ISSN 1028-334X, Doklady Earth Sciences, 2016, Vol. 471, Part 1, pp. 1158–1163. © Pleiades Publishing, Ltd., 2016. Original Russian Text © G.I. Gorchakov, A.V. Vasiliev, K.S. Verichev, E.G. Semoutnikova, A.V. Karpov, 2016, published in Doklady Akademii Nauk, 2016, Vol. 471, No. 1, pp. 91–97.
GEOPHYSICS
Finely Dispersed Brown Carbon in a Smoggy Atmosphere G. I. Gorchakova*, A. V. Vasilievc, K. S. Vericheva, E. G. Semoutnikovab, and A. V. Karpova Presented by Academician G.S. Golitsyn June 4, 2014 Received October 8, 2014
Abstract—It is shown that the absorption capacity of smoke aerosol during mass forest and forest–peat fires is determined to a considerable degree by light absorbing organic compounds or brown carbon. According to the data from the AERONET global network of stations [1], the absorption spectra of smoke aerosol vary significantly if airborne particulate matter is contained in brown carbon. It is established that in several cases, the absorption spectra of smoke aerosol are approximated with satisfactory accuracy by exponents. It is shown that the finely dispersed (submicron) fraction of the smoke aerosol makes a major contribution to its optical characteristics in the 0.44–1.02 μm spectral region. Strong variation in the single scattering albedo is discovered in the presence of brown carbon in the smoke aerosol. It is shown that the optical characteristics of coarsely dispersed and finely dispersed fractions of smoke aerosol differ considerably. DOI: 10.1134/S1028334X16110039
During mass forest and forest–peat fires, large amounts of smoke aerosol (SA) are released into the atmosphere [2–4] and strongly affect the transport of solar shortwave radiation in the atmosphere [5] and the ecological situation [4, 6]. The radiation conditions of the smoggy atmosphere are determined by the SA distribution in the atmosphere stratum and its optical (radiation) characteristics. Due to the operation of the AERONET global network of stations equipped with CIMEL sun photometers [1], the spectral dependences τ ex (λ) = τ sc (λ) + τ ab (λ) of aerosol optical thickness of extinction (weakening) averaged over the atmosphere stratum are currently available, where λ is the wavelength of light, τ sc and τ ab are the aerosol optical thicknesses of scattering and absorption. The single scattering albedo (SSA) or the quantum survival probability is estimated by the ratio of Λ = τ sc / τ ex . The spectral dependences of the refraction coefficient n(λ) and κ(λ), as well as the asymmetry parameter g(λ) of the light scattering indicatrix and the distribution of particle volumes by size dV (r ) , are reconstructed using the measured v(r ) = d ln r a Obukhov Institute of Atmospheric Physics, Russian Academy
of Sciences, Pyzhevsky per. 3, Moscow, 119017 Russia b GPBU Mosecomonitoring, Moscow, 119019 Russia c St. Petersburg State University, St. Petersburg, 199034 Russia * e-mail:
[email protected]
values of the weakening spectrum τ ex (λ) and the sky brightness indicatrix, where r is the radius of the aerosol particles and V is the accumulated volume of the particles [8]. The variation observed in the SA optical properties, primarily τ ab (λ ) and Λ(λ) , cannot be completely explained [9, 10] by the content of elemental or black carbon in the smoggy atmosphere or its external and internal mixtures with airborne particulate matter components that do not absorb solar shortwave radiation. Therefore, attention is currently focused on study of sunlight-absorbing brown carbon [9]. The absorption of solar radiation by gaseous organic compounds has been studied quite well, which makes it possible to measure the near-surface concentrations of formaldehyde, benzene, toluene, and other organic compounds in urbanized territories, including the megalopolis Moscow by optical open pass gas detectors [11]. The organic compounds