Title: Air Quality Management of Particulate Matter Emitted from Vehicular Sources in Delhi
1Air Quality Management of Particulate Matter
Emitted from Vehicular Sources in Delhi
- by
- P. Goyal, Neeru Jaiswal, Rashmi Mittal
- Centre for Atmospheric Sciences,
- Indian Institute of Technology, Delhi
2Overview of the Presentation
- Introduction
- Objective of the study
- Model Formulation
- Study Area
- Emission Inventory
- Results and Discussions
- Conclusions
3Introduction
- Delhi is one of the 10 polluted cities of the
world. - Particulate Matter (PM) are mainly emitted from
industrial - process, auto exhaust and domestic sources.
- Total number of vehicles registered in Delhi
was 4.17 - million in 2004.
- PM levels are increasing continuously despite
number of - mitigation measures.
4Objectives of the Study
Air Quality Management of PM in Delhi through
- Development of line source model designed for
low and - moderate wind conditions.
- Detailed source inventory of vehicles.
- Forecast of emission of PM as per the existing
scenario in - next 10 years.
- Temporal variation of PM in Delhi.
- Spatial variation of PM concentration in Delhi.
- Identification of hot spots in Delhi due to PM.
5Model Formulation
The concentration of PM due to line source in low
winds (ult 1 m/sec) conditions is C and the
concentration for moderate winds (ugt 1 m/sec) is
C Where, C concentration (mg/m3) Q
Source strength (mg/sec) U Wind speed
(m/sec) (x, y, z) location of source (m)
(x, y, z) location of receptor (m) (sx,
sy,sz) dispersion parameters (m) in x, y, z
directions respectively.
6Study Area
- A gridded source inventory
- over an area of Delhi City
- (26Km 30Km) has been
- made.
- In each grid, curved roads are
- approximated by number of
- straight lines and each of
- these straight line is
- considered as a separate source.
- The traffic volume for each
- road has been obtained
- individually.
- Total emission rate of each grid
- is estimated from the
- contributions of all the source
- located within that grid.
7Emission Inventory
Emission rate of PM has been estimated in each
grid on hourly basis by using secondary data of
vehicular traffic and apportioned according to
the following methods Method I
Where, E Emission
rate (gm/sec) i Index, varies over six
categories of vehicles. Ni Number of
registered vehicles in i th category TDi
Travel demand for vehicular category i
(km/day) VKT Vehicle Kilometer traveled
(Km) ei Emission factor (g/km) for
the pollutant of the vehicular category i
Vehicles are categorized into 6 vehicle
category Cars, Two wheeler, Three wheeler,
Buses, Taxi, and Goods vehicles.
8Emission Inventory.
- Method II
- Different vehicles were monitored at the major
traffic intersections and national - highways, nearly at 36 places in Delhi.
- Emission rates of PM are estimated by using
the emission factors of each - pollutant for each vehicle group.
- Where,
- E Emission rate(gm/sec)
- i Index, varies over six categories of
vehicles - Ni Number of vehicle of i th category monitored
per hour (vehicle/hour) - L Road length in each monitored grid (km)
- ei Emission factor (g/km) for the pollutant
of the vehicular category i - Emission of PM in each monitored grid over the
road length in that particular grid - is calculated.
- The linear interpolation technique is used to
estimate the emission made in the rest - of the grids of the study area
9Emission Inventory.
- Method III
- Lengths of major, minor and residential roads
were estimated in each grid through - the measurements.
- Monitored traffic data reveals that the traffic
volume due to major, minor and - residential roads is in the ratio of 43
3324 respectively. - Total estimated load of PM on the study area
is taken as 12.77 tons/day (CPCB) - Emission of PM in the i th grid is calculated
by the following expression - Where,
- Ei total emission in i th grid
- LMi total length of the major roads in the i th
grid - Lmi total length of the minor roads in the i th
grid - Lri total length of the residential roads in
the i th grid - C total emission of the pollutant in whole
study area.
10Results and Discussion
- Hourly variation of emission rates of PM has been
determined by different methods, which reveals
that emission of PM from different methods are
well in agreement with observed values.
An emission inventory of vehicles has been
prepared over the map of Delhi, which gives
emission rate of PM in each grid.
PM Emission at ITO
11Results and Discussion
- Forecasting of PM emissions, on the basis of
present emission scenario, for future years
(2005-2014) is showing slightly increasing
values.
EURO-I
EURO-II
EURO-III
EURO-IV
PRE-EURO
12Results and Discussion
- Hourly variation of concentration and emission
rates of PM - shows two peaks during the day hours. First
peak occurs - between 9 AM to 11 AM and second is between
5 to 7 PM. - A comparative study of model and observed
concentration - of PM during the day hours shows that model
is always over - predicting but remains within a factor of
two.
Temporal variation of PM concentration and Source
Strength
4-hourly averaged concentration at Nizzamuddin
monitoring station
13Results and Discussion
- Isopleths of PM reflects that hotspots in Delhi
at ITO, Shivaji Stadium, Cannaught Place, Dhaula
Kuan and Laxmi Nagar have maximum values of PM
ranging from 300-600 mg/m3 which are beyond NAAQS
(200 mg/m3).
Spatial variation of PM concentration in Delhi
(January, 2004).
14Conclusions
- The present line source model, appropriate for
low wind and - traffic condition of Delhi can be used for
air quality impact - assessment due to vehicular traffic in
other cities like Delhi. - The emission inventory of Delhi with respect
to PM can be - used for emission control as a first step
for air quality - management.
- Forecasting of emission of future years may be
used for - making the future strategy of air quality
management. - Model computation of PM shows that
concentration of PM - are beyond the NAAQS at most of the places
in Delhi. - The present isopleths of various concentration
of PM can be - used for emission control and regulatory
purposes.
15Thanks