AERMOD Modeling System

1
AERMOD Model Case Study Mohit C.
Dalvi Computational Atmospheric Sciences
Team Centre for Development of Advanced Computing
(C-DAC) Pune University Campus, Pune
Pune City ?
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Overview

• About C-DAC
• Air Pollution overview
• Air Quality Management Components
• Air Quality Modeling overview
• AERMOD Model
• Case study using Linux AERMOD
• Use of AQ Model for scenario analysis

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About C-DAC
High Performance Computing
Hardware solutions
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Scientific Computing
Advanced Computing Training
Artificial Intelligent
Language Technology
Medical Informatics
Evolutionary Computing
4
Computational Atmospheric Sciences

• Activities
• Computational Research
• Workflow Environment Development
• Technology Development
• Parallel Programming
• Model Porting, Optimisation Simulations
• Grid Computing

• Joint Collaborative Research
• Turnkey solutions
• Contract Projects
• Consultancy

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Computational Atmospheric Sciences

• Global Forecast Models
• NCEP's T170/T254/T382/PUM
• Multi-institutional ERMP program
• Regional Weather Research
• MM5 / WRF / MM5 Climate / RegCM / RSM
• Real Time Weather System (RTWS)
• Coupled system development (IITM Collaboration)
• Climate Models
• CCSM
• Climate Change Studies
• Ocean Models
• MOM4 / POM / ROMS / HYCOM
• Coupled system development (IITM collaboration)
• Ocean response studies

• Air Quality/Environmental Computing
• GIS based emissions modeling with IITM
• Offline WRFChem with NOAA/FSL
• WRFAERMOD for Pune AQM with USEPA
• Aerosol studies using LMDzT Off-line version
  with IIT-B

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Air Pollution

• Air quality-------- degree of purity of the air
  to which people and natural resources are exposed
  at any given moment.
• Definitions Air (Prevention Control of
  Pollution) Act, 1981
• Air pollutant" means any solid, liquid or
  gaseous substance 2(including noise) present in
  the atmosphere in such concentration as may be or
  tend to be injurious to human beings or other
  living creatures or plants or property or
  environment
• Air pollution" means the presence in the
  atmosphere of any air pollutant
• Primary air pollutants chemicals that enter
  directly into the atmosphere. E.g carbon oxides,
  nitrogen oxides, sulfur oxides, particulate
  matter, hydrocarbons
• Secondary air pollutants chemicals that form
  from other already present in the atmosphere. E.g
  ozone, sulfurous acid, PAN

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Air Pollution
Pollutants- Sources Effects
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Particulate Matter
Air Pollution
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Air Pollution
Global Warming
Pune City ?
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ATMOSPHERIC CHEMISTRY
Air Pollution

• Interactions of Pollutants
• Primary Pollutant Prim. Pollutant ? Sec
  Pollutant
• Prim. Pollutant Existing component ? Sec
  Pollutant
• Primary/ Secondary Pollutant ? Decay/ Removal
• - Photolysis
• - Dry Deposition (on soil, vegetation)
• - Wet Deposition (washout by rain, on fog,
  cloud droplet)
• - Radioactive decay
• - Absorption/ uptake by plants/ animals
• - Dissolution in water body/ ocean

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Air Pollution Legislations Brief History
Air Pollution

• Some reference in Factories Act, 1860s/ 1948
• 1952 London smog Inversion conditions for 4
  days smoke from coal (fireplaces, boilers)
  stagnated - 4000 deaths
• Clean Air Act (UK) 1956 1968
• Clean Air Act (USA) 1970
• Air (Prevention Control of Pollution) Act, 1981
• Bhopal Gas Tragedy, 1984
• Environmental Protection Act, 1986

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Air Pollution

• National Ambient Air Quality Standards

24 hourly values should be met 98 of the time
in a year. However, 2 of the time it may exceed
but not on two consecutive days. Annual average
annual arithmetic mean of minimum 104
measurements in a year taken twice a week 24
hourly at uniform interval
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Air Quality Management
Air Quality Management - Components
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Air Quality monitoring methods
Air Quality Management

• Passive Methods
• Remote Sensing Satellite Imageries cloud/
  haze
• Satellite mapping (TOMS NASA for Aerosol
  Ozone)
• LIDAR Light Detection Ranging

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Emission Inventory
Air Quality Management

• Is a comprehensive listing of the sources of air
  pollution and an estimate of their emissions
  within a specific geographic area for a specific
  time interval.
• Inventories can be used to
• Identify sources of pollution
• Identify pollutants of concern
• Amount, distribution, trends
• Identify and track control strategies
• Input to air quality modeling

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Emission Inventory
Air Quality Management

• Steps
• - Identify sources of pollution
• - Measure/ estimate pollutant release from
  single unit
• - Extrapolate to expected no. of sources of same
  type

