Particulate Matter Modeling: Including Nanoparticles

Transcription

1 Particulate Matter Modeling: Including Nanoparticles Ted Russell Air Resources Engineering Center Georgia Tech April 9, 2001

2 Issues Much of the suspected health and welfare effects from air pollution due to particulate matter Health (the main concern) Morbidity and mortality concerns Asthma etc. Welfare Visibility Deposition Particulate matter modeling has significant challenges Modeling techniques in development input/output uncertainties impact model evaluation

3 Outline Air quality modeling Role Scientific foundation Model vs. process Particulate matter models State of the science Current research directions Conclusions

4 Role of Air Quality Modeling In Air Quality Management Air Quality Goals Air Quality/Health Impacts Controls Chemistry Pollutant Distributions Air Quality Model Emissions Meteorology

5 Air Quality Model Representation of physical and chemical processes Numerical integration routines Scientifically most sound method to link future emissions changes to air quality Chemistry Emissions Meteorology Numerics C=AxB+E 100 species x 5000 hor. grids x 10 layers= 5 million coupled, stiff non-linear differential equations c t Air Quality Model ci = ( uci) + ( K ci) + Ri + Si t Atmospheric Diffusion Equation Discretize + L(x,t)c = f(x,t) 5-20 Computational Planes Operator splitting c(t+2 t) = L x ( t) L y ( t) L cz (2 t) L y ( t) L x ( t) c(t)

6 Air Quality Model Chemistry and Aerosol Dynamics Photochemical Reactions Heterogeneous Processes Aerosol Dynamics Homogeneous Processes Thermochemical Reactions Surface Deposition Sink Processes Chemical Processes (R) Emissions 1. Anthropogenic 2. Geogenic 3. Biogenic Transported Pollutants Sources Sources (E,S, BCs) Numerical Solution Techniques Air Quality Computed Concentrations Meteorology Transport (U, K, V d ) Topography & Land use Temperature Radiation Turbulence Geographical Features Cloud Cover Wind

7 Evolution in Air Quality Model Development Aerosols Model Comprehensiveness Gaussian plume Photochemical box (...EKMA in 1977) PiG Multidimensional Photochemical Nested Models Advanced diagnostic features Models 3(?) Infancy Golden era Maturation

8 Evolution in Air Quality Model Application Model Comprehensiveness Application of Gaussian plume for source impact Understanding ozone chemistry Air quality planning Understanding urban and regional ozone dynamics Quantifying uncertainty Understanding PM dynamics Infancy Science focus Golden era? Regulatory focus *or denial

9 EKMA Important Milestones Aerosol thermodynamics RRS: Start of the 3D & UAM era NAPAP: RADM Second generation AQMs Identified emissions errors major Organic aerosol dynamics Biogenics important External aerosol CB Series of chem. mechanisms SAPRC Series of chem. mechanisms Use of prognostic met fields OTAG: UAM-V CAAA call for 3D AQM Prognostic met fields with FDDA

10 How Good Are They? All evidence suggests that they describe the processes most affecting the evolution of ozone and (if equipped) particulate matter (o.k., many components of PM) after pollutant emission Current limitations Input errors Emissions Meteorology Monitoring data Sparse, ground level Don t effectively use upper-air data Model components/formulation/design

11 Particulate Matter Dynamics Particulates are exceptionally complex Complete description must include size and chemical composition Continuous size distribution Chemical composition varies continuously with size Phase conversion important PM function concentration function is more complex: C(x,t,d p ): space, time and particle diameter Composition may not be uniform for a given size: C(x,t,d p, s i ): s i is source i Makes ozone modeling look really easy

12 Sources of Particulate Matter PM has both primary and secondary components Primary Organic & elemental carbon (OC/EC), crustals, metals, water Mobile sources, industry, utilities, dust Secondary Sulfate, nitrate, ammonium and organic carbon Utilities, mobile sources, industry, biogenic, fertilizer, emissions control equipment

13 Particulate Sulfate Formation SO 2 H 2 O 2 O 3 H 2 SO 4 NH 3 (g) Emissions: Utilities, mobile sources OH Cloud and gasphase processing: Oxidants all impacted by NOx emissions H 2 SO 4 NH 4 HSO 4 (NH 4 ) 2 SO 4 Condensation & ammonia scavenging

14 Particulate Nitrate & OC Formation NO x oxides of nitrogen (NO + NO 2 ) O 3 + +HNO 3 (g)+org (pm) ROG reactive organic gases NH 3 (g)

