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Pune City
AERMOD Model Case Study
Mohit C. Dalvi
Computational Atmospheric Sciences Team
Centre for Development of Advanced Computing (C-DAC)
Pune University Campus, Pune
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OverviewOverview
• 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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Language Technology
GIS Solutions
Medical Informatics
High Performance Computing
Hardware solutions
Scientific Computing
Artificial Intelligent
Evolutionary Computing
Advanced Computing Training
About C-DACAbout C-DAC
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Computational Atmospheric SciencesComputational 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 SciencesComputational 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
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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
Air PollutionAir Pollution
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Pollutant Natural Sources Anthropogenic Sources Effects
Nitrogen Oxides (NO, N20)
Bacterial activity,Lightning
Fuel combustion,Chemical process
Acid rain,Aerosols, PAN, ozone, smoglung damage, leaf damage,carcinogen
Sulphur dioxide Volcano, forest fires, bacterial activity
Fuel combustion, Chemical process
Forms H2SO3 aerosols-smog, burning sensation, @ 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.
Pollutants- Sources & Effects
Air PollutionAir Pollution
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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
Air PollutionAir Pollution
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Pune City
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 1
CH4 1.72 ppmv 10-13 ppbv 12-17 58 24.5q**
N2O 310 ppbv 0.8 ppbv 120 206 320
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.
Air PollutionAir Pollution
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ATMOSPHERIC CHEMISTRYATMOSPHERIC CHEMISTRY
• Interactions of PollutantsInteractions of Pollutants
• Primary Pollutant + Prim. Pollutant Primary Pollutant + Prim. Pollutant Sec Pollutant Sec Pollutant
• Prim. Pollutant + Existing component Prim. Pollutant + Existing component Sec Pollutant Sec Pollutant
• Primary/ Secondary Pollutant Primary/ Secondary Pollutant Decay/ Removal Decay/ Removal
- Photolysis- Photolysis
- Dry Deposition (on soil, vegetation)- Dry Deposition (on soil, vegetation)
- Wet Deposition (washout by rain, on fog, cloud droplet)- Wet Deposition (washout by rain, on fog, cloud droplet)
- Radioactive decay- Radioactive decay
- Absorption/ uptake by plants/ animals- Absorption/ uptake by plants/ animals
- Dissolution in water body/ ocean- Dissolution in water body/ ocean
Air PollutionAir Pollution
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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
Air PollutionAir Pollution
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National Ambient Air Quality Standards
PollutantsTime-weighted
Average
Concentration in ambient air
IndustrialAreas
Residential Areas
Sensitive Areas
Sulphur Dioxide (SO2)
Annual Average* 80 g/m3 60 g/m3 15 g/m3
24 hours** 120 g/m3 80 g/m3 30 g/m3
Oxides of Nitrogen as NO2
Annual Average* 80 g/m3 60 g/m3 15 g/m3
24 hours** 120 g/m3 80 g/m3 30 g/m3
Suspended Particulate Matter (SPM)
Annual Average* 360 g/m3 140 g/m3 70 g/m3
24 hours** 500 g/m3 200 g/m3 100 g/m3
Respirable Particulate Matter (RPM) (size <10)
Annual Average* 120 g/m3 60 g/m3 50 g/m3
24 hours** 150 g/m3 100 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
Air PollutionAir Pollution
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Air Quality Management - Components
Impacts AssessmentImpacts Assessment
Air Pollution ModelingAir Pollution Modeling
Strategies, Planning, Strategies, Planning, DevelopmentDevelopment
Meteorological Meteorological DataData
GIS based Emission
gridding
Emission InventoryEmission Inventory
Monthly variation PM10 levels
0
100
200
300
400
500
600
700
800
1 2 3 4 5 6 7 8 9 10 11 12Month
Le
ve
l ug
/m3
Sw argate
Nalstop
Bhosari
Air Quality Air Quality MonitoringMonitoring Source ApportionmentSource Apportionment
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Air Quality monitoring methods
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
“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
Steps: - Identify sources of pollution - Measure/ estimate pollutant release from single unit - Extrapolate to expected no. of sources of same type
Air Quality ManagementAir Quality Management
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
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• Main driver for movement of pollutants (and interactions)
Air Quality ManagementAir Quality Management
Meteorological Data
wind
Buoyancy
turbulence
Inversion layer
Deposition, washout
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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
Meteorological Data
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TYPES OF AIR QUALITY MODELSTYPES OF AIR QUALITY MODELS
• Physical ModelsPhysical Models – Laboratory representations of real life phenomenon
• Mathematical ModelsMathematical Models – Set of analytical/ numerical algorithms representing physical and chemical aspects of the behaviour of pollutant in atmosphere.Can be broadly divided into :- Statistical ModelStatistical Model – – Semiempirical statistical relations among available data & measurements
- Determinisitic ModelsDeterminisitic Models - Fundamental mathematical descriptions of atmospheric processes. Include the analytical and numerical models.
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PHYSICAL MODELSPHYSICAL MODELS
– 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
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; R=0.36
Where, T- temperature, P - pressure, WD - wind direction and WV - wind velocity.
