Air Quality Modeling University of North CarolinaCommunity Modeling and Analysis System 1 Air...

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Air Quality Modeling

University of North Carolina Community Modeling and Analysis System


Zac Adelman and Craig MattocksCarolina Environmental Program

dx =

(Reco

sφ)d

λ e

∂C/ ∂t = - ∂(uC)/ ∂x - ∂(vC)/ ∂y - ∂(wC)/ ∂z

J = ∫ 4πIλ,T Aλ,T QYλ,T dλ ∞

0

krkr = Ar(300/T)Bexp(Cr/T) = Ar(300/T)Bexp(Cr/T)

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Outline Why model air pollution? Air quality modeling system components

– Meteorology– Emissions– Chemistry and transport

Technical and operational details Problems in air quality modeling Application examples Future directions in atmospheric modeling

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Adapted from Mackenzie and Mackenzie, “Our Changing Planet”, Prentice Hall, New Jersey, 1995.

Anthropogenic Emissions

Biogenic Emissions

Advection Diffusion

Boundary Conditions

Aerosol Processes:

Nucleation & Coagulation

Condensation & Deposition Evaporation &

Sublimation

O3 + NO NO2 + O2

O + O2 + M O3 + M

Gas Chemistry: Aqueous Chemistry:

SO2 (g) SO2 (aq)

Source Sink

Geogenic Emissions

4



Why model air pollution?

Air pollution models are frameworks that integrate our understanding of individual processes with atmospheric measurements

Air pollution systems are non-linear– Need to establish the link between emissions sources and

ambient concentrations

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Air Quality Modeling Components

Meteorology modeling Emissions processing Initial/boundary conditions processing Photolysis rate processing Chemistry and transport modeling

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WRF-ARW Basics Fundamentals of Numerical Weather Prediction

– Real vs. artificial atmosphere– Map projections– Horizontal grid staggering– Vertical coordinate systems

Definitions & Acronyms Flavors of WRF

– ARW core– NMM core

Other Numerical Weather Prediction Models– MM5– ARPS– Global Icosahedral

WRF Model Governing Equations– Vertical coordinate and grid discretization– Time integration– Microphysics

Current Defects of WRF

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Real vs. Artificial Atmosphere

FVkfdtVd

p

True analytical solutions are unknown!Numerical models are discrete approximations of a continuous fluid.

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Map Projections

Example of a regional high resolution grid

(projection of a spherical surface onto a 2D plane) nested within a global (lat,lon)

grid with spherical coordinates

x = r cos y = r Differences in map projections require

caution when dealing with flow of information across grid boundaries.

WRF offers polar stereographic, Lambert conformal, Mercator and rotated Lat-Lon map projections.

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Arakawa “A” Grid

Unstaggered grid - all variables defined everywhere.

Poor performance, first grid geometry employed in NWP models.

Noisy - large errors, short waves propagate energy in wrong direction, additional smoothing required.

Poorest at geostrophic adjustment - wave energy trapped, heights remain too high.

Can use a 2x larger time step than staggered grids.

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Arakawa “B” Grid

Staggered, velocity at corners. Preferred at coarse resolution. Superior for poorly resolved inertia-gravity

waves. Good for geostrophy, Rossby waves:

collocation of velocity points. Bad for gravity waves: computational

checkerboard mode. Used by MM5 model.

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Arakawa “C” Grid

Staggered, mass at center, normal velocity, fluxes at grid cell faces, vorticity at corners.

Preferred at fine resolution. Superior for gravity waves. Good for well resolved inertia-gravity waves. Simulates Kelvin waves (shoulder on boundary)

well. Bad for poorly resolved waves: Rossby waves

(computational checkerboard mode) and inertia-gravity waves due to averaging the Coriolis force.

Used by WRF-ARW, ARPS, CMAQ models.

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Arakawa “D” Grid

Staggered, mass at center, tangential velocity along grid faces.

Poorest performance, worst dispersion properties, rarely used.

Noisy - large errors, short waves propagate energy in wrong direction.

