01 introduction air quality model

8/17/2019 01 Introduction Air Quality Model

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Air Quality Modeling


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Model Vs Measurement

x

Model Measurement


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Air Quality Modeling

• Areas that are not incompliance with the AmbientStandards should use modelingto

– Estimate the efects o growth(and expected controlmeasures) on uture air uality

– E!aluate alternati!e"additionalmeasures

– #emonstrate uture compliance


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• Modeling eforts or most

areas include' –scoping study to assess the

nature and extent o the

problem and data gaps"needs –collection and analysis o

meteorological air uality

emissions and land&use data –development and

continued refnement o

modeling databases andca abilities


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Typical Air Quality Modeling Process

onceptual Model #e!elopment

Select episodes and domains

*repare"re+ne inputs

Apply emiss met models

ompare model results tomeasurements

*erormance ,-.

*repare uture&year emissions

onduct uture&year e!aluations

/es

0o

ModelE!aluation


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• 1agrangian employ a coordinatesystem that mo!es with air parcels

• Eulerian the coordinate system is+xed in space

• 2ybrid incorporate eatures o1agrangian types into a

Eulerian ramewor3

Classifcation o Photochemical Air Quality Simulation Models


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Eulerian !s4 1agrangian

• Eulerian – 5ixed coordinate

– 5ocus on the statisticalproperties o 6uid

!elocities – Eulerian statistics are

readily measurable

– #irectly applicablewhen there are

chemical reactions – losure problem 7 no

generally !alid solutions

• 1agrangian – Mo!ing coordinate

– 5ocus on the statisticalproperties o the

displacements ogroups o particles

– 0o closure problem – #i8cult to accurately

determine the reuiredparticle statistics

– 0ot directly applicableto problems in!ol!ingnonlinear chemicalreactions


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Eulerian Model


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EulerianModel


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Eulerian Model


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Scheme 1agrangian Model


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1agrangian output15/02/02 12TU 9.5-10.5km

19/02/02 12TU 8-9km


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1agrangian


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9op&!iew o distribution o particles 7San #iego harbor

14

1 km horizontal resolution 444 m horizontal resolution

Meteorology: MM5 model; Dispersion: Lagrangian Random Particle

Dispersion Model


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15


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Lagrangian Models

• Many Simpliying Assumptions – *roduce a simple closed&orm

analytical expression

or difusion

– #o not reuire numerical integration• !n"o#e the Assumption o Air Parcel $oherency

– :rea3s down uic3ly not ar rom an

emissions source especially incomplex wind 6ow situations

• $ost%eecti"e solution at relati"ely close rangesor a relati"ely small num&er o sources


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Lagrangian Models (continued)

• $hemical interactions &et'een pus(segments( or particles cannot &e properly

treated

• Readily produce source%receptorrelationships

• Se"ere technical limitations especially or)

– large numbers o sources – regional&scale transport

applications

– photochemically reacti!e


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*aussian Plume Models

• +he earliest air models

• !n"o#e many simpliying assumptions to o&tain

closed%orm analytical solutions – Steady&state (i4e4 time in!ariant)

– Spatially uniorm (homogeneous) dispersion

– *lume coherency

– ;nert or +rst&order decay• *aussian plume models are not capa&le o treating

photochemistry


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Gaussian Plume Model


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,-

The simplest dispersion modeling – Gaussian approximation for the

plume spread

Not applicable to regional scales – complex terrain con!ecti!e

conditions and ground"le!el sources#


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Model Assumptions

• ind speed direction and difusion

characteristics o the plume are constant – Mass transer due to bul3 motion in the x&

direction ar outshadows the contribution due tomass difusion

– onser!ation o mass i4e4 no chemicaltransormations ta3e place

– >ind speeds are ?@ m"sec4

– 1imited to predicting concentrations ? B mdownwind

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Atmospheric Stability lasses

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( ) ( )

σ−+

σ−

σπσ=

,

,

,

,

,1e5p

,((

$ y $ y

% $ yu

& $ y x'

Gaussian Dispersion Equation


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6amples o *aussian Plume models include)

• !S$

•$7MPL62

• R+DM

• A6RM7D


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*aussian Pu Models

• .e'er simpliying assumptions

– employ analytical solutions or each puf but

– computers are reuired to trac3 the large

number o pufs

– still retain the plume coherency assumption

– a ew ha!e been de!eloped or indi!idual reacti!eplumes

e4g4 C*M&;V

–numerical solution methods are needed to sol!echemical

3inetics euations


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6amples o *aussian Pu models include)• $ALP8..

