4104-Assomadi-M 7 Model Dispersi Gauss

MODEL DISPERSI

PENCEMAR

UDARA

Abdu F. Assomadi

2

Main concepts in air quality modeling

Parameter-parameter dalam Model Sumber (Karakteristik emisi)

laju emisi (massa/waktu)lokasi sumber (koordinat)temperatur gas emisi (K)tinggi plume rise (m)

MeteorologyTemperatur AtmosferStabilitas Atmosfer (diperlukan untuk menentukan koefisien

dispersi) Kecepatan dan arah angin, turbulensi

Sifat Kimia AtmosferReaksi-reaksi kimia di atmosferDeposisi (basah / kering)

Parameter permukaangeometri permukaan, kekasaran/terrain, lautan, daerah kota atau

desa

4

Beberapa Model Kualitas Udara Model point source

Model at urban and large scale

Model Fotokimia

Model-model lain:

Model secara meteorologi

Model plume rise dan Model Dispersi

Model Statistik - Model Penerima/receptor di ambien

Model deposisi

Model bau/odor

Model partikulat

Model Point Source

Model pertama yang dikembangkan untuk

kualitas udara (contoh, Sutton, 1932,

Bosanquet, 1936) menjelaskan sifat dispersi

plume yang diemisikan dari cerobong industri.

Dalam sejarahnya dinilai sukses, dari sini

dikembangkan model dispersi sederhana

Model Plume Gaussian.

Model ini telah diaplikasikan untuk menghitung

impact plume pada MGL (max ground level)

dan jarak maksimum dari sumber.

Plume Boundary

H

X

Y

Z

u

Q

7

The simplest dispersion modeling Gaussian approximation for

the plume spread

Not applicable to regional scales complex

terrain, convective conditions, and ground-level sources.

Gambaran Model Gaussian

Asumsi-asumsi pada Model

Dispersi Gauss

Plume disebar mengikuti sebaran gauss normal baik dalam bidang horisontal maupun vertikal,dengan deviasi standar konsentrasi plume padaarah horisontal dan vertikal y, and z;

Laju emisi polutan seragam, Q;

Kondisi plume pada ground (z=0) dianggap secara total direfleksikan oleh permukaan; dan

Plume bergerak downstream (horisontal padaarah x) dengan rata-rata kecepatan angin, u,

10

Limitations of Gaussian-plume models Causality effects

Gaussian-plume models assume pollutant material is transported in a straight line instantly (like a beam of light) to receptors that may be several hours or more in transport time away from the source.

Low wind speeds

Gaussian-plume models 'break down' during low wind speed or calm conditions due to the inverse wind speed dependence of the steady-state plume equation, and this limits their application.

Straight-line trajectories

In moderate terrain areas, these models will typically overestimate terrain impingement effects during stable conditions because they do not account for turning or rising wind caused by the terrain itself. CTDM and SCREEN are designed to address this issue.

Spatially uniform meteorological conditions

Gaussian steady-state models have to assume that the atmosphere is uniform across the entire modelling domain, and that transport and dispersion conditions exist unchanged long enough for the material to reach the receptor.

Convective conditions are one example of a non-uniform meteorological state that Gaussian-plume models cannot emulate.

No memory of previous hour's emissions

In calculating each hour's ground-level concentration the plume model has no memory of the contaminants released during the previous hour(s).

Wark & Warner, Air Pollution: Its Origin & Control

Mixed layer

TRAPPED BY MIXED LAYER

Sistem Koordinat Model Dispersi

Gauss

Short term model, steady-state Gaussian plume equation untuk sumber kontinyu dan punya elevasi tertentu.

Koordinat asal (0,0) ditempatkan pada ground surface tepat di dasar stack.

Sumbu X positif pada arah angin (downwind direction), sumbu y tegak lurus arah angin (crosswind searah sumbu X) dan sumbu Z merupakan arah vertikal.