that can absorb light in a condensed phase [9] remain little studied. The possible influence of brown carbon on the SA optical properties can be evaluated by the calculated values of an imaginary component of the refraction coefficient of airborne particulate matter. It is known that that the imaginary component of the refraction coefficient of elemental black carbon, which was considered in [10, 12] as the only particulate matter in the SA that absorbs solar shortwave radiation, varies by no more than 10% in the spectral region of 0.4–1.0 μm [12]. For the internal mixture of aerosol with elemental carbon as the particulate matter, which does not absorb solar radiation, the imaginary component of
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Imaginary component of the refraction coefficient (10 3 κ) from the AERONET data No. 1 2 3 4 5 6 7 8
Station AF Ms Tm Yek Tk BL Yak Ch
Date
Time
Aug. 28, 2005 Sept. 7, 2002 July 21, 2006 Aug. 10, 2010 July 26, 2012 Aug. 20, 2010 July 30, 2006 July 20, 2010
12:25 11:30 10:53 13:23 0:32 22:16 9:05 21:40
Wavelength, μm 0.44
0.675
0.87
1.02
10.1 8.44 6.55 8.64 22.2 10.2 6.28 12.2
5.43 5.72 4.88 6.88 15.3 9.04 4.68 13.4
5.11 3.82 3.99 5.83 10.5 6.76 3.56 13.7
5.11 2.94 3.64 5.00 8.32 5.57 3.33 13.0
ζ
2.0 2.9 1.8 1.7 2.7 1.8 1.9 0.94
AF, Alta Floresta, Brazil; Ms, Moscow; Tm, Tomsk; Yek, Yekaterinburg; Tk, Tiksi; BL, Bratts Lake, Canada; Yak, Yakutsk; Ch, Chapais, Canada.
the refraction coefficient will also vary within a narrow range. Brown carbon differs from black carbon in selective absorption in the shortwave range of the visible spectral region [9], which is caused by a strong dependence of the imaginary component of the refraction coefficient κ for brown carbon on the wavelength of light [9]. It is evident that if the concentrations of brown carbon are sufficiently high, the absorption selectivity will also be manifested clearly in internal mixtures of black and brown carbon, primarily as a noticeable excess of the imaginary component of the refraction coefficient κ in the shortwave range of the visible spectrum over the values of κ in the longwave range of the visible spectrum and in the nearest infrared spectral region. During the analysis of the AERONET data, selectivity of the spectral course of κ can be quantified by the parameter ζ = κ (λ = 0.44 μ m)/ κ (λ = 1.02 μ m), that can evaluate the influence exerted by brown carbon on the SA absorption capacity. The purpose of this work is to study the variation in the optical properties of brown carbon–containing smoke aerosol during mass fires in the boreal forests of Russia and Canada using the AERONET data. The analysis of aerosol monitoring at the AERONET network of stations indicates strong variation in the SA optical properties. In order to evaluate the influence of brown carbon on the SA optical properties, we selected six examples (nos. 2–7 in table) of observations (the data level 2.0) of aerosol optical characteristics in the strongly smoggy atmosphere of the boreal forests of Russia and Canada ( τ ex for λ = 0.5 μm from 0.92 to 2.0), for which the spectral dependence κ (λ ) is to a considerable degree determined by brown carbon (ζ = 1.7 −2.9), including the observations (the time is shown in table) from stations in Moscow, Tomsk, Yekaterinburg, Tiksi, and Yakutsk, and Bratts Lake in Canada. We also presented an example of observations (no. 1 in table) in a strongly smoggy atmosphere ( τ ex = 2.86 for λ = 0.5 μm) at the Alta Floresta station in Brazil (ζ = 2.0 ). For comparison, we considered an example of observation results for a strongly smoggy DOKLADY EARTH SCIENCES
