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 

2. 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

3. About C-DAC High Performance Computing Hardware solutions
GIS Solutions
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

5. 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
UKMO: PUM Model Output (JJAS 2005)
Average Daily Precipitation (mm/day)
Air Quality/Environmental Computing
GIS based emissions modeling with IITM
Offline WRFChem with NOAA/FSL
WRF+AERMOD for Pune AQM with USEPA
Aerosol studies using LMDzT – Off-line version with IIT-B

6. 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

7. Pollutants- Sources & Effects
Air Pollution
Pollutants- Sources & Effects
Pollutant
Natural Sources
Anthropogenic Sources
Effects
Nitrogen Oxides (NO, N20)
Bacterial activity,
Lightning
Fuel combustion,
Chemical process
Acid rain,Aerosols, PAN, ozone, smog
lung damage, leaf damage,carcinogen
Sulphur dioxide
Volcano, forest fires, bacterial activity
Fuel combustion, Chemical process
Forms H2SO3 aerosols-smog, burning 20ppm-lung, eye damage
Carbon monoxide
Oxidation of hydrocarbons by bact, plants, ocean
Combustion (incomplete), chemical reaction
carboxyhaemoglobin (HbCO), smog formation
Hydrocarbons, Volatile Organic Carbons (VOC)
Decomposition, plants,soil
Fuel combustion (incomplete), evaporation, chemical processes
Particles, smog, respiratory damage, carcinogens, global warming, ozone damage.

8. Air Pollution Particulate Matter Non respirable (>10μm)
Wind blown, dust, pollen
Crushing, shredding
Visibility, plant damage, carriers
Respirable – Coarse
(2.5 – 10 μm)
Dust,forest fires, volcanoes,
Crushing, grinding, traffic
Visibility, plant, lungs-asthma
Respirable – fine (<2.5 μm)
Ocean spray, fires, dust, volcano
Construction, combustion, processes
Lung, eyes, plants,allergens carcinogens, property
Average composition – Elemental/ organic carbon, sulphates, nitrates, ammonium, soil, pollen, cotton

9. Air Pollution Global Warming
Global warming potential (GWP) and other properties of CO2, CH4, and N2O.
Gas
Concentration
Annual increase
Lifetime (years)
Relative absorption capacity *
GWP
CO2
355 ppmv
1.8 ppmv
120 1
CH4
1.72 ppmv
10-13 ppbv
12-17 58
24.5q**
N2O
310 ppbv
0.8 ppbv
206
320
ppmv = parts per million by volume ppbv = parts per billion by volume * per unit mass change from present concentrations, relative to CO2 GWP Global Warming Potential following addition of 1kg of each gas, relative to CO2 for a 100 year time horizon ** Including the direct effect of CH4 and indirect effects due to the production of tropospheric ozone and stratospheric water vapour.
Source: Bouwman, 1995.
Pune City 

10. ATMOSPHERIC CHEMISTRY
Air Pollution
ATMOSPHERIC CHEMISTRY
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

11. Air Pollution Legislations– Brief History
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

12. National Ambient Air Quality Standards Concentration in ambient air
Air Pollution
National Ambient Air Quality Standards
Pollutants
Time-weighted
Average
Concentration in ambient air
Industrial
Areas
Residential
Sensitive
Sulphur Dioxide (SO2)
Annual Average*
80 g/m3
60 g/m3
15 g/m3
24 hours**
120 g/m3
30 g/m3
Oxides of Nitrogen as NO2
Suspended Particulate Matter (SPM)
360 g/m3
140 g/m3
70 g/m3
500 g/m3
200 g/m3
100 g/m3
Respirable Particulate Matter (RPM) (size <10)
50 g/m3
150 g/m3
75 g/m3
CO Concentration
8 hours
5.0 mg/m3
2.0 mg/m3
1 mg/m3
1 hour
10.0 mg/m3
4.0 mg/m3
2 mg/m3
** 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

13. Air Quality Management - Components
Impacts Assessment
Air Pollution Modeling
Strategies, Planning, Development
Meteorological Data
GIS based Emission gridding
Emission Inventory
Air Quality Monitoring
Source Apportionment

14. Air Quality monitoring methods
Air Quality Management
Air Quality monitoring methods
Passive Methods:
Remote Sensing – Satellite Imageries – cloud/ haze
Satellite mapping (TOMS – NASA for Aerosol & Ozone)
LIDAR – Light Detection & Ranging

15. 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
Let me first define what an emission inventory is.
It is a comprehensive listing of the sources of air pollution and an estimate of their emissions within a specific geographic area and time interval.
It is important thing to note is that an emission inventory is an ESTIMATE of the emissions from sources.
It is not a complete accounting because for the most part, emissions are not actual measurements.