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Arakawa “E” Grid

Semi-staggered grid. Equivalent to superposition of 2 C-grids,

then rotated 45 degrees. Center set to translated (lat,lon) = (0,0) to

prevent distortion near edges, poles. Developed for Eta step-mountain

coordinate to enhance blocking, overcome PGF errors caused by sigma coordinates.

Controls the cascade of energy toward smaller scales.

Used by WRF-NMM and Eta models.

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p

Continental orography

The vertical structure of a typical pressure vertical coordinatesystem (or p-system). The dashed lines represent constantpressure-levels ( p0 to p7 ). Note that p7 < p0 .

p7

p6

p5

p4p3p2p1p0

Pressure (p) coordinate system

Advantages:Density no longer appears in the pressure gradient force term:

The continuity equation is a pure diagnostic equation:

Disadvantages:Lower boundary of atmosphere is not a coordinate surface

FVkfd

Vdp

0p

Vp

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Sigma () coordinate system

43

2

1

0

The vertical structure of a typical sigma vertical coordinatesystem (or -system). The dashed lines represent constantsigma () levels. = 0 where p= 0 and = 1 where p=PS .

p


= P-Ptop / Ps-Ptop

Advantages:

Lowest sigma-level ( = 1) follows the topography.

Disadvantages:

Absence of constant pressure levels in the upper atmosphere.

Error in PGF along topography - near cancellation of large terms.

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Hybrid coordinate systems

4

3

2

1

0

The vertical structure of a typical hybrid vertical coordinatesystem (or -system). The dashed lines represent constant hybrid() levels.

p


p = AO + BPS ; PS = Surface pressure

Prescribed pre-defined conditions are required to determine thevalue of A O and B at each model level.

p-system: More suitable for upper

atmosphere simulations

-systems: More suitable for surface simulations

BPAp SO

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Definitions & Acronyms

WRF: Weather Research & Forecasting numerical weather prediction model

ARW: Advanced Research WRF [nee Eulerian Model (EM)] core

NMM: Nonhydrostatic Mesoscale Model core WRF-SI: Standard Initialization (4 components) -

prepares real atmospheric data for input to WRF WRF-VAR: Variational 3D/4D data assimilation

system (not used for this class) IDV: Integrated Data Viewer - Java application for

interactive visualization of WRF model output

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Flavors of WRF (ARW) ARW solver (research - NCAR, Boulder, Colorado)

– Fully compressible, nonhydrostatic equations with hydrostatic option– Arakawa-C horizontal grid staggering– Mass-based terrain following vertical coordinate

• Vertical grid spacing can vary with height• Top is a constant pressure surface

– Scalar-conserving flux form for prognostic model variables– 2nd to 6th-order advection options in horizontal &vertical– One-way, two-way and movable nest options– Runge-Kutta 2nd & 3rd-order time integration options– Time-splitting

• Large time step for advection• Small time step for acoustic and internal gravity waves• Small step horizontally explicit, vertically implicit• Divergence damping for suppressing sound waves

– Full physics options for land surface, PBL, radiation, microphysics and cumulus parameterization

– WRF-chem under development: http://ruc.fsl.noaa.gov/wrf/WG11/

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Flavors of WRF (NMM) NMM solver (operational - NCEP, Camp Springs, Maryland)

– Fully compressible, nonhydrostatic equations with reduced hydrostatic option – Arakawa-E horizontal grid staggering, rotated latitude-longitude – Hybrid sigma-pressure vertical coordinate– Conservative, positive definite, flux-corrected scheme used for horizontal and

vertical advection of TKE and water species– 2nd-order spatial that conserves a number of 1st-order and quadratic

quantities, including energy and enstrophy– One-way, two-way and movable nesting options– Time-integration schemes: forward-backward for horizontally propagating fast

waves, implicit for vertically propagating sound waves, Adams-Bashforth for horizontal advection and Coriolis force, and Crank-Nicholson for vertical advection

– Divergence damping & E subgrid coupling for suppressing sound waves– Full physics options for land surface, PBL, radiation, microphysics (only