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atmosphere at τ ex (λ = 0.5 μm) ≃0.5 at the Chapais station in Canada where the SA absorption capacity is determined by black carbon (ζ = 0.94) and the influence of brown carbon can be neglected. The observed spectral dependences of optical aerosol thickness weakened ( τ ex ) in the spectral region of 0.34–1.02 μm for the cases under study are shown in Fig. 1 (curves 1–8). We note that in a strongly smoggy atmosphere, they are approximated with satisfactory accuracy (in the coordinates of ln τ ex − ln λ ) by convex parabolas in the spectral region of 0.44–1.02 μm [4]. During analysis of the AERONET data, the absorption capacity of aerosol is as a rule characterized by the SSA spectral dependences Λ(λ). The results of determining SSA for the above cases are shown in Fig. 2 (curves 1–8). In accordance with the data presented, the values of Λ vary within 0.91–0.98 in the spectral region of 0.44–1.02 μm if SA contains brown carbon. Typically for the cases under consideration, SSA grows as the wavelength increases. In particular, for Tiksi (curve 5), SSA grew from 0.91 to 0.94 as the wavelength changed from 0.44 to 1.02 μm. The dependences Λ(λ) for Tomsk and Yakutsk (curves 3 and 7) resembled similar dependences in the smoggy atmosphere of Alaska [10]. The spectral course of SSA is noticeably different from that stated above at the Alta Floresta station (curve 1). When black carbon dominated at the Chapais station (curve 8), SSA decreased relatively quickly as the wavelength of light increased, which is consistent with the empirical model of Λ(λ) in [2] for the boreal forests in Canada (curve 12) and with the empirical model of Λ(λ) for the Amazonian forests [2]. Thus, in accordance with the data presented, the spectral dependences of SSA are quite varied in the presence of brown carbon in the smoke aerosol and differ noticeably from the dependences Λ(λ) for the cases where the imaginary component in the refraction coefficient of smoke aerosol particulate matter is determined by black carbon. The spectral dependences of aerosol absorption optical thickness τ ab (λ ) are of primary interest. Figure 3а
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ln τex(1−8), ln τexf (9−11), ln 3τexс (12) 1
1.2
9
2
9
3
10
10
0.8 0.4 0 −0.4
11 12
4
5 6
7 11
8
−0.8 −1.2 −1.6 12 −2.0 0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0 1.1 λ, μm
Fig. 1. Spectral dependences of weakening of the aerosol optical thickness τ ex (λ), where λ is the wavelength of light in the smoggy atmosphere from the measured data (1–8) at the (1) Alta Floresta, (2) Moscow, (3) Tomsk, (4) Yekaterinburg, (5) Tiksi, (6) Bratts f Lake, (7) Yakutsk, and (8) Chapais AERONET stations; reconstructed for the finely dispersed aerosol fraction τ ex (9–11) at the
(9) Alta Floresta, (10) Moscow, and (11) Yakutsk stations; and reconstructed for the coarsely dispersed fraction (3τ cex ) at (12) the Moscow station.
shows the spectral dependences of τ ab in the coordinates ln τ ab − λ for the eight cases under consideration (curves 1–8). We note that to represent the data conveniently, we indicated 0.75 of τ ab for Tomsk in Fig. 3 (curves 3 in Figs. 3а and 3b). Our attention is drawn to the various shapes of the observed dependences τ ab (λ ). The analysis showed that in several cases the observed absorption spectra are approximated with satisfactory accuracy by the exponents
τ ab (λ) = τ 0 exp(−λ / λ ) , *
(1)
where λ is the scale coefficient and τ 0 is the coeffi* cient that determines the absolute values of τ ab . In particular, for Moscow (Fig. 3а, 2) λ = 0.39 μm, for * Tiksi (Fig. 3а, 5) λ = 0.32 μm, and for the Bratts Lake * station in Canada (Fig. 3а, 6) λ = 0.37 μm. The * mean-square deviations of the observed values from the corresponding approximating functions do not exceed 2% (at several points, up to 4%). As the wavelength of light increased, the absorption decreased exponentially [13] in photoemulsions [14] and in ultradisperse systems of metallic particles [13], including island films [15].