15 PM Nitrate Formation Gaseous nitric acid formed from NOx emissions Ammonia derived directly from emissions Combine via equilibrium reaction Sensitive to temperature K K=[HNO3(g)][NH3(g)] T

16 Diurnal Nitrate Pattern Source: R. Weber

17 PM Nitrate Formation Nitric acid reacts with (free) ammonia As ammonia emissions increase, nitrate will increase until gas phase nitric acid depleted Sulfate reduces free ammonia/increases acidity, reducing nitrate formation Reducing NOx will decrease HNO3 formation, but may not decrease PM nitrate much Aerosol Nitrate SO2 NH3 NOx

18 Particulate Matter Dynamics External Mixture HNO 3 (g) Thermodynamics H+, NO3 -,SO4 = EC, Org Semivolatile organics Coagulation Org, EC: SO 2 +H 2 O 2 ==> H 2 SO 4 SO 2 (g) Mass transfer Internal Mixture In droplet chemistry

19 Particle size distribution p )) Mass distribution (dm/d(d Aitkin mode Nucleation mode Sulfate fraction changes with size Accumulation mode Particle diameter (d p ) Coarse mode

20 Particle number distribution p )) Number distribution (dn/d(d Nucleation mode Aitkin mode Accumulation mode Particle diameter (d p ) Coarse mode

21 Particulate Matter Modeling Approaches Size distribution None Sectional Modal Gas-to-particle conversion Inorganic Organic Aerosol particle description Internal mixture External mixture

22 Size Distribution No description All PM is together: no information on size or composition as a function of size Sectional Size distribution made up of a user defined number of bins Can have many bins (>20) or few (4) Describes compositional changes as a function of size Modal Size distribution made up of 2-4 modes corresponding to modes in size distribution Mode shaped like log-normal profiles

23 Sectional Approach Aerosol Histogram dci/dlogdpi (micrograms/m3/log(micrometers)) Diameter (micrometers)

24 Modal Approach Mass distribution (dm/d(d p )) Particle diameter (d p ) Sulfate fraction

25 Gas-to-Particle Conversion Gas phase species can condense upon, and volatilize from, particulate matter Inorganics Thermodynamic equilibrium usually assumed ISORROPIA AIM Organics Semivolatiles and low-vapor pressure organics One and two-step approach One step: gas phase chemistry leads to condensable species which goes to particulate phase Two step: gas phase chemistry leads to semivolatile product that partitions between gas and condense phases

26 Organic Partitioning Coefficient Yield=M 0 [αk i /(1+K i M 0 )] Fraction condensed K i =1/C sat,i =RT/(γp i m i ) Low vapor pressure Temperature

27 Internal vs.external Mixtures Particle composition can be very inhomogenous even in the same size distribution Traditional approaches assume homogenous mixture in each size range/bin: C(x,t,d p ): Internal mixture See prior slides Theory and evidence suggests that particle composition varies within a size range Source-based differences: C(x,t,d p, s i )

28 Internal Mixture Sulfate Nitrate Sulfate Nitrate OC OC EC Metals Metals

29 External Aerosol Modeling From Kleeman, Cass and Eldering, 1997

30 Nanoparticle Modeling Nanoparticles represent an important fraction, in terms of total number but not total mass, of PM, and are unique Very short lifetimes Directly emitted or, possibly, due to nucleation in short term events Models have not dealt so much with nanoparticles because of their short lifetimes and small fraction of the mass Three approaches for modeling: Sectional Can add multiple sections in the nano-modes Modal Adding a new nucleation mode in addition to Aitkin mode External, source oriented approach

31 Modal Approach to Nanoparticles Number distribution (dn/d(d p )) Nucleation mode Aitkin mode Particle diameter (d p, um)

32 Sectional Approach for p )) Number distribution (dn/d(d Nanoparticles Particle diameter (d p )

33 Strengths and Weaknesses Modal Strengths Computationally efficient Weaknesses Lack of detailed information (all nano-particles similar) Sectional Strength: Tremendous capacity for detail, very flexible Weakness: Computationally time consuming External mixture Strengths: Tremendous detail, particles tied directly to sources Weakness: Computationally expensive All Nucleation theory is highly uncertain Not a major factor if particles are primary in origin

