Top-down constraints to aerosol emissions from open biomass burning: the role of gasparticle partitioning and secondary organic aerosol formation

Transcription

1 Top-down constraints to aerosol emissions from open biomass burning: the role of gasparticle partitioning and secondary organic aerosol formation Igor B. Konovalov 1, Matthias Beekmann 2, Evgeny V. Berezin 1, Hervé Petetin 2 1 Institute of Applied Physics, Russian Academy of Sciences, Russia 2 Laboratoire Inter-Universitaire de Systèmes Atmosphériques, CNRS, Créteil, France IBBI 3 d workshop, Schloss Ringberg, Bavaria

2 MOTIVATION Chemistry transport models enabling simulations of aerosol evolution in the atmosphere are indispensible tools for validating and constraining aerosol emissions as well as for assessing radiative and climate effects of aerosols Several modeling studies indicated major discrepancies between aerosol simulations driven by the state-of-the-art emission inventories and satellite measurements of AOD over regions affected by major fires (Reid et al., 2009; Petrenko et al., 2012; Huneeus et al., 2012; Kaiser et al., 2012 ). Furthermore, the results by Huijnen et al., 2012, Konovalov et al., 2014 indicate that the aerosol (OA&BC) emission factors may be underestimated relative to the CO emission factors; the ratios of OA&BC to CO emissions appear to be underestimated by the bottom-up inventories. Can these issues be explained by imperfection of the methods employed to simulate organic aerosol in chemistry transport models?

3 MODELING ORGANIC AEROSOL: TRADITIONAL APPROACH primary organic aerosol (POA) SOA SOA VOC semivolatile and volatile species oxidation primary aerosol emissions volatile organic compounds aerosol precursors Saturation pressure (volatility)

4 A MORE REALISTIC VIEW UPON ORGANIC AEROSOL PROCESSES See, e.g. Robinson et al., Science, 2007 POA VOC SOA VOC SOA VOC VOC dilution + oxidation POA POA VOC VOC primary organic compound emissions Saturation pressure (volatility)

5 MODELING ORGANIC AEROSOL: VOLATILITY BASIS SET (VBS) APPROACH [Donahue et al, 2006; Robinson et al., 2007] X p = EF EF OA tot fi = i C 1 + C * i OA - from partitioning theory [Pankow,1994] X p ~2 under typical conditions POA i + OH OPOA i-1 OPOA i + OH OPOA i-1 i = 2,, N.

6 WILDFIRES IN WESTERN RUSSIA IN SUMMER 2010 Active fires in the period from July 15 to August 15 [FIRMS, Air pollution in Moscow (CO and PM 10 ) [Konovalov et al., ACP, 2011; Konovalov et al., GRL, 2012]

7 MODEL AND MEASUREMENT DATA CHIMERE chemistry transport model Model domains: (1) A coarse resolution domain, 1 0 x1 0 covering Western Russia (2) A nested domain (Moscow region), x0.1 0,12 layers (up to 200 hpa) Chemistry: MELCHIOR2 gas phase chemical mechanism (>100 reactions of 40 species) Anthropogenic emissions: EMEP data (0.5 0 x0.5 0 ) downscaled with the population density Boundary conditions: LMDZ Meteorology: NCEP Reanalysis-2 data processed with MM5 model Photolysis rates: calculated with the TUV model using the observed AOD 550 from MODIS measurements [Konovalov et al., ACP, 2011, Konovalov et al., GRL, 2012] Satellite measurement data 1. MODIS FRP L2 (MOD/MYD14) NASA data product (orbital data with ~1 km optimal resolution) 2. MODIS AOD L3 (MYD/MOD08_D3) NASA data product (daily data on the 1 0 x1 0 grid) 3. IASI total CO columns (Clerbaux et al., 2009): DOFS (degrees of freedom for signal) > 1.7 Ground based measurements CO and PM10 data from the Mosecomonitoring network in the Moscow region (provided by E. Semutnikova)

