presents:\camx/training1198.ppt introduction to camx presented by chris emery environ international...

$: Presents:\camx/training1198.ppt Introduction to CAMx Presented by Chris Emery ENVIRON International Corporation June 2003$
presents:\camx/training1198.ppt

Introduction to CAMx

Presented by

Chris Emery

ENVIRON International Corporation

June 2003

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presents:\camx/traininggenerictrng1198.ppt

Topics

• Conceptual Overview– Ozone Chemistry– Modeling Atmospheric Dispersion

• Description of CAMx– Model Design– Comparison to Other Models

• Application of CAMx– Model Setup– Replication of Historical Case/Performance Evaluation– Evaluation of Future Case/Control Strategies


Conceptual OverviewIntroduction to Ozone Chemistry

• Sources and sinks for tropospheric ozone• Ozone formation from VOC and NOx• Control strategy implications

– Sensitivity to VOC and/or NOx

– VOC reactivity

– NOx suppression

• Condensed chemical mechanisms


• Sources– Smog chemistry involving VOCs and NOx

– Global methane and CO oxidation

– Stratosphere

• Sinks– Chemical reactions (e.g., NO, alkenes)

– Deposition (dry and wet)

Conceptual OverviewIntroduction to Ozone Chemistry


Conceptual OverviewOzone Formation from VOC and NOx

no sunlight no ozone production no NOx no ozone production

no VOC no ozone production


Conceptual OverviewCentral Role of Hydroxyl Radical (•OH)


Conceptual OverviewOzone and Precursor Relationships:

EKMA Diagram

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Conceptual OverviewOzone and Precursor Relationships:

Ozone vs. NOz• NOz = NOy - NOx• Observed ozone/NOz relationship for a rural

location in eastern U.S.• Interpret slope as production efficiency for ozone

from NOx

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Conceptual OverviewControl Strategy Implications

• Sensitivity to emission reductions– VOC sensitive, NOx sensitive, or both

• VOC reactivity– Depends upon chemical nature: reaction rate, radical

yields, products

– Approximated by reactivity scales, e.g., Carter MIRS


Conceptual OverviewControl Strategy Implications

• NOx Suppression– If Ozone is highly VOC sensitive, NOx reduction may

incur disbenefits by two effects:(1) direct titration (or scavenging) of ozone by fresh NO

emissions

(2) scavenging of radicals by NO2 (inhibits ozone production)


Conceptual OverviewCondensed Chemical Mechanisms

• Hundreds of VOCs and thousands of reactions in the atmosphere

• Need to condense this chemistry for modeling• Condensation strategies:

– Focus on the most important reaction pathways

– Lump together VOCs with similar reactions


Conceptual OverviewVOC Lumping Strategies: Lumped Structure (CB4)

CH3 - CH2 - CH3

CH3 - CH = CH2

CH3 - CH2 - CH2 - CH3

CH3 - CH2 - CH = CH2

PAR

OLE


Conceptual OverviewVOC Lumping Strategies:

Lumped Molecule (SAPRC)

CH3 - CH2 - CH3

CH3 - CH = CH2

CH3 - CH2 - CH2 - CH3

CH3 - CH2 - CH = CH2

ALK1

OLE1


Conceptual OverviewLumped Mechanisms for Modeling

Species

Mechanism Reactions Transported Radical Total

CB4 (CAMx Mech 3) 96 25 12 37

SAPRC99 (CAMx Mech 5) 211 56 18 74


Conceptual OverviewIntroduction to Atmospheric Dispersion

• All models solve some form of the Continuity Equation

• Relates changes in pollutant concentration to:– “Dispersion”

• Advection (transport by mean/resolved wind)

• Turbulent diffusion (transport by unresolved motion)

– Chemical reactions

– Deposition

– Emissions


Conceptual OverviewGeneral Form of the Continuity Equation

(Eulerian Form)

Mathematical solution (integration) of the general form is difficult

– Simplifying assumptions are required


• Lagrangian types -- Coordinate system followsair parcels

• Eulerian types – Coordinate system is fixed in space

• Hybrid types – Incorporate features of Lagrangian types into an

Eulerian framework

Conceptual OverviewThree General Types of Air Quality Models


Conceptual OverviewLagrangian Models

• Many simplifying assumptions– Air parcel coherency

– Break down quickly, especially in complex wind flows

• Cost-effective solution at relatively close range for a relatively small number of sources

