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Uncertainties in Global Aerosols and Climate Effects Due to Biofuel Emissions

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Uncertainties in Global Aerosols and Climate Effects Due to Biofuel Emissions Atmos. Chem. Phys., 15, 8577–8596, 2015 www.atmos-chem-phys.net/15/8577/2015/ doi:10.5194/acp-15-8577-2015 © Author(s) 2015. CC Attribution 3.0 License. Uncertainties in global aerosols and climate effects due to biofuel emissions J. K. Kodros1, C. E. Scott2, S. C. Farina1, Y. H. Lee3, C. L’Orange1, J. Volckens1, and J. R. Pierce1 1Colorado State University, Fort Collins, CO, USA 2University of Leeds, Leeds, LS2 9JT, UK 3Duke University, Durham, NC, USA Correspondence to: J. K. Kodros ([email protected]) Received: 10 March 2015 – Published in Atmos. Chem. Phys. Discuss.: 7 April 2015 Revised: 2 July 2015 – Accepted: 13 July 2015 – Published: 3 August 2015 Abstract. Aerosol emissions from biofuel combustion im- aerosol schemes. The sign and magnitude of these effects pact both health and climate; however, while reducing emis- have a strong regional dependence. We conclude that the cli- sions through improvements to combustion technologies will mate effects of biofuel aerosols are largely unconstrained, improve health, the net effect on climate is largely uncon- and the overall sign of the aerosol effects is unclear due strained. In this study, we examine sensitivities in global to uncertainties in model inputs. This uncertainty limits our aerosol concentration, direct radiative climate effect, and ability to introduce mitigation strategies aimed at reducing cloud-albedo aerosol indirect climate effect to uncertain- biofuel black carbon emissions in order to counter warm- ties in biofuel emission factors, optical mixing state, and ing effects from greenhouse gases. To better understand the model nucleation and background secondary organic aerosol climate impact of particle emissions from biofuel combus- (SOA). We use the Goddard Earth Observing System global tion, we recommend field/laboratory measurements to nar- chemical-transport model (GEOS-Chem) with TwO Moment row constraints on (1) emissions mass, (2) emission size dis- Aerosol Sectional (TOMAS) microphysics. The emission tribution, (3) mixing state, and (4) ratio of black carbon to factors include amount, composition, size, and hygroscop- organic aerosol. icity, as well as optical mixing-state properties. We also eval- uate emissions from domestic coal use, which is not biofuel but is also frequently emitted from homes. We estimate the direct radiative effect assuming different mixing states (ho- 1 Introduction mogeneous, core-shell, and external) with and without ab- sorptive organic aerosol (brown carbon). We find the global- Close to half of the world’s population relies on combustion mean direct radiative effect of biofuel emissions ranges from of domestic solid fuel use as a source of energy (Bruce et −0.02 to C0.06 W m−2 across all simulation/mixing-state al., 2000), creating concerns for both air quality (Bruce et combinations with regional effects in source regions rang- al., 2006) and climate (Bond et al., 2004b; Venkataraman et ing from −0.2 to C0.8 W m−2. The global-mean cloud- al., 2005). Domestic solid fuel combustion is dominated by albedo aerosol indirect effect (AIE) ranges from C0.01 to wood, charcoal, and agricultural waste (Bond et al., 2007; −0.02 W m−2 with regional effects in source regions rang- Fernandes et al., 2007). Biofuel combustion is especially ing from −1.0 to −0.05 W m−2. The direct radiative effect is prevalent in developing countries where a significant portion strongly dependent on uncertainties in emissions mass, com- of the population lacks access to electricity or clean com- position, emissions aerosol size distributions, and assumed bustion technology (Bruce et al., 2000). Gaseous and partic- optical mixing state, while the indirect effect is dependent ulate matter emitted from biofuel combustion degrades air on the emissions mass, emissions aerosol size distribution, quality and may lead to detrimental health risks (Akbar et and the choice of model nucleation and secondary organic al., 2011). The recent Global Burden of Disease Study ranks household air pollution from solid fuels and ambient air pol- Published by Copernicus Publications on behalf of the European Geosciences Union. 8578 J. K. Kodros et al.: Uncertainties in global aerosols and climate effects lution from particulate matter (all sources) as the third and (Lack and Cappa, 2010); Bond et al. (2006) estimated that a ninth largest contributors, respectively, to the global burden core-shell morphology would produce an average amplifica- of disease (Lim et al., 2012). Improved combustion devices tion factor of approximately 1.5 above that of an externally that reduce human exposure to pollutants should reduce the mixed particle. Laboratory studies have observed absorp- burden of disease from household air pollution; however, the tion enhancements of 1.3 for thin coatings (Schnaiter et al., net climate effect resulting from changing emissions remains 2003) and approximately 2 for thick coatings (Schnaiter et uncertain. al., 2005; Zhang et al., 2008) due