Ferrier scheme) and cumulus parameterization– Note: Many ARW core options are not yet implemented! Nesting still under

development– NMM core will be used for HWRF (hurricane version of WRF), operational in

summer of 2007

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Other NWP Models (MM5) MM5 (research - PSU/NCAR, Boulder, Colorado)

– Progenitor of WRF-ARW, mature NWP model with extensive configuration options

– Support terminated, no future enhancements by NCAR’s MMM division

– Nonhydrostatic and hydrostatic frameworks– Arakawa-B horizontal grid staggering– Terrain following sigma vertical coordinate– Unsophisticated advective transport schemes cause dispersion,

dissipation, poor mass conservation, lack of shape preservation – Outdated Leapfrog time integration scheme– One-way and two-way (including movable) nesting options– 4-dimensional data assimilation via nudging (Newtonian

relaxation), 3D-VAR, and adjoint model– Full physics options for land surface, PBL, radiation,

microphysics and cumulus parameterization

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Other NWP Models (ARPS) ARPS (research - CAPS/OU, Norman, Oklahoma)

– Advanced Regional Prediction System– Sophisticated NWP model with capabilities similar to WRF– Primarily used for tornado simulations at ultra-high (25 meter)

resolutions and assimilation of experimental radar data at mesoscale

– Elegant, source code, easy to read/understand/modify, ideal for research projects, very helpful scientists at CAPS

– Arakawa-C horizontal grid staggering– Currently lacks full mass conservation and Runge-Kutta time

integration scheme– ARPS Data Assimilation System (ADAS) under active

development/enhancement (MPI version soon), faster & more flexible than WRF-SI, employed in LEAD NSF cyber-infrastructure project

– wrf2arps and arps2wrf data set conversion programs available– http://www.caps.ou.edu/ARPS/arpsdownload.html

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Global Icosahedral Model

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WRF Model Governing Equations(Eulerian Flux Form)

∂U/∂t + ( · Vu) − ∂(pφ∇ η)/∂x + ∂(pφx)/∂η = FU

∂V/∂t + ( · Vv) − ∂(pφ∇ η)/∂y + ∂(pφy)/∂η = FV

∂W/∂t + ( · Vw) − g(∂p/∂η − μ) = F∇ W

∂Θ/∂t + ( · Vθ) = F∇ Θ

∂μ/∂t + ( · V) = 0∇

∂φ/∂t + μ−1[(V · φ) − gW] = 0∇

Momentum:

Potential Temperature:

Continuity:

Geopotential Height:

∂φ/∂η = -μ

where:μ = column massV = μv = (U,V,W)Ω = μ d(η)/dtΘ = μθ

Diagnostic Hydrostatic (inverse density ):

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WRF Vertical Coordinate

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Vertical Grid Discretization

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Φ∗ = Φt + t/3 R(Φt )

Φ∗∗ = Φt + t/2 R(Φ∗)

Φt+t = Φt + t R(Φ∗∗)

“2.5” Order SchemeLinear: 3rd orderNon-linear: 2nd order

Runge-Kutta Time Integration

Square Wave Advection Tests:

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Runge-Kutta Time Step Constraint RK3 is limited by the advective Courant number (ut/x) and the

user’s choice of advection schemes (2nd through 6th order) The maximum stable Courant numbers for advection in the RK3

scheme are almost double those in the leapfrog time-integration scheme

Maximum Courant number for 1D advection in RK3

Time Scheme

Spatial order

3rd 4th 5th 6th

Leapfrog Unstable 0.72 Unstable 0.62

RK2 0.88 Unstable 0.30 Unstable

RK3 1.61 1.26 1.42 1.08

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Microphysics Includes explicitly resolved water vapor, cloud and precipitation processes Model accommodates any number of mixing-ratio variables Four-dimensional arrays with 3 spatial indices and one species index Memory (size of 4th dimension) is allocated depending on the scheme Carried out at the end of the time-step as an adjustment process, does not

provide tendencies Rationale: condensation adjustment should be at the end of the time step

to guarantee that the final saturation balance is accurate for the updated temperature and moisture

