Source apportionment studies: relevance to air quality management

1
Source apportionment studies relevance to air
quality management
Round table conference for transport
practitioners in India Transport and climate
change an action plan for mitigation November
27, 2008

• T S Panwar
• Energy Environment Policy, TERI

2
Source Apportionment studies

• Overall coordination by CPCB and MoEF
• Technical committee headed by the Chairman, CPCB
• Steering committee headed by the Secretary, MoEF
• Currently ongoing in 6 cities (Delhi, Pune,
  Bangalore, Mumbai, Chennai, Kanpur)
• TERI Executing agency in Bangalore
• Financial support by CPCB and consortium of oil
  companies (IOC, HPCL, BPCL, RIL)

3
Source apportionment study why to quantify
the share of pollutionObjectives

• To measure air pollutant concentrations in
  different parts of Bangalore (which includes
  source specific hot spots viz.
  kerbside/industrial zones)
• To prepare emission inventory for different air
  pollutants (including spatial/temporal
  distribution)
• To conduct source apportionment study of
  particulate matter (essentially PM10 PM2.5)
• To assess impacts of sources on AAQ under
  different management options and draw a roadmap
  for short/long term cost-effective measures.

4
Overall approach
5
(No Transcript)
6
Ambient AQ monitoring

• Air Quality Monitoring (7 stations)
• Domlur (residential)
• Kamanahalli (residential)
• Victoria road (kerbside )
• Silkboard junction (kerbside)
• Peenya (industrial)
• Indira Gandhi Institute of Child Health
  (Hospital)
• Whitefield (background)
• Monitoring period one year covering 3 seasons
• (Sampling period different for various
  pollutants)
• Pollutants SPM, RSPM, PM 2.5, NOx, SO2, CO,
  OC/EC, Ions, VOC(Benzene, 1-3 butadiene), Ozone,
  Aldehyde, NMHC, HC, PAHs, molecular markers
• Onsite weather monitoring (temperature,
  humidity, wind speed/direction, insolation)

7
Instruments at background monitoring location
8
PM10/PM2.5 chemical characterisation

• PM samples collected in different seasons (3) at
  various sites (7) analysed for -
• Carbon (elemental and organic)
• Elements
• Ions (cations and anions)
• Focus on PM 10 chemical speciation. Only limited
  chemical speciation of PM2.5 samples

Samples collected on Teflon/Quartz filter and
analysed for- Carbon OC, EC Ions F, Cl, Br,
NO2, NO3, SO4, K, NH4, Na, Ca, Mg Elements Na,
Mg, Al, Si, P, S, Cl, Ca, Br, V, Mn, Fe, Co, Ni,
Cu, Zn, As, Ti, Ga, Rb, Y, Zr, Pd, Ag, In, Sn,
La, Se, Sr, Mo, Cr, Cd, Sb, Ba, Hg, and Pb
Molecular markers Alkanes, Hopanes, Alkanoic
acid, PAHs, Others (Selected)
9
Emission Inventorisation

• Specific 2 km x 2 Km survey for various sectors
  in the zone of influence around each of the
  monitoring locations
• Overall city level emission projections (based on
  primary and secondary data)
• Impact of alternative strategies in the various
  sectors on emission loading
• Sources considered
• Transport
• Industries, power plant
• Road dust re-suspension, Construction, DG sets,
  Domestic,Hotels/restaurants/bakeries.

10
Emission factors for Indian vehicles

• Source http//www.cpcb.nic.in/Source_Apportionmen
  t_Studies.php
• Draft report on emission factor development for
  Indian vehicles by ARAI, Pune
• Emission factors developed for various vehicle
  categories, vintages and engine cubic capacities
• Pollutants CO, HC, NOx, CO2, PM, Benzene, 1-3
  Butadiene, aldehydes, PAH
• Considered fuel effect and maintenance effect
• 62 emission factors developed (total 450 emission
  tests)
• However, sample size still a limitation

11
Quantification using modelling tools

• CMB 8.2 Receptor model (USEPA)
• CMB Receptor model consists of a solution to
  linear equations that express each receptor
  chemical concentration as a linear sum of
  products of source profile abundances and source
  contributions.
• The source profile abundances (i.e. the mass
  fraction of a chemical from each source type) and
  the receptor concentrations, with appropriate
  uncertainty estimates, serve as input data to
  CMB.
• The output consists of the amount contributed by
  each source type represented by a profile to the
  total mass, as well as to each chemical species.
• Source profiles for vehicular sources from ARAI,
  other sources based on source profiling studies
  by IIT Mumbai and default values based on
  literature.

12
Source Apportionment Analysis
13
Air quality dispersion modeling

• ISCST3 (Industrial source complex short term)
  model for assessing the air quality
  concentrations under different scenarios.
• Based on the emission inventory and onsite
  meteorological data, ambient concentrations
  predicted for PM10 and model validated against
  observations
• Future ambient concentrations (2012, 2017)
  predicted under different scenarios using future
  emission loads

14
Air quality management plan

• On completion of data collection, analysis, and
  interpretation of the assimilated information, a
  detailed road map is to be drawn considering all
  possible measures for air quality improvement
  policy implications
• Special focus on transport sector interventions
• (BS-V,BS-VI, Electric vehicles, Hybrid, CNG,
  ethanol, bio-diesel, DOC,DPF)
• Other interventions
• (Metro, ban on commercial vehiclesgt10yr, Shift
  of PKT to public transport, Ban on new
  industries, road-wall to wall paving, better
  construction practices, DGsets IM)
• These measures would be classified into short
  term and long term

15
Thank You