Trends Analysis of Ambient Air Pollutants in Agra City … Journal of Air Pollution Control, Vol...

Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017

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Trends Analysis of Ambient Air Pollutants in Agra City -2002-2013


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ISSN 0250-5231

Indian Association for Air Pollution Control

(Delhi Chapter)

C/o Envirotech Instruments Pvt. Ltd., A – 271, Okhla Industrial Area, Phase- 1, New Delhi- 110020

EXECUTIVE COMMITTEE

President

Dr. B. Sengupta

National Vice -President Prof. A. L. Aggarwal

Vice - Presidents Dr. J. S. Sharma

Sh. H. K. Parwana

Dr. J. K. Moitra

Dr. S. K. Jain

General Secretary Sh. S. K. Gupta

Treasurer Dr. Rajendra Prasad

Joint Secretary Dr. D. Saha

Sh. A. Pathak

Executive Member Dr. T. K. Joshi

Dr. S. D. Attri

Dr. Shankar Agarwal

Sh. Rakesh Agarwal


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Dr. P. C. Jha

Dr. M. A. Patil

EDITORIAL BOARD

Editor- in- Chief Dr. S.K. Tyagi

Editors Dr. P.B. Rastogi

Dr. M. P. George


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Published by:

INDIAN ASSOCIATION FOR AIR POLLUTION CONTROL

(Delhi Chapter)

c/o Envirotech Instruments Pvt. Ltd.

A-271, Okhla Industrial Area, Phase-I, New Delhi - 110020

E-mail: [email protected] ; Website: www.iaapc.in

ISSN 0250-5231

Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017


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Indian Journal of Air Pollution Control

(Vol XVI, No.2 & Vol XVII, No. I, September 2016 / March 2017)

CONTENTS

From the Editor-in-Chief

A Report from the Secretary

Research Papers

1. Trends Analysis of Ambient Air Pollutants in Agra City -2002-2013

Kamal Kumar, V.K.Shukla

08 - 20

2. 1. Site-Specific Variation Study of Particulate Matter with Traffic

Kirti Bhandari, Rina Singh , Anuradha Shukla

21 - 36

3. 1. Impact of Trace Gases (Seasonally) and Meteorology on Concentration

of Particulate matter (PM2.5) in Delhi

Nikki Choudhary, Atul Dwivedi2,

37 - 53

4. 2. Worsening Of Urban Air Quality: Role of Meteorology and Episodic Events

during Winter Month

Rohit Sharma, Kamna Sachdeva and Anu Rani Sharma

54 - 60

5. Recent Development on the Understanding of Aerosol Nucleation and Growth

Bighnaraj Sarangi, Deepak Sinha, Prashant Patel, Shankar G. Aggarwal

61-75

6. A study on Ambient Air Quality and Non-Attainment Cities in North Zone of India

Anchal Garg, Tarun Darbari, S.K. Tyagi and N.C. Gupta

76-84

7. The Diurnal Trend of Urban Ground Level Ozone during Monsoon, Post-Monsoon &

Winter Months in Delhi, India

Harveen Kaur, Sushil K. Tyagi

85-98

8 Proceedings of Training Workshop on Volatile Organic Compounds (VOC) and

Hydrocarbon (HC) : Monitoring and Management, Organised by ONGC in Association

with CPCB and IAAPC (DC) during March 2-3, 2016

99-104

9 Recommendations of the Workshop on Requirement, Practices, Gaps and Challenges in Air

Quality Study for Preparation of EIA Report , Organised by IAAPC (DC) on August 27,

2016

105-107

10 Instruction for Authors 108-109

11 Membership Form 110-111


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From the Editor-in-Chief

Delhi is the 11th most polluted city in the world - WHO Report in 2016

Delhi & NCR have high level of air pollution, which remains in very poor or critical level during winter season

due to higher levels of PM10, PM2.5 most of the time. The Central Pollution Control Board has issued direction to

the 22 Towns of the NCR for necessary action plan to control air pollution in these town. The vehicles are supposed

pre-dominant source of air pollution in cities. Besides vehicles, a wide range of other emission sources exits in

Indian urban areas. Therefore, it is important to carry out the monitoring of molecular markers in ambient air of

important cities intending to find out the major sources to the air borne particulate matter including vehicles, coal

combustion, agriculture residue/ refuse burning etc to provide scientific basis to the policy makers and other

stakeholders, for formulation of appropriate strategies and prioritizing actions for improving air quality in urban

areas.

Elemental and ion analysis show abundance of soil constituents (e.g. Si, Fe, Ca, Na). This clearly suggests that

there could be significant sources of particulate pollution from soil, and road dust. The organic molecular markers

are individual compounds or groups of related compounds (homologous compounds such as n-Alkanes, n-

Alkanoic acids, Hopans and PAHs, which at a molecular level comprise the chemical profile or "fingerprint" for

specific emission source types.

An individual molecular marker or groups of marker compounds is linked quantitatively to major emission sources

of urban fine particles. The markers like hopanes, those indicate gasoline and diesel burning are present in all

cities. Stigmasterol / Sterans indicates presence of Biomass burning & Levoglucosan indicates the presence of

Hardwood & Softwood burning.

Besides, these parameters specific metals/ elements, black carbon (absorption coefficient for black carbon),

Benzene, Toluene, Cyclohexane, Methyl-Cyclohexane (Toluene/ Cyclohexane ratio & other various component

ratios etc) are also used as molecular marker for source apportionment.

In the current issue of Indian Journal of Air Pollution Control, Vol. XVI, 2, 2016 and Vol. XVII, 1, 2017 are

merged in the research section, we present the very first research paper is by Kamal Kumar and V.K.Shukla on

trends analysis of ambient air pollutants in Agra city for criteria pollutants during -2002-2013 . The fine particulate

matter has also shown continuously increasing trend, this may be due to increase in the anthropogenic activities.

There was increasing trend observed in PM10 at all stations. PM10 concentration levels exceed the prescribed

national ambient air quality standards for sensitive areas and found in the critical category (EF :> 1.5).


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The next research paper is on site-specific variation study of particulate matter with traffic by Kirti Bhandari et.

al. The PM data analysis shows highest concentration of coarse particulate of PM10 with average value of

882.71±219.84 μg/m3 followed by fine PM concentrations of PM2.5 and PM1.0 with average values of 214.34±98.16

μg/m3 and 167.24±88.34 μg/m3 respectively. The paper also focuses on the variation of meteorological

characteristics such as wind speed, wind directions, relative humidity and temperature with PM10, PM2.5 and PM1.0

concentrations measured near a busy urban road during the same month of study. The next paper is by Nikki

Choudhary and Atul Dwivedi on impact of trace gases (seasonally) and meteorology on concentration of particulate

matter (PM2.5) in Delhi. The authors observed the concentrations of PM2.5, SO2, NO, NO2 and CO were highest

during the winter season whereas O3 concentration peaked during summer. The high concentration of PM2.5 and

trace gases during the winter season could be attributed to the increased combustion activity and vehicular

emission. The significant positive correlation was observed between PM2.5 and CO, NO2 whereas PM2.5 and

temperature, wind speed was found negatively correlated. The mixing layer ventilation coefficient was calculated,

which ranged from 403m2/s to 5455 m2/s in study area during the festival time have been described in the next

paper is on the worsening of urban air quality: role of meteorology and episodic events during winter months by

Rohit Sharma et.al. The next paper on recent development on the understanding of aerosol nucleation and growth

by Bighnaraj Sarangi et.al is the brief review covering the basic understanding of nucleation and growth process

of atmospheric aerosols, and the recent development on this topic. In a study on ambient air quality and non-

attainment cities in north zone of India by Anchal Garg et.al evaluated those cities in north zone of India which

are exceeding the National Air Quality Standards. This paper on NAC with respect to ambient air quality

monitoring is mainly focused on the north zone of India and analyzes the data of PM10, SO2, and NO2 for the year

2011-2013. They found 38 cities to be NAC in case of PM10, two cities to be NAC in case of NO2 with one city as

NAC for SO2.

In the next paper on the diurnal trend of urban ground level ozone during monsoon, post-monsoon & winter

months in Delhi, India, Harveen Kaur and Sushil K. Tyagi describe the concentration values and the diurnal

concentration of ozone during the study period. The average range of concentration was found to be between

19.68 ppb to 65.36 ppb.

The next two articles are on the proceedings of training workshop on volatile organic compounds (VOC) and

hydrocarbons (HCs): monitoring and management, organised by ONGC in association with CPCB and IAAPC

(DC) during March 2-3, 2016 and recommendations of the workshop on requirement, practices, gaps and

challenges in air quality study for preparation of EIA report, organised by IAAPC (DC) on August 27, 2016

Indian Association of Air Pollution Control is whole heartedly working for the better air quality for better liveable

environment to the society at large. In this endeavour organizes various conferences for the professionals and

conclaves to create mass awareness, to involve public and increase peoples’ participation in this campaign time to

time. Dr. B. Sengupta, President & Shri S.K. Gupta, Secretary General of the Association needs to be

complimented for their untiring efforts along with dedicated team of executive members of the Association.

We can hope that we shall succeed to abate the air pollution in the coming years, especially with the launch of

various governmental initiatives to curb the urban air pollution and augmenting public transport.

S. K. Tyagi


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Secretary Report

The GBM of IAAPC-DC held on 23rd Sept. 15 reposed faith in the dynamic leadership of Dr. B. Sengupta and

requested him to lead the IAAPC-DC for another two years.

The year started with the Late Prof. Nilay Chaudhury Memorial Lecture delivered by Smt. Maneka Sanjay Gandhi,

former Union Minister of Environment, an Activist and currently Union Minister of Women & Child

Development. She urged the Association to take up Environmental issues with the Govt. persuasively.

IAAPC has been consistently & proactively brain storming the important emerging issues. Whether it was the need

for revision of ambient air quality standards or impacts due to burning of agricultural residues or importance of

monitoring of fine dust, all have become now part of our Indian Air Quality Management Policy. Association is

fully aware that for good health, control of pollution & better environment quality are the key issues and all our

efforts have to be centralised around these. We need to keep an eye on emerging toxic pollutants like VOC’s,

Mercury, Heavy Metals & specially ultrafine particulate (PM1) which are acting as carriers.

Thus Association has finalised an ambitious plan to organise following Workshops & Conferences during the next

two years.

a). Impact, Assessment & control of VOC’s

b). Workshop on development of guidelines for Air Quality study required in EIA report

c). Workshop on Development of Green Belts around Industries in Association with TERI University

d). Workshop on gaps in Monitoring Protocols & Q/A & Q/C in Air Quality Monitoring

e). Workshop on Occupational Exposure on Human Health

f). Review of Govt. Policies for protecting Air Quality

g). Seminar by Eminent Experts

First of the above was very successfully organised in association with ONGC on 3rd & 4th March at Hotel Claridges,

New Delhi. It was very well attended and led to very useful recommendations & guildlines.

Dr. S. K. Tyagi, Editor-in-Chief is making efforts to publish the Journal on time but due to lack of good quality

research papers, it is getting delayed. All are requested to extend their help to him.

S.K. Gupta


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Kamal Kumar1, V.K. Shukla2 1Scientist-C, Central Pollution Control Board, Agra

2Scientist-D & In-charge, Central Pollution Control Board, Agra

(Email: [email protected])

Abstract

Central Pollution Control Board was initiated the Ambient Air Quality Monitoring (AAQM) with the establishment

of air monitoring stations to monitor Suspended Particulate Matter, Respirable Particulate Matter, Sulphur-di-oxide

and Nitrogen-di-oxide at specified four locations in Agra city since 2002. In this paper statistical interpretation of

annual average of SPM, PM10, SO2, NO2 data of four monitoring stations has been taken for the period of 2002 to

2013(12 years). In general, there has been a slight decreasing trend in concentration of SPM since 2002 in consent

of ambient air at all monitoring stations and there was increasing trend observed in PM10 at all stations. PM10

concentration levels exceed the prescribed National Ambient Air Quality Standards for sensitive areas and found in

the critical category (EF :> 1.5). The annual average concentration of SO2 and NO2 remained almost constant during

the study period at all locations and found within the notified ambient air quality standards except NO2 at Nunhai

Monitoring stations. During 2002-13, SO2 falls in the low polluted category (EF :< 0.5). The Exceedence Factor of

NO2 during 2002-13 has been found in low to moderate polluted category (EF: 0.5 - 1.0) at all stations except at

Nunhai, where it fall in high polluted category (EF: 1.0 - 1.5) in Agra. Upon analysis of the overall annual average

concentration data, it may be seen that the Tajmahal in terms of all monitored parameters remained the least polluted

AAQ monitoring station and Nunhai was the most polluted monitoring station with highest concentrations of

pollutants in Agra. The fraction of fine particulate matter has also shown continuously increasing trend, this may be

due to increase in the anthropogenic activities.

Key Words: Suspended Particulate Matter (SPM), PM10, PM2.5, Sulphur Di-oxide (SO2), Nitrogen Di-Oxide

(NO2), Ambient Air Quality Trend Analysis, NRSPM (SPM-PM10), Exceedance Factor.

1. Introduction

According to World Bank study (2000), the number of premature deaths due to air pollution in India has

increased by almost 30%. (Mahajan S.P., 2009) The physical addition of materials that turns the air impure

or unclean and sources for such undesirable additions to atmosphere are natural and anthropogenic

activities. (Ambasht et al, 2006) There are various sources (mobile & stationery sources) of air pollutants

in the form of solid (particulate matters), gaseous (NO2, O3, SO2 etc.) and liquid. (Barthwal R.R., 2002)

There are two different type of air pollution problem in urban areas, one is the release of primary pollutants

(those released directly from sources) and the other is the formation of secondary pollutants (those that are

formed through chemical reaction of the primary pollutants). (Richard W. et al, 2005)

PM10 inhalable particles, with diameters that are generally 10 micrometers and smaller; and

PM2.5 are fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller.

Particulate matter contains microscopic solids or liquid droplets that are so small that they can be

inhaled and cause serious health problems. Particles less than 10 micrometers in diameter pose the greatest

problems, because they can get deep into your lungs, and some may even get into your bloodstream. Fine

particles (PM2.5) are the main cause of reduced visibility (haze) in parts of the United States, including

many of our treasured national parks and wilderness areas. (EPA 2016) Air borne carbonaceous aerosols

are largest contributor to fine particulates with an aerodynameter smaller than 2.5µm which have been

mailto:[email protected]


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found to be associated with human health problems causing serious respiratory and cardiovascular diseases

and air quality problems such as visible reduction. (Pachauri Tripti, et al, 2013)

Sulfate and organic matter are the two main contributors to the annual average PM10 and PM2.5 mass

concentrations, except at kerbside sites where mineral dust (including trace elements) is also a main

contributor to PM10. On days when PM10 > 50 µg/m3 , nitrate becomes also a main contributors to PM10

and PM2.5. Black carbon contributes 5–10% to PM2.5 and somewhat less to PM10 at all sites, including

the natural background sites. Its contribution increases to 15–20% at some of the kerbside sites. (WHO,

2003) NO2 is one component of the complex mixture of different pollutants found in ambient air and from

studies of NO2 exposure indoors where its sources include unvented combustion appliances. Interpretation

of evidence on NO2 exposures outdoors is complicated by the fact that in most urban locations, the nitrogen

oxides that yield NO2 are emitted primarily by motor vehicles, making it a strong indicator of vehicle

emissions (including other unmeasured pollutants emitted by these sources). NO2 (and other nitrogen

oxides) is also a precursor for a number of harmful secondary air pollutants, including nitric acid, the nitrate

part of secondary inorganic aerosols and photo oxidants including ozone. (WHO, 2003)

According to background information and human health risk, the standard for airborne particulate matter

was revised by CPCB/MoEF during Nov. 2009, maintaining the previous indicator of particulate matter of

less than or equal to 10 µm in aerodynamic diameter (PM10) and creating a new indicator for fine particulate

matter of less than or equal to 2.5µm in aerodynamic diameter (PM2.5). Most pollutants are emitted both by

natural as well as by anthropogenic sources. Natural sources are not influenced by humans or by human-

induced activities. Due to industrialization and development of urban areas the pollution has increased. The

ratio between anthropogenic and natural emissions is very important, as only the anthropogenic portion can

be influenced and controlled, the ever increasing threat from anthropogenic activities are creating imbalance

in the natural environment and resulting the increase in pollution levels.

Particulate Matters (PM) in all the major cities in India are higher than the prescribed standards of Central

Pollution Control Board, India as well as WHO guidelines. Over last 12 years various changes in fuel

quality, vehicle technologies, industrial fuel mix and domestic fuel mix have taken place resulting in

changes in air quality in cities. To protect the world wonder Tajmahal and improvement in ambient air of

Taj Trapezium Zone (TTZ), the monitoring of particulate matters (SPM, PM10) and gases (SO2, NO2) was

started in 2002 by CPCB at four locations in Agra city till date. The monitoring is continued as per the

direction and guidance of CPCB. The data for the period of 2002 to 2013 have been taken for the analysis

in this paper.

2. Material & Method

The monitoring of SPM and PM10 standard methodology as per CPCB Manual (CB/CL/TM/9, 2001) and

based on USEPA provisions Gravimetric Method was adopted. High volume samplers (HVS APM 430)

were used for SPM monitoring to trap all the particulate matter up to size 100µm on the Whatman GF/A

(glass microfibre filters, 25.4cm x 20.3cm). The PM10, SO2 and NO2 sampling has been done with

Respirable Dust Sampler. The SO2 samples were collected and analyzed by following the West and Gaeke

Method and NO2 samples were analyzed by following the Jacob and Hochheiser Modified (Na-arsenite)

method with spectrophotometer in the CPCB laboratory. PM2.5 is measured by FRM sampler (2000i).

3. Quality Control

The performances check of instruments, data validation, temp., humidity control and standard monitoring

protocol (SOP) were followed at all stages. The quality control, during the filter paper numbering, pre-

conditioning, weighing, handling, monitoring, then post weighing and recording was the thrust attention.

For maintaining the quality control all instrument used during monitoring such as balance, RDS, HVS,

PM2.5 sampler, spectrophotometer were calibrated on regular interval and recorded. The complete


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analytical procedures were provided by Central Pollution Control Board, Delhi. The outlier values have

been removed during the validation and recording of AAQM data.

4. Result & Discussion

The annual average trends of four pollutants SPM, PM10, SO2, NO2 for the period of monitoring 2002 to 2013 of four monitoring stations are discussed and analyzed; graphs (fig.1-16) and table (table no. 1- 4) are

presented below.

The annual average concentrations of SPM, PM10, SO2 and NO2 and the percentage change in annual

average concentration of the pollutants with respect to 2002 to 2013 have been depicted at fig.01 - 12 of 04

ambient air quality monitoring stations. The level of SPM concentration has been decreased 26.9%, 27.1%,

27.6% and 30.1% at Tajmahal, Etmad-ud-daulah, Rambagh and Nunhai respectively with respect to 2002.

During 2013, the concentration of PM10 has increased 4.1% at Tajmahal and 3.4% at Rambagh, while

decreased 3.0% at Nunhai and no increased has been observed at Etmad-ud-daulah with respect to 2002.

In general, the level of PM10 has increased at all monitoring stations. The concentration of PM10 has been

Fig.3: %Change in SO2 & NO2

w.r.t 2002 at Tajmahal

Fig.2: %Change in PM10 & SPM

w.r.t 2002 at Tajmahal

Fig.1: Trend of Ambient Air Pollutants at Tajmahal

in Agra-2002-2013


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found always above the national ambient air quality standard i.e. annual average standard is 60µg/m3 as per

the AAQ standard 2009. The Exceedence Factor of PM10 has been found between 2.6 (least at Tajmahal)

to 3.8 (highest at Nunhai) in Agra, which is in the critical polluted category (EF :> 1.5). In general, there

was no more change have been observed in SO2 and NO2 with respect to 2002; only with fluctuating trend

in small range during 2002 to have been observed and it have been found within the national ambient air

quality standard (i.e. SO2 annual average standard is 20µg/m3 and NO2 annual average standard is

30µg/m3), except NO2 concentration at Nunhai. The Exceedence Factor of SO2 during 2002-2013 has been

found in low polluted category (EF :< 0.5) in Agra. The Exceedence Factor of NO2 during 2002-2013 has

been found in low to moderate polluted category (EF: 0.5 - 1.0) at all stations except at Nunhai, where it

fall in high polluted category (EF: 1.0-1.5) in Agra.

The AAQM data for the period of 2002-13 (12 years) has been statistically analysed and summarized in

Table-4. It may be seen that during 12 years, the values of SPM ranging 275 - 376 µg/m3 (with avg. 319

µg/m3) and SD is 28.2, the PM10 concentration has been found between 133 - 178 µg/m3 (with avg. 154

Fig.6: %Change in SO2 & NO2 w.r.t 2002 at

Etmad-ud-daulah Fig.5: %Change in PM10 & SPM w.r.t

2002 at Etmad-ud-daulah

Fig.4: Trend of Ambient Air Pollutants at Etmad-ud-daulah in Agra-2002-2013


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µg/m3) and SD is 14.1, the NO2 level ranging 17 - 23 µg/m3 (with avg. 21 µg/m3) and SD is 2.0 and SO2

has been found between 4 - 9 µg/m3 (with avg. 6 µg/m3) and SD is 1.4 at Tajmahal. Upon seeing the Etmad-

ud-daulah AAQM data, the values of SPM ranging 352 - 519 µg/m3 (with avg. 422 µg/m3) and SD is 46.3,

the PM10 concentration has been found between 166 - 214 µg/m3 (with avg. 188 µg/m3) and SD is 15.2, the

NO2 level ranging 22 - 29 µg/m3 (with avg. 25 µg/m3) and SD is 2.0 and SO2 has been found between 4 -

10 µg/m3 (with avg. 6 µg/m3) and SD is 1.8.

At Rambagh, It may be seen that the values of SPM ranging 338 - 541 µg/m3 (with avg. 425 µg/m3) and

SD is 52.1, the PM10 concentration has been found between 157 - 278 µg/m3 (with avg. 186 µg/m3) and

SD is 32.3, the NO2 level ranging 22 - 27 µg/m3 (with avg. 25 µg/m3) and SD is 1.2 and SO2 has been

found between 4 - 8 µg/m3 (with avg. 5 µg/m3) and SD is 1.3. Being the industrial area surrounding, the

values of SPM ranging 472 - 675 µg/m3 (with avg. 585 µg/m3) and SD is 70.7, the PM10 concentration has

been found between 205 - 306 µg/m3 (with avg. 251 µg/m3) and SD is 29.1, the NO2 level ranging 33 - 38

µg/m3 (with avg. 35 µg/m3) and SD is 1.5 and SO2 has been found between 4 - 11 µg/m3 (with avg. 6

µg/m3) and SD is 1.8 at Nunhai.

