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outdated automotive technology add to the increase in vehicular emissions.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).

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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.

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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.

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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

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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.

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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.

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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.

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• 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 -

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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

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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).

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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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