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Chapter - III
^Amoient\Mir Qualify
JiLonJforJng
AMBIENT AIR QUALITY MONITORING
INTRODUCTION
Air pollution is the result of rapid industrialisation, urbanization,
modernization and development of new technologies. Although emissions
from industrial establishments, biodegradation and uncontrolled burning of
garbage accumulated in dumping yards contribute to urban air pollution, the
major cause for urban air pollution is undoubtedly automobile exhaust. The
sharp rise in air pollution, especially in developing country like India is due to
the sudden increase in the number of petrol and diesel fuelled vehicles. More
than half of the pollution load in our cities is due to automobile exhaust. The
dramatic rise in air pollution in most Indian metropolitan cities over last two
decades is a direct result of an inefficient state both in terms of lack of
balancing responsibilities and precautionary activities (Sharma & Chowdhury,
1996).
Disorganised public transport system has led to an increase in the use
of personalised vehicles. A car or two-wheeler is most sought after family
acquisition when earnings rise. Congested traffic, poor road conditions and
outdated automotive technology add to the increase in vehicular emissions.
Indian cities are considered as the most polluted cities in the world. Delhi has
been ranked as the fourth most polluted mega cities in the world. Pollution
levels in the other 23 cities in India with a population of over one million,
exceeds the WHO recommendations (Brandon & Hommann, 1996). The total
estimated pollution load from the transport sector in the country increased
from 0.15 million tonnes in 1947 to 10.3 million tonnes in 1997 (State of
Environment Report & Action Plan, 1999).
91
The total number of vehicles in India has increased from about 11
million in 1986 to more than 40 million in 1998. About one third of the
vehicular population in India is concentrated in major metropolitan cities like
Bangalore, Calcutta, Chennai, Delhi, Hyderabad and Mumbai (OSC, 2000).
The number of vehicles in the state of Karnataka, India has increased from
14.33 lakh in 1990-91 to 39.96 lakh in 2001-02, showing almost a three-fold
increase over twelve years. Of the total number of vehicles in Karnataka,
nearly 38.22% are plying in Bangalore urban area. Two wheelers constitute
71.81% followed by cars (9.50%) and other vehicles (9.57%). The highest
numbers of two wheelers were seen in Bangalore district (10,49,281)
followed by Mysore (1,95,307) (State of the Environment Report & Action
Plan, 2003). The total number of vehicles in Mysore city has increased from
6,333 in 1970 to 3,06,430 in 2004.
The principal pollutants emitted by vehicles are Carbon monoxide
(CO), Hydrocarbons (HC), Suspended Particulate Matter (SPM), Oxides of
nitrogen (NOx), polynuclear hydrocarbons and varying amounts of Sulphur
dioxide (S02). The quantity of emission of these pollutants depends on the
fuels used. Exhaust gases from petrol driven vehicles contain lead compounds
because of the addition of tetraethyl lead in motor vehicles. Lead is a
cumulative poison and can be stored in bones as lead triphosphate. It can be
very dangerous for those who are in direct contact with it. Of the airborne
particles, whether from traffic or other sources, a fraction eventually comes
down in precipitation or as dust. The metal that deposited on soil and crop
enters the food chain through roots and foliar absorption. NOx and SPM are,
the major emissions from heavy-duty diesel powered vehicles, whereas CO
and HC are the major emission from motor vehicles. Unleaded petrol release
benzene and toluene. Benzene, a known carcinogen, is present in petrol to
some extent as a replacement for lead.
92
As per the Air (Prevention and Control of Pollution) Act 1981, Central
Pollution Control Board (CPCB) has set up ambient standards for various
locations in India and also standards for the pollutants on health, vegetation
and economic criteria. Since then, monitoring of ambient air in major cities
has commenced.
There has been a significant increase in the vehicular density since
1970 in Mysore city. Consequently this growth leads to the deterioration of
air quality especially in commercial areas. The quantity and duration of
exposure of the automobile pollutants on people and plants will be high in
these areas. Hence, the ambient air quality monitoring was undertaken in the
city of Mysore.
MATERIALS AND METHODS
Ambient air quality monitoring
Ambient Air Quality was monitored seasonally for a period of two
years from rainy season of the year 2000 to summer season of the year 2002.
For the present study, conventional method was adopted for selecting ten
circles represented by industrial, residential, commercial and sensitive areas.
Since the study is focused on the impact of vehicular pollution on avenue
trees, more circles were selected from the commercial areas. The air
monitoring was carried out in accordance with the Bureau of Indian
Standard's specification (BIS): BIS -5182.
The ten locations selected for the ambient air quality monitoring in
Mysore city is depicted in the map (Fig. 3.1). Photographs of selected circles
are shown in Figs. 3.2 and 3.3.
93
Map of Mysore City
K.R.Circle
Sub-urban Bus stand circle
Metropole Circle
Fig : 3.1 Air Monitoring Sites
• KIADB , Industrial Area
Highway Circle
Milk Dairy Circle, T.N.Road
Canara Bank circle, Nazarbad Area
Fountain Circle
Ramaswamy Circle
Vijaya Bank Circle, Kuvempunagar
PHOTOGRAPHS OF CIRCLES SELECTED FOR THE STUDY
FIG. 3.2 : a. K.R. Circle (KR); b. Suburban Bus-stand Circle (SU);
c. IMetropole Circle (IMP); d. Canara Bank Circle (CB);
e. Fountain Circle (FT); f. KIADB Industrial Area (KIADB)
PHOTOGRAPHS OF CIRCLES AND CONTROL AREA SELECTED FOR THE STUDY
FIG. 3.3 : a. Highway Circle (HW); b. Milk Dairy Circle (MD);
c. Ramaswamy Circle (RS) d. Vijaya Bank Circle (VB);
e & f. Mahadevapura (Control)
The circles selected for the study include: K.R. Circle, Suburban bus
stand area, Metropole circle, Canara bank circle - Nazarbad area, Fountain
circle, KIADB-Industrial area, Highway circle, Milk dairy circle,
Ramaswamy circle and Vijaya Bank Circle - Kuvempunagar. Mahadevapura,
which lies 20 km away from Mysore city, was selected as control as there was
negligible traffic at this region.
Ambient Air Quality was monitored using High Volume Air Sampler
Enviro Tech APM-415 (Fig. 3.4). The parameters analysed include
Suspended Particulate Matter (SPM), Sulphur dioxide (S02) and Oxides of
nitrogen (NOx) and lead. The sampling was carried out for a duration of 8
hours per day / season at each of the eleven sites during all the seasons (rainy,
winter and summer during 2000 to 2002). The locations were also selected as
per the BIS specifications. Accordingly, the sampler was placed at the
breathing level at the height of 1.5 to 3 mts above the ground level. The gases
such as sulphur dioxide and oxides of nitrogen were collected for 8 hours at
an interval of 4 hours absorption in the mercuric chloride for S02 and sodium
hydroxide for NOx. They were analyzed spectrophotometrically.
*
Counting of vehicles
The number of Two Wheelers (TW), Light Motor Vehicles (LMV)
(Cars, Jeeps and Autorickshaws) and Heavy Motor Vehicles (HMV) (Mini
bus, Lorries and Buses) plying during peak hours of the day (9.00 am to 11.00
am) were counted for 2 hours using hand tally counter, at the time of
monitoring.
