only fit methodology for quantifying coating thickness on rBC- containing particles Rather than stratifying particles as thinly or thickly-coated, numerous studies (see Introduction) have employed the leading edge only (LEO) fit method to quantify coating thickness on rBC particles. In the same way that you briefly describe the theory behind lag time, it’d be good to do this briefly here. In this study, I use the Paul Scherrer Institute’s Single-Particle Soot Photometer Toolkit (PSI-TK) version 4.100a (originally developed by Martin Gysel with the help of Marie Laborde and others) in IGOR v. 6.36 to perform the LEO method. Please see the Appendix 3 for a detailed description of the implementation of the PSI-TK method to quantify coating thickness. For the LEO analysis, I report coating thickness for rBC-containing particles that have rBC core diameters ranging from 240 – 280 nm. 4.2.7. Estimation of Photochemical Age Photochemical age PCA was assessed using colocated NOx and NOy measurements supplied by the SCAQMD from their Rubidoux site (500 N. Dearborn St. Redlands, CA 92374), which is approximately 30 km southwest (i.e. upwind, assuming 92 typical afternoon westerly sea breezes) of Redlands. This estimate of PCA was computed using the same method described in Cappa et al. (2012) and Krasowsky et al. (2016) where NOx is assumed to be the source of all NOy and HNO 3 is the dominant loss product of NOx. This metric is intended to give a relative estimate of the sample age by ranking measurements from least to most aged. I assume that PCA derived from measurements at Rubidoux are fairly representative of values for Redlands. Because the analysis lacked the required NOx and NOy measurements at the Redlands location to perform a direct analysis of PCA at the measurement site, I also used hourly ozone mixing ratios supplied by the SCAQMD for Redlands to get a sense of photochemical air pollutant production per day. 4.2.8. Weekday and Weekend Analyses An analysis of differences in physical properties of rBC-containing particles for weekdays versus weekends is presented in this study for measurements at the Redlands site. I define weekdays as Tuesdays through Thursdays and weekends as Sundays. This avoids confounding weekday/weekend analysis by aerosols with lifetime greater than one day in the LA basin. For example, measurements made on Mondays likely include a contribution of aerosols that were emitted within the basin on Sunday. 4.3. Results and discussion 4.3.1. Near-road campaign 4.3.1.1. rBC mass and number concentrations and number fraction of thickly-coated particles at different distances from the highway Figure 4.1 shows rBC mass concentration, rBC number concentration, and f versus distance from the highway. Markers and error bars shown here represent the mean 93 ± 95% confidence intervals for measurements made during the four sampling days in August and September. Figure 4.1. rBC mass concentration (µg m -3 ), rBC number concentration (cm -3 ), and fraction of black carbon that is thickly-coated (f) versus downwind distance from Interstate 405 in Los Angeles, California. While results are not monotonic as distance from the highway increases, likely due to turbulent eddies and flow irregularities near the highway, there is an overall decrease in rBC mass concentrations as distance increases, as was similarly reported in Zhu et al. (2002). In addition, increases in distance are associated with overall decreases in rBC number concentrations and increases in f. The observed trend in rBC mass and number concentrations likely occur because of vehicle emissions from the highway being transported away from the source and entraining background air with lower rBC concentrations. Regarding f, three possible explanations for the observed trend are proposed. The first is analogous to the driver of rBC mass and number concentration decreases; as distance from the highway increases, the plume dilutes and entrains “background” air that would likely include a greater fraction of thickly-coated aged 1.00 0.75 0.50 0.25 0.00 375 350 325 300 275 250 225 200 175 150 125 100 550 500 450 400 350 300 250 200 150 0.060 0.055 0.050 0.045 0.040 0.035 0.030 0.025 0.020 110 100 90 80 70 60 50 40 30 94 particles. Because f is a relative measurement that bins particles as either thinly or thickly-coated, and values of f are small near the highway, entraining aged rBC into the highway plume could have large relative impacts on the fraction of particles that are thickly-coated. A second possible explanation is that rBC may be acquiring coatings as it is transported away from the highway, likely dominated by condensation of condensable vapors onto rBC, with a lower relative contribution of coagulation of externally mixed particles. Zhang et al (2004) found that condensation led to particle growth as distance from the highway increased at the same site. A third possible explanation is that as rBC mass and number concentrations decrease as distance from the highway increases, the availability of condensation sites (i.e. rBC particles) may decrease relative to condensable vapor concentrations, would could lead to increased coating thicknesses. This is somewhat analogous to the aerosol “indirect effect” in which cloud droplet size can increase when there are a reduced numbers of cloud condensation nuclei (IPCC, 2007). Zhu et al (2002) investigated ultrafine particles at the same site over a decade ago. As a part of their campaign, they measured BC mass concentrations using a dual-beam aethalometer (Model AE-20, Andersen Model RTAA-900, Andersen Instruments, Inc.) at 30, 60, 90, 150, and 300 m downwind and upwind of Interstate 405. I compare here this study’s SP2 measurements of refractory black carbon made at 30, 61, and 91 m downwind of the highway to their measurements made 30, 60, and 90 m downwind of the highway. Relative to values at 30 m, my study suggests that rBC mass concentrations at 61 and 91 m decrease by 54% and 58%, respectively. Corresponding values for Zhu et al. are 41% and 54% (Table 4.1). Values from my measurements were about an order of magnitude lower than the 2001 measurements. I suggest that this decrease is primarily the 95 result of stringent and effective policy implementation aimed at curbing carbonaceous aerosol emissions from vehicles as has shown to be the case at locations across the Los Angeles basin (e.g. Hasheminassab et al., 2014). It is important to note that differences may partially be attributed to variations in measurement technique. 