Latent heating forcing for potential temperature during dynamical sub-steps (saving the microphysical heating as an approximation for the next time step)

Sedimentation process is accounted for, a smaller time step is allowed to calculate vertical flux of precipitation to prevent instability

Saturation adjustment is also included

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WRF Microphysics Options Mixed-phase processes are those that result from the interaction of ice

and water particles (e.g. riming that produces graupel or hail) For grid sizes ≤ 10 km, where updrafts may be resolved, mixed-phase

schemes should be used, particularly in convective or icing situations For coarser grids the added expense of these schemes is not worth it

because riming is not likely to be resolved

Scheme Number of Moisture Variables

Ice-Phase Processes

Mixed-Phase Processes

Kessler 3 N N

Purdue Lin 6 Y Y

WSM3 3 Y N

WSM5 5 Y N

WSM6 6 Y Y

Eta GCP 2 Y Y

Thompson 7 Y Y

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Current Defects of WRF Serious deficiencies in PBL parameterizations and land surface models

produce biases/errors in the predicted surface and 2-meter temperatures, and PBL height. WRF cannot maintain shallow stable layers.

3D/4D Variational data assimilation and Ensemble Kalman Filtering (EnKF) still under development, EnKF available to community from NCAR as part of the Data Assimilation Research Testbed (DART).

Not clear yet what to do in “convective no-man’s land” – convective parameterizations valid only at horizontal scales > 10 km, but needed to trigger convection at 5-10 km scales.

Multi-species microphysics schemes with more accurate particle size distributions and multiple moments should be developed to rectify errors in the prediction of convective cells.

Heat and momentum exchange coefficients need to be improved for high-wind conditions in order to forecast hurricane intensity. Wind wave and sea spray coupling should also be implemented. Movable, vortex-following 2-way interactive nested grid capability has recently been incorporated into the WRF framework.

Upper atmospheric processes (gravity wave drag and stratospheric physics) need to be improved for coupling with global models.

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Emissions Processing

Area Mobile Point Biogenic

Emissions Processing Steps

AQM-ready Emissions:

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Emissions Terminology

Inventory: estimate of pollutant emissions at a given spatial unit

Model Grid: 3-d representation of the earths surface based on discrete and uniform spatial units, i.e. grid cells

Speciation: conversion of inventory pollutant species to model pollutant species

Gridding: conversion of inventory spatial units to model grid cells

Temporalization: conversion of inventory temporal units to those requires by an air quality model

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Emissions Terminology

Plume rise: calculation of the vertical distribution of emissions from point sources and the subsequent allocation of the emissions to the model layers

Spatial surrogate: GIS-based estimate of the fraction of a grid cell covered by a particular land-use category (e.g. population or rural housing)

Profiles: emissions distributions in space, time, or to chemical species

Cross-referencing: relating profiles to specific emissions sources

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Area sources

Most basic inventory unit Country/province/municipality wide estimate Requires spatial surrogates to map to a model

grid Examples

– Construction and agricultural emissions– Road dust– Fires

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Mobile sources

On-road: Estimate by road way and vehicle type– Requires emissions factors for local vehicles and activities/speeds for

local roads– Gridding by road way distribution or links– Can use local meteorology to adjust emissions factors for temperature

and humidity– Examples: Heavy-duty diesel trucks on primary highways, light-duty

gasoline cars on rural roads

Non-road: Area-like estimates– Examples: Construction and mining vehicles, recreational vehicles

(boats, ATV’s),

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Point sources

Emissions at specific latitude-longitude coordinates Often elevated sources that require stack parameters (e.g.

stack height, exit gas velocities, exit gas temperatures, etc.) Can use annual, daily, or hourly emissions estimates Examples:

– Electricity generating units (EGU’s)– Smelters– Wildfires

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Biogenic sources

Estimates of emissions from vegetation and soils Uses gridded land-use data and emissions factors by

vegetation type Uses local meteorology to calculate emissions based on

photosynthetically active radiation (PAR) and to adjust for temperatures

Examples:– VOC emissions from specific tree species– Soil NO

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Gridded sources

Pre-gridded emissions from global databases Normalize to the model grid to combine with

other sources Can encompass any of the emissions categories Top-down vs. bottom-up emissions estimate

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Emissions Processing

Purpose: convert emissions data to formats required by air quality model

Primary functions– Import data into system– Spatial allocation (gridding)– Chemical allocation (speciation)– Temporal allocation– Merge– Quality assurance

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Emissions Processing Steps Data Import

– Inventory categories

• Area

• Point

• Mobile

• Biogenic

• Gridded– ASCII or gridded binary– Country/state/county estimates– Annual estimates– Pollutants include bulk VOC and

PM2.5

Spatial Allocation– Inventory spatial units model

grid– Requires spatial surrogates

EI Grid Cell Mapping

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Emissions Processing Steps Chemical Allocation

– Tons Moles– Converts inventory

pollutants to air quality model species

– Model-dependent speciation profiles

– NOx NO + NO2

– VOC PAR, OLE, etc.– PM2.5 NO3, SO4, etc.

Temporal allocation– Inventory units

hourly emissions– Requires temporal

profiles

0

0.02

0.04

0.06

0.08

0.1

0.12

J F M A M J J A S O N D

% E

mis

sion

s0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

M T W Th F Sa Su

% E

mis

sion

s

0

0.01

0.02

0.03

0.04

0.05

0.06

% E

mis

sion

s

Monthly Weekly

Diurnal

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Emissions Processing Steps

Merging– Combine all

intermediate steps to create AQM-ready emissions

– Combine individual source categories

– Formatting– Units– Output file naming

Quality Assurance– Report base inventory

values and changes at each processing step

– Customize reporting

• e.g. by state and SCC, by SCC and temporal profiles, by grid cell and surrogate I.D.

– Means to determine why a result occurred

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Other Emissions Processing Steps

Plume Rise– Allocate elevated

emissions sources to vertical model layers

– Compute layer fractions

– Require meteorology to calculate plume buoyancy

– Stationary point, fires, in-flight aircraft

Projections– Grow and/or control

inventories for future year modeling

– Source-based projection information

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Emissions Processing Paradigms

Linear– Sequential steps that follow a particular order– Requires completing one step before completing the next

Parallel– Flexible sequence with steps in any order

Import Grid Speciate Temporal Merge

Import

Grid

Speciate

Temporal

Merge AQM

AQM

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Initial and Boundary Conditions

Initial conditions define the chemical conditions at the start of a simulation

– Defined using vertical profiles of clean background concentrations

Boundary conditions define the chemical conditions on the horizontal faces of the modeling domain

– Static and dynamic boundaries are possible Initial conditions decay exponentially with

simulation time; boundary conditions on the upwind boundary continue to affect predictions through an entire simulation.

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Initial/Boundary Condition Processing

Processing requires generating IC/BC estimates on a model-grid

Nested simulations extract BC’s from a parent grid

Multi-day simulations extract IC’s from the last hour of the previous day

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Photolysis Rate Processing

Photolysis: Chemical dissociation caused by the absorption of solar radiation

Photolysis rate: rate of reaction for pollutants that undergo photolysis

Processing calculates clear sky photolysis rates at different latitudes and altitudes

Air quality models adjust rates with cloud cover estimates from meteorology


0

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What are air quality models?