Determination of Suspended Particulate Matter (SPM)
Small solid particles along with liquid droplets suspended in the air are
collectively termed as particulates. The size of SPM ranges from a diameter
94
PHOTOGRAPHS OF HIGH VOLUME AIR SAMPLER
HIGH VOLUME SAMPLER
ENVIROTECH APM 415
FIG. 3.4 : a. Sampler showing stage for G.F. filter paper placement.
b. Sampler showing gas impingers - gaseous pollutant sampling attachment and a manometer
of 0.002 U, to 500 u,. The SPM was sampled for 8 hours using GF/A Whatman
Filter paper by the gravimetric method.
The Air was drawn into the covered high volume air sampler at a flow
rate of 1.13 to 117 mVminute using Glass fiber filter paper. Hourly manometer
readings were recorded to compute the average air flow rate. After 8 hours of
sampling, the filter paper was dried at 105°C in an oven for 24 hours and
weighed after cooling in a dessicator. Drying and weighing was repeated to
obtain concordant weights. The concentration of SPM deposited on the filter
paper was calculated using the equation given below.
W f - W j X l O 6
SPM ng/m3 = V
where
Wj = initial weight of the filter paper
Wf = final weight of the filter paper
V = Volume of air sampled, m3
The sampling and analysis of SPM was carried out as per IS 5182-
1973 part 4.
Determination of Sulphur dioxide by West and Gaeke method
West and Gaeke method for S02 - IS-5182 1969 - Part II was used to
determine the SO2 content in the samples. The sample was collected in the
absorbing solution and made upto 30 ml by using distilled water. From this
solution, 15ml of sample was taken and 5ml of reagent solution consisting of
2ml Formaldehyde, 1ml sulfamic acid and 2ml Pararosaniline was added. The
optical density was measured at 560 nm with the help of systronic UV VIS
(visible) spectrophotometer-108. S02 concentration in the analyzed sample
95
was determined graphically and SO2 concentration in the air sample was
calculated as follows :
A - A 0 x l 0 3 x B S02 ug/ m3 =
V
where
A = Sample absorbance
A0 = Reagent blank absorbance
103 = Conversion of litres to cubic meters
B = Calibration factor ug/ absorbance
V = Volume of air sampled in litres
Preparation of reagents
Absorbing solution (Sodium tetrachloromercurate): 27.2 gms of mercuric
chloride and 11.7 gms of sodium chloride were dissolved in 1000 ml distilled
water.
Pararosaniline hydrochloride, acid bleached : Step - I : 0.5 mg of
pararosaniline hydrochloride was dissolved in 100 ml of distilled water.
Filtered after 2 days and refrigerated.
Step - II : 4 ml of pararosaniline hydrochloride and 6 ml of concentrated
hydrochloric acid were dissolved in 100 ml of distilled water. This was used
as colouring reagent during analysis.
Formaldehyde (0.2%) : 5 ml of 40% formaldehyde was diluted with 1000
ml of distilled water. This was prepared freshly each time.
Sulfamic acid : 0.6 gm of sulfamic acid was dissolved in 100 ml of distilled
water.
96
Solution of sodium metabisulphite was used as standard for SO2. To
standardize Sodium metabisulphite solution 0.1N potassium dichromate
solution, 0. IN sodium thiosulphate solution, potassium iodide solution, starch
solution, and iodine solution were used.
• 0.1N potassium dichromate solution : 6 gms of K2Cr207 was dried at
103°C for 2 hours. After cooling, 1.226 gms of K2Cr207 was dissolved in
250ml of distilled water.
• 0.1N sodium thiosulphate solution : 7 gms of sodium thiosulphate and 2
ml of chloroform were dissolved in 250 ml of double distilled water.
• Potassium iodide solution : 10 gms of potassium iodide was dissolved in
10 ml of distilled water.
• Starch solution : Starch paste was prepared with distilled water and 1.25
gms of starch paste was added to 250 ml of boiling distilled water and
allowed to boil. The cooled supernatant was taken for experiments.
Standardization of sodium thiosulphate
For standardization of sodium thiosulphate, 80 ml of distilled water
was taken and 1 ml of concentrated H2S04, 10 ml of 0.1N potassium
dichromate solution and 1 ml of potassium iodide solution were added.
Reaction mixture was allowed to settle for 6 minutes in dark. This solution
was titrated with stock solution of sodium thiosulphate until the yellow colour
of iodine appears. 1 ml of starch indicator was added and the titration
continued until the blue colour appears. The final solution had a bluish green
tinge because of the chromus ions in it.
• 0.1N iodine solution (stock) : 10 gms of potassium iodide and 3 gms of
resublimed iodine were dissolved in 20 ml of distilled water. The same was
kept overnight and the final volume was made up to 250 ml with distilled
water.
97
Standardization of iodine solution
10 ml of Iodine solution from the stock solution was dissolved in 20 ml
of distilled water. It was titrated against 0. IN sodium thiosulphate solution to
get pale yellow coloured solution. 2 ml of starch solution was added and
titration was continued to get colourless solution. Titration was repeated to
confirm the titrant volume.
• Step I : Sodium metabisulphite (stock) solution : 0.40 gm of sodium
metabisulphite solution was mixed with distilled water and made upto 250
ml. 10 ml of this metabisulphite solution was taken in 250 ml of starch
solution. This solution was titrated against standard iodine solution until
the appearance of blue colour.
Strength of metabisulphite solution was calculated as follows : One ml
of 0.0IN metabisulphite solution contains 320ug S02.
Therefore one ml of sodium metabisulphite solution contains
320 xS = = Yug of SO2 (value of Y in the range of
0.01 about lOOOfj. S02)
where 'S' is the strength (N) of the stock metabisulphite solution.
Step II : 10 ml of metabisulphite solution, in 100 ml of distilled water contain
lOxY = = Zug of SO2 per ml of solution
1000
Step III: The volume of metabisulphite solution for 2, 5, 7, 10, 16, 20, 25 ug
in S02 was calculated. The SO2 concentration was estimated using sodium
metabisulphite as standard.
98
Determination of NOx by Modified Jacob and Hochheiser Method
Modified Jacob and Hochheiser Method for NOx - IS-5182 - Part VI
was used to determine the NOx contents in the samples. 10 ml of sample was
added to 20 ml of reagents consisting of 1 ml of H2O2, 1.4 ml of NED A, 10ml
of Sulfanilamide solution and 7.6 ml Sodium hydroxide, Absorbing solution
and kept for 30 minutes. The optical density was measured at 540 nra with the
help of systronic UV VIS (visible) spectrophotometer 108.
The concentration of NOx in ug/m3 in the sample was calculated as
follows:
ug/NOx~xVs
ugNOx/m3 = x D Va x 0.82 x V,
where
u.g/NOx~ = NOx" concentration in analyzed sample
Va= Volume of air sampled, m3
0.82 = Sampling efficiency
D = dilution factor (D = 1 for no dilution ; D = 2 for 1:1 dilution).
Vs= Final volume of sampling solution
Vt = Aliquot taken for analysis
Reagents
• Absorbing reagents : 4.0 gms of sodium hydroxide and 1.0 gm of sodium
arsenite were dissolved in 1000 ml of distilled water.
• Sulfanilamide solution : 20 gms of sulfanilamide was dissolved in 700 ml
of distilled water and 50 ml of concentrated phosphoric acid was added and
the volume was made upto 1000 ml with distilled water.
99
• N-(l-Naphthal) - Ethylenediamine dihydrochloride (NEDA) : 0.5 gm of
NEDA was dissolved in 500 ml distilled water.