Fig.9: %Change in SO2 & NO2

w.r.t 2002 at Rambagh


w.r.t 2002 at Rambagh

Fig.7: Trend of Ambient Air Pollutants at


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Fig.10: Trend of Ambient Air Pollutants at

Nunhai in Agra-2002-2013

Fig.12: Change in SO2 & NO2 w.r.t. 2002

at Nunhai


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Table-1: %Change in PM10& PM100 w.r.t. 2002 at 04 AAQM stations in Agra

Tajmahal Etmad-ud-daulah Rambagh Nunhai

Year PM10 PM100 PM10 PM100 PM10 PM100 PM10 PM100

2003 -1.4 -6.4 10.3 -5.4 5.1 0.2 -15.5 2.7

2004 -9.5 -17.8 2.9 7.5 13.1 15.8 -28.8 -17.8

2005 0.0 -18.6 6.9 -13.7 5.7 -16.5 -14.5 -8.5

2006 -9.5 -16.0 23.0 -17.0 58.9 -7.7 -40.3 -10.3

2007 13.6 -21.3 16.7 -21.9 16.0 -6.0 -3.5 -7.8

2008 13.6 -19.1 22.4 -21.1 -1.1 -12.8 21.3 4.7

2009 6.8 -11.2 6.9 -11.4 -8.6 -8.6 -2.2 -9.2

2010 13.6 -11.4 5.2 -13.3 -10.3 -14.8 5.1 -21.5

2011 1.4 -22.9 -4.6 -14.5 -8.6 -19.9 -12.4 -25.8

2012 21.1 -11.7 5.2 -12.6 2.9 -10.9 1.7 -17.9

2013 4.1 -26.9 0.0 -27.1 3.4 -27.6 -3.0 -30.1

Table-2: %Change in SO2 & NO2 w.r.t. 2002 at 04 AAQM stations in Agra


Year SO2 NO2 SO2 NO2 SO2 NO2 SO2 NO2

2003 -20.0 0.0 0.0 8.0 -20.0 -18.5 -20.0 3.0

2004 0.0 -18.2 20.0 4.0 20.0 -14.8 20.0 3.0

2005 80.0 0.0 100.0 0.0 60.0 -7.4 120.0 3.0

2006 20.0 0.0 40.0 -4.0 40.0 -7.4 40.0 3.0


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2007 20.0 4.5 0.0 8.0 0.0 -7.4 0.0 12.1

2008 40.0 0.0 40.0 16.0 0.0 -7.4 20.0 15.2

2009 20.0 -9.1 0.0 0.0 0.0 -7.4 0.0 9.1

2010 0.0 -9.1 -20.0 -8.0 -20.0 -7.4 0.0 3.0

2011 -20.0 -9.1 -20.0 -4.0 -20.0 -7.4 0.0 3.0

2012 0.0 -18.2 -20.0 -12.0 -20.0 -7.4 0.0 3.0

2013 -20 -22.7 -20.0 -8.0 -20.0 -7.4 0.0 2.9

Table-3: NRSPM (PM100-PM10) & PM100/PM10 Ratio at four monitoring stations in Agra


Years NRSPM PM100/PM10 NRSPM PM100/PM10 NRSPM PM100/PM10 NRSPM PM100/PM10

2002 229 2.6 309 2.8 292 2.7 441 2.9

2003 207 2.4 265 2.4 284 2.5 347 2.3

2004 176 2.3 340 2.9 343 2.7 396 2.4

2005 159 2.1 231 2.2 205 2.1 339 2.3

2006 183 2.4 187 1.9 153 1.6 331 2.1

2007 129 1.8 174 1.9 236 2.2 310 2.1

2008 137 1.8 168 1.8 234 2.4 298 2.4

2009 177 2.1 242 2.3 267 2.7 407 2.6

2010 166 2.0 236 2.3 241 2.5 284 2.2

2011 141 1.9 247 2.5 214 2.3 296 2.4

2012 154 1.9 239 2.3 236 2.3 316 2.3

2013 122 1.8 178 2.0 157 1.9 245 2.1

All NRSPM values are in µg/m3 except ratio (PM100/PM10)


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Table-4: AAQM data Analysis in Agra-2002-13

SO2 NO2 RSPM SPM

Tajmahal

monitoring

station

max. 9 23 178 376

min. 4 17 133 275

Avg. 6 21 154 319

SD 1.4 2.0 14.1 28.2

Etmad-ud-

daulah

monitoring

station

max. 10 29 214 519

min. 4 22 166 352

Avg. 6 25 188 422

SD 1.8 2.0 15.2 46.3

Rambagh

monitoring

station

max. 8 27 278 541

min. 4 22 157 338

Avg. 5 25 186 425

SD 1.3 1.2 32.3 52.1

Nunhai

monitoring

station

max. 11 38 306 675

min. 4 33 205 472

Avg. 6 35 251 585

SD 1.8 1.5 29.1 70.7

All values are in µg/m3 except SD

The trend of NRSPM (SPM -PM10) and the ratio of SPM /PM10 of ambient air quality of 04 stations have

been shown in figures 13 -16. The trend line of NRSPM (SPM -PM10) plotted in figures clearly indicated

the decreasing trend at all monitoring stations. The inverse slop shows the rate of decrease of NRSPM. The

linear trend line of NRSPM (SPM -PM10) at Tajmahal found y = -6.552x + 207.5 with R² = 0.552; at Etmad-

ud-daulah y = -7.489x + 283.3 with R² = 0.255, at Rambagh y = -8.042x + 290.7 with R² = 0.285 & at

Nunhai y = -11.16x + 406.7 with R² = 0.511.


17

Fig.16: Trend of NRSPM & SPM/PM10 at Nunhai Fig.15: Trend of NRSPM & SPM/PM10 at Rambagh

Fig.14: Trend of NRSPM & SPM/PM10 at Itmad-ud-daulah Fig.13: Trend of NRSPM & SPM/PM10 at Tajmahal


18

Fig.18: Trend of %Fraction of PM at Tajmahal in Agra-2013-14

Fig. 17: %Fraction of PM at Tajmahal in Agra-2013-14


19

On the basis of average concentration of pollutants, Tajmahal monitoring station is least polluted and

Nunhai is highest polluted monitoring station in Agra. The level of SPM and PM10 have been found always

above the national air quality standard for sensitive zone, while SO2 and NO2 are within the norms, except

NO2 at Nunhai, where is has been found marginally above the norm.

To know the fraction variation of the particulate matter in Agra data of PM2.5, PM10 & SPM of Tajmahal

monitoring station for the year 2013-14 taken. Upon seeing the annual average particulate matter data, it

was observed that various fractions of particulate matter are present as PM2.5 38%, PM (10-2.5) 18% and

PM (100-10) 44% (fig.17). The percentage variation of particulate matter fraction of PM2.5, PM (10-2.5)

and PM (100-10) during the year may be seen at fig.18.

5. Conclusion

During the study period (2002 to 2013), the values of PM10 were found higher than the notified national

ambient air quality standards for sensitive areas at all monitoring stations in Agra and found in critical

polluted category (EF: >1.5). There has been found little variation in the trend of SO2 and the values were

well within the notified national ambient air quality standards at all the monitoring stations and found in

low polluted category (EF: <0.5). The NO2 concentration has been found almost constant (with fluctuating

trend in small range during 2002 to 2013), while the least concentration found at Tajmahal and highest at

Nunhai. The annual average of NO2 concentration values found within the notified ambient air quality

standard at all monitoring stations in Agra except Nunhai during 2002-13. The Exceedence Factor of NO2

during 2002-2013 has been found in low to moderate polluted category (EF: 0.5 - 1.0) at all stations except

at Nunhai, where it fall in high polluted category (EF: 1.0 - 1.5) in Agra.

Tajmahal monitoring station has been found to be least polluted during 2002 to 2013 among four

monitoring stations at Agra and Nunhai monitoring station is most polluted. This may be due to local

geographical locations and various actions initiated like development of more green area, control measures

such as implementation of clean fuel, restriction on vehicles movement within 500 meter, ban on generators

& new/polluted industries, introduction of CNG for vehicles & industries, control on vehicles etc. taken by

various agencies in and around Tajmahal and Agra city. The Nunhai monitoring station is situated near to

road and industrial activities due to this the highest pollution found at Nunhai during the year 2002-2013.

The concentrations of pollutants at Etmad-ud-daulah and Rambagh have been found in between Tajmahal

and Nunhai. The various fractions of particulate matter of PM2.5 have been found 38% and PM (10-2.5)

18%, which is 56% of total particulate matter and PM (100-10) 44%. Finer fraction of particulate matter is

higher during winter and least during monsoon period, which indicated that it is locally generated fraction

that is not dispersed during winter due to low mixing height and low temperature.

In general, the pollutants levels of SO2 and NO2 have not increased since 2002 to 2013 may be due to

increase in green cover area and implementation of CNG fuel in vehicles and industry in Agra. The

concentration of SPM has decreased since 2002 to 2013, clearly indicated the reduction in coarser fraction,

but finer fraction (<10µm) of pollutants has increases may be due to increase in anthropogenic activities.

6. Acknowledgements The authors express their sincere thanks to the Chairman, CPCB, Member Secretary, CPCB and north zonal

in-charge, CPCB for constant support and guidance all the time, besides expressing thanks to all officials

and research scholars of P.O. Agra for monitoring and analytical support.


20

7. References

1. Mahajan S.P., 2009, Air Pollution Control, Common wealth of learning IISc, TERI press, New

Delhi, page1

2. Ambasht R.S., & Ambasht P.K., 2006, Environment & Pollution, 4th edition, New Delhi, CBS

Publishers & Distributors, page 162,

3. Barthwal R.R., 2002, Environmental Impact Assessment, New Delhi, New Age International (P)

Ltd. publishers, page 23

4. Richard W. Boubel, Donald L. Fox, D. Bruce Turner and Arthur C. Stern, 2005, Fundamentals of

Air Pollution, 3rd edition, New Delhi, Elsevier, page no.36,

5. EPA, Particulate matter; 2016, https://www.epa.gov/pm-pollution/particulate-matter-pm-

basics#PM

6. Pachauri Tripti, Saraswat RK, Singla V, Laxmi Anita & Kumari Maharaj K., 2013,

Characterization of Organic and Elemental Carbon in PM2.5 Aerosols at Agra, Research Journal

of Recent Sciences, Vol. 2(ISC-2012), 255-260, India.

7. Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide; Bonn,

Germany 13–15 January 2003, EUR/03/5042688, page 8, 46

(http://www.euro.who.int/data/assets/pdf file/ 0005/ 112199/ E79097.pdf).

8. Central Pollution Control Board, Ministry of Environment & Forest, “Chemical laboratory test

method”, I, C-1 to C-30 (2001).

https://www.epa.gov/pm-pollution/particulate-matter-pm-basics#PM

https://www.epa.gov/pm-pollution/particulate-matter-pm-basics#PM

http://www.euro.who.int/data/assets/pdf%20file/%200005/%20112199/%20E79097.pdf


21

Site-Specific Variational Study of Particulate Matter with Traffic

Kirti Bhandari*1, Rina Singh2, Anuradha Shukla3

1Principal Scientist, 2Senior Scientist, 3Chief Scientist, CSIR-CRRI, New Delhi, 110025

(*E-mail – [email protected])

Abstract

This paper presents analysis and interpretation of 24 hours average particulate matter (PM10, PM2.5 and PM1.0)

concentrations and traffic volume count measured at a roadside location in Delhi, at Nehru Place near Kalkaji Mandir,

India, in the month of March 2013. During the morning hours traffic (7.00am-8.00am), the values of PM10, PM2.5 and

PM1.0 has been found to be highest (PM10 - 1284.23μg/m3, PM2.5 - 425.75μg/m3 and PM1.0 - 347.04μg/m3) while

lowest values were found during 4am-5am for PM10 and during 4pm-5pm for PM2.5 and PM1.0 (PM10 - 494.67μg/m3,

PM2.5 - 71.51μg/m3 and PM1.0 - 40.99μg/m3) respectively. PM data analysis shows highest concentration of coarse

particulate of PM10 with average value of 882.71±219.84 μg/m3 followed by fine PM concentrations of PM2.5 and

PM1.0 with average values of 214.34±98.16 μg/m3 and 167.24±88.34 μg/m3 respectively. The paper also focuses on

the variation of meteorological characteristics such as wind speed, wind directions, relative humidity and temperature

with PM10, PM2.5 and PM1.0 concentrations measured near a busy urban road during the same month of study. The R2

values for above said parameters have been estimated using SPSS. From the analysis between meteorological

parameters and PM it was observed that PM10 were negatively correlated for wind speed and relative humidity and

positively correlated for wind direction and temperature. Whereas, PM2.5 and PM1.0 were negatively correlated for

temperature and wind direction but statistically positively correlated for relative humidity and wind speed.

Key words: Particulate Matter (PM), Meteorological Parameters, Pearson’s Correlation,

Heterogeneous Traffic.

Introduction

The issue of urban air quality in general and, particulate matter (PM) concentrations in particular, are

receiving more attention as an increasing share of the world’s population lives in urban centres (UN 2004).

The traffic–generated emissions are accounting more than 50% of the total PM emissions in the urban

areas (Wrobel et al. 2000). At present, over 600 million people living in urban areas worldwide are being

exposed to dangerous levels of traffic–generated air pollutants (Cacciola et al. 2002). About 30% of the

respiratory diseases are related to personal exposure to high level ambient PM concentrations (WHO

2000). At global scale, more than 0.5 million deaths per year are due to exposure to ambient PM

concentrations (AQEG 2005). In developed countries, PM emissions are mainly responsible for respiratory

health problems (Yang 2002; Shendell and Naeher 2002; Wang et al. 2003). The main sources for ambient

PM concentrations at urban roadways are vehicle exhausts, emissions from tyre and brake wear and re–

suspension of road dust. Motorized vehicles is an important source of harmful emissions of particulate

pollution in cities of the developing world, where economic growth, coupled with a lack of effective

transport and land use planning is resulting in increasing vehicle ownership and traffic congestion. These

factors combine to create air pollution hotspots near roads.



22

Delhi is spread over an area of 1,484 km2 (573 sq mile), of which 783 km2 (302 sq mile) is designated

rural and 700 km2 (270 sq mile) urban. The city's population is increasing rapidly with a consequent

increase in the number of vehicles without a commensurate increase in road length. The number of

registered vehicles in Delhi has crossed 6 million mark and a sizeable vehicular traffic ply’s on roads in

Delhi from the neighbouring states (DSH 2010). It has been observed that vehicles alone contribute about

64% of the pollution in Delhi while other sources like power plants, industries, and domestic contribute

16%, 12% and 8% respectively (MoEF 1997). The maximum contribution of air pollution is growing

rapidly from vehicular sources (Mitra and Sharma 2002). Deteriorating air quality due to the increased

mobile anthropogenic emissions of airborne particulate matter (PM) is a major environmental problem in

urban environment. PM is an heterogeneous mixture of solid and liquid particles suspended in air, that

continuously vary in number, size, shape, surface area, chemical composition, solubility and origin in both

space and time. Based on aerodynamic diameter, PM is divided into coarse (PM10) and fine PM (PM2.5 and

PM1.0). Meteorological and topographical conditions affect dispersion and transport of PM, which can

result in high level ambient PM concentrations that may have adverse impact on human health, global

climate.

Different studies have shown that the particle mass concentration of fine particles (up to PM2.5) at roadsides

is dependent on the wind speed (Ruellan and Cachier 2001; Vecchi et al. 2004). With higher wind velocity

the particle concentration decreases because of a dilution effect (Gupta et al. 2004). For coarse particles,

a different behaviour was discovered, indicating that other processes such as resuspension of road dust can

superpose the diluting effect of wind speed on the coarse particle fraction (Ruellan and Cachier 2001;

Kuhlbusch et al. 1998). Not only the wind speed, but also the wind direction, may influence the particle

concentration (Jung et al. 2002). Furthermore, the air temperature plays a role (Papanastasiou et al. 2007).

This dependence, with higher values of PM2.5 at higher temperatures (above 21 °C) and wind speeds being

lower than a threshold, was found by Jung et al. ( 2002) and Correlations were also found with other

meteorological parameters such as precipitation and relative humidity (RH) (Vecchi et al.,2004 ; Davidson

1994, Mariani, et al. 2007; Marcazzan et al. 2002). Mu¨ller (1999) reported that the duration of

precipitation influences the aerosol concentration stronger than the amount of precipitation. It is confirmed

that the atmospheric stability (i.e., BLH - the boundary layer height) affects the particle concentrations

(Vecchi et al. 2004; Davidson 1994, Mariani, et al. 2007; Marcazzan et al. 2002; Mu¨ller 1999; Hien, et

al. 2002). Ruellan and Cachier (2001) believed that the combination of a+ higher BLH and increased wind

speed furthers the dilution effect of PM. However, it has been verified that not all components of PM show

an identical response to the meteorological parameters (Mariani et al. 2007).

In this paper, an attempt has been made to cover the main objectives of the study that is to analyze and

interpret the 24 hours observations of coarse (PM10) and fine particulate matter (PM2.5 and PM1.0)

concentrations measured at an urban roadway having heterogeneous traffic flow. Different categories of

vehicles such as two–wheelers, three– wheelers, cars, buses, auto goods, LCV and HCV have been

characterise. The influences of consequent meteorology study measurements for the given site on the

particulate matter concentrations are also studied.

Objectives of the Study

1. To interpret and analyse the particulate matter concentration of PM10, PM2.5 and PM1.0 and

correlate with the traffic flow.

2. To examine the influence of the meteorological parameters and traffic to the particle

concentration.


23

2. Methodology

The site selected for the study was Nehru place (28 32 58.53 N and 77 15 32.61 E), New Delhi (figure 1).

Nehru Place is a commercial business district (CBD) in Delhi, India. It is accessible by all forms of public

transport including MRT system. Observations were taken for 24 hours at an interval of one hour.

Fig. 1 Map Showing Survey Site, Nehru Place (New Delhi)

Analysis was done to examine the relationships between the PM concentrations (PM10, PM2.5 and PM1.0),

meteorology and traffic metrics. Particulate emissions from the road surfaces are due to direct emissions of

vehicles from the exhaust, from brake and tire wear, and from the re-suspension of loose material on the

road surface. Studies on spatial variation showed that the concentration of particulates were higher at

locations impacted by traffic emissions compared to non-traffic areas (Buzorious et al. 1999; Gehrig and

Buchmann 2003; Janssen 1997).

3. Results and Discussion

3.1 Particulate Matters

Observations for the PM particles were done using Grimm Dust Monitor (Enviro-check Model 107). It

monitors particulate matter of micron sizes (0.2μm to 15μm) and mass (1 to 1.500μg/m3) in ambient air.

The Grimm dust monitor is a portable instrument designed to provide continuous concentrations of

http://en.wikipedia.org/wiki/Delhi

http://en.wikipedia.org/wiki/India


24

particulate matter (PM10, PM2.5 and PM1.0) suspended in the ambient air. It is build up of single piece

stainless steel and has optical unit able to measure particulate matter of sizes PM 10, PM2.5 and PM1.0 at the

same time for 24 hours. The dust particles are measured by the physical principle of orthogonal light

scattering. It is designed to measure particle size distribution and particle mass based on a light scattering

measurement of individual particles in the sampled air. Each single particle is illuminated by a defined laser

light and each scattering signal is detected at an angle of 90° by a photo diode. Table 1 shows concentrations

of particulate matters (PM10, PM2.5 and PM1.0) in μg/m3 with time period (hours). It has been observed that

the concentrations of PM10, PM2.5 and PM1.0 were highest during 7am to 8am (PM10 - 1284.23μg/m3, PM2.5

- 425.75μg/m3 and PM1.0 - 347.04μg/m3) while lowest values were found during 4am-5am for PM10 and

during 4pm-5pm for PM2.5 and PM1.0 (PM10 - 494.67μg/m3, PM2.5 - 71.51μg/m3 and PM1.0 - 40.99μg/m3).

Table 1: Concentrations of Particulate Matters (PM10, PM2.5 and PM1.0) in μg/m3 with Time Period (hours)

Time Period PM 10 PM 2.5 PM 1.0

08.00-09.00 930.55 218.97 182.36

09.00-10.00 1154.11 206.28 162.11

10.00-11.00 976.11 188.86 145.92

11.00-12.00 792.92 155.47 84.45

12.00-13.00 818.95 94.13 63.23

13.00-14.00 851.22 74.52 45.36

14.00-15.00 773.45 83.63 55.62

15.00-16.00 959.29 93.86 53.89

16.00-17.00 1019.8 71.515 40.99

17.00-18.00 1155.56 99.97 61.78

18.00-19.00 855.53 123.52 84.49

19.00-20.00 972.79 176.13 128.59

20.00-21.00 972.78 276.13 219.71

21.00-22.00 1146.59 276.12 195.22

22.00-23.00 1132.52 252.17 198.41

23.00-24.00 797.27 246.22 204.86

00.00-01.00 678.69 244.49 229.86

01.00-02.00 534.65 269.33 229.75

02.00-03.00 636.51 306.78 253.95

03.00-04.00 509.13 306.12 254.58


25

04.00-05.00 494.67 303.55 253.85

05.00-06.00 679.99 312.12 257.53

06.00-07.00 1057.77 338.61 260.30

07.00-08.00 1284.23 425.75 347.04

3.2 Meteorology

In order to study the impact of local meteorology on PM levels, meteorological parameters such as

temperature, relative humidity, wind speed, and wind direction at the study region were collected for which

the PM concentrations were measured. The impact of meteorology on the particulate matter emissions is

discussed below in section 3.3. The 24-hr meteorological values were calculated. Pearson’s regression

analyses using the statistical software of SPSS were also performed to assess the relation between

meteorological parameters and ambient PM10, PM2.5 and PM1.0 concentrations which has been shown in

results and discussion section. Table 2 shows the meteorological parameters for 24hours observed at a site.

For each parameter and PM concentrations, the values of correlation has been determined.