• Readily produce source-receptor relationships• Severe technical limitations for:

– Large numbers of sources

– Regional-scale transport applications

– Non-linearly reactive pollutants



• Gaussian Plume Models– The earliest air models– Many simplifying assumptions provide closed-form

analytical solutions• Steady-state (i.e., time invariant)• Spatially uniform (homogeneous) dispersion• Inert or first-order decay

– Examples include:• ISC• COMPLEX• RTDM• AERMOD


Conceptual OverviewGaussian Plume Model



• Gaussian Puff Models– Fewer simplifying assumptions

• Retain plume coherency assumption– Employ analytical solutions for each puff, but:

• Must track large numbers of puffs– Examples include:

• CALPUFF• SCIPUFF

– Some models developed for individual reactive plumes; e.g., RPM

• Numerical solution methods needed for chemistry• Chemical interactions between puffs, segments, or

particles cannot be fully treated


Conceptual OverviewEulerian Models

• Generally considered to be technically superior– Allow more comprehensive, explicit treatment of

• Physical processes

• Chemical processes

• Interactions of numerous sources

• Require sophisticated solution methods– Employ discrete time steps and operator splitting

– Computational grid (“grid models”)

– Relatively expensive to apply for long periods


Conceptual OverviewEulerian (Grid) Model Concept


Conceptual OverviewGrid Cell Processes


Conceptual OverviewCoupling Between Grid Cells


Conceptual OverviewEulerian (Grid) Model Results



• Subgrid resolution can be a limitation– As grid size and time step are reduced

• Accuracy increases, but

• Computational time increases

– Advanced grid models can• Employ improved accuracy in critical locations

• Allow cost effective application on urban to regional scales



• Before source apportionment techniques, many runs were required to:– Establish source-receptor relationships

– Evaluate effective control strategies

• Examples of early photochemical grid models include:– UAM-IV

– RADM

– CALGRID


Conceptual OverviewHybrid Models

• Incorporate features of Lagrangian models into grid model framework– Overcome many of the limitations of sub-grid

processes– Provides practical advantages of Lagrangian models,

by development of:• Plume-in-Grid (PiG) sub-models• Variable (nested) grid resolution• Source apportionment techniques

• Capitalize on availability of low-cost high-speed computers


Conceptual OverviewHybrid Models

• Examples of hybrid photochemical grid models include:– CAMx

– MODELS3/CMAQ

– UAM-V


Conceptual OverviewSummary

• Photochemical hybrid models– Preferred means for addressing complex and nonlinear

processes affecting reactive tropospheric air pollutants– Variable grid spacing (nested grids)– Lagrangian PiG sub-models to treat subgrid plume

dispersion and chemistry– Source apportionment and process analysis tools

• These models invoke fewer assumptions, but require:– More computer resources– Sophisticated numerical integration methods


Description of CAMxComprehensive Air quality Model with

extensions (CAMx), Version 4.00

3-D Eulerian/hybrid tropospheric photochemical transport model– Treats emissions, chemistry, dispersion, removal

– Chemical species• Photochemical gasses (NOx, VOC, CO, O3)

• Aerosols (sulfate, nitrate, organics, inert)

– Applicable scales• From individual point sources (< 1 km)

• To regional (>1000 km)


Description of CAMx CAMx v4.00

Unifies features required of “state-of-the-science” models– New coding of several industry-accepted algorithms

– Computational and memory efficient

– Easy to use

– Modular framework permits easy substitution of revised and/or alternate algorithms

– Publicly available (www.camx.com)



• Technical features:– Grid nesting

• Horizontal and vertical nesting

• Supports multiple levels

• Variable meshing factors

– Plume-in-Grid (PiG) sub-model

– Multiple, fast and accurate chemical mechanisms

– Mass conservative and mass consistent transport scheme



– Multiple map projections• Geodetic (latitude/longitude)

• Universal Transverse Mercator (UTM)

• Lambert Conformal Projection (LCP)

• Rotated Polar Stereographic Projection (PSP)

– Multiple probing tools• Ozone Source Apportionment Technology (OSAT)

• Decoupled Direct Method (DDM) of sensitivity analysis

• Process Analysis tools (IPR, IRR, CPA)


Description of CAMxTechnical Approach

Overview– Solves continuity equation for each species

– Time splitting operation• Each process solved individually each time step, each

grid

– Master time step:• Maintains stable solution of transport on master grid

• Multiple nested grid steps per master step

• Multiple chemistry steps per master step



Transport– Advection and diffusion solvers are mass conservative– 3-D advection is mass consistent