• Hydrogen peroxide solution : 0.2 ml of 30% hydrogen peroxide was
diluted in 250 ml of distilled water.
• Standard nitrate solution : Desiccated sodium nitrate was dissolved in
1000 ml of distilled water.
The amount of NaNC>2 was calculated as follows :
1.500 G= xlOO
A
where
G = Amount of NaN02 in grams
1.500 = Gravimetric factor converting N02 into NaN02.
A = Assay, percent
The volume of air sample was calculated as follows :
Fi+F2
V= xtxlO - 6
2
where
V = volume of air sampled, M3
F[ = Measured flow rate before sampling, cm3/min.
F2 = Measured flow rate after sampling, cm3/min.
t = Time of sampling, min.
10"6 = Conversion of cm3 torn3.
100
Meteorological parameters
The data was collected from meteorological center, Bangalore for the
study period. The data was collected for parameters such as minimum and
maximum temperature, rainfall, wind velocity and relative humidity.
Determination of Lead in the Ambient Air
Glass fibre filter paper, capable of accumulating Suspended Particulate
Matters was used to estimate the amount of lead in ambient air. 1/4 of G.F.
filter paper was used for digestion in 50 ml of distilled water and 10 ml of
10% HN03. After half an hour, the volume was made upto 50 ml with
distilled water. Heavy metal like lead (Pb) was analyzed using Atomic
Absorption spectrophotometer at 217.0 nm as per standard methods
prescribed by CPCB (1998).
Central Pollution Control Board under section 16 (2) of the Air
(Prevention and Control of Pollution) Act, 1981, has notified the national
ambient air quality standard for lead in the ambient air (Table 3.1).
Table 3.1: Average concentration of lead in ambient air
Pollutants
Lead
Time
Annual average
24 hours
Commercial area
1.0ug/m3
1.5ug/m3
Industrial area
0.75 ug/m3
1.00ug/m3
Sensitive area
0.50ug/m3
0.75ug/m3
Method of measurement
AAS method after sample in GF filter
paper
Determination of air quality index
Air quality index has been validated using National Ambient Air
Quality Standards (NAAQS) (Table 3.2) to determine the permissible
concentration of pollutants in the air. As per the NAAQS, pollutants such as -
101
SPM, SO2 and NOx were analyzed and the threshold value in each circle was
highlighted. Table 3.2a shows the NAAQS Standards established by the
United States Environmental Protection Agency (USEPA).
The Air Quality Index is the overall measure of the status of the place
under consideration. The index is a compilation of the terms that define the air
quality as understandable by a layman, unlike the air quality data, which is
complex to comprehend by all groups of people. The Air Quality Index (AQI)
is a measure of the ratio of the pollutant concentration to the standard
concentration is often used to express the status of the ambient air in a place.
Table 3.3 gives rating scale of AQI values.
The following computation was used to derive the Air Quality Index of
the sites under consideration (Rao & Rao, 1989).
r AQI = 1/3
SPM S02 NO + +
>>
v >SPM >S02 >NOx
xlOO
J
Where SSPM> SSo2 and SNOx represent the ambient air quality standards
as prescribed by the CPCB for particulates, Sulphur dioxide, Oxides of
nitrogen respectively and SPM, SO2 and NOx represent the actual values of
pollutants obtained on sampling.
Table 3.2 : National Ambient Air Quality Standards (NAAQS)
Pollutants
S02
NOx
SPM
Time
8hrs.
8hrs.
8hrs.
Concent <
Industrial
120
120
500
tration in ambient air uality (ng/m3) Residential
80
80
200
Sensitive
30
30
100
Method of measurement
Improved West and Gaeke Dioxide method
Modified Jacob and Hochheiser (Na-Arsenite) method
Average flow rate not less than 1.1 nrVminute
102
The National Ambient Air Quality Standards (NAAQS) are standards established by the United States Environmental Protection Agency (USEPA).
NAAQS has set standards on six criteria pollutants:
1. Ozone (03) 2. Particulate Matter
* PM10, course particles: 2.5 micrometers (urn) to urn in size (although current implementation includes all particles 10 u.g or less in the standard)
* PM2.5, fine particles: 2.5 um in size or less 3. Carbon monoxide (CO) 4. Sulfur dioxide (S02) 5. Nitrogen oxides (NOx) 6. Lead (Pb)
Table 3.2a : The National Ambient Air Quality Standards (USEPA)
Pollutant
S02
S02
S02
PM10
PM10
PM2.3
PM2 5
CO
CO
03
03
N0X
Pb
Type
Primary
Primary
Secondary
Primary and Secondary
Primary and Secondary
Primary and Secondary
Primary and Secondary
Primary
Primary
Primary and Secondary
Primary and Secondary
Primary and Secondary
Primary and Secondary
Standard
0.14 ppm (365 ug/m3)
0.030 ppm (80 |ig/m3)
0.5 ppm (1,300 ug/m3)
150 ug/m3
50 ug/m3)
65 ug/m3)
15 ug/m3)
35 ppm (40 mg/m3)
9ppm(10mg/m3)
0.12 ppm (235 |xg/m3)
0.08 ppm (235 Ug/m3)
0.053 ppm (100 ug/m3)
1.5 ug/m3
Averaging Time"
24-hour
annual
3 - hour
24-hour
annual
24-hour
annual
1-hour
8-hour
l-hourb
8-hour
annual
Quarterly
Regulatory Citation
40 CFR 50.4(b)
40 CFR 50.4(a)
40 CFR 50.5(a)
40 CFR 50.6(a)
40 CFR 50.6(b)
40 CFR 50.7(a)
40 CFR 50.7(a)
40 CFR 50.8(a)(2)
40 CFR 50.8(a)(1)
40 CFR 50.9(a)
40 CFR 50.10(a)
40 CFR 50.11(a) & (b)
40 CFR 50.12
Primary : Protect human health, including sensitive populations such as children, the elderly and individuals suffering from respiratory disease.
Secondary: Protect Public Welfare a: Each standard has its own criteria for how many times it may be exceeded, in some
cases using a three year average, b: As of June 15, 2005, the 1-hour ozone standard no longer applies to areas
designated with respect to the 8-hour ozone standard (which includes most of the United States, except for portions of 10 states).
Source: USEPA (http://epa.gov/air/criteria.html)
102a
Table 3.3 : Rating scale of AQI values
Index Value
0-25
26-50
51 -75
76 - 100
>100
Remarks
Clean air (CA)
Light air pollution (LAP)
Moderate air pollution (MAP)
Heavy air pollution (HAP)
Severe air pollution (SAP)
Statistical analysis
Mean and standard deviation was calculated for the data obtained from
the present study. Two-way ANOVA and Tukey's post-hoc test were also
applied for the data and graphs were plotted.
RESULTS
The results of the density of vehicles counted, concentration of
pollutants monitored and meteorological parameters observed at different
circles including control area and their seasonal variations have been
presented in the tables (3.4 to 3.12) and figures (3.5 to 3.11).
Two-wheelers (TW)
The number of two-wheelers observed showed significant difference at
different circles (F = 26. 220; P< 0.000) (Table 3.4). The number of two-
wheelers was minimum (1.17) at control area and maximum at KR Circle
(Mean - 7527.33) (Table 3.5). There was a significant difference in the
number of two-wheelers during different seasons (F = 3.559; P<0.040) (Table
3.4). At majority of the circles, maximum numbers of two-wheelers were
present during rainy season, while winter season showedminimuaa«^umber
(Table 3.7). The interaction effect between circlesy^d^seasons-was^sp^
significant (F = 1.628; P<0.105) (Table 3.4).