Table2: Meteorological Parameters for 24-Hours Observed at a Study Site

Time Period (hrs)

Wind Speed (m/s)

Wind Direction (degree)

Temperature (degree C)

Relative Humidity (%)

08.00-09.00 0.2 110 24 57.5

09.00-10.00 0.21 110.14 24.90 58.35

10.00-11.00 0.49 251.58 26.79 49.79

11.00-12.00 0.46 239.74 27.88 42.73

12.00-13.00 0.45 254.20 29.77 33.26

13.00-14.00 0.41 256 31 24.31

14.00-15.00 0.43 246 32 20.19

15.00-16.00 0.43 266 32 20.28

16.00-17.00 0.4 270 32 20.03

17.00-18.00 0.44 274 30 25.12

18.00-19.00 0.4 273 26 35.25


26

19.00-20.00 0.6 294 25 47.24

20.00-21.00 0.49 307 24 58.16

21.00-22.00 0.5 284 23 62.02

22.00-23.00 0.5 259 23 62.88

23.00-24.00 0.37 272 22 67.07

00.00-01.00 0.6 223 22 67.44

01.00-02.00 0.58 225.93 21 71.55

02.00-03.00 0.61 224 21 72.59

03.00-04.00 0.61 221 20 73.12

04.00-05.00 0.61 223 20 74

05.00-06.00 0.62 222 19 77.75

06.00-07.00 0.62 221 19 77.22

07.00-08.00 0.62 222 19 73.22

3.3 Volume count of traffic and its composition

A detailed traffic volume count proforma was prepared with a detailed classification of vehicles. The

vehicles were classified into nine categories viz., two–wheelers, three-wheelers (passenger), cars, buses,

auto goods, commercial vehicles (light and heavy). Fig 2 shows the vehicle wise hourly variation of traffic

for 24 hours. The total number of vehicles passing through the study stretch was 95921. The morning peak

was observed between 10am-11am and evening peak was observed between 6pm-7pm. Amongst all the

characteristics traffic, the volume count of cars was highest followed by 2-wheelers, buses, 3-wheelers,

HCV, auto goods and LCV. From the composition of traffic showing in Figure 3, the traffic is dominated

by cars by 43% followed by 2 wheelers by 27%, buses by 18%, 3 wheelers by 8%, auto goods by 1%, LCV

by 1% and HCV by 1%.


27

Fig. 2 Traffic Peaks with Time Period

Fig. 3 Composition of Traffic

3.4 Hourly variation of PM concentrations with traffic


28

Data was collected during the survey held in the month of March 2013 at Nehru place near Kalkaji Mandir.

The results of the data obtained have been summarized into tables and graphs to clarify the vehicular traffic

and transport patterns in the metropolis and their subsequent relation with the particulate matters of micron

sizes (PM1.0, PM2.5 and PM10). In PM10 graphs (fig. 4a, 4b and 4c) particulate concentration increases

during the peak traffic and decreases as the traffic decreases.

Fig. 4a Variation of Motorised Vehicles with PM10 Concentrations during 24 Hours at Study Site

Fig.4b Variation of Bus with PM10 Concentrations during 24 Hours at Study Site


29

Fig.4c Variation of Goods Vehicles with PM10 Concentrations during 24 Hours at Study Site

Whereas, for PM2.5 (fig. 5a, 5b and 5c) and PM1.0 graphs (fig. 6a, 6b and 6c), quite similar trend is seen,

particulate concentration decreases first with the time period when the traffic concentration was quite high

and then it starts increasing after 4pm-5pm and between 8pm-10pm somewhat constant variation of PM

concentration has been observed and then it again starts increasing.

Fig. 5a Variation of Motorised Vehicles with PM2.5 Concentrations during 24 Hours at Study Site


30

Fig. 5b Variation of Bus with PM2.5 Concentrations during 24 Hours at Study Site

Fig. 5c Variation of Goods Vehicles with PM2.5 Concentrations during 24 Hours at Study Site


31

Fig.6a Variation of Motorised Vehicles with PM1.0 Concentrations during 24 Hours at Study Site

Fig. 6b Variation of Bus with PM1.0 Concentrations during 24 Hours at Study Site


32

Fig.6c Variation of Goods Vehicles with PM1.0 Concentrations during 24 Hours at Study Site

3.5 Impact of Meteorology on PM Concentrations

The 24-h average PM10, PM2.5 and PM1.0 mass concentrations measured at a busy roadside (Nehru place) in Delhi city,

are analysed along with key meteorological variables. The particulate matter concentration varies considerably with

time, location and depending on meteorological conditions and source emissions rate (Beer 2001; Elminir 2005).

Under poor meteorological conditions i.e. inversion conditions the PM concentrations may rise to several times higher

than the normal level (Elminir 2005). The correlations between meteorological factors (wind speed, wind direction,

temperature and humidity) and PM10, PM2.5 and PM1.0 mass concentrations at the study site are regressed at 0.001

and 0.005 confidence levels by using the statistical software of SPSS to understand their interrelationships (Table 3).

The Pearson’s correlation values for wind speed and particulate matter concentrations (PM10, PM2.5 and PM1) are

found to be -0.334, 0.552 and 0.544 respectively. Whereas, for wind direction, temperature and relative humidity, the

R2 values are 0.066, -0.246 and -0.285; 0.226, -0.950 and -0.958; -0.245, 0.945 and 0.955 respectively.

Table 3: Pearson’s Correlation Values between Meteorological Parameters and Particulate Matters

Wind Speed

Wind

Direction

Temperature RH PM10 PM2.5 PM1.0

W S Pearson Correlation

1 0.373 -0.535** 0.492* -0.334 0.552** 0.544**


33

Sig. (2-tailed) N

24

0.073

24

0.007

24

0.015

24

0.111

24

0.005

24

0.006

24

W D Pearson

Correlation

Sig. (2-tailed) N

0.373

0.073

24

1

24

0.279

0.188

24

-0.334

0.110

24

0.066

0.761

24

-0.246

0.246

24

-0.285

0.178

24

Temp. Pearson

Correlation

Sig. (2-tailed) N

-0.535**

0.007

24

0.279

0.188

24

1

24

-0.982**

0.000

24

0.226

0.289

24

-0.950**

.000

24

-0.958**

0.000

24

RH Pearson Correlation

Sig. (2-tailed) N

0.492*

0.015

24

-0.334

0.110

24

-0.982**

0.000

24

1

24

-0.245

0.249

24

0.945**

0.000

24

0.955**

0.000

24

PM10 Pearson Correlation

Sig. (2-tailed) N

-0.334

0.111

24

0.066

0.761

24

0.226

0.289

24

-0.245

0.249

24

1

24

-0.071

0.742

24

-0.143

0.505

24

PM2.5 Pearson Correlation

Sig. (2-tailed) N

0.552**

0.005

24

-0.246

0.246

24

-0.950**

.000

24

0.945**

0.000

24

-0.071

0.742

24

1

24

0.989**

0.000

24

PM1.0 Pearson Correlation

Sig. (2-tailed) N

0.544**

0.006

24

-0.285

0.178

24

-0.958**

0.000

24

0.955**

0.000

24

-0.143

0.505

24

0.989**

0.000

24

1

24

** Correlation is significant at the 0.01 level (2-tailed)

* Correlation is significant at the 0.05 level (2-tailed)

4. Conclusions

The authors analyzed PM concentrations at an urban arterial and studied the correlations with traffic

intensity and various meteorological parameters. It was observed that goods traffic is lowest in number

during daytime and predominant during night hours (22:00 to 04:00). In addition to the time of day and

temperature, relative humidity (RH) and wind also play an important role in this study. The frequent

changes in meteorological conditions and variation in emission rate (complex dispersion conditions) are


34

the main factor which determines the correlation values at the study site. The analysis of PM concentrations

shows that the variations observed for PM10 concentration is opposite to variations observed for PM2.5 and

PM1.0 concentrations. During 7.00am-8.00am in the morning, the value of PM10 has been found to be

highest (1284.23 μg/m3) followed by PM2.5 (425.75 μg/m3) and PM1.0 (347.04 μg/m3) respectively, while

the lowest value of PM10 concentration has been found between 4.00am-5.00am (494.67 μg/m3) followed

by PM2.5 (71.51 μg/m3) and PM1.0 (40.99 μg/m3) between the same duration of 4.00pm-5.00pm. From the

graphs, (between traffic and PM concentrations (PM10, PM2.5 and PM1.0) shown in figures 4a, 4b and 4c;

5a, 5b and 5c and 6a, 6b and 6c) it has been analysed that traffic has weak correlation with respect to

particulate concentration. As for PM10 graphs, with increase in traffic in the morning and evening hours

PM10 shows positive but weak correlation with respect to traffic while in the late-night, levels of PM10

shows gradual decrease as the corresponding traffic decreases. For PM2.5 and PM1.0 graphs, particulate

concentrations shows weak or no correlation with respect to traffic flow. Also, for PM2.5 and PM1.0 graphs,

with increase in traffic flow in morning and evening hours, particulate concentrations decrease and vice-

versa observed for late-night hours. Particulate concentration decreases in the night-time due to reduction

in source emission rate (trickle traffic flow). The average PM2.5 and PM1.0 concentrations showed marginal

variation between traffic flow hours (6:00 am to 10:00 pm) and trickle traffic flow hours (10:00 pm to 6:00

am). This is mainly because of the slower settling of fine particles. During daytime, considerable amount

of PM mass is generated because of movement of vehicles (exhaust emissions and re–suspension of road

dust). The PM emissions released during evening rush hours were accumulated (trapping of pollutants) in

the ambient air because of inversion conditions. These PM concentrations are gradually reduced during

night-time and reach to minimum levels at midnight. From the observation of Pearson’s correlations (R2)

calculated between meteorological parameters and PM concentrations, it has been analysed that for PM2.5

and PM1.0, temperature shows strong negative correlation and relative humidity shows good positive

correlation whereas for wind speed, weak positive correlation is observed and wind direction shows weak

negative correlation. In contrast for PM10, meteorological parameters such as wind speed and relative

humidity shows weak negative correlation whereas for wind direction and temperature, weak positive

correlation is observed.Wind speed shows weak correlation with PM. The negative correlation between

wind speed and PM concentrations indicates the predominance of local sources. Strong winds flush

pollution out of the system and low winds allow pollution levels to rise. It has been reported that the impact

of wind speed on PM concentrations are strongly correlated (Cheng and Lam 1998; Ruellan and Cachier

2001).

5. References

1. Air Quality Expert Group (AQEG). (2005). Particulate Matter in the United Kingdom‐Summary,

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37

Impact of Trace Gases (Seasonally) and Meteorology on Concentration of Particulate matter (PM2.5) in Delhi

Nikki Choudhary 1, Atul Dwivedi 2, 1 Delhi Pollution Control Committee, Govt. of NCT of Delhi, 4th Floor,

ISBT Building, Kashmere Gate. Delhi-110006. 2 Envirotech online equipments Pvt. Ltd. New Delhi.

(*E-mail: [email protected])

Abstract

Increasing Particulate Matter concentration affects the ambient air quality of Delhi, India and therefore is the major

concern in recent past. In the present, study near-surface measurements of sulfur dioxide (SO2), nitrogen monoxide

(NO), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and PM2.5 were done from January 2014 to

December 2014 over the site R.K. Puram, Delhi.

The concentrations of PM2.5, SO2, NO, NO2 and CO were highest during the winter season whereas O3 concentration

peaked during summer. High concentration of PM2.5 and trace gases during the winter season could be attributed to the

increased combustion activity and vehicular emission. Significant positive correlation was observed between PM2.5 and

CO, NO2 whereas PM2.5 and Temperature, wind speed was found negatively correlated. Air mass trajectories at the

receptor site indicate that air parcels were mostly originated from the arid region of (Western) Indian sub-continent.

Keywords: Atmospheric chemistry, Trace gases, Correlation, PM2.5, Trajectories.

1. Introduction Atmospheric trace gases have grown to become one of the challenging environmental issues in urban and

industrial areas (Wang and Hao, 2012). Particularly trace gases such as nitrogen dioxide (NO2) and sulfur

dioxide (SO2), which have the second and third highest exceedance rate in India, respectively, after

particulate matter less than 10 μm (PM10), as per the National Ambient Air Monitoring Program

(NAAQMP) (CPCB 2012).

The expansion of industrialization and increasing population have seen an inevitable increase in fossil-

and bio-fuels combustion, characteristic to the needs of a country like India high energy demand and

higher agricultural land cultivation have caused enormous emission of pollutants into the atmosphere

(Tie et al., 2009; Sharma et al., 2014).

Combustion is one of the chief causes of the emission of trace gases and aerosols into the atmosphere

(Khare, 2012; Andreae and Merlet, 2001), including several noxious pollutants such as SO2, CO, NOx,

volatile organic compounds (VOCs), metal oxides, and PM. However, it must be noted that, various

meteorological parameters may also influence urban air pollution (Akpinar et al., 2008). This is seen

from positive correlations between primary pollutant (CO, SO2) concentrations being usually higher in

winter than in summer, whereas the concentrations of the secondary pollutants (NO2 and O3) are higher

in summer than in winter (Barrero et al., 2006). The origin of Particulate matter (PM2.5) is attributable to

various natural and sources emit primary particles and gases (SO2, NO, NO2, NH3 etc.) leading to aerosol

formation through gas-to-particle conversion. The NO2 in the atmosphere comes from two sources, either

directly from emission sources (primary pollutant) or from chemical reactions in the atmosphere (Han

and Naeher., 2006). Further, Nitrogen monoxide (NO), in turn, is converted to NO2 by reactions with

peroxy radicals (RO2) or O3 (Aneja et al., 1996). It is only seen that higher concentrations of CO generally

occur in areas with heavy traffic and congestions. The point sources of CO emission also include

industrial processes, non-transportation fuel combustion (Han and Naeher., 2006).


38

The presence of SO2 in air is related to the fuel combustion and industrial processes. Noted authors

(Chhabra et al., 2001; Maureen et al., 1997; Barman et al., 2010 and Ramalinga Swami et al., 1999)

reports that pollutants such as CO, PM (10 μm and 2.5 μm size), NO, NO2 and SO2 cause health effects

related to lungs, throat, cardiovascular disorders, etc. Gaseous pollutants have been linked to have major

negative impacts on health. They also play significant role in environmental changes and have also been

linked to changes in the atmospheric chemistry. It is expected that the increasing load of atmospheric

SO2, NO2, CO, O3 will add to global climate change; therefore, it is necessary to quantify the emissions

in the very near future. Also the linked issues of ozone and oxidant production as well as particulate

matter pollution have been significant problems in the field of tropospheric chemistry for many years.

The purpose of this paper is to show the diurnal and seasonal variation of trace gases (CO, SO2, NO2,

NO, O3), PM2.5, correlation of PM2.5 with trace gases and the influence of meteorological parameters in

Delhi. 2. Methodology

1. Study area

Delhi, the capital city of India, is situated in North India (28°12’–28°63’ N, 75°50’–77°23’ E) at an

altitude of 293m above sea level. It is surrounded by the Thar Desert of Rajasthan to the west and the hot

plains of Central India to the south. This region experiences four dominant seasons each year: winter

(December-February), pre-monsoon (March-May), monsoon (June-August), and post-monsoon

(September-November). The climate of Delhi is semi–arid and is mainly influenced by its inland position

and prevalence of continental air during most of the year (CPCB, 2011).This area is under the influence

of air mass flow from north-east to north-west in winter and from south-east to South-west in the summer.

In addition, Delhi experiences severe fog and haze weather conditions and poor visibility during

wintertime. The temperature of Delhi varies from minimum (monthly average: ~ 13.2°C) in winter

(December-February) to maximum (monthly average: 35.6°C) in summer (March-May). The average

rainfall in Delhi during monsoon (July to October) is in the order of ~825 mm (Sharma et al., 2010a).


39

Figure i. Map of sampling location R.K Puram, Delhi.

The area of study, Rama Krishna Puram, Delhi has a continuous ambient air monitoring station situated

at 28°33’–46.29’’ N, 77°11’–10.26’’ E. The area is roughly rectangular, enclosed by ring road to North

and outer ring road to South. Arterial roads crisscross the residential locations as shown in Figure i. Tables

i summarizes the monthly averaged meteorological parameters such as ambient air temperature, relative

humidity, solar radiation and wind speed observed over January -December 2014. The monthly mean

minimum (12.6±1.6 °C) and maximum (33.63± 2.69 °C) temperature occurred in January and June,

respectively. The monthly mean minimum (24.515.4%) and maximum (74.7± 7.6 %) relative humidity

was observed in April and January, respectively.

Table i. Monthly average meteorological conditions observed at R.K Puram, Delhi during study period (January -December 2014).

Month Temperature

(°C)

(Range)

Relative humidity

(%RH) (Range)

Wind speed

(ms-1) (Range)

Vertical wind speed

(m s-1)

(Range)

January 12.6±1.6

(9.8- 15.8)

74.7±7.64

(58.6-86.4)

1.2±0.38

(0.4-1.9)

-0.18±0.2

(-0.7-0.01)

February 15.4±2.2

(12.1-19.5)

68.4±6.9

(57.3-81.2)

1.2±0.4

(0.6-2.1)

-0.09±0.04

(-0.1- -0.01)

March 21.7±3.05

(15.4-26.2)

56.9±7.3

(39.9-79.1)

1.56±0.57

(0.9-3.8)

-0.1±0.04

(-0.1-0.04)

April 32.3±7.7

(22.5-42.5)

24.5±15.4

(7.1-50.1)

1.02±0.8

(0.3-2.7)

-0.13±0.04

(-0.2 - -0.1)

May 29.9±2.6

(22.7-34.2)

38.2±9.1

(18.8-9.8)

1.5±0.3

(0.7-2.4)

-0.13±0.04

(-0.2 - 0.03)

June 33.6±2.6

(27.6-37.5)

37.5±11.6

(18.4-57.9)

1.8±0.5

(1.1-3.3)

-0.17±0.07

(-0.3- -0.04)

July 30.4±3.2 59.6±12 1.85±0.6 0.03±0.07


40

2. Instrumentation and Data Analysis Procedures

Mass concentration of PM2.5 was continuously measured using beta attenuation particle monitor

(BAM) (Model: BAM 1020, MetOne, USA). At the beginning of each sample hour, a small 14C

(carbon-14) element emits a constant source of high-energy electrons (known as beta rays) through

a spot of clean filter tape. These beta rays are detected and counted by a sensitive scintillation detector

to determine a zero reading. The BAM-1020 then advances this spot of tape to the sample nozzle,

where a vacuum pump pulls a measured and controlled amount of outside air through the filter tape,

loading it with ambient dust. At the end of the sample hour, this dust spot is placed back between the

beta source and the detector, thereby causing an attenuation of the beta ray signal which is used to

determine the mass of the particulate matter on the filter tape. This mass is used to calculate the

volumetric concentration of particulate matter in ambient air. The Serinus 10 Ozone analyzer was

used for determination of ozone. Non-dispersive ultraviolet (UV) photometric Technology was used

to measure ozone to a sensitivity of 0.5ppb in the range of 0-20ppm. The Measurement of the Ozone

is determined by UV photometric analysis. The Ecotech Serinus 30 Carbon Monoxide analyzer was

used to measure CO in ambient air (range: 0- 200ppm to a sensitivity of 0.05 ppm). The Serinus 50

Sulfur Dioxide Analyzer was used to determine SO2 concentration. UV fluorescent radiation

technology is used to detect SO2 (sensitivity- ppb, range 0-20 ppm). The Serinus 40 Oxides of

Nitrogen analyzer was used to measure NOx (LDL <0.4 ppb, Range 0-20 ppm).

The meteorological parameters were also simultaneously measured at the same site (Table i). Hybrid

Single Particle Lagrangian Integrated Trajectory (HYSPLIT) was run every day starting at 0500

hours, IST (Indian Standard Time), at a height of 500m above the ground level (AGL) on an hourly

basis during January 2014 to December 2014 and have been calculated (using GDAS meteorological

(25.9-35.6) (40.1-76.0) (0.7-2.9) (-0.11- 0.16)

August 29.8±2.07

(26.3-35.0)

59.3±10.6

(42.1-79.7)

1.58±0.6

(0.6-3.3)

-0.13±0.11

(-0.4- 0.03)

September 27.5±1.4

(24.1-29.1)

62.1±10.1

(48.1-81.2)

1.4±0.6

(0.5-3.3)

-0.07±0.08

(-0.2- 0.14)

October 24.4±2.9

(18.9-29.2)

56.2±5.1

(46.3-66.7)

0.9±0.3

(0.5-1.8)

-0.07±0.02

(-0.1-0.007)

November 17.4±2.6

(14.0-21.9)

50.6±5.3

(41.2-62.1)

0.9±0.2

(0.5-1.4)

-0.07±0.01

(-0.12- 0.05)

December 10.4±3.9

(4.9-17.5)

65.3±13.1

(41.1-82.8)

1.03±0.37

(0.4-2.1)

-0.07±0.04

(-0.15- 0.05)


41

data). This height was chosen so as to show the diminishing effects of surface friction and to represent

winds in the low boundary layer.

3. Results and Discussion

1. Seasonal variation of trace gases (CO, SO2, NO, NO2, O3) and PM2.5

Table ii shows the annual and seasonal average mass concentrations of PM2.5 and trace gases during

study period. The annual average mass concentration of PM2.5 was recorded as 140.3 ± 87.9 µg m-3

with a range of 23.8- 482.2 µg m-3. The annual average concentration of SO2, NO, NO2 were

recorded as 13.8 ± 6.9 µg m-3, 6.5 ±4.2 µg m-3, 49.0 ± 21.3µg m-3 respectively during Jan-Dec 2014.

Annual average mass concentration of CO and O3 were given on 8 hour’s basis (Table ii). Figure ii

shows the monthly average variation in mass concentration of PM2.5, SO2, NO, NO2.

During winter highest mass concentration of PM2.5, NO, NO2, CO was recorded whereas lowest

concentration of O3 was recorded. Average mass concentration of PM2.5 (211.2 ± 97.7 µg m-3) was

noted highest during winter may be due to combined effect of source strength and lower boundary

layer height. Generally during winter season, the meteorology of Delhi is dominated by high

pressure centered over Western China causing increased atmospheric stability which in turn allows

less general circulation engulfing more stagnant air masses (Datta et al., 2010). Additionally, lack

of precipitation during winter may also reduce the potential of wet deposition and associated

cleansing mechanisms of the atmosphere. During monsoon season lowest concentration of PM2.5

(84.1±34.7 µg m-3), NO, NO2 and CO was recorded due to a combined effect of large near-surface

anthropogenic emissions, boundary layer processes.

Table ii: The annual and seasonal average contribution of PM2.5 and Trace gases (CO, SO2, NO, NO2,O3) over Delhi during study period.


