Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
i
Trends Analysis of Ambient Air Pollutants in Agra City -2002-2013
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
ii
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
i
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
ii
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
iii
Trends Analysis of Ambient Air Pollutants in Agra City -2002-2013
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
iv
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
v
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
vi
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
vii
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
8
Trends Analysis of Ambient Air Pollutants in Agra City -2002-2013
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
9
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
10
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
11
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
12
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
Fig.8: %Change in PM10 & SPM
w.r.t 2002 at Rambagh
Fig.7: Trend of Ambient Air Pollutants at
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
13
Fig.11: %Change in PM10 & SPM
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
14
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
Tajmahal Etmad-ud-daulah Rambagh Nunhai
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
15
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
Tajmahal Etmad-ud-daulah Rambagh Nunhai
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)
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
16
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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%.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
27
Fig. 2 Traffic Peaks with Time Period
Fig. 3 Composition of Traffic
3.4 Hourly variation of PM concentrations with traffic
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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**
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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,
Department for the Environment. Food and Rural Affairs, Nobel House, 17 Smith Square, London.
2. Beer, T. (2001). Air Quality as a Meteorological Hazard. Natural Hazards, 23 (2), 157-169.
3. Buzorious, G., Hameri, K., Pekkanen, J., Kulmala, M. (1999). Spatial variation of aerosols number
concentration in Helsinki city. Atmospheric Environment, 33 (4), 553-565.
4. Cacciola, R.R., Sarva, M., Polosa, R., (2002). Adverse respiratory effects and allergic
susceptibility in relation to particulate air pollution: flirting with disaster. Allergy 57 (4), 281–286.
5. Cheng, S., Lam, K. (1998). An analysis of winds affecting air pollution concentrations in Hong
Kong. Atmospheric Environment, 32 (14-15), 2559-2567.
6. Davidson, A. (1994). The Los Angeles Aerosol Characterization and Source Apportionment
Study: a Meteorological Air Quality Analysis. Aerosol Science and Technology, 21(4), 269-282.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
35
7. DSH (Delhi Statistical Handbook) (2010). Directorate of Economics & Statistics, Government of
National Capital Territory of Delhi, various issues. Accessed on October 2010:
http://www.delhi.gov.in/.
8. Elminir, H. K. (2005). Dependence of urban air pollutants on meteorology. Science of the Total
Environment, 350 (1-3), 225– 237.
9. Gehrig, R., Buchmann, B. (2003). Characterizing seasonal variations and spatial distribution of
ambient PM10 and PM 2.5 concentration based on long-term Swiss monitoring data. Atmospheric
Environment, 37 (19), 2571-2580.
10. Gupta, A. K.., Patil, R.S., Gupta, S.K. (2004). A Statistical Analysis of Particulate Data Sets for
Jawaharlal Nehru Port and Surrounding Harbour Region in India. Environmental. Modelling and
Assessment, 95 (1-3), 295-309.
11. Hien, P.D., Bac, V.T., Tham, H.C., Nhan, D.D., Vinh, L.D. (2002). Influence of Meteorological
Conditions on PM2.5 and PM2.5–10 Concentrations during the Monsoon Season in Hanoi,
Vietnam. Atmospheric Environment, 36(21), 3473-3484.
12. Janssen, N A H., Dimphe Van Manson D F M., Jagt, K V D., Harssema, H., Hoaek, G. (1997).
Mass concentration and elemental composition of airborne particulate matter at street and
background locations, Atmospheric Environment, 31(8), 1185-1193.
13. Jung, I., Kumar, S., John, K., Christ, K. (2002). Impact of Meteorology on the Fine Particulate
Matter Distribution in Central and South-eastern Ohio. In Preprints of the American
Meteorological Society 12th Joint Conference on Applications of Air Pollution Meteorology with
the A&WMA Norfolk, VA. American Meteorological Society: Boston, MA.
14. Kuhlbusch, T.A.J., John, A.C., Fissan, H., Schmidt, K.-G., Schmidt, F., Pfeffer, H.-U., Gladtke,
D. (1998). Diurnal Variations of Particle Number Concentrations- Influencing Factors and
Possible Implications for Climate and Epidemiological Studies. Journal of Aerosol Science, 29(1-
2), 213-214.
15. Marcazzan, G.M., Valli, G., Vecchi, R. (2002). Factors Influencing Mass Concentration and
Chemical Composition of Fine Aerosols during a PM High Pollution Episode. Science of the Total
Environment, 298(1-3), 65-79.
16. Mariani, R.L., de Mello, W.Z. (2007) PM2.5–10, PM2.5, and Associated Water- Soluble
Inorganic Species at a Coastal Urban Site in the Metropolitan Region of Rio de Janeiro.
Atmospheric Environment, 41(13), 2887-2892.
17. Mitra, AP., Sharma, C. (2002). Indian aerosols: present status. Chemosphere, 49 (9), 1175–1190
18. MoEF (1997). White paper on pollution in Delhi with an action plan. Ministry of Environment
and Forest, Government of India, New Delhi.