Statistical models: describe concentrations in the future as a statistical function of current chemical and/or meteorological conditions

Chemistry-transport models (CTM): based on fundamental descriptions of physical and chemical processes in the atmosphere

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How do CTMs work? Air Quality Model processes

– Dynamical/thermodynamical: meteorology, land surface conditions (soil, water)

– Transport: emissions, advection, diffusion, dry deposition, sedimentation

– Gas phase chemistry: photochemistry, phase changes

– Radiative: optical depth, visibility, energy transfer– Aerosol/clouds: nucleation, coagulation,

heterogeneous chemistry, aqueous chemistry

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Overlay 3-D boxes on a grid

Lagrangian/Trajectory Models

Moves relative to the coordinate

Different locations at different times

Only emissions enter the cell

No material leaves the cell

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Overlay 3-D boxes on a grid

Eulerian Models

Fixed relative to the coordinate

All locations at all times

Materials move through all cell faces*

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Conceptual approach to CTMs

Extend the 2-D box model to three dimensions

∆x

∆y

u1C1 u2C2

∆y

∆x ∆zu2C2u1C1Ci Ci

2-D 3-D

u = wind vector

Ci = concentration of species i

Basic Continuity Equation (flux in 1 direction):

∆C ∆x ∆y ∆z = u1C1∆y ∆z ∆t – u2C2 ∆y ∆z ∆t

Divide by ∆t and volume: ∂C/ ∂t = - ∂(uC)/ ∂x

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Expanded Continuity Equation

∆y

∆x ∆z

E

u2C2u1C1

Ri

3-D

S

u,v,w = wind vectors

E = emissions

S = loss processes

Ri = Chemical formation of species I

D = Molecular diffusion coefficient

Expanded Continuity Equation Derivation:

Expand to flux three dimensions:

∂C/ ∂t = - ∂(uC)/ ∂x - ∂(vC)/ ∂y - ∂(wC)/ ∂z

= - ∙ (vC) (flux divergence form)

Add additional production and loss terms:

∂C/ ∂t + ∙ (vC) = D 2 C + R + E - S

w1C1

w2C2

X v1C1

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Advection: - ∙ (vC)

Boundary Conditions

Aerosol Processes:Gas Chemistry: Aqueous

Chemistry:

Remis

Rdep

Rchem

Rwash

Rnuc + Rc/e + Rdp/s + Rds/e + Rhr

Rhr

Diffusion: D 2 C

R = Rate

chem=chemical production/loss hr=heterogeneous reactions nuc=nucleation c/ev=condensation/evaporation dp/s=depositional growth/sublimation ds/e=dissolution/evaporation wash=washout dep=deposition emis=emissions

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Full Continuity Equations Gas Continuity Equation

∂C/ ∂t + ∙ (vC) = D 2 C + Rchemg + Remisg + Rdepg+ Rwashg + Rnucg + Rc/eg + Rdp/sg + Rds/eg + Rhrg

Particle Continuity Equation (number)

∂n/ ∂t + ∙ (vn) = D 2 n + Remisn + Rdepn+ Rsedn + Rnucn

+ Rwashn + Rcoagn

Particle Continuity Equation (volume concentration)

∂V/ ∂t + ∙ (vV) = D 2 V + Remisv + Rdepv+ Rsedv + Rnucv

+ Rwashv + Rcoagv+ Rc/ev + Rdp/sv + Rds/ev

+ Regv + Rqgv + Rhrv

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CTM Coordinate Systems

Convert all motion equations from Cartesian to spherical coordinates

Horizontal grids typically on the order of 1 to 36 km

Recent applications extending to 500m and 108 km

Lambert conformal, polar stereographic, and Mercator are the most common modeling projections

dx = (Recosφ)dλe dy = Red φ

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CTM Coordinate Systems

Vertical grids extend from the surface to 10 km Altitude coordinate: layers are defined as

surfaces of constant height with variable pressure

Pressure coordinate: layers are defined as surfaces of constant pressure with variable height

Sigma-pressure coordinate: layers defined as surfaces of constant σ, where

ppaa – p – pa,topa,top

pa, surf - pa,topσσ = = (0,1)

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Boundary Layer Processes Boundary layer more difficult

to model because of greater turbulence and larger emissions forcing terms than in free troposphere; land surface interactions and planetary boundary layer (PBL) dynamics dominate

Surface temperature and soil moisture affect energy and moisture flux; affect mixing heights, winds, and pollutant concentrations

