Estimating Black Carbon Aging Time-Scales with a Particle-Resolved Aerosol Model

Understanding the aging process of aerosol particles is important for assessing their chemical reactivity, cloud condensation nuclei activity, radiative properties and health impacts. In this study we investigate the aging of black carbon containing particles in an idealized urban plume using a new approach, the particle-resolved aerosol model PartMC-MOSAIC. We present a method to estimate aging time-scales using an aging criterion based on cloud condensation nuclei activation. The results show a separation into a daytime regime where condensation dominates and a nighttime regime where coagulation dominates. There is also a strong dependence on supersaturation threshold. For the chosen urban plume scenario and supersaturations ranging from 0.1% to 1%, the aging time-scales vary between 11 and 0.068 h during the day, and between 54 and 6.4 h during the night.

[1]  M. Petters,et al.  Chemical aging and the hydrophobic‐to‐hydrophilic conversion of carbonaceous aerosol , 2006 .

[2]  B. Vogel,et al.  Modeling aerosols on the mesoscale‐γ: Treatment of soot aerosol and its radiative effects , 2003 .

[3]  M. Andreae,et al.  Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols , 2006 .

[4]  R. C. Easter,et al.  Simulating the evolution of soot mixing state with a particle-resolved aerosol model , 2008, 0809.0875.

[5]  D. Streets,et al.  A technology‐based global inventory of black and organic carbon emissions from combustion , 2004 .

[6]  C. Liousse,et al.  Construction of a 1° × 1° fossil fuel emission data set for carbonaceous aerosol and implementation and radiative impact in the ECHAM4 model , 1999 .

[7]  D. Rivin,et al.  Particulate carbon and other components of soot and carbon black , 1982 .

[8]  Kimberly A. Prather,et al.  The influence of chemical composition and mixing state of Los Angeles urban aerosol on CCN number and cloud properties , 2008 .

[9]  Michael J Kleeman,et al.  Size and Composition Distributions of Particulate Matter Emissions: Part 2—Heavy-Duty Diesel Vehicles , 2007, Journal of the Air & Waste Management Association.

[10]  K. Prather,et al.  Single particle characterization of ultrafine and accumulation mode particles from heavy duty diesel vehicles using aerosol time-of-flight mass spectrometry. , 2006, Environmental science & technology.

[11]  D. Gillespie.  Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[12]  Michael J. Kleeman,et al.  SIZE AND COMPOSITION DISTRIBUTION OF FINE PARTICULATE MATTER EMITTED FROM MOTOR VEHICLES , 2000 .

[13]  Daniel T Gillespie,et al.  Stochastic simulation of chemical kinetics. , 2007, Annual review of physical chemistry.

[14]  Leonard K. Peters,et al.  A computationally efficient Multicomponent Equilibrium Solver for Aerosols (MESA) , 2005 .

[15]  G. Cass,et al.  Source-oriented model for air pollutant effects on visibility , 1996 .

[16]  H. Horvath,et al.  A study of the aerosol of Santiago de Chile. I: Light extinction coefficients , 1993 .

[17]  H. Burtscher,et al.  Hygroscopic properties of carbon and diesel soot particles , 1997 .

[18]  P. Brimblecombe,et al.  Thermodynamic Model of the System H + − NH 4 + − Na + − SO 42-− NO 3-− Cl-− H 2 O at 298 . 15 K , 2009 .

[19]  David B. Kittelson,et al.  On-road and laboratory evaluation of combustion aerosols-Part1: Summary of diesel engine results , 2006 .

[20]  Vincent R. Gray.  Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[21]  Daniel T. Gillespie,et al.  An Exact Method for Numerically Simulating the Stochastic Coalescence Process in a Cloud , 1975 .

[22]  D. Gillespie.  A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions , 1976 .

[23]  Jerome D. Fast,et al.  Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) , 2008 .

[24]  D. Gillespie.  Markov Processes: An Introduction for Physical Scientists , 1991 .

[25]  D. Gillespie,et al.  Stochastic Modeling of Gene Regulatory Networks † , 2005 .