42

Parameters

Annual average

(Range)

Winter

(Dec-Feb)

Pre monsoon

(Mar- May)

Monsoon

(Jun- Aug)

Post monsoon

(Sep-Nov)

PM2.5

(µg m-3)

140.3±87.9

(23.8- 482.2) 211.2 ±97.7 105.4±36.7 84.1±34.7 162.9±95.3

CO

(mg m-3)

12am-8am

1.2 ± 0.69

(0.1-3.8) 1.8 ± 0.6 0.8 ± 0.4 0.7 ± 0.2 1.4 ± 0.7

8am-4pm 1.0 ± 0.58

(0.1-3.3) 1.6 ± 0.4 0.5 ± 0.4 0.7 ± 0.2 1.1 ± 0.5

4pm-12pm 2.3 ± 1.6

(0.3-9.1) 3.4 ± 1.8 1.7 ± 1.2 1.2 ± 0.7 2.9 ± 1.7

SO2

(µg m-3)

13.8 ± 6.94

(1.9-54.7) 12.2 ± 6.2 17.9 ± 6.6 9.1 ± 2.4 15.9 ± 7.6

NO

(µg m-3)

6.5 ± 4.2

(1.5-31.8) 8.6 ± 7. 1 5.5 ± 2.3 4.4 ± 3.7 8.1 ± 4.9

NO2

(µg m-3)

49.0 ± 21.3

(15.6-205) 61.1 ± 27.5 43.9 ± 14.3 43.7 ±14.3 54.7 ± 24.0

O3

(mg m-3)

12am-8am

13.6 ± 10.4

(0.6-68.9) 8.1 ± 5.9 19.5 ± 15.09 14.9 ±7.02 11.8 ± 7.6

8am-4pm 83.5 ± 55.03

(5.9-257.8) 54.1 ± 30.8 130.8 ± 50.1 61.0 ±55.1 87.5 ± 44.6

4pm-12pm 23.7 ± 22.8

(1.1-220) 13.5 ± 9.5 37.3 ± 18.0 26.8 ±26.1 14.3 ± 6.95


43

Figure ii. Monthly average variation in concentrations of PM2.5, NO, NO2, SO2.

2. Diurnal variation of PM2.5 and Trace gases (CO, SO2, NO, NO2, O3)

Figure iii shows the seasonally averaged diurnal variation of PM2.5 and trace gases. The average PM2.5

concentrations was at the lowest value during early morning (80.6 μg m-3) followed maximum value

during night (1800 to 2200 hours). The data for the site indicate that vehicular emissions have a clear

influence on PM2.5. The date for the mornings and evenings, show a clear influence from the rush hour

traffic on PM2.5 readings in all seasons. During the other hours PM2.5 seems to be influenced by wind and

temperature inversion. In the afternoon time (1100 to 1800 hours) there is a decrease in PM2.5

concentrations. Diurnal variations in O3 concentration show daytime photochemical production all

through the study period. The diurnal average maximum is observed during day time (150.3 mg m-3)

whereas the minimum (13.5 mg m-3) appears at night. Photochemical production of O3 takes place during

daytime initiated by oxidation of its precursors. O3 production during day time is driven by the

photochemical reaction between hydroxyl radicals (OH), organic peroxy radicals and NO, while it is

removed at night by dry deposition and destruction by alkenes and NO. The conversion of NO to NO2 by

O3 during the night is the primary reaction that increases NO2 at night, while the reverse reaction

dominates during the day time. In addition, the relatively low air temperature near the ground at night

prevents the vertical dispersion of NOx, contributing to its accumulation and resulting in higher night-

time concentrations. According to photochemical reaction, CO reacts with water vapor producing OH

radical in the presence of UV radiation and leads to formation of ozone in the presence of sufficient NOx.

In the presence of maximum sunlight (UV radiation), low CO and low humidity during midday indicates

the possibility of photochemical reaction (Gaur et al., 2014; Saini et al., 2014).

Both NO2 and CO starts to build up during evening hours (1600 hours) and attain their maximum

concentrations during night time (2100 hours), which are different from the variation in ozone. The SO2

concentration starts to increase in the morning hours (0600 hours) and attains its maximum concentration

0

40

80

120

160

200

240

280

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

NO NO2 SO2 PM 2.5

Con

cen

trati

on

[µ

gm

-3]

Month


44

in afternoon followed by a decrease till it sees rise in evening hours till night. A sink is seen from late

night to early morning in all seasons. Whereas the lowest values for both [NO2 (14.3µg m-3) at 1300 hours

and CO (0.43 mg m-3) at 1400 hours in pre monsoon] were observed during the morning and afternoon

hours. During winter season the largest diurnal peak concentration of PM2.5 (282 µg m-3) was observed

followed by post-monsoon (203.9 µg m-3), pre-monsoon (i.e. summer) (154.04 µg m-3), and monsoon

(103.9 µg m-3) respectively. During pre-monsoon season highest diurnal peak concentration of O3 (150.3

µg m-3), SO2 (31.5 µg m-3), was observed followed by post-monsoon, winter and monsoon season

respectively whereas lowest concentration of NO2 and CO was noted in pre monsoon season. This

seasonal diurnal pattern is similar to that of other locations in India (Pulikesi et al., 2006; Reddy et al.,

2010; Ahammed et al., 2006; Beig et al., 2007).


45

Figure iii. Seasonally averaged diurnal variation of Trace gases and PM2.5

0.0

100.0

200.0

300.0

400.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

WINTER

0

50

100

150

200

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

PRE MONSOON

0.0

20.0

40.0

60.0

80.0

100.0

120.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

MONSOON

0.0

50.0

100.0

150.0

200.0

250.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

PM2.5 O3 NO2 SO2 NO CO

POST MONSOON

Hours

CO

[m

g m

-3]

PM

2.5

,O3, N

O2, S

O2,N

O [

µg

m-3

]


46

1. Correlation of PM2.5 with trace gases and meteorological parameters

The correlation analysis of PM2.5 with trace gases (CO, NO, NO2, SO2, O3) and meteorological parameters

had been carried out during winter, pre-monsoon, monsoon and post-monsoon seasons over R.K.Puram,

Delhi (Table iii). PM2.5 showed significant positive correlation with CO (0.65) and NO2 (0.44) in all

seasons. It thus suggests that all three species (PM2.5, CO, and NO2) share similar source profiles, such

as traffic emissions (Smith et al., 2001). PM2.5 shows non- significantly positive correlation with NO

(0.19) and SO2 (0.16) in all seasons. There is a negative correlation between PM2.5 and O3 in all seasons

except monsoon season in which slight positive correlation was seen in monsoon season (0.08). The

negative PM2.5-O3 correlations indicate thus lower O3 concentrations being associated with higher

particulates concentrations (associated with increased NOx, which leads to lower O3 concentrations)

(Lorga et al., 2015).

There was a significant negative correlation was observed between the temperature and PM2.5

concentrations, with correlation coefficients of -0.58 in post monsoon followed by winter (-0.33), pre

monsoon (-0.23) except monsoon season (-0.18). High temperatures, especially in summer, may lead to

intense vertical dispersion of pollutants which induce an inverse relation between temperature and PM,

especially in the fine particle categories (PM2.5 and PM1). Atmospheric PM is transported quickly and

effectively, allowing its accelerated dispersion, and thus decreasing local mass concentrations.

Conversely, low temperatures and the temperature inversion layer caused by radiative cooling weaken

convection (Lin et al., 2009); in these circumstances, atmospheric PM remains suspended under the

inversion layer, leading to higher atmospheric PM concentrations.


47

PM2.5 and relative humidity was positively correlated: 0.25 (winter), 0.19 (pre monsoon), 0.07 (post

monsoon) except monsoon season (-0.29) in which negative correlation was observed. When the relative

humidity is high, aerosol hygroscopic increase is significant, which can induce the increasing of PM

concentration and scattering capability (Li et al., 2010). When the relative humidity is low, the aerosol

hygroscopicity increase is weak which can induce the aerosol scattering capability to decrease.

PM and wind speed are also negatively correlated in all seasons: -0.47(winter), -0.38(post monsoon), -

0.22(pre monsoon), 0.01 (monsoon). This is consistent with the fact that PM concentrations decrease as

wind speed and atmospheric dilution increase. In winter season low speed wind conditions and lower

temperature could result in a low boundary layer that traps pollution to the ground. In summer and pre

monsoon season, more intense winds and higher temperature (that could reflect positive correlations with

solar radiation) and higher boundary layer could result in pollution transport (Lorga et al., 2015).

Table iii: Seasonal and Annual Pearson Correlation of CO, SO2, NO, NO2, O3, Temperature, Humidity, Wind speed, solar radiation with PM2.5 at R.K Puram, New Delhi.

x/y

PM2.5

Annual Winter Pre monsoon Monsoon

Post

Monsoon

CO 0.65 0.58 0.56 0.29 0.66

SO2 0.16 0.12 0.42 0.07 0.18

NO 0.19 0.09 0.07 0.25 0.20

NO2 0.44 0.42 0.55 0.19 0.59


48

Figure iv. Correlation of PM2.5 with trace gases.

Figure v. Correlation of PM2.5 with meteorological parameters during study period.

O3 -0.23 -0.29 -0.42 0.08 -0.11

Temp. -0.50 -0.33 -0.23 0.18 -0.58

Humidity 0.22 0.25 0.19 -0.29 0.07

Wind speed -0.32 -0.47 -0.22 -0.01 -0.38

Solar radiation -0.15 0.19 -0.31 0.01 -0.28


49

2. Regional source identification

Wind is one of the most important processes for the transfer and dispersion of air pollutants. Besides, the

local sources contributing to in-situ PM2.5 concentrations, long range transport may possibly be other

reason for high concentration of PM2.5. As per location (Figure i) the polar plot (Figure vi) represents that

most of the pollutants comes from NE (ring road) and NW to SW (outer ring road).Backward trajectories

are drawn to examine the origin of air mass arriving from different locations to the present experimental

site to find the possible sources of pollution. The HYSPLIT model was used to investigate the source of

origin. Figure vii represents 120 h back trajectory ending at the observational site at 500 m altitude.

Lagrangian Integrated Trajectory (HYSPLIT) has also shows air mass parcel from long range transport

at the receptor site (Figure vii). During the observational period the approaching air mass at the receptor

site was mainly from Rajasthan (Thar-desert), Gujarat, Pakistan, Afghanistan, Arabian Sea, Bay of

Bengal and surrounding areas. Datta et al., 2010 reported the long distance source of air mass during

winter at Delhi.

Figure vi. Polar plot showing wind directions for PM2.5.


50

Figure vii. Air parcel back trajectory (using HYSPLIT model) during Jan - Dec 2014 (GDAS meteorological data).

4. Conclusion

This paper presented the continuous measurements of PM2.5 and SO2, NO, NO2, CO, O3 from an urban

site in Delhi. The period covered was during January 2014 to December 2014. Statistical analysis of SO2,

NO, NO2, CO and O3 was performed to characterize their monthly, seasonal as well as diurnal patterns

together with meteorological parameters and their influence on PM2.5.The hourly averaged mean SO2,

NO, NO2, CO, and O3 and PM2.5 concentrations over the entire study period ranged from 1.9-54.7µg m-

3,1.5-31.6µg m-3, 15.6-205µg m-3, 0.3-9.1mg m-3, 0.6-68.9 mg m-3 and 23.8-482.2 µg m-3 respectively,

with a mean and one standard deviation of 13.8 ± 6.9µg m-3, 6.5±4.2µg m-3, 49.0±27.4 µg m-3, 2.3±1.6mg

m-3, 83.5± 55.0mg m-3, 140.3±87.9µg m-3, respectively. PM2.5, NO, NO2, and CO concentrations were

highest during the winter season, perhaps due to a combined effect of large near-surface anthropogenic

emissions, boundary layer processes, and retarded photochemical loss owing to lower solar intensity as

well as local surface wind patterns. Contrary, O3 concentrations were observed highest during pre-

monsoon season, with its direct linear relationship with incoming solar radiation. It was also seen that

the lowest concentrations for all trace gases and PM2.5 were observed during the monsoon season, mainly

due to wet scavenging of pollutants. The averaged diurnal patterns also showed similar seasonal variation.

The average PM2.5 concentrations were at the lowest value during early mornings, followed by an increase

and then stabilizing 0900 hours till noon. After 1100 hours it starts decreasing till 1800 hours and then

again increasing to its maximum .The data for the site indicate that vehicular emissions may have the


51

influence on PM2.5 concentration. In the morning and evening, a clear influence was seen from the rush

hour traffic on PM2.5 in all seasons. NO, NO2 and CO showed peaks during morning and evening traffic

hours and a valley in the afternoon irrespective of the seasons, clearly linked to the boundary layer height

evolution. Contrary, O3 depicted a reverse pattern with highest concentrations during afternoon hours and

lowest in the morning hours. PM2.5 showed negative correlation with Temperature and wind speed.

During the observational period the approaching air mass at the receptor site was mainly from Rajasthan

(Thar-desert), Gujarat, Pakistan, Afghanistan, Arabian Sea, Bay of Bengal and surrounding areas.

Nevertheless, such continuous measurements of trace gases, PM2.5 and meteorological variables are

crucial to a better understanding and the characterization of air pollutants at diverse locations, including

urban areas which are at a high health and economical risks to developing nations.

5. Acknowledgement

The authors are grateful to Sh. Chandraker Bharti, IAS, Chairman, Sh. Sayed Musawwir Ali, Member

Secretary and Dr. M.P George, Scientist- D, Delhi Pollution Control Committee, Department of

Environment, for his support and guidance for conducting the study. We are also thankful to the technical

staff and trainees at the air laboratory of Delhi Pollution Control Committee (DPCC) for their steady

efforts in data compilation.

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Negi, M.P.S., Pandey, P., Verma, A.K., Jain, G. and Bhargava, S.K., 2010. Assessment of

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54

Worsening of Urban Air Quality: Role of Meteorology and Episodic Events during Winter Months

Rohit Sharma1, Kamna Sachdeva2* and Anu Rani Sharma3

1. PhD Scholar, 2. Assistant Professor ([email protected]), 3.Assistant Professor,

All from Department of Natural Resources, TERI University, Vasant Kunj, New Delhi - 110070.

Abstract

The urban air quality especially during winter months has always been a point of concern with respect to the impact

they pose over the human health and visibility. The episodic events like Diwali festivities further worsen the air quality

making it more noxious to the urban population.To assess the air quality over urban region of Delhi, PM1, PM2.5 and

PM10 samples were collected using GRIMM spectrometer. The settling velocity of each channel and visibility

reduction due to respective size fraction has also been calculated. The result showed that the particle with diameter

<1µm took 346 hours to settle whereas particle with diameter >1 µm settled within 12 hours of the release. Further,

in order to evaluate transport rate of the emitted pollutants within the mixing layer ventilation coefficient was

calculated, which ranged from 403m2/s to 5455 m2/s in study area during the festival time. Urban air quality was

further evaluated with respect to the role of meteorology. These meteorological coefficients and varied pollution levels

derived the possibility of the defining urban pollution islands (UPI) within the city. The study shows the opportunity

of using such coefficient based approach to define pollution zoning of the city especially during the time of the episodic

events like Diwali. Also the prospect of releasing public health warning on such episodic events could be undertaken.

Keywords: Stokes law, Settling velocity, Ventilation coefficient, Urban Pollution Islands (UPI)

1. Introduction Air quality remains a major concern for most of the urban cities worldwide. Annually, air pollution

contributes to 3.2 million deaths in Asian countries (Dholakia et al. 2013). The fate of the generated air

pollutants is principally decided by the meteorology of the particular location at certain point of time.

Meteorology i.e. study of lower atmosphere (Seinfeld et al., 2012) determines the variability of the pollutants

and their precursors within the ambient air hence plays a significant role in deciding the uphold of the

atmosphere for the released pollutants. Urban cities like Delhi, being the capital has grown at a prompt pace

in all sectors be it commercial, transport and housing which has contributed immensely to the city’s air

quality (Guttikunda et al., 2013). Over the years such kind of urban development has made several industrial,

commercial or transport hubs or islands within the city, throughout the country.

India being a culturally diverse country has ample number of festivals and to which the countrymen get

actively involved and celebrate these festivals with great enthusiasm. Be it any manner of celebration,

bursting of crackers is a common practice. Diwali, being one such kind of festival is passionately celebrated

all over the country every year during October/ November (Sarkar et al., 2010). The celebration usually

starts by late evening and continues till mid night. So for that short duration of time there is an intensive

firecracker bursting episode taking place whole over the region enfolding it in to a thick hazy layer. This

layer is formed by the release of several elements like potassium nitrate, sulphur, charcoal, and other trace

elements (Barman et al. 2009), trace gases like SO2,NOx (Sarkar et al. 2010) and ozone (Attri et al., 2001).

Through this study we tend to point out the blend of the impact of such kind of episodic activities (Diwali

in this case), the role of the meteorology governing the pollutant behavior and the formation of the Urban

Pollution Islands (UPI). During such festival events where of the pollutants released, in short time intervals



55

within small volume create local air pollution episode. These episodes along with supporting meteorological

conditions and already buildup pollution levels may worsen the quality air further, which can sustain for a

longer period and may affects the sensitive population like elderly and school-age children.

Through this paper, we report that meteorology plays crucial role in deciding the incremental exposure levels

during episodic events in Delhi, which coincides with the calm wind regimes of the winter. Fine particulate

load is main culprit for all kind of pollution related diseases and along with combustion byproducts released

from fire crackers becomes reason of attributable health risk in the region. Particles settling velocity have

also been assessed to relate the impacts of finer particulates in later days after the episodic event. Visibility,

mixing height, wind, relative humidity and temperature were correlated to find out urban ventilation co-

efficient during episodic event like Diwali. This ventilation coefficient can be used as indicator for urban

particulate pollution levels. On the basis of these simple meteorological coefficients urban pollution islands

(UPI) can be detected and varied behavior of pollution levels can be explained within the city.

2. Study area and methodology

Real time particulate matter sampling was carried out on the rooftop of TERI (The Energy Resources

Institute) University, Vasant Kunj which lies in the south western part of Delhi using GRIMM aerosol

spectrometer. The site is located at the latitude 28032’89’’N and longitude 77008’54’’E and reported as

receptor site as per the predominant south western wind direction (Agrawal et al. 2011) (Figure 1). GRIMM

Spectrometer (Series 1.108, Germany) was used to get 15 channeled (0.30 to 20 µm) particle count per liter

sampling. It measures number of particle per unit volume of air using light scattering technology, ambient

air with flow rate of 1.2 liters per minute is drawn in to the instrument through a volume controlled pump.

The instrument initiates a self-test and zero calibration before every start (Adak 2014). Sensitivity of the

spectrometer is 1 particle/L with a reproducibility of ± 2% (Cheng 2010).

The sampling was divided under the Pre-Diwali, Diwali and Post Diwali days covering the whole Diwali

week i.e. from 20, October 2014 (Monday) to 25, October 2014 (Saturday), of which 23, October 2014

(Thursday) was the Diwali day. The prime objective of this study was to focus on the night time particulate

load added by the bursting of firecrackers; hence forth the sampling hours were fixed so as to maximize the

coverage of the episode i.e. 1900 hours to 2300 hours. Settling velocity was calculated using Stoke’s law,

which determines the velocity of an aerosol particle undergoing gravitational settling in still air (William

C. Hinds 1983). When particles are released in the air it quickly reaches its terminal velocity, which is

expressed by the term given below -

18

2

cp

ts

gCdV

Where ρp is density of the particle, g is the acceleration due to gravity and η is the viscosity and Cc is slip

correction factor (applied on particle d<1 µm). Further, the limit of visibility (Lv) was calculated for the

channels PM1, PM2.5 & PM10 on the basis of concentration observed by GRIMM using following equation.

𝐿𝑣 ≈

1.2 × 103

𝐶

Where C is the PM concentration in microgram per cubic meter and Lv is limit of visibility in kilometers.

These parameters along with ventilation coefficient were used to determine long-term impact of episodic


56

event on the air quality. Further, in order to calculate ventilation coefficient, mixing height was estimated

using thermodynamic diagrams and sounding profiles of Delhi were obtained from University of Wyoming,

Department of Atmospheric Science (http://weather.uwyo.edu/upperair/sounding.html) providing the

meteorology of Delhi for selected dates. The morning maximum surface temperature for the day and dry

adiabatic line from maximum surface temperature are plotted, the height at which these two intersects is

considered as the maximum mixing height of the day.

Figure 1: Location map of study area

The product of the maximum mixing depth and the average wind speed within the mixing depth is sometimes

used as an indicator of the atmosphere’s dispersive capability. This product is known as the ventilation

coefficient*. Further, in order to examine Urban Pollution Island (UPI) phenomena, ventilation coefficient

was calculated for all the sampling days. .

http://weather.uwyo.edu/upperair/sounding.html


57

*Note: Ventilation coefficient (VC) is a product of mixing layer height and average wind speed. The

ventilation coefficient reflects the transport rate of pollution in the mixing layer. The calculation of VC is

given by,

VC=ZiU

Where, Zi is atmospheric mixing layer height above the ground at height of i meters (m); U is average wind

velocity near the ground (m/s).

3.0 Results and discussion:

3.1 Settling Velocity and Visibility

Visibility degradation is the most readily apparent effect of air pollution. The longer the pollutants remain

suspended in the air, extended is the time for the reduced visibility. Hence, settling velocity of each of the

GRIMM observatory channel and visibility reduction due to it was calculated in order to understand the

effect of additional pollutants due to bursting of fire crackers during Diwali festivities (Table 1).

Table 1: Terminal settling velocity and settling time for different channels of GRIMM Spectrometer.

Channel

Diameter (µm)

Terminal Settling

Velocity (cm/s)

Settling Time

(hours)

0.3 5.6 x 10-4 346.29

0.4 9.9 x 10-4 194.79

0.5 15.6 x 10-4 124.66

0.65 26.3 x 10-4 73.77

0.8 39.9 x 10-4 48.70

1 62.3 x 10-4 31.17

1.6 159.7 x 10-4 12.17

2 249.5 x 10-4 7.79

3 561.5 x 10-4 3.46

4 998.2 x 10-4 1.95

5 1559.7 x 10-4 1.25

7.5 3509.4 x 10-4 0.55

10 6239 x 10-4 0.31


58

The average density of particulate matter emitted from the episodic event to calculate settling velocity is

taken to be 2.12 g/cm3 (Agrawal et al. 2011) which may have some uncertainty. The observed variations in

the particle count showed that the lesser the channel diameter, more is the time taken by particle to settle

down. This implies that the fine particles often take longer time to settle than that of the larger particles.