• Linked via divergent atmospheric incompressible continuity equation

– Order of east-west and north-south advection alternates each master step

– Two options for horizontal advection solver:• Bott (1989): area-preserving flux-form solver• Colella and Woodward (1984): piecewise-parabolic

method



– Vertical transport solved with implicit scheme• Resolved vertical velocity

• Mass exchange across variable vertical layer structure

– Vertical diffusion solved with implicit scheme• Dry deposition rates used as surface boundary condition

– Horizontal diffusion solved with explicit scheme• 2-D simultaneous



• Pollutant removal– Dry deposition

• First order removal rate based on deposition velocity (Weseley, 1989)

• Dependent upon: season, land cover, solar flux, surface stability, surface wetness, gas solubility and diffusivity, aerosol size

– Wet scavenging• First order removal rate based on scavenging coefficient• Gas rates depend upon solubility and diffusivity• Aerosol rates depend upon size• Separate in-cloud and below-cloud rates (Seinfeld and

Pandis, 1998)



• Photochemistry– CBM-IV (Gery et al., 1989)

• 3 variations available

– SAPRC99 (Carter, 2000)• Chemically up-to-date

• Tested extensively against environmental chamber data

• Uses a different approach for VOC lumping

– All mechanisms are balanced for nitrogen conservation

– Photolysis rates derived from TUV preprocessor• Can be affected by cloud optical depth



• Gas-phase chemistry solvers– Most “expensive” component of simulation

– CAMx CMC solver• Increases efficiency and flexibility

• Adaptive hybrid approach:– Radicals (fastest reactions) – in steady state

– Fast state species – second-order Runge-Kutta

– Slow state species – solved explicitly

• “Adaptive” = number of fast species can change according to chemical regime



– Implicit-Explicit Hybrid solver (Sun et al, 1994)• Accuracy comparable to reference methods (LSODE)

• IEH vs. CMC:– Accuracy similar during the day

– IEH more accurate during the night

– IEH several times slower



• Aerosol chemistry– Gas-phase mechanism (CBM-IV Mech 3) with:

• Additional biogenic olefin (terpenes)• Condensible organic gas species• Chlorine and HCl chemistry• Homogeneous SO2 to sulfate• Photochemical production of nitric acid

– Aerosol mechanism calculates:• Aqueous SO2 to sulfate (CMAQ/RADM-AQ)• Condensible organic gasses to organic aerosols (SOAP)• NO3/SO4/NH3/Na/Cl equilibrium (ISORROPIA)• Size spectrum is static, user-defined



– Aerosol species treated:• Sulfate

• Nitrate

• Ammonium

• Sodium

• Chloride

• Secondary Organics

• Primary Organics

• Elemental Carbon

• Primary Fine (+dust)

• Primary Coarse (+dust)



• Plume-in-Grid (PiG)– Resolves chemistry/dispersion of large NOx plumes– Tracks plume segments (puffs) in Lagrangian frame

• Each puff moved independently by local winds• Puff growth (diffusion) determined by local diffusion

coefficients– GREASD PiG: fast, conceptually simple

• Reduced NOx chemistry set (NO-NO, NOx/ozone equilibrium, HNO3 production)

• Puffs leak mass according to growth rates and grid cell size

• Puffs terminated by age or sufficiently dilute NOx



• Probing Tools– Utilize CAMx routines for all dispersion and chemistry

– Ozone Source Apportionment Technology (OSAT)• Determines source area/category contribution to ozone

anywhere in the domain

• Uses tracers to track NOx and VOC precursor emissions, ozone production/destruction, and initial/boundary conditions

• Estimates ozone production as NOx- or VOC formed



• HOWEVER: cannot quantify ozone response to NOx or VOC controls

• Chemical allocation methodologies:– OSAT: standard approach

– APCA: attributes ozone production to anthropogenic (controllable) sources only

– GOAT: tracks ozone based on where it formed, not where precursors were emitted



– Decoupled Direct Method (DDM) for sensitivity analysis

• Calculates pollutant concentration sensitivity to input parameters

– First-order sensitivity to emissions, initial/boundary conditions

• Allows estimates of effects of emission changes

• Allows ranking of source region/categories by their importance to ozone formation

• Slower than OSAT, but:– Provides information for all species (not just ozone)