If we plot the absorption spectra in the coordinates ln τ ab − ln λ (Fig. 3b), it would be easy to understand that some of the observed SA absorption spectra that contain brown carbon are approximated with satisfactory accuracy by the power functions
τ ab (λ) = B λ β,
(2)
where β is the power exponent (parameter B makes it possible to reconstruct τ ab in absolute units). In particular, the absorption spectra τ ab (λ ) for Tomsk (curves 3 in Fig. 3) and Yakutsk (curves 7 in Fig. 3) are approximated with satisfactory accuracy (Fig. 3b) by power functions with exponents β = –1.90 and –1.95, respectively. These values of β differ strongly from the known value β s = –1 for fine soot particles (r ! λ ) or black carbon particles [12]. We mention that four power spectra of SSA absorption with exponents of β from –1.35 to –1.96 were recorded [10] in the smoggy atmosphere of Alaska. Not all recorded absorption spectra are approximated by exponents and power functions, such as for example, spectra 1 and 8 in Fig. 3. To quantify the variation in the SA absorption spectral shape, we also introduced parameter η = τ ab (λ = 0.44 μ m)/ τ ab
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Λ (1−8), Λf (9−11), ΛD(12) 0.98 11
2
7
3
10
4 6
0.96
5
0.94 9
1
0.92 12
9 10 11 12
0.90
8
0.88 0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1 λ, μm
Fig. 2. Spectral dependences of the quantum survival probability Λ from the AERONET data (1–8) at the (1) Alta Floresta, (2) Moscow, (3) Tomsk, (4) Yekaterinburg, (5) Tiksi, (6) Bratts Lake, (7) Yakutsk, and (8) Chapais stations; reconstructed for the finely dispersed aerosol fraction Λ f at the (9) Alta Floresta, (10) Moscow, and (11) Yakutsk stations; empirical model of the spectral dependence the spectral dependence of ΛD [2] for the boreal forests in Canada (12).
(λ = 0.87 μ m) , which equals approximately 2.0 for finely dispersed soot aerosol (β = –1). According to the data presented, if SA contains brown carbon, parameter η varies from 3.0 to 4.5 (examples 1–7), and when black carbon (example 8) dominates, η = 2.1. The analysis of variation in η in the smoggy atmosphere showed that when black carbon is dominant, the values of η are always close to 2. On the other hand, if SA contains large amounts of brown carbon, parameter η is always significantly greater than 2 and sometimes reaches 5–6. From this it follows that a comparatively large value of this parameter (η≥ 2.5) indicates a relatively large amount of brown carbon in the SA. The optical characteristics of aerosol depend on the particle size. The analysis of data on the SA structure obtained during global monitoring showed that the distribution of the SA particles by size is represented by the sum of finely dispersed (submicron) and coarsely dispersed fractions [2]. Therefore, it is necessary to assess the contributions made by the stated fractions to the total optical characteristics of SA. To estimate the ratios of contributions made by the finely dispersed and submicron fractions approximately, we calculate parameter ξ = v f / v c , where v f and v c are the maximum values of v(r ) for the stated fractions. It appeared that, for examples 1–8 under DOKLADY EARTH SCIENCES
