Pune City

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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


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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


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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.


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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


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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


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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


24 hours** 120 g/m3 80 g/m3 30 g/m3

Suspended Particulate Matter (SPM)


24 hours** 500 g/m3 200 g/m3 100 g/m3

Respirable Particulate Matter (RPM) (size <10)


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


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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

Air Quality ManagementAir Quality Management

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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


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)


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.

Air Quality ModelingAir Quality Modeling

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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


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Gaussian Plume Model

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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


)]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


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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

AERMOD Modeling SystemAERMOD Modeling System

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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]


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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


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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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- 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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- 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


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- Vehicular Sources – CNG 2010/ 2015

- 3-Wheelers – 40% conversion by 2010; 100% by 2015

- Passenger Cars – 5% by 2010, 10% by 2015

-Results



% 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


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- 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



% 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


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- 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


% diff. (ISBT –Base) 2007 1.98 to 4.48 (avg 3.13) 2.90 3.14 3.30 3.01


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- 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


% 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


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- 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


% 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


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- Scenarios At A Glance


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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

Pune City

Documents

Transcript of Pune City