8 , ESTIMATION OF EMISSIONS FROM WILDFIRES E s ( t ) = αφ d β l s l ρ h l l ( t ) The idea was suggested by Ichoku & Kaufman [2005]; is used e.g. in GFAS [Kaiser et al., 2012] E s (g s -1 m -2 ) is the emission rate of a model species s Φ d (W/m 2 ) is the daily mean FRP density (derived from the MODIS measurements as in Konovalov et al. [2011, 2014]) α (g[organic matter] s -1 W -1 ) is the FRP -to- biomass burning rate conversion factor β sl (g [model species])/g[organic matter]) is the emission factor for a given type l of the land cover [the data base of M. Andreae, 2013] ρ l is the fraction of land cover type l in a given grid cell h l is the assumed diurnal cycle of fire emissions α opt = arg min [ J(V,V )] V o and V m are the observed and modeled daily values of the characteristics considered (CO total columns and AOD at 550 nm) o Please see Konovalov et al., ACPD, 2014 for further details m

9 VBS SCHEME IN THE CHIMERE MODEL: ADAPTATION FOR THE CASE OF BIOMASS BURNING AEROSOL Zhang et al., ACP, 2013: VBS scheme in CHIMERE for the case of anthropogenic emissions Volatility distributions of biomass burning emissions derived by fitting thermodenuder measurements in a combustion chamber [May et al., JGR, 2013] POA i + OH OPOA i-1 OPOA i + OH OPOA i-1 i = 2,, N. Robinson et al., 2007: k= cm 3 molec 1 s 1; 7.5% increase in mass per generation Grieshop et al. ACP, 2009: initial rapid burst of OA production that occurs in the first hour suggests that there may initially be a pool of much more reactive precursors; Based on the observed evolution of O:C ratio, the organic mass was assumed to increase more rapidly (40% per each generation with a two-bin shift in volatility) Hennigan et al., 2011; Ortega et al., 2013: SOA production in BB smoke oxidation experiments strongly depends on a type of the fuel burned

10 MODELING CASES Case No. k OH cm 3 s 1 mass increment, % accommodation coefficient reference" OA concentration (µg С m -3 ) * * * * EF EF OA tot a model run with the standard aerosol module in CHIMERE 1 (Case No. 0) * C f 1 + i i C C OA is the reference OA concentration = i OA

11 OPTIMIZED SIMULATION OF CO COLUMNS VS IASI MEASUREMENTS The FRP to biomass burning rate conversion factor constrained by IASI CO measurements: γ =0.67(±0.08) 10-3 g/j (reflects contribution of peat fires?) cf: α =0.368(±0.015) (g/j) [Wooster et al., 2005] mean total CO columns: 15/07-20/07/2010: IASI mean total CO columns: 15/07-20/07/2010:CHIMERE Average total CO columns: IASI vs. CHIMERE (bias corrected) Near surface CO concentrations in Moscow

12 RESULTS: TOP-DOWN VS. BOTTOM-UP ESTIMATES OF OA&BC/CO EMISSION RATIOS The ratios of aerosol and CO total pyrogenic emissions in the study region in the period from 15 July to 20 August, 2010 Our statistical hypothesis: the top-down and bottom-up estimates are equal; purple crosses show the probability of false rejection of the hypothesis (the Type 1 error)

13 AOD: MODIS MEASUREMENTS VS SIMULATIONS CONSTRAINED BY IASI CO COLUMNS AOD averaged over the period from 15 July to 20 August, 2010 Time series of AOD averaged over the study region

14 AOD: MEASUREMENTS VS SIMULATIONS CONSTRAINED BY THE AOD MODIS MEASUREMENTS AOD averaged over the period from 15 July to 20 August, 2010 Simulations constrained by AOD measurements vs. air pollution monitoring data from the Moscow region

15 CONCLUSIONS The Volatility Basis Set (VBS) approach to ОА modeling was applied in the framework of the CHIMERE chemistry transport model (CTM) to analysis of OA&BC emissions from the 2010 Russian fires. Unlike the traditional approach used in most of CTMs, the VBS approach allows taking into account gas-particle partitioning and oxidation of lowervolatility organic emissions. The VBS approach, unlike the traditional approach, can enable consistency between the top-down and bottom-up estimates of the pyrogenic emissions of aerosol and CO. The agreement between the PM 10 monitoring data and near-surface PM 10 calculated by CHIMERE using the OA&BC emissions constrained by AOD measurements drastically improves if the VBS scheme is used in the simulations instead of the standard aerosol module. However, a significant inconsistency between AOD and near-surface PM 10 concentrations related through the CTM still remains and calls for further analysis.