Lokasi penerima dikonversi dari sistem koordinat sumber, untuk perhitungan konsentrasi jangka waktu tertentu (sesuai arah angin dominan)

Sistem Koordinat Distribusi Gauss Arah

Horizontal dan Vertikal

Persamaan Model Dispersi Gauss

Model Gauss distribusi konsentrasi

Rumus menghitung C gas atau aerosol (

Model Gaussian

Diformulasikan dengan penentuan sebaran plume scr horisontal / vertikal dan distribusi konsentrasiplume dengan deviasi standar plume mengikutisebaran normal

Distribusi/sebaran polutan dihitung sebagai fungsistabilitas atmosfer dan jarak dari sumber searahangin

Stabilitas Atmosfer parameter karakteristikturbulensi atmosfer (Range F A; menyatakansanga stabil sangat tidak stabil; D netral)

Pengaruh Stabilitas Atmosfer pada

Plume

Typical Velocity, and Plume Shapes

The lateral dispersion coefficient function and, the vertical dispersioncoefficient functions depend on the downwind distance and the atmosphericstability class. These coefficients in meters can be obtained using Pasquill-Gifford-Turner estimates shown in the equations below

Gaussian Parameters

where ,

s = an integer [1-6] representing the atmospheric stability shown in Table 1

kx,x = empirical constants, values for each of the stability class can be obtained from Green et al. (1960)

Surface wind

speed at 10 m

(m/s)

Day Night

Incoming Solar radiation Cloud Cover

Strong Moderate Slight Thinly Overcast Mostly Cloudy

< 2 A (s = 1) A-B B (s = 2)

2-3 A-B B C (s = 3) E (s = 5) F (s = 6)

3-5 B B-C C D E

5-6 C C-D D (s = 4) D D

>6 C D D D D

Table 1. Constants a,b,c,d depend on Pasquill Stability categories defined by Turner (1995)

Horizontal Dispersion Coefficient as a Function of Downwind Distance from Source

Vertical Dispersion Coefficient as a Function of Downwind Distance from Source

11/20/2014 22

Gaussian Dispersion Equation

If the emission source is at ground level with no

effective plume rise then

2

2

2

2

2

1exp,,

zyzy

zy

u

QzyxC

H is the sum of the physical stack height and

plume rise.

Plume Rise

stackactualriseplume hhH

11/20/2014 23

Plume Rise

For neutral and unstable atmospheric

conditions, buoyant rise can be calculated by

)/ 55F( 425.21 34

75.0

smu

Fh riseplume

)/ 55F( 71.38 34

6.0

smu

Fhplume rise

Sass TTTdgVF 4/)(2

where buoyancy flux isVs: Stack exit velocity, m/s

d: top inside stack diameter, m

Ts: stack gas temperature, K

Ta: ambient temperature, K

g: gravity, 9.8 m/s2

Buoyant plume: Initial buoyancy >> initial momentum

Forced plume: Initial buoyancy ~ initial momentum

Jet: Initial buoyancy

11/20/2014 24

Carson and Moses: vertical momentum & thermal

buoyancy, based on 615 observations involving 26 stacks.

(stable) 24.204.1

(neutral) 64.235.0

(unstable) 15.547.3

u

Q

u

dVh

u

Q

u

dVh

u

Q

u

dVh

hsriseplume

hsriseplume

hsriseplume

asph TTCmQ

MWRT

PV

dm

s

s4

2

(heat emission rate, kJ/s)

(stack gas mass flow rate. kg/s)

When pollutants are dispersed to the ground level,

how should we handle the situation?

11/20/2014 25

Wark & Warner, Air Pollution: Its Origin & Control

2

2

2

2

2

2

2exp

2exp

2exp

2,,

zzyzy

HzHzy

u

QzyxC

What if the surface is absorbing?

How should the concentration profile look like w/ reflection?

11/20/2014 26

Ground level concentration

2

2

2

2

2exp

2exp

zyzy

Hy

u

QC

11/20/2014 27

Maximum Ground Level Concentration

Under moderately stable to near neutral conditions,

zy k1

The ground level concentration at the center line is

2

2

2

1 2exp0,0,

zz

H

uk

QxC

The maximum occurs at

2 0/

HddC zz

Once z is determined, x can be known and subsequently C.

u

Q

u

QxC

zyzy

1171.01exp0,0,

11/20/2014 28

Example

An industrial boiler is burning at 12 tons (10.9

mton) of 2.5% sulfur coal/hr with an emission

rate of 151 g/s. The following exist : H = 120 m,

u = 2 m/s, y = 0. It is one hour before sunrise,

and the sky is clear. Determine downwind

ground level concentration at 10 km.