•

RPM%!9

• S$!P8..

• S$!$6M


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ulerian (Grid) Model Concept


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Processes Treated in a Grid Model

• 6missions – Surace emitted sources (on&road and non&road mobile

area low&le!el point biogenic +res)

– *oint sources (electrical generation industrial other +res)

• Ad"ection :+ransport

• Dispersion :Diusion• $hemical +ransormation

– V, and 0,x chemistry radical cycle

– D5or *M aerosol thermodynamics and aueous&phasechemistry

• Deposition – #ry deposition (gas and particles )

– >et deposition (rain out and wash out gas and particles)


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Photochemical Modeling Concepts

All Air Models Sol"e Some .orm o theAtmospheric Diusion 6


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General !orm o the Species

Conser"ation (Continuity) #uation$

Mathematical solution (integration) of generalforms of the diffusion equation is difficult --simplifying assumptions are required


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ulerian Grid Cell Processes


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Coupling %et&een Grid Cells


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ulerian Models

•*enerally considered to &e technically superior – allow more comprehensi!e explicit treatment

o physical processes

– chemical processes included

– interactions o numerous sources

• Re


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ulerian Models (continued)

•Su&grid resolution can &e a limitation – as grid si=e and time step length are reduced

• accuracy increases but

• computation time also increases

– ad!anced grid models employ !ariable gridspacing or nesting

• impro!es accuracy in critical locations

• allows cost efecti!e application on urban to regionalscales

• AMx has 6exi&nesting capability


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'yrid (Lagrangianulerian)Models

• ;ncorporate eatures o 1agrangian modelsinto

grid model ramewor3

– o!ercome many o the sub&grid modellimitations

• ,!ercome many o the prior practicalad!antages o 1agrangian models

through the de!elopment o' – !ariable (nested) grid resolution

– source apportionment techniues

• apitali=e on the a!ailability o low&costhi h s eed com uters


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Su*Grid*Scale Plume Concept


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CAM+ G,AS- PiG Concept


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'yrid Models (concluded)

• 6amples o hy&rid photochemical gridmodels include)

– M,#E1S&H"MAQ – MAQS;*

– IAM&V

– AMx


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Modules in a Grid Model

•6missions Modeling System – E*S$x SM,-E and EMS&$BBH

• Meteorological Modeling System – MM and CAMS (SA;MM and A1ME9)

• Preprocessors or 7ther !nputs – 9IV (photolysis Cates)

– ;nitial oncentrations and :oundary onditions

• Air Quality Model – AMx"*MAMx Models&H"MAQ (IAM&V MAQS;*)

• Post%Processors and 9isualization – Model *erormance E!aluation (MA*S AMJtrct Excel

SIC5EC)

– *AVE

– 5lying #ata


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General Model Summary

Photochemical grid=hy&rid models ha"e e"ol"ed as the preerredmeans o addressing comple and nonlinear processes aecting

reacti"e air pollutants in the troposphere

+hese models in"o#e e'er assumptions &ut re


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."er"ie& o the CAM+ ,egionalPhotochemical Model

• Simulates the physical andchemical processes go!erning

the ormation and transport oo=one in the troposphere – three&dimensional Eulerian (grid&

based) model

– reuires speci+cation ometeorological emissions land&useand other geographic inputs

– output includes hourly concentrations


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CAM+ ."er"ie& (continued)

Mathematically simulates the ollo'ing processes)

–emission o o=one precursors

(anthropogenic and biogenic) –ad!ection and difusion

(transport)

–*hotochemistry

–deposition


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+ample o a Multiply /ested CAM+-omain$ -en"er 0*hr AC Study

%&3m grid

@4H&3m grid

HK&3m grid

@$&3m grid


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CAM+ Model !ormulation

( ) ( ) ( )

++−= $

(c

y

!c

x

uc

t

c iiii

δ

δ

δ

δ

δ

δ

δ

δ

hange inoncentration

> Ad!ection by >inds

9urbulent#ifusion

? Ci ? Si ? 1ihemicalCeaction

Emissions SuraceCemo!al"#eposition

+

+

+

$

c )

$ y

c )

y x

c )

x

i*

i %

i %

δ

δ

δ

δ

δ

δ

δ

δ

δ

δ

δ

δ

CAM+ Modeling System


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CAM+ Modeling System!eatures