19. Muller, K. (1999). A 3-Year Study of the Aerosol in Northwest Saxony (Germany). Atmospheric
Environment, 33(11), 1679-1685.
20. Papanastasiou, D.K., Melas, D., Kioutsioukis, I. (2007). Development and Assessment of Neural
Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-Sized
Mediterranean City; Water Air and Soil Pollution, 182(1), 325-334.
21. Ruellan, S., Cachier, H. (2001). Characterization of Fresh Particulate Vehicular Exhausts near a
Paris High Flow Road. Atmospheric Environment, 35(2), 453-468.
22. Shendell, D.G., Naeher, L.P. (2002). A pilot study to assess ground‐level ambient air
concentrations of fine particles and carbon monoxide in urban Guatemala. Environment
International, 28(5), 375–382.
23. United Nations (UN). (2004). World Urbanization Prospects: The 2003 Revision,
ST/ESA/SER.A/237. Department of Economic and Social Affairs, Population Division, New
York.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
36
24. Vecchi, R., Marcazzan, G., Valli, G., Ceriani, M., Antoniazzi, C. (2004). The Role of Atmospheric
Dispersion in the Seasonal Variation of PM1 and PM2.5 Concentration and Composition in the
Urban Area of Milan (Italy). Atmospheric Environment, 38(27), 4437- 4446.
25. Wang, G., Wang, H., Yu, Y., Gao, S., Feng, J., Gao, S., Wang, L. (2003). Chemical
characterization of water‐soluble components of PM10 and PM2.5 atmospheric aerosols in five
locations of Nanjing, China. Atmospheric Environment, 37 (21), 2893–2902.
26. World Health Organization (WHO). (2000). Air Quality Guidelines for Europe. WHO Regional
Publications, European Series No. 91, WHO Regional Office for Europe, Copenhagen.
27. Wrobel, A., Rokita, E., Maenhaut, W. (2000). Transport of traffic‐related aerosols in urban areas.
Science of the Total Environment, 257 (2-3), 199– 211.
28. Yang, K.L. (2002). Spatial and seasonal variation of PM10 mass concentrations in Taiwan.
Atmospheric Environment,36(21),3403-3411.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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)
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
]
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
6. References
1. Akpinar, S., Oztop, H., Kavak Akpinar, E., 2008. Evaluation of relationship between
meteorological parameters and air pollutant concentrations during winter season in Elazığ,
Turkey. Env. Monit. Assess., 146(1–3), 211–224.
2. Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass
burning. Glob. Biogeochem. Cycles, 15(4), 955–966.
3. Aneja, P. V., Kim, D. S., & Chameides, W. L., 1996. Trends and analysis of ambient NO,
NO2, CO, and ozone concentrations in Raleigh, North Carolina. Chemosphere, 34, 611–
623.
4. Barrero, M. A., Grimalt, J. O., & Canton, L., 2006. Prediction of daily ozone concentrations
and maxima in urban atmosphere. Chemometr. Intell. Lab. Syst., 80, 67–76.
5. Barman, S.C., Kumar, N., Singh, R., Kisku, G.C., Khan, A.H., Kidwai, M.M., Murthy, R.C.,
Negi, M.P.S., Pandey, P., Verma, A.K., Jain, G. and Bhargava, S.K., 2010. Assessment of
Urban Air Pollution and its Probable Health Impact. J. Env. Biol. , 31, 913-920.
6. 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. J. Atmos. Chem., 57(3), 239–
253.
7. Chhabra, S.K., Pande, J.N., Joshi, T.K. and Kumar, P., 2001. Air Quality and Health.
Workshop on Land Use, Transportation and the Environment, Pune, 3-4 December, 1-24.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
52
8. CPCB: National ambient air quality status and trends in India-2010. In: Central pollution
control board, ministry of environment and forests, NAAQMS/ 35 /2011-2012. (2012)
9. Datta, A., Saud, T., Goel, A., Tiwari, S., Sharma, S.K., Saxena, M., Mandal, T.K., 2010
Variation of ambient SO2 over Delhi. J. Atmos. Chem., 65(2–3), 127–143.
10. Draxler, R.R., Rolph, G.D. HYSPLIT (Hybrid Single-Particle Lagrangian Integrated
Trajectory) Model access via NOAA ARL READY Website
(http://ready.arl.noaa.gov/HYSPLIT.php). NOAA Air Resources Laboratory, Silver
Spring, MD.
11. Gaur, A., Tripathi, S. N., Kanawade, V. P., Tare, V., and Shukla, S. P., 2014. Four-
year measurements of trace gases (SO2, NOx, CO, and O3) at an urban location, Kanpur,
in Northern India. J. Atmos. Chem. , 71, 283–301. DOI 10.1007/s10874-014-9295-8
12. Han, X., & Naeher, P. L., 2006. A review of traffic-related air pollution exposure
assessment studies in the developing world. Environ. Int., 32, 106–120.