96 Table 4.1. Mean rBC mass concentration for the Redlands measurement campaign and mean rBC (and black carbon) mass concentrations corresponding to three measured distances downwind of Interstate 405 in the Los Angeles National Cemetery in 2016 (and 2001). Study Location Time period BC Mass Concentration (µg m -3 ) This Study a Redlands Campaign September 16-October 10, 2016 0.14 ± 0.10 hourly mean ± st. dev. 30 m downwind of Interstate 405 August 4, 2016 August 5, 2016 September 12, 2016 September 14, 2016 0.67 61 m downwind of Interstate 405 August 4, 2016 August 5, 2016 September 12, 2016 September 14, 2016 0.31 91 m downwind of Interstate 405 August 4, 2016 August 5, 2016 September 12, 2016 September 14, 2016 0.28 Zhu et al. 2002 b 30 m downwind of Interstate 405 May 15 to July 18, 2001 5.4 (3.4-10.0) 60 m downwind of Interstate 405 May 15 to July 18, 2001 3.2 (3.0-3.5) 90 m downwind of Interstate 405 May 15 to July 18, 2001 2.5 (2.4-2.6) a Measurements of refractory black carbon were made using a Single-Particle Soot Photometer (Droplet Measurement Technologies, Inc.) b Measurements of black carbon were made using a dual-beam aethalometer (Model AE- 20, Andersen Model RTAA-900, Andersen Instruments Inc.) 97 Figure 4.2a and 4.2b show f versus rBC mass and number concentrations with color coding highlighting the dependence of these variables on distance from the highway. Each point represents 10 second averages. The highest values of f (~0.2) are associated with the lowest values of rBC mass (~0.1 µg m -3 ) and number concentrations (~100 cm -3 ). Similarly, the lowest values of f (~0.01) are associated with the highest values of rBC mass (~0.3 – 1.1 µg m -3 ) and number concentrations (~200 – 1000 cm -3 ). rBC mass concentration and f are anti-correlated with r = -0.21; similarly, rBC number concentration and f are anti-correlated with r = -0.25. There appears to be a denser population of thickly-coated rBC at distances greater than roughly 60 m from the highway. Given that rBC mass concentrations can be considered a conservative tracer, Figure 4.2a shows systematic increases in f as emissions from motor vehicles become increasingly diluted away from the highway. Though rBC number concentrations are theoretically not conserved due to possible coagulation of rBC-containing particles, Figure 4.2b nonetheless shows systematic increases in f as rBC number concentrations decrease. 98 Figure 4.2. Fraction of rBC that is thickly-coated (f ) versus (a) rBC mass concentration (µg m -3 ) and (b) rBC number concentration (cm -3 ). Colors depict varying downwind distances from Interstate 405 in Los Angeles, California. The black lines represents a 7 th degree polynomials of best fit. 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 1600 1400 1200 1000 800 600 400 200 0 99 4.3.1.2. rBC mass and number size distributions at different distances from the highway Refractory black carbon mass and number size distributions were computed for three distances from the highway averaged over the 4 sampling days (Figure 4.3a and b). I report size distributions for rBC-containing particles with mass equivalent diameters (MED) ranging from 70 to 450 nm, similar to previous studies (Gao et al., 2007; Moteki and Kondo, 2007; Dahlkötter et al., 2014; Krasowsky et al., 2016). rBC mass and number concentration decreased at all core diameters measured as distance from the highway increased. Concentrations at most sizes were substantially greater nearest the highway (i.e. 31 m) relative to other distances. Diameter of rBC corresponding to peaks in the mass size distribution are ~180 nm MED for 30 m from the highway, higher than values for 61 and 114m of ~80 and 100 nm, respectively. Figure 4.3. (a) Mass and (b) number size distributions of rBC cores versus rBC core diameter. Size distributions were measured at 30 m (100 ft.), 61 m (200 ft.), and 114 m (375 ft.) downwind of Interstate 405 in Los Angeles, California. 10 -2 2 4 10 -1 2 4 10 0 2 4 10 1 10 1 2 4 6 10 2 2 4 6 10 3 10 0 10 1 10 2 10 3 10 4 10 1 2 4 10 2 2 4 10 3 100 4.3.1.3. Quantifying coating thickness for rBC near the highway using LEO-fit In this section, I investigate coating thickness on rBC-containing particles near the highway. Median coating thickness using the LEO method was determined for all measured distances from the highway over one of the sampling days (August 4). The histogram of coating thickness for each measured rBC-containing particle is shown in Figure 4.4. The median coating thickness was -1 nm or approximately 0 nm (Figure 4.4). While some particles with coating thickness up to ~240nm were measured, the majority of rBC contained coating thicknesses ranging from -40 to 40 nm. This implies that while there are some rBC particles with thick coatings, as can also be observed from the reported values of f (Figure 4.1), the majority of particles have little to no coatings. Note, past studies have reported a range of uncertainties associated with the LEO method. One study reported a coating thickness uncertainty of ±20 nm due to noise, assumptions of particle refractive indices, and non-core-shell structure of rBC-containing particles; however, another study suggested that uncertainty decreases as coating thickness increases (Metcalf et al., 2012). Even though results from this study show coating thickness ranging from -40 to 40 nm near the highway, I suggest this is within acceptable uncertainty due to the overall lack of thick coatings at the measurement location to reduce uncertainty. 101 Figure 4.4. Histogram depicting the frequency of occurrence of specific coating thicknesses on rBC-containing particles as estimated with the Leading-Edge-Only (LEO). 4.3.2. Redlands Campaign 4.3.2.1. Campaign overview Figure 4.5 shows an overview from September 16 to October 10 of hourly average results for the Redlands measurement campaign, including rBC mass concentration, f, ozone mixing ratio, and an estimate of PCA. The overall rBC mass concentration (mean ± standard deviation) was 0.14 ± 0.097 µg m -3 . rBC mass concentrations reach values up to about 0.6 µg m -3 , not including what appears to be an outlier on October 9 at 03:00, which could have been due to a strong nearby source. Values of f vary by day and reach values up to about 0.2. Values of PCA show strong diurnal variation, as expected, with daily peaks generally occurring in the early afternoon and ranging in value up to a maximum of about 7 hours. Diurnal cycles for PCA are similar in shape to those for O 3 , providing confidence that PCA derived from 24x10 -3 20 16 12 8 4 0 240 200 160 120 80 40 0 -40 102 measurements in Rubidoux, California, can be used to reasonably approximate the photochemical age of air in nearby Redlands, California. Figure 4.5. Mean hourly fraction of rBC that is thickly-coated (f), rBC mass concentration (µg m -3 ), and ozone mixing ratio (ppb) measured in Redlands, California from September 16 to October 10, 2016. Photochemical age (PCA) was computed concurrently using the ratio of NOx to NOy for air masses in Rubidoux, California, a location roughly 30 km (20 miles) to the southwest. 