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Meteorological Data
Air Quality Management

• Main driver for movement of pollutants (and
  interactions)

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Air Quality Management
Meteorological Data

• Parameters of importance
• Wind components driving force for advection.
• Temperature, Surface Heat, lapse rate for
  buoyancy, plume rise, stability, vertical
  transport
• Rainfall, humidity removal by wet deposition
• Cloud cover wet deposition, light intensity
  (for photochemistry), radiation balance
• Landuse, albedo for biogenic/ geogenic
  emissions, chemistry, dry deposition
• Terrain impact on wind, obstacle to movement
• Source Weather stations, balloons, SODAR,
  satellites
• For forecasting/ projections numerical
  weather prediction models

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TYPES OF AIR QUALITY MODELS
Air Quality Modeling

• Physical Models Laboratory representations of
  real life phenomenon
• Mathematical Models Set of analytical/
  numerical algorithms representing physical and
  chemical aspects of the behaviour of pollutant in
  atmosphere.
• Can be broadly divided into
• - Statistical Model Semiempirical statistical
  relations among available data measurements
• - Determinisitic Models - Fundamental
  mathematical descriptions of atmospheric
  processes. Include the analytical and numerical
  models.

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PHYSICAL MODELS
Air Quality Modeling

• Scaled Down version of real phenomenon
• Attempt to replicate phenomenon under controlled
  conditions
• E.g Wind Tunnel, Fluid Tanks

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STATISTICAL MODELS
Air Quality Modeling

• Statistical models are based on the time series
  (or any other trend) analysis of meteorological,
  emission and air quality data. These models are
  useful for real time analysis and short term
  forecasting.
• Eg. Air Quality Monitoring and Modeling for
  Coimbatore City - P.Meenakshi and R.Elangovan
  (CIT)
• Use of "least squares" method to analyse how
  a single dependent variable is affected by the
  values of one or more independent variables.
• - The monitored data in Coimbatore City are
  analyzed by multi regression
• SPM -82.0703 T - 80.5704 P - 0.76381 WD -
  2.03456 WV 64531.68 R 0.5
• SO2 2.397 T - 1.1481 P 0.016 WD 1.173 WV
  831.5413 R 0.2
• NOx 5.728 T 3.2582 P - 0.0636 WD 2.1923
  WD 2.192 WV - 2601.85 R0.36
• Where, T- temperature, P - pressure, WD - wind
  direction and WV - wind velocity.

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RECEPTOR MODELS
Air Quality Modeling
Receptor Models use the chemical physical
characteristics of measured concentrations of
pollutants at source as well as receptor to
identify the presence and contribution of the
source to the pollutant level at receptor. e.g
Chemical Mass Balance Equation Ci Fi1S1 Fi2
S2 . FiJ SJ Ci Concentration of
ith species Fij Fraction of species i
from source j Sj Sources contribution
from sources 1 J Dj Ej
Ej Emission rate
Dj 0 ? T d u(t),s(t),x dt
u wind velocity
s stability parameter
x distance of source from
receptor
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DETERMINISTIC MODELS
Air Quality Modeling

• Calculate/ predict the concentration field based
  on mathematical manipulations of the inputs
• - source emission characteristics
• - atmospheric processes affecting transport
• - chemical processes affecting mass balance
• Eg
• - Diffusion models Gaussian models
• - Numerical models
• - Eulerian Models
• - Lagrangian models

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Gaussian Plume Model
Air Quality Modeling
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Gaussian Plume Model
Air Quality Modeling
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Gaussian Plume Model - Assumptions
Air Quality Modeling
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Gaussian Plume Model
Air Quality Modeling

• Simplified form
• c concentration (x,y,z,H) ,
• Q emission rate (g/s) ,
• u-wind speed_at_z
• ?y standard deviation of conc. in y
  direction (stability dependant)
• ?z - standard deviation of conc. in z
  direction
• Standard deviations determined by using
  Briggs/ Pasquill-Gifford formaulas as a function
  of x (downwind distance) and stability class

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PLUME RISE
Air Quality Modeling

• Initial vertical dispersion of the plume emitted
  from stack due to momentum (exhaust velocity)
  and buoyancy (higher temperature than
  surroundings.
• Briggs Buoyancy Flux parameter Fb
• Fb v2r2g(Ts-Ta)/Ts v
  velocity at exit, r radius
• Ta air temp, Ts stack temp
• Distance to final plume rise xf 49(Fb)5/8
  for Fb gt 55
• 119(Fb)2/5 for Fb lt 55
• Plume rise unstable/ neutral conditions
• ?h (1.6 (Fb)1/3 (xf)2/3)/u
• Plume rise stable conditions
• ?h 2.4( (Fb / us)1/3 ) s
  stability parameter (g/Ta) (??/?z)
• Effective stack height Ht hs ?h