• $ar&on%@ond%!9 chemical mechanism 'ithenhanced isoprene and toics chemistry

• +'o%'ay interacti"e nested%gridcapa&ilities

•Plume%in%grid :P%i%* treatment

• Accepts output rom a "ariety o dynamicmeteorological models


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CAM+ 1nput ,e#uirements

•Meteorological !nputs – 9hree&dimensional winds

– 9hree&dimensional temperatures

– 9hree&dimensional water&!apor concentration

–Surace pressure

– 9hree&dimensional !ertical difusi!ity (efecti!emixing height)

– 9wo&dimensional co!er

– Cainall rate


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CAM+ 1nputs (continued)

• Emissions ;nputs – 1ow&le!el anthropogenic emissions

• *oint sources

• Area sources

• ,n&road motor !ehicles

• 0on&road sources

– Ele!ated point source emissions

– :iogenic emission estimates


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CAM+ 1nputs (continued)

• Air


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,egional Scale .2one Tile Plots

Daily Maimum %hr 7zone $oncentrations :pp& on B1 Culy ,--1

7"er the 6astern 8nited States) B #m $AM *rid Domain


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Local Scale .2one Tile Plots

Daily Maimum %hr 7zone $oncentrations :pp& on B Aug ,--- 7"er

+he San Cuan @asin=.our $orners Region) 4 #m $AM *rid Domain


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.2one Time Series Plots

ourly 7zone $oncentrations :pp& on at Mesa 9erde Eational Par#

.or B1 Culy F 4 August ,--- 7"er the San Cuan @asin=.our $orners Region


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.2one Time Series Plots

ourly 7zone $oncentrations :pp& on at Su&station .or B1 Culy to

4 August ,--- 7"er the San Cuan @asin=.our $orners Region) 4 #m *rid


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'ourly .2one Scatter Pots

Scatterplot o ourly 7zone $oncentrations :pp& on ,, Cune ,--1

7"er Lo'er La#e Michigan) 4 #m $AM *rid Domain


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Quartile*Quartile .2one Plots

Q%Q Plot o ourly 7zone $oncentrations :pp& on ,4 Cune ,--1

7"er Lo'er La#e Michigan) 4 #m $AM *rid Domain


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CAM+ 3*- Animation


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ase Study' La3arta city ;ndonesia

Asep Soyan( +0 Gitada( and0 *0 Gurata( umeri!al "tud# o$ %& and "%&

D#nami!s under 'and/"ea (ree)es in Dr# "eason in *akarta+ ,ndonesia(

+ournal of Global ,n!ironment ,ngineering 9olume 1B( ,--( pp0 H%H0

Gitada +0( Asep Soyan( and Gurata *0:,-- umeri!al simulation o$ air

pollution transport under sea/land ree)e situation in *akarta+ ,ndonesia in

dr# season. ir ollution Modelin and its ppli!ation ,( $0 @orrego and

A0C0 Miranda :eds0( ,4B%,51( Springer0

Asep Soyan( +0 Gitada( and0 *0 Gurata( Di$$eren!e o$ "ea (ree)e in *akarta

(eteen Dr# and 3et "easons4 ,mpli!ation in %2 and "%2 Distriution in

*akarta( +ournal of Global ,n!ironment ,ngineering 9olume 1,( ,--/( pp0

B%50

Pu&lications)


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:ac3 9raectory 9echniues in Air *ollution

9raectories' the paths o in+nitesimally smallparticles o air as they mo!e through timeand space4

Such 6uid particles Nmar3edO at a certainpoint in space at a gi!en time can be tracedorward or bac3ward in time along theirtraectory4 :ac3ward (bac3) traectories'

indicate the past path o a particle

5orward traectories' indicate the uture path o a particle


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re!ept

or

6ample @ac# +raIectory

/%day @ac# traIectories rom the ship

:receptor ha"e &een calculated using

the 3SPL!+ 4 model :3&rid

Single%Particle Lagrangian !ntegrated

traIectory0


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Applications o :ac3 9raectories

Synoptic meteorology ;n!estigate air mass 6ow around mountains

(Steinac3er @P%)

limatology

;dentiy pathways o water !apor transport(#OAbreton and 9yson @PPK) or desert dust(hiapello et al4 @PPR)

En!ironmental Sciences Establish source&receptor relationships o air

pollutants (Stohl @PPKa) 1aw Enorcement

ombine with pollen measurements to +nd possiblelocations o mariuana culti!ation (abe=udo etal4@PPR)


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Air Quality Research


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Air Quality Research

–#ata collection' Emissionin!entory

–Management' Scenario and

policy or transportationindustry orest +re

–limate change' co&bene+t

–;mpact' 2ealth *lant –Measurement' gas

particulateblac3 carbon

01 introduction air quality model

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