Stability class =

y =

z =

C(10 km, 0, 0) =

11/20/2014 29

If emissions are from a ground level source with H = 0, u = 4 m/s, Q = 100 g/s, and the stability class = B, what is downwind concentration at 200 m?

At 200 m:

y =

z =

C(200 m, 0, 0) =

Exercise

11/20/2014 30

Calculate H using plume rise equations for an 80 m high

source (h) with a stack diameter = 4 m, stack velocity =

14 m/s, stack gas temperature = 90o C (363 K), ambient

temperature = 25 oC (298 K), u at 10 m = 4m/s, and

stability class = B. Then determine MGLC at its location.

F =

h plume rise =

H =

z =

y =

Cmax =

Example

Residents around Florida Rock Cement Plant are complaining its emission

being violating its allowed level. The plant has its facility within 0.5 km

diameter. Its effective stack height is 60 m. You are a FLDEP environmental

specialist. Where are you going to locate your air quality monitors? Why?


Konsentrasi Plume (A)parameter unit case 1

wind speed (from or to south west) m/s 2

stack height m 45

h m 90,602efective stack height (He) m 135,602

parameter polutan (gas) n

Nitrogen Oxides

beban (loading) g/s 19,834

stabilitas atmosfer a


Konsentrasi Plume (B)parameter unit case 1


stack height m 45

h m 60,401efective stack height (He) m 105,401


Nitrogen Oxides


stabilitas atmosfer b


Konsentrasi Plume (C)parameter unit case 1


stack height m 45

h m 36,241

efective stack height (He) m 81,241parameter polutan (gas) n

Nitrogen Oxides


stabilitas atmosfer c


Konsentrasi Plume (D)parameter unit case 1


stack height m 45

h m 25,886

efective stack height (He) m 70,886


Nitrogen Oxides


stabilitas atmosfer d


Konsentrasi Plume (E)parameter unit case 1


stack height m 45

h m 36,241

efective stack height (He) m 81,241


Nitrogen Oxides


stabilitas atmosfer e


Konsentrasi Plume (F)parameter unit case 1


stack height m 45

h m 60,401

efective stack height (He) m 105,401parameter polutan (gas) n

Nitrogen Oxides


stabilitas atmosfer f

Beberapa pengembang model

ini Hgstrom (1964), Turner (1964), Briggs (1965) Moore

(1967), Klug (1968) Penggunaan dan aplikasi meluas danmenjadi teknik standar di berbagai negara untuk menghitungtinggi stack yang dibutuhkan untuk perijinan

Beryland (1975) telah mem-publis standar kerja di Rusia

Holzworth, (1967), Deardorff, (1975) pengaruh mixing height dalam memeperhitungkan konsentrasi MGL

Yamartino, (1977) teri refleksi distribusi ditambahkan padaModel Plume Gaussian

MODEL-MODEL LAIN

DALAM DISPERSI

PENCEMAR UDARA

Abdu F. Assomadi

Model pada skala urban dan luas

Sekitar 1970 diketahui bahwa polusi udara bukan hanyafenomena lokal (tidak cocok didekati dengan model gaussian) SOx dan Nox di eropa hujan asam di wilayah luas (antar

negara)

Ozon urban dan industri impact

Didekati dengan model Lagrangian dan Model Eulerian Lagrangian modeling parcel udara (atau puff) megikuti

lintasannya dan diasumsikan tetap identitasnya selama dalamperjalanan

Eulerian modeling area studi dibagi menjadi grid cell, dalamarah vertikal maupun horisontal

Lagrangian modeling

Diarahkan pada deskripsi transport sulfur jarak jauh (long-range), dimulai dengan penelitian Rohde (1972, 1974), Eliassen (1975) dan Fisher (1975).