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RECEPTOR MODELSRECEPTOR 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
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DETERMINISTIC MODELSDETERMINISTIC 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
Air Quality ModelingAir Quality Modeling
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Air Quality ModelingAir Quality Modeling
Gaussian Plume Model
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Air Quality ModelingAir Quality Modeling
Gaussian Plume Model
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Air Quality ModelingAir Quality Modeling
Gaussian Plume Model - Assumptions
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• Simplified form
c = concentration (x,y,z,H) , Q emission rate (g/s) , u-wind speed@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
Gaussian Plume Model
)]2
exp()2
)[exp(2
exp(2 222 z
Hz
z
Hz
y
y
zyu
Qc
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PLUME RISEPLUME 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
Air Quality ModelingAir Quality Modeling
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• 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
EULERIAN MODELS
process chemical depositionwet
depositiondry emission
diffusion vertical diffusion horizontal
advection
)()(
)()..,,(
)]([]cos
1[
))cos()(
(cos
1
321
2
2
2
2
2
RcPcdRcH
WIdf
dtz
VdfssssQ
z
cKz
zy
c
x
c
yKxy
y
yvc
x
uc
yrt
c
w
ddn
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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 tS(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
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Comparison – Eulerian, Lagrangian framesComparison – Eulerian, Lagrangian frames
Eulerian approach
z
y
x
t t1
Lagrangian approach
t
t1
Combined models: Eulerian models where individual puff/particle are handled by Langragian module till it attains grid dimensions
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Pune City
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
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AERMOD Modeling System
ReceptorsDEM Data
Site Met. Data
Sources & Emissions Point, Area, Volume
Surface Obs. Upper Air Data
Concentration Profiles Average, Exceedance, Source Contributions
AERMAPTERRAIN
PREPROCESSOR
AERMETMETEOROLOGICAL PREPROCESSOR
AERMODMAIN MODEL
Version 02222
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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 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]
AERMOD Modeling SystemAERMOD Modeling System
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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 ,. . .
Pune - Air Quality Modeling Pune - Air Quality Modeling
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Pune City
Case Study
- Rural Area – One processing plant, two clusters of households
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Pune City
Case Study
Emission InventoryIndustry: 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
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Pune City
Emission InventoryHousehold cooking: Stoves using firewood and kerosene in 65:35 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
Case Study
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GUI for AERMOD model on Linux PlatformGUI for AERMOD model on Linux Platform
Pune City
• 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
AERMOD Modeling SystemAERMOD Modeling System
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Case Study Demo
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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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Pune Air Quality Modeling – Scenario Analysis
- Base Case Run : 2006-07
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
AERMOD Scenario AnalysisAERMOD Scenario Analysis
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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. (2010-2007) 21.95 to 36.69 (avg 26.15)
23.53 31.06 23.67 31.1
%diff (2015 – 2007) 74.75 to 96.16 (avg 80.72)
76.77 87.84 77.14 87.98
AERMOD Scenario AnalysisAERMOD Scenario Analysis
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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 -0.64 (avg -1.53) -1.5 -1.46 -1.5 -1.35
% diff. (CNG –BAU) 2015 -34.6 to -31.35 (avg –32.18)
-32.12 -32.6 -32.13 -32.22
AERMOD Scenario AnalysisAERMOD Scenario Analysis
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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
PM10 (μg/m3) from Vehicles
Pune City AQM Cell K. Park Oasis Mandai
% diff. (PMT –BAU) 2010 -10.69 to -0.08 (avg -2.43) -0.35 -6.33 -0.43 -6.38
% diff. (PMT –BAU) 2015 -10.34 to -0.24 (avg -2.47) -0.37 -6.13 -0.44 -6.31
AERMOD Scenario AnalysisAERMOD Scenario Analysis
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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
ResultsPM10 (μg/m3) from Vehicles
Pune City AQM Cell K. Park Oasis Mandai
% diff. (ISBT –Base) 2007 1.98 to 4.48 (avg 3.13) 2.90 3.14 3.30 3.01
AERMOD Scenario AnalysisAERMOD Scenario Analysis
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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) -90.45 to 157.54 (avg -54.84)
-69.19 -26.07 -72.19 -41.08
%diff (SLM2015 – Base) -91.0 to 350.0 (avg -37.26)
-39.66 -34.92 -75.44 -25.96
AERMOD Scenario AnalysisAERMOD Scenario Analysis
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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 22.72 (avg 18.04)
15.56 17.36 16.56 16.94
%diff (SLMCNG2015 – SLMVEH-07)
14.86 to 34.42 (avg 21.78)
18.82 27.26 18.45 27.07
AERMOD Scenario AnalysisAERMOD Scenario Analysis
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Pune Air Quality Modeling – Scenario Analysis
- Scenarios At A Glance
AERMOD Scenario AnalysisAERMOD Scenario Analysis
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•Resources
http://www.epa.gov/ttn/scram
University website – Atmospheric Sciences Lectures/ Handouts
http://www.cpcb.nic.in http://www.envfor.nic.in
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