103
Light Motor Vehicles (LMV)
Light motor vehicles showed significant differences in number at
different circles (F = 34.916; PO.000) (Table 3.4). The number of light motor
vehicles was minimum (0.83) at control area and maximum at KR Circle
(5728.83) (Table 3.5) followed by Ramaswamy circle, Sub-urban bus stand,
Metropole circle, Fountain circle, Canara Bank, Highway circle, Vijaya Bank,
Milk Dairy circle, KIADB Industrial Area. There was no significant
difference in the mean value of light motor vehicles in different seasons (F =
0.519; P<0.600). The interaction effect between circles and seasons was also
found to be insignificant (F = 0.894; P<0.595) (Table 3.4).
Heavy Motor Vehicles (HMV)
The mean value of heavy motor vehicles at different circles was found
to be significant (F = 2.586; P<0.020) (Table 3.4). There was no heavy motor
vehicle at control area. Their number was minimum at Vijaya bank circle and
maximum at Canara Bank circle (Table 3.5).
No significant difference was found in the mean value of heavy motor
vehicle numbers at different seasons (F = 1.618; PO.214). The interaction
effect between circle and season was also found to be insignificant (F = 0.605;
PO.880) (Table 3.4).
POLLUTANTS
Ambient air at selected circles was trapped and the concentration of
SPM, SO2, NOx and lead were estimated as per the protocol described under
materials and methods.
Suspended Particulate Matter (SPM)
A significant difference was seen in the mean value with respect to
SPM at different circles (F = 11.717; P<0.000) (Table 3.4). The control area
104
showed a minimum mean value (23.00) and Canara Bank circle showed
maximum mean value (655.67). Sub-urban bus stand, K.R. Circle and
Metropole circles showed maximum mean value of 503.50, and 474.33, 414
respectively (Table 3.6). KIADB and Vijaya Bank circles however showed a
minimum mean value. There was no significant difference in the mean value
with respect to SPM during different seasons (F = 0.379; P<0.688) (Table 3.4;
Fig. 3.5a). The interaction effect between circles and seasons was found to be
insignificant (F = 1.086; PO.406) (Table 3.4).
In control area, where vehicle numbers were limited to two, SPM level
was also found to be minimum. The levels were compared with national
ambient air quality standards (NAAQS) (Table 3.2). In all the circles the
levels of SPM were within permissible range while, only at Canara Bank
circle (>600 u.g/m3) and Suburban bus stand circle (>500 u.g/m3) the value
were slightly higher than permissible limit (Table 3.6). Even Vijaya bank
circle showed SPM level above the threshold value (>260 ug/m3).
Sulphur dioxide (S02)
There was no significant difference in the mean value with respect to
S02 at different circles (F = 0.545; P<0.845) (Table 3.4). However a
significant difference was found in the mean value during different seasons
(F=8.33; P<0.001). A minimum mean value was found during rainy season
and maximum value during winter at all the circles (Table 3.8; Fig. 3.5b). The
interaction effect between circle and season was found to be insignificant (F =
0.295; P<0.997) (Table 3.4).
Oxides of Nitrogen (NOx)
There was no significant difference found in the mean value with
respect to NOx at different circles (F = 1.417; P<0.216). But a significant
difference was observed in the mean value during different seasons (F =
105
12.678; P<0.000) (Table 3.4). A minimum mean value (24.00) was found
during summer season and a maximum mean value (49.86) was observed
during winter at all the circles (Table 3.7). At majority of the circles minimum
mean value was observed during summer season except KIADB industrial
area, which showed minimum mean value during rainy season. Maximum
mean value, were observed during winter at all the circles (Fig. 3.5c).
Interaction effect between circles and seasons was found to be insignificant
(F= 0.435; P<0.973) (Table 3.4).
Correlation between pollutants and type of vehicles
Figure 3.6 provides a correlation between two wheelers and the levels
of pollutants such as SPM, S02 and NOx. As indicated in Fig. 3.6, maximum
number of Two. Wheelers was observed in K.R. circle (7527) followed by
Ramaswamy circle (3984) and Metropole circle (3909). SPM level follows
the same order as that of vehicles except at Canara Bank circle and K.R.
circle, indicating that two wheelers were responsible for increase in SPM in
the ambient air. Significant increase in SPM at Canara Bank could be due to
contribution of vehicles other than two wheelers. Results are substantiated by
the correlation with heavy motor vehicles (Fig. 3.8).
Correlation between LMV, HMV and pollutants - SPM, S0 2 and NOx
has been depicted in Figs. 3.7 and 3.8 respectively. There was a proportional
change in the SPM content depending on the raise or fall in the number of
LMV and HMV, except at Canara Bank circle, where approximately 200 fold
increase in SPM was observed, although there was no significant increase in
the number of LMV. However SPM correlated perfectly, with HMV at
Canara bank circle, (Fig. 3.8a). These results suggest that SPM pollution may
be contributed more from HMV than either two wheelgES=erLMS^Qn the
contrary, as depicted in Fig. 3.7b and 3.7c, Fig. 3.8n!^u^^.8l;rSe^te^lde
fluctuation in the number of vehicles varying ax§kC ~4 to >20Q_ foldVno\i
106
significant change in S02 and NOx contents were observed. This indicates
that LMV and HMV were not contributing towards the pollutants S02 and
NOx in the ambient air.
Of the pollutants studied such as SPM, S02 and NOx, only SPM level
showed significant correlation as indicated in Table 3.10. Correlation factors
were relatively higher in summer season followed by wint irCSl&sorr'&Wd'j:
season. / S x ^
METEOROLOGICAL DATA
Temperature
*4f \$
Temperature showed no significant differenceXi^different circles. The J •vC'
mean value of minimum temperature (F=0.943; P<0>508) and"rnaxir
temperature (F=0.578; P<0.820) at different circles showed no significant
difference (Tables 3.8). However there was a significant difference in the
mean value of minimum (F = 48.685; PO.000) and maximum (F = 18.168;
P<0.000) temperature at different circles during different seasons (Table 3.8).
For minimum temperature, least mean value was found in winter season and
highest mean value during summer. Least mean value for maximum
temperature was found during winter and highest value during summer season
(Table 3.7). The.interaction effect between circles and seasons was found to
be insignificant with respect to the level of minimum (F=0.686; P<0.811) and
maximum (F=0.292; P<0.997) temperature (Table 3.8).
Wind Speed
Wind speed showed no significant difference at different circles
(F = 1.040; P<0.433) and during different seasons (F=2.600; P<0.089). The
interaction effect between circles and seasons was also found to be
insignificant with respect to the wind speed (F = 0.620; P< 0.869) (Table 3.8).
107
Rainfall
There was no significant difference in the mean value regarding the
extent of rainfall at different circles (F = 1.198; P<0.328) and during different
seasons (F = 1.249; P<0.300). The interaction effect between circles and
seasons was found to be insignificant with respect to the extent of rainfall
(F = 0.816; P<0.679) (Table 3.8).