To quantify the change in the fine range particle concentration, the emphasis was made on the PM1, which

were divided under six sub channels in GRIMM spectrometer i.e. size ranges of 0.3, 0.4, 0.5, 0.65, 0.8 and

1 µm respectively. It was observed that there has been an increase of ~200% particle per liter of air for 0.3

µm channel, ~150% for 0.4 µm, ~130% for 0.50 µm, ~110% for 0.65 µm, ~85% for 0.8 µm and ~65% for

1 µm channel diameter.

Further, it was found that the particulate matter of the channel size <1µm (i.e. 0.3, 0.4, 0.5, 0.65, 0.8 and 1

µm) would take longest time to settle reaching up to 346 hours once released. On the other hand the particles

>1 µm (i.e. 1.6, 2, 3, 4, 5, 7.5 and 10 µm) would tend to settle within approximately 12 hours of the release.

Further the limit of visibility for each PM1, PM2.5 & PM10 channel was calculated (Table 2).The increased

PM concentration resulted in the reduction of visibility as well (Clark 1997). The pre Diwali visibility of

32, 20 and 6 km from Lv PM1, Lv PM2.5 and Lv PM10 reduced to 7, 6 and 3 km making it ~22%, 30% and

50% visibility reduction subsequently on Diwali night. This clearly shows the impact particulate matter

renders on visibility.

Table 2: GRIMM observations and visibility calculated on its basis.

Date

Oct, 2014

PM1

PM2.5

PM10

LvPM1

LvPM2.5

LvPM10

20th 104 129 315 12 9 4

21st 38 60 208 32 20 6

22nd 81 104 245 15 12 5

23rd* 170 198 355 7 6 3

24th 97 119 275 12 10 4

25th 108 133 306 11 9 4

Hence, the Diwali event doesn’t impact the air quality of a particular region for a day or two, the impact is

been observed for the next two weeks as well. This additional load along with favorable meteorological

conditions, may cause formation of UPI and cause severe health issues to north Indian peoples.

3.2 Ventilation coefficient and mixing height

Calculated on basis of PM load (km) GRIMM Observations (µg/m3)


59

Ventilation coefficient (VC) of a particular location depends on number of meteorological parameters, i.e.

wind speed, ambient temperature, relative humidity, pressure and solar radiation. Various studies have found

that solar radiation determines mixing height and is highly correlated (~0.8) with it during daytime (Chan et

al. 2012). In the present study the ventilation coefficient of different location within the city has been

calculated using the meteorological data (Figure 2), the locations were selected on the basis of the

meteorological data availability. The selected locations were, NSIT Dwarka, IGI Airport, Shadipur, and

Safdarjung. The VC ranged as high as 5455 m2/s for Safdarjung and recorded the minimum of 403m2/s for

NSIT Dwarka respectively during the Diwali week. There has been a certain dip in VC on Diwali (23

October 2014) and post Diwali (24 October 2014) day, making these two days very prone to higher

concentrations throughout the observed locations. Out of the four chosen locations Safdarjung station was

identified as urban pollution island on the day of Diwali.

Figure 2: Estimated ventilation coefficient of different location.

Further, NAQI (National Air Quality Index) was also analyzed for the Diwali dates which indicated that

south of Delhi was highly polluted than Northern and Eastern part (Figure 1).

4.0 Conclusion

The study examines role of local meteorology in governing impacts of the episodic events like “Diwali”

over urban air quality. Further, a new concept of urban pollution islands (UPI) has been introduced, which

emphasizes on the role of urban infrastructure planning in maintaining air quality of the region. The observed

variation in the particulate matter (PM1, PM2.5 and PM10) concentration showed that smaller the particle size,

the longer time it took in settling down. The finer range particle size <1µm (i.e. 0.3, 0.4, 0.5, 0.65, 0.8 and

1 µm) took 346 hour to settle down once released. Similarly particles with size >1 µm (i.e. 1.6, 2, 3, 4, 5,

7.5 and 10 µm) settled down within ~ 12 hours to the release. The ventilation coefficient ranged as high as

5455 m2/s for Safdarjung and recorded the minimum of 403m2/s for NSIT Dwarka during the Diwali week.

Out of the four locations chosen, NSIT Dwarka, IGI Airport, Shadipur and Safdarjung, Safdarjung was

found to be acting as UPI on the day of Diwali. Such parameter based approach can be utilized in defining


60

pollution zoning of the city especially during the time of the episodic events like Diwali and public health

warning can be issued.

5.0 Acknowledgement

The first author acknowledges the support of HSBC for the scholarship and Central Pollution Control Board

(CPCB) and University of Wyoming for providing the required datasets.

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Climate Change, John Wiley & Sons. Available at:

https://books.google.com/books?hl=en&lr=&id=YH2K9eWsZOcC&pgis=1 [Accessed May 26,

2015].

12. William C. Hinds, 1983. Aerosol technology. Journal of Aerosol Science, 14(2), p.175.


61

Recent Development on the Understanding of Aerosol Nucleation and Growth

Bighnaraj Sarangi1,2*, Deepak Sinha3, Prashant Patel1, Shankar G. Aggarwal1

1CSIR-National Physical Laboratory, New Delhi 110012 2now at Physical Research Laboratory, Ahmedabad 380 009

3Government Nagarjun Post Graduate College of Science, Raipur 492010

(*Correspondence: [email protected])

Abstract

The study of atmospheric aerosol is important because it has deleterious effects on climate, atmospheric composition,

air quality, and human health. It is well known that aerosols influence the climate by changing radiative forcing via

two basic classes of mechanisms: direct and indirect. The direct radiative forcing of aerosols changes atmospheric

scattering and the absorption of radiation.The indirect effect is connected with the role of aerosols as cloud

condensation and ice nuclei. Although there is much progress in understanding the aerosol characteristics, still

uncertainties are remaining in the current global climate predictions largely because aerosol mass and particle number

concentrations are highly variable with location and time. Therefore, the understanding in the formation and growth

of this ubiquitous species is very much important. Current nucleation and growth theories are also hampered by high

uncertainties because of the lack of laboratory and atmospheric measurements. This paper is the brief review covering

the basic understanding of nucleation and growth process of atmospheric aerosols, and the recent development on this

topic.

Keywords: Nucleation, Growth, Secondary Formation

1. Introduction

Atmospheric aerosols are solid or liquid particles suspended in the gaseous medium, which is usually

air. Depending upon the source of origin aerosols are of two types: primary and secondary. Primary

aerosols (e.g., soot, mineral dust, sea-salt particles or pollen) are the particles introduced directly into the

atmosphere, almost near to the ground whereas secondary aerosols are form through gas-to-particle

conversion usually observed in boreal forest, coastal areas, urban areas, near boundary layer and upper

troposphere (Hinds, 1999; Kulmala et al., 2004; Twohy et al., 2009). Secondary aerosols are become

climatically important because they are able to grow to sizes of 50 nm and larger. Particles in this size range

can serve as cloud condensation nuclei (Twomey 1974; Pirjola et al., 2002; Laaksonen et al., 2005;

Kaufman and Koren, 2006) and they contribute to indirect aerosol effect on the climate (Lehtinen and

Kulmala, 2003). Furthermore, if the particles grow to sizes about 100 nm and above, they scatter light very

efficiently, and have thereby a direct (cooling) effect on the Earth climate (Coakley, 2005). The formation

of such particles takes place frequently in the ambient atmospheric condition and at different geographic

locations. A considerable fraction of the atmospheric particles is formed by gas-to-particle conversion, and

this process is known as nucleation process. In order to determine the causes of atmospheric nucleation

events, and to better understand the characteristic of these events in different environments, it is important

to know the underlying processes causing the particle formations, growth and the physiochemical

mechanisms which control their effects on the regional and global atmosphere.

Several methods have been developed to study the aerosols formation and growth from atmospheric

observations. Significant progress has been achieved in understanding these processes in laboratory and

http://en.wikipedia.org/wiki/Aerosol#cite_note-4


62

ambient atmospheric conditions. Still these processes describe through complex mathematical equations,

which rely on particle number, mass, size distribution measurements and chemical compositions. To

estimate aerosol formation and growth accurately, models must include microphysical processes such as

nucleation, coagulation, scavenging, condensation/evaporation, etc. However, estimation and contribution

of these processes are not so straightforward which leads to gathering of several information, such as

nucleation range particle with size, number and charge; vapour concentration of atmospheric constituents,

and heterogeneous processes, etc. Moreover, to collect this information, there is hardly any instrumentation

available to measure these molecular clusters at atmospheric conditions.

We discussed here a straightforward nucleation and growth mechanism of atmospheric aerosols

reported by researchers around the globe and highlighting the guideline to identify these atmospheric

phenomena.

2. Nucleation

Nucleation is condensation of vapours in the atmosphere under favourable condition to form molecular

clusters known as critical nuclei, which subsequently grow in to larger size particles (Kulmala et al., 2004).

Therefore, nucleation is responsible for the production of tiniest particle through gas to particle conversion.

Figure 1 shows general assumption of gas-to-particle conversion through various physical transformations

in three different steps. In step I suspended condensable vapour molecule (< 0.3 nm) condenses under

favourable condition. In the next step (II), they form cluster that leads to a critical size (~1 nm) and in step

III these cluster is activated (~1.5 nm) and grow faster to form large number of new aerosol particle (> 3

nm).

Figure1: Description of the nucleation results in to a particles through gas-to-particle conversion


63

Nucleation can be homogeneous or heterogeneous (Sheinfield and Pandis, 2006). Homogeneous nucleation

is the nucleation of vapour on embryos comprised of vapour molecules only, in the absence of foreign

substances. Heterogeneous nucleation is the nucleation on a foreign substance or surface, such as an ion or

a solid particle.

2.1 Concept of nucleation

Nucleation is in general formation of molecular embryos or clusters prior to formation of a new phase during the

transformation of vapor → liquid →solid (see Figure 2). This process is characterized by a reduction in both enthalpy

and entropy of the nucleating system (i.e., ΔH < 0 and ΔS < 0, favourable according to first law of thermodynamics and second law of thermodynamics). But in general nucleation is hindered by increase of entropy and often free energy

changes from spontaneous (ΔG = ΔH-TΔS < 0) to non-spontaneous (ΔG= ΔH-TΔS > 0) crosses the nucleation barrier

(1.3 to 1.5 nm) once reaches to the new phase as a result spontaneous growth observed in particles at sub 10 nm

ranges (Kulmala, 2000) .

During nucleation or formation of the clusters there is an equilibrium existed between condensation and

evaporation (saturation vapour pressure) where chemical potential (µ) of both the phases should be equal.

Figure 2: Shows the equilibrium between condensation and evaporation; Vi,gas is molar volume of gas phase, Vi,aerosol is the molar volume of aerosol phase, Pi,sat is the saturation vapour pressure and T is the temperature at both the phase.

µi,gas = µi,aerosol (1)

dµi,gas = dµi,aerosol (2)

−Si,gasdT + Vi,gasdp = −Si,aerosoldT + Vi,aerosoldp (3)

( dP

dT) =

Si,gas−Si,aerosol

Vi,gas−Vi,aerosol =

∆S

∆V =

∆H

T∆V (Murphy and Koop, 2005) (4)

Equation (4) is known as Clausius-Clapeyron-equation for the respective state of equilibrium. In the

ambient atmosphere molar volume of gas phase is always larger than the molar volume of aerosols.

Therefore

Vi,gas > Vi,aerosol

Equation (4) for saturation vapour pressure can be written as

( dPsat

dT) =

∆Hvap

TVi,gas (5)

Vi, aerosol

Vi, gas Gas

AerosolPi,sat

T

T


64

The modified form of equation (5) by using molar charge on aerosols particles as suggested by Kelvin

(known as Kelvin equation)

Psat = P0 × exp (2σVaerosol

Rtr) (Seinfeld and Pandis, 2006) (6)

Where P0 is vapour pressure at the flat surface, σ is the surface tension at the surface of the particle, Vaerosol

is the molar volume of aerosols molecule, and r is the radius of the particle.

2.2 Theory to establish the nucleation process

Numerous theories have been established to better model the aerosol nucleation process. These

theories are based on assumption and approximation to better characterized the nucleation process.

Theories such as classical nucleation theory (CNT), attempt to obtain the free energy of formation of the

critical nucleus from macroscopic parameters, e.g., surface tension, bulk liquid density, etc. Theory such

as kinetic theory, derive the cluster distribution and hence the estimation of formation rate. Using Monte

Carlo simulations, and density functional theory, one can apply the first principles to calculate the cluster

structure and free energy of cluster formation. Among these theories, CNT still forms the basis for the

thermodynamic interpretation of aerosol nucleation processes. The study of Gibbs free energy change

(∆G) during nucleation process is important which can be derived easily from the natural variable

temperature and pressure. For example if a substance is supersaturated (vapour pressure (P) > equilibrium

vapour pressure (p0) over a flat surface of the bulk substance) in the gas phase and away from any other

surfaces on which the gas phase molecules could condense on, the system is meta-stable and the vapour

molecules would generally be preferred to undergo phase transition to the condensed phase as reduction

in G could be obtained due to the lower chemical potential of the bulk liquid. For a single substance, the

thermodynamics of the nucleation can be explained by equation (7):

∆G = − 4

3 π rp

3 kT

vllnS + 4πrp

2σ (7)

This equation explains the free energy change (∆G) as a function of the nucleating particle’s radius rp.

Here S is the saturation ratio, S = p/p0, k is the Boltzmann constant, T is the temperature, vl is the volume

occupied per molecule, and σ is the surface tension of nucleating substance.


65

Figure 3: Thermodynamic representation of aerosol nucleation for a single substance. A nucleation barrier of height ∆G∗ exists. The critical cluster size rp

* is defined by the maximum of the barrier (Curtius, 2006). Equation (7) is illustrated in Figure 3. As long as the system is supersaturated (S>1), the first term of the

right hand side of the equation is negative. Generally, this is the driving force for the vapour phase

molecules to condense and thereby increase the radius of the nucleating substance. Just in the beginning,

for small Rp, the second term plays an important role. As the particle forms, a new surface (4πRp2) has to

be built up, costing surface energy. In the beginning, this surface energy is bigger than the energy won from

changing from vapour phase to particle phase and therefore for small Rp, an effective energy barrier exists

(the so-called nucleation barrier) that prevents the vapour from nucleation. The location of maximum barrier

is known as critical radius of nucleating substance. Once the nucleating substance reaches to this critical

radius (rp*) then a droplet persists, and which further grow by condensation of vapour molecules. To

quantify the nucleation process, nucleation rate (J) is defined, the number of clusters that grow beyond the

critical size per second. The nucleation rate is connected to the height of the nucleation barrier is expressed

in equation (8) (Seinfeld and Pandis, 2006)

J = K exp (−∆G∗

kT) (8)

where K is a preexponential factor. Using this equation one can able to measure the nucleation rate and

study the cluster formation capability of individual chemical species under different supersaturating

conditions.

CNT is considered to be successful than the others in describing the nucleation process but still large

differences exist between observation and prediction of nucleation process. There are several factors

responsible for this i.e., this theory limited as the bulk phase parameters like surface tension and density

that require in CNT are not suitable to describe the critical nucleus formation correctly (Curtius, 2006).

Although some more sophisticated approaches exist, but agreement between atmospheric measurement and

theoretical prediction is still required a better approach. This limited agreement causes the theory

incomplete and make us difficult to measure the size, concentration and chemical composition of nucleating

cluster less than 3 nm.


66

2.3 Nucleation Bursts

In ambient atmosphere the continuous production of non-volatile species by means of anthropogenic

or natural origin eventually leads to their nucleation, formation of new aerosol particles, and their

subsequent growth. Thus formed aerosol and the pre-existing are able to retard the nucleation process

because of condensation of non-volatile substances onto their surfaces. This process is referred to as the

nucleation burst (Friedlander, 2000). Nucleation bursts are heterogeneous nature in most of the cases and

were regularly observed in the atmospheric conditions. This serves as an essential source of cloud

condensation nuclei and can thus affect the climate and weather conditions on Earth (Kulmala et al., 2004;

Seinfeld and Pandis, 2006). The present opinion connects nucleation bursts with the production of non-

volatile species which is able to form new particle under atmospheric condition. But the production of non-

volatile species in turn requires special conditions (e.g. emissions from the vegetation, atmospheric

chemical composition, exchange process of boundary layer, etc.).

A huge number of field and laboratory measurements and modelled based study done to characterise

nucleation bursts dynamics during the last decade (Boy and Kulmala, 2002; Kulmala, 2004). Most of these

studies are centred on a commonly accepted point that the chemical reactions of trace gases are responsible

for the formation of non-volatile precursors, which then lead to the formation of sub-nanometre particles.

Several models work on the nucleation burst involves nonlinear approach because of the chemical cycling

involves in the production of non-volatile species are highly uncertain.

2.2 Nucleation Experiment

2.2.1 Laboratory experiments

There have been numerous experiments performed to measure the nucleation rate that involve numbers

of method using high-tech analytical instruments. The main goal of these experiments is to find a way to

achieve the supersaturated state of condensing vapour. These experiments are performed initially for single

component (one species of condensing vapour) and then for multi component (more than one species)

system. For single component system the supersaturated state of condensing vapour can be obtained by

cooling the vapour by temperature gradient or adiabatic expansion. For multicomponent, supersaturated

state is achieved by turbulent mixing of vapours followed by intense cooling or generation of nucleating

vapours photochemically. Most common approaches to study the nucleation process experimentally are

adiabatic expansion (Schmitt, 1981), diffusion chamber experiment (Katz, 1970), laminar flow chamber

experiment (Nguyen et al., 1987), turbulent mixing chamber experiment (Zhang et al., 2009) and generation

of nucleating vapours from different chemical sources (Wyslouzil et al., 2000). Most of these approaches

are non or semi continuous and associated with diffusion losses of nucleating vapours. Among the different

approaches discussed, turbulent mixing chamber experiment and generation nucleating vapour from

different chemical sources consider to be more effective than the others. In the later approach sulfuric acid

vapour is produced chemically from SO2 inside flow chamber through ozonolysis of alkenes or photolysis

of ozone. OH redical is formed in both the process which convert sulfur dioxide to sulfate and the reaction

mechanism is discussed below

O3+ hγ(254 nm) → O(D1) + O2 (r1)


67

O3 + alkene → OH + Other products (r2)

SO2+ OH → HSO3 (r3)

HSO3 + O2 → SO3 + HO2 (r4)

SO3 + 2H2O → H2SO4 + H2O (r5)

These processes (r1-r5) leads to continuous generation of nucleating molecules (e.g., H2SO4) in the

vapour phase by chemical reactions and further leads to formation of nucleation clusters then condensation

continues on existing clusters promoting their growth to detectable sizes. Using these experimental

approaches, the accuracy of nucleation rate measurements can be achieved. Similar approach has also been

used to better simulate the atmospheric nucleation event using smog chamber experiments which involve

atmospheric relevant nucleating vapour sources.

2.2.2 Atmospheric observations The formation of new particle post to the nucleation is referred as nucleation event. Dal Maso et al.

(2005) described the classification of nucleation events based on the strength and visual distinction of new

particle mode. The classification of nucleation event and non event day is subjected to observed nucleation

mode (< 25 nm, now about <10 nm because of today’s instrumental capability) from the size distribution

analysis. Days to be classified as new particle formation (NPF) event, the following criteria need to be met

1. There must be a distinct new mode appear in the size distribution.

2. The mode must originate in the nucleation mode size range (< 10 nm).

3. The mode must prevail over a time span of hours.

4. The new mode must show signs of growth up to Aitken or accumulation (>100 nm) range

Figure 4 display a flowchart illustrating the classification of nucleation event and non-event (Dal Maso

et al., 2005).


68

Figure 4: A flowchart displaying decision made during the nucleation event classification

Figure 5 shows such a nucleation event as it is frequently observed during spring at a measurement site

(CSIR-National Physical Laboratory, New Delhi) located in central part of Delhi. The figure shows a

banana plot of the measured particle size distribution as a function of time. While hardly any particles exist

that are smaller than 25 nm for most of the time, suddenly in the late morning numerous particles of below

25 nm size are detected. Over the day, these particles grow by coagulation among the particles and

condensation of further condensable gases. These observations are similar to a typical particle nucleation

event in a boreal forest at Hyytiälä, Finland, where numerous freshly nucleated particles appear at the

smallest measurable sizes (>3nm) and grow within hours to sizes of around 50 nm (Boy and Kulmala,

2002).


69

Figure 5: Time series of size-resolved particle number concentrations using scanning mobility

particle sizer (SMPS) observed in Delhi (Sarangi and Aggarwal, 2017).

In general a typical nucleation events produce about 1 particle cm−3 s−1 and number concentrations freshly

formed particles in thosands per cubic centimetre are usually detected after a nucleation event (Kulmala et

al., 2004) where the growth rate of the particles after nucleation is observed to be on the order of 1nm h−1

(Kulmala et al., 2004). In most of the cases, the nucleation event takes place during daytime, preferentially

in the late morning because of the involvement photolytic processes formation of OH redical taking place.

This highly reactive redical react with atmospheric trace gases to form precursor gases that produce the new

particles.

Identification of the gaseous precursors responsible for atmospheric nucleation and growth event requires

detailed analysis of the particle chemical compositions. Therefore, a combined measurements of size

distributions and chemical compositions of nanoparticles is essential and represent a key approach to better

understand the underlying mechanisms of nucleation events or new particle formation in the atmosphere.

In general the formation of new particles from gaseous sulfuric acid and water is considered to be the most

important atmospheric nucleation process. As already discussed atmospheric SO2 is converted into sulfuric

acid in the gas phase by reaction with the hydroxyl radical OH (r3-r5) (Seinfeld and Pandis, 2006) and

served as precursors for nucleation events. It has also been shown that organic compounds, such as the

organic acids from photochemical oxidation of terpenes (Ortega et al., 2012) and alkylamines (Willis et al.,

2016) are important components in the ultrafine particles produced during nucleation events. Therefore,

various mechanisms suggested base on the observations and tested through laboratory experiment for the

atmospheric nucleation events. The commonly suggested mechanism are (1) binary nucleation of H2SO4

and H2O (2) Ternary Nucleation of H2SO4-H2O Involving Ammonia or Amines and (3) Nucleation of

H2SO4-H2O assisted by Organics (4) nucleation due to iodine oxides and (5) ion induced nucleation

(Seinfeld and Pandis, 2006).