– More flexible in selecting which parameters to track



– Process Analysis (PA)• Designed to provide in-depth analyses of all physical

and chemical processes operating in model

• Operates on user-defined species and any portion of the modeling grid

• Three components:– Integrated Process Rate (IPR): provides detailed process rate

information for each physical process (emissions, advection, diffusion, chemistry, deposition)

– Integrated Reaction Rate (IRR): provides detailed reaction rate information for all chemical reactions

– Chemical Process Analysis (CPA): like IRR, but designed to be more user-friendly and accessible


Description of CAMxInput Requirements

Meteorology (Fortran binary)– 3-D time-varying fields to define the state of the

troposphere• Winds, temperature, pressure, humidity, clouds, rain,

turbulent diffusion rates– Affects transport, chemistry, deposition– Defines vertical grid structure

• Air quality (UAM Fortran binary)– Initial and boundary conditions– Time constant or varying– All species or a sub-set


Description of CAMxInput Requirements

• Emissions (UAM Fortran binary)– 2-D time varying emission fields

– Individual time-varying elevated point sources

• Other Inputs– User control file (text)

– Chemistry parameters file (text)

– Land use / land cover (Fortran binary)

– Albedo / haze / ozone column definition (text)

– Photolysis rates lookup table (text)

– Probing Tool definition files (text)


Description of CAMxModel Output

Average concentration (UAM Fortran binary)– 2-D (surface) or 3-D time varying

– User-selected species (ppm gasses, g/m3 aerosols)

– Separate master grid and fine grid files

• Instant concentration (UAM Fortran binary)– 3-D time varying (output last 2 hours of simulation for

model restart)

– All species (mol/m3 gasses, g/m3 aerosols)

– Separate master grid and fine grid files



Deposition (UAM Fortran binary)– 2-D time varying

– User-selected species

– Dry deposition velocity (m/s)

– Dry and wet deposition fluxes (mol/ha gasses, g/ha aerosols)

– Precipitation liquid concentration (mol/l gasses, g/l aerosols)

– Separate master grid and find grid files



PiG restart (Fortran binary)– All relevant PiG information for model restart

• Diagnostic files (text)– Repeat run control parameters and I/O file names

– Diagnostic messages and warning/error messages

– CPU timing

– Mass budgets



Probing Tool files– Master and fine grid instantaneous tracer files (UAM

Fortran binary)

– Master and fine grid average tracer and PA rates files (UAM Fortran binary)

– OSAT and DDM receptor file (text)• Tracer/sensitivity concentrations at discrete receptors

– IPR and IRR rates files (Fortran binary)• Time-varying rate information for user-selected cells


Description of CAMxComputer Requirements

Dependent upon size of CAMx application– Standard model vs. large Probing Tool configuration– Number of nested grids needed and number of cells per

grid– Chemistry mechanism

• SAPRC99 slower than CBM-IV• PM chemistry slows model significantly

– Chemistry solver• IEH slower than CMC

– Plan according to episode length and desired throughput for desired number of simulations



Example: CAMx application on 1 Ghz Pentium III PCCBM-IV Mech 3 (no aerosols), CMC solver

Application Memory

Disk Usage

Execution Time

1 grid OTAG domain (64 x 63 x 5) No OSAT

49 Mb

90 Mb/episode day

5 min/episode day

2 grids OTAG domain (64 x 63 x 5) and (137 x 110 x 7) No OSAT

115 Mb

200 Mb/episode day

40 min/episode day

2 grids OTAG Domain (64 x 63 x 5) and (137 x 110 x 7) 17 OSAT source regions 4 OSAT source groups Total OSAT tracers = 280

387 Mb

500 Mb/episode day

5 hours/episode day



Recommended hardware – Linux workstation– Minimum 256 Mb memory– Fastest affordable PC chipset available– Minimum 10 Gb available disk space– Graphics monitor and associated drivers

• Recommended software– Portland Group F77/F90 compiler for Linux– PAVE, Vis5d, Surfer (or some other graphics viewer)– Ancillary support software for pre- and post-processing

• SAS (for EMS-95), ArcInfo or other GIS systems• MM5, RAMS, NCAR Graphics (for meteorological

preprocessing)


Description of CAMxComparison to Other Grid Models

UAM UAM-V REMSAD MAQSIP Models-3/CMAQ

CAMx v4

Developers/history SAI 1970-1990

SAI 1991-present

SAI (UAM-V) 1991-1995

SAI (REMSAD) 1995-present

NCAR (RADM) 1985-1987

SUNY (SAQM) 1992-1997

MCNC (DAQM/MAQSIP)