Relative Humidity
A significant difference was found in the mean value of relative
humidity at different circles (F = 4.044; PO.001) (Table 3.8). Compared to
control, majority of the circles showed no significant difference in mean value
of relative humidity. However Highway circle differed significantly in
relative humidity compared to control (Table 3.9). Significant difference in
relative humidity was observed during different seasons (F = 44.453;
P<0.000) (Table 3.8). Minimum mean value for relative humidity was
observed during winter season and maximum during rainy season (Table 3.7)
in majority of the circles. The interaction effect between place and season was
found to be significant (F = 2.198; P<0.022) with respect to relative humidity
(Table 3.8). The data clearly indicates that there was no significant change in
these parameters, despite large variation in vehicular density (Fig. 3.9).
Further, attempts were made to understand the correlation between
meteorological data and vehicular pollutants such as SPM, SO2 and NOx
(Table 3.11). Since, no significant change was observed in various parameters
of meteorological data during various seasons, correlation has been made for
the consolidated data for overall seasons. Interestingly, consolidated data
indicated a correlation with SPM and S02, while NOx showed significant
negative correlation particularly with reference to the temperature ranging
from 17° C to 21° C. Oxides of nitrogen thus, minimized with the increase in
temperature. The results substantiated by seasonal studies indicated that
reduction in level of NOx with the increase in temperature could be due to the
decomposition of NOx, which are quite unstable. NOx however showed a
positive correlation with relative humidity.
108
Table 3.4 : Result of 2-way ANOVA of different type of vehicles and pollutants
Source
Two wheelers
Circle
Season
Circle * season
Error
LMV
Circle
Season
Circle * season
Error
HMV
Circle
Season
Circle * season
Error
SPM
Circle
Season
Circle * season
Error
S02
Circle
Season
Circle * season
Error
NO,
Circle
Season
Circle * season
Error
Sum of squares
232987659.8
6324370.45
28927528.21
29322887.50
148392959.5
441051.758
7603185.242
14025038.50
5154776.606
645149.212
2413922.121
6578590.500
1845822.697
11940.212
342078.121
519870.500
836.030
2555.848
903.152
5058.000
. 4354.697
7790.939
2671.394
10140.000
Df
10
2
20
33
10
2
20
33'
10
2
20
33
10
2
20
33
10
2
20
33
10
2
20
33
Mean square
23298765.979
3162185.227
1446376.411
888572.348
14839295.948
220525.879
380159.262
425001.167
515477.661
322574.606
120696.106
199351.227
184582.270
.5970.106
17103.906
15753.652
83.603
1277.924
45.158
153.273
435.470
3895.470
133.570
307.273
F-ratio
26.220
3.559
1.628
34.916
.519
.894
2.586
1.618
.605
11.717
.379
1.086
.545
8.33
.295
1.417
12.678
.435
P (Sig.)
.000
.040
.105
.000
.600
.595
.020
.214
.880
.000
.688
.406
.845
.001
997
.216
.000
.973
109
Table 3.5 : Result of Tukey's post-hoc test for two wheelers, LMV and HMV
Source
Two wheelers
LMV
HMV
Circles
Control
Kiadb
Md
Hw Cb
Vb
Su1
Ft
Mp
Rs
Kr
Control
Kiadb
Md
Vb
Hw
Cb
Ft
Mp
Su
Rs
Kr
Control
Vb
Kiadb
Md
Mp
Rs
Ft
Hw
Kr
Su
Cb
Subset (Sig. Level at 0.05)
1
1.17
639.33
.83
314.50
929.83
1207.33
1266.33
0.00
148.17
190.67
273.00
412.67
583.33
585.17
599.50
613.33
661.00
2
639.33
2085.67
2295.83
2515.00
929.83
1207.33
1266.33
1996.67
2111.67
148.17
190.67
273.00
412.67
583.33
585.17
599.50
613.33
661.00
1031.33
3
2085.67
2295.83
2515.00
2949.17
3305.50
3560.67
3909.50
1207.33
1266.33
1996.67
2111.67
2449.17
4
2295.83
2515.00
2949.17
3305.50
3560.67
3909.50
3984.33
1996.67
2111.67
2449.17
2806.67
2906.00
5
7527.33
5728.83
110
Table 3.6 : Results of Tukey's post-hoc test for SPM
Source
SPM
Place
Control Kiadb Vb Rs Md Ft Hw Mp Kr Su Cb
Subset (Sig. Level at 0.05) 1
23.00 113.17 260.67
2
113.17 260.67 292.50 294.67 300.50
3
260.67 292.50 294.67 300.50 395.83 414.00 474.33 503.50
4
414.00 474.33 503.50 655.67
Table 3.7 : Result of Tukey's post-hoc test for two wheelers, S02, NOx, Temperature and relative humidity
Source
Two wheelers
S02
NOx
Minimum temperature
Maximum temperature
Relative humidity
Season
Winter Summer Rainy
Rainy Summer Winter
Summer Rainy Winter
Winter Rainy Summer
Winter Rainy Summer
Winter Summer Rainy
Subset (Sig. Level al 1
2668.27 2868.27
21.14 29.91
24.00 31.50
17.2909
28.6909 29.5059
59.2273
2
2868.27 3401.68
29.91 36.32
49.86
19.2818
33.0282
63.7273
10.05) 3
21.9318
74.4545
111
SEASONAL VARIATION BETWEEN SPM, S0 2 AND NOx & TOTAL NUMBER OF VEHICLES
1600
1400
1200
1000
800
600
400
200
0
160
140
120
100
80
60
40
20
0
160
140
120
100
80
60
40
20
0
• Total No. vehicles - • — Rainy -A- • Winter • • • Summer
a
• Total No. vehicles - • — Rainy -A- - Winter *- - • Summer
.
\
• -
1 i
— -A- • Winter
- - • • • Summer
T 1 1 1 1 1 1
c
—1 ! — • —
•& & ^Z & «F" . P ^ ^ <? 4° ^ 4?
Circles 0o>
Fig. 3.5 : a. SPM; b. S0 2 and c. NOx
CORRELATION BETWEEN TWO WHEELERS AND SPM, S02 & NOx
2000 - -
1000 -
0
8000
7000
6000 |
5000
4000
3000
2000
1000 }
0
H 1 1 1 1 1 1 1 \
-- 50
^ & & <y <£*.$> 4* ^ <& ^ £ *>
V c° Circles
Fig. 3.6 : a. SPM; b. S02 and c. NOx
CORRELATION BETWEEN LIGHT MOTOR VEHICLES AND SPM, S0 2 & NOx
7000 -i-
6000 - -
5000 - -
4000 --
3000--
2000 --
1000 --
0--
6000 --
Circles
Fig. 3.7 : a. SPM; b. S02 and c. NOx
CORRELATION BETWEEN HEAVY MOTOR VEHICLES AND SPM, SOz & NOx
£ M 3 s 5B
>
i — e la
u E a 2:
4> c? ^ tf <• I5 ^ ^ «? i? ^
Circles c?
Fig. 3.8 : a. SPM; b. S02 and c. NOx
Table 3.8 : Result of 2-way ANOVA of meteorological data
Source
Minimum temperature Circle Season Circle * season Error Maximum temperature Circle Season Circle * season Error Wind speed Circle Season Circle * season Error Rainfall Circle Season Circle * season Error Relative humidity Circle Season Circle * season Error
Sum of squares
23.105 238.511 33.619 80.835
37.198 233.805 37.642
212.346
28.364 14.182 33.818 90.000
132.737 27.688 180.975 365.725
1224.939 2692.758 1331.242 999.500
Df
10 2 20 33
10 2 20 33
10 2 20 33
10 2 20 33
10 2 20 33
Mean square
2.310 119.256 1.681 2.450
3.720 116.903 1.882 6.435
2.836 7.091 1.691 2.727
13.274 13.844 9.049 11.083
122.494 1346.379 66.562 30.288
F-ratio
.943 48.685
.686
.578 18.168 .292
1.040 2.600 .620
1.198 1.249 .816
4.044 44.453 2.198
P (Sig.)