4.3.2.2. Diurnal cycles of rBC mass concentrations and number fraction of thickly- coated particles Campaign average diurnal cycles of rBC mass concentrations and number fraction of thickly-coated particles are shown separately for weekdays and weekends in Figure 4.6a and b. On weekdays, the highest mass concentrations occur between 7:00−9:00 when commuter traffic peaks and the atmospheric mixing height is low. There is a secondary peak in the early evening when commuter traffic increases and mixing heights start decreasing (relative to mid-day). rBC concentrations on weekends show less hour- to-hour variation during daytime than weekdays, as expected, due to more consistent traffic flows. rBC concentrations are higher at night than during the day due to low nocturnal mixing heights. Note that the peak at 03:00 for weekends stems from the 9/16 9/17 9/18 9/19 9/20 9/21 9/22 9/23 9/24 9/25 9/26 9/27 9/28 9/29 9/30 10/1 10/2 10/3 10/4 10/5 10/6 10/7 10/8 10/9 10/10 1.0 0.8 0.6 0.4 0.2 0.0 0.25 0.20 0.15 0.10 0.05 0.00 8 6 4 2 0 120 100 80 60 40 20 0 103 previously discussed outlier on October 9. At the 95% confidence level there were not statistically distinguishable differences between weekdays and weekends at most times of day. (Note that error bars are larger for weekends than weekdays due to the reduced weekend days sampled.) However, rBC mass concentrations were systematically higher for weekdays than weekends for all hours of the day besides the early morning hours. This is likely due to higher diesel truck activity on weekdays versus weekends (Marr and Harley 2002; Lough et al., 2006). Note that the average diurnal cycle for weekdays in this study shows less variability than my prior study (Krasowsky et al., 2016) reporting measurements in Rubidoux during winter. This is likely because (a) Rubidoux is closer to downtown Los Angeles where a large fraction of the emissions from the LA basin occur, and (b) atmospheric mixing heights during winter are generally lower than during summer. 104 Figure 4.6. Mean diurnal cycle of ambient (a) rBC mass concentration (mg m 3 ) and (b) number fraction of particles that are thickly-coated (f) averaged over the entire measurement campaign and shown separately for weekdays and weekends. Error bars are 95% confidence intervals using the Student t-distribution and computed using day- to-day variability in each hourly average. Values for weekend at 23:00 are removed because data for only day was available. Hour 3:00 for rBC weekend has a 95% confidence of 0.347 with mean=0.28547 with an interval of [-0.06153, 0.63247]. 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 0.20 0.15 0.10 0.05 0.00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 105 Campaign average diurnal cycles for f are shown in Figure 4.6b. On weekdays, values of f are relatively consistent throughout the day. However, values of f for weekends show a discernable peak at 14:00 when PCA and O 3 (Figure 4.7) values are at (or near) their diurnal peak. f is systematically higher on weekends than weekdays, though differences are generally not statistically significant. Previous studies have shown that coatings on rBC in urban plumes can be sensitive to differences in the vehicle fleet for weekdays versus weekends (Metcalf et al., 2012; Krasowsky et al., 2016). Traffic is overall lower on weekends than weekdays, but relative decreases in diesel truck traffic are larger than for light-duty vehicles. Thus, reductions in NO x are larger than those for non-methane volatile organic compounds (NMVOC), which due to nonlinearities in ozone chemistry can lead to higher ozone concentrations on weekends relative to weekdays. During the campaign, the weekday and weekend mean (± 95% confidence interval) O 3 mixing ratio at 15:00 was 60.4 ± 13.6 ppb and 68.9 ± 16.5 ppb, respectively (see Figure 4.7). Since ozone can be used as a surrogate for secondary organic aerosol (Turpin and Huntzicker, 1991;Turpin et al., 1994; Bahreini et al., 2012; Pollack et al., 2012; Warneke et al., 2013; Heo et al., 2015), I expect that SOA concentrations would also be higher on measured weekends than weekdays. Thus, weekends are expected to have higher SOA and lower rBC concentrations relative to weekdays, leading to a higher fraction of rBC particles that are thickly-coated, as shown in Figure 4.6b. 106 Figure 4.7. Mean diurnal cycles of the ozone mixing ratio (ppb) measured in Redlands, California. Weekdays represent Tuesday−Thursdays, and weekends represent Sundays as measured during the campaign from September 16−October 10, 2016. The weekday mean (± 95% confidence interval) was 36.3 ± 2.50 ppb, while the weekend mean (± 95% confidence interval) was 47.1 ± 3.20 ppb. 4.3.2.3. Number fraction of thickly-coated particles versus photochemical age Figure 4.8 shows the number fraction of rBC-containing particles that are thickly- coated versus photochemical age, using hourly average values between the hours of 13:00−16:00. Boxes depict the 25 th and 75 th percentiles, whiskers depict the 10 th and 90 th percentiles, and the horizontal lines within the boxes show the median. Only afternoon values were included to highlight coatings that likely result from photochemistry. As the photochemical age of the measured air increases, so does the fraction of particles that are thickly-coated. For PCA < 2 hours, the median, 25 th , and 75 th percentiles for f are about 0.04, 0.02, and 0.05, respectively. For PCA values ranging from 6 to 8 hours, corresponding values for f are about 0.06, 0.04, and 0.14. Krasowsky et al. (2016) performed a similar analysis comparing f versus PCA, but results were for wintertime measurements in Rubidoux and included f for all hours of the day. The previous study 100 75 50 25 0 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 107 also used a greater lag-time cutoff of 3 µs. Both studies show increases in f as PCA increases, though the previous study shows slightly higher median values for f at the highest PCA bin even though the lag-time cutoff is higher. 90 th percentiles of f for the highest PCA bin are quite similar for both studies. Nonetheless, the two studies are not directly comparable given the (a) difference in lag-time cutoffs used to define thickly- coated rBC particles, and (b) the difference in time of day analyzed. Including the entire daily cycle as in Krasowsky et al. (2016) could confound results by including times when coatings develop via processes other than photochemistry. Note, NOx and NOy data supplied by SCAQMD used to estimate PCA was hourly, and thus boxes and whiskers summarize hourly values. Figure 4.8. Number fraction of rBC particles that are thickly-coated versus photochemical age for the hours of 13:00 to 16:00 Boxes depict the 25 th and 75 th percentiles, whiskers depict the 10 th and 90 th percentile, and the horizontal lines within the boxes show the median. 