16. 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
Pollutant from 1 car of type A
(gm/km or gm/lt fuel) x
Avg distance travelled
(or lts of fuel consumed) X
No of cars of type A in given area
= Total emissions from car type A in given region/ street

17. Meteorological Data Air Quality Management Inversion layer wind
Main driver for movement of pollutants (and interactions)
wind
Buoyancy
turbulence
Inversion layer
Deposition, washout

18. Meteorological Data Air Quality Management 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

19. TYPES OF AIR QUALITY MODELS
Air Quality Modeling
TYPES OF AIR QUALITY MODELS
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.

20. Air Quality Modeling PHYSICAL MODELS
Scaled Down version of real phenomenon
Attempt to replicate phenomenon under controlled conditions
E.g Wind Tunnel, Fluid Tanks

21. Air Quality Modeling STATISTICAL MODELS
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= T P WD WV ; R = 0.5
SO2= T P WD WV ; R = 0.2
NOx= T P WD WD WV ; R=0.36
Where, T- temperature, P - pressure, WD - wind direction and WV - wind velocity.

22. Air Quality Modeling RECEPTOR MODELS
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

23. Air Quality Modeling DETERMINISTIC MODELS
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

24. Air Quality Modeling
Gaussian Plume Model

25. Air Quality Modeling
Gaussian Plume Model

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

27. Air Quality Modeling Gaussian Plume Model Simplified form
c = concentration (x,y,z,H) ,
Q emission rate (g/s) ,
u-wind
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

28. Air Quality Modeling PLUME RISE
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 = v2*r2*g*(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 >= 55
119(Fb)2/5 for Fb < 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

29. Air Quality Modeling EULERIAN MODELS
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, Δz =plume ht
W=washout coeff., I=prep. Intensity,H=layer ht
Pc=Product matrix, Rc=Reactant matrix
Soln : Finite differences, FiniteElement, Parabolic – req initial & boundary conditions

30. Air Quality Modeling LAGRANGIAN MODELS
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
<c(r,t)> = -α t  p(r,t|r’,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’ & t>t’ p<=1) (solved statistically e.g Monte Carlo)
Chemistry/ dry/wet removal handled by change in mass at each step:
m (t+Δt) = m (t) exp(-Δt/R) ,
R: rate of reaction/dry/wet deposition
Preferred method for particle tracking
Puff simulation by simulation at centre of mass of puff

31. Comparison – Eulerian, Lagrangian frames
Air Quality Modeling
Comparison – Eulerian, Lagrangian frames
Eulerian approach
Lagrangian approach z t t1 t t1 y x
Combined models: Eulerian models where individual puff/particle are handled by Langragian module till it attains grid dimensions

32. Air Quality Modeling AERMOD (AERMIC MODEL)
Developed by AMS/ EPA Regulatory Model Improvement Committee
– 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 

33. AERMOD Modeling System
Air Quality Modeling
AERMOD Modeling System
DEM Data
Receptors
Surface Obs.
Upper Air Data
AERMAP
TERRAIN PREPROCESSOR
AERMET
METEOROLOGICAL PREPROCESSOR
AERMOD
MAIN MODEL
Site Met. Data
Concentration Profiles
Average, Exceedance, Source Contributions
Sources & Emissions
Point, Area, Volume
Version 02222

34. AERMET – Meteorological Preprocessor
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

35. AERMOD Model AERMOD Modeling System
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]

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

37. Case Study
Pune City 
Rural Area – One processing plant, two clusters of households

38. 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 = tn/day
(0.288 tn/day x 1,000,000 gm/tn )/ sec/day = 3.33 gm/sec
Stack details : ht = 25 m , top dia = 0.5 m,
exit velocity = 5 m/s, exit temp = K
Pune City 

