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Introduction to CAMx. Presented by Chris Emery ENVIRON International Corporation June 2003. Topics. Conceptual Overview Ozone Chemistry Modeling Atmospheric Dispersion Description of CAMx Model Design Comparison to Other Models Application of CAMx Model Setup

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Introduction to camx

Introduction to CAMx

Presented by

Chris Emery

ENVIRON International Corporation

June 2003


Topics
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 overview introduction to ozone chemistry
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


Conceptual overview introduction to ozone chemistry1
Conceptual OverviewIntroduction to Ozone Chemistry

  • 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 overview ozone formation from voc and nox
Conceptual OverviewOzone Formation from VOC and NOx

no sunlight  no ozone productionno NOx no ozone productionno VOC  no ozone production


Conceptual overview central role of hydroxyl radical oh
Conceptual OverviewCentral Role of Hydroxyl Radical (•OH)


Conceptual overview ozone and precursor relationships ekma diagram
Conceptual OverviewOzone and Precursor Relationships: EKMA Diagram


Conceptual overview ozone and precursor relationships ozone vs noz
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

NOz ppb


Conceptual overview control strategy implications
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 overview control strategy implications1
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 overview condensed chemical mechanisms
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 overview voc lumping strategies lumped structure cb4
Conceptual OverviewVOC Lumping Strategies: Lumped Structure (CB4)

CH3 - CH2 - CH3

CH3-CH = CH2

CH3 - CH2 -CH2 - CH3

CH3 - CH2- CH = CH2

PAR

OLE


Conceptual overview voc lumping strategies lumped molecule saprc
Conceptual OverviewVOC Lumping Strategies: Lumped Molecule (SAPRC)

CH3 - CH2 - CH3

CH3 -CH = CH2

CH3 - CH2 -CH2 - CH3

CH3 - CH2 - CH = CH2

ALK1

OLE1


Conceptual overview lumped mechanisms for modeling
Conceptual OverviewLumped Mechanisms for Modeling


Conceptual overview introduction to atmospheric dispersion
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 overview general form of the continuity equation eulerian form
Conceptual OverviewGeneral Form of the Continuity Equation(Eulerian Form)

Mathematical solution (integration) of the general form is difficult

  • Simplifying assumptions are required


Conceptual overview three general types of air quality models
Conceptual OverviewThree General Types of Air Quality Models

  • Lagrangian types -- Coordinate system follows

    air parcels

  • Eulerian types – Coordinate system is fixed in space

  • Hybrid types – Incorporate features of Lagrangian types into an

    Eulerian framework


Conceptual overview lagrangian 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


Conceptual overview lagrangian models1
Conceptual OverviewLagrangian Models

  • 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 overview gaussian plume model
Conceptual OverviewGaussian Plume Model


Conceptual overview lagrangian models2
Conceptual OverviewLagrangian Models

  • 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 overview eulerian models
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 overview eulerian grid model concept
Conceptual OverviewEulerian (Grid) Model Concept


Conceptual overview grid cell processes
Conceptual OverviewGrid Cell Processes


Conceptual overview coupling between grid cells
Conceptual OverviewCoupling Between Grid Cells


Conceptual overview eulerian grid model results
Conceptual OverviewEulerian (Grid) Model Results


Conceptual overview eulerian models1
Conceptual OverviewEulerian Models

  • 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


Conceptual overview eulerian models2
Conceptual OverviewEulerian Models

  • 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 overview hybrid models
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 overview hybrid models1
Conceptual OverviewHybrid Models

  • Examples of hybrid photochemical grid models include:

    • CAMx

    • MODELS3/CMAQ

    • UAM-V


Conceptual overview summary
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 camx comprehensive air quality model with extensions camx version 4 00
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
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)


Description of camx camx v4 001
Description of CAMx CAMx v4.00

  • 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


Description of camx camx v4 002
Description of CAMx CAMx v4.00

  • 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 camx technical approach
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


Description of camx technical approach1
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach2
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach3
Description of CAMxTechnical Approach

  • 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)


Description of camx technical approach4
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach5
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach6
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach7
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach8
Description of CAMxTechnical Approach

  • Aerosol species treated:

    • Sulfate

    • Nitrate

    • Ammonium

    • Sodium

    • Chloride

    • Secondary Organics

    • Primary Organics

    • Elemental Carbon

    • Primary Fine (+dust)

    • Primary Coarse (+dust)


Description of camx technical approach9
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach10
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach11
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach12
Description of CAMxTechnical Approach

  • 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


Description of camx technical approach13
Description of CAMxTechnical Approach

  • 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 camx input requirements
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 camx input requirements1
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 camx model output
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


Description of camx model output1
Description of CAMxModel Output

  • 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


Description of camx model output2
Description of CAMxModel Output

  • 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


Description of camx model output3
Description of CAMxModel Output

  • 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 camx computer requirements
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


Description of camx computer requirements1
Description of CAMxComputer Requirements

Example: CAMx application on 1 Ghz Pentium III PC

CBM-IV Mech 3 (no aerosols), CMC solver


Description of camx computer requirements2
Description of CAMxComputer Requirements

  • 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


Description of camx comparison to other grid models
Description of CAMxComparison to Other Grid Models


Application of camx
Application of CAMx

  • CAMx Setup

  • Base Case Development

  • Performance Evaluation

  • Future Base Case

  • Emission Control Scenario Evaluation


Application of camx camx setup domain definition
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 camx camx setup domain definition1
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


Application of camx camx uses arakawa c grid
Application of CAMxCAMx Uses “Arakawa C” Grid


Application of camx horizontal nesting structure
Application of CAMxHorizontal Nesting Structure


Application of CAMxVertical Nesting Structure


Application of CAMxOSAT/DDM Source Area Definition


Application of camx camx setup air quality 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 camx camx setup chemistry definition
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


Application of camx camx setup chemistry definition1
Application of CAMxCAMx Setup – Chemistry Definition

  • 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


Application of camx camx setup chemistry definition2
Application of CAMxCAMx Setup – Chemistry Definition

Mechanism options in v4.00


Application of camx camx setup chemistry definition3
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


Application of camx camx setup chemistry definition4
Application of CAMxCAMx Setup – Chemistry Definition

  • 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


Application of camx camx setup chemistry definition5
Application of CAMxCAMx Setup – Chemistry Definition

  • 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 camx camx setup meteorological inputs
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 DevelopmentEmission Inventories


Application of camx base case development emission inventories1
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


Application of camx base case development emission inventories2
Application of CAMxBase Case Development – Emission Inventories

  • 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


Application of camx base case development emission inventories3
Application of CAMxBase Case Development – Emission Inventories

  • 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 camx model performance evaluation
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


Application of camx model performance evaluation1
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation2
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation3
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation4
Application of CAMxModel Performance Evaluation

  • 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)


Application of camx model performance evaluation5
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation6
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation7
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation8
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation9
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation10
Application of CAMxModel Performance Evaluation

  • 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


Application of camx model performance evaluation11
Application of CAMxModel Performance Evaluation

  • 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 camx future base case
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 camx future emissions control scenarios
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


Application of camx future emissions control scenarios1
Application of CAMxFuture Emissions Control Scenarios

  • 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


Application of camx future emissions control scenarios2
Application of CAMxFuture Emissions Control Scenarios

  • 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


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