13. Khare, M., 2012. Air pollution – monitoring, modeling, health and control. InTech Janeza
Trdine 9, 51000 Rijeka, Croatia.
14. Lin, J.; Liu, W.; Yan, I., 2009. Relationship between meteorological conditions and particle
size distribution of atmospheric aerosols. J. Meteor. Environ., 25, 1–5.
15. Lorga, G., Raicu, C., Stefan, S., 2015. Annual air pollution level of major primary
pollutants in Greater Area of Bucharest. Atmos. Pollut. Res., 6, 2015.
16. Pulikesi, M., Baskaralingam, P., Rayudu, V.N., Elango, D., Ramamurthi, V., Sivanesan,
S., 2006. Surface ozone measurements at urban coastal site Chennai, in India. J. Hazard.
Mater. , 137(3), 1554–1559.
17. Ramalingaswami, V., Aggarwal, P., Chhabra, S.K., Desai, P., Ganguly, N.K.,
Gopalkrishnan, K., Kacker, S.K., Kalra, V., Kamat, R., Kochupillai, V., Nag, D., Pande,
J.N., Raina, V., Ray, P.K., Saiyed, H., Seth, P.K., Trehan, N. and Wasir, H.S., 1999. Urban
Air Pollution. Curr. Sci., 77, 334-336.
18. Reddy, B.S.K., Kumar, K.R., Balakrishnaiah, G., Gopal, K.R., Reddy, R.R., Ahammed,
Y.N., Narasimhulu, K.,Reddy, L.S.S., Lal, S., 2010. Observational studies on the
variations in surface ozone concentration at Anantapur insouthern India. Atmos. Res.,
98(1), 125–139.
19. Reddy, B.S., Kumar, K.R., Balakrishnaiah, G., Gopal, K.R., Reddy, R.R., Sivakumar, V.,
Lingaswamy, A.P.,Arafath, S.M., Umadevi, K., Kumari, S.P., Ahammed, Y.N., Lal, S.,
2013. Analysis of diurnal and seasonal behavior of surface ozone and its precursors (NOx)
at a semi-arid rural site in southern India. Aerosol. Air. Qual. Res. , 12, 1081–1094 .
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
53
20. Saini, R., Singh, P., Awasthi, B.B., Kumar, K., and Taneja, A., 2014. Ozone distributions
and urban air quality during summer in Agra – a world heritage site. Atmos. Pollut. Res. ,
5, 796‐804.
21. Sharma, A.P., Kim, K., Kim, K., Ahn, J., Shon, Z., Sohn, J., Lee, J., Ma, B.R.J.C., 2014.
Ambient particulate matter (PM10) concentrations in major urban areas of Korea during
1996–2010. Atmos. Pollut. Res. , 5, 161–169.
22. Sharma. S.K., Mandal. T.K., Rohtash, Kumar. M., Gupta. N.C., Pathak. H., Harit. R.C and
Saxena. M., 2014a. Measurement of ambient ammonia over the National Capital Region
of Delhi, India, MAPAN-J. Met. Society of India.
23. Sharma, S. K., Datta, A., Saud, T., Mandal, T. K., Ahammed, Y. N., Arya, B. C., Tiwari,
M. K., 2010a. Study on concentration of ambient NH3 and interactions with some other
ambient trace gases. Environ. Monit. Assess. , 162, 225-235.
24. Tie, X., Geng, F., Peng, L., Gao, W., Zhao, C., 2009. Measurement and modeling of O3
variability in Shanghai, China: application of the WRF-chem. model. Atmos. Environ.
43(28), 4289–4302.
25. Wang, S., Hao, J., 2012. Air quality management in China: issues, challenges, and options.
J. Environ. Sci., 24(1), 2–13.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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. .
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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)
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
6.0 References
1. Adak, A., 2014. Atmospheric Fine Mode Particulates at Eastern Himalaya, India: Role of
Meteorology, Long-Range Transport and Local Anthropogenic Sources. Aerosol and Air Quality
Research, pp.440–450. Available at: http://www.aaqr.org/Doi.php?id=42_AAQR-13-03-OA-
0090&v=14&i=1&m=2&y=2014 [Accessed December 30, 2014].
2. Agrawal, A., Upadhyay, V.K. & Sachdeva, K., 2011. Study of aerosol behavior on the basis of
morphological characteristics during festival events in India. Atmospheric Environment, 45(21),
pp.3640–3644. Available at: http://linkinghub.elsevier.com/retrieve/pii/S1352231011003621
[Accessed December 16, 2014].
3. Attri, A.K., Kumar, U. & Jain, V.K., 2001. Formation of ozone by fireworks. Nature, 411(June),
p.2001. Available at: http://www.nature.com/nature/journal/v411/n6841/abs/4111015a0.html.
4. Barman SC, Singh R, Negi MPS, Bhargava SK. Fine particles (PM2.5) in ambient air of Lucknow
city due to fireworks on Diwali festival. J Environ Biol. 2009;30(September):625–32.