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EULERIAN MODELS
Air Quality Modeling

• Based on conservation of mass of a given
  pollutant species (r,t)
• Modeling Domain is a fixed 3-Dimensional grid of
  cells
• Atmospheric parameters are homogenous for a given
  cell at t
• Computations for each cell at each timestep
• u,v wind
  velocity in x, y direction
• 
  Kxy, Kz Horizontal, vertical diffusion
  coeff.
• 
  Vd dep velocity, ?z plume
  ht
• 
  Wwashout coeff., Iprep.
  Intensity,Hlayer ht
• 
  PcProduct matrix,
  RcReactant matrix
• Soln Finite differences, FiniteElement,
  Parabolic req initial boundary conditions

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LAGRANGIAN MODELS
Air Quality Modeling

• Lagrangian approach derived from fluid mechanics
  simulate fluid elements following instantaneous
  flow
• Frame of reference follows the air mass/ particle
• Advection not computed separately as against
  Eulerian
• ltc(r,t)gt -a? t ? p(r,tr,t) S(r,t) dr
  dt
• c(r,t) conc. At locus r at time t
• S(r,t) source term (g/m3s)
• p probability density function that parcel
  moves from r,t to r,t
• (for any r tgtt plt1) (solved
  statistically e.g Monte Carlo)
• Chemistry/ dry/wet removal handled by change in
  mass at each step
• m (t?t) m (t) exp(-?t/R) ,
• R rate of reaction/dry/wet deposition
• Preferred method for particle tracking
• Puff simulation by simulation at centre of mass
  of puff

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Comparison Eulerian, Lagrangian frames
Air Quality Modeling
Eulerian approach
Lagrangian approach
z
t
t1
y
t
t1
x
Combined models Eulerian models where individual
puff/particle are handled by Langragian module
till it attains grid dimensions
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Air Quality Modeling
AERMOD (AERMIC MODEL)

• Developed by AMS/ EPA Regulatory Model
  Improvement Committee
• - 1994 2001 till first version
• - Steady-state Gaussian Plume Dispersion Model
• Improvements over traditional Gaussian Models
  (ISC)
• - Computes turbulence before dispersion
• - Separate schemes for Convective Stable BL
• - Inbuilt computation of vertical profiles
  (PDF)
• - Urban handling- nighttime boundary layer
• - Specified as Preferred Regulatory Model by
  USEPA in 2006

Pune City ?
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AERMOD Modeling System
Air Quality Modeling
Receptors
DEM Data
Surface Obs.
Upper Air Data
Site Met. Data
Concentration Profiles Average, Exceedance,
Source Contributions
Sources Emissions Point, Area, Volume
Version 02222
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AERMOD Modeling System
AERMET Meteorological Preprocessor

• Extract, Quality check Preprocess- Raw Met.
  data
• Inputs
• Surface Observation Parameters (Hourly)
• - Minimum Ambient Temperature,Wind
  direction speed, sky cover
• - File formats NWS, CD-144, TD-3280,
  Samson
• Upper Air Data
• - Supports NWS (twice daily) UA soundings,
  NOAA-FSL data
• - Parameters (Levelwise) Atmospheric
  Pressure, Height, Temperature (dry bulb),Wind
  direction, Wind speed
• Onsite Meteorological Records
• - Optional User specified format
• - Output
• 1. Surface File with PBL parameters
• 2. Profile file with levelwise data

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AERMOD Modeling System
AERMOD Model

• Inputs Outputs from AERMAP AERMET
• Source Emission Information
• - Point sources
• - Locations, Emission Rate, Stack
  parameters. Building dimensions
• - Area Sources
• - Location dimensions, Emission rate
• - Volume Sources
• Location, initial dimensions, Emission
  Rate
• Urban Source Option Population and Surface
  Roughness

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WRF-AERMOD coupling for Pune Air Quality Modeling
(MOEF-USEPA Program for Urban Air Quality
Management)
Pune - Air Quality Modeling

• C-DAC role Emission inventory, data processing,
  air quality modeling
• Hourly meteorology req. for AERMOD air quality
  model
• First time in the world Development of
  Preprocessor for coupling WRF and AERMOD
• Stakeholders PMC, NEERI, MPCB, C-DAC ,. . .

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Case Study
Pune City ?