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consideration, parameter ξ varies approximately from 5 to 32, including 4.8 for Moscow (example 2) and 10.5 for Yakutsk (example 7). The ratio of contributions made by the finely and coarsely dispersed SA fractions was quantified accurately by the approximation of the corresponding counting distributions of the particles by the log-normal distributions f c g f (r; r0 , σ f , N f ) and g c (r; r0 , σ c , N c ) and the corresponding calculations by the Mie theory. In particular,
g f (r ) =
⎧⎪ (ln r − ln r0f ) 2 ⎪⎫ Nf exp ⎨− ⎬, 2πσ f r 2σ 2f ⎪⎩ ⎪⎭
(3)
where r0f , σ f , and N f are the distribution parameters for the finely dispersed aerosol fraction. In the examples under study, r0f varies from 0.12 to 0.18 μm and the parameter σ f changes from 0.3 to 0.425. Figure 1 demonstrates the results of calculating the spectral dependences of weakening of the aerosol optical thickness for the finely dispersed SA fraction τ exf (λ ) in the spectral region of 0.44–1.02 μm for the Alta Floresta, Moscow, and Yakutsk stations (curves 9, 10, and 11, respectively). It is not difficult to understand that the calculated values of τ exf are close to the recorded values of τ ex . For Moscow (example 2), the
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ln τab(1, 2, 4−8), ln 0.75τab(3), ln τabf (9−11), ln 3τabс (12) −1
(a) 9 10
−2
−3
11 12
10 4 6
1 9
8 −4
5 2
12
3 7
−5
11 0.4
0.5
0.6
0.7
0.8
0.9
1.0
−1 9
(b)
10 −2
11 12
10 −3
4 6
1 9
8 −4
5 2
12
3 7
−5
11 0.4
0.5
0.6
0.7
0.8
0.9
1.0 λ, μm
Fig. 3. Spectral dependences of the aerosol absorption optical thickness τ ab (λ) , where λ is the wavelength of light in the smoggy atmosphere in (а) the coordinates λ − ln τ ab and (b) ln λ − ln τ ab from the AERONET data (1, 2, 4–8) at the (1) Alta Floresta, (2) Moscow, (4) Yekaterinburg, (5) Tiksi, (6) Bratts Lake, (7) Yakutsk, and (8) Chapais stations, 0.75 for τ ab at (3) the Tomsk f station; reconstructed for the finely dispersed aerosol fraction τ ab (9–11) at the (9) Alta Floresta, (10) Moscow, and (11) Yakutsk
stations; the value of 3τ cab reconstructed for the coarsely dispersed fraction at the Moscow station (12).
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contribution of the finely dispersed fraction of τ ex is 98% for the wavelength of 0.44 μm and 86% for the wavelength of 1.02 μm, and for Yakutsk (example 7), it is 94% for λ = 0.44 μm and 83% for λ = 1.02 μm. The values of Λ f differ noticeably (up to 0.04–0.06) from the corresponding observed values of Λ towards an increase in the value of Λ f for Moscow and Yakutsk (curves 10 and 11 in Fig. 2). f The relative differences in τ ab (λ) and τ ab are significantly greater than the relative differences in τ exf (λ) and τ ex (λ) . In particular, for Moscow, the contribuf tion made by τ ab to τ ab ranges from 85% (λ = 0.44 μm) to 68% (λ = 1.02 μm), and for Yakutsk, this contribution varies from 79% (λ = 0.44 μm) to 72% f (λ = 1.02 μm). The differences in τ ab and τ ab are noticeably less at the Alta Floresta station (1 and 9 in f Fig. 3). The dependences τ ab (λ), as well as τ ab (λ ), in the above cases (2, 4–6) are approximated by the exponents with somewhat greater values of the scale of λ*. In particular, for Moscow, λ f = 0.27 μm. In * f cases 3 and 7, the absorption spectra τ ab (λ) are approximated by the power law spectra (for Yakutsk, β f = –2.05).