4.3.2.4. rBC mass concentration versus number fraction of thickly-coated particles Figure 4.9 shows rBC mass concentration versus number fraction of rBC particles that are thickly-coated. Unlike Figure 4.8, which summarizes hourly averages, Figure 4.9 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 108 shows one-minute values. This higher temporal resolution allows for observing short periods with much higher values of f (i.e. > 0.6) relative to hourly averages. Median values of rBC mass concentrations are found to decrease as f increases. For f ranging from 0 to 0.1, rBC mass concentrations are 0.21 µg m -3 , while f > 0.6 is associated with much lower rBC concentrations of 0.04 µg m -3 . Overall, rBC mass concentration and f are anti-correlated with correlation coefficient r = -0.087. As sampled air becomes more aged, rBC concentrations in general are expected to decrease primarily due to dilution, while f would be expected to increase. In addition, the weekend-effect discussed in the previous section is represented in this figure; weekend values show lower rBC mass concentrations and higher f (Figure 4.7). Figure 4.9. Refractory black carbon mass concentration (µg m -3 ) versus the number fraction of rBC particles that are thickly-coated versus for measurements made in Redlands, California from September 16−October 10, 2016. Data here represents 1- min temporal resolution. Boxes depict the 25 th and 75 th percentiles, whiskers depict the 10 th and 90 th percentiles, and the horizontal lines within the boxes show the median. rBC mass concentration and f are anti-correlated with correlation coefficient r = -0.087. 0.50 0.40 0.30 0.20 0.10 0.00 109 4.3.2.5. rBC size distributions and mixing state analysis using LEO-fit for different days We conducted more in-depth analysis of the physical properties and mixing state of rBC-containing particles for four afternoons (15:00-16:00) for the Redlands campaign. Days were chosen to sample over a variety of meteorological conditions (e.g. wind speed, wind direction, ambient temperature), as well as both weekdays and weekends. For the four afternoons chosen, the National Oceanic and Atmospheric Administration’s HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess, 1998) was employed to assess 12-hour back trajectories starting at 15:00 on each day (Figure 4.10). Properties quantified include coating thickness distributions using LEO-fit and rBC number and mass particle size distributions. Figure 4.10. 12-hour back trajectories from 15:00 local time, 22:00−23:00 UTC, depicted from the HYSPLIT model (NOAA) during four unique meteorological regimes in Redlands, California. The first afternoon examined was Sunday, September 18 (Figure 4.11, top row). During 15:00-16:00 winds were westerly at 2.7 m s -1 , and the daily maximum temperature was quite high (40.6°C). Back trajectories suggest that sampled air included contributions from both nearby regions to the west of Redlands, and “low desert” regions 10/07/2016 09/25/2016 09/18/2016 500 N. DEARBORN ST. REDLANDS, CA 92374 09/28/2016 110 to the east including the city of Palm Desert. The median coating thickness was 14 nm, and a few relatively high coating thickness bins (e.g. centered at 130 nm) show local peaks. rBC mass concentrations on this day were typical of campaign-average weekend values at this time of day (see Figure 4.6a). The rBC mass size distribution shows a peak at core size of about 135 nm MED, while the number size distribution indicates that its peak was at particle sizes smaller than the lower limit of detection (70 nm). 111 a.) b.) c.) d.) e.) f.) g.) h.) 24x10 -3 20 16 12 8 4 0 240 200 160 120 80 40 0 -40 10 2 4 100 2 4 1000 0.1 1 10 100 1000 0.001 0.01 0.1 1 10 24x10 -3 20 16 12 8 4 0 240 200 160 120 80 40 0 -40 10 2 4 100 2 4 1000 0.1 1 10 100 1000 0.001 0.01 0.1 1 10 24x10 -3 20 16 12 8 4 0 240 200 160 120 80 40 0 -40 10 2 4 100 2 4 1000 0.1 1 10 100 1000 0.001 0.01 0.1 1 10 24x10 -3 20 16 12 8 4 0 240 200 160 120 80 40 0 -40 10 2 4 100 2 4 1000 0.1 1 10 100 1000 0.001 0.01 0.1 1 10 112 Figure 11. Investigation of the physical properties of rBC-containing particles through Leading-Edge-Only (LEO) analysis and characterization of size dependence on rBC mass and number concentrations for a mix of meteorological regimes during the Redlands, California measurement campaign. (a and b) depict (a) LEO histogram of coating thickness (nm) and (b) mass/number size distribution of rBC cores, dN/dlogDp MED , (µg m -3 ) for air masses measured on Sunday, September 18, 2016. (c and d) depict (c) LEO histogram of coating thickness (nm) and (d) mass/number size distribution of rBC cores, dN/dlogDp MED , (µg m -3 ) for air masses measured on Sunday, September 25, 2016. (e and f) depict (e) LEO histogram of coating thickness (nm) and (f) mass/number size distribution of rBC cores, dN/dlogDp MED , (µg m -3 ) for air masses measured on Sunday, September 28, 2016. (g and h) depict (g) LEO histogram of coating thickness (nm) and (h) mass/number size distribution of rBC cores, dN/dlogDp MED , (µg m -3 ) for air masses measured on Sunday, October 7, 2016. We examined another Sunday, September 25 (Figure 4.11, second row), with noticeably different atmospheric flow patterns (Figure 10). During 15:00-16:00, winds were from the south at 1.8 m s -1 . Back trajectories indicate that sampled air largely originated from the desert regions to the Northeast as far away as Las Vegas, NV. On this day, the median coating thickness was 24 nm. Compared to Sunday, September 18, several coating thickness bins above 80 nm show higher frequencies of occurrence. rBC mass concentration was very low (0.02 ± 0.01 µg m -3 ), below the campaign-average weekend value shown in Figure 4.6a. Particle size distributions indicate that all sizes of rBC were at lower concentrations relative to September 18, though the rBC core size associated with the peak in mass size distribution was similar on both days. Differences in computed back trajectories, relatively lower rBC mass concentrations, and relatively higher amounts of rBC with thick coatings, all suggest that a higher fraction of measured rBC came from farther away sources and was increasingly aged compared to September 18. Lower ambient temperatures on September 25 may have also played a role by favoring partitioning of semi-volatile species to the particle phase, though I have no way of determining the relative contribution of this effect. 