465809 Source Identification for Non-Volatile Particulate Matter By Laser Derivatization

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Randy L. Vander Wal, Energy and Mineral Engineering & The EMS Energy Institute, Pennsylvania State University, University Park, PA and Joseph Abrahamson, Energy and Mineral Engineering, Penn State University, University Park

According to the EPA, soot from diesel sources is responsible for more than 8,300 premature deaths, 360,000 asthma attacks, 23,000 cases of bronchitis, 10,000 hospital admissions and 1.5 million lost work days in the US per year [1]. Nearly 50% of all soot from aircraft is not released at ground level but in the upper troposphere. Though constituting only ~ 1% by mass of jet engine emissions, the black carbon emission presents the largest aerosol surface area [2]. New findings show soot may be contributing to changes happening near the North Pole, such as accelerating melting of sea ice and snow and changing atmospheric temperatures [3]. Although black carbon is hydrophobic, chemical and physical changes occurring at its surface can create functional groups that attract and retain water, thereby causing it to become hydrophilic [4]. Such changes determine whether soot acts as cloud condensation nuclei, its susceptibility to washout and participation in atmospheric heterogeneous reactions, thereby significantly impacting the atmospheric radiative balance [5]. Recent estimates place black carbons’ radiative forcing as ~ 0.5 to 0.8 W/m2, nearly 50% that of CO2 [3].

The “bottom-up” or “source model” is one of the common soot identification methods. This approach begins by identifying pollution sources and their emission strengths, which are converted to emissions (via emission factors by category) and then by utilizing meteorological patterns predicting pollution advection (movement) and compositions over time and space [6]. With an inventory of known sources such models have great potential yet presently are limited because a) a complete inventory of sources is not usually known and b) meteorological dispersion is not accurate. The “top-down” or “receptor model” approach begins by sampling air in a given area and inferring the likely pollution sources by matching common chemical and physical characteristics between source and air pollution samples [6]. Top-down methods offer the promise of quantifying the relative contributions of the different sources to ambient air pollution, where rather little may be currently known. Additionally, top-down methods may require few atmospheric measurements and relatively simple analysis.

In a regulatory world, the top-down approach is more acceptable, primarily due to the involvement of direct pollution measurements at hot spots, analyzing the samples in a lab, and determining (statistically) the contribution of various sources to the pollution at that particular spot. On the downside, the measurement points are few, due to the costs involved in monitoring and chemical analysis, which is (and can be) compensated by a comprehensive bottom-up approach to cover as many hot spots, and a better mapping of the pollution sources in the city. Nevertheless the proposed work should be applicable to source apportionment studies by either a source-model or receptor-model based approach. Both approaches are used to understand air pollution at urban, regional, and global levels [7].

The main objective of the particulate matter (PM) pollution source apportionment is to identify the potential contribution of various sources to the ambient pollution levels (indicators), which can be utilized for effective informed decision making (planning). Though the methodology focuses primarily on the PM pollution, it includes the contribution of other pollutants in the secondary form. PM and in particular, soot can serve as a tracer for other pollutants.

Soot varies in physical structure and chemical composition. Over the past two decades it has become understood that different physical and chemical processes contribute to soot nucleation versus growth, that a variety of species contribute to soot growth and that nascent fuel molecular structure and composition, timescales and temperature all affect its chemical makeup and physical nanostructure [8,9]. Soot is a complex aerosol formed under non-equilibrium conditions, highly dependent upon the particulars of the combustion process. Though there is commonality of radical reactions driving soot formation, the variability of species identities, relative concentrations and associated rates conspire to create soot with system dependent nanostructure. That soot from practical scale power generation and engine combustion varies in composition and structure should not be surprising [10,11]. That nanostructure varies is well known and quantification tools for analyzing high-resolution transmission electron microscope (HRTEM) images are well developed [12,13]. These image analysis algorithms have been vetted by comparison to traditional single parameter analytical characterization methods such as X-ray diffraction for lamella spacing [14], and Raman spectroscopy for the in-plane lattice dimension La [12].

In this context it has been shown that the nanostructure of soot depends upon its formation and growth conditions for both applied systems [15] and by fundamental laboratory studies [16]. We define soot nanostructure as the degree of atomic order as manifested by graphitic layer plane segments and their physical relation to each other. Different physical measures can describe nanostructure: a) lamellae length, b) lamellae curvature, and c) lamellae spacing. Soots from different sources are distinguishable based upon structure and composition, as published elsewhere[10,11,17]. Yet often differences are small and variability from real sources and large-scale facilities is high, given both the complexity and variations in the combustion process in large-scale systems. It would be advantageous to accentuate recognizable structural differences and bring out subtle chemical differences.