Thus, detailed quantitative analysis showed that the finely dispersed fraction makes a major contribution to the observed spectra of SA weakening and absorption. The spectral dependences for the optical characteristics of the finely dispersed fraction fundamentally differ from the corresponding spectral dependences of the SA finely dispersed fraction. Figure 1 (curve 12) exemplifies the spectral course of the triplicate (for convenience) value of weakening of the aerosol optical thickness (τ cex ) in the coarsely dispersed SA fraction for Moscow. The quantum survival probability Λ c of the coarsely dispersed SA fraction varies approximately from 0.71 (λ = 0.44 μm) to 0.9 (λ = 1.02 μm). Figure 3 (curve 12) presents the spectral dependence of the aerosol absorption optical thickness τ сab (the triplicate values) for Moscow. It is approximated with satisfactory accuracy by the exponent with the scale of с λ = 0.47 μm, which is considerably greater than the * corresponding value of λ f = 0.27 μm. We note that * m c the value calculated τ ab differs from the = τ exf + τ ex value observed τ ex by ±1% for the spectral region of 0.44–0.87 μm and by 5% for λ = 1.02 μm, and the m f c value τ ab differs from the corresponding = τ ab + τ ab values observed τ ab (to the smaller side) by 2–4%. DOKLADY EARTH SCIENCES
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This work shows that the recorded variation in the SA absorption spectra is determined to a considerable degree by the solar radiation–absorbing organic compounds and black carbon contained in SA particles. It is established that the finely dispersed SA fraction makes a major contribution to its optical characteristics in the spectral region of 0.44–1.02 μm. The influence of brown carbon on the optical characteristics of smoke aerosol is recorded not only during fires in the boreal forests, but also in the other regions of the planet, which we exemplified here by the noticeable effect produced by brown carbon on the optical characteristics of smoke aerosol in Brazil (the Alta Floresta AERONET station).
ACKNOWLEDGMENTS The authors thank the AERONET team and the organizers of the observations for providing the opportunity to use the measurements of optical and microphysical characteristics of smoke aerosol and are grateful to G.S. Golitsyn for helpful advice. This work was supported by the Russian Science Foundation, project no. 14-47-00049. REFERENCES 1. B. N. Holben, T. F. Eck, I. Slutsker, et al., Remote Sens. Environ. 66 (1), 1–16 (1998). 2. O. Dubovik, B. Holben, T. F. Eck, et al., J. Atmos. Sci. 59 (2), 590–608 (2002). 3. G. I. Gorchakov, P. P. Anikin, A. A. Volokh, et al., Izv., Atmos. Ocean. Phys. 40 (3), 323–336 (2004). 4. G. I. Gorchakov, M. A. Sviridenkov, E. G. Semoutnikova, et al., Dokl. Earth Sci. 437 (2), 513–517 (2011). 5. S. A. Sitnov, G. I. Gorchakov, M. A. Sviridenkov, et al., Issled. Zemli Kosmosa, No. 2, 28–41 (2013). 6. G. I. Gorchakov, E. G. Semoutnikova, A. A. Isakov, et al., Opt. Atmos. Okeana 24 (6), 452–458 (2011). 7. O. Dubovik and M. D. King, J. Geophys. Res. 105 (D16), 20673–20696 (2000). 8. L. A. Remer, Y. J. Kaufman, B. N. Holben, et al., J. Geophys. Res. 103 (D24), 31879–31891 (1998). 9. J. Feng, V. Ramanatan, and V. R. Katamarthi, Atmos. Chem. Phys. 13, 8607–8621 (2013). 10. T. F. Eck, B. N. Holben, J. S. Reid, et al., J. Geophys. Res. 114 (D11), D11201 (2009). 11. G. I. Gorchakov, E. G. Semoutnikova, E. V. Zotkin, et al., Izv., Atmos. Ocean. Phys. 42 (2), 156–170 (2006). 12. R. W. Bergstrom, P. B. Russel, and P. Hignett, J. Atmos. Sci. 59 (2), 567–577 (2002). 13. E. A. Bondar’, in Physic of Atmospheric Aerosol (A. M. Obukhov Institute of Atmospheric Physics Russ. Acad. Sci., Moscow, 1999), pp. 79–90 [in Russian]. 14. F. Urbach, Phys. Rev. 125 (5), 1324–1329 (1962). 15. G. V. Rozenberg, Optics of Thin-Layer Coatings (Fizmatgiz, Moscow, 1958) [in Russian].
Translated by L. Mukhortova