70

3. Growth of atmospheric aerosols

After nucleation, there is spontaneous growth of aerosol particle up to 10 nm (Kulmala et al., 2004)

which further grow by condensation and coagulation under atmospheric condition (Leppä et al., 2011; Dal

Maso et al., 2005). Particles can change their size and composition by condensation of vapor species or by

evaporation, by coagulating with other particles, by chemical reaction, or by activation in the presence of

water supersaturation then they cloud condensing nuclei (CCN) (see Figure 6). Particles are eventually

removed from the atmosphere by two mechanisms deposition at the Earth's surface (dry deposition) and

incorporation into cloud droplets during the formation of precipitation (wet deposition) (Seinfild and

Pandis, 2006).

Figure 6: Illustration of the nucleation and growth process of particles under atmospheric

conditions.

Condensation growth is condensation of vapors onto pre-existing particles. Under this process particle grow

through the Kelvin effect (i.e., equilibrium vapours pressure over a curve surface (Seinfeld and Pandis,

2006)). Coagulation is a kinetic process in which particles those are in relative motion, collide and fuse. In

this process, the effect of particle charge is dominant for small particles (Fuchs, 1964). There are two types

of coagulation processes discussed in general, (i) self-coagulation, which is defined as fusion of similar

sized particles, and (ii) coagulation scavenging, which is referred as scavenging of freshly form nucleation

range particles by the pre-existing relatively bigger sized particles. A pictorial presentation is shown in

Figure 7, which discussed all the physical process lying behind the growth of the particles.


71

Figure 7: Pictorial presentation of the processes of growth of the particle due to self-coagulation,

coagulation scavenging and condensational growth (Sarangi et al., 2015).

The quantity use to measure the growth of the particle known as growth rate. The temporal variation of

particle size distribution in which the mode peak of the distributions is either shift to higher mode size

(growth) or towards lower mode size (shrinking). This shifting is depends on the prevailing atmospheric

conditions. Growth rate can be calculated from the particle size distribution if size range and the number

concentrations are known. Then Geometric mean diameters (GMD) for each size distribution can be used

to examine particle growth processes (Jeong et al., 2010). The growth rate (GRtotal) of the ambient particle

measured by fitting the GMD of the particle in modal ranges during the growth process over a period of

time ‘t’. Here we are using total growth rate as it may involves the growth due to condensation and

coagulation. The mathematical expression for the total growth is expressed in equation (9)

GRtotal = ∆GMD

∆t (9)

GMD (dg) = exp∑ (lndpi)×Nii

∑ Nii (10)

Where dg is the geometric mean diameter of the particles, dpi is the particle diameter of size bin i, and

Ni is the particle number concentration in size bin i (Hinds, 1998). The growth rate due to individual

processes (e.g. coagulation and condensation) can also be calculated using mathematical expression (Leppä

et al., 2011; Sarangi et al., 2015) as discussed below

3.1 Self-coagulation

Growth rate due to self-coagulation (procedure described in Leppä et al., 2011) can be defined as:

GRscoag(dp) = dp

6k(dp)N (11)

Where N is the total number concentration of particles in the mode peak, and k(dp) is the Brownian

coagulation coefficient between the particles of similar size. k(dp) can be determined as:

k(dp) = 3 × 10−16 × Cc (12)

Where Cc is known as Cunningham slip factor (Hind, 1998).

3.2 Coagulation scavenging

The coagulation of pre-existing particle with the newly formed particles resulted as decrease in number

concentration of nucleation range particles and growth of Atkins and accumulation range particles. Using

particle number size distribution, the value of coagulation sink for the particles in a mode can be calculated

as discussed in Leppä et al. (2011):

Particle growth due to self-

coagulation (GR )

Particle growth due to

coagulation scavenging

Particle condensational

growth (GR ) Condens

able

Freshly

formed

Similar

sized


72

CoagSi = ∑ k(dp)ij × NJqj=p (13)

Where k(dp)ij is the Brownian coagulation coefficient between the particles in sections i and j and the

particles in the nucleation range are in the sections from p to q. Here Nj is the nucleation range particle

number concentration, which are scavenged by the larger one.

k(dp)ij = (Didi + Djdj + Djdi + Didj)πβ (14)

Where D and d denote the diffusion coefficients and particle diameter of class i and j, respectively, is the

correction factor for the particles in transition and free molecular regime as suggested by Fuchs (1964).

Diffusion coefficient can be calculated as (Hind, 1998):

𝐷 = 𝑘𝑇𝐶𝑐

3𝜋𝜂𝑑𝑝 (15)

Where dp is the particle diameter and k, T and η are Boltzmann constant, temperature at standard condition,

and the coefficient of viscosity, respectively. Cc is called Cunningham slip correction factor. Equation (8)

agrees with the correlation (adjusted for mean free path, ) developed by Allen and Raabe (1985) for all

particle sizes. Leppä et al. (2011) derived an equation to estimate the growth rate due to scavenging as:

𝐺𝑅𝑠𝑐𝑎𝑣 = 𝑑𝑝∗ ∑ 𝐶𝑜𝑎𝑔𝑆𝑖𝑁𝑖

𝑛𝑖=1

∑ 𝑁𝑖𝑛𝑖=1

−∑ 𝐶𝑜𝑎𝑔𝑆𝑖𝑁𝑖𝑑𝑝𝑖

𝑛𝑖=1

∑ 𝑁𝑖𝑛𝑖=1

= 𝑑𝑝∗ × 𝐶𝑜𝑎𝑔𝑆∗ − (𝑑𝑝 × 𝐶𝑜𝑎𝑔𝑆)

∗

(16)

Where dp* is the count mean diameter, dpi, Ni and CoagSi are the particles diameter, number concentration

and coagulation sink for the particles in class i, respectively, and * denotes the count mean value over the

nucleation mode.

3.3 Condensation

Under most atmospheric conditions, aerosol particles grow mainly due to condensation of vapours on them.

The growth rate of a particle diameter due to condensation is then calculated as (Seinfeld and Pandis, 2006):

GRcond =1

2× Vm × v × α(C∞ − Cs) (17)

Where Vm is the volume of the condensing vapour molecule, v is the mean speed of the molecules; C∞ and

Cs are the number concentration of condensing molecules far away from the particle and the saturation

vapour concentration at the particle surface, respectively, and α is the molecular accommodation

coefficient.

4. Current challenges and future needs in aerosols research

Over a span of a decade, we have witnessed tremendous expansion in study of aerosol research

especially in the secondary aerosols (nucleation and growth of aerosols due to gas to particle conversion),

and this trend will likely to be continued. Several scientific challenges related to complex, multi-phase

chemistry and physics of aerosols that are yet to be resolved. Although significant progress achieved in

understanding the new particle formation events, which accounts for a major fraction of atmospheric

aerosols in various environments, still the fundamental chemical processes responsible for aerosol


73

nucleation and growth yet to be established. Available results on aerosol nucleation and growth from

previous experimental, theoretical, and field studies are conflicting, hindering efforts to develop

atmospheric models to simulate formation and growth of secondary aerosols on the regional and global

scales. The following challenges especially in aerosol nucleation and growth need to be address

1. Understanding the chemico-physical processes that lead to the formation of new particles by

nucleation will require information on the composition and concentration of the molecular clusters

that serve as their precursors. Research community looking for the concrete evidence that “Bridging

the gap” between molecules and nanoparticle (>3 nm) particles which is still remains a challenge

in current theoretical understanding.

2. Quantification of diverse gaseous nucleating precursors present in ambient atmosphere at the ppb

or even lower levels is very much essential. Further detection chemical composition of critical

nucleus less than sub-10-nm diameter particles still a challenge to the current understanding of

formation of aerosol.

3. Chemical characterization of NPF is a challenging analytical problem. Knowledge gaps remain,

especially with regard to the organic composition of ambient particles. Because of the most of the

urban and rural atmosphere influenced with vehicular emission, biogenic emission, biomass

burning and industrial exhaust. Therefore it is difficult to characterize the chemical components

which responsible for high secondary aerosol loading.

4. Formation and growth of atmospheric aerosol strongly involve chemical interactions relative to our

understanding are of purely gas-phase chemistry. This is due, in large part, to the fact that chemists

have played a secondary role to physicists and engineers in aerosol science. Many of the challenges

for the future are chemical, and recent work shows a rapid increase in the sophistication with which

aerosol chemistry is treated. It is likely that this trend will continue.

5. Regarding measurement of aerosol properties i.e., physical and chemical parameters: accurate

instrumentation and well-defined methodology is still essential and must be acceptable to global

community. In this process a single protocol will be followed by different research community

working on the same issues and the resulted data would be comparable and concluding.

6. Most of analytical measurements measuring the secondary aerosols are bulky and expensive.

Efforts should be done quite effectively on the portable real time measurement system. Apart from

this efficiency and accuracy of the instruments need to be improved to better quantify the chemical

and physical behaviour gas-aerosol phase which is still a challenge to current development of

research.

As reported earlier measurements of NPF in the free troposphere are mostly consistent with the binary

water-sulfuric acid nucleation. In the boundary layer, however, binary nucleation not always explaining

atmospheric nucleation events and several alternative nucleation mechanisms may play a crucial role,

including ternary nucleation of sulfuric acid with ammonia or organics and ion induced nucleation. The

contribution from organics likely explains high aerosol concentrations observed in polluted environments

(Passonen et al., 2010), where high concentrations of low-volatility organic species can be produced by

direct emissions and by photochemical oxidation of hydrocarbons. Although each of these mechanisms

may explain new particle formation are site specific, none of them provides a consistent explanation of

particle nucleation under a wide range of environmental conditions.

Advanced field measurements are needed to improve the understanding of atmospheric NPF and growth,

further laboratory based experiments and field measurements needed to monitor the gas phase nucleating


74

vapours including chemical compositions of neutral, ionic clusters and nanoparticles simultaneously. To

witness this level of chemical details, development of more advanced analytical techniques is required.

Further developments in theoretical methods are required to well established and validate the results of

laboratory experiments and ambient measurements. More importantly field and theoretical studies need to

be implemented into regional and global atmospheric models to assess the impacts of aerosols on climate,

weather, air quality, and human health.

On current understanding basis, the aforesaid mechanism behind the formation and growth of aerosols may

be feasible. However, the current state of science is not capable of assessing all of the potential side effects.

Over the next decade, research on global climate change should build the framework necessary for such an

assessment (contribution of effects aerosols to the global atmosphere with respect to green house gases).

We need a full understanding of the temporal and spatial characteristics of the observed global warming

and we need to better quantify how natural and anthropogenic aerosols form and affect the climate system.

A clear assessment of the negative potential effects of aerosols is needed and strategies should be developed

to minimize these deleterious effects.

5.0 References

1. Allen, M. D. and Raabe, O. G., 1985, Slip Correction Measurements of Spherical Solid

Aerosol Particles in an Improved Millikan Apparatus, Aerosol Sci. Technol., 4, 269–286.

2. Boy, M. and Kulmala, M., 2002, Nucleation events in the continental boundary layer:

Influence of physical and meteorological parameters, Atmos. Chem. Phys., 2, 1–16,

doi:10.5194/acp-2-1-2002.

3. Coakley, J., 2005, Reflections on Aerosol Cooling, Nature, 438, 1091–1092.

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https://doi.org/JCPSA6

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http://aip.scitation.org/servlet/linkout?suffix=r24&dbid=32&doi=10.1063%2F1.451486&key=1%3ACAS%3A528%3ADyaL38Xlt1Kqsb0%253D

http://aip.scitation.org/doi/abs/10.1063/1.1312274

http://aip.scitation.org/doi/abs/10.1063/1.1312274


76

A study on Ambient Air Quality and Non-Attainment Cities in North Zone of India

Anchal Garg1*, Tarun Darbari2, S.K. Tyagi2 and N.C. Gupta1

1University School of Environment Management

GGS Indraprastha University, Sec-16C, Dwarka, New Delhi – 110078 2Central Pollution Control Board (CPCB)

Parivesh Bhawan, East Arjun Nagar, New Delhi –110032

(1*Corresponding author: [email protected])

Abstract

India is facing an acute air pollution problem in cities due to economic and industrial development, increase in population,

and exponential growth in registered automobile sector. The overall increase in these activities results in the formation of

non-attainment cities (NAC) i.e., unfit for human health. The cities not complying with any one of the criteria pollutants

monitored consecutively over three years time are considered to be non-attainment cities with respect to ambient air quality

norms. The aim of this paper is to evaluate those cities in north zone of India which are exceeding the National Air Quality

Standards. This paper on NAC with respect to ambient air quality monitoring is mainly focused on the north zone of India

and analyzes the data of PM10, SO2, and NO2 for the year 2011-2013. In this paper, we have found 38 cities to be NAC in

case of PM10, two cities to be NAC in case of NO2 with one city as NAC for SO2. We conclude that many of the ecologically

sensitive areas like Dehradun, Firozabad, and Rishikesh are also found under risk and classified as non-attainment due to

higher concentration of pollutants in their ambient air.

Key words: Ambient air quality, NAC, Vehicular pollution, SO2, NO2, PM10

1. Introduction

The recent report on Global Burden of Disease has ranked air pollution among the top ten killers in the

world, and as the sixth largest killer in South Asia (Murray, C. J., et al. 2013). In India, various studies have

been conducted and suggests that pollution levels vary significantly in different areas with reference to its

location, time, and period of sampling and climatic conditions. Rapid urbanization and industrialization has

adversely affected the ambient air quality and results in increasing the concentration of gaseous and

particulate pollutants (Police et al. 2016). Environment Protection Agency (EPA) has established national

ambient air quality standards (NAAQS) for six criteria pollutants— particulate matter (PM), nitrogen

dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), lead (Pb), and ground-level ozone (O3).

Nitrogen oxides cause respiratory problems, lung irritation, asthma and pneumonia. Higher concentration

of oxides of sulphur causes bronchitis. It also causes acid rain, sulfurous smog and results in the reduction

of atmospheric visibility. Combination of particulate matter with sulphur oxides is more harmful than either

of them separately (Balashanmugam et al.2012). It has been found that PM10 is responsible for respiratory

hazards in human health. Such particulates can also obstruct lung function without reacting chemically, by

depositing in human lungs and interfering with normal functioning.


77

In India, it has been found that SO2 and NO2 concentrations are generally within permissible limits in many

areas but and PM10 and PM2.5 concentrations are exceeding the limits as per Indian air quality standards

(NAAQS, 2009). PM10 has highest exceedance rate among all the pollutants, other than this NO2 and SO2

has the second and third highest exceedance rate respectively, as per the National Ambient Air Quality

Monitoring Program (NAAQMP) (CPCB 2012). Areas of the country that meet or violate air quality

standards are classified as non-attainment areas by EPA. The areas of the country where air pollution levels

persistently exceed the national ambient air quality standards may be designated as "non-attainment. The

cities not complying with any one of the criteria pollutants monitored consecutively over three-year period

are considered non-attainment cities with respect to ambient air quality norms. (CPCB). Policy

interventions and cleaner technologies were found to play a very significant role in controlling the pollution

level and making the area to be attainment city (Garg et. al, 2016).

2. Materials and Methods

Study Area: The predominant geographical features of North India are: (1) The Indo- Gangetic plain,

which spans the states of Punjab, Haryana and Uttar Pradesh. (2) The Himalayas, which lie in the states of

Uttrakhand, Himachal Pradesh and Jammu & Kashmir. North India lies mainly in the North Temperate

Zone. Though cool or cold winters, hot summers and moderate monsoons are the general pattern. It is one

of the most climatically diverse regions on Earth.

Table 1. Details of North Zone of India

NORTH ZONE OF INDIA

POPULATION (2011) 543,937,430

AREA 726,133 km2

STATES Himachal Pradesh, Jammu & Kashmir, Punjab, Uttrakhand, Uttar

Pradesh, Haryana, Delhi

MOST POPULUS CITIES Delhi, Jaipur, Lucknow, Kanpur, Ghaziabad, Ludhiana, Faridabad,

Meerut, Varanasi, Allahabad, Jabalpur, Chandigarh, Gurgaon

Secondary air quality data of SO2, NO2 & PM10 for the year 2011 to 2013 was collected from CPCB for all

the monitoring sites of North Zone of India. From the list of all the cities of north zone, we identified only

those cities which are non-attainment with respect to SO2, NO2 and PM10. Cities have non-attainment status

only when its concentration is more than the standard (NAAQS value) in all the 3 consecutive years of data

observation regarding any one of the criteria pollutant.


78

Figure 1. Increasing vehicular population trends for various states of north-zone

(Source: https://data.gov.in/catalog/stateut-wise-registered-motor-vehicles-1000-population)

3. Results & discussion

3.1. PM10

Annual average NAAQS standards for PM10 is 60 µg/m3. In this study, we found a total of 38 NAC.

In Uttar Pradesh, 16 cities were found under NAC category (fig. 2(c)), among which Allahabad, Bareilly,

Firozabad, Ghaziabad, Kanpur & Lucknow has PM10 concentration more than 180 µg/m3 (fig 2(c)). The

reason for which is continuous increase in vehicular population in Uttar Pradesh from 2002 to 2012 (i.e

4389 thousand to 12424 thousand). In Uttar Pradesh, Ghaziabad (under Delhi NCR) has highest amount of

PM10. The reasons for which is increase in population (9,68,256 in 2001 & 16,36,068 in 2011) and increase

in the number of vehicles supplemented with industrial pollution. It has been found that continuous increase

in number of vehicles along with rapid urbanization has made Delhi as non-attainment city with respect to

particulate matter (fig. 2(b)).In Haryana, only the city Faridabad has found NAC, the reason for which is

relatively less number of vehicles per unit area in Haryana, but still in Faridabad due to rapid urbanization

and industrialization, city comes under NAC category. In Faridabad concentration of PM10 is increasing

more than 180 µg/m3 (fig. 2(b)).In Punjab, major sources of air pollution include industries, vehicular sector

and agricultural burning (CPCB, 2010). Ludhiana, Khanna & Amritsar has PM10 concentration more than

180 µg/m3 (fig. 2(e)).Himachal Pradesh and Uttrakhand are one of the most important geographic regions

of India in terms of agriculture, providing horticulture products and maintaining weather conditions in the

northern part of the country (Mallick et al., 2012). Tourist inflow, vehicular density, roadside dust and

burning of coal and fuel wood on a large scale are attributed to the air pollution in these areas.In Uttrakhand-

Dehradun, Kashipur & Rishikesh are NAC (fig. 2(d)). Since Dehradun and Rishikesh come under

ecologically sensitive areas hence that much high concentration should not be permitted. Tourism is the

largest retail industry in Uttrakhand state (Davies and Cahill, 2000). Trends of tourist arrivals show that in

Uttrakhand both foreign and domestic tourists have gradually increased. Most tourism-related air pollution

comes from automobiles (Andereck, 1993).In Himachal Pradesh total 7 cities are come under NAC

category (figure 2(a)).

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Veh

icu

lar

pop

ula

tion

in

th

ou

san

ds

Himachal Pradesh

Jammu & Kashmir

Punjab

uttrakhand

Uttar Pradesh

Haryana

Delhi


79

Figure 2. PM10 annual average concentration of NAC in various states

In case of Jammu & Kashmir & Uttrakhand the number of vehicles are almost same and showing the same

trend of increasing vehicular population. The temperature of both states is less, which reduce the kinetics

of particulate matter, hence they have relatively low concentration, but these are still come under the

category of NAC because of more population per unit area.

0

20

40

60

80

100

120

140

160

180 HIMACHAL PRADESH

2011

2012

2013

standard

(a)

0

50

100

150

200

250

Delhi Faridabad Jammu

Delhi Haryana Jammu &Kashmir

2011

2012

2013

standard

(b)

0

60

120

180

240

300

360

Co

nce

ntr

atio

n i

n µ

g/m

3

UTTAR PRADESH

2011

2012

2013

standard

(c)

0

60

120

180

240

300

360

Dehradun Kashipur Rishikesh

UTTRAKHAND

2011

2012

2013

standard

(d)

0

60

120

180

240

300 PUNJAB

2011

2012

2013

standard

(e)


80

3.2. NO2

Annual average NAAQS standards for NO2 are 40 µg/m3 for Industrial, Residential, Rural and other Areas

& 30 µg/m3 for ecologically sensitive area. In this study, we found two non-attainment cities (Delhi and

Firozabad) with respect to NO2 in north zone of India. Delhi has significantly high concentration of NO2.

The reason of which may be attributed to vastly growth in manufacturing sector in Delhi. The key

manufacturing sectors in Delhi are (Electrical and Electronics sector, Textiles sector, Leather industries

sector, Metals and Minerals sector, Plant and Machinery sector, Pharmaceutical sector). Also rapid

urbanization in Delhi is the main reason of air pollution in Delhi as well as the increasing trend of vehicle

population i.e. burning of fuel results in increasing NOx concentration in the air. Firozabad of Uttar Pradesh

being an ecological sensitive area comes under NAC category. Although the concentration of NO2 is (in

range of 22-31 µg/m3) in Firozabad but still this concentration is also harmful for the city. Glass

manufacturing become a major hub of manufacturing different Glass based items. All sorts of glass articles,

including jars, candle stands, glasses, flower vases, and electric wares such as decorative lights, bulbs and

every other sort of glass articles are prepared in this city results in increase in pollutant concentration.

Figure 3. NO2 annual average concentration of NAC

3.3. SO2

Annual average NAAQS standards for SO2 are 50 µg/m3 for Industrial, Residential, Rural and other Areas & 20 µg/m3

for the ecologically sensitive areas. In this study, only one city i.e., Dehradun has been found as a non-attainment city

with respect to SO2. Dehradun is an ecologically sensitive area and is distinguished from most other cities in the state

by the existence of very large forests chiefly stocked with Sal. Besides, supplying fuel, fodder, bamboos and medicinal

herbs, they also yield a variety of products like honey, gum, resin, catechu, wax, horns and hides. The forests account

for 1477 sq. kms of area, giving a 43.7% of the total area of the district. Chir is the only coniferous species in the old

reserved forests of Dehradun. Since Dehradun is predominant by forest hence even the concentration of 25µg/m3 is

still harmful for flora and fauna, because when rain drops react with oxides of sulphur, result in the formation of acid

rain and degradation of the forests.

0

10

20

30

40

50

60

70

Delhi Firozabad

Co

nce

ntr

atio

n i

n µ

g/m

3

2011

2012

2013

annual average standard


81

Figure 4. SO2 annual average concentration of Dehradun

Table 2. represents the lit of NAC along with information regarding its population, major source of pollution

and status of air pollutants.