113 The remaining two days under investigation were weekdays. Wednesday September 28 (Figure 4.11, third row) had slower and more irregular winds and cooler temperatures relative to the Sundays already discussed. Back trajectories indicate that sampled air originated from relatively close to Redlands compared to the other days (Figure 4.10), which is consistent with the observed low wind speeds. The median coating thickness was 8 nm, lower than the Sundays already discussed. The rBC mass concentration was 0.04 ± 0.01 µg m -3 , higher than Sunday, September 25 but lower than Sunday, September 18. Number size distributions for this day are quite similar to those for September 18; however, mass size distributions on September 28 show lower concentrations at most sizes relative to September 18, and the size of rBC associated with the peak mass concentration is shifted toward smaller mass equivalent diameter (i.e. 94 nm). Given the differences in back trajectories, coating thickness histograms, size distributions, and the fact that this was a weekday with higher black carbon emission rates in the LA basin than for weekends, I conclude these measurements were dominated by rBC that was emitted by nearby sources and did not have sufficient time to acquire thick coatings. The last day of interest was Friday, October 7 (Figure 4.11, bottom row), with winds from the northwest at 1.3 m s -1 from 15:00-16:00 and the same daily maximum temperature as the other weekday under investigation here. Back trajectories indicate that sampled air largely came from the “high desert” region of southern California including the Barstow area (Figure 4.10). Interestingly, of the four days discussed in this section, rBC sampled on this afternoon had the smallest median coating thickness (3 nm), and the least discernable peaks for coating thickness > 80 nm. rBC mass concentrations were 114 higher than other days investigated in this section, presumably due to both higher weekday emissions in the LA basin and distinct atmospheric flow patterns for this day. I cannot isolate individual factors that contributed to the low coating thicknesses measured on this afternoon. However, I suggest that important contributors include the fact that (a) this was a weekday, and (b) back trajectories indicate that sampled air came from regions with low source emissions rates, suggesting that measured rBC was dominated by relatively fresh emissions from nearby sources. 4.3.3. Comparison of near-road and Redlands campaigns We have evaluated the physical properties of rBC at (a) different distances from a major highway on the west side of Los Angeles, and (b) the east side of the Los Angeles basin where secondary pollutant (e.g. ozone) concentrations are among the highest observed in the basin (Hersey et al., 2011). rBC mass concentrations at 30 m from the highway were about a factor of 3.0 higher than those measured 114 m from the highway. rBC mass concentrations 114 m from the highway were quite similar to campaign average values for midday on weekdays in Redlands. Particle number size distributions for rBC indicate that the smallest measured sizes (~70-100 nm mass equivalent diameter), which dominate total rBC number concentrations, decreased by a factor of about 2.7 from 30 m to 114 m from the highway. Interestingly, rBC number concentrations for the smallest measured diameters showed values that were about an order of magnitude higher at 114 m from the highway relative to those measured in Redlands (i.e. compare Figure 4.3a to 4.11). Thus, rBC number concentrations were significantly higher at all near-highway distances measured relative 115 to Redlands, while rBC mass concentrations were quite similar at Redlands versus 114 m from the highway at the near-road site. Assessing the number fraction of rBC that was thickly-coated indicates that while f increased as the measurement location moved from 30 m to 114 m from the highway, values observed 114 m from the highway were roughly similar to the median for Redlands at PCA values of 6 to 8 hours (Figure 4.1 vs 4.8). This suggests that the residence time of air in the Los Angeles basin under typical conditions measured during this campaign may not be long enough for rBC to acquire thick coatings. On the other hand, as indicated by the boxes and whiskers in Figure 4.8, there were hourly time periods during the sampling campaign where f reached nearly 0.20 in Redlands, and shorter time periods where f reached values greater than 0.6 (Figure 4.9). Assessing coating thickness histograms suggests that most measured rBC was uncoated or thinly-coated near the highway. Measurements in Redlands showed relatively more rBC with coating thickness > 80 nm under certain atmospheric conditions. This occurred especially when atmospheric flows favored rBC being transported from the east on weekends, causing rBC emissions from nearby traffic sources to be lower and the relative contribution of more remote sources to be higher. 116 4.4. Summary Improving understanding in spatiotemporal distributions of refractory black carbon (rBC), as well as evolution of rBC physical properties and mixing state at both (a) rapid timescales near sources (e.g. road-to-ambient processing), and (b) longer timescales as pollutants are transported on urban, continental, and global scales, is critical for reducing uncertainty on the impacts of aerosols on human health and regional and global climate. This study carries out measurements of ambient particles containing refractory black carbon in two distinct locations during the hottest months in southern California to systematically evaluate differences in rBC physical properties and mixing state near a highway and downwind of urban Los Angeles. The results reported here attempt to highlight the influence of road-to-ambient processing, varying meteorological regimes, and changing vehicle fleets, on the physical properties and mixing state of rBC. Two techniques for quantifying coatings on rBC particles (i.e. the lag-time and LEO method) were employed using measurements made with a single-particle soot photometer (SP2). Sampling for the first location was completed near Interstate 405 at the Los Angeles National Cemetery between 12:00−14:00 local time on August 4, August 5, September 12, and September 14, 2016. Measurements were made in ~8 m increments from 30 to 114 m downwind of the highway using a mobile platform. As distance from the highway increased, rBC mass concentrations decreased. A previous study (Zhu et al., 2002) that measured BC with an aethalometer in 2001 at the same site reported similar trends with respect to distance from the freeway, though concentrations reported here are about an order of magnitude lower than the values reported in the previous study. This highlights the efficacy of stringent policies for reducing black carbon emissions in California. rBC number concentrations decreased, while the number fraction of thickly-coated rBC 117 particles (f) showed an overall increase, as distance from the highway increased from 30 to 114 m. rBC mass concentrations