5. Chan L, Qi-hong D, Wei-wei L, Bo-liang H, Ling-zhi S. Characteristics of ventilation coefficient
and its impact on urban air pollution. J Cent South Univ Technol (Engl Ed) [Internet].
2012;15(6):830–4. Available from: http://link.springer.com/article/10.1007/s11771-012-1047-9
6. Cheng, Y.-H., 2010. Measurement of Particle Mass Concentrations and Size Distributions in an
Underground Station. Aerosol and Air Quality Research, pp.22–29. Available at:
http://www.aaqr.org/Doi.php?id=3_AAQR-09-05-OA-0037&v=10&i=1&m=2&y=2010
[Accessed December 30, 2014].
7. Clark, H., 1997. New directions. Light blue touch paper and retire... Atmospheric Environment,
31(17), pp.2893–2894.
8. Dholakia, H.H. Purohit P, Rao S, Garg A, 2013. Impact of current policies on future air quality and
health outcomes in Delhi, India. Atmospheric Environment, 75, pp.241–248. Available at:
http://dx.doi.org/10.1016/j.atmosenv.2013.04.052.
9. Guttikunda, S.K. & Goel, R., 2013. Health impacts of particulate pollution in a megacity-Delhi,
India. Environmental Development, 6, pp.8–20. Available at:
http://dx.doi.org/10.1016/j.envdev.2012.12.002.
10. Sarkar, S. Khillare PS, Jyethi DS, Hasan A, Parween M, 2010. Chemical speciation of respirable
suspended particulate matter during a major firework festival in India. Journal of hazardous
materials, 184(1-3), pp.321–30. Available at: http://www.ncbi.nlm.nih.gov/pubmed/20817345
[Accessed December 4, 2014].
11. Seinfeld, J.H. & Pandis, S.N., 2012. Atmospheric Chemistry and Physics: From Air Pollution to
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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)
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
4. Curtis, J., 2006, Nucleation of atmospheric aerosol particles, C. R. Physique, 7, 1027–1045.
5. Dal Maso, M., Kulmala, M., Riipinen, I., Wagner, R., Hussein, T., Aalto, P. P. and
Lehtinen, K. E. J., 2005, Formation and Growth of Fresh Atmospheric Aerosols: Eight
Years of Aerosol Size Distribution Data from SMEAR II, Hyytiälä, Finland. Boreal
Environ. Res., 10, 323–336.
6. Friedlander, S. K., 2000, Smoke, dust, and haze: Fundamentals of aerosol dynamics (2nd
ed.). New York, NY: Oxford University Press.
7. Fuchs, N. A., 1964, The Mechanics of Aerosols, Pergamon Press, Oxford, UK.
8. Hinds, W. C., 1999, Aerosol Technology: Properties, Behavior, and Measurement of
Airborne Particles, Wiley, New York.
9. Jeong, C. H., Evans, G. J., McGuire, M. L., Chang, R. Y.-W., Abbatt, J. P. D.,
Zeromskiene, K., Mozurkewich, M., Li, S. M., and Leaitch, W. R., 2010, Particle
Formation and Growth at Five Rural and 20 Urban Sites. Atmos. Chem. Phys. 10: 7979–
7995, doi: 10.5194/acp-10-7979-2010.
10. Katz, J. L., 1970: J. Chem. Phys., 52, 4733.
11. Kaufman, Y. J and Koren, I., 2006, Smoke and Pollution Aerosol Effect on Cloud Cover,
Science, 313, 655–658.
12. Kulmala, M., Pirjola, L., and Mäkelä, J. M., 2000, Stable Sulfate Clusters as a Source of
New Atmospheric Particles, Nature, 404, 66–69.
13. Kulmala, M., Vehkamäki, H., Petäjä, T., Dal Maso, M., Lauri, A., Kerminen, V. M. W.,
Birmili, W., and McMurry, P.H., 2004, Formation and Growth Rates of Ultrafine
Atmospheric Particles: A Review of Observations, J. Aerosol Sci., 35, 143–176.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
75
14. Laaksonen, A., Hamed, A., Joutsensaari, J., Hiltunen, L., Cavalli, F., Junkermann, W.,
Asmi, A., Fuzzi, S., and Facchini, M. C., 2005, Cloud condensation nucleus production
from nucleation events at a highly polluted region, Geophys. Res. Lett., 32, L06812,
doi:10.1029/2004GL022092.
15. Lehtinen, K. E. J. and Kulmala, M., 2003, A model for particle formation and growth in
the atmosphere with molecular resolution in size, Atmos. Chem. Phys., 3, 251–257.
16. Leppä, J., Anttila, T., Kerminen, V. M., Kulmala, M., and Lehtinen, K. E. J., 2011,
Atmospheric New Particle Formation: Real and Apparent Growth Ofneutral and Charged
Particles, Atmos. Chem. Phys., 11, 4939–4955.