The implications are significant: soot nanostructure is dependent upon the source and it can be quantified. This would permit definitive resolution of the soot source and its contribution to any particular receptor site. Our initial results using HRTEM validated this approach for the soots at-hand. Yet three parameters (length, separation and curvature) is not an exhaustive dataset, nor a unique set of parameters. Fuel-rich combustion conditions producing soot offer sufficiently similar chemistries that render distinctiveness in nanostructure subtle. This, coupled with variability in image processing details related to thresholding and contrast level, and extraction of the nanostructure parameters suggests that a more robust set of parameters is needed for distinctive recognition and identification of soot based upon physical structure.

How best to bring out small differences in nanostructure and other seemingly subtle differences in chemistry? The derivitization of analytes is very important in several branches of analytical chemistry, in fact often necessary to make a measurement possible [18]. It can bring out small differences in composition; render subtle structural differences distinguishable by providing recognition for and sensitizing differences to well known analytical techniques. Unconsidered to-date is the process of derivitization as applied to combustion produced soots, let alone by pulsed laser annealing. The goal of this work is to exploit the non-equilibrium formation history of combustion soot and bring out the subtle details of chemical and physical variations that while obscured by bulk analyses, can be highlighted by laser-based derivitization. With details as dependent upon combustion conditions, subtle compositional and structural differences may be brought to light thereby distinctively and uniquely identifying the source of the soot. Thus the proposed technique is referred to as, Soot Source Identification by Laser Derivitization, (SSILD).


[1] U.S. Environmental Protection Agency (EPA). (2002) Health assessment document for diesel engine exhaust. Prepared by the National Center for Environmental Assessment, Washington, DC, for the Office of Transportation and Air Quality. EPA/600/8-90/057F.

[2] Aviation Aerosols, Contrails and Cirrus Clouds (A2C3), (2000) Proceedings of a European Workshop, Sachem German, July 10-12, Air Pollution Report 74, Report EUR 19428, European Commission, Brussels.

[3] J. Hansen, L. Nazarenko, Proc. Natl. Acad. Sci. U. S. A. 101 (2003) 423-428.

[4] S. Picaud, P.N.M. Hoang, S. Hamad, J.A. Mejias, S. Lago, J. Phys. Chem. B 108 (2004) 5410-5415.

[5] V. Ramanathan, P.J. Crutzen, J.T. Kiehl, D. Rosenfeld, Science 294 (2001) 2119-2124.

[6] S.K. Guttikunda, Handbook on Particulate Pollution Source Apportionment, (2008). World Bank. (www.urbanemissions.info/pmsa)

[7] S.K. Guttikunda, R.V. Kopakka, Air Qual., Atmos. Health 7 (2014) 195-207.

[8] C.S. McEnally, L.D. Pfefferle, B. Atakan, K. Kohse-Hoinghaus, Prog. Energy Combust. Sci. 32 (2006) 247-294.

[9] M. Frenklach, Phys. Chem. Chem. Phys. 4 (2002) 2028-2037.

[10] M.D. Hays, R.L. Vander Wal, Energy Fuels 21 (2007) 801-811.

[11] R.L. Vander Wal, V.M. Bryg, M.D. Hays, Anal. Chem. 83 (2011) 1924-1930.

[12] R.L. Vander Wal, A.J. Tomasek, K.W. Street, D.R. Hull, W.K. Thompson, Appl. Spectrosc. 58 (2004) 230-237.

[13] K. Yehliu, R.L. Vander Wal, A.L. Boehman, Combust. Flame 158 (2011) 1837-1851.

[14] R.L. Vander Wal, A.J. Tomasek, M.I. Pamphlet, C.D. Taylor, W.K. Thompson, J. Nanopart. Res. 6 (2004) 555-568.

[15] R.L. Vander Wal, C.J. Mueller, Energy Fuels 20 (2006) 2364-2369.

[16] R.L. Vander Wal, A.J. Tomasek, Combust. Flame 136 (2004) 129-140.

[17] R.L. Vander Wal, V.M. Bryg, M.D. Hays, J. Aerosol Sci. 41 (2010) 108-117.

[18] J.M. Rosenfeld, TrAC, Trends Anal. Chem. 22 (2003) 785-798.

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