.508
.000
.811
.820
.000
.997
.433
.089
.869
.328
.300
.679
.001
.000
.022
Table 3.9 : Result of Tukey's post-hoc test - Relative humidity at different places
Circles
Md Su Vb Kr Kiadb Rs Cb Ft Control Mp Hw
Subset (Sig. Level at 0.05) 1 61.0000 61.5000 63.1667 63.5000 64.0000 64.5000 66.5000 66.8333 67.5000 67.8333
2
66.8333 67.5000 67.8333 77.5000
112
Circles
•— Total No. vehicles x 1000 - -o- • Rain fall •*— Min. temperature - - » - • Max. temperature *— Wind speed —•— Relative humidity
Fig. 3.9 : Correlation between total number of vehicles and meteorological data
Table 3.10 : Correlation between different types of vehicles and SPM during different seasons
Two wheeler
LMV
HMV
Rainy season
.323
.429*
.573**
Winter season
.431*
.379
.643**
Summer season
.698**
.609**
.736**
Overall
.430**
.458**
.553**
** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)
Table 3.11 : Correlation between SPM, S 0 2 and NOx and meteorological data
Rain fall
Min. temperature
Max. temperature
Wind speed
Relative humidity
SPM
-.034
-.098
-.127
-.053
-.070
so2
-.120
-.102
.211
.072
.058
NOx
-.198
-.502**
-.510**
-.126
.251*
** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)
113
Lead in ambient air
There was no significant difference in the mean value of Lead in
ambient air in different circles and in different seasons (F=0.919; P<0.528)
(F - 2.334; P<0.113) respectively. The interaction effect between place and
season was found to be insignificant with respect to the presence of Lead in
ambient air (F=0.897; P<0.593) (Table 3.12).
Table 3.12 : Result of 2-way ANOVA for mean lead concentration in ambient air in different circles and seasons
Source
Circle
Season
Circle * Season
Error
Sum of squares
.127
.06462
.248
.456
Df
10
2
20
33
Mean square
.01270
.03226
.01240
.01382
F-ratio
.919
2.334
.897
P (Sig.)
.528
.113
.593
The level of lead in various circles in comparison with standard
permissible limit provided by NAAQS, which is 1.5ug/m3 is given in the
Fig. 3.10. However, in the circles tested, lead levels were very minimal,
ranging from 0.03 to 0.172ug/m3 including control which has a lead value of
0.173ug/m3. Result clearly indicates that there is no significant lead
accumulation both in the control and traffic zone despite heavy traffic flow in
few places.
Air Quality Index (AQI)
Air Quality Index was calculated during the study period considering
accepted limit for Clean Air (CA), Light Air Pollution (LAP), Moderate Air
Pollution (MAP), High Air Pollution (HAP) and Severe Air Pollution (SAP)
with respective standards set or rating scale for commercial, residential and
sensitive area (Table 3.3).
114
1.5
1 =
J • o
I 0.5
COMPARISON OF LEAD WITH THRESHOLD VALUE
• • • • -i—*—i r
^ /V V & ^ d? • .#> <£* ^ <t* 4°
Threshold value of lead and places
Fig. 3.10 : Lead in ambient air at different circles and control
AIR QUALITY INDEX OF DIFFERENT CIRCLES AND CONTROL AREA DURING DIFFERENT SEASONS
<
o <
120 -t
110-
100 -
90 -
80-
70-
60-
50 -
40 -
30-
2 0 -
10 -
100 -
90 -
80 -
70-
6 0 - 1 50 -1 *
4 0 -
30-
2 0 -
10-
0 -
a |
I^^H
•
•
•
^S c <tv .JP tf <& V 4» J-•7 Oo*
| C
• •
• • •
«
<
80-
70 -
60 -
50 -
40 -
3 0 -
20 -
10 -
[i -
| |
It
ii i i i
. ,
r d
r,
V *> <$ „*S <y * <fi •¥•
Fig. 3.11 : a - Rainy ; b - Winter ; c - Summer; d - Overall
The data revealed that the control area showed clean air during all
the seasons (Fig. 3.11a-d). When all the seasons were considered, majority of
the circles showed MAP, while Canara bank circle and Vijaya bank circle
showed Heavy Air Pollution (HAP). The remaining circles showed LAP.
Most of the circles belong to light air pollution and moderate air pollution
during all the seasons (Fig. 3.1 Id). Moderate air pollution was observed at
Metropole, Canara Bank and Vijaya Bank circles during rainy season
(Fig. 3.11a). K.R, Suburban bus stand and Highway circles during winter
season (Fig. 3.11b) and K.R. and Suburban bus stand circles during summer
season (Fig. 3.1 lc). KIADB comes under clean air zone during rainy season.
Canara Bank and Vijaya Bank circle belongs to heavy air pollution zone
during winter and Vijaya Bank circle belongs to heavy air pollution during
summer season.
DISCUSSION
The drastic rise in the urban population in India along with economic
activities has intensified the problem of urban transport problem.
Unfortunately the supply of mass transport services is not keeping pace with
the demand, which is growing in geometric progression. This widening in
demand - supply gap is leading to a proliferation of personalized modes of
transport. With an improvement in the standard of living and easily available
schemes within the reach of common man to own a vehicle, the private
vehicles have been growing at a rate of 10 - 15 percent per annum. Two-
stroke engine powered vehicles such as motor cycles, scooters and mopeds
are getting increasingly popular in India, because of their greater fuel
economy, better power output, lower optional maintenance costs and low
production costs (Mathur, 1985).
115
A distinctive feature of vehicular population in India is the fact that
two wheelers and cars in metropolises contribute to 78 percent and 11 percent
of vehicular population respectively. Three wheelers contribute around 5
percent, buses contribute 2 percent and trucks contribute 4 percent. Evidently
two wheelers are the main cause of road congestion and high fuel
consumption in the metropolitan cities (Patankal, 2000). Apart from the
increase in vehicular population in the urban area, which contributes to the
increased emission, the other factors include, the type of engines and fuels
used, age of the vehicles, congested roads, poor road condition and out dated
automotive technology. The four basic source of the air pollution from the
automobiles are fuel tank, carburetor, crankcase and exhaust pipe. The
exhaust pipe is the major source of air pollution from automobiles which
account for 65 percent to 70 percent of pollution, while about 20 percent
occurs through blow from the crank case breather and the remaining through
evaporative emission from the fuel tank breather, carburetor, and spillage
losses (Biswas & Dutta, 1994).
Emissions from vehicles are of two types, regulated and unregulated
pollutants. Regulated pollutants include CO, HC, NOx and SPM whose levels
in the atmosphere are governed by the emission regulations in force.
Unregulated or toxic pollutants such as lead (Pb), sulphur dioxide (S02),
benzene and poly aromatic hydrocarbons (PAN) etc., are mostly dependent on
the quality of fuel and are not governed by the emission regulations but are
more harmful (Subramanian, 2000). Emissions of the regulating pollutants are
governed more by the design of the engines and vehicles, the operating
conditions and the environment. They demand better fuel quality to achieve
target norms whereas emissions of the unregulated pollutants are mostly the
direct effect of fuel constituents. The ambient air quality (AAQ) in this case
will improve only if there is a reduction of these fuel constituents as they
influence the emission from both old and new vehicles.