17. Murphy, D. M. and Koop, T., 2005, Review of the vapour pressures of ice and supercooled
water for atmospheric applications, Quart. J. Royal Met. Soc., 131, 1539–1565.
18. Nguyen, H. V.; Okuyama, K.; Mimura, T.; Kousaka, Y.; Flagan, R. C.; Seinfeld, J. H. J.,
1987, Colloid Interface Sci., 119, 491.
19. Ortega, I. K., Suni, T., Boy, M., Gronholm, T., Manninen, H. E., ¨ Nieminen, T., Ehn, M.,
Junninen, H., Hakola, H., Hellen, H., ´ Valmari, T., Arvela, H., Zegelin, S., Hughes, D.,
Kitchen, M., Cleugh, H., Worsnop, D. R., Kulmala, M., and Kerminen, V.-M., 2012, New
insights into nocturnal nucleation, Atmos. Chem. Phys., 12, 4297–4312, doi:10.5194/acp-
12-4297-2012.
20. Pirjola, L., O’Dowd, C. D., and Kulmala, M., 2002, A Model Prediction of the Yield of
Cloud Condensation Nuclei from Coastal Nucleation Events., J. Geophys. Res., 107, 8098,
doi: 10.1029/2000JD000213.
21. Sarangi, B., Aggarwal, S. G., and Gupta, P. K., 2015, A Simplified Approach to Calculate
Particle Growth Rate Due to Self-Coagulation, Scavenging and Condensation Using SMPS
Measurements during a Particle Growth Event in New Delhi, Aerosol Air Qual. Res., 15,
166–179.
22. Sarangi, B., and Aggarwal S. G., 2017, Observed particle growth: a case study in an urban
city, International Conference on Aerosol Climate Change Connection (AC3) (Centenary
Celebration of Bose Institute),25-27 April, 2017,CP-50, 165-168.
23. Schmitt, J. L., Adams, G. W., and Zalabsky, R. A., 1982, J. Chem. Phys., 77,
2089, https://doi.org/JCPSA6, Scitation, CAS.
24. Seinfeld, J. H. and Pandis, S. N., 2006, Atmospheric Chemistry and Physics: From Air
Pollution to Climate Change, 2nd edn., John Wiley & Sons, Inc., New Jersey.
25. Twohy, C. H., Kreidenweis, S. M., Eidhammer, T., Browell, E. V., Heymsfield, A. J.,
Bansemer, A. R., Anderson, B. E., Chen, G., Ismail, S., DeMott, P. J., and Heever, S. C.
V. D., 2009, Saharan dust particles nucleate droplets in eastern Atlantic clouds, Geophys.
Res. Lett., 36, L01807, doi:10.1029/2008GL035846.
26. Twomey, S., 1974, Pollution and Planetary Albedo. Atmos. Environ., 8, 1251–1256.
27. Willis, M. D., Burkart, J., Thomas, J. L., Köllner, F., Schneider, J., Bozem, H., Hoor, P.
M., Aliabadi, A. A., Schulz, H., Herber, A. B., Leaitch, W. R., and Abbatt, J. P. D., 2016,
Growth of nucleation mode particles in the summertime Arctic: a case study, Atmos.
Chem. Phys., 16, 7663–7679, doi:10.5194/acp-16-7663- 2016.
28. Wyslouzil, B. E., Heath, C. H., Cheung, J. L., and Wilemski, G., 2000, Binary
condensation in a supersonic nozzle, J. Chem. Phys., 113, 7317.
29. Zhang, R., Wang, L., Khalizov, A. F., Zhao, J., Zheng, J., McGraw, R. L., and Molina, L.
T., 2009, Formation of nanoparticles of blue haze enhanced by anthropogenic pollution, P.
Natl. Acad. Sci. USA, 106, 17650–17654.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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)
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March 2017
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.
References: 1. Andereck, K.L., 1993, The impacts of tourism on natural resources. Parks and Recreation. 28(6), pp:
26-32.
2. Balachandran, S., Meena, B.R., and Khillare, P.S., 2000, Particle size distribution and its elemental
composition in the ambient air of Delhi. Environment International. 26 , pp :49–54.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
84
3. Central Bureau of Health Intelligence, 2012. Directorate General of Health Services, Ministry of Health
and Family Welfare, Government of India, pp 53-83.
4. CPCB (Central Pollution Control Board). 2012. National Ambient Air Quality Status and Trends 2010.
[NAAQMS//2011-12]. New Delhi: CPCB.