to the lensing effect. Field Combustion of biofuel emits greenhouse gases (such as observations have not always agreed with laboratory mea- carbon dioxide and methane) (Johnson et al., 2008; Yevich surements. Cappa et al. (2012) found absorption enhance- and Logan, 2003) as well as carbonaceous aerosol particles, ments of only 6 % over two California regions and suggest such as black carbon (BC) and organic aerosol (OA) (Bond this may be caused by BC inclusions at the edge of the parti- et al., 2007). In the atmosphere, carbon dioxide and methane cle. Conversely, Q. Wang et al. (2014) found absorption en- are generally well mixed due to long lifetimes, and their hancement of 1.8 over China. It is therefore uncertain where impacts on climate are better understood than those from and with what magnitude the enhancement of absorption in aerosols (Boucher et al., 2013). Conversely, BC and OA have core-shell mixtures occurs. As a result, modeling studies fre- short lifetimes with more complex climate effects necessitat- quently use the external mixture assumption but multiply the ing the use of aerosol microphysical models to understand absorption by a fixed enhancement factor (e.g., 1.5 as de- the net impacts (e.g., Pierce et al., 2013; Spracklen et al., scribed above) (Hansen et al., 2007; X. Wang et al., 2014). 2011a). Carbonaceous aerosols can affect climate through The first AIE, or cloud albedo effect, refers to aerosols alter- scattering/absorbing solar radiation (direct radiative effect), ing reflectivity of clouds by changing the cloud droplet num- changing the radiative properties of clouds (the cloud-albedo ber concentration (CDNC) (Twomey, 1974). OA and mixed and cloud-lifetime indirect aerosol effects), changing the ab- BC from biofuel combustion can serve as nucleation sites for sorption of snow (snow albedo effect), and changing the water vapor, called cloud condensation nuclei (CCN) (Pierce temperature profile of the atmosphere (semi-direct effect) et al., 2007; Spracklen et al., 2011a). Increasing OA and BC (Boucher et al., 2013). In this study, we will be limited to concentrations may lead to an increase in CDNC, which will the direct radiative effect and the cloud-albedo aerosol indi- increase cloud albedo and thus yield a negative forcing. The rect effect (AIE) but acknowledge that this is not the total ability for OA and BC particles to act as CCN is a function aerosol climate forcing. of particle size and hygroscopicity as well as the maximum The direct radiative effect (DRE) refers to direct scatter- supersaturation of water vapor in the cloud (Petters and Krei- ing and absorption of incoming solar radiation (Charlson et denweis, 2007). Larger particles can activate into cloud drops al., 1992). BC has a strong absorbing component while OA more easily than smaller particles (due to higher saturation is usually considered to be entirely scattering; however, re- vapor pressures over curved surfaces); however, larger parti- search has shown that under certain combustion conditions cles may deplete water vapor concentrations, lower the max- OA may have an absorbing component (Kirchstetter et al., imum supersaturation, and limit activation of smaller sized 2004; Lack et al., 2012; McMeeking et al., 2014; Saleh et al., particles. 2013, 2014). Absorbing OA, commonly termed brown car- Emission factors from biofuel combustion are dependent bon, has a strong wavelength dependence (Andrea and Ge- on combustion conditions, which can vary with the type and lencsér, 2006), which varies with the BC to OA ratio from size of fuel (Li et al., 2009; L’Orange et al., 2012), the com- combustion (Saleh et al., 2014). bustion device (Bond et al., 2004a; Jetter et al., 2012), and the The magnitude of the DRE is strongly dependent on the operator (Roden et al., 2009). In general, flaming conditions size and mixing state of the particles (Jacobson, 2001; Kling- tend to emit relatively more BC mass and larger sized parti- müller et al., 2014). Aerosol-climate models generally as- cles (Janhäll et al., 2010) compared to smoldering. Grieshop sume that BC is mixed with other particle-phase species et al. (2011) found that the particle matter (PM) emission in several different ways: homogeneously with scattering mass can vary by a factor of 4 based on different stove and species, as a BC core surrounded by a homogeneously mixed fuel combinations. Wood and agricultural waste emit mostly shell (core-shell), or as separate from other aerosol species carbonaceous particles, while coal (used in domestic fuel use (external) (Jacobson, 2000). For a fixed amount of BC and but is not biofuel) has a higher sulfur content and so emits scattering mass, assuming a homogeneous internal mixture more SO2 gas, which reacts to form condensable H2SO4 yields the most absorption and an external mixture the least vapor in the atmosphere that contributes to particle forma- (Jacobson, 2000; Klingmüller et al., 2014); neither of these tion and growth. PM mass and composition can vary signif- states are realistic in the atmosphere, but they do provide icantly between different types of technologies used mainly upper and lower bounds for the DRE.
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