Table 2. NON-ATTAINMENT CITIES (2011-2013)

0

5

10

15

20

25

30

2011 2012 2013

Co

nce

ntr

atio

n in

(µ

g/m

3 )

S.No. State City Population

in 2001

Population

in 2011

Major Sources of

Pollution Status

1 Delhi Delhi 12877470 11007835 Vehicles, industries PM10, NO2

2 Haryana Faridabad 1055938 1404653 Vehicles, industries PM10

3

Himachal

Pradesh

Baddi 22601 29911 Vehicles, industries PM10

Damtal NA 3682 Natural Dust PM10

Kala Amb NA NA Industries, Natural

Dust PM10

Nalagarh 9443 10708 Vehicles, industries PM10

Parwanoo 8609 8758 Vehicles PM10

Paonta Sahib 19090 25183 Industries PM10

Sunder Nagar 23986 23979 Industries PM10

4

Jammu &

Kashmir Jammu 612163 503690 Industries, Natural

Dust PM10

5

Punjab

Amritsar 1003917 2490656 Vehicles, industries PM10

Dera Bassi 15841 26295 Vehicles PM10

Pathankot/Dera

Baba 168485 148357 Vehicles, industries

PM10

Gobindgarh 60677 82266 Vehicles, industries PM10


82

Jalandhar 714077 873725 Industries PM10

Khanna 103099 128130 Vehicles, industries PM10

Ludhiana 1398467 1613878 Vehicles, industries PM10

Naya Nangal 45368 48497 Vehicles, industries PM10

Patiala 323884 1895686 Vehicles, industries PM10

6

Uttar

Pradesh

Agra 1331339 1574542 Vehicles, industries PM10

Allahabad 1042229 1117094 Industries PM10

Anpara 22358 22385 Industries PM10

Bareilly 748353 4448359 Vehicles, industries PM10

Firozabad 432866 603797 Vehicles, Industries,

Natural dust PM10, NO2

Gajraula 39790 55048 Vehicles, industries PM10

Ghaziabad 968256 1636068 Vehicles PM10

Jhansi 460278 507293 Vehicles, industries PM10

Kanpur 2715555 2767031 Vehicles, industries PM10

Khurja 98610 142636 Vehicles, industries PM10

Lucknow 2245509 2815601 Vehicles, industries PM10

Meerut 1161716 1309023 Industries PM10

Moradabad 641583 4772006 Vehicles, industries PM10

Noida 305058 642381 Industries PM10

Varanasi 1203961 1201815 Vehicles, Natural dust PM10

Rae Bareli

169333

3405559

Vehicles, industries

PM10

7

Uttrakhand

Kashipur 92967 521623 Industries PM10

Rishikesh 78805 102138 Industries PM10

Dehradun 530263 578420 Vehicles, Natural dust PM10, SO2

Table 3. Air quality and impact studies in different states

Study State Result

Nautiyal J. et al., 2007 Punjab The population in Gobindgarh (Industrial town)

shows more number of cardiovascular disease as

compared to Morinda (Non-Industrial) area.

This result is attributed to higher levels of PM

levels.

Kumar et al., 2012 Punjab Total annual welfare loss in terms of health

damages due to air pollution caused by burning

of rice straw in rural Punjab amounts to 76

million.

Tyagi S.K. et al., 2016 Uttrakhand and

Himachal Pradesh

The study showed increase in concentration of

air pollutants during peak tourist activity but

local meteorology also plays an important role

in defining the air quality of the region.

Central Bureau of

Health Intelligence,

2012

Uttar Pradesh According to Ministry of Health and Family

Welfare (2009), 1,500,000 patients are

registered for TB treatment in India, of which 2,

77,000 are alone from Uttar Pradesh, while it

was 6,734 in Lucknow city.

Balachandran et al.,

2000 and Kumar et al.,

2001

Delhi Vehicular emissions and industrial activities

were found to be associated with indoor as well

as outdoor air pollution in Delhi

4. Conclusions

In this paper, we have found total 38 cities to be NAC in case of PM10, two cities to be NAC in case of

NO2 & one city as NAC for SO2. In this study, many of the ecologically sensitive areas like Dehradun,

Firozabad, and Rishikesh are also found under risk and classified as non-attainment due to higher

concentration of pollutants. Uttrakhand and Himachal are two important tourist places in India, over

the time the air quality has been affected in these areas due to high influx of tourist transportation.

These areas need special attention for protection as these are source of various medicinal plant species

and rich in biodiversity. Our main emphasis in this paper was to awake policy makers to formulate and

implement certain policies to reduce pollution. Promotion of cleaner technologies to reduce vehicular

pollution and to ensure better fuel quality may be panacea and which will support the transition to

healthier air in Indian cities. The governmental efforts alone are not enough. Participation of the

community is crucial in order to make a great effect in the reduction of pollution.

Acknowledgments The authors would like to thank Central Pollution Control Board (CPCB) for providing all the ambient air quality

data and information. This study was conducted during summer training as an intern (Anchal Garg) at CPCB.

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Diurnal Trend of Urban Ground Level Ozone during Monsoon, Post-Monsoon & Winter Months in Delhi, India

Harveen Kaur1, S. K. Tyagi2 1Doctoral Research Scholar, Department of Resource Management & Design Applications (RMDA),

Lady Irwin College, University of Delhi (Email: [email protected]) 2Scientist, “E”, Central Pollution Control Board, East Arjun Nagar, Delhi-110032 ([email protected])

Abstract

The concern about ozone in the stratosphere is that it is depleting which is known to be “good” ozone; the concern

at ground level is that it is increasing known to be “bad” ozone. In the upper atmosphere, ozone has a beneficial

effect by absorbing the harmful ultraviolet rays of sunlight. At the earth’s surface, ozone is harmful to crops,

forests, building materials and the health of humans and animals. The tropospheric ozone remains an important

phytotoxic air pollutant and is also recognised to be one of the most important greenhouse gases (IPCC, 2001).

Not only a greenhouse gas, it is the precursor of other reactive gases as well. The review of the literature revealed

a dearth of research in this area in India.

Thus, it was considered important to understand various factors associated with ozone as a pollutant and therefore,

the study was conceptualised to understand the variation of ozone concentration in different seasons. It was found

that ozone concentration was correlated to temperature and increased with increase in temperature. Also, it was

revealed that ozone dynamics is sensitive to day-to-day, season-to-season and night- to- night changes in weather

patterns. The study was carried out in Delhi as it’s amongst one of the biggest metropolitan city which emits a

huge amount of greenhouse gases in the environment. Central Road Research Institute (CRRI), Delhi was selected

for the monitoring of ozone concentration. The readings of Ozone concentration were taken for five months

namely July, August, September, October and November during the year 2010-11. The time framework for

recording the same was 1100 hours 1600 hours. The concentration values recorded over the monitoring duration

during each day in a month were then averaged to find out the diurnal concentration of ozone. The average range

of concentration was found to be between 19.68 ppb to 65.36 ppb. The study shows that the concentration of

ozone was found the maximum in July and August followed by November and October. The concentration was

found to be least in the month of September. It is also seen that the ozone concentration was found to be maximum

during afternoon and minimum in evening. Analysis on diurnal scale revealed that the timing and magnitude of

the peak in ozone maximum value were found to vary according to the season. The average ranges recorded

during various months were also compared with national ambient air quality standards notified by Central

Pollution Control Board which is 91.84 ppb.

Keywords: Tropospheric ground level ozone, urban pollution, diurnal trend

1.0 Introduction Tropospheric ozone pollution is a global environmental issue. Many researches report about this issue

from locations as diverse as India, Germany, Taiwan Hong Kong, South Korea, Spain, Greece, Canada,

and the United States. Ozone is a serious pollutant in the troposphere owing to its hostile effects on the

well-being of plants, and on the respiratory systems, eyes, and mucous membranes of humans (Unger,

2005). Tropospheric ozone is a key constituent of an urban smog, which is also known as ozone

pollution, is produced by a complex series of chemical reactions involving automotive and industrial

emissions of volatile organic compounds (VOCs, mainly hydrocarbons), nitrogen oxides (NOx) from


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the same sources, and sunlight. As temperatures increase during the day, solar energy from the sun

enhances those chemical reactions and increases the amount of ozone produced.

Towns and cities that have more traffic or more industrial plants have a higher potential for ozone

formation, especially towns that also experience many warm sunny days with little wind. Several

meteorological variables such as cloud cover, ambient temperature, and relative humidity (or dew point

temperature) can disturb tropospheric ozone production (National Research Council 1991). However,

cloud cover is negatively correlated with high ozone levels (Wang et al. 2003), because ozone is

photochemically produced, and cloud cover reduces solar radiation (Chudzyński et al. 2001, Dani and

Devara 2002). Temperature is positively correlated (Vukovich and Sherwell 2003, Wang et al. 2003),

and it has been observed that most atmospheric chemical reaction rates increase with temperature

(McElroy 2002). Relative humidity inclines to be positively correlated with high ozone because it leads

to an increase in radicals, which help initiate chain reaction mechanisms, and also tends to increase

precursor BVOC emissions (Sillman 1999).

A comprehensive emission inventory for megacity of Delhi, India, for the period 1990–2000 was

developed in support of air quality, atmospheric chemistry and climate studies by Gurjar (2004) which

revealed that SO2 and total suspended particles (TSP) are largely emitted by thermal power plants (68%

and 80%, respectively), while the transport sector contributes most to NOx, CO and non-methane

volatile organic compound (NMVOC) emissions (80%). The relatively strong growth of NOx

emissions indicated that photochemical O3 formation in the regional environment is increasing

substantially, in particular in the dry season. During the summer, on the other hand, convective mixing

of air pollutants reduces regional but increase large-scale, i.e. hemispheric effects.

Beig et al, 2007 carried out simultaneous observations of surface ozone (O3) with its precursors namely,

carbon monoxide (CO) and oxides of nitrogen (NOx) on a diurnal scale from a tropical semi-urban site,

Pune. The peak in the amplitude of ozone was found during noontime whereas CO and NOX , the peak

was observed in the morning hours between 0800 and 0900 hr. The concentrations of these pollutants

were observed to drop down considerably during south-west monsoon months and the diurnal pattern

also became very weak. The diurnal trend of these gases was found to be different for different seasons.

Analysis on diurnal scale revealed that the timing and magnitude of the peak in ozone maximum are

found to vary according to the season.

Another study was done by Shukla et al, 2010 on the impact of the vehicular exhaust on ambient air

quality of Rohtak city in Haryana state. Air quality was measured by High Volume Sampler after

selecting Sulphur dioxide (SO2), Nitrogen dioxide NO2), Ozone (O3) and Suspended particulate matters

(SPM) parameters to judge the quality of air. Ozone concentration was found below the national

ambient air quality standards (NAAQS) 2009 . The concentrations of ozone were observed maximum

in summer in comparison to winter and monsoon season. Ghude, Sachin. D. et al (2008) in a study

based on Ozone in ambient air at a tropical megacity, Delhi compared seven-year data (1997-2004) of

hourly surface ozone concentration and analysed diurnal cycle, trends, excess of ozone levels above a

threshold value and cumulative ozone exposure indices. The study revealed a sharp increase in the

ozone levels during forenoon and a sharp decrease in the early afternoon. Also, relatively high levels

of ozone were observed during summer whereas low ozone levels were noticed during monsoon.

A study by Lippman (1989), Tilton (1989), McElroy (2002), Parmet et al. (2003,) states that Ozone

concentration in the lower troposphere are an imperative concern, as it may extremely harm biological

organisms, including humans and plants. In humans, ozone primarily cause damage to the respiratory


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system, eyes, and mucous membranes of the nose and throat. Parmet et al. (2003) also highlighted

similar concerns and stated that ozone can be the basis for irritation to the eyes, nose, and throat and

may result in difficulty in taking deep breaths, and leads to augmented necessities for asthma

medications. Exposure to ozone decreases lung functioning in both healthy and compromised subjects,

such as asthmatics or heart patients (Folinsbee et al. 1994, Brauer and Brook 1997). Mikkelson and

Heide-Jørgensen (1996) and Chappelka et al. (2003), Davison et al. emphasised destructive effects of

ozone in plants like leaf mottling, accelerated leaf senescence and leaf necrosis.

2.0 Methodology

The study was conducted with major objectives (1) to measure ozone concentration during different

seasons, (2) to find out diurnal variations & (3) to compare ozone concentrations in different months &

with selected meteorological parameters in the metropolitan city of Delhi, the capital of India.

2.1 About the Study Area

The study was carried out in Delhi as it’s amongst one of the biggest metropolitan city which emits

a huge amount of greenhouse gases in the environment. According to the World Health

Organization (WHO), New Delhi is one of the top ten most polluted cities in the world. It has been

observed that over the past few decades due to vehicular and industrial emissions air pollution has

reached alarmingly high levels in Delhi. The entrance gate of the Central Road Research Institute

(CRRI) was chosen as monitoring site which is located on National Highway No.2 (Delhi-

Mathura/Agra Road). This is a south-east outer zone of Delhi represented by heterogeneous traffic.

During heavy traffic hours in the morning and early evening, vehicular speed varies between 35-

50 Km/h. Moreover, there is one traffic light at the intersection of this highway which makes the

traffic idle after every 10 minutes and the idle traffic leads to high pollution levels at this particular

point. Other polluting sources near to highway & the monitoring location are emissions produced

from Okhla sewage treatment plant, few petrol pumps, construction site besides heavy traffic jams,

some of which are the precursors for ozone formation.

2.2 The monitoring of Ozone

The Ozone concentration monitoring was conducted for five months namely July, August,

September, October and November in 2010 using Ozone monitor with a measuring range from

lower limit of detection of 1.5 parts-per-million by volume (ppbv) to an upper limit of 100 parts-

per-million based on the well-established technique of absorption of ultraviolet light at 254 nm.The

monitoring was usually conducted during 1100 hours 1700 hours. Various readings recorded on

various days during each month were averaged to get monthly ozone concentration value.

Likewise, average readings of ozone concentration for five months were analysed and the results

were interpreted. Apart from ozone, the temperature for all months was averaged to plot the graphs

along with the wind speed and rainfall.


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Image 1: Ozone Monitor

3.0 Results and Discussions

3.1 Diurnal variation of ozone concentration

The hourly diurnal variation of ozone at the entrance gate of CRRI building was monitored for

consecutive five months i.e., from July to November 2010 as shown below. The data was monitored on

these particular dates during the year 2010; 2nd & 19th July; 9th & 11th Aug; 2nd & 3rd Sept; 26th, 28th &

29th Oct and 10th & 11th Nov, 2010. The values for a particular hour were averaged for a month to get

the diurnal variation in a particular month.

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Fig 1: Average diurnal variation in ozone concentration at CRRI main gate site, July 2010

The variation of average ozone concentration with time in minutes for the month of July is shown in

Fig 1. The average temperature during the monitoring period was observed to be 34°C. It was observed

that the ozone level was at a peak during afternoon time i.e. between 1319 hours and 1434 hours with

the ozone concentration as 72.3 ppb and 70.4 ppb respectively. During this time period, a sudden dip

in the ozone concentration has been also observed. After this time period, the ozone concentration

gradually decreased and was monitored to be 64.9 ppb at 1630 hours. On the average, the diurnal

variation of ozone concentration for the month of July is 65.15 ppb , which is below the Central

Pollution Control Board standard of 91.84 ppb.

Fig 2: Average diurnal variation in ozone concentration at CRRI main gate site, Aug 2010

Fig 2 shows the average variation of ozone concentration with time for the month of August at the

CRRI main gate site. During this month, ozone concentration was found to be increasing during

morning time and the maximum concentration was noted as 60.5 ppb and 59.5 ppb at 1118 hours and

1135 hours respectively. Ozone concentration values were observed to be gradually decreasing after

1400 hours and were found to be below the monthly average concentration of 39.94 ppb. The monitored

ozone concentration values for the month of August were found to be below the Central Pollution

Control Board standard of 91.84 ppb.

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Fig 3: Average diurnal variation in ozone concentration at CRRI main gate site, Sept 2010

During the month of September, the monitored ozone concentration values were found to be showing

varied patterns as seen in Figure 3. The ozone concentration was observed to gradually increase during

morning time i.e. 1130 hours onwards and then decrease after 1344 hours. The ozone concentration

once again rose after 1500 hours. The range of the monitored values of ozone concentration during this

month was found to vary between 12.1 ppb and 23.0 ppb. The maximum concentration noted was

however 23.0 ppb at 1544 hours. The average monitored ozone concentration was observed to be 19.53

ppb and all the monitored values during this month were below the Central Pollution Control Board’s

standard of 91.84 ppb.

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Fig 4: Average diurnal variation in ozone concentration at CRRI main gate site, Oct 2010

Figure 4 shows the variation of average ozone concentration with time for the October month. During

this month, the average concentration of ozone was noted as 39.73 ppb which was higher than the

average concentration of ozone during the month of September. Peak values were found to be between

the time period range of 1300 hours to 1500 hours and most of the readings monitored were found to

be below the average value. The average ozone concentration value for the month of October was below

the Central Pollution Control Board’s national standard limit of 91.84 ppb.

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Fig 5: Average diurnal variation in ozone concentration at CRRI main gate site, Nov 2010

Fig 5 shows the variation of mean ozone concentration with time for the November month. During this

month, ozone concentration remained low during morning time, which had been the case in the previous

four months as well. The ozone concentration increased during the afternoon period and remained

above average from 1200 hours to 1345 hours. The peak ozone concentration value for this month was

noted at 70 ppb approx at 1500 hours. The ozone concentration began to decrease after 1600 hours and

was lowered to 20.3 ppb from 70 ppb in a time difference of about one hour.

3.2 Comparison of diurnal ozone concentration during various months

The following Figure shows the average ozone concentrations for the five months namely July, August,

September, October and November during the year 2010.

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Fig 6: Monthly comparison of diurnal ozone concentration during the study period

Fig 6 shows that during the study period ozone concentration at CRRI main gate is highest in the month

of July while it is lowest in the month of September. The trend in ozone concentration values for the

period of August, October and November lies between these two months. Ozone concentration in the

months of October and November show an almost similar trend. In the month of August, the monitored

ozone concentration shows a visible decline in the third half of the day.

3.3 Hourly Variations of Ozone Concentration during various months (Year 2010)

Table 1: Hourly Variations of Ozone Concentration

S.No. Months

Ozone concentration (Year 2010)

Average 11.00-

12.00 hr

12.00-

13.00 hr

13.00-

14.00 hr

14.00-

15.00 hr

15.00-

16.00 hr

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1. July 51.81 65.87 68.65 71.06 67.63 67.19 65.36833

2. Aug 45.45 45.54 46.44 34.21 33.2 35.66 40.08333

3. Sept 17.97 21.88 19.11 18.26 21.65 19.26 19.68833

4. Oct 33.64 40.49 39.66 46.65 37.8 38.73 39.495

5. Nov 26.35 35.5 44.75 34.82 34.25 20.45 32.68667

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Fig 7: Hourly Variations of Ozone Concentration for various months (year 2010)

Fig 7 shows that hourly concentration was found to be high during July month at time intervals of 1300-

1400 and 1400-1500 which are below 1 hourly standard limits of Central Pollution Control Board i.e.

91.84. The ozone concentration variation on hourly basis was found to be least during September

month at time intervals of 1100-1200 and 1400-1500.

3.4 Comparative monthly ozone concentration with meteorological parameters

The figure 8 shows the monthly variation of average ozone concentration with respect to temperature

and wind speed.

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HOURLY VARIATIONS OF OZONE CONCENTERATION FOR VARIOUS MONTHS (YEAR 2010)

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Fig 8: Monthly comparison of ozone concentration with respect to meteorological parameters

The Fig 8 shows that during the month of July and August, a high temperature of 34°C and 32°C

respectively corresponds to a comparatively higher average ozone concentration of 65.36 ppb and 40.08

ppb respectively. In the month of September, the average wind speed is highest among all months for

which the study has been carried out, and consequently, low average ozone concentration is observed

i.e. 19.68 ppb. We have already noted in Fig 6 that the average ozone concentration variation for the

month of October and November was 39.6 ppb and 32.6 ppb as expected. The mean temperature in the

month of October is greater than that in the month of November.

The study shows that the concentration of ozone was found the maximum in July and August followed

by November and October. The concentration was found to be least in the month of September. It is

also seen that the ozone concentration was found to be maximum during afternoon and minimum in

evening. Also, if we see there is not much difference in the mean temperature and concentration for

July and August months. The reasons for high mean ozone concentrations is an increase in air

temperature as well as reduced cloudiness and precipitation due to climate change which promoted high

ozone concentrations. The maximum ozone concentration during July month is due to higher

temperature which leads to the conversion of ozone precursors like VOCs and NOx etc to the formation

of ozone in the presence of sunlight.

The reason for least concentration in the month of September could be probable actions taken to

improve ambient air quality during CWG 2010 (3 Oct 2010 – 14 Oct, 2010) by the Delhi Govt.

Moreover, it is a monsoon month so pollutants do not disperse considerably in the atmosphere. Other

reasons include the absence of blue-line buses from the roads to reduce pollution levels as well as traffic

loads in the city. Apart from this, green cover of the city was improved and a lot of plantation was done

on the roadside areas like along the footpaths, dividers and walkways etc. Actions were not only taken

to minimise public traffic load but as well as for the private communicators, as specific timelines were

allotted which were area and time specific. Also, some routes were also closed and traffic was diverted.

July Aug Sept Oct Nov

Mean Conc (ppb) 65.36 40.08 19.68 39.64 32.68

Mean temp (°C) 34 32 28 22 18

Mean Wind speed (km/h) 9.01 13.4 13.13 9.58 11.7

Rainfall (mm) 272.5 238.6 248.4 2.6 14.8

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During this time, most of the Delhi population was at home as offices, schools and colleges were closed

which also resulted in less emission in the city.

Ozone concentration was also found comparatively high in the month of November i.e. 33.83 with a

mean temperature of 18°C and wind speed rate at 11.7 km/h. This is the only month in which mean

ozone concentrations were found to be greater inspite of low mean temperature. The reason for this is

the increase in built up of exhaust emissions from automobile, CO, VOCs and other incomplete

combustion products like hydrocarbons that were released along with NOx during this month at the

CRRI building site because of frequent traffic jams. As mentioned earlier, both NO2 (product of NOx)

and organic compounds resulting from incomplete combustion are key to the formation of ozone.