1995-present

U.S. EPA 1995-present

ENVIRON 1996-present

Horizontal Scale Vertical Extent

urban 2-3 km

urban-regional 3-4 km

regional-continental 100 mb

urban-regional 100 mb

urban-regional 100 mb

urban-regional, 5-8 km

Model Formulation Horizontal Grid UTM UTM

geodetic UTM

geodetic Lambert conformal

Lambert conformal Lambert conformal UTM Lambert conformal polar stereographic

geodetic Vertical Grid terrain following

mixing height terrain following

height sigma-p sigma-p sigma-p terrain following

height Grid Nesting none 2-way 2-way 1-way 1-way 2-way Horizontal Advection Smolarkiewicz Smolarkiewicz Smolarkiewicz Bott

Smolarkiewicz PPM Bott

PPM Bott

Vertical Advection Crank-Nicholson Lorenz cycle Bott Bott PPM Bott

Implicit

Mass Conservative yes yes no no yes yes Mass Consistent yes no no yes yes yes Horizontal Diffusion K-theory

constant K-theory variable

K-theory variable

K-theory constant

K-theory variable

K-theory variable

Vertical Diffusion K-theory K-theory K-theory K-theory convective mixing

K-theory convective mixing

K-theory

Gas-Phase Chemistry CBM-IV CBM-IV CBM-tox

Micro CBM-IV

CBM-IV RADM

CBM-IV RADM SAPRC

CBM-IV SAPRC

Inorganic PM Chem Aqueous Chem SOA

none GATOR

MARS Empirical

SOA yields

MARS RADM aqueous

SOA yields

ISORROPIA RADM aqueous

SOA yields

ISORROPIA RADM aqueous

SOAP


Description of CAMxComparison to Other Grid Models

UAM UAM-V REMSAD MAQSIP Models-3/CMAQ

CAMx v4

PM Size Distribution none modal-sectional hybrid

constant fine, coarse

modal modal constant fine, coarse

Cloud Processes none reduced photolysis Aqueous PM

reduced photolysis Aqueous PM

vertical mixing

modified photolysis Aqueous PM

vertical mixing


vertical mixing


vertical mixing Plume in Grid none PiG none none PinG GREASD PiG Dry Deposition simple resistance Wesely resistance Wesely resistance Wesely resistance Wesely resistance,

Pleim Wesely resistance

Wet Deposition none yes yes yes yes yes Probing Tools none none none none DDM-3D

PA OSAT DDM PA

Practicality/Regulatory Acceptability Comp Requirements low medium medium high high medium Documentation good fair fair poor fair fair Ease of Use fair fair fair poor poor fair Availability publicly available restricted restricted restricted publicly available publicly available Peer Review yes yes yes no yes yes Regulatory Use yes yes yes yes yes yes


Application of CAMx

• CAMx Setup• Base Case Development• Performance Evaluation• Future Base Case• Emission Control Scenario Evaluation


Application of CAMxCAMx Setup – Domain Definition

• Coverage– Geographical/political issues– Influence of boundary conditions– Need for fine resolution in key areas– Depth– Resource/time constraints

• Resolution– Master grid: met model, coordinate projection, layer

structure– Nested Grids: where, how many, vertical nesting


Application of CAMxCAMx Setup -- Domain Definition

OSAT and DDM source/receptor areas– Source mapping assigns each grid cell to a source area

• Can specify one source area (entire domain)• Can specify multiple source areas on master or nested

grids– Receptor locations:

• Optional user-specified receptor locations• Can select: point, single cell, cell average, or wall of

cells

• Landuse– Depends upon available geographic databases– Often developed from emission surrogates


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Application of CAMxCAMx Uses “Arakawa C” Grid

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Application of CAMxHorizontal Nesting Structure


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Application of CAMxVertical Nesting Structure


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Application of CAMxOSAT/DDM Source Area Definition


Application of CAMxCAMx Setup -- Air Quality Definition

Initial Conditions– Model starts from this definition

– May be defined by ambient measurements• Usually available data is sparse, not 3-D

– May be defined as spatially invariant• Need to spin up model over a day or more to remove

• Boundary Conditions– Lateral boundaries (may be time, space varying)

– Concentrations aloft (space invariant)