34 Particulate Matter Sensitivity Analysis and Source Attribution AQM s major function is to link source emissions to air quality: Source attribution Individual vs. regional/category analysis Assessing impact of individual sources difficult Small perturbation to noisy process Small difference between two large numbers» e.g.: 10 Ton/day source in a 1000 ton/day area (10 ton/day/1000 ton/day)*0.1(% change in O3/%change in NOx)*120 ppb=0.12 ppb Can a model see this accurately? Assessing categories/regions/complete strategies more appropriate for typical approach if reductions are reasonable Unrealistic changes to minimize noise raises additional issues New approach: direct sensitivity analysis

35 Source Attribution using Direct Sensitivity Analysis NO o NO 2 o VOC i o... T K u, v, w E i k i BC i... J R k i j 3-D Air Quality Model DDM-3D Sensitivity Analysis decoupled O 3 (t,x,y,z) NO(t,x,y,z) NO 2 (t,x,y,z) VOC i (t,x,y,z)... c(t) i s ij (t) = p j Concentrations Simultaneously Response to emissions changes: accurate for very small changes

36 Response of Fine Nitrate to SO2 reductions

37 Source Attribution: Sulfate by Source Region Direct Sensitivity Analysis Sulfate Reduction (%) Atlanta Daily Average Sulfate Sensitivity (10% Reduction in SO2 Emissions) 7/11/95 7/12/95 7/13/95 7/14/95 7/15/95 7/16/95 WI NI SI EI AO

38 Particulate Matter Modeling and Chemical Mechanisms Current generation of gas-phase mechanisms (e.g., SAPRC99+, RACM) in pretty good shape for ozone Flexible Evolutionary Appear to adequately describe gas phase kinetics for ozone, etc Limited information for determining organic composition of PM Important information for identifying sources and impacts lost Aqueous phase mechanisms Likely adequate for inorganics and ozone Questions about organic oxidation

39 PM Modeling State of the Science: Where are We? Ozone models are mature PM Models still evolving One atmosphere / 3rd generation urban-toregional models are at the forefront Combined gas/aerosol/deposition & nested/multiscale Some built in diagnostic features Sensitivity analysis Regional Multiscale Model

40 Attributes of Advanced Models: Internal Mixture Models Usual attributes of advanced internal mixture models Advanced chemical mechanism Sectional or modal approach Thermodynamic inorganic One-step organic formation Two step on the way, but large uncertainties Advanced diagnostic features Examples: URM, CMAQ, CIT-AERO, UAM-AERO URM extensively evaluated over eastern US as part of SAMI CMAQ is to become the community model

41 Attributes of Advanced Models: External Mixture Models Features Limited applications to date Very time and resource consuming AIM thermodynamics/growth Trajectory and grid-based versions of CIT model See Cass, Kleeman and co-workers Expect wider application in next 10 years

42 Example Model Application: SAMI Southern Appalachians Mountains Initiative (SAMI) Stakeholder process to develop regional strategy to deal with: Ozone (Sum06), PM, haze, acid deposition Single model applied to suite of 5, 10 day episodes Episodes chosen to represent typical year

43 SAMI Modeling Air Quality: URM-1ATM (Urban-to-Regional Multiscale One Atmosphere) Model Horizontal cells of varying dimensions ( km) 7 vertical layers extending from surface to 12.8 km Meteorology: RAMS (Regional Atmospheric Modeling System) temperature, air density, wind speed and direction, total solar radiation, ultraviolet radiation, mixing height, turbulent momentum diffusivity, precipitation, cloud parameters Emissions: EMS-95 (Emission Modeling System) Gas: NO x, VOCs, CO, NH 3, SO 2 Aerosols: OC, EC, Ca, Mg, K, NO 3, SO 4, other PM

44 Urban-to-Regional Multiscale One Atmosphere (URM-1ATM) Model Three-dimensional Eulerian photochemical model Finite element, multiscale transport scheme (Odman & Russell, 1991) Gas-phase chemistry SAPRC-93 mechanism (Carter, 1994) Aqueous-phase heterogeneous sulfate chemistry Aerosol dynamics Sectional approach (Gelbard and Seinfeld, 1980) ISORROPIA thermodynamic equilibrium (Nenes, et al., 1998) Organic aerosol yields (Pandis, et al., 1992) Acid deposition Wet: Reactive Scavenging Module (Berkowitz, et al., 1989) Dry: three-resistance approach One atmosphere modeling approach