5. Davies, T., and Cahill, S., 2000. Environmental implications of the tourism industry. Discussion paper
0014. Resources for the Future, NW Washington, DC 20036.
6. Finlayson-Pitts, B.J., and Pitts, J.N, 1986. Atmospheric Chemistry: Fundamentals and Experimental
Techniques, John Wiley & Sons; 1 edition (April 1986) ed. Chemical analysis: a series of monographs
on analytical chemistry and Its applications, New York: John Wiley (1986)
7. Garg, A., Tyagi, S.K., Bhattacharya, P., 2016. RISK ASSESSMENT OF BENZENE IN AMBIENT
AIR OF DELHI, International journal of current research, 8(8), pp.37532-37538
8. Kumar, A., Phadke, K.M., Tajne, D.S., Hasan, M.Z., 2001. Increase in inhalable particulates
concentration by commercial and industrial activities in the ambient air of a select Indian
metropolis. Environmental Science and Technology, 35, pp:487–92.
9. Kumar, P., & Kumar, S., 2012. Valuing the Health Effects of air pollution from agricultural residue
burning. ACIAR: Policy Instruments to address air pollution issues in agriculture – Implications for
happy Seeder technology in India.
10. Mallik, C., Venkataramani, S., & Lal, S., 2012. Study of a high SO2 event observed over an urban site
in western India. Asia-Pacific Journal of Atmospheric Sciences, 48(2), pp : 171–180. doi:10.1007/
s13143- 012-0017-3
11. Mavroidis, I., Ilia, M., 2012. Trends of NOx, NO2 and O3 concentrations at three different types of air
quality monitoring stations in Athens, Greece. Atmospheric Environment 63, pp:135–147 doi:10.1016/
j.atmosenv. 2012.09.030
12. Murray, C. J., Ezzati, M., Flaxman, A. D., Lim, S., Lozano, R., Michaud, C., & Lopez, A. D., 2013.
GBD 2010: design, definitions, and metrics. The Lancet, 380(9859), pp : 2063-2066.
13. Nautiyal J. et al., 2007 Air Pollution and Cardiovascular Health in Mandi-Gobindgarh, Punjab, India -
A Pilot Study. Int. J. Environ. Res. Public health, 4 (4), pp: 262-282.
14. P. Balashanmugam, A. R. Ramanathan and V. Nehru Kumar, 2012, Ambient Air Quality Monitoring
in Puducherry, International Journal of Engineering Research and Applications, 2(2), pp: 300-307
15. Police S, Sahu SK, Pandit G.G., 2016. Chemical characterization of atmospheric particulate matter and
their source apportionment at an emerging industrial coastal city, Visakhapatnam, India. Atmospheric
Pollution Research. 7, pp: 725-733.
16. Seinfeld, J.H., Pandis, S.N., 1998, Atmospheric chemistry and physics. From Air Pollution to Climate
Changes, Wiley, New York.
17. Stroud, C., Madronich, S., Atlas, E., Ridley, B., Flocke, F., Weinheimer, A., Talbot, B., Fried, A., Wert,
B., Shetter, R., Lefer, B., Coffey, M., Heikes, B., Blake, D., 2003, Photochemistry in the arctic free
troposphere: NOx budget and the role of odd nitrogen reservoir recycling. Atmospheric Environment,
37(24), 3351–3364. doi:10. 1016/S1352-2310(03)00353-4.
18. Sun, Y., Wang, L., Wang, Y., Quan, L., Zirui, L., 2011, In situ measurements of SO2, NOx, NOy, and
O3 in Beijing, China during august 2008. Science of the Total Environnment. 409(5), 933–940.
19. Tyagi, S.K., Upadhyay, V.K., Kulshreshtha, D., Kumar, S., Krishnamurthy, P., and Sen, A.K., 2016,
Study of Background Ambient Air Quality in Northern Himalayan Regions: Uttrakhand and Himachal
Pradesh, India, Indian Journal of Air Pollution Control, Vol XVI, No.1, pp: 1-9.
U.S. Environmental Protection Agency. 2010. The Green Book Nonattainment Areas for Criteria
Pollutants. U.S. EPA. Online available at- https://www.epa.gov/green-book
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
85
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
86
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
87
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
88
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.
0102030405060708090
100
11
:19
:55
11
:29
:55
11
:39
:55
11
:49
:55
11
:59
:55
12
:09
:55
12
:19
:55
12
:29
:55
12
:39
:55
12
:49
:55
12
:59
:55
13
:09
:55
13
:19
:55
13
:29
:55
13
:39
:55
13
:49
:55
13
:59
:55
14
:09
:55
14
:19
:55
14
:29
:55
14
:39
:55
14
:49
:55
14
:59
:55
15
:09
:55
15
:19
:55
15
:29
:55
15
:39
:55
15
:49
:55
15
:59
:55
16
:09
:55
16
:19
:55
16
:29
:55
16
:39
:55O
zon
e C
on
cen
trat
ion
(p
pb
)
Time (mins)
OZONE CONCENTRATION VARIATION JULY MONTH
Ozone concAverageNational standard value
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
89
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.