Application of CAMxCAMx Setup – Chemistry Definition

Chemical Processes in CAMx– Gas phase chemistry

• Define mechanism, rate constants, photolysis rates

– Aerosol chemistry• Define species, representative sizes, density

– Dry deposition• Define Henry’s law solubility, diffusivity, reactivity

– Wet deposition• Define Henry’s law solubility



Contents of chemistry parameters file– Mechanism (choose from 1 through 5 or inert)– Photolysis reaction parameters and options– Gas phase species list

• Lower bound values• Deposition parameters (KH, diffusivity, reactivity)

– Aerosol species list (mechanism 4 only)• Lower bound values• Size parameters• Density

– Gas phase reaction rate constants



Mechanism options in v4.00 Mechanism

Gas Phase

Aerosol Phase

1

Mechanism 3 with added chlorine chemistry

none

2

CBM-IV with updated radical reactions

none

3

CBM-IV with updated radical reactions and isoprene chemistry

none

4

Mechanism 3 with added chlorine chemistry, condensible organics, biogenic olefins

CMAQ/RADM-AQ

SOAP

ISORROPIA 5

SAPRC99

none


Application of CAMxCAMx Setup -- Chemistry Definition

Photolysis Rates Input– Two options:

• Directly input using a lookup table, or

• Set by ratio to a reaction given in lookup table

• Thus, must directly input at least one reaction

• Input files:– Chemistry parameters

• Specifies photolysis rate options by reaction number



– Photolysis Rate Input File• Look-up tables for all directly specified photolysis

reactions

• Function of:– Zenith angle

– Altitude

– UV Albedo

– Haze opacity

– Ozone column

• Prepared using TUV light model from NCAR



– Albedo/haze/ozone column (ALHZOZ or AHO) file• Categorize albedo, haze and ozone column into “bins”

• Bin ranges must match photolysis rate file definition

• ALHZOZ contains gridded fields of bin indices

• Ozone column from NASA satellite data (TOMS)

• Albedo based on input land use data

• Haze opacity generally set to a constant default value


Application of CAMxCAMx Setup – Meteorological Inputs

Prognostic meteorological models (recommended)– Solve coupled predictive equations of motion,

thermodynamics, and continuity– Consistent fields of winds, temperature, humidity,

turbulence– ENVIRON has developed translation software for

RAMS and MM5• Diagnostic models (out-dated, not recommended)

– Adjust objective analyses for effects of terrain, stability, divergence minimization

– Independent fields of winds, temperature (density), humidity


Application of CAMx

Base Case Development

Emission Inventories


Application of CAMxBase Case Development – Emission Inventories

• Available Emission Processors– EPS2x

• FORTRAN based, straightforward to use• Modular and flexible, good QA capability• New extensions allow for rapid turnaround of emission

scenarios• Widely used in many US and international applications

– EMS-95• SAS based, uses ArcInfo GIS system

– Requires familiarity with 3rd party software

• Complex but flexible, good QA capability• Used for LMOS, OTAG, SAMI, CCOS



– SMOKE• Fortran based

• All processing performed from a single matrix of adjustment factors

• Limited QA capabilities

• Highly scripted, complex

• Currently under development– unstable over different platforms



• Estimating Biogenic Emissions– VOC emissions from trees and other plants

• Isoprene, terpenes (e.g., -pinene), oxygenated VOC (e.g., alcohols)

• Depend on plant species, temperature, sunlight, season– NO emissions from soils, enhanced by fertilizer– Models:

• BEIS – EPA recommended• GLOBEIS – developed by NCAR• BIOME – a component of EMS-95


Application of CAMxModel Performance Evaluation

• All air quality models have inherent limitations, uncertainties, and weaknesses– Discretization/resolution

– Simplifications (chemistry lumping, closure, etc.)

– Uncertainties and inaccuracies in input data

• Limit the range of their applicability• Affect their ability to replicate actual conditions• It is difficult to separate the effects of each source

of error



• A 3-step evaluation should be conducted before the control strategy assessment

Step 1 – Calculate performance statistics

Step 2 – Compare statistics to EPA performance goals

Step 3 – Undertake diagnostic evaluation of the model



• Statistical model performance evaluation– Quantitative comparison between predictions and

observations– Provides measure of performance, but

• Sheds little or no light on reasons for poor performance• Provides few indications of the robustness of good

performance• Often leaves users without a clear picture of model

reliability



• Statistical measures– Ability to replicate peak hourly ozone observations

• Unpaired (time and space) accuracy of the peak

• Unpaired (time) accuracy of the peak (paired in space)