45 Aerosol Module Inorganic aerosols - ISORROPIA sulfate, nitrate, ammonium, chloride, sodium, hydrogen ion condensation/evaporation (thermodynamic equilibrium) Organic aerosols experimental and estimated aerosol yields from VOC oxidation Inert Species EC, Mg, Ca, K, other PM Sectional Size Distribution dci/dlogdpi (micrograms/m3/log(micrometers)) < P M > < P M > Diam eter (m icrom eters)

46 SAMI Modeling Domain and Grid * *

47 a) Observed Performance Average PM 2.5 concentration b) Sim ulated 28.4 µ g/m 3 Average PM 2.5 concentration 31.6 µ g/m 3 16% 12% 3% 21% 11% 3% Ammonium Nitrate Sulfate 37% 28% 28% 34% EC OC Other 4% 3% Ozone (ppb) AIRS Station ; Nashville, Davidson Co, TN (urban) Time (starting July 11, 1995) Ozone (ppb) AIRS Station ; Look Rock, Blount Co, TN (high elevation) Time (starting July 11, 1995)

48 Performance on July 15, 1995 Simulated (L) and Observed (R) PM 2.5 for July 15, 1995 PM 2.5 (µg/m 3 ) Other Soils EC NO3 NH4 ORG SO BRIG DOSO GRSM JEFF LYBR MACA OKEF ROMA SHEN SHRO SIPS UPBU WASH Station

49 Sensitivity Analysis to Emissions DDM - Decoupled Direct Method: Extended to Particulate Matter Use direct derivatives of governing equations Perform numerous sensitivity calculations in one model run. Inaccurate sensitivities may result due to non-linear response Assessed response of PM to emissions Regionally By source region

50 Sulfate Sensitivity to SO 2 Emissions

51 Geographic Sensitivity Regions

52 SO 4 & its Change on July 15, 1995 for a 10% Reduction of 2010-OTW SO 2 Emissions from Different Regions 2010-OTW Midwest Northeast Central SAMI FL + MS µg/m 3

53 SO 4 & its Change on July 15, 1995 for a 10% Reduction of 2010-OTW SO 2 Emissions from SAMI States KY WV VA OTW TN NC AL GA SC µg/m 3

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55 Sulfate Sensitivity at GRSM to 10% SO2 Emission Reductions 7/15/95 7/31/91 7/27/91 7/12/95 5/27/95 8/11/93 6/24/92 4/29/95 8/07/93 8/04/93 5/15/93 4/26/95 3/24/93 2/09/94 3/27/ AO SE NE MW CN WV VA TN SC NC KY GA AL SO Sulfate Concentration ( g/m 3 ) Class 1 Class 2 Class 3 Class 4 Class 5 Sulfate Sensitivity at SHEN to 10% SO2 Emission Reductions 7/15/95 7/12/95 5/24/95 8/11/93 8/07/93 5/12/93 6/27/92 6/24/92 7/31/91 7/24/91 7/19/95 5/27/95 8/04/93 5/15/93 5/03/95 4/29/95 4/26/95 3/24/93 2/09/94 3/31/ AO SE NE MW CN WV VA TN SC NC KY GA AL SO4 Sulfate Concentration ( g/m3) Class 1 Class 2 Class 3 Class 4 Class 5 Sulfate Sensitivity (%) Sulfate Sensitivity (%)

56 Summary Ozone models are mature PM Modeling is developing Much more involved More uncertainties Performance is acceptable Main Features Internal mixtures External computationally huge Sectional distribution More flexible than modal Inorganic thermodynamics Useful features Direct sensitivity analysis Future External mixture approach more common more detailed organic chemistry

57 7/19/95 7/15/95 7/12/95 5/27/95 5/24/95 5/3/95 7/19/95 7/15/95 7/12/95 5/27/95 5/24/95 5/3/95 4/29/95 4/29/95 Great Smoky Mt. National Park 4/26/95 2/12/94 2/9/94 8/11/93 8/7/93 8/4/93 5/15/93 5/12/93 3/31/93 3/27/93 3/24/93 6/27/92 Observations Model Shenandoah National Park 4/26/95 2/12/94 2/9/94 8/11/93 8/7/93 8/4/93 5/15/93 5/12/93 3/31/93 3/27/93 Observations Model 6/24/92 7/31/91 7/27/91 7/24/ /24/93 6/27/92 6/24/92 7/31/91 7/27/91 7/24/ PM 2.5 Conc. (µg/m 3 ) PM 2.5 Conc. (µg/m 3 )