0102030405060708090
100
11
:00
:59
11
:12
:59
11
:23
:59
11
:34
:02
11
:45
:59
11
:56
:59
12
:16
:59
12
:27
:59
12
:38
:59
12
:49
:59
13
:00
:59
13
:11
:59
13
:22
:59
13
:33
:59
13
:44
:59
13
:55
:59
14
:06
:59
14
:17
:59
14
:28
:59
14
:39
:59
14
:50
:59
15
:01
:59
15
:12
:59
15
:23
:59
15
:34
:59
15
:45
:59
15
:56
:59
16
:07
:59
16
:18
:59
16
:29
:59
16
:40
:59
16
:51
:59O
zon
e C
on
cen
trat
ion
(p
pb
)
Time (mins)
OZONE CONCENTERATION VARIATION AUG MONTH
Ozone Conc
Average
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
90
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.
0102030405060708090
100
11
:04
:05
11
:19
:05
11
:34
:05
11
:49
:05
12
:04
:05
12
:19
:05
12
:34
:05
12
:49
:05
13
:04
:05
13
:19
:05
13
:34
:05
13
:49
:05
14
:04
:05
14
:19
:05
14
:34
:05
14
:49
:05
15
:04
:05
15
:19
:05
15
:34
:05
15
:49
:05
16
:04
:05
16
:19
:05
16
:34
:05
16
:49
:05
17
:04
:05
17
:19
:05
17
:34
:05
17
:49
:05O
zon
e C
on
cen
trat
ion
(p
pb
)
Time (mins)
OZONE CONCENTRATION VARIATION SEPT MONTHOzone concAverageNational standard value
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
91
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.
0102030405060708090
100
11
:00
:06
11
:09
:06
11
:18
:06
11
:27
:06
11
:36
:06
11
:45
:06
11
:54
:06
12
:03
:06
12
:12
:06
12
:21
:06
12
:30
:06
12
:39
:06
12
:48
:06
12
:57
:06
13
:06
:06
13
:15
:06
13
:24
:06
13
:33
:06
13
:42
:06
13
:51
:06
14
:00
:06
14
:09
:06
14
:18
:06
14
:27
:06
14
:36
:06
14
:45
:06
14
:54
:06
15
:03
:06
15
:12
:06
15
:21
:06
15
:30
:06
15
:39
:06
Ozo
ne
Co
nce
ntr
atio
n (
pp
b)
Time (mins)
OZONE CONCENTRATION VARIATION OCT MONTH
Ozone concAverageNational standard value
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
92
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.
0102030405060708090
100
11
:00
:17
11
:10
:17
11
:20
:17
11
:30
:17
11
:40
:17
11
:50
:17
12
:00
:17
12
:10
:17
12
:20
:17
12
:30
:17
12
:40
:17
12
:50
:17
13
:00
:17
13
:10
:17
13
:20
:17
13
:30
:17
13
:40
:17
13
:50
:17
14
:00
:14
14
:10
:14
14
:20
:14
14
:30
:14
14
:40
:14
14
:50
:14
15
:00
:14
15
:10
:14
15
:20
:14
15
:30
:14
15
:40
:14
15
:50
:14
16
:00
:14
16
:10
:14
16
:20
:14
Ozo
ne
Co
nce
ntr
atio
n (
pp
b)
Time (mins)
OZONE CONCENTRATION VARIATION NOV MONTHOzone ConcAverageNational standard value
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
93
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
16.00-
17.00 hr
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
0
10
20
30
40
50
60
70
80
90
1001
1:0
0:5
9
11
:15
:59
11
:29
:59
11
:43
:59
11
:57
:59
12
:20
:59
12
:34
:59
12
:48
:59
13
:02
:59
13
:16
:59
13
:30
:59
13
:44
:59
13
:58
:59
14
:12
:59
14
:26
:59
14
:40
:59
14
:54
:59
15
:08
:59
15
:22
:59
15
:36
:59
15
:50
:59
16
:04
:59
16
:18
:59
16
:32
:59
16
:46
:59
Ozo
ne
Co
nce
nte
rati
on
(p
pb
v)
MONTHLY OZONE VARIATION OF ALL MONTHS
July Conc
Aug Conc
Sept Conc
Oct Conc
Nov Conc
Time (mins)
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
94
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.
0
10
20
30
40
50
60
70
80
90
100
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 16.00-17.00 hr
HOURLY VARIATIONS OF OZONE CONCENTERATION FOR VARIOUS MONTHS (YEAR 2010)
July Aug Sept Oct Nov Hourly Standard Limit
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
95
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
0
50
100
150
200
250
300Fr
eq
ue
ncy
OZONE VARIATIONS WITH RESPECT TO METEOROLOGICAL PARAMETERS (YEAR 2010)
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
96
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).