• Rural Area One processing plant, two clusters
  of households

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Case Study
Emission Inventory Industry Manufacturing plant
using coal. Requires 10 tonnes coal/ day with ash
36. Pollution control equipment scrubber with
90 efficiency Particulate matter emissions 10
tonnes/day coal x 0.36 tonnes/ton ash x 0.8
(percent flyash) 2.88 tonnes/day fly ash Scrub
2.88 x (100-90)/100 0.288 tn/day (0.288
tn/day x 1,000,000 gm/tn )/ 86400 sec/day 3.33
gm/sec Stack details ht 25 m , top dia 0.5
m, exit velocity 5 m/s, exit temp
453. 0K
Pune City ?
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Case Study
Emission Inventory Household cooking Stoves
using firewood and kerosene in 6535 usage ratio.
Consumption firewood - 175 kg/p/yr kerosene
56 kg/p/yr (PMC) Emission factors firewood
1.7 g/kg kerosene 0.6 g/kg (URBAIR) Population
cluster1 500. cluster2 245. Area
cluster1 800 sq.m cluster2 550 sq.m
Amount of firewood Cluster1 500 persons
x 0.65 x 175 56875 kg/yr 155 kg/day
Cluster2 245 persons x 0.65 x 175 27878
kg/yr 76.3 kg/day Kerosene Cluster1
500 persons x 0.35 x 56 9800 kg/yr 26.84
kg/day Cluster2 245 persons x 0.35 x 56
4802 kg/yr 13.15 kg/day Emissions
Cluster1 (155 x 1.7) (26.84 x 0.6) 279.6
g/day 0.0032 gm/sec / 800 4.0E-6 g/sec-m2
Cluster2 (76.3 x 1.7) (13.15 x 0.6) 137.1
g/day 0.0016 gm/sec / 550 2.91E-6 g/sec-m2
Pune City ?
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AERMOD Modeling System
GUI for AERMOD model on Linux Platform

• AERMOD designated by USEPA as
• replacement for ISC model.
• AERMOD set-up (sources, receptors,
• options) cumbersome
• Linux based graphical user interface for
• ease of use
• Features
• Drawing tools to specify the source/
• receptors
• Simplified forms to specify options.
• Online validation of parameters
• Automatic generation of the input file.
• Actual AERMOD runs through the GUI
• Post-processing for contour plots

Pune City ?
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Case Study Demo
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AERMOD Modeling System
Pune Air Quality Modeling Scenario Analysis

• Feasibility of using Pune AQM system for Control
  Scenarios
• Simplifying the process Inventory ?Model input
• Scenarios
• Planned Development/ Controls (PMC)
• Probable/ Likely situations/ measures
• Sourcewise controls and emissions impacts
• Projected 2010, 2015
• Currently Relative impacts on contribution from
  specified sources

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AERMOD Scenario Analysis
Pune Air Quality Modeling Scenario Analysis

• Base Case Run 2006-07

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AERMOD Scenario Analysis
Pune Air Quality Modeling Scenario Analysis

• Vehicular Sources BAU 2010/ 2015
• Increase in Vehicle population as per RTO/ PMC-
  AQM Cell survey
• Results

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AERMOD Scenario Analysis
Pune Air Quality Modeling Scenario Analysis

• Vehicular Sources CNG 2010/ 2015
• 3-Wheelers 40 conversion by 2010 100 by
  2015
• Passenger Cars 5 by 2010, 10 by 2015
• Results

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AERMOD Scenario Analysis
Pune Air Quality Modeling Scenario Analysis

• Vehicular Sources PMT 2010/ 2015
• Improvement in PMT bus service increased no/
  frequency-
• Expected to benefit about 20000 passengers
  daily
• Reduction in personal vehicle trips by these
  passengers
• Results

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AERMOD Scenario Analysis
Pune Air Quality Modeling Scenario Analysis

• Vehicular Sources Bus Shifting 2007-08
• Shifting of Interstate Bus stations to
  outskirts
• Reduction in Heavy vehicle traffic ( 2000
  state, 120 private) thru city
• Increase in personal (2/4W) and public (3/W)
  trips to new Bus stands
• Current / Immediate future only
• Results

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AERMOD Scenario Analysis
Pune Air Quality Modeling Scenario Analysis

• Slum Fuel Use SLUM 2010/ 2015
• Traditionally biofuels ? kerosene ? LPG
• As per AQM Cell survey, faster shift from biofuel
  to LPG
• Expected ratio 50 LPG 35 kerosene 15
  biofuel
• - Increase in slum population 6 / yr (AQM
  Cell)
• Results

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AERMOD Scenario Analysis
Pune Air Quality Modeling Scenario Analysis

• Combined Scenarion CNG Slum Fuel Use
  SLMCNG 2010/ 2015
• Most likely scenarios
• Contribution from Vehicular Slum fuel use
• Results

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AERMOD Scenario Analysis
Pune Air Quality Modeling Scenario Analysis

• Scenarios At A Glance

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• Thank You

• Resources
• http//www.epa.gov/ttn/scram
• University website Atmospheric Sciences
  Lectures/ Handouts
• http//www.cpcb.nic.in http//www.envfor.ni
  c.in