116
Unlike most other sources of pollution, the impact of emission from
motor vehicles is more because they emit pollutants in close proximity to the
breathing zone of the people. The high raise buildings close to the roads affect
dispersion of pollutants naturally thereby increasing the concentration of
pollutants in the ambient air. This in turn will affect the health of the people
who reside and work in the near vicinity and also the vegetation growing near
the roadside. With a view to control further deterioration of AAQ, a
comprehensive legislation for prevention and control of air pollution has been
enacted under which all states have constituted "Air Pollution Control Board"
that coordinates the regulatory norms and enact strategies for effective
prevention and control or abatement of air quality in the country. For adopting
any strategy or work plan for controlling the AAQ, it is essential to formulate
air quality standards (AQS) or emission standards (ES) along with other
regulatory actions. To derive logical AQS recommendations, the background
information on effects on health and vegetation are essential. Studies on short
term and long term effects on vegetation can form the basis in the initial
stages for formulating certain criteria for AQI.
In India the ambient air quality standards were first notified in the year
1984 followed by the introduction of automotive vehicular emission norms in
the year 1991. These were subsequently revised twice in 1996 and 2000 to
meet the air quality standards. A number of reports have been prepared by
various government ministries in the recent past on vehicular emission norms,
standards on quality of auto fuel and measures to reduce air pollution. A
committee consisting of experts of National repute was constituted in August
2001 to formulate national auto fuel policy together with a road map for
implementation. A interim report was submitted in December 2001, and the
117
final report was published in 2002. This would go a long way in formulating a
long term auto fuel policy for the country, which would play a major role in
improving quality of ambient air of environment particularly the metros.
The continuous assessment of ambient air quality status of industrial as
well as urban areas is an important task in any air pollution management
programme. The National Environmental Engineering Research Institute
(NEERI) in Nagpur, initiated the National Air Quality Monitoring Network
Programme (NAQMN) in 1978 in 10 cities. This was the first organized effort
to record continuous concurrent data on gaseous and particulate pollutant
level in the ambient air. The study revealed that the ambient air quality in
many Indian cities has reached a level, which requires immediate action for
its control. The nation wide programme of monitoring ambient air quality was
initiated in 1984 in the country. As on March 31st 1995, the network
comprises 290 stations covering over 90 towns and cities distributed over 24
states and 4 union territories. The NAQMN is operated through respective
state pollution control boards, NEERI and CPCB.
Monitoring of ambient air quality in Kanarataka state, India, is being
carried out by the Karnataka State Pollution Control Board (KSPCB), at
various locations of Bangalore, Belgaum, Bidar, Davangere, Dharwad,
Hassan, Mangalore and Mysore. Monitoring in Bangalore city was initiated in
1981 at few commercial stations. Between 1985 to 1990 it was extended to 10
monitoring stations and the parameters analyzed were total suspended
particulates, S02 and NOx. Since 1991 such data was restricted to only three
NAQMN stations. In the city of Mysore the ambient air quality monitoring is
being carried out only at two stations by KSPCB, one commercial and another
an industrial area. However this monitoring will be inadequate if one wants to
118
see the status of pollution in situ, collection of time series data will enable one
to analyze the dynamics of pollution or dispersion and also integrate it to an
AAQ management programme.
There has been a drastic increase in the number of registered vehicles
in Mysore city over three decades. During the study period the total number
of vehicles increased from 2,22,238 in 2000 to 2,56,985 in 2002. By the end
of 2004 total number vehicles had increased to 3,06,430 out of which two
wheelers constituted more than 50 percent. Urbanization and industrialization
are inherent part of the process of economic development in the city, which
started in the beginning of 1970. Its rate was indicated, by the gradual
increase in the population over the years in Mysore city. Increased population
and vehicular density, as a consequence of industrialization and urbanization
may contribute towards the deterioration of air quality in Mysore city in
future.
The number of two-wheelers, light motor vehicles (autorickshaws,
cars, mini vans, jeeps etc.) and heavy motor vehicles (Buses and Lorries) that
were plying in the study areas, at the time of monitoring showed significant
variations. Two wheelers were found to be maximum in number at majority
of the circles, except at Canara Bank and Suburban bus stand circles. Majority
of the circles showed maximum number of two wheelers during rainy season
and minimum during winter season. The increase in the number of two
wheelers in the city of Mysore may be attributed to the following reasons. The
city transportation is limited to certain areas and is time specific hence more
people prefer personal vehicles for transportation. Mysore city is also well
known for its educational institutions, which attracts large number of students
from all parts of the country, consequently there is an increase in the number
of two-wheelers. Maximum numbers of two wheelers and light motor
vehicles were observed at KR circle out of ten selected circles. Canara Bank
119
and Suburban bus stand circles showed highest number of heavy motor
vehicles. This was due to the location of a private bus terminal near Canara
Bank circle where buses arrive from nearby villages. K R circle showed
maximum number of two wheelers and light motor vehicles during all the
three season.
Mysore city is one of the historical, centers of India, which attracts
large number of tourists during the months of October to December (winter)
and March to June (Summer). During these periods there was increase in the
total number of vehicles in and around the city of Mysore. Similar,
observations were made by Gupta and Vidya (1994) at Shimla town during
winter and summer.
In the present study ambient air quality monitoring in the selected
circles revealed that the primary pollutants SPM, SO2 and NOx were within
the CPCB permissible limit at majority of the circles when the average of all
the seasons were taken into consideration. However Canara Bank, Sub Urban
bus stand and Vijaya bank circles showed maximum concentration of SPM,
which was above the permissible limit. These results agrees with the ambient
air quality studies at some of the cities, such as Visakapatnam (Mudri et al,
1986), Mexico, Sau Paulo Bucros Aires and Rio de Janeiro (Kretzschmar,
1994), Germany (Pfeffer et al, 1995), Indore (Joshi & Mishra, 1998), Mysore
(Hosmani & Doddamani, 1998), Dhaka (Alam et al, 1999), Bangalore (Dayal
& Nandini, 2000), Hyderabad (Bhagyalakshmi & Saroja, 2003), Delhi (Gupta
& Indrani, 2003), Chennai (Senthilnathan and Rajan, 2003), Margao (Antao,
2004), Bangalore (Mahendra & Krishnamurthy, 2004) and Pondicherry
(Ramesh, 2004).
Studies carried out only on SPM, which showed high values have been
reported by Gupta and Vidya (1994) at Shimla Town, Wilson (1998) at
120
Bangalore, Samanth et al. (1998) at Calcutta, Rajashekar et al. (2001) at
Madurai city, Nanda and Tiwari (2001) at Orissa, Kumar et al. (2003) at
Bombay. Ambient air monitoring studies showed similar results at industrial
areas of Auriya, U.P. (Gupta & Shukla, 2004), Industrial complex Hyderabad
(Raza et al, 1988), Begusari, Bihar (Kannan & Sengupta, 1993), Korba
thermal power plant, M.P. (Williams et al, 1996), Jharia coal fields,
Dhanbad, Jharkhand (Ghouse & Majee, 2001), mining site Joda - Barbil belt,
Orissa (Das et al, 2003) Lakhanpur coal field, Orissa (Chaulya, 2004) at
Kakinada (Rao et al, 1999) and Hazira town (Reddy & Suneela, 2001). In the
present study at K.R. and Metropole circles the levels of SPM was high and
may reach the permissible limit in the near future.