[26]  Maria Cristina Facchini,et al.  The effect of physical and chemical aerosol properties on warm cloud droplet activation , 2005 .

[27]  Leonard K. Peters,et al.  A new lumped structure photochemical mechanism for large‐scale applications , 1999 .

[28]  J. Hansen,et al.  Climate Effects of Black Carbon Aerosols in China and India , 2002, Science.

[29]  A study of the aerosol of Santiago de Chile—II. Mass extinction coefficients, visibilities and Ångström exponents , 1993 .

[30]  E. Feil,et al.  Climate Effects of Black Carbon Aerosols in China and India , 2002 .

[31]  Yinon Rudich,et al.  Aging of organic aerosol: bridging the gap between laboratory and field studies. , 2007, Annual review of physical chemistry.

[32]  Peter V. Hobbs,et al.  Aerosol-Cloud-Climate Interactions , 1993 .

[33]  Ulrich Pöschl,et al.  Atmospheric aerosols: composition, transformation, climate and health effects. , 2005, Angewandte Chemie.

[34]  B. Vogel,et al.  Soot aging time scales in polluted regions during day and night , 2004 .

[35]  J. E. Glynn,et al.  Numerical Recipes: The Art of Scientific Computing , 1989 .

[36]  M. Petters,et al.  A single parameter representation of hygroscopic growth and cloud condensation nucleus activity , 2006 .

[37]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[38]  M. Stolzenburg,et al.  On the sensitivity of particle size to relative humidity for Los Angeles aerosols , 1989 .

[39]  J. Seinfeld,et al.  Global distribution and climate forcing of carbonaceous aerosols , 2002 .

[40]  A. Wexler,et al.  Thermodynamic Model of the System H , 2009 .

[41]  Markus Kraft,et al.  A new method for calculating the diameters of partially-sintered nanoparticles and its effect on simulated particle properties , 2006 .

[42]  William H. Press,et al.  Numerical recipes: the art of scientific computing, 3rd Edition , 2007 .

[43]  K. Prather,et al.  Assessment of the relative importance of atmospheric aging on CCN activity derived from field observations , 2007 .

[44]  Erik Swietlicki,et al.  Hygroscopic growth and critical supersaturations for mixed aerosol particles of inorganic and organic compounds of atmospheric relevance , 2005 .

[45]  David B. Kittelson,et al.  On-road and laboratory evaluation of combustion aerosols—Part 2:: Summary of spark ignition engine results , 2006 .

[46]  이동근,et al.  Single-particle mass spectrometer , 2005 .

[47]  K. Prather,et al.  Chemically segregated optical and microphysical properties of ambient aerosols measured in a single‐particle mass spectrometer , 2008 .

[48]  Linda R. Petzold,et al.  Stochastic Modeling of Gene Regulatory Networks y , 2005 .

[49]  A. Wexler,et al.  Thermodynamic Model of the System H+−NH4+−Na+−SO42-−NO3-−Cl-−H2O at 298.15 K , 1998 .

[50]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

[51]  James G. Hudson,et al.  Evaluation of aerosol direct radiative forcing in MIRAGE , 2001 .

[52]  I. J. Ackermann,et al.  Modeling the formation of secondary organic aerosol within a comprehensive air quality model system , 2001 .

[53]  Linda R. Petzold,et al.  Stochastic modelling of gene regulatory networks , 2005 .

[54]  D. Koch.  Transport and direct radiative forcing of carbonaceous and sulfate aerosols in the GISS GCM , 2001 .

[55]  Anthony S. Wexler,et al.  A new method for multicomponent activity coefficients of electrolytes in aqueous atmospheric aerosols , 2005 .

[56]  Kan,et al.  Thermodynamic Model , 2005 .

[57]  Ulrike Lohmann,et al.  Erratum: ``Prediction of the number of cloud droplets in the ECHAM GCM'' , 1999 .

[58]  U. Lohmann,et al.  Black carbon ageing in the Canadian Centre for Climate modelling and analysis atmospheric general circulation model , 2005 .

[59]  Sonia M. Kreidenweis,et al.  Cloud droplet activation of secondary organic aerosol , 2007 .