Eliassen (1975) mulai dengan model well-known EMEP-trajectory digunakan beberapa tahun untuk menghitungpolusi udara trans-boundary spesies penyebab keasaman , dan kemudian foto-oksidan

Lagrangian modeling digunakan mencakup periode waktuyang lebih panjang bahkan sampai skala tahun

Secara umum dipakai di Eropa, fokus utama pada SO2 yang terdistribusi jauh dan waktu yang lama

Prinsip Perubahan Konsentrasi dideskripsikan relatifterhadap pergerakan fluida

Eulerian modeling

Dimulai dengan studi Reynolds (1973) untuk ozondi area urban; Shir and Shieh (1974) untuk SO2 diarea urban, Egan (1976) dan Carmichael (1979) untuk sulfur skala regional.

Reynolds (1973) Los Angeles, simulasifotokimia well-known Urban Airshed Model-UAM.

Pemodelan Eulerian grid secara luas diaplikasikandi US, meliputi area urban dengan fokus utamapada O3.

Prinsip Eulerian Model

Sifat/perubahan spesies dideskripsikan relatifterhadap sistem koordinat yang tetap (fixed)

Ada dua model yang dikembangkan:Single Box Model

Focus: Kimia Atmosfer Lack physical realism - horizontal and vertical transport,

etc

Multi-dimensional grid-based air quality model Potentially the most powerful Involving the least-restrictive assumption

Beberapa Pengembang Model

Lagrangian dan Eulerian Sklarew et al., (1971) pendekatan hybrid, seperti metode

particle-in-cell.

Paper mengenai pemodelan Eulerian and Lagrangianmodeling Friedlander and Seinfeld (1969), Eschenroeder -Martinez (1970) dan Liu and Seinfeld (1974).

A comprehensive overview of long-range transport modeling in the seventies Johnson (1980).

Selanjutnya, global modeling of earths troposphere. Model Global 2-D troposfer global dimodelkan dalam rata-rata

arah longitudinal (Isaksen, 1978).

model global 3-D dikembangkan Peters (1979) (dan jugaZimmermann, 1988).

Sekitar tahun 1980, konsep dasar modeling danperangkatnya sudah avalaible.

Pengembangan setelah 1980 difokuskan pada fine-tuningdari konsep dasar tersebut

Model Fotokimia

AQM dengan pendekatan Fotokimia dibuatsecara luas dan rutin digunakan sebagai alatanalisis regulasi dan menguji efektifitas strategipengendalian

Model fotokimiamodel kualitas udara skalabesar yang mensimulasikan perubahankonsentrasi polutan di atmosfer menggunakansederet persamaan matematik, karakteristik kimiadan proses fisika di atmosfer.

Model ini diaplikasikan pada skala multiple spatial mulai lokal, regional, nasional, dan global

Beberapa Model Foto kimia CMAQ Tujuan utama Models-3/Community Multiscale Air Quality

(CMAQ) untuk mengimprove 1) kemampuan environmental

management community untuk evaluasi impact pengelolaan kualitas

udara multi polutan pada multi skala, dan 2) kemempuan peneliti

untuk memahami dan mensimulasi lebih baik interaksi polutan

secara kimia-fisika di atmosfer

CAMX model kualitas udara komperehensif a publicly

available open-source computer modeling system untuk assesment

terintegrasi (pencemar udara gas-gas dan partikel)

Simulate air quality over many geographic scales

Treat a wide variety of inert and chemically active pollutants:

Ozone

Inorganic and organic PM2.5/PM10

Mercury and toxics

Provide source-receptor, sensitivity, and process analyses

Be computationally efficient and easy to use

Beberapa Model Foto kimia

UAM The Urban Airshed Model (UAM) mulai dikembangkan oleh ystems Applications International (SAI) sejak sekitar tahun 1970paling luas digunakan dalam photochemical air quality model saat ini

Model photochemical lain telah dikembangkannamun tidak ada model sampai saat ini yang lebihreliable dan superior secara teknik