39. Case Study Emission Inventory
Household cooking: Stoves using firewood and kerosene in 65:35 usage ratio.
Consumption : firewood 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 = kg/yr = 155 kg/day
Cluster2 : 245 persons x 0.65 x 175 = kg/yr = kg/day
Kerosene :
Cluster1 : 500 persons x 0.35 x 56 = 9800 kg/yr = kg/day
Cluster2 : 245 persons x 0.35 x 56 = 4802 kg/yr = kg/day
Emissions:
Cluster1 : (155 x 1.7) + (26.84 x 0.6) = g/day = gm/sec / 800 = 4.0E-6 g/sec-m2
Cluster2 : (76.3 x 1.7) + (13.15 x 0.6) = g/day = gm/sec / 550 = 2.91E-6 g/sec-m2
Pune City 

40. GUI for AERMOD model on Linux Platform
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 

41. Case Study Demo

42. Pune Air Quality Modeling – Scenario Analysis
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

43. Pune Air Quality Modeling – Scenario Analysis
AERMOD Scenario Analysis
Pune Air Quality Modeling – Scenario Analysis
Base Case Run :
Average Contribution of Sources to PM10 over Pune – Base Case run
Pune City*
AQM Cell
K. Park
Oasis
Mandai
6.63 – 115.0
(avg: 51.64)
93.64
71.99
61.92
106.72

44. Pune Air Quality Modeling – Scenario Analysis
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
2-Wheelers
3-Wheelers
4-Wheelers incl. Light Comm Veh
Heavy Vehicles
8.3%
7.0%
9.0%
3.0%
PM10 (μg/m3) from Vehicles
Pune City
AQM Cell
K. Park
Oasis
Mandai
% diff. ( )
21.95 to (avg 26.15)
23.53
31.06
23.67
31.1
%diff (2015 – 2007)
74.75 to (avg 80.72)
76.77
87.84
77.14
87.98

45. Pune Air Quality Modeling – Scenario Analysis
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
PM10 (μg/m3) from Vehicles
Pune City
AQM Cell
K. Park
Oasis
Mandai
% diff. (CNG –BAU) 2010
-4.38 to (avg -1.53)
-1.5
-1.46
-1.35
% diff. (CNG –BAU) 2015
-34.6 to (avg –32.18)
-32.12
-32.6
-32.13
-32.22

46. Pune Air Quality Modeling – Scenario Analysis
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 passengers daily
Reduction in personal vehicle trips by these passengers
Results
PM10 (μg/m3) from Vehicles
Pune City
AQM Cell
K. Park
Oasis
Mandai
% diff. (PMT –BAU) 2010
to (avg -2.43)
-0.35
-6.33
-0.43
-6.38
% diff. (PMT –BAU) 2015
to (avg -2.47)
-0.37
-6.13
-0.44
-6.31

47. Pune Air Quality Modeling – Scenario Analysis
AERMOD Scenario Analysis
Pune Air Quality Modeling – Scenario Analysis
Vehicular Sources – Bus Shifting
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
PM10 (μg/m3) from Vehicles
Pune City
AQM Cell
K. Park
Oasis
Mandai
% diff. (ISBT –Base) 2007
1.98 to (avg 3.13)
2.90
3.14
3.30
3.01

48. Pune Air Quality Modeling – Scenario Analysis
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
PM10 (μg/m3) from Slum cooking
Pune City
AQM Cell
K. Park
Oasis
Mandai
% diff. (SLM2010 -Base)
to (avg )
-69.19
-26.07
-72.19
-41.08
%diff (SLM2015 – Base)
-91.0 to (avg )
-39.66
-34.92
-75.44
-25.96

49. Pune Air Quality Modeling – Scenario Analysis
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
PM10 (μg/m3) from Slum + Vehicles
Pune City
AQM Cell
K. Park
Oasis
Mandai
% diff. (SLMCNG2010 –SLMVEH-07)
2.25 to (avg 18.04)
15.56
17.36
16.56
16.94
%diff (SLMCNG2015 – SLMVEH-07)
14.86 to (avg 21.78)
18.82
27.26
18.45
27.07

50. Pune Air Quality Modeling – Scenario Analysis
AERMOD Scenario Analysis
Pune Air Quality Modeling – Scenario Analysis
Scenarios At A Glance

51. Thank You Resources http://www.epa.gov/ttn/scram
University website – Atmospheric Sciences Lectures/ Handouts