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Modeled cloud processes

Radiative transfer: reflecting, scattering, and trapping heat

Atmospheric component of the hydrologic cycle

Wet deposition of gases and particles

Medium for aqueous phase chemistry

Vertical transport/convective mixing

Energy balance: temperature effects and photolysis rates

Aerosol Processes Condensation

Evaporation

Gas Phase

pA pA(a) A(a) A(r)

B(a) B(r)

pC pC(a) C(a) C(r)

Water Droplet

Aerosol particle

New aerosol particle

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Energy/Radiative Effects Visibility

Optical depth scattering and absorption between top of atmosphere and altitude x

Photolysis rates

Radiative transfer

Sun

Single scattering

Multiple scattering

Direct

Beam

DiffuseΘs

Θ(μ,Θ) Fs (-μ,Θ)

dI/dx = σ bIB - σ ext I

σ = extinction coefficient

I = visible radiance


0

4πI = actinic flux A = absorption cross section QY = quantum yield

61



Gas Phase Chemistry

Thousands of different organic and inorganic gases react to form smog and PM

Gas phase chemistry is a “stiff” system

Parameterized chemistry mechanisms represent the system with a few surrogate organic pollutants

Surrogates based on molecular or atomic structures of pollutants

Carbon bond lumpingPropane = 3 PAR:

H3C-CH2-CH3

1-Butene = 2 PAR + 1 OLE:

H2C=CH-CH3-CH3

– averaged reaction rates

Molecular lumping– Surrogates represent

similarly reactive species

– Explicit or averaged reaction rates

62



Gas Phase ChemistryImportant Inorganic Reactions

NO + O3 NO2 + O2

NO2 + hv NO + O (λ<420 nm)

O + O2 + M O3 + M

O3 + hv O2 + O(1D) (λ<310 nm)

O3 + hv O2 + O3P (λ>310 nm)

O(1D) + H2O 2OH

OH + O3 HO2 + O2

OH + NO HONO

HONO + hv OH + NO (λ<400 nm)

NO2 + OH HNO3

HNO3 + hv NO2 + OH (λ<335 nm)

Important Organic Reactions

(methane example)

OH + CH4 H2O + CH3∙

CH3∙ + O2 CH3O2∙

CH3O2∙ + NO NO2 + CH3O∙

CH3O∙ + O2 HO2 + HCHO

CH3O2∙ + HO2 O2 + CH3O2H

CH3O2H + hv CH3O∙ (λ<360 nm)

CH3O2H + OH H2O + CH3O2∙

63



Gas Phase Chemistry

Key reaction sequence for smog

ROG∙ + NO NO2 + ROG∙∙

NO + O3 NO2 + O2

NO2 + hv NO + O

O + O2 + M O3 + M

64



Gas Phase Chemistry Kinetics

aA + bB eE + fF Rate = kr[A]a[B]b

Rate constant calculations

d[A]t/dt = -kF[A]t = -kS[A]t[B]0 = -kT[A]t[B]0[C]0

1st order: A D + E kF = -(1/t) ln [A]t/[A]0

2nd order: A + B D + E kS = -(1/[B]0t) ln [A]t/[A]0

3rd order: A + B + C D + E kT = -(1/[B]0[C]0t) ln [A]t/[A]0

Arrhenius equation for temperature dependence

kr = Ar(300/T)Bexp(Cr/T)

Troe equation for temperature and pressure dependence

kr = {(k∞,T k0,T [M])/(k∞,T+k0,T[M])}Fc [1+(logk0[M]/k∞)^2]^-1

Photolysis rate equation


0

65



Aqueous Chemistry

Gases equilibrate with the aqueous phase by Henry’s law:

[A(aq)] = HApA

Dissolved gases react in solution to form new compounds

Sequence: droplet formation gases dissolve in droplet chemical reactions evaporation

66



Aerosol Dynamics

Size distribution: ratio of # aerosols in a diameter range to the size of the range; discrete function of the number of particles

Ni = ni∆Dp

Number distribution: continuous function of the diameter of the particles

N = ∫ nN(Dp)dDp

Aerosol moments: properties of the distributions (e.g. mean, variance)