were overall anti-correlated with f at this measurement site, suggesting that the fraction of thickly-coated rBC-containing particles increased as the plume from the highway diluted. On August 4, 2016, the LEO method was used to quantify coating thickness histograms; the median coating thickness for rBC- containing particles at all distances measured was about 0 nm. While f indicated that a small fraction of rBC-containing particles (i.e. 5%) acquired coatings as downwind distances approached ~100 m away from the highway, most particles were essentially uncoated or thinly-coated. Sampling for the second location was completed in Redlands, California, approximately 100 km east of downtown Los Angeles, from September 16−October 10, 2016. The overall rBC mass concentration (± standard deviation) was 0.14 ± 0.097 µg m - 3 . Campaign-average diurnal cycles of rBC mass concentration and f were analyzed separately for weekdays and weekends. During daytime, hourly values of rBC mass concentrations were larger on weekdays than weekends, though differences were not statistically distinguishable at the 95% confidence level. There was less hour-to-hour variation in rBC mass concentrations on weekends relative to weekdays presumably due to more consistent traffic flows throughout the day. Values of f were systematically higher on weekends than weekdays, with the peak value occurring at 14:00 when photochemistry is prevalent. I suggest that the higher weekend values in f are analogous to the ozone weekend effect, but in this case apply to higher secondary organic aerosol loadings that condense onto reduced available rBC, leading to more thickly-coated refractory black carbon particles. Previous research by Metcalf et al. (2012) and 118 Krasowsky et al. (2016) corroborate this theory. I also investigated f as a function of photochemical age (PCA) for the hours of 13:00−16:00 and found that f increased as PCA increased. Similar to the near-road site, f was anti-correlated with rBC mass concentrations. An examination on how various meteorological regimes impact the physical properties and mixing state of rBC-containing particles in Redlands was completed for four afternoons (two weekdays and two weekend days at 15:00-16:00) using 12-hour back trajectories computed with the HYSPLIT model. I found that the afternoon with the most prevalent mode of thickly-coated rBC corresponded to a Sunday with back trajectories indicating that measurements were dominated by air originating from the desert regions to the northeast of the Los Angeles basin. Relatively lower weekend emissions from diesel truck traffic in the Los Angeles basin and transport of air from the northeast suggests that measured rBC contained a larger contribution of aged particles emitted from remote locations than the other days under investigation. 119 4.5. Appendix 3 For the analysis on ambient air, I loaded one of every “n=5” particles into Igor Pro. The SP2 can accurately size scattering particles down to ~170 nm VED (Krasowsky et al., 2016). However, for the LEO analysis, care was taken to set the lower size thresholds in the PSI-TK to a more conservative value (200 nm). The more conservative limit reduces noise in LEO verification statistics by eliminating particles with size near the lower detection limit of the split detector. Note, the instrument used in this study was particularly prone to noise at lower optical diameters (<200 nm VED), and even though 200 nm was the minimum criteria for scattering VEDs to be included in the general PBP data and for beam shape statistics, I have elected to only report LEO coating thickness data for rBC cores ranging from 240−280 nm ensuring that the smallest rBC-containing particle would have a 240 nm rBC core with 0 nm coating thickness. The aforementioned minimum scattering VED threshold of 200 nm is only for the LEO analyses described here, and the lower threshold for other analyses presented in this study was 170 nm. Under the LEO-fit tab the first step is to choose “get beam and PSD properties”. For this step, I chose scattering low gain (SCLG) for the scattering channel to be used and split low gain (SPLG) for the split detector channel to be used. The split detector helps determine the position of an individual rBC-containing particle within the laser beam. I used the default “minimum peak height to be considered” of 1600 with 40 “points before the peak center to be considered”. Upon completion of this step, a “LEO_BeamShapeStats” plot should display a perfect Gaussian response with a shown split detector position. A second output of the PSI-TK is a plot titled “LEO_BeamFWHM_and_SplitTime_Graph”. The full width at half maximum for my analysis was approximately 8 µs with a split point to beam center delay time of 120 approximately 4 µs. The second step is to perform the “LEO trace analysis”. The fast LEO fit with 3 points was used for my analysis using the SPLG channels for PSD split position. For this section, the PSI-TK will prompt the selection of the “BeamAndCalib” folder within the “LEO” folder of the IGOR Pro data browser. I only fit the scattering low gain data and split low gain data by selecting the “fit SCLG” and “fit SPLG” boxes in the PSI-TK. I concurrently selected “run LEO post processing,” which causes “verification of optical sizing” to be performed at this time. The broadband high gain broadband low gain combined signal (BHBL) for the “incandescence channel for BC cores” was selected along with RI=2.26+1.26i for rBC cores and RI=1.50+0i for “Mie data for coated BC core” based on previous literature (Gao et al., 2007; Moteki et al., 2010; Taylor et al., 2014; Dahlkötter et al., 2014). Excellent linear agreement should be found on the PSI-TK outputted plot entitled “LEO_verification_SCLG_vs_SCLG.” If necessary, adjustments can be made based on the given SP2 configuration and the “slope fudge factor”. 