In the present study, a positive correlation was observed between the
levels of the SPM and total number of vehicles at K.R, Metropole, Fountain,
KIADB, Highway, Milk dairy, Ramaswamy and Vijaya Bank circles. In these
areas two wheelers are found in maximum number than LMV and HMV.
Hence two wheelers were contributing factors for the levels of pollutants in
the ambient air at these circles. Aleksandropoulou and Lazaridis (2004) have
attributed energy production, agriculture and road transports as major factors
for increased emission of gaseous and particulate matter in Greece. Beevers
and Carslaw (2005) have observed a significant reduction in the emissions of
NOx and PMio with increase in vehicular speed in London.
On the contrary Canara Bank and Suburban bus stand circles showed
no significant correlation between SPM and two wheelers as well as LMV.
However these circles showed maximum number of HMV, which directly
correlates to the higher level of SPM in these circles. In addition at Canara
Bank circle, bad (mud) road condition was also responsible for the higher
levels of SPM. Increase in the SPM level due to diesel driven vehicles have
been reported by Mishra et al (1993). Higher smoke density in the diesel
121
vehicles was attributed to the lack of improper maintenance, improper heat
absorption, ventilation and age of the vehicle. Dayal and Nandini (2000) have
also observed higher levels of SPM in Bangalore city due to diesel vehicles
such as lorries, trucks and buses. In the present study, Vijaya Bank circle
though a residential area, showed SPM level above the threshold value. This
was because of the increase in the number of two wheelers. This is one of the
busiest, densely populated residential areas in the city. Joshi and Mishra
(1998), Anto (2001) and Chaulya (2004) have also reported an increase in
SPM level at residential areas.
The levels of SO2 and NOx were within permissible limits of CPCB at
all the circles in the present study. Relatively higher values of these pollutants
were observed during winter compared to summer and rainy season probably
due to slow dispersion of pollutants on account of thermal inversion effect
and low wind velocity (NBPJ, 2004). Oanh et al. (2006) have observed high
levels of PM10 and PM25 during the dry season and less during wet season in
six Asian Urban areas. Increase in particulate matter during winter season
was observed by Goyal et al. (2006) and Song et al. (2006) in Gantok, India
and Beijing, China respectively. The total number of vehicles did not show
any correlation with SO2 and NOx levels in the ambient air in the present
study. Higher S02 and NOx concentrations due to autoexhaust from the motor
vehicles however have been observed by Mudri et al. (1986), Alam et al.
(1999), Samal and Santra (2002), Gupta and Indrani (2003), Rajput and
Agarwal (2004) and Das et al. (2003). Williams et al. (1996) observed that
the increased S02 concentration was strongly influenced by the wind direction
and wind velocity. Increased level of S02 and NOx in the ambient air was due
to wind direction and wind velocity during winter compared to summer and
rainy season (Alam et al, 1999). Saiz-Lopez et al. (2006) observed that the
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levels of N02 in Spain enhanced during winter, which was strongly
influenced by levels of motor traffic. According to Wang and Lu (2006)
usage of diesel fuel by buses resulted in increased emission of S02.
In the present investigation the meteorological parameters like wind
velocity and rainfall did not show any correlation with pollutants. During the
study period Mysore district experienced drought hence arid condition
prevailed during rainy season. Temperature showed negative correlation with
NOx while relative humidity showed a positive correlation with NOx. Kannan
and Sengupta (1993) have observed higher SPM levels in summer due to
higher wind speed and wind direction compared to winter and monsoon.
According to Williams et al. (1996) the concentration of SPM were strongly
influenced by wind direction and velocity. Hargreaves et al. (2000) had
observed that high wind speed decreased the concentration of N02 in U.K.
Das et al. (2003) have observed high SPM during pre-monsoon, which was
attributed to high arid condition and suspension of dust in the ambient air.
Alam et al. (1999) and Samal and Santra (2002) have observed higher SPM
during winter due to predominant wind direction and other
micrometeorological parameters and slow dispersion of the pollutants.
According to Gupta and Vidya (1994) and Pfeffer et al. (1995) temperature
and wind velocity aggregate at higher altitude and also help the SPM to settle
at the higher altitude from lower altitude. Gupta and Shukla (2004) have
observed higher level of SPM during summer due to volcanic eruptions,
blowing dust and soil by wind.
Lead (Pb) - Tetra ethyl lead (TEL, (C2H5)4 Pb) was being used in
petrol as an anti-knock agent. The combustion of the leaded petrol deposits
metallic lead on engine valve seats, which serve to improve valve life. 1, 2
dibromothiane is also added to petrol, which converts lead into volatile lead
bromide that is carried away in the exhaust. Lead and its compounds are toxic
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and retained by the human body over a long period of time. This causes
adverse effects like digestive disorders, brain damage and mental retardation
in children. Before the introduction of unleaded petrol, studies have shown
higher lead level in the ambient air (Sadashivan, 1987; Wilson, 1998;
Kulshretha et al, 1994a and Samanth et al, 1998). Since the use of leaded
petrol in vehicle was prohibited by Indian government from April 2000, lead
pollution load in ambient air has come down. The present study also showed
negligible lead content (less than 0.1 ug/m ) in the ambient air at all the
circles. Similar observations were made by Gupta and Shukla (2004) at town
area of Auriya district U.P, due to the use of unleaded petrol in the vehicles.
On the contrary, Rajashekar et al. (2004) have observed lead concentration
exceeding the permissible limit in Madurai city due to heavy movement of
vehicles.
Swamy and Lokesh (1993) have studied the dispersion of lead from
automobile exhaust on soil surface along low and high traffic density on roads
in Mysore city. They found that the concentration of lead decreased with the
increase in distance from the heavily polluted area in Mysore city. They also
noticed less deposition of lead with increase in depth of the soil.
Air quality index (AQI) was calculated for different circles based on
air monitoring data to assess the quality of air in the city. In the present study
control area showed clean air during all the three seasons. Majority of the
circles showed Light Air Pollution and Moderate Air Pollution. KIADB
showed clean air during winter season, while Canara Bank circle, showed
Heavy Air Pollution only during winter season and Vijaya Bank during
summer and winter seasons, when average of all the seasons was considered.
Similar works on AQI were conducted by Gupta and Sharma (1995) Alam
et al. (1999), Dayal and Nandini (2000), Reddy and Suneela (2001),
Senthilnathan and Rajan (2003), Das et al. (2003), Ghose and Mrinal (2004)
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and Ramesh (2004). According to Reddy and Suneela (2001), the lower air
quality index at industrial areas was attributed to location, which was far
away from the thickly vehicular populated area and use of gaseous fuel
instead of solid and liquid fuels, by these industries. In the present study,
Vijaya Bank circle, a residential area showed moderate air pollution and
heavy air pollution mainly due to movement of two wheelers. Similar
observations were made by Senthilnathan and Rajan (2003) in residential
areas of Chennai which showed high air pollution due to increased vehicular
movements, leading to increase in exhaust in the ambient air and also due to
bad road conditions in these areas. Das et al. (2003) observed in one of the
study areas moderate air pollution due to higher mining activities leading to
increased SPM which remains suspended in the air column during pre-
monsoon periods, while the other areas belongs to "Fairly clean air category".
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