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
97
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
98
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
99
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:-
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
100
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
101
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
102
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
103
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
104
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
105
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.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
106
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
107
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
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
108
Instructions for Authors
The Indian Journal of Air Pollution Control aims to focus attention on all problems and their solutions
related to the subject of air pollution. Authors wishing to have their papers published in the journal are
requested to send their manuscripts to the Chief Editor c/o Envirotech Instruments (P) Ltd., A–271,
Okhla Industrial Area – Phase I, New Delhi – 110020 (e-mail address: [email protected]) or
directly to the Editor – in – Chief (e-mail address: [email protected]).Three copies (original plus
two copies) of the paper must be submitted. Two reviewers will review the manuscripts. All
correspondence regarding the manuscript will be made with the first author unless specified. Articles
reporting current R & D and in-depth studies on subjects of current interest are welcomed. Submission
of the manuscript for publication in the journal will imply that it has not been previously published and
is not under consideration for publication elsewhere; and further that, if accepted, it will not be
published elsewhere. We shall particularly wish to encourage papers that are likely to be of interest to
more than one professional group, either because the work is fundamental or because it reflects the best
in current technology or it offers extensive critical reviews, especially of subjects of interest in the
country. Papers on developments in Indian technology must pay special attention to the problems
peculiar to the country. The editors assume no responsibility for the statements and opinions expressed
by individual authors.
All parts of the script must be typed single-spaced on one side of the white bond paper of A-4 size. A
4 cm margin must be provided for insertion of printer’s instructions. Title page must contain the title
of the paper, the initials and names of the authors and the name and address of the institution where the
work was done and a brief running title of not more than 50 letter spaces. The title should be as concise
as possible, generally no more than two lines. If necessary for clarity, a glossary of mathematical
symbols may be included under an unnumbered heading ‘Notation’ after the acknowledgements.
Abstract must be informative and not just indicative, and must contain the significant results reported
in the paper. Keywords, not more than six in number, may be provided for indexing and information
retrieval. The text must be divided into sections, generally starting with ‘Introduction’ and ending with
‘Conclusions’. The main text should be followed by a list of references. Tables with legends must be
numbered consecutively numerals in the order of occurrence in the text on the top of the table. They
should be self-contained and have a descriptive title. Figures, in black and white, with suitable captions,
should be numbered consecutively in numeral in the order of occurrence in the text at the bottom of the
figure. Equations must be written clearly, each on its own line, well away from the text but punctuated
to read with it. Units and associated symbols must invariably follow SI practice. Footnotes must be
avoided. Appendices if any should be labelled A, B etc., in order of appearance. A copy of the original
tables and figures must be sent separately.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
109
References should be cited in the text by author’s name and year, not by number. If there are more
than two authors, references should be made in the name of the first author followed by et al in the text.
References at the end of the paper should be listed alphabetically by author’s names, followed by
initials, year of publication, title of the paper, name of the journal, volume number and page
numbers. Reference to books should include name(s) of author(s), initials, year of publication, title of
the book, edition if not the first, initials and name(s) of editor(s) if any, place of publication, publisher,
and chapter or pages referred to. Reference to thesis, must include the year, the title of the thesis, the
degree for which submitted and the University.
The reviewed manuscript (hard copy) along with its copy on the floppy disc/CD (alternately e–mail
copy) in the prescribed format must be resubmitted for consideration of publication within three months
of the receipt of the referee’s comments. In the final copy, A4 (297X 210mm) size, margin should
be as follows: top 25 mm; bottom 30 mm; sides 20mm. Paper title should be in 14 pt. Arial font,
(Title case) and centered. Author’s names should be in 12 pt. Times New Roman, sentence case,
spaced 2.5 mm (or 7 pt.) beneath the title, with Author’s affiliations and addresses in 10 pt. Times
New Roman spaced 1 mm (or 3 pt) below the authors name. Please include e-mail addresses in
brackets. Abstract heading should be in 12 pt. Arial, sentence case with 10 mm (28 pt.) space
above and 1 mm (3 pt.) below, followed by the text in 10 pt. Times New Roman justified (single
spaced). Main section headings should be in 12 pt. Arial bold, numbered (with hanging indent),
sentence case, left justified and 14 pt. space above and 3 pt. space below. First level sub-headings
should be in 11 pt. Arial, numbered (with hanging indent), sentence case, left justified with 10 pt.
space above and 3 pt. space below, and second level sub-headings should be in 10 pt. Arial italic,
numbered (with hanging indent), sentence case, left justified, with 7 pt. space above and 2 pt.
space below. Body text should be in 11 pt. Times New Roman justified (single spaced), first line
indented 10 mm except for the paragraph following a heading. Fully justify each line, hyphenating if
necessary. Insert only a single space after a sentence. Avoid hyphens at ends of two or more consecutive
lines. Do not number the pages except by pencil on the back of each sheet if you send a printed
copy. Please send manuscripts as attachment in MS word or in printed form strictly as per instructions
above. In the floppy disc, manuscript must be composed in MS word with single spacing, tables
and figures to be inserted as a part of the text. Tables and figures must be properly formatted.
The titles and/or figure numbers must not be made part of the tables and figures when inserting scanned
copies. Printed copies of original diagrams/figures and tables used in the text should also be sent
separately along with the final version.
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
110
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
111
Indian Journal of Air Pollution Control, Vol XVI, No.2 & Vol XVII, No. 1, September 2016 / March
2017
112