3.5 Correlation & Regression Equation for VOC & O3

The R value representing the simple correlation between VOC & Ozone was calculated as 0.646 which

indicates a low degree of correlation. The R2 value indicates how much of the total variation in the

dependent variable, Ozone can be explained by the independent variable, VOC. The ANOVA indicates

the statistical significance of the regression model. Here, p < 0.0005, which is less than 0.05, and

indicates that overall the regression model statistically significantly predicts the outcome variable (i.e.,

it is a good fit for the data). From the values of coefficients, regression equation comes out to as follows:

Ozone= -32.606 – 0.562 (VOC)

4.0 CONCLUSION

The average range of concentration of ground-level ozone was found to be between 19.68 ppb to 65.36

ppb during the non-peak summer season that is in the monsoon, post-monsoon & winter months which

are below the national standard limit of 91.84 ppb but the peak concentrations are approaching the same

in the month of July. As Ozone is an extremely harmful pollutant and worsens the symptoms of several

health related diseases like asthma; it damages lung tissues and also leads to lung function impairment

and other heart-related diseases. Common symptoms include coughing, headache and chest pain etc.

So, even a few hours of exposure to it may trigger serious health and environmental hazards.

International and national studies state that people with pre-existing diseases are more prone to deaths

due to ozone exposure, the precautions are required to further reduce the O3 concentration in the urban

area. So, urgent steps are required to control the pollutants that help in ozone formation in the

atmosphere.

5.0 Recommendations and Suggestions

The government should come with ozone standards and should take an initiative to aware the general

public about its harmful effects from the real-time data it generates on daily basis. It should also

implement control strategies for ozone-forming pollutants. Most of these precursor gases responsible

for ozone formation comes from vehicles e.g. NOx and other Volatile Organic Compounds (VOCs).


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Moreover, in India, advanced cleaner fuel & vehicular emission control technologies are needed to be

made available.

6.0 ACKNOWLEDGEMENTS

I want to thank my thesis Supervisor Dr Renuka Gupta, Head of the Department of Biology and

Associate Professor, Lady Irwin College, the University of Delhi. Thanks are due to, Dr Anuradha

Shutla and Dr Rina Singh at Central Road Research Institute, Delhi, India for their willingness to

provide many of the resources and instruments needed in the course of this study. Special thanks to Dr

Govind Singh and Dr Amulya Chevuturi for their assistance in data analysis.

7.0 REFERENCES

1. Brauer, M. and Brook, J. (1997). Ozone personal exposures and health effects for selected groups

residing in the Fraser Valley. Atmospheric Environment 31(14) 2113-2121. DOI: 10.1016/S1352-

2310(96)00129-X

2. Beig, G. & Gunthe, S. & Jadhav, D.B. (2007). Simultaneous measurements of ozone and its

precursors on a diurnal scale at a semi-urban site in India. Journal of Atmospheric Chemistry,

Volume 57, Issue 3, pp 239–253. DOI:10.1007/s10874-007-9068-8

3. Chappelka, A., Neufeld, H. and Davison, A., Somers, G. and Renfro, J. (2003). Ozone injury on

cutleaf coneflower (Rudbeckia laciniata) and crown-beard (Verbesina occidentalis) in Great Smoky

Mountains National Park. Environmental Pollution 125(1) 53-9.

4. Chudzyński, S, Czyzewski, A. et al. (2001). Observation of ozone concentration during the solar

eclipse. Atmospheric Research 57 (2001): 43-49.

5. Folinsby, L., Hortman, D. and Kehrl, H., et al. (1994). Respiratory responses to repeated prolonged

exposure to 0.12 ppm ozone. American Journal of Respiratory and Critical Care Management

149(1):98-105. DOI: 10.1164/ajrccm.149.1.8111607

6. Ghude, S.., Jain, S.., Arya, B., et al. (2008). Ozone in ambient air at a tropical megacity, Delhi:

characteristics, trends and cumulative ozone exposure indices. Journal of Atmospheric

Chemistry 60 (3) 237-252. DOI: 10.1007/s10874-009-9119-4

7. Gurjara, B.R., et., al. (2004). Emission estimates and trends (1990–2000) for megacity Delhi and

implications. Atmospheric Environment 38 (2004) 5663–5681

8. Lippman, M. (1989). Health effects of ozone: a critical review. Journal of the Air Pollution Control

Association 39 (5) 672-695. DOI 10.1080/08940630.1989.10466554

9. McElroy, M. (2002). The Atmospheric Environment: Effects of Human Activity. Princeton

University Press 175-187, 231-262. ISBN: 9780691006918

10. Mikkelson, T., and Heide-Jørgensen, H. (1996). Acceleration of leaf senescence in Fagus sylvatica

L. by low levels of tropospheric ozone demonstrated by leaf colour, chlorophyll fluorescence and

chloroplast ultrastructure.” Trees. 10 (3): 145-156. DOI:10.1007/BF02340766

11. National Research Council (1991). Rethinking the Ozone Problem in Urban and Regional Air

Pollution. National Academy Press, Washington.

12. Parmet, S., Lynm, C., and Glass, R. (2003). Health effects of ozone. Journal of the American

Medical Association 290 (14) 1944.

13. Sillman, S. (1999).The relation between ozone, NOX, and hydrocarbons in urban and polluted rural

environments. Atmospheric Environment 33: 1821-1845.


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14. Shukla, V., Dalal, P. and Chaudhry, D. (2010). Impact of vehicular exhaust on ambient air quality

of Rohtak city, India. Journal of Environmental Biology 31(6):929-32.

15. Tilton, B. (1989). Health effects of tropospheric ozone. Environmental Science and Technology 23

(3) 257-263. DOI: 10.1021/es00180a002

16. Unger, E.E. The Relationship Between High Ozone Days and Atmospheric Patterns in Atlanta,

Georgia. Masters thesis, Georgia State University, 2005.

17. Wang, X., Lu, W. , Wang, W. and Leung, A. (2003). A study of ozone variation trend within area

of affecting human health in Hong Kong. Chemosphere 52 (9) 1405-10


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Proceedings of Training Workshop on Volatile Organic Compounds (VOC) and Hydrocarbon (HC): Monitoring and Management

Organised by:

ONGC in Association with CPCB and IAAPC (DC)

March 2-3, 2016

The training workshop was inaugurated by Sh. Hem Pande, Special Secretary, MoEF&CC and attended

by Sh. M.C. Das, ED (HSE- ONGC), Dr. R.K. Garg, Ex. Chairman, EAC, Sh. P.C. Tyagi, Ex.

Chairman, CPCB and Chairman, Accreditation Committee of NABET/QCI, Prof. J.M. Dave, Ex. Dean,

JNU, Prof. C.K. Varshney, Emeritus Prof. JNU, Dr. A.L. Agarwal, Ex. Deputy Director, NEERI, Dr.

Anjali Srivastava, Ex Deputy Director, NEERI, Dr. J.S. Sharma, GM (HSE- ONGC), Dr. B. Sengupta,

Ex-Member Secretary, CPCB and President, IAAPC(DC) besides other senior officials of CPCB,

IAAPC(DC) and ONGC. About 80 participants (35 accredited EIA consultants, 35 ONGC officials &

10 scientists / engineers from CPCB / MoEF&CC etc.) attended the Training Workshop. EIA

consultants having accreditation from NABET/QCI on chemical industry, oil drilling and oil refinery

sector participated in the Training Workshop.

In his welcome address Dr. J.S. Sharma highlighted the requirement of VOC monitoring in ambient

as well as stack emission. He also explained the process of data generation by ONGC. In the

inaugural speech, Sh. Hem Pande, Special Secretary, MoEF&CC emphasised the need of correct

sampling and analysis method for VOC / NMHC especially for baseline data generation for EIA

study. He explained that due to unreliable VOC data in EIA Reports, the decision making process

for environmental clearance under EIA Notification 2006 is affected. He explained the EIA appraisal

process and role of QCI accredited consultants for preparing EIA reports. Sh. Hem Pande,

congratulated ONGC, CPCB & IAAPC (DC) for taking the lead and organising two days training

workshop on VOC / Hydrocarbon Monitoring and Management. Dr. B. Sengupta in his opening

remark mentioned that this training programme has been organized as advised by EAC of

MoEF&CC to ONGC and MoEF&CC has also suggested that this type of training programmes to

be organised in other parts of the country so that all accredited EIA consultants are trained on VOC

monitoring.

During training workshop half day hands-on training was also organised at CPCB Lab at East Arjun

Nagar, Delhi where the participants got chance to see the latest methods of VOC / Hydrocarbon

sampling and analysis. The practical demonstration of advanced instrumentations like GCMS,

GCATD,Sorbent tube followed by gas chromatography (GC) separation, Non-dispersive infrared

(NDIR) detection, Differential optical absorption spectrometry (DOAS), Mass Spectroscopy, Flame

Ionization Detector (FID) & Photo Ionisation Detector (PID) were given to participants by CPCB

Scientists.

During the training workshop eminent scientists / engineers have given detailed presentation on

following topics:-


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S. No. Topic Speaker

1. Emission of VOCs /HC from different sources and

National and International Standard

Dr. B Sengupta,

Former MS, CPCB

2. Measurement of HC, NMHC in stack and ambient

air

Dr. Anjali Srivastava, Director grade

Scientist, NEERI

3. Measurement of VOC & BTX and calibration of

equipment and analysers

Dr. S K Tyagi,

Addl. Director, CPCB

4. Measurement techniques for VOC / NMHC Dr. Rens Zijlmans, MD- Synspec,

Netherlands

5. Baseline VOC/ HC data generation for EIA studies Dr. A. L. Agarwal, Former Director

Grade Scientist, NEERI

6. VOC/HC emission control from Oil Refineries Shri S C Tandon, Ex-IOC/ MRPL

7. Health effect of VOCs/HC Dr. T. K. Joshi, Emeritus Prof Maulana

Azad Medical College, Delhi

8. VOC/HC Control from Industries Prof. Mukesh Sharma, IIT Kanpur

9. LDAR Management from oil Industry- Case

studies

Dr Rajendra Prasad, MD, Ecotech

Instruments

10. Specific VOC (Benzene, Toluene, Methylene

chloride, Formaldehyde etc.) control from

industries-Case Studies

Sh.

DVS Narayana Raju, Director

Deccan Fine Chemicals

11. Plants- a source of VOC Emission Prof. C.K. Varshney, Emeritus

Professor, JNU

12. Air Quality monitoring with special reference to

HAP/ VOC Monitoring

Dr. Abhijit Pathak- Sr Scientist, CPCB

Techniques for monitoring individual VOCs as discussed during the training workshop

1. Sorbent tube followed by gas chromatography (GC) separation

Solid adsorbents are versatile media for collecting hundreds of types of VOCs. They work by

collecting the VOC on the surface of the media, which is usually contained within a tube. Prior to


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analysis, the sampled VOCs are removed by either thermal desorption or by extrusion using a

suitable solvent.

2. Non-dispersive infrared (NDIR) detection

All VOCs absorb electromagnetic radiation, while different compounds absorb energy at different

frequencies. This means that VOCs have an electromagnetic finger-print which is known as a

spectrum. This feature can be exploited for measurements by targeting a peak or peaks in a

compound's spectrum.

3. Fourier transform infrared (FTIR)

FTIR uses the same basic principle as simple infra red (IR) analysers, but resolves interfering

spectra by splitting the beam into two. One beam is then bounced off a fixed mirror while the

other is bounced off a moving mirror. This causes the beams to be slightly out of phase. The

beams are then directed by mirrors to collide, and the resulting new spectrum creates both

constructive and destructive interference in such a way that software can carry out a Fourier

transform calculation to identify distinct compounds. All VOCs absorb IR radiation and most can

be detected by FTIR.

4. Differential optical absorption spectrometry (DOAS)

Most DOAS instruments use either UV or IR absorption to distinguish between different species.

The technique can measure a selected handful of VOCs, such as benzene, toluene, ethyl benzene,

xylene and formaldehyde

5. Mass Spectroscopy

In electron impact Mass Spectrometry (MS), organic molecules are bombarded with electrons and

converted to energetic, positively charged ions, which can break up into smaller ions. The

charged ions are deflected by a series of either electric or magnetic fields to allow the selection of

specific mass to charge species. Data is recorded in terms of either a full mass spectrum or by

selected ion recording techniques.

6. Flame Ionization Detector (FID)

FIDs do not differentiate between different compounds since they respond to carbon-hydrogen

bonds, rather than specific compounds. FIDs measure Total Organic Carbon (TOC), including


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methane. If methane is present in the air sample, the measurement will be for both VOCs and

methane.

The main strength of the FID is that it is a useful instrument for measuring total hydro carbon in a

gas stream. As a general rule, the response of a FID is mostly influenced by the number of carbon

atoms in a sample. Furthermore, FIDs only respond to gaseous or vapour phase molecules which

contain carbon-hydrogen bonds.

If the stack gas stream is relatively hot and wet, or if the VOCs are concentrated, then there is a

high probability of condensation in the sampling probe / column when the gas sample touches a

surface cooler than that the stack temperature. A FID for stack monitoring should, therefore, have

some system for preventing condensation of either moisture or VOCs in the sample line (i.e. the

probe / column be equipped with a heated-line, heated detector and a heated by-pass).

Where continuous or periodic sampling is carried out with an analyser close to the duct and the

sampled gases are above ambient temperature, then the line (and its filter) carrying the sample to

the analyser must be heated to prevent condensation and reduce adsorption losses. The lines within

an analyser also need to be heated, while all gas chromatographs must have heated injection ports,

ovens to heat the column and detectors in heated housings.

7. Photo Ionisation Detection (PID)

These work on a similar principle to FIDs, in that the sample gas is ionised. The difference is that

the source of ionisation is an intense UV light and not a flame, so there is no need for support gases.

They are not as suitable as FIDs for total carbon counting, especially from combustion processes.

The other major differences between FIDs and PIDs are the response factors are much more

variable than in FIDs and they have much weaker responses for the small saturated hydrocarbons.

They are not used as CEMs because of the problems caused by the high variability of response

factors and difficulties with sample conditioning.

During panel discussion, which was chaired by Sh. P.C. Tyagi, Ex. Chairmen of CPCB and panel

members were Dr. R.K. Garg, Prof. J.M. Dave, Dr. A.L. Agarwal, Dr. Anjali Srivastava, Dr. C.K.

Varshney & Dr. B. Sengupta, the recommendations of the training workshop were formulated

considering views given by panellists and also based upon discussion among participants.


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Recommendations:-

Based on technical presentations, discussion at CPCB lab, panel discussions, discussion among

participants, following recommendations were prepared by the Panel of Experts during the

concluding session of the Training Workshop:-

1. There is an urgent need to prepare simple and cost effective sampling and analysis technique /

protocol for measuring the volatile carbon compounds, so that MOEF approved laboratories &

EIA Consultants can generate reliable and factual baseline data. The measurement protocol is

to be clarified for Total HC, NMHC and VOC separately, so that Lab / EIA Consultant / EAC-

MOEF / SEAC-State Govt. can choose the exact measurement requirement for specific EIA

Study.

2. Feasibility of using Passive Sampling methodology for TOC/ NMHC/ VOC in EIA Study like

Diffusion Tubes exposed to air environment for a particular period needs to be clarified.

3. As various solvents used by chemical industries (Pharma, pesticides, agro-chemical etc.) are

responsible for VOC emission, it is recommended that solvent balance should be made part of

consent management and it should be regulated by SPCBs while granting CTO to industries.

4. Fugitive emission standards specially LDAR (leak detection and repair) as notified under EP

Act to be reviewed. Also proper protocol for LDAR measurement to be developed.

5. Industry specific VOC standard or atleast Total HC standards for chemical industry should be

developed and notified under EP Act for implementation by SPCBs.

6. Evaporative emission control standards for vehicles and petrol filling stations to be evolved on

priority. This will reduce VOC / Benzene level in urban air and improve air quality.

7. List of HAPs (hazardous air pollutants), specific to Indian Industry to be identified by CPCB.

8. Ambient air quality standards for VOC / Total HC / NMHC may be developed by CPCB /

MoEF for which it is recommended to constitute an expert group. The TOR of expert group

should include the following:-

1. Identification of HC / VOCs / HAPs based on solvents used in Indian Industries.

2. Standardization of sampling and analysis methodologies for these HC/ VOCs / HAPs.

3. Health effects of such HC / VOCs / HAPs based on available knowledge in this area.

4. Review of various solvent recovery techniques and best management practices for

VOC emission control followed in industry (India and abroad).

5. Based on above, recommendations on following are to be made by expert group:-

1. Ambient standards for Total HC/ NMHC / VOCs / HAPs.

2. Best cost effective and simple sampling and analysis techniques in Indian

conditions to be followed.

3. Source specific emission standards for various types of chemical and pharma industries.


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9. The composition of expert group as suggested by participants are as follows:-

1. Dr. R.K. Garg, Ex. Chairman, EAC, MoEF&CC Chairman

2. Dr. A.B. Akolkar, Member Secretary, CPCB Member

3. Dr. B. Sengupta, Ex. Member Secretary, CPCB Member

4. Dr. A.L. Agarwal, Representative of IAAPC(DC) Member

5. Dr. Anjali Srivastava, Ex. Addl. Director, NEERI Member

6. Representative of MoEF&CC Member

7. Dr. J.S. Sharma, General Manager (Env.) ONGC Member

8. MS, Gujarat SPCB or his representative Member

9. MS, Telangana SPCB or his representative Member

10. Representative of HSE, IOC Member

11. Representative of Pharma / Agro-chemical industry Member

12. Additional Director of Air Toxic Lab, CPCB Member Convener


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Recommendations of the Workshop on Requirement, Practices, Gaps and Challenges in Air Quality Study

for Preparation of EIA Report

Organised by IAAPC (DC) on August 27, 2016

The workshop was organised by IAAPC (DC) and more than 100 QCI accredited consultants and experts

working in the field of air quality monitoring attended. The following experts have given technical

presentations in this workshop.

1. Dr. B. Sengupta, Former Member Secretary, CPCB

2. Prof. A.L. Aggarwal, Former Director Grade Scientist, NEERI

3. Dr. S.D. Attri, Deputy Director General, IMD, New Delhi

4. Dr. G.V. Subrahmanyam, Member EAC-I, MoEF & Former Advisor, MoEF

5. Dr. D. Saha, Additional Director, CPCB

6. Dr. J.S. Sharma, General Manager (Environment), ONGC

7. Dr. J.K. Moitra, MD, EMTRC

8. Dr. Mohit Roy, Independent Expert on Air Pollution

9. Dr. Rajendra Prasad, MD, Ecotech

10. Dr. Bhasker, Representative of EIA Consulting Group

11. Mr. Rajesh Kanungo, Representative of EIA Consulting Group

12. Mr. Sameer Kadam, Representative of EIA Consulting Group

Dr. C.K. Varshney, Emeritus Professor, JNU; Dr. G.V. Subrahmanyam, Former Advisor, MoEF; Dr.

Nalini Bhat, Former Advisor, MoEF; Dr. S.D. Attri, DDG, IMD & Dr. P.B. Rastogi, Former Advisor,

MoEF chaired and co-chaired various technical sessions.


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Based on deliberation the following recommendations are made which was decided to be sent to MoEF / CPCB for consideration:-

1. Only industry specific pollutants (Not all 12 parameters given in NAAQS) to be monitored for generation of baseline

data for EIA study. For example:- for Thermal power plants only PM10, PM2.5, SO2, NOx and mercury shall be

monitored in ambient air and not benzene, PAH, Ozone etc. MoEF may be requested to issue necessary guidelines

for all 36 categories of industries required EC under EIA 2006.

2. Number of monitoring stations to be setup for baseline data generation should be indicated in TOR. Also the basis

of identifying the locations of monitoring stations should be based on modelling studies and consideration of

sensitive receptors close the project site. CPCB may be requested to issue guidelines for the same. The

proponent/consultant can source the meteorological data from nearby IMD meteorological station (or some other

weather monitoring station). This data can be used to produce one-month wind rose. Based on the site and period

specific wind rose 70-80 % time of wind direction(s) can be easily projected. Then covering about 70-80% of time

the U/W or D/W direction can be projected.

3. It should be clearly stated in TOR that simultaneous monitoring in all the monitoring locations are necessary for

baseline data generation. MoEF may be requested to advise Centre/ State expert appraisal committees that TOR

should clearly specify that simultaneous (synoptic) measurement of air quality parameters is necessary (i.e.

measurement at all sites to be carried simultaneously) for generating baseline air quality data for EIA study. Also a

detailed account of ecological/social features, including receptor(s) identification, prevailing at each monitoring site

to be provided.

4. Once the emission data and meteorological data (project specific and location specific) is available then simple

modelling can be used to calculate the location (zones) of maximum GLC for different major categories of wind

speeds. The locations can be prioritized and fixed accordingly

5. SCREEN3 can be used to estimate ambient impacts from point, area, and volume sources and flares to a distance of

10 km at 100 % of emission load (with and without proposed controls). There should be Significant Impact

Threshold (SIT) which can be given under TOR (guidelines can be easily developed based on regional pollution

levels) for worst met conditions (F Stability). The set of 54 worst-case meteorological conditions are built in to these

SCREEN models.

SCREEN3 simply demonstrating that the maximum predicted impacts (without the addition of background concentrations)

are below the acceptable adverse impact levels.

In case the worst case GLC is exceeding the SIT then comprehensive modelling based on ISC3 can be applied.

6. CPCB / MoEF may be requested to issue guidelines / standards for parameters like Hg, VOC, NMHC etc. in ambient

air for which no Indian standards exist.

7. In case of expansion projects, where industry is maintaining CAAQMS data, MoEF should allow to use such data

for EIA study.

8. CPCB/MoEF may be requested to prescribe “calibration protocol" to be followed for instruments used for ambient

air quality monitoring.

9. CPCB / MoEF should come-up urgently with a certification scheme for air quality monitoring instruments required

for air quality monitoring.

10. There may be a porter maintained at MOEF, where in the base line data monitored/reported under the respective

project should be posted at the project latitude/longitude of the site with time lines for the project under EC process.

The subsequent post project monitoring data (as reported by project proponent at the frequency of six monthly

reports) should also be posted at the same latitude/longitude. By maintaining such a porter the spatial & temporal

trend of air quality levels could be monitored and checks and balances could be traced for other projects that are


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planned for future. This could also serve the secondary data base for future projects also and national scenario can

be generated.

11. Monitoring of SO2 using Mercuric Chloride in absorbing media should be reviewed (as mercury bearing reagent

disposal is an issue). Similarly monitoring of NO2 using Sodium Arsenite in absorbing media should be reviewed.

CPCB may consider to introduce new methods for monitoring these 2 pollutants.

It was suggested that IAAPC (DC) should organise orientation programme for expert members of EAC /SEIAA on air quality

monitoring for baseline data generation for EIA Study. Also the personnel involved in air quality monitoring in field should be

properly trained


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