CALGRID

Model-model Lain

Model Meteorologi CALMET

MM5

RAMS

Model Plume Rise dan Dispersi AERMOD termasuk PRIME sistem algoritma untuk

mensimulasi efek plume rise, termasuk downwash ketikaplume melintas diatas/melewati gedung-gedung

Model Statistik Model Receptor Air Quality Forecast and Alarm Systems

Model Deposisi

Model Bau

Model Partikulat Kinematic Simulation Particle (KSP) Model in CALPUFF

MONTECARLO (Zannetti and Sire, 1999)

Model-model yang populer dalam

AQM Industrial Source Complex Short-Term Model (ISCST3)

American Meteorological Society- Environmental Protection Agency Regulatory Model (AERMOD)

Guideline on Air Quality Models (GAQM)

Office of Air Quality Planning and Support (OAQPS)

Support Center for Regulatory Air Models (SCRAM)

ISC-COMPDEP model (a merger of the ISCST2 and COMPLEX I model)

COMPLEX I, from which a new modelCOMPDEP

Rough Terrain Diffusion Model (RTDM) RTDMDEP

California Puff Model (CALPUFF)

ISCST with the new downwash algorithm, ISC-PRIME

AERMOD dan CALPUFFRekomendasi US-EPA saat ini dua model komputasi untuk simulasi polutan

non reaktif (misal, SO2):

AERMOD: http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod steady-state Gaussian plume model, menggunakan angin tunggal yang diturunkan dari permukaan lapisan atas, dan pengamatan meteorological onsite. AERMOD juga menggabungkan data geofisika (terrain, elevasi) untuk mendapatkan parameter boundary layer ( panjang monin-obukhov, mixing height, stabilitas, turbulrnsi , dsb) AERMOD saat ini menggantikan ISC models untuk sebagian besar regulasi di US

CALPUFF: http://www.epa.gov/scram001/dispersion_prefrec.htm#calpuff model dispersi puff non-steady state Lagrangian Dibanding dengan Gaussian-based model CALPUFF lebih realistik mensimulasi

transport polutan pada kondisi calm, stagnant, complex terrain, dan coastal regions with sea/land breezes.

CALPUFF direkomendasikan untuk long-range simulations (lebih dari 50 miles) danassesment visual impact plume.

Dengan VISTAS Version 6 model2, CALPUFF digunakan dengan data meteorological sub-hourly dan dijalankan dengan tahap waktu sub-hourly. CALPUFF versi ini juga didekati untuk kedua simulasi long-range and short-range.

http://www.epa.gov/scram001/dispersion_prefrec.htmhttp://www.epa.gov/scram001/dispersion_prefrec.htm

ISCST3 modelSecara teknik mampu untuk menghitung dan evaluasi Laju dispersi gaussian vertical dan horizontal plume cross-

section;

Koefisien dispersi Urban dan rural;

Efek Terrain;

Karakteristik sumber point diskrit, area dua dimensi, volume 3-D;

Rerata Short-term dan long-term (1jam - tahunan);

Data meteorologi permukaan termasuk data pengamatan kecepatanangin tiap jam , arah angin, stabilitas (6 kelas), dan temperatur;

Interpolasi Data Mixing height dihubungkan dengan data kondisipermukaan;

Proses deposisi massa konservatif , wet /dry deposisi partikel danremoval

Laju peluruhan ekponensial order satu tunggal

Sumber dan referensi

Human Health Risk Assessment Protocol Chapter 3: Air Dispersion and Deposition Modeling, U.S. EPA Region 6 Multimedia Planning and Permitting Division Center for Combustion Science and Engineering, september 2005

Daly, A. and P. Zannetti. 2007. Air Pollution Modeling An Overview. Chapter 2 of AMBIENT AIR POLLUTION (P. Zannetti, D. Al-Ajmi, and S. Al-Rashied, Editors). Published by The Arab School for Science and Technology (ASST) (http://www.arabschool.org.sy) and The EnviroCompInstitute (http://www.envirocomp.org/).

4104-Assomadi-M 7 Model Dispersi Gauss

Documents

Transcript of 4104-Assomadi-M 7 Model Dispersi Gauss