• Average paired (time and space) accuracy of the peak

• Bias in the timing of the paired peak

• A minimum observation (60 ppb) is normally set



– Ability to replicate hourly ozone observations over a day

• Normalized bias and gross error (60 ppb minimum observation)

• Fractional bias and gross error (60 ppb minimum observation)

– Weights over- and under-predictions equally

• Ratio of mean observation to mean prediction (no minimum)

• Ratio of bias to mean observation (no minimum)



• Model Performance Goals– One-hour average ozone

• EPA (1991) has developed performance goals for 3 statistical measures (Guideline for the Regulatory Application of the UAM)

– Unpaired accuracy of the peak: < ±20 %

– Normalized bias: < ±15 %

– Normalized gross error: < ±35 %

• Although there are no goals for fractional bias and gross error, it is useful to compare these with the goals above



– Eight-hour average ozone• EPA (1999) provides draft guidance for 8-hour ozone

performance evaluation (Use of Models and Other Analyses in Attainment Demonstration for the 8-hour Ozone NAAQS)

• Guidance is much more qualitative and flexible than for 1-hour ozone

• Stops short of recommending specific statistical goals– Suggests use of same statistical measures as for 1-hour ozone

– Downplays importance of unpaired peak performance



• Some additional metrics are suggested– Compute statistics only between 8 AM – 8 PM

– New comparisons for hours above the standard (85 ppb)

• Suggests performance evaluation for precursors and secondary species

• Suggests using source apportionment and process analysis as corroborative evidence for performance

• Strongly recommends diagnostic evaluations



• Diagnostic model performance evaluation– Diagnostic evaluations supplement statistics

• Assess reasons for poor statistical performance

• Confirm robustness of good statistical performance

• Reconciliation of differences yields:– a more informative assessment of numerical model performance

and applicability

– an aide in designing model sensitivity runs

– more objective justification for altering model inputs to improve model performance



– Model results are compared against a conceptual model• A general description of what “probably” occurred based

on:– Analyses of observational data– Experience– Physical/chemical considerations– Intuition

• Provides a framework on which to unravel cumulative modeling uncertainties

• Conceptual models are imperfect and occasionally over-speculative, so they must be used with considerable discretion

– Significant discrepancies between a predictive and conceptual model may indicate problems with either or both



– Graphical products are essential tools and provide useful insights

• Station (time-series) plots – temporal alignment of predictions and observations

• Isopleth plots – spatial alighnment of predicted fields

• Scatter plots – performance as a function of concentration level

• Difference plots – effects of model input changes



– Sensitivity runs are made to test hypotheses and explore model response to input modification

• Helps prioritize effort to improve/refine model inputs within their range of uncertainty

• Typical sensitivity tests include:– Varying initial/boundary conditions

– Varying vertical diffusion rates

– Alternative meteorological simulations

– VOC and NOx precursor sensitivity

– Week-end vs. weekday emission sensitivity

– Scaling of emissions and/or emission components (mobile vs. biogenic vs. area, etc.)


Application of CAMxFuture Base Case

• Application to attainment demonstrations and/or control strategy evaluation

• Base-year emissions must be adjusted for conditions expected in a future year– Growth due to new sources and increased activity

– Controls expected as mandated by existing regulations

• Model runs use future base emissions to predict future air quality


Application of CAMxFuture Emissions Control Scenarios

• If attainment is not predicted for the future base case, then additional controls must be considered– “Attainment” of air quality standards must be

demonstrated according to EPA guidelines

• A variety of control scenarios are developed and their effectiveness explored



• An emissions control scenario is a collection of generic or specific control measures on individual sources or groups of source– Sources are often grouped by type or location– Examples of typical control measures are:

• Reduction of Vehicle Miles Traveled• Low NOx burners on power plant or industrial boilers• Lower auto tail pipe standards (LEV, ULEV, ZEV)• Fugitive VOC detection and control programs• Consumer product reformulation• Gasoline reformulation• Vapor recovery at fueling stations



• Since the number of combinations of feasible control measures is almost endless,– A means of narrowing the list to a manageable number

of scenarios is needed

– Prior experience and economic feasibility usually enter into the selection process

– CAMx source apportionment is also an aide in identifying the most effective control scenarios

presents:\camx/training1198.ppt introduction to camx presented by chris emery environ international...

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