121 Chapter 5 - Conclusions In the first study presented in this dissertation, emissions from a large sample of locomotives were measured in the Alameda Corridor at the intersection of E. Greenleaf Blvd and S. Alameda Street. The Alameda Corridor is a train path that runs virtually north/south connecting the Ports of Los Angeles and Long Beach with the rest of California and the United States. I quantified fuel-based emission factors (g of pollutant per kg of fuel consumed) of black carbon (BC), particle number (PN), fine particulate mass (PM 2.5 ) and lung-deposited surface area (LDSA) for trains that are reasonably representative of typical freight locomotives across the nation. Care was taken to ensure the exhaust plume remained undiluted during sampling. Emission factors of BC were slightly skewed where the dirtiest 10% of trains responsible for about 20% of the total BC emissions. Results presented here are similar to diesel truck emissions prior to the implementation of required diesel particle filters in 2007 (Ban-Weiss et al., 2009; Dallman et al., 2012). I quantified a cumulative distribution of BC emission factors and found results to be nearly linear, further highlighting the lack of locomotive regulations when measurements were performed in 2013. Variation between emission factors was influenced mostly by wear and tear rather than discrepancies in control devices, contrasting similar measurements performed for diesel trucks where a relatively larger fraction of total emissions is associated with relatively smaller fraction of the diesel truck fleet. In this study, the LDSA emission factor, a potentially important health related metric for quantifying population exposure to air pollution, was reported for the first time to my knowledge. Using results presented here along with previous measurements, I compared for freight trains versus diesel trucks the amount of BC emissions associated with pulling an 122 intermodal freight container over a given distance. This comparison has associated uncertainty because it depends on accurately characterizing the relative differences in fuel use and total number of containers hauled by each category. I provide ranges of possible outcomes associated with a range of assumptions and find that, in most cases, locomotives emit less BC per container hauled than diesel trucks. However, particulate matter emissions from diesel trucks are continuing to decrease as more of the on-road fleet adopts diesel particle filters. This suggests that unless particulate matter emissions from locomotives are decreased in the near future, BC emissions associated with hauling a container for a given distance could become lower for diesel trucks than locomotives. This study reports mean emission factors for locomotives measured in late 2013/early 2014. I recommend that future studies repeat the measurements presented here after new emissions regulations for locomotives are adopted in 2015 (EPA, 2009). Note, because the regulations apply only to new and remanufactured locomotives, fleet-wide PM reductions may be relatively slow. In the second study, described in this dissertation I quantified the mass absorption cross-section enhancement (MAC E ) for coated versus uncoated refractory BC (rBC) particles. Late winter measurements of ambient rBC and thermally-denuded rBC were performed in Rubidoux, California. The site is presumably representative of aged emissions from the downtown area. I developed a system of sampling between alternating cycles of heating and non-heated air to remove soluble coatings from ambient black carbon. In this way, I successfully compared ambient (coated) to thermally- danuded (uncoated) rBC. Results indicated very slight enhancement of rBC when coatings are present and that there may be interesting distinctions from coatings 123 associated with secondary organic aerosol formation and possibly some aqueous phase SOA production at night (Venkatachari et al., 2005; Lim et al., 2010; Ervens et al., 2011; Hersey et al., 2011), which could lead to an interesting future analysis. The overall finding of the second study is that black carbon was largely uncoated during this time. However, when coatings were present there was minimal absorption enhancement. This study provoked increased desire to understand how coatings vary in time and space, so measurements were then performed in additional environments to quantify the mixing state of rBC. The goal for the last study was to further the understanding on how rBC acquires coatings, but also to find a location where rBC was more heavily coated. In the third study, rBC physical properties and mixing state was investigated in two distinct environments. The first location was downwind of a major highway (Interstate 405) in Los Angeles, California. The second site was approximately 100 km downwind of the center of Los Angeles in Redlands, California. I have presented a summer comparison of the evolution of rBC physical properties and mixing state at rapid and longer spatiotemporal scales. At the furthest measured downwind distance from Interstate 405, rBC mass concentrations were similar to campaign average values for midday on weekdays in Redlands, California. The rBC number concentration 114 m from the roadway for the smallest size range measured was about an order of magnitude higher than measured in Redlands, California. While values of f increased as downwind distance increased from the highway, the observed values of f at 114 m from the roadway were about the same as median f values measured in Redlands when photochemistry was most prevalent, indicating that there may be insufficient 124 residence time in the Los Angeles basin for rBC particles to accumulate thick coatings. An assessment of coating thickness using the LEO-fit method demonstrates that near the highway, the majority of rBC particles are uncoated while rBC particles in Redlands have coatings > 80 nm under certain atmospheric conditions. Comparing rBC at the near-road site versus Redlands shows remarkable similarity in some properties and divergence in others. At the furthest measured downwind distance (114 m) from Interstate 405, rBC mass concentrations were similar to campaign average values for midday on weekdays in Redlands, California. On the other hand, the rBC number concentrations near the highway for the smallest size range measured (70 – 100 nm MED) was about an order of magnitude higher than for Redlands. While values of f increased as distance from the highway increased, the observed values of f at 114 m from the roadway were about the same as median f values measured in Redlands when photochemistry was most prevalent. This suggests that the residence time of air in the Los Angeles basin under typical conditions measured during this campaign may not be sufficient for rBC to acquire thick coatings. However, under certain meteorological conditions, f was observed to be ~0.20, with coating thickness histograms showing a larger contribution of rBC particles with coating thickness > 80 nm. This occurred during a weekend day when local emissions from diesel vehicles were lower (compared to weekdays) and winds brought air from the desert regions to the Northeast of Los Angeles, both of which increase the relative contribution of remote sources of rBC. The overall findings from the last two studies help highlight mechanisms that govern the way rBC acquires soluble coatings; however, both studies report values in f that may still be too low to significantly enhance absorption properties of rBC. The Los 125 Angeles basin a bustling metropolis full of activity related to emissions of black carbon. 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Abstract (if available)