∞

0

67



Aerosol Dynamics

Mass transfer solutions to transport mass between gas/aqueous/solid phases

Solve gravitational settling, diffusion, and advection for moving particles around

4 classes of nucleation for particle formation:– Homogenous Homomolecular– Homogenous Heteromolecular– Heterogenous Homomolecular– Heterogenous Heteromolecular

68



CTM Setup and Configuration All CTM’s are free and open source Compile on UNIX/Linux with Fortran Script interfaces for compiling and running Begin with download, installation, and compilation Set up computing environment

– I/O directories– Check for processor availability and disk space

69



CTM Setup and Configuration

CTM’s are at the end of a long sequence of preprocessing steps

– Prepare meteorology inputs with research and forecast met models

– Prepare emissions inputs with specialized emissions processors

– Prepare initial and boundary condition inputs with preprocessors packaged with CTM

– Generate clear sky photolysis rates with a preprocessor packaged with CTM

70



CTM Process Schematic

MetModeling

Emissions Modeling

IC Preparation

BC Preparation

PhotolysisRates

2-D/3-D Met Files

Model Ready Emissions

IC Input File

BC Input File

Clear SkyJ Rates

CTM Modeling

HourlyConcentrations

CumulativeWet Dep

CumulativeDry Dep

VisibilityMetrics

DiagnosticOutputs

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Modeling Conventions

Establish base case model performance Simulate sensitivities off the base case Select episodes or time periods that illustrate the

problem being addressed– O3 episodes– PM episodes– High flow regimes/transport scenarios– Annual episodes for one-atmosphere modeling

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One Atmosphere Approach

Changing paradigm from multiple models that address individual process to a single unified model

Conceptually more realistic: all atmospheric processes are coupled

In practice very difficult because of confounding errors

“Right answer for the wrong reasons”

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What is the right answer for CTMs?

Evaluation techniques – Comparisons to ambient measurements– Sanity checks– Looking for known trends (diurnal/seasonal patterns,

chemical signatures (ratios) Comparison to measurements

– Paired in space and time– Compare predicted vs observed maximums– Paired in space but not in time– Statistical metrics include paired/unpaired peak

prediction accuracy, mean normalized bias, mean error

74



Problems in air quality modeling

Inconsistencies in spatial scales and speciation when comparing models to measurements

Incomplete measurement database (PBL, radiation budget, short lived pollutants, observations aloft)

Huge uncertainties in all input data

Garbage in = Garbage out

75



Problems in air quality modeling

Met and chemistry models are tuned for certain conditions– Meteorology models generally don’t work well

under stagnant, low flow conditions– Chemistry models break down at night and

during background ambient conditions Incomplete science

– For various reasons, some important atmospheric processes are either not represented at all or are using crude approximations

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Significant CTM Studies

Regional Ozone Model: early 1980’s, first regional scale model, early studies on regional transport

Regional Acid Deposition Model: mid 1980’s, 1st multi-pollutant model, predecessor to current modeling systems

National Acid Precipitation Assessment (NAPAP): 1980’s, established links between S emissions in the Midwest to acid rain in the Northeast; first modeling studies of emissions trading programs

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Significant CTM Studies Ozone Transport

Assessment Group (OTAG): mid 1990’s, established multi-scale problem of ozone, relationships between 1-hr vs. 8-hr ozone standard and transport, prevailing regional conditions for poor air quality, weekday-weekend trends in air quality

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Model Application ExamplesRegional Ozone Sensitivities - NCDAQ

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Model Application Examples

Intercontinental transport of pollutants

80




Continental one atmosphere modeling

1 day, 24-hour average ozone

Observations overlaid on plot

81




Summer, 2002 Sulfate Winter, 2002 Nitrate

Continental one atmosphere modeling

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Future Directions

Quantify model uncertainties Expand the ability of the models to represent and

integrate all atmospheric processes 2-way coupling between global, regional, and

neighborhood scale models Source apportionment technologies Couple with other media/disciplines

(water,soil,risk, economics) Community/open source development

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