Abstract Black carbon (BC) particles can have deleterious human health consequences and impact regional and global climate. However, to date, global estimates on the impact of BC climate-forcing remain uncertain. Constraining estimates of BC has proved challenging due to the heterogeneous distribution of BC across the globe. Additionally, there is a fundamental lack of knowledge describing how optical properties of BC vary in time and space. This work aims to reduce uncertainty in current climate-forcing estimates of BC by focusing on (1) characterizing understudied sources of anthropogenically emitted BC, (2) evaluating the influence of “mixing state” (describing how black carbon exists with other species) on optical properties of refractory BC (rBC), and (3) confining the spatiotemporal evolution of rBC physical properties and mixing state in distinct environments. In part 1, an important non-road source of BC, locomotives, was characterized. Particulate matter emissions from a large sample of in-use line-haul freight locomotives were measured in the Alameda Corridor, located near the ports of Los Angeles and Long Beach. Emission factors for BC, particle number, fine particulate mass (PM₂.₅), and lung-deposited particle surface area (LDSA) were computed based on 1 Hz measurements of the rise and fall of particulate emissions and CO₂ concentrations as the locomotives passed the sampling location. LDSA emission factors are included as relevant for near-source human exposures. BC emissions associated with pulling an intermodal freight container over a given distance was compared for freight trains versus diesel trucks. Results suggest that unless emissions from locomotives are decreased in the near future, emissions associated with hauling a container could become lower for diesel trucks than locomotives. In part 2, I evaluated how accumulation of soluble coatings on rBC particles influences optical properties of black carbon. Ambient rBC was measured in Rubidoux, California, approximately 90 km downwind of downtown Los Angeles. Collocated NOₓ and NOy measurements were used to estimate the photochemical age of the sampled air. Both ambient and thermally-denuded air was sampled to compare the physical and optical properties of coated versus uncoated rBC particles. Physical properties of individual rBC particles including mass and coating thickness were measured using a Single-Particle Soot Photometer (SP2), and rBC optical properties were measured using a Photoacoustic Extinctiometer (PAX) at 870nm. Results indicated minimal enhancement from coatings or rBC particles reported in this study

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Creator Krasowsky, Trevor Serge (author)

Core Title Characterization of black carbon: from source to evolution of physical and optical properties in the atmosphere

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Environmental Engineering

Publication Date 10/19/2017

Defense Date 10/12/2017

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag aerosol,Air pollution,black carbon,Climate,emissions,environment,global warming,locomotive,OAI-PMH Harvest,particulate matter,refractory,single-particle,soluble coatings,Train

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ban-Weiss, George (committee chair), Fruin, Scott (committee member), Sioutas, Constantinos (committee member)

Creator Email krasowsk@usc.edu,trevor.krasowsky@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-446470

Unique identifier UC11264416

Identifier etd-KrasowskyT-5845.pdf (filename),usctheses-c40-446470 (legacy record id)

Legacy Identifier etd-KrasowskyT-5845.pdf

Dmrecord 446470

Document Type Dissertation

Rights Krasowsky, Trevor Serge

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA