qSXX

1. DEVELOPMENT OF EMISSION INVENTORY FOR VIZIANAGARAM
CITY
Submitted in partial fulfilment of the requirement for the award of degree of
MASTER OF TECHNOLOGY
IN
CIVIL ENGINEERING
With specialization in
ENVIRONMENTAL ENGINEERING AND MANAGEMENT
By
GANDI MAHESH
Regd no. 321206308007
Under the guidance of
Prof. S. BALA PRASAD
(Professor of Environmental, Engineering & Management)
DEPARTMENT OF CIVIL ENGINEERING
ANDHRA UNIVERSITY COLLEGE OF ENGINEERING
VISAKHAPATNAM – 530003
2021-2023

2. DEPARTMENT OF CIVIL ENGINEERING
ANDHRA UNIVERSITY COLLEGE OF ENGINEERING (A)
VISAKHAPATNAM
ISO 9001-2015 CERTIFIED
CERTIFICATE
This is to certify that the Dissertation work entitled “DEVELOPMENT OF
EMISSION INVENTORY FOR VIZIANAGARAM CITY” is the bonafide work
done by Mr. GANDI MAHESH, Regd. No: 321206308007, Postgraduate student of
Department of Civil Engineering, College of Engineering (A), Andhra University,
Visakhapatnam, during the academic year 2021-2023 in the partial fulfilment of the
requirements for the award of Degree of Master of Technology in Environmental
Engineering and Management of Andhra University and is a record of
student’s work, carried out under my supervision and guidance.
Prof. S. BALA PRASAD
Department of Civil Engineering,
Andhra University College of Engineering (A),
Visakhapatnam-530003,
Andhra Pradesh.

3. ANDHRA UNIVERSITY COLLEGE OF ENGINEERING (A)
VISAKHAPATNAM
DISSERTATION EVALUATION REPORT
This dissertation entitled “DEVELOPMENT OF EMISSION INVENTORY FOR
VIZIANAGARAM CITY” submitted by GANDI MAHESH, Regd. No.
321206308007 of 2021-2023 batch in partial fulfilment of the requirements for the
award of the degree of Master of Technology with specialization in
ENVIRONMENTAL ENGINEERING AND MANAGEMENT in CIVIL
ENGINEERING of ANDHRA UNIVERSITY COLLEGE OF ENGINEERING
(A), Visakhapatnam, has been approved.
EXAMINERS:
1) ..................................................... DISSERTATION GUIDE
(Prof. S. BALA PRASAD)
2) ..................................................... EXTERNAL EXAMINER
(Prof. B.V. SARADHI)
3) ..................................................... CHAIRMAN, BOARD OF STUDIES
(Prof. P.V.V. SATYANARAYANA) (DEPARTMENT OF
CIVIL ENGINEERING)
4) ..................................................... HEAD OF THE DEPARTMENT
(Prof. C.N.V. SATYANARAYANA REDDY) (DEPARTMENT OF CIVIL
ENGINEERING)
Station: Visakhapatnam
Date:

4. ACKNOWLEDGEMENT
It is my privilege to express my deep sense of gratitude to Prof. S. Bala Prasad, a faculty
member of the Environmental Engineering and Management Division, Department of
Civil Engineering for the continuous support of my M.Tech dissertation for his patience,
motivation, enthusiasm, and immense knowledge. His guidance helped me in all the time
of research and writing of this thesis.
Besides my advisor, I would like to thank Prof. C.N.V.Satyanarayana Reddy, Head of,
Department of Civil Engineering, Prof. P.V.V.Satyanarayana, Chairman Board of
Studies, Department of Civil Engineering, AUCE (A), for providing all facilities required
for the completion of this dissertation.
A dissertation of this nature cannot be prepared without the tremendous background
information made available by various research works by authors of excellent books and
technical articles by various professional bodies which have been referred to and listed at
the end of the dissertation and I am very thankful to them.
I would like to thank all the supporting staff of the environmental engineering laboratory
and non-teaching staff of the department and engineering library for their support while
working on this dissertation.
Last but not least I would like to thank my friend Narasimha Naidu, his encouraging
words and PC is helpful for documentation work in the period of my dissertation work.
GANDI MAHESH
(Regd. No: 321206308007)

5. DECLARATION
I GANDI MAHESH, hereby declare that the dissertation work entitled
“DEVELOPMENT OF EMISSION INVENTORY FOR VIZIANAGARAM
CITY” has been carried out by me, in partial fulfilment of the requirements for the
award of MASTER OF TECHNOLOGY in Civil Engineering with specialization
in Environmental Engineering and Management is an original work for the best
of my knowledge and was not submitted to any Educational Institute (or)
University in India (or) Abroad for the award of MASTER OF TECHNOLOGY.
DATE:
GANDI MAHESH
Regd. No. 321206308007

6. i
ABSTRACT
Emission inventory studies play a vital role in comprehending the intricate interplay
between urban development, industrialization, and environmental quality. This paper
focuses on an emission inventory analysis conducted for Vizianagaram city, aiming to
provide a comprehensive understanding of pollutant releases and their implications for air
quality and sustainable urban development. Through meticulous data collection,
advanced modelling techniques, and source characterization, this study offers valuable
insights into the sources, levels, and trends of key pollutants like particulate matter (PM).
By identifying dominant sources and quantifying their impact, the study contributes to
informed decision-making and targeted policy measures to enhance air quality and
promote a greener, healthier urban environment in Vizianagaram city. The total
emissions from the transport sector in Vizianagaram city is computed PM10 & PM2.5 as
4307 Kg/y & 3876 Kg/y and the particulate emissions from road dust sources are
computed PM10 &PM2.5 as 4323.17 Kg/y & 1033.84 Kg/y. The study results shows that
the main contributing source for particulate emissions is Road dust and Transport sector.

7. ii
TABLE OF CONTENTS
ABSTRACT i
TABLE OF CONTENTS ii
LIST OF FIGURES iv
LIST OF TABLES v
Chapter-1
INTRODUCTION 1
1.1 GENERAL 1
1.1.1 Emission Inventory 2
1.1.2 Source Apportionment and Carrying Capacity Studies 3
1.2 OBJECTIVES OF THE PROJECT 4
1.3 SCOPE OF THE PROJECT 5
1.4 DEMOGRAPHY OF VIZIANAGARAM CITY 5
1.4.1 Air Pollution Sources In Vizianagaram City 5
Chapter 2
THEORY AND LITERATURE REVIEW 6
2.1. GENERAL 6
2.1.1 Air Pollution 6
2.1.2 Air Pollutants 6
2.2 AIR QUALITY MONITORING 7
2.2.1 Ambient Air Quality Assessment 7
2.2.2 Air Quality Index 8
2.3 SOURCE APPORTIONMENT 8
2.4. EMISSION INVENTORY IN INDIA 9
2.4.1 Emission Inventory and Associated Parameters 9
2.4.2 Emission Inventory Studies in Indian Cities 10
2.5 LITERATURE REVIEWS RELATED TO EMISSION INVENTORY 12
2.6 CONCLUSION 24
Chapter-3
METHODOLOGY 25
3.1 GENERAL 25
3.2 DEVELOPMENT OF EMISSION INVENTORY METHODOLOGY 25
3.3 DEVELOPMENT OF EMISSION INVENTORY FOR VIZIANAGARAM CITY 30
3.3.1 NCAP Monitoring Locations of Vizianagaram City 30
3.3.1.1 The Details Of The 1km x 1km Grid At The Lanka Veedhi Monitoring Station 32
3.3.1.2 The Details Of The 1km x 1km Grid At The Sitam College Monitoring Station 32
3.3.1.3 The Details Of The 1km x 1km Grid At The Ashok Nagar Monitoring Station 33
3.3.1.4 The Details of The 1km x 1km grid at Complex Area Monitoring Station 34

8. iii
3.4 DATA COLLECTION FOR EMISSION INVENTORY 36
3.4.1 Analysis of Collected data 36
3.5 VEHICULAR COUNT ESTIMATION 37
3.6 ROAD DUST SAMPLING 38
3.7 CONCLUSION 38
Chapter-4
RESULTS AND DISCUSSION 39
4.1 GENERAL 39
4.2 POSSSIBLE SOURCES OF EMISSIONS IN VIZIANAGRAM CITY 39
4.3 ESTIMATION OF EMISSION LOADS 39
4.3.1 EMISSION FACTORS 40
4.4 ESTIMATION EMISSION LOAD FOR VIZIANAGARAM CITY 42
4.4.1 Emissions From Residential Sector 42
4.4.2 Emissions From Restaurants 45
4.4.3 Emissions From Open Eat outs 46
4.4.4 Emissions From Bakeries 46
4.4.5 Emissions From Building Construction 47
4.4.6 Emissions From Crematoria 48
4.4.7 Emissions From Diesel Generator (DG) Sets 49
4.4.8 Emissions From Transport 51
4.4.8.1 Traffic Counts and Vehicle Fleet Characteristics 51
4.4.9 Emissions From Road Dust 55
4.5 TOTAL EMISSION LOADS FOR STUDY AREA 58
4.5.1 Total Emission Loads for Lanka Veedhi & Sitam College grids 59
4.5.2 Total Emission Loads for Grid Ashok Nagar& Complex Area Grids 61
Chapter-5
SUMMARY AND CONCLUSION 63
5.1. SUMMARY 63
5.2 CONCLUSIONS 63
Chapter-6
REFERENCES 65
Annexure -1 69
Annexure -2 90

9. iv
LIST OF FIGURES
Figure 3.1 Process for Emission Inventory Development 25
Figure 3.2 Proposed Framework on Emission Inventory 26
Figure 3.3 Typical 1 km x 1 km Grid Map for Vizianagaram City 27
Figure3.4(a)Typical 1 km x 1 km Grid Map for Vizianagaram City using ArcGIS 28
Figure3.4(b)Typical 1 km x 1 km Grid Map for Vizianagaram City using ArcGIS 28
Figure 3.5 Vizianagaram Monitoring stations with 1km x 1km grid 30
Figure 3.6 Vizianagaram Ward wise Map Shape file 30
Figure 3.7 Vizianagaram Ward wise Map 31
Figure 3.8 Monitoring station at Lanka Veedhi with 1km x1km grid with roads 32
Figure 3.9 Monitoring station at Sitam College with 1km x1km grid with roads 33
Figure 3.10 Monitoring station at Ashok Nagar with 1km x 1km grid with roads 33
Figure 3.11 Monitoring station at Complex Area with 1km x 1km grid with roads 34
Figure 4.1 Data collection in Sachivalayams at Vizianagaram city 43
Figure 4.2 ADT Traffic Survey in Vizianagaram City 54
Figure 4.3 Road Dust sample collection at Vizianagaram city 54
Figure 4.4 Emission Loads for Lanka Veedhi Grid 55
Figure 4.5 Emission Loads for Sitam College Grid 55
Figure 4.6 Emission Loads for Ashok Nagar Grid 57
Figure 4.7 Emission Loads for Complex Area Grid 57
Figure 4.8 B.C Sector Emission Loads for All Grids 58
Figure 4.9 Total Emission Loads for 4 Grids 59

10. v
LIST OF TABLES
Table 3.1: Possible Sources of Air Pollution Emissions in Each of the City 29
Table 3.2 Methodology for Vehicular emission estimation 37
Table 4.1 Emission factors for various types of sectors 40
Table 4.2 Sample sets count for monitoring locations 43
Table 4.3 Residential Emission load for Lanka Veedhi grid 44
Table 4.4 Restaurants Emission load for Lanka Veedhi grid 45
Table 4.5 Open eat outs Emission load for Lanka Veedhi grid 46
Table 4.6 Bakeries Emission load for Lanka Veedhi grid 47
Table 4.7 Building construction Emission load for Lanka Veedhi grid 48
Table 4.8 Crematoria Emission load for Lanka veedhi grid 47
Table 4.9 Diesel Generator Emission load for Lanka Veedhi grid 49
Table 4.10(a)Vehicular count at monitoring locations from ADT survey 52
Table 4.10(b) Vehicular count in monitoring locations from ADT survey 53
Table 4.11 Emission factors for Vehicles from ARAI 2007-18 and NEERI 2019 54
Table 4.12 Vehicle Kilometer Travelled(VKT) for Monitoring locations 54
Table 4.13 Transport Emission load for Lanka Veedhi grid 55
Table 4.14 Road dust analysis for collected samples 57
Table 4.15 Road dust Emission loads for Monitoring locations 57
Table 4.16 Emission loads for Lanka Veedhi & Sitam College Monitoring locations 58
Table 4.17 Emission loads for Ashok Nagar & Complex Area Monitoring locations 60

11. vi

12. vii

13. 1
CHAPTER-1
INTRODUCTION
1.1 GENERAL
Air pollution has increasingly become a serious concern due to the increased concentrations of
various constituents that may influence the health of the living beings. In recent years, even some
of the towns have also witnessed an increase in air pollution. The emission of the following
pollutants such as Particulate matter (PM10 and PM2.5), SOx, NOx, CO, BTX results in air pollution.
The pollutants in the region should meet the permissible limits set by NAAQ Standards. If not, it
results in categorizing such cities or towns as non-attainment town or cities of India. Hence, the
issues need a comprehensive understanding to develop suitable strategy in air quality management.
The objective of implementing such strategy is to improve quality life of the citizens. As part of its
continuous efforts to provide clean air environment, Government of India launched National Clean
Air Programme (NCAP), in January 2019, as a national-level strategy for reducing the levels of air
pollution at both the regional and urban scales. The National Clean Air Programme (NCAP)
identified 132 non-attained cities, which are required to achieve 20%–30 % reduction in PM2.5 and
PM10 by 2024, considering 2017 as the base year. The goal of the NCAP is to meet the prescribed
annual average ambient air quality standards at all locations in the country in a stipulated timeframe.
However, for effective air-quality management plans for achieving the targets, as well as to track
the progress of control initiatives, it is important to have comprehensive studies to understand
various aspects of air pollution. In addition, the Hon’ble NGT directed the government to conduct
the Source Apportionment (SA) and Carrying Capacity (CC) studies in major cities across India.
Source Apportionment and Carrying Capacity are necessary concepts for effective environmental
management. Source apportionment helps identify and target pollution sources, while carrying
capacity guides sustainable resource use and ecosystem preservation. Both concepts contribute to
informed decision-making, regulatory compliance, and long-term environmental health.
In accordance of the policy of Government of India and the Hon’ble NGT directions, the
Government of Andhra Pradesh initiated to conduct the SA and CC studies in major non-attained
cities in the state of Andhra Pradesh through APPCB with the support of CPCB. The National

14. 2
Knowledge Network (NKN) identified Andhra University, Visakhapatnam as one of the IoRs,
which signed a MoU with the Srikakulam, Vizianagaram, Visakhapatnam, Rajamahendravaram,
and Eluru urban local bodies along with the APPCB. Hence, as IoR, it is proposed to conduct the
SA and CC studies of the above said five cities in coastal Andhra Pradesh. This study will help in
understanding various aspects of air pollution in the cities under consideration. It will provide
necessary inputs to develop appropriate air pollution management strategies and plans aimed at
pollution reduction.
The SA and CC studies consists of Air Quality assessment, Emission Inventory, Source Profiling,
Receptor and Dispersion Modelling. Emission Inventory is an important component of the study.
It helps in understanding the source and the concentration of its emissions in the atmosphere.
1.1.1 Emission Inventory
An emission inventory is a comprehensive list of sources and quantities of air pollutants released
into the atmosphere from various activities within a specific geographical area, such as a city or
region. An emission inventory is one of the important components of the source apportionment
studies. It consists of identification of various sources contributing to the air pollution of the city.
The major sources identified for the present cities under consideration pertaining the transport,
industrial, diesel generator sets, road dust emissions, construction and demolition sectors, domestic
sector etc. within the city and its vicinity. Identified mobile sources are light duty vehicles, medium
duty trucks, heavy duty vehicles, auto rickshaws, motorcycles, trains, farm equipment (both
passenger and goods vehicles using various fuels such as diesel, petrol, CNG). In addition to the
above, the fuel storage and handling facilities are included. The stationary sources include fuel
combustion, waste disposal, cleaning and surface coatings, petroleum products and marketing,
industrial process etc. The area wise sources include solvent evaporation, consumer products,
pesticides & fertilizers, architectural coatings and related process solvents, asphalt paving
activities, etc. There are other processes which consist of residential fuel combustion, farming
operations, construction & demolition, paved road dust, unpaved road dust, fugitive windblown
dust, fires, managed and unmanaged burning and disposal, cooking, etc. The sectoral description
of each of the activities and the methodology of the emission inventory is an important part of this
study.

15. 3
It provides a quantitative assessment of the magnitude and spatial distribution of air pollutants
emitted from various sources, and serves as a useful tool for policymakers, regulators, and the
public to understand the sources and levels of air pollution in a given area and develop effective
strategies to reduce emissions and improve air quality. The process of creating an emission
inventory involves collecting data on the activities that emit pollutants, such as fuel consumption,
vehicle miles travelled, industrial production rates, and population demographics. This data is then
combined with established or locally developed emission factors to estimate the total amount of
pollutants emitted from each source. Emission factors are estimates of the amount of pollutants that
are released into the atmosphere for a given unit of activity, such as one liter of fuel burned, one
kilometer travelled, or one ton of industrial production. These factors are usually developed through
laboratory experiments or field studies and can vary depending on the type of source and activity
being measured, as well as local conditions such as climate and geography. The accuracy and
reliability of an emission inventory depends on the quality of the data and emission factors used.
To ensure accuracy, quality control measures are employed throughout the process of developing
an emission inventory. This includes verifying the accuracy of the activity data, checking the
validity of the emission factors, and assessing the uncertainty associated with the estimates. Once
an emission inventory is complete, it can be used to inform air quality management decisions.
• Line Sources
• Area Sources
• Point Sources
1.1.2 Source Apportionment and Carrying Capacity Studies
Source apportionment studies are conducted to identify and quantify the contribution of different
sources to ambient air pollution. These studies are essential for developing effective air quality
management strategies by identifying the sources that are most responsible for air pollution and
targeting actions to reduce their emissions. Air pollution is caused by a variety of sources, including
industrial activities, transportation, energy production, and natural sources such as wildfires and
dust storms. Source apportionment studies use various techniques to determine the relative
contribution of each source to ambient air pollution. The goal of source apportionment is to provide
information that can be used to develop effective air quality management strategies. By identifying
the most significant sources of air pollution, regulators and policymakers can target their efforts to

16. 4
reduce emissions from those sources, which can help to improve air quality and protect public
health. In Source Apportionment studies, the ambient concentration levels of SO2 and NO2 can be
predicted by using the ISCST3 model (Chalapathi Rao C V et.al… ,2005). It is identified that in
the emission inventory the Particulate Matter emissions dominated by Vehicular, Industrial sources
(Guttikunda S. et al…,2008)
The Carrying Capacity Studies for air quality involve evaluating emissions and capacity within a
city's boundary. This includes field inspections to understand the local scenario, creating an
emission inventory, assessing population through census and migration data, and conducting traffic
surveys. The study also evaluates various emission sources, environmental indicators like
population and traffic, and determines carrying capacity considering meteorology, terrain, and
emissions. The comprehensive approach aims to gauge current air quality and forecast future
impacts.
1.2 OBJECTIVES OF THE PROJECT
The Source Apportionment (SA) and Carrying Capacity (CC) studies proposed for the non-
attainment cities primary aims at providing necessary input for effective air pollution management.
The emission inventory studies will help in developing an appropriate air quality management
plan/program and suitable inputs to decision makers to reduce the air pollutant concentrations in
each of these cities significantly.
The Objectives of the proposed study are to
i. To identify key sources responsible for specific pollutants, enabling focused control
measures.
ii. To provide accurate and comprehensive data on pollutant emissions to regulatory
authorities for compliance with environmental regulations and standards.
iii. To support air quality modeling and prediction, helping authorities understand the
potential impacts of emissions on public health and the environment.
iv. To apportion the contribution of different sources and sectors to overall emissions.
v. To select the appropriate Emission Factor for each of emission loads from the
activities in the region

17. 5
1.3 SCOPE OF THE PROJECT
The scope of an emission inventory encompasses a wide range of considerations related to the
collection, compilation, and analysis of data on pollutant emissions from various sources. It
involves a comprehensive approach to understanding and quantifying emissions, and it can vary
based on the specific goals and objectives of the inventory.
• The inventory may cover a range of pollutants, including criteria pollutants (e.g., particulate
matter, sulfur dioxide, nitrogen oxides, volatile organic compounds) and hazardous air
pollutants (e.g., benzene, lead, mercury).
• To select appropriate Emission Factors for different sources and activities.
• The inventory can be used to compare emissions against air quality standards, emission
reduction goals.
• Evaluate vehicle population, emissions, and practices. To Conduct receptor modeling for
source apportionment.
1.4 DEMOGRAPHY OF VIZIANAGARAM CITY
Vizianagaram is identified as one of the non-attainment cities in India by Government
of India. As a part of the National Clean Air Programme, a strategy to improve air quality the
Government of Andhra Pradesh initiated to conduct the SA and CC studies in major non-attained
cities in the state of Andhra Pradesh through APPCB with the support of CPCB. Vizianagaram is
the district headquarters of Vizianagaram district, Andhra Pradesh. The city is situated on the
eastern coast of Andhra Pradesh, Vizianagaram city is located at 18.12°N and 83.42°E. The city
has a population of 2.28 lakh as per 2011 census & flourishing with additional population floating
into the city everyday.
In order to maintain Ambient Air quality, the emission inventory study has been carried out.
1.4.1 Air Pollution sources in Vizianagaram city
Reviews shows that the major sources contributing to PM10 and PM2.5 are re-suspension of road
dust, emissions from vehicle movement, burning of waste and construction activities.

18. 6
CHAPTER -2
THEORY AND LITERATURE REVIEW
2.1. GENERAL
The theoretical aspects related to Air Pollution, Air Quality Assessment in general, the Emission
Inventory in particular, are presented in this chapter. A review of literature on projects carried out
on Emission inventory and Air Quality Assessment are presented here.
2.1.1 Air Pollution
Air Pollution refers to the presence of harmful or excessive substances in the Earth's atmosphere
that can have adverse effects on human health, the environment, and overall well-being. These
pollutants can come from both natural sources, such as wildfires and volcanic eruptions, and
human activities, including industrial processes, transportation, and energy production. Air
pollution and air quality are closely related concepts, as air pollution directly affects air quality.
2.1.2 Air Pollutants
Air pollutants are substances present in the Earth's atmosphere that have the potential to harm
human health, the environment, and the overall quality of the air we breathe. These pollutants can
be both natural and human-made, and they can originate from various sources such as industrial
processes, transportation, agriculture, and natural events. Air pollutants can have detrimental
effects on air quality, leading to a range of negative consequences.
The Common Air Pollutants include,
Particulate Matter (PM): Tiny solid particles and liquid droplets suspended in the air. They are
categorized by size, with PM2.5 (particles with a diameter of 2.5 micrometers or smaller) and PM10
(particles with a diameter of 10 micrometers or smaller) being of particular concern due to their
ability to penetrate deep into the respiratory system.
Nitrogen Dioxide (NO2): A reddish-brown gas primarily released from burning fossil fuels,
particularly in vehicles and power plants. It can irritate the respiratory system and contribute to the
formation of smog and acid rain.
Sulfur Dioxide (SO2): A gas produced by burning fossil fuels containing Sulfur, often from
industrial processes and power generation. It can cause respiratory problems and contribute to the
formation of acid rain.

19. 7
Carbon Monoxide (CO): A colorless, odorless gas produced by incomplete combustion of carbon-
containing fuels. High levels of CO can interfere with oxygen transport in the body and lead to
health issues.
Ground-Level Ozone (O3): This is not emitted directly but forms in the atmosphere through
chemical reactions involving precursor pollutants like Nitrogen oxides (NOx) and Volatile organic
compounds (VOCs), often emitted by vehicles, industrial processes, and certain natural sources.
Ground-level ozone can cause respiratory issues and other health problems.
Volatile Organic Compounds (VOCs): These are emitted from various sources including vehicle
exhaust, industrial processes, and certain household products. VOCs can contribute to the
formation of ground-level ozone and smog, and some can have harmful health effects.
The presence of these pollutants in the atmosphere affects the air quality and causes air pollution.
2.2 AIR QUALITY MONITORING
Air Quality refers to the condition or cleanliness of the air in a specific area, reflecting the
concentration of pollutants present. It is often assessed based on the levels of various pollutants
and their potential impact on human health and the environment. Good air quality implies that the
concentration of pollutants is within acceptable limits and poses minimal risks to health, while
poor air quality indicates that pollution levels are elevated and could lead to health problems,
reduced visibility, and environmental degradation. Air quality monitoring and regulation are
crucial for safeguarding human health and the environment. To evaluate the quality of air, air
quality assessment is to be done.
2.2.1 Ambient Air Quality Assessment
Ambient Air Quality Assessment is the process of evaluating and measuring the cleanliness of the
air in a specific area to determine the levels of pollutants present and their potential impact on
human health and the environment. An Air Quality Assessment is an assessment undertaken to
establish the baseline air quality and is usually required for any development that has the potential
to impact the existing environment or if the environment has the potential to affect the sensitive
development. An indoor air quality assessment tests the levels of air quality within the premises of
a building or structures. The process of an indoor air quality sampling differs but would include
the collection and analysis of air samples using swab/sticky pads. An outdoor air quality
assessment can be a simple air quality screening assessment or a detailed air quality assessment

20. 8
involving monitoring and dispersion modeling. The air quality assessment depends on a number
of factors including the size of the development, its proposed location and the extent of current
knowledge about levels of pollutants close to the site.
2.2.2 Air Quality Index
The air quality index (AQI) is an index for reporting air quality in an area during a period. The
purpose of the AQI is to help people know how the local air quality and possible impacts on their
health. The AQI determination considers five major air pollutants, for which national air quality
standards have been established to safeguard public health. Five major pollutants are Ground-level
Ozone, Particulate Matter (PM2.5/PM10), Carbon Monoxide, Sulfur dioxide and Nitrogen Dioxide.
The higher the AQI value, the greater the level of air pollution and the greater the health concerns.
The concept of AQI has been widely used in many developed countries for over the last three
decades. AQI quickly disseminates air quality information in real-time. As technology advances,
a vast amount of data on ambient air quality is generated and used to establish the quality of air in
different areas. The studies carried out under Ambient Air Quality Assessment are Pollutant
Monitoring, Source Apportionment and Emission inventory.
2.3 SOURCE APPORTIONMENT
Source apportionment studies involve identifying and quantifying the contributions of different
pollution sources such as industries, vehicles, natural sources to the overall pollution levels in a
specific area. Through detailed analysis of pollutants and their chemical characteristics, as well as
data on emissions and atmospheric conditions, these studies help to attribute the relative impacts
of various sources. The results guide targeted pollution control measures, policy formulation, and
mitigation strategies, aiming to effectively reduce pollution and improve ambient air quality while
understanding the specific sources driving pollution in that region. To identify the sources and
activities which contribute to the emissions to the atmosphere, emission inventory is to be done.
Different approaches are used to determine and quantify the impacts of air pollution sources on air
quality. Commonly used SA techniques are
Explorative methods - Exploratory methods use simple mathematical relationships and number
of assumptions to achieve a preliminary estimation of the source contribution.
Emission inventories - Emission inventories are detailed compilations of the emissions from all
source categories in a certain geographical area and within a specific year. Emissions are estimated

21. 9
by multiplying the intensity of each relevant activity (activity rate) by a pollutant dependent
proportionality constant (emission factor).
Inverse modelling - In inverse modelling, air quality model parameters are estimated by fitting the
model to the observations. The inverse technique consists of a least squares optimization with an
objective function defined as the sum of squared deviations between modelled and observed
concentrations.
Lagrangian models - Lagrangian models use a moving frame of reference to describe the
trajectories of single or multiple particles as they move in the atmosphere.
Gaussian models - Gaussian plume models assume that turbulent dispersion can be described using
a Gaussian distribution profile. This type of model is often used to estimate emissions from
industrial sources
Eulerian models - Eulerian models encompass equations of motion, chemistry and other physical
processes that are solved at points arranged on a 3D grid.
Receptor models - Receptor models focus on the properties of the ambient environment at the
point of impact, as opposed to the source-oriented dispersion models which account for transport,
dilution, and other processes that take place between the source and the sampling or receptor site.
2.4 EMISSION INVENTORY IN INDIA
An emission inventory is a comprehensive record of the number of pollutants and greenhouse gases
released into the atmosphere from various sources within a specific geographic area over a defined
period of time. It serves as a valuable tool for assessing air quality, understanding the impact of
emissions on the environment and human health, and developing strategies for air quality
management and environmental protection. Emission inventories are used by regulatory agencies,
researchers, policymakers, and industries to track and manage emissions and their associated
effects.
2.4.1 Emission Inventories and Associated Parameters
In preparing EIs for various cities, researchers have considered different polluting sources and
study boundaries. Particulate matter (PM10 and PM2.5), considered to have the most impact on the
human body, has been estimated as the primary pollutant. Other pollutants estimated in most of
the EIs are Sulphur dioxide (SO2), Nitrogen oxides (NOx), Ammonia (NH3), Carbon monoxide

22. 10
(CO), and Volatile organic compounds (VOCs).Though PM is primarily emitted from both natural
and anthropogenic activities, a significant portion of it comes from human activities such as
agricultural operations, industrial and commercial processes (combustion of wood and fossil fuels),
construction and demolition activities, and re-suspension of road dust (ARAI, 2010; CPCB, 2010;
Guttikunda et al., 2015; IITM, 2010; NEERI, 2010b; Sarkar et al., 2010).As both natural and
anthropogenic activities contribute to a city’s emission load, the corresponding polluting sources
need to be identified for developing mitigation policies. The sources contributing to the emission
load may vary across various cities depending on geographical conditions, economic activities,
and livelihood patterns. This makes every city unique, and thus, city-specific strategies are required
to mitigate pollution. However, a few of the pollution sources such as transportation, domestic and
commercial fuel consumption, and dust from road and construction-demolition activities remain
the same for almost all cities. Emission factors (EF’s) are crucial for estimating the emission loads,
for developing an EI. EF’s for various sectors have been estimated by the United States
Environmental Protection Agency (USEPA) (AP 42) (US EPA). India’s Central Pollution Control
Board (CPCB) has adopted the EF of PM 10 from the AP 42 list. EF’s for the transportation sector
have been developed by ARAI (ARAI, 2010; TERI & ARAI, 2018a). Moreover, other global lists
such as EDGAR (Janssens-Maenhout et al., 2015) provide EF’s for various pollutants. Various
factors influence the EF value, such as geographical condition variations, technology changes, fuel
changes, and others (Janssens-Maenhout et al., 2015; Li et al., 2017; NEERI, 2010). EIs prepared
at the city level help in understanding the major polluting activities/sectors in the city, and
dispersion modelling help in understanding the spread of the various pollutants.
However, modelling requires an understanding of regional- and country-level emissions too, so
that the boundary conditions are known. In the absence of regional- and country-level EIs for India,
open-source EIs can be used, such as Emission Database for Global Atmospheric Research
(EDGAR).
2.4.2 Emission Inventory Studies in Indian Cities
In the last decade and a half, many studies have been conducted to estimate and quantify air
pollution in India. However, 2010 was a milestone year for India’s EI studies as city-specific EIs
were developed for six cities—Delhi, Mumbai, Chennai, Bengaluru, Kanpur, and Pune. Although
country-specific (Baidya & Borken-Kleefeld, 2009; T. V. Ramachandra & Shwetmala, 2009;
Reddy & Venkataraman, 2002) EIs have been developed earlier, city-specific inventories helped

23. 11
to understand the pollution landscape at the city level. In these EI studies, researchers had
considered most of the pollutants to understand the impact of various sources on the cities’
emission load. The EIs developed for the six cities followed a CPCB-approved methodology.
Moreover, the EIs focused on the city area and not on the air-shed area. The developed EIs had a
spatial resolution of 2 km × 2 km and helped to understand sectoral emission loads and their share
in the cities’ total emission load. All the Studies confirmed that the transportation sector (tailpipe
emission and re-suspension of dust) contributed the most to PM 10 emission load, whereas the
domestic sector contributed the least. After 2010, many studies were conducted to develop EIs for
different cities (Mishra & Goyal, 2015; Pandey & Venkataraman, 2014; Sadavarte &
Venkataraman, 2014; Sahu, Ohara, et al.,2015; Sahu, Schultz, et al., 2015; M. Sharma & Dikshit,
2016; Sindhwani et al., 2015; TERI & ARAI, 2018a).
List of cities developed Emission Inventory in India,
Delhi: As Delhi is the capital of India and one of the most polluted cities in the world, many studies
have been conducted to estimate the emission load share of different polluting sectors(Guttikunda
& Calori, 2013; Mishra & Goyal, 2015; M. Sharma & Dikshit, 2016; Sindhwani et al., 2015; TERI
& ARAI, 2018a). All these studies had different objectives, and hence, the total emission load
(PM10) for Delhi as estimated by these studies ranged from 38,230 tonnes per year to 114,000
tonnes per year. The variation in the estimated PM 10 emission load was due to the variation in
the selected study area (780 km2 to 6400 km 2) and the polluting sectors considered.
Mumbai: For Mumbai, the only EI was developed in 2010 by the National Environmental
Engineering Research Institute (NEERI). The study was conducted for an area of 1056 km2
. The
total PM 10 emission was estimated to be 26810.8 tonnes/year. Re-suspension of dust (from paved
and unpaved roads) was identified as the biggest polluting source of PM 10, and industrial emission
was identified as the biggest polluting source of SO2.
Chennai: For Chennai, the only inventory was prepared by IIT Madras (IITM, 2010), for an area
of 812 km 2. Re-suspension of dust was identified as the biggest polluting source of PM 10.
Kanpur: Multiple studies (Gaur et al., 2014; A. Goel et al., 2017; M. Sharma, 2010) have been
conducted to estimate the pollution sources in the city. The major contributors of SO2 were found
to be vehicular emission, garbage burning, and coal combustion. Wood combustion was found to
be limited to the city’s outskirts.
Pune: For Pune, the only inventory was prepared by the Automotive Research Association of India

24. 12
(ARAI) (ARAI, 2010), for an area of 440 km 2. Total PM 10 from all the sources was estimated
to be 11789 tonnes/year. The study identified re-suspension of dust (PM 10) and tailpipe emissions
(NOx) as the biggest pollution sources.
Bengaluru: For Bengaluru, the only EI was developed in 2010 by The Energy and Resources
Institute (TERI). This exercise was part of a source apportionment study, which covered an area
of 624 km 2 and estimated a PM 10 emission load of around 19,856 tonnes/year. Since then, the
existing EI has not been revised. However, an EI was developed on the basis of secondary data by
Guttikunda et al., 2019, which estimated a PM10 emission load of 67,100 tonnes/year for an air-
shed area of 3600 km 2. For reducing air pollution, TERI, 2010 prescribed limiting heavy vehicles
to peripheral ring roads. The institute also suggested using compressed natural gas (CNG) in public
buses and installing diesel oxidation catalysts (DOCs) and diesel particulate filters (DPFs) in all
pre-2010 vehicles. Wall-to-wall paving was recommended for reducing road dust. Prohibition of
diesel generator (DG) sets and implementation of better construction practices were also
recommended.
2.5 LITERATURE REVIEWS RELATED TO EMISSION INVENTORY
The following are the past studies on Emission Inventory studies by few researchers and
Institutions are presented.
Bhanarkar A.D. et al. (2005) developed a comprehensive and spatial emission inventory was
carried out for Sulphur dioxide (SO2), particulate matter (PM) and toxic metals from industrial
sources in Greater Mumbai, India. Fuel consumption database was developed for industrial
sources. Emission factors for various pollutants were compiled from the literature, scrutinized and
used appropriately as applicable under Indian conditions. Emissions of SO2, PM and toxic metals
were estimated for 2001–02 and extrapolated to 2010. SO2 emissions from fossil fuel combustion
covering 215 point sources for 2001–02were computed as 55.591 Gg/ y whereas those for PM
were calculated as 9.794 Gg/ y. The total metal emissions from industrial sources were computed
as 0.375 Gg/ y. Total fossil fuel energy consumption in industrial sector during 2001–02 was 145
PJ, which included fuel consumption (29%) in power plants. It was found that among the
industries, thermal power plants (TPP) were the major source of emissions in the region
contributing 27% share towards SO2, 19% PM and 62% metals.

25. 13
Rao C.V.C et al. (2005) Contribution of pollution from different types of sources in Jamshedpur,
the steel city of India, has been estimated in winter 1993 using two approaches in order to delineate
and prioritize air quality management strategies for the development of region in an environmental
friendly manner. The first approach mainly aims at preparation of a comprehensive emission
inventory and estimation of spatial distribution of pollution loads in terms of SO2 and NO2 from
different types of industrial, domestic and vehicular sources in the region. In the second approach,
contribution of these sources to ambient air quality levels to which the people are exposed to, was
assessed through air pollution dispersion modelling. Ambient concentration levels of SO2 and NO2
have been predicted in winter season using the ISCST3 (Industrial Source Complex Short Term)
model. The results of the modelling exercise showed that in the city area, concentration levels of
SO2 and NO2 would be relatively high. Source contribution analysis carried out through emission
loads estimation and model predictions revealed that SO2 and NO2 concentrations in the city area
have been dominated by 77% and 68% of total emissions from industrial sources which contribute
to 54% and 51% of total SO2 and NO2 concentrations in the urban area. Even though the share of
SO2 and NO2 emissions from domestic and vehicular sources, respectively, is smaller in relation
to the total emissions as compared to industrial sources, the contribution of domestic (38%SO2)
and vehicular (41% NO2) sources to the concentration in ambient air is high. More than 50% of
the city area is dominated by industrial sources where contribution to SO2 and NO2 concentrations
by industrial sources is 50% and 89% of the total, respectively. Nearly 30% of the city area is
affected by vehicular pollution, and its contribution to NO2 concentration is 50% of the total. The
researcher identified that the SO2 and NO2 are the major emissions from the steel industries in
Jamshedpur.
Guttikunda S. et al. (2008) develop emission inventory in Hyderabad city. The area around
punjagutta circular grid network with the radius of 5 km. The next 5 km distance (penultimate
grid) covers areas with high to moderate pollution. Similarly, the next 5 km distance (outer
grid) covers areas with moderate to low pollution.In this grid, monitoring stations were installed
in a phased manner. Six stations are installed at Abids, Punjagutta, Paradise, Charminar, Zoo
Park, KBRN Park and all the stations are manually operated. An aerosol samples were collected
using Mini Volume Portable Air Samplers operating for 24 hour sampling periods. The sampling

26. 14
was conducted in three phases based on the climatic conditions to represent the three predominant
seasons - winter, summer and rainy. Overall, PM emissions are dominated by vehicular, industrial,
and fugitive sources. The garbage burning, a very uncertain source of emissions due to lack of
necessary information on the amount burnt and proper emission factors, is a significant
unconventional source. Only one landfill to the southeast of MCH border is estimated to burn on
average 5 percent of the trash collected and combined with the domestic fuel consumption accounts
for ~10 percent of the annual PM10 emissions. Emissions of PM10, SO2, NOx, and CO2 are
estimated at 29.6 kt, 11.6 kt, 44.5 kt, and 7.1 million tons respectively. For CO2, a major GHG
gas, the transport sector accounts for 90 percent of the emissions. Based On this study the
researcher identified that the Transportation sector accounts for more emissions than other sectors.
Sahu S. K. et al. (2010) presented a report on emissions inventory (EI) of PM10 and PM2.5 for the
metropolitan city Delhi for the year 2010. The comprehensive inventory involves detailed activity
data and developed for domain of 70 km × 65 km with a 1.67 km × 1.67 km resolution covering
Delhi and surrounding region using Geographical Information System (GIS) technique. The major
sectors considered are, transport, thermal power plants, industries, residential and commercial
cooking along with windblown road dust which is found to play a major rolefor Delhi
environment. It has been found that total emissions of PM10 and PM2.5including wind-blown dust
over the study area are found to be 236 Gg /yr and 94 Gg/ yr respectively. The contribution of
windblown road dust is found to be as high as 131 Gg yr−1
for PM10. This study concluded that the
unattended source of windblown dust from paved and unpaved roads is surprisingly found to be
the major contributor in PM10. Hence the researcher stated that the transport sector which has direct
contribution through fossil fuel combustion and indirect related to road condition provide the key
to better air quality in NCRD if properly mitigated along with road condition and construction
activities.
Sailesh N. et al. (2011) developed a model on GIS-based emission inventory, Dispersion
Modelling, and Assessment for Source Contributions of Particulate Matter in an Urban
Environment, The Industrial Source Complex Short Term (ISCST3) model was used to discern
the sources responsible for high PM10 levels in Kanpur City, a typical urban area in the Ganga
basin, India. A systematic geographic information system-based emission inventory was

27. 15
developed for PM10 in each of 85 grids of 2 × 2km. The total emission of PM10was estimated at
11 TPD with an overall breakup as follows: (a) industrial point sources, 2.9TPD (26%); (b)
vehicles, 2.3TPD (21%); (c) domestic fuel burning, 2.1TPD (19%); (d) paved and unpaved road
dust, 1.6TPD (15%); and the rest as other sources. To validate the ISCST3 model and to assess air-
quality status, sampling was done in summer and winter at seven sampling sites for over 85days;
PM10levels were very high 89 – 632μgm−3). The researcher claimed that the model-predicted
concentrations are in good agreement with observed values, and the model performance was
found satisfactory.
Sarath K. et al. (2013) developed an emission inventory in Delhi, at seven monitoring stations, the
daily average of particulates with diameter <2.5 μm (PM2.5) was 123 ± 87 μg m−3
and particulates
with diameter <10 μm (PM10) was 208 ± 137 μg m−3
The bulk of the pollution is due to
motorization, power generation, and construction activities. In this paper, they presented a multi-
pollutant emissions inventory for the National Capital Territory of Delhi, covering the main district
and its satellite cities – Gurgaon, Noida, Faridabad, and Ghaziabad. For the base year 2010, we
estimate emissions (to the nearest 000's) of 63,000 tons of PM2.5, 114,000 tons of PM10, 37,000
tons of sulfur dioxide, 376,000 tons of nitrogen oxides, 1.42 million tons of carbon monoxide, and
261,000 tons of volatile organic compounds. The inventory is further spatially disaggregated into
80 × 80 grids at 0.01° resolution for each of the contributing sectors, which include vehicle exhaust,
road dust re-suspension, domestic cooking and heating, power plants, industries (including brick
kilns), diesel generator sets and Waste burning. The GIS based spatial inventory coupled with
temporal resolution of 1 h, was utilized for chemical transport modeling using the ATMoS
dispersion model. The modeled annual average PM2.5 concentrations were 122 ± 10 μg m−3
for
South Delhi; 90 ± 20 μg m−3
for Gurgaon and Dwarka; 93 ± 26 μg m−3
for North-West Delhi; 93
± 23 μg m−3
for North-East Delhi; 42 ± 10 μg m−3
for Greater Noida; 77 ± 11 μg m−3
for Faridabad
industrial area. The researcher stated that results have been compared to the CPCB standard values
found 3 times higher in South Delhi which are far beyond the permissible limits of CPCB
guidelines.
Vicente F. et al. (2013) prepared a review on development of road vehicle emission factors. For
proper planning and execution of air quality strategies, pollutant emissions must be precisely
estimated. The most popular emission measurement methods, such as engine and chassis

28. 16
dynamometer measurements, remote sensing, road tunnel investigations, and portable emission
measurement systems (PEMS),are described in this article. Regarding emissions modeling, the
key benefits and drawbacks of each approach are discussed. A discussion of the methods for
deriving EFs from test data is also carried out, with a specific distinction made between
measurements made in real- world operation and data produced under controlled circumstances
(engine and chassis dynamometer measurements using conventional driving cycles). The
development of accurate EFs found in road vehicle emission models is a joint enterprise among
several parties that requires intensive testing to adequately cover all the relevant vehicle types and
driving conditions, and substantial research and modelling efforts to keep up with technological
advances and improve the methodologies to accurately reflect real-world emissions. All this needs
to be accomplished with limited resources. However, given their inherent inability to capture the
full range of real-world driving parameters (even when real-world test cycles are used) these should
not be the only data sources that emission modelers tap into. Indeed, the role of the technologically
less mature real-world techniques (such as PEMS, remote sensing or tunnel studies) in EF
development should not be downplayed, as they have often proved to be valuable resources of data
for key aspects of emissions modelling such as EF validation, investigation of off-cycle emissions,
characterization of emission trends, identification of high emitters, assessment of alternative fuels
and evaluation of the influence of real-world conditions upon the emission profile of vehicles and
the formation of secondary pollutants. All of the above contribute to the quality of emission
models, and to the achievement of long-term environmental goals.
Outapa P. et al. (2014) developed emission inventory using the IVE (International Vehicle
Emission) model, & created dynamic emission factors to more correctly project vehicle emission
inventory based on journey distance data. These emission inventories are created using a bottom-
up methodology and are based on dynamic emission variables. This project creates air hazardous
emission inventories for mobility sources in Bangkok from 2009 through 2024. This study also
assessed the variables, factors that affected the emission variables, and the inventory of air
hazardous emission sources in the study area. The basis year for this study is 2009, which has been
chosen. In order to predict the number of vehicles in Bangkok he utilized the average yearly growth
rate of each vehicle type from 2000 to 2010, the number of cars is predicted from 2009 to 2024.
The anticipated pollution and fuel criteria are taken into consideration while setting the fleet

29. 17
characteristics for each predicted year. The study's classification of vehicle types includes
passenger cars, vans and pickup trucks, taxi motorcycles, public motorcycles, buses, public vans,
and trucks and the researcher highlights the significance of dynamic emission factors and the IVE
model in enhancing emission inventories for vehicle pollution. Their findings provide valuable
insights into emission reduction scenarios, emphasizing the importance of data-driven strategies
for improving air quality and environmental sustainability.
Prasanth G. et al. (2014) developed PM10 inventory for Delhi in conjunction with source profiles
was used to estimate emissions of major PM10 components including organic and elemental
carbon (OC and EC respectively), Sulphates (SO4-2), and Nitrates (NO-3), as well as selected
toxic trace metals (i.e., Pb, Ni, V, As, and Hg), some of whichare subject to India’s National
Ambient Air Quality Standards (NAAQS). Emission inventories were constructed in the NEERI
(2010) study for PM10 mass and other criteria pollutants in 2 × 2 km2
zones of influence (Chow et
al., 2002) surrounding each monitoring site. PM10 source attributions for seven emission sources
by fuel types (i.e., including vehicular categories but excluding road dust, construction, and non-
fuel specific sources [i.e., open burning and waste incineration]) in the city-wide inventory. And
he also analyzed the emitting sources and their pollution concentrations. Paved road dust,
construction activities, power plants, domestic cooking and vehicles are the main sources of
PM10 in Delhi. He was further stated that continue ambient measurements of PM2.5, PM10, and
their major chemical components order to establish a long-term database for evaluating the
effectiveness of pollution control measures.
Sindhwani R. et al. (2016) conducted a study that aims to develop a spatial high-resolution
emission inventory (2 km × 2 km) of criteria air pollutants (CO, NOx, SO2 and PM10) for National
Capital Region (NCR), Delhi. The inventory is centered at the metropolitan area of Delhi, and
includes adjoining parts of the neighboring states of Haryana and Uttar Pradesh within an area of
70 km X 70 km. The bottom-up gridded emission inventory has been prepared taking into account
land use pattern, population density as well as industrial areas which includes major emission
sources of the region, namely vehicular exhaust, road-dust re-suspension, domestic, industrial,
power plants, brick kilns, aircrafts and waste sectors. Data corresponding to various sectors along
with related emission factors have been acquired from literature and various regulatory bodies for

30. 18
the study domain. The results reveal that total estimated emissions from vehicular exhaust, road
dust and power plants contribute nearly 52%, 83%, 74% and 54% of PM10, SO2, NOx and CO
emission respectively. Transport sector has been found as the bulk contributor towards CO and
NOx emissions. Coal-fired power plants corresponds to the most polluting sector with regard to
SO2 contributing ~67%. Power plants Badarpur, Rajghat, Indraprastha and Faridabad power plant
emerged as the primary hotspots for SO2 and PM10 emissions. Further, Primary and secondary
emission hotspots for each criteria pollutant has been identified and discussed in detail and In
addition to it, the researcher has performed forward trajectory analysis to assess the impact of
emissions over the regional scale this gives a qualitative approach to assess the uncertainty in the
emission estimates.
Sharma M. et al. (2016) presented a Comprehensive Study on Air Pollution and Green House
Gases (GHGs) in Delhi, The primary data were collected by IITK team. Parking lane survey at
18 locations was done to assess types of vehicles on the road. Construction and demolition data
was collected by field survey and validated by satellite imagery. Road dust sampling at 20
locations was conducted. Physical survey of industrial areas was also done. The main sources of
secondary data collection are from DPCC, Delhi Metro Rail Corporation (DMRC), Census of
India, CPCB website, AAI (Airport Authority of India), Indian Railways, and Central Electricity
Authority (CEA). Information has also been collected through Internet by visiting various
websites. The land-use map of the study areais prepared in terms of settlements, forests,
agriculture, road network, water bodies, etc. The entire city was divided into 441 grid cell of 2
km x 2 km. Different types of area sources are compiled to calculate emissions. The assessment
of contributions from different sources yields a comprehensive overview of the PM10 emission
landscape in Kanpur. The study estimates a total PM10 emission of approximately 11.2 tons per
day. The breakdown of sources includes industrial point sources (26%), industrial area sources
(7%), vehicles (21%), domestic fuel burning (19%), road dust (15%), open burning (5%), hotel
and restaurant fuel use (4%), diesel generator sets (1%), and other miscellaneous sources (2%).
Industrial point sources, notably a 200 MW coal-based thermal power plant, emerge as the largest
contributor to PM10 emissions.
Dhananjay S. et al. (2016) prepared an assessment of road dust contamination in India. The road
dusts (RD) are fugitive in nature causing potential health hazards to people livingin highways.

31. 19
They are generated from different sources on the roads and being a valuable archive of
environmental information. In the present work, contamination assessment of 18 heavy metals
and ions in road dusts of the country are described. The road dust samples were collected
from 42 locations of the country, near high way. The most of sampling locations were chosen
from the Chhattisgarh state of the country due to running of several industries and coal based
thermal power plants. Other samples were taken from 5 cities and towns of India. Total 42 surface
road dust samples (0 - 10 cm) over area of 6 × 6 cm2
were collected from various locations of the
country in year, 2008. Four samples from different points of each location were collected, and a
composite sample was prepared by mixing them in equal mass ratio. Techniques i.e. ion selective,
ion chromatography and atomic absorption spectrophotometers were used for analysis of the ions
and metals. The main dominating species in the road dust is the Fe, contributing ≈75% fraction
of the content of 18 elements (i.e. F−
, Cl−
, NO−3
, SO4
−2
, NH4
+
, Na+
, K+
, Mg2+
, Ca2+,
As, Cr, Mn,
Fe, Ni, Cu, Zn, Pb and Hg). However, the fraction of Na and Ca includes 4% and 8%, respectively.
The road dust is a sodic in nature at hazardous levels. The motor vehicle exhaust emissions are
expected to be main sources for contaminating the road dust with Cl-
, SO4
-2
, Cu, Zn and Pb nearby
highways. The higher concentration of F−
was marked in two locations: Raipur and Korba of the
country due to huge coal burning and running of an Aluminum Plant.
Pallavi P. et. al (2016) developed article on Characterization of traffic Related Particulate Matter
Emissions In a road tunnel, Birmingham Road. In this study, PM samples were collected
simultaneously in a road tunnel and at a background site in Birmingham (UK) and analyzed.
The tunnel samples show a large enrichment of trace elements relativeto the urban background
with a mode at ca. 3 µm in the mass size distribution, indicative of emissions resulting from
resuspension/abrasion sources. Cu, Ba and Sb were found to have the characteristic non-exhaust
(brake wear) emission peaks in the coarse size range in the tunnel. A composite PM2.5 traffic
profile was prepared using the data from the two sites, and was compared against previously
reported profiles. The profile was also comparedagainst other traffic profiles from Europe and
USA, and was found to be very similar to the previously reported PM2.5 composite traffic profile
from the UK. However, the uncertainties associated with the species were found to be much
lower in the case of tunnel profile from this study, and they conclude that this profile would

32. 20
be very suitable for use in Chemical Mass Balance Model analyses for the UK and other countries
with a similar road traffic fleet mix. Road transport constitutes an important source of particulate
matter (PM) emissions in urban areas, and motor vehicles are an important source of carbonaceous
aerosols particularly for the particles in the fine size range (aerodynamic diameter < 2.5 µm) (Kam
et al., 2012; Keuken et al., 2012). Average PM2.5 concentrations in the UK range between 12–15
µg/ m3
(Harrison et al., 2012a) and primary road traffic emissions contribute nearly 30% of
the total PM2.5 and contribute 30–50% of the urban and road side increments of PM.
Yan Z. et al. (2017) studied the rapid development of China’s container port industry, the
emissions of air pollutants in port areas have been increasing. Cargo handling equipment asa
non-road mobile source of emissions has become a focus of public attention. This article adopted
a full activity-based ‘‘bottom-up’’ method to establish the inventory of emissions by cargo
handling equipment at a container port. Drawing on the OFFROAD model of the USEPA (United
States Environmental Protection Agency) to study on the emission characteristics of non-road
diesel engine with various emission data sources, conductedinvestigation and analysis of cargo
handling equipment holdings, activity levels, and equipment-related parameters and modified the
emission factors. Cargo handling equipment produced more PM and HC emissions than any other
emission source at the port. Enginetypes of the bridge crane were entirely changed from diesel
into electricity, as well as a few power of RTG cranes were changed from diesel into electricity,
in which the CO and NOx emissions were reduced to about 68%. Accelerate the implementation
of the engine ‘‘tooptimize the fuel + SCR (selective catalytic reduction) technology roadmap’’
and control the emission of PM and NOx. The method and main conclusions of this article
provide support for future work on energy conservation and emission reduction in port areas.
Nishad K. et al. (2019) studied Air quality, emissions, and source contributions analysis for the
Greater Bengaluru region of India, Urban emissions inventory at 1-km spatial resolution was
established for the Greater Bengaluru region for sources including road/rail/aviation/shipping
transport, power generation through diesel generator sets, small and medium scale industries,
urban road dust resuspension, domestic cooking/heating/lighting, construction activities, and
open waste burning. Regional emission sources, where relevant, are also considered in the
modeling exercise includingopen fires, sea salt, dust storms, biogenic, and lightning, but are not

33. 21
included in the urban emissions inventory calculations presented in this paper.The methodology
for estimating emissions is based on activity data by sector (for example fuel consumed for
vehicle exhaust, vehicle km traveled for road dust, waste collected or left behind for open waste
burning) and relevant emission factors. The emissions inventory is developed for total PM in
four bins (PM10 and PM2.5, black carbon (BC), organic carbon (OC)), SO2, nitrogen oxides
(NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), and
carbon dioxide (CO2).
Bin xu. et al. (2020) Proposes the latest high-resolution emission inventory through the emission
factor method and compares the results with the rest of the urban agglomeration. This study
summarized the emission factor database suitable for Changzhutan urban agglomeration. The
emission factors of SO2, NOx, PM10, PM2.5, VOCs, and NH3 come from the latest literature. Used
eight major data sources to ensure the refinement of emission inventory data, including
longitude/latitude coordinates of pollution sources, product types, fuel categories, technical
processes, and pollution control measures. The emission inventory shows that the estimates for
Sulphur dioxide (SO2), Nitrogen oxides (NOx), Particulate matter 10 (PM10), Particulate matter
2.5 (PM2.5), Volatile organic compounds (VOCs), and Ammonia (NH3). From the 3 × 3 km
emission grid, the spatial difference of air pollutant emissions in the Changzhutan urban
agglomeration was more obvious, but the overall trend of monthly pollutant discharge was
relatively stable. They also provide the source and transmission path of the air mass in different
seasons. Simultaneously, industrial emissions, vehicle exhaust, and dust are still three main
sources that cannot be ignored. With the support of these data, the results of this study may
provide a reference for other emerging urban agglomerations in air quality. In this study, we
developed a high-resolution CZT urban agglomeration air pollutant emission inventory for the
year 2015. Conclusions are as follows: The total emissions of SO2, NOx, PM10, PM2.5, VOCs, and
NH3 are 132.5, 148.9, 111.6, 56.5, 119.0, and 72.0 KT, respectively. The discharge of atmospheric
pollutants in the CZT urban agglomeration shows obvious spatial differences. The monthly
variation trend of major air pollutants is relatively stable, and the monthly emission of some
pollutants peak in autumn and winter. The chemical composition data indicate that the main
species in the PM2.5 of the CZT urban agglomeration in 2015 are SO4
2-
, OC, and NO3
-
, and the

34. 22
annual average concentrations are 13.06, 8.24, and 4.84 μg/m3, respectively. The regional PM2.5
pollution shows obvious seasonal differences, and the PM2.5 concentration in winter varies greatly.
The results show that the influence of the source types of Changsha, Zhuzhou, and Xiangtan on
PM2.5 is not significant and consistent, but pollution causes of PM2.5 are similar.
Erin E. et al.(2020) develop a global anthropogenic emission inventory of atmospheric pollutants
from sector- and fuel-specific sources (1970–2017), an applicationof the Community Emissions
Data System (CEDS).They have updated the open-sourceCommunity Emissions Data System
(CEDS) to develop a new global emission inventory, CEDSGBD-MAPS. This inventory includes
emissions of seven key atmospheric pollutants (NOx ; CO; SO2; NH3; NMVOCs; black carbon,
organic carbon) over the time period from 1970-2017 and reports annual country-total emissions
as a function of 11 anthropogenic sectors (agriculture; energy generation; industrial processes;
on-road and non-road transportation; separate residential, commercial, and other sectors (RCO);
waste; solvent use; and international shipping) and four fuel categories (total coal, solid biofuel,
the sum of liquid-fuel and natural-gas combustion, and remaining process- level emissions). The
CEDSGBD-MAPS inventory additionally includes monthly global gridded (0.5 × 0.5) km
emission fluxes for each compound, sector, and fuel type to facilitate their use in earth system
models.
Debananda R. et al. (2021), conducted a survey on Emission inventory of PM10 in Dhanbad/Jharia
coalfield (JCF), India. Inventory of natural (mine fire) and anthropogenic (mining and non-
mining) was considered to create actual database in the study area. It is a unique approach for a
complex coal mining zone associated with mine fire in India. The multiple emission sources such
as anthropogenic (open coal mining, industrial and local) and natural (coal mine fire) are
responsible for the complexity in the study area. Gridding systems of 129 grids (2km × 2km each)
were developed to build up a detailed database of sources/activities throughout the study area.
The total 9409 kg/day emission load of PM10 was estimated during study period. Between all
the sources, emission from the open-castcoal mining (19.97%), thermal power plant (18%),
vehicles (16%), the paved/unpaved road (14%), domestic fuel burning (12%), open coal burning
and mine fire (6%) and garbage burning (5%) were generated a significant amount of PM10
throughout the study area.

35. 23
Claudie W. et al. (2021) developed an Emission Inventory Report of Hong Kong in the year 2019.
In order to assist in the development of efficient air quality management strategies in Hong
Kong. This inventory analyzes thequantity of local air pollutant emissions and their principal
sources of emission. Additionally, it offers the information required to conduct impact analyses
on air quality. The following topics are covered in the emission inventory by source category; (i)
the emission trends for six key air pollutants from 2001 to 2019; (ii) the sectoral analyses for
six source categories of emissions; and (iii) The emissions from hill fires. The emission inventory
includes estimates of emissions for six major air pollutants: Sulphur dioxide (SO2), nitrogen
oxides (NOx), Respirable suspendedparticulates (RSP or PM10), fine suspended particulates (FSP
or PM2.5), volatile organic compounds (VOC), and carbon monoxide (CO). These emissions come
from seven source categories. The production of public energy, transportation by road, navigation,
and civil aviation, other combustion sources, non-combustion sources, and hill fires are some of
the sources of emissions.
Abdullah k. et al. (2022) investigated on PM national inventory data and mass concentration
trends for Lithuania. This analysis considers primary (sum of filterable and condensable) PM2.5
and PM10 emissions from point, mobile on-road and off-road, industry, agriculture, and waste
sectors. The Lithuanian emission inventory is based mainly on statistics published by the
Lithuania Statistics Department (Statistical Yearbooks of Lithuania, sectoral yearbooks on
energy balance, agriculture, commodities production, etc.), emission data collected by the
Environment Protection Agency, and others. Additionally, a major part ofthe NFR categories
in the 2019 EMEP/EEA methodology with provided emission factors was applied. The mass
concentrations of PM2.5 and PM10 were measured at 15 automatedair pollution monitoring sites
by EPA in Lithuania, accessed on 28 October 2022. In this study, by examining both the
emissions and the mass concentrations of PM10, the effects of emissions decreasing with a
concentration decrease were revealed. The total national emissions expressed in Gg from the year
2005 to 2020. The total PM2.5, PM10, and BC emissions amounted to 6.51 Gg, 17.75 Gg, and 1.90
Gg, respectively, in 2020. In 2020 PM2.5, PM10, and BC decreased by 1.26, 1.06, and 1.27%,
respectively, compared to thebase year (2005). The slower decreasing tendency of PM10 and
BC (0.03 Gg/year) than thatof PM2.5 (0.1 Gg/year) should be noted. The researcher is claimed

36. 24
that the prevention or reduction of air pollutant emissions should be ensured by establishing
ambient air quality targets while taking into account relevant WHO 2021 standards, guidelines,
and programs.
Saidi L. et al. (2023) analyzed emission inventory provided by the Moroccan Ministry of
Environment, Mines, and Sustainable Energy (MEMSD) includes annual emission fluxes for the
reference year 2013 of seven air pollutants: NOx, SO2, NMVOC, NH3, CO, PM10, PM2.5 and the
greenhouse gas CH4 (Moroccan Ministry of Environment, Mines, and Sustainable Energy, 2018).
The MEMSD inventory is compiled following a top- down approach based on national energy
consumption data and emissions factors related to each activity. The major component of
summertime PM10 composition is dust particles, whereas in winter PM10 consists mainly of
primary organic aerosol. PM10 simulated concentrations are closer to in-situ surface
measurements during summer than during winter with an overestimation of 13% in summer
versus an underestimation of 37% in winter. For particle concentrations we showed that the
CAMS inventory underestimates significantly the primary organic particles at the urban location,
suggesting emissions in residential emissions. The MEMSD inventory in mapped to the 2 km
× 2 km resolution mesh of the air-quality simulation for NO2 and PM2.5. And also compares
emission fluxes obtained with the described methodology (MEMSD) against the global CAMS
anthropogenic emission inventory (Granier et al., 2019). The road network is finely resolved in
the maps of NO2 and at a certain degree PM2.5 emissions. They observed an underestimation of
particulate matter concentrations atthe rural location during wintertime. Transport of dust from
the Sahara Desert being rare in wintertime, this underestimation has been attributed to the
emission of local dust emissions over the arid rural area in the model.
2.6 CONCLUSION
In this chapter, methods to conduct emission inventory which are in the literature are
discussed and in the next chapter-3 methodology that was used to develop emission inventory
for Vizianagaram City is presented.

37. 25
3.1 GENERAL
CHAPTER-3
METHODOLOGY
This chapter outlines the systematic approach employed to quantify and analyze pollutant
emissions. The processes encompass source identification, data collection, application of emission
factors, and spatial-temporal allocation. This methodology serves as the foundation for informed
decision-making and the development of effective pollution control strategies.
3.2 DEVELOPMENT OF EMISSION INVENTORY METHODOLOGY
The methodology involves the identification of the possible sources of air emissions in the
entire city. The city is divided into different zones using the present activity. The monitoring
stations were identified for the AAQ assessment. These monitoring stations are marked on the grid
map developed. The zones of influence around the monitoring stations and the relevant activities
were identified and categorized as point, line and area sources. The available data and information
related to each of the activities of the city is analyzed to know the profile of each of the cities. The
fieldwork is proposed to collect the primary data for each of the sectors identified for AAQ
monitoring. The primary and secondary data collected/ available is evaluated to arrive at the
possible air pollution sources of a city. The detailed data collected from these four or five zones of
influence will be used to develop emission inventory of these zones.
Figure 3.1 The Process for Emission Inventory Development

38. 26
Figure 3.2 Proposed Framework on Emission Inventory
Phase-1: Estimating city- specific sectoral emission load contribution, based on the secondary
data, to perform the basic EI assessment.
• Understanding the pollution scenario in each of the cities
• Literature review to understand the land use and land cover (LULC) of the
cities, to identify the polluting sectors
• Compilation of activity specific emission factors (ARAI, CPCB, USEPA and others)
• Secondary-data collection:
• Data collection from government reports, publications, and online data scraping
• Sectoral emission estimations:
• Estimating emission load from various sectors using secondary data.

39. 27
Phase-2: Developing an emission inventory
The study identified the activities contributing to the pollution loads in all four cities.
Subsequently, emission loads will be estimated based on the activities and emission factors.
The primary data is being collected relating to various sectoral activities via surveys, personal
interaction and group discussions with concern state departments and pollution control boards.
The Land Use and Land Cover (LULC) scenario and the population density are assessed for
each of the grid and ward of a city. This is used to arrive at the spatially distributed EI which
can be used as an input for air pollution modelling studies to be carried out as part of the SA
and CC studies.
Figure 3.3 Typical 1 km x 1 km Grid Map for Vizianagaram City

40. 28
Figure 3.4(a) Typical 1 km x 1 km Grid Map for Vizianagaram City using ArcGIS
Figure 3.4(b) Typical 1 km x 1 km Grid Map for Vizianagaram City using ArcGIS

41. 29
The data collection pertaining to the EI studies include the point, line and area sources. The sources
listed in Table 3.1 are identified as the possible sources contributing to the air pollution of a city.
Table 3.1: Possible Sources of Air Pollution Emissions in Each of the City
Type of Source Possible sectors and sub sectors contributing to air pollution
Point Fuel Combustion: Electric utilities, cogeneration, oil and gas
production, petroleum refining, manufacturing industries, food &
agricultural activities, services utilities / establishments, commercial
establishments (such as bakery, hotel & restaurants) etc.
Industrial Processes: Chemical, food and agriculture, mining, stone
crushers, mineral, metal, wood, paper, glass & related, electronics etc.
Petroleum Production and Marketing: Oil and gas production, petroleum
refining, petroleum or similar product storage & marketing, etc.
Petroleum Products and Marketing: Oil & gas production, petroleum
refining, production of products similar to petroleum, petroleum
marketing, etc.
Waste Disposal: Sewage treatment plant, landfill or solid waste dump
yard, incinerator, open burning, etc.
Line Light, medium and heavy-duty passenger and goods vehicles that runs
using CNG, Petrol and Diesel as fuel.
Two/Three wheelers such as Motorcycles, trains, farm equipment, fuel
storage & handling, Aircrafts, motor boats etc.
Area Residential fuel combustion, construction & demolition, paved and
unpaved road dust, farming operations, pesticides & fertilizers, fugitive
windblown dust, fires, cooking, asphalt paving or proofing, wood and
tyre burning in winter, commercial hotels and restaurants, bakeries,
crematoria and other commercial activities, etc.

42. 30
3.3 DEVELOPMENT OF EMISSION INVENTORY FOR VIZIANAGARAM CITY
Emission inventory is developed for the selected 4 NCAP locations named Lanka veedhi, Sitam
college, Ashok Nagar and Complex Area. The resultant emission loads calculated from various
sources in the chosen monitoring locations is extrapolated to entire city based on LULC, population
density, building density and activity rate.
3.3.1 NCAP Monitoring Locations of Vizianagaram City
The monitoring stations for air quality monitoring are located at Lanka Veedhi, Sitam
College, Ashok Nagar and Complex Area. A grid of 1km x 1km is drawn at the four monitoring
stations is indicated in the figure below.
Figure 3.5 Vizianagaram Monitoring stations with 1km x 1km grid

43. 31
Figure 3.6 Vizianagaram Ward wise Map Shape file
Figure 3.7 Vizianagaram Ward wise Map

44. 32
Selected monitoring locations in different categories of residential, industrial, commercial and
traffic areas by analyzing the previous air quality statistics, meteorology, geographic boundaries,
pollutant of interest and availability of data. The city is divided into number of 1km x 1 km grid by
based on land use and land cover area of the selected locations. Secondary data collected from
Vizianagaram Municipal Corporation and Pollution Control Board (PCB) like total ward wise
population, buildings, road wise details etc. Prepared questionnaires for primary data collection
using Questionnaire Method for different fields like residential, commercial, open eat outs,
restaurants, hospitals, bakeries, open burnings etc. as mentioned in (ANNEXURE-1). From based
on secondary data, computed the sample size (No. of questionnaire) for distribution.
3.3.1.1 The Details Of The 1km X 1km Grid At The Lanka Veedhi Monitoring Station
The Lanka veedhi monitoring grid consists of nearly 920 residential buildings and 200
apartments. The monitoring grid of 1km x 1km consists of 3 major district road, 6 minor district
roads and 16 other roads. In Lanka Veedhi, the total road length is 12.41 kilometres, with major
roads spanning 2.34 kilometres and minor roads covering 10.07 kilometres.
Figure 3.8 Monitoring station at Lanka Veedhi with 1km x1km grid with roads

45. 33
3.3.1.2 The Details Of The 1km X 1km Grid At The Sitam College Monitoring Station
The Sitam College monitoring grid consists of nearly 25 residential buildings and 3
apartments. The monitoring grid of 1km x 1km consists of 2 major district roads, 4 minor district
roads and 2 other roads. In Sitam College, the total road length is 5.43 kilometres, of which major
roads account for 1.1 kilometres, and minor roads extend over 4.33 kilometres.
Figure 3.9 Monitoring station at Sitam College with 1km x1km grid with roads
3.3.1.3 The Details Of The 1km X 1km Grid At The Ashok Nagar Monitoring Station
The Ashok Nagar monitoring grid consists of nearly 1500 residential buildings and 50
apartments. The monitoring grid of 1km x 1km consists of 2 major district roads, 5 minor district
roads and 25 other roads. Ashok Nagar consists a total road length of 13.18 kilometres, with 2.02
kilometres attributed to major roads and 11.16 kilometres to minor roads.

46. 34
Figure 3.10 Monitoring station at Ashok Nagar with 1km x 1km grid with roads
3.3.1.4 The Details Of The 1km X 1km Grid At The Complex Area Monitoring Station
The Complex Area monitoring grid consists of nearly 941 residential buildings and 169
apartments. The monitoring grid of 1km x 1km consists of 3 major district roads, 6 minor district
roads and 35 other roads. In Complex Area, the total road length measures 17.97 kilometers,
comprising 2.34 kilometers of major roads and 15.63 kilometers of minor roads.

47. 35
Figure 3.11 Monitoring station at Complex Area with 1km x 1km grid with roads
The emission inventory for the rest of the 1 km x 1 km grid zones is developed using the trends of
the four to five zones and analogies or similarities with these zones of influence. The emission
inventory developed is used to determine the emission loads. The existing CPCB and ARAI
recommended emission factors will be used during the study. The emission loads along with other
necessary inputs including the meteorological parameter is used for the dispersion and SA studies.
Typical 1 km x 1 km grid map for each of the cities is shown in Figure 5. The primary and secondary
data pertaining to the activities and the population was collected. The traffic surveys are made
through the usage of the CC cameras at the prominent traffic junctions taking into the traffic
convergence and divergence. This will help in the assessment of the types of vehicles operating on
the roads. Construction and demolition data were collected by field survey with the help of the
municipal official and the ward volunteers. Physical survey of industrial area is to be done. The
main sources of secondary data collection are from APPCB, Census of India, and CPCB website,
the urban local body of the respective city, local industries, and research article if any.

48. 36
3.4 DATA COLLECTION FOR EMISSION INVENTORY
The data collected from Vizianagaram Municipal Corporation includes the total ward
wise population, total buildings and ward wise areas. The sample size is computed based on the
building density, population density, household density and type of activity. Further the sample sets
are divided for each secretariat based on the area of covered in monitoring grid. The point sources
data taken from APPCB.
3.4.1 Analysis of Collected data
The data collected from Vizianagaram city includes various sources like domestic,
commercial and industrial sources in the monitoring locations with the help of Vizianagaram
Municipal Corporation and APPCB. In Lanka Veedhi, the sample includes 233 households, 15
commercial establishments, 5 hospitals, 20 temples, 30 restaurants, 45 educational institutions, and
15 open eat outs. Meanwhile, Sitam College encompasses 22 households, 10 commercial
establishments, 5 hospitals, 1 temple, 3 restaurants, 8 educational institutions, and 15 open eat outs.
In Ashok Nagar, the sample size consists of 266 households, 15 commercial establishments, 7
hospitals, 20 temples, 10 restaurants, 17 educational institutions, and 15 open eat outs. Finally,
Complex Area features 100 households, 70 commercial establishments, 8 hospitals, 4 temples, 14
restaurants, 9 educational institutions, and 15 open eat outs.
Notably, data was collected for all available bakeries in each of these areas. And there is no point
sources & working industries with in the city limits. The figures provide valuable insight into the
distribution of various activities within each locality and can be instrumental for source
apportionment assessments and urban planning initiatives.

49. 37
3.5 VEHICULAR COUNT ESTIMATION
Vehicular count is estimated by ADT survey. An Average Daily Traffic (ADT) survey
is a specific type of vehicular survey that focuses on collecting data about the average number
of vehicles that pass a specific location on a road or highway over the course of a typical day.
ADT survey includes
• Data Collection: ADT surveys involve the continuous monitoring of traffic flow at a specific
location (usually a designated counting point) for a period of 24 hours for 7 days.
• Counting Methods: ADT survey is conducted by using advanced technologies like video
cameras with computer vision algorithms.
• Selection of Traffic monitoring locations: Selected Major traffic hotspots are:
1. Etthu Bridge Junction
2. Three Lamp Junction
3. Ring Road Junction
Table 3.2 Methodology for Vehicular emission estimation
S.No. Step Approach
1 Assessing the number of vehicles Traffic counts Traffic counts
2 Analyzing the distribution of vehicles
based on vintages, technologies, and fuel
types
parking lot surveys
3 Computation of vehicle kilometer travelled
(VKT) for all sub-categories of vehicles
Traffic counts and road length
Traffic counts and road length
4 Selection of emission factors for each
subcategory
ARAI, 2011
ARAI 2007, NEERI
5 Computation of emissions VKT*Emission factor

50. 38
3.6 ROAD DUST SAMPLING
Road dust sampling was done to collect and analyze dust particles and other particulate
matter that accumulate on road surfaces. The sampling is important for understanding air
quality, environmental pollution, and potential health risks associated with airborne particles
generated from road traffic and other sources. The procedure for road dust collection and
sampling is adopted from USEPA AP-42.
• Site Selection: Sampling sites are strategically chosen to represent different types of roads
(e.g., urban roads, highways, residential streets) and varying traffic densities. Factors like
proximity to industrial areas, construction sites, and residential neighborhoods are also
considered. Selected Major traffic hotspots are:
1. Etthu Bridge Junction
2. Three Lamp Junction
3. Ring Road Junction
Sampling Equipment: Specialized equipment is used to collect dust samples. The equipment used
are broom, pan and vacuum filter bags.
• Tests Conducted: Moisture analysis and silt analysis.
3.7 CONCLUSION
In conclusion, the methodology elucidated in this chapter forms a road dust framework for
conducting precise and comprehensive emission inventory assessments. Through systematic source
identification, meticulous data collection, emission factor application, and thoughtful spatial-
temporal allocation, a holistic understanding of pollutant releases is achieved. Emission loads are
calculated from the various emission factors for the selected monitoring locations in Chapter 4.

51. 39
CHAPTER 4
RESULTS AND DISCUSSION
4.1 GENERAL
The development of Emission Inventory for a Geographical area consists of Various
sectors like Residential, Restaurants, Bakeries, Open Eat outs, Commercial, Diesel Generators,
Industries, Road dust resuspension and Transport sectors. The city level emissions are
estimated by using Land use Land cover (LULC).
The present emissions from various sources in Vizianagaram city is estimated from the
primary data collected from Vizianagaram Municipal Corporation and APPCB. Emission
factors are selected for various sectors to estimate emissions and calculated the emission loads
for the selected monitoring locations. Hence, it helps to apportion the contribution of different
sectors to overall emissions of the city. The details of analysis are presented in this chapter.
The procedure is presented in the previous chapter (3.2 Methodology) is the same
followed to develop the EI for the city of Vizianagaram.
4.2 POSSIBLE SOURCES OF EMISSIONS IN VIZIANAGARAM CITY
Vehicles are among the dominant sources of air pollution and are responsible for high toxic
exposure. The cooking activity from the residential, restaurants, eateries, were contribute
Particulate matter. Apart of them, the combustion activities from Crematoria and diesel
generators also emits the particulate matter. There are no major industries located within the
city and as per PCB. Identified major sources contributing to PM10 and PM2.5 are re-
suspension of road dust, emissions from vehicle movement, burning of waste and
construction activities.
4.3 ESTIMATION OF EMISSION LOADS
Emission load refers to the total amount of pollutants or contaminants released into the
environment from various sources, such as industrial processes, transportation, residential
activities, and natural sources. It is a crucial concept in environmental science and air quality
management, as it helps quantify the impact of human activities on the air, water, and soil.
Emission load is typically measured in terms of mass or volume of pollutants emitted over a
specific time period, often in units like kilograms per day, tons per year, or similar metrics. The

52. 40
pollutants can include a wide range of substances, such as greenhouse gases (like Carbon
dioxide and Methane), Particulate Matter (PM2.5 and PM10), Nitrogen oxides (NOx), Sulphur
dioxide (SO2), Volatile organic compounds (VOCs), and more.
The general equation for emission estimation is:
E= A*EF*(1-ER/100) 4.1
Where,
E= Emission rate
A= Activity rate
EF= Emission factor and
ER= Overall emission reduction efficiency, %
4.3.1 Emission Factors
Emission factors quantify the relationship between activities and the pollutants they
release, aiding in estimating environmental impact. They express the amount of a specific pollutant
emitted per unit of activity, such as fuel burned or production output. These factors are crucial for
air quality management, guiding policies and regulations to control pollution sources. By using
emission factors, industries and regulators can make informed decisions to minimize their
environmental footprint and enhance overall sustainability. The following emission factors are
selected from various standard reports like CPCB, NEERI, TERI, USEPA AP-42 and studies of
IITs.
Table 4.1 Emission factors for various types of sectors
S.No Source Fuel Type
Emission Loads Factor
Source
PM 10 PM 2.5
1 Residential
LPG 2.1 Kg/MT 1.89 Kg/MT
CPCB
Annexure_3.1_27.02.201
8
Wood 17.3 Kg/MT 11.76 Kg/MT
CPCB
2011Annexure_VIII
Coal 12 Kg/ MT
8.16
Kg/ MT
SA_Kol-How_NEERI
Report Table 3.14
2 Restaurants
LPG 2.1 Kg/MT 1.89 Kg/MT
CPCB
Annexure_3.1_27.02.201
8
Wood 17.3 Kg/MT 11.76 Kg/MT
CPCB
2011Annexure_VIII

53. 41
3 Open Eat outs
LPG 2.1 Kg/MT 1.89 Kg/MT
CPCB
Annexure_3.1_27.02.201
8
Wood 17.3 Kg/MT 11.76 Kg/MT
CPCB
2011Annexure_VIII
Coal 12 Kg/ MT 8.16 Kg/ MT
SA_Kol-How_ NEERI
Report Table 3.14
4
Bakeries
LPG 2.1 Kg/MT 1.89 Kg/MT
CPCB
Annexure_3.1_27.02.201
8
Wood 17.3 Kg/MT 11.76 Kg/MT
CPCB
2011Annexure_VIII
Coal 12 Kg/ MT 8.16 Kg/ MT SA_Kol-How_ NEERI l
Report Table 3.14
5
Road
Construction
-
0.009884
Kg/ day-m2
0.001976
Kg/ day-m2
AP-42, Chapter
13_Section 13.2.3
SA_Kol-How_ NEERI
Report Table 3.14
UNEP-IITKanpur_2021-
Final-Report Table 1
6
Building
Construction
-
1.2
MT/ Ac-M
0.24
MT/ Ac-M
AP-42,Section 13.2.3
SA_Kol-How_ NEERI
Report_
Table 3.14
EI&SA Study for
Mumbai City_Table 3.24
7 Crematoria
EF Body
0.000025
kg/body
0.000017
kg/body
CPCB
Annexure_3.1_27.02.201
8
Wood 17.3 kg/MT 11.76 kg/MT
ARAI_Pune September
2022 Report Table 9
Gas 0.40 kg/MT 0.25 kg/MT
Kerosene 0.61 kg/MT 0.024 kg/MT
SA_Kol-How_ NEERI
Report Table 3.14
Dung Cake
10.5 kg/MT 4.4 kg/MT
8
Diesel
Consumption
in DG sets
Diesel
0.00047988
kg/ kwh
0.000407898
kg/ kwh
TERI_2021 Table 21
,Development Of
Spatially Resolved Air
Pollution Emission
Inventory Of India
Air pollutant emissions
scenario for India Table
7.4

54. 42
9
Point source
Industry
Furnace oil 3.228 kg/MT 2.152 kg/MT
TERI_2021 Table 21
,Development Of
Spatially Resolved Air
Pollution Emission
Inventory Of India
Coal 6.9 kg/MT 1.8 kg/MT
Air pollutant emissions
scenario for India_Table
7.4
furnace oil EI&SA Study
for Mumbai
City_Table3.30
Wood/Rice
husk/Briquet
es
17.3 kg/MT 11.76 kg/MT
coal AP 42 Vol 1
CH1_Table 1.2-4
EI&SA Study for
Mumbai City_Table3.30
4.4 ESTIMATION OF EMISSION LOADS OF VIZIANAGARAM CITY
Emission loads for the selected grids Lanka Veedhi, Sitam College, Ashok Nagar and
Complex Area of Vizianagaram city is estimated by using the emission factors and emission
load formulae.
4.4.1 Emissions From Residential Sector
The residential data is collected using the questionnaire mentioned in Annexure-1. Sample sets
are calculated based on the population density and building density. Six major fuels are used in
the residential households for cooking and lighting purposes– a) Fuel wood, b) dung cake, c)
crop residue, d) coal, e) kerosene and f) LPG and were included in the estimation of emissions.
In this study, analysis for domestic sector is carried out using both secondary data and the data
collected during the primary surveys. Primary data is collected from 1x1km2
area of all 4 sites
and door-to-door sample surveys conducted in these sites in order to collect data on fuel
consumption, pattern of fuel usage and other details related with fuels used in domestic sector.
Based on property sizes (as surrogate to income classes), housing societies/ bungalows are
randomly selected for the surveys. Three broad categories (High,middle and low income
categories) covering minimum of 200 houses per category were surveyed. This implies that
total of around 1000 samples in residential sector were selected. The information on the use of
Diesel generator (DG) sets was also collected. Data on parameters such as fuel consumption,
price of fuel, etc. from various households is collected to understand the use of fuel for various
purposes such as cooking, heating, lighting etc.

55. 43
Primary data has been analysed by way of calculating the averages for various parameters
collected from 3 income groups at different locations. It is seen that kerosene consumption is
mainly in low income houses.
Table 4.2 Sample sets count for monitoring locations
Sample size
Activity Lanka Veedhi Sitam College Ashok Nagar Complex Area
Houses 233 20 266 100
Commercial 15 10 15 70
Hospitals 5 5 7 8
Temples 20 1 20 4
Restaurants 30 3 10 14
Educational 45 8 17 9
Open Eat Outs 15 15 15 15
Bakeries ALL ALL ALL ALL
The basic equation employed for emission estimation from the residential sector is:
E= Fi * EFi * No. of Households 4.2
Σ Fi = Population* %population using fuel*per capita fuel consumption 4.3
Where,
Σ Fi – Total Fuel consumption in MT/yr
E- Emission load in kg/yr
Fi – Fuel consumption in MT/yr
EFi – Emission Factors in kg/MT

56. 44
Figure 4.1 Data collection in Sachivalayams at Vizianagaram city
Table 4.3 Residential Emission load for Lanka Veedhi grid
Domestic LPG Consumption (for Fi - 0.1704 MT/yr EF PM 10 - 2.1
kg/MT &
PM 2.5 - 1.89 kg/MT)
S.No Sub Grid
No of House
Holds
Emission Load (kg / y)
PM 10 PM 2.5
1 1 360 128.52 115.668
2 2 647 230.979 207.8811
3 3 550 196.35 176.715
4 4 524 187.068 168.3612
TOTAL 2081 742.92 668.63
E = Σ Fi * EFi * No. of Households
E = 0.1704 * 2.1 * 360 = 128.52 kg/yr (for PM 10)
E = 0.1704 * 1.89 * 360 = 115.668 kg/yr (for PM 2.5)

57. 45
4.4.2 Emissions From Restaurants
Emissions from this sector are generated due to LPG and coal use in tandoors/barbeques. The
common fuels used by restaurants/hotels in Vizianagaram city are and LPG, coal and wood.
The formula used for calculating emissions by this sector includes
E= Σ Fi * EFi * No. of. Restaurants 4.4
Where,
Σ Fi – Total Fuel consumption in MT/yr
Fi= Average Consumption of Ith
fuel in city per restaurant in MT/yr
EFi= EF= Relevant emission factor for ith
fuel in kg/MT
Primary surveys are conducted in different localities of Vizianagaram Municipal Corporation,
municipal councils, and rural areas of Vizianagaram to understand the fuel usage pattern in
hotels, restaurants. The data collected fuel consumption in restaurants is used to quantify the
emissions. It is also assumed that no control devices are installed in the restaurants to control
the emissions.
Table 4.4 Restaurants Emission load for Lanka Veedhi grid
Restaurant LPG Consumption ( EF PM 10 - 2.1 kg/MT & PM 2.5 - 1.89 kg/MT)
Total
Restaurants
LPG cylinders
Consumption
month
Avg LPG
Consumption
/ month
Total LPG
Consumption
T/ year
Emission Load (kg / yr)
PM 10 PM 2.5
7 275 40 9.0514 133.06 119.75
E= Σ Fi * EFi * No. of. Restaurants
= (40*19.2*12/1000) * 2.1 * 2
= 133.06 kg / yr (PM 10)
E= Σ Fi * EFi * No. of. Restaurants
= (40*19.2*12/1000) * 1.89 * 2
= 119.75 kg / yr (PM 2.5)

58. 46
4.4.3 Emissions From Open Eat outs
Emissions from this sector are generated due to coal and wood use in tandoors/barbeques. The
common fuels used by open eat outs in Vizianagaram city are and LPG, coal and wood. The
formula used for calculating emissions by this sector includes
E= Σ Fi * EFi * No. of. Open Eatouts 4.5
Where,
Σ Fi – Total Fuel consumption in MT/yr
Fi= Average Consumption of ith
fuel in city per restaurant in MT/yr
EFi= EF= Relevant emission factor for ith
fuel in kg/MT
Table 4.5 Open eat outs Emission load for Lanka Veedhi grid
Total Ope
eatouts
LPG cylinders
Consumption
month
Avg LPG
Consumption
month
Total LPG
Consumption
T/ year
Emission Load (kg / yr)
PM 10 PM 2.5
4 8 2 0.3408 2.86 2.58
E = Σ Fi * EFi * No. of. Open Eat Outs
= (2*14.2*12/1000) * 2.1 *4
= 2.86 kg/yr (PM 10)
E = Σ Fi * EFi * No. of. Open Eat Outs
= (2*14.2*12/1000) * 1.89 * 4
= 2.58 kg/yr (PM 2.5)
4.4.4 Emissions From Bakeries
Vizianagaram city consists of various types of bakeries and primary data like fuel consumption
and number of bakeries are taken from field survey.
The formula used for calculating emissions by this sector includes
E= Σ Fi * EFi * No. of. Bakeries 4.6
Where,
Fi= Average Consumption of ith fuel in city per restaurant in MT/yr
EFi= EF= Relevant emission factor for ith fuel in kg/MT

59. 47
Table 4.6 Bakeries Emission load for Lanka Veedhi grid
Total
Bakeries
LPG cylinders
Consumption
/ month
Avg LPG
Consumption
/ month
Avg. LPG
Consumption T
year
Emission Load
(kg / yr)
PM 10 PM 2.5
1 2 2 0.3408 0.72 0.64
E = Σ Fi * EFi * No. of. Bakeries
= (2*14.2*12/1000) * 2.1 * 1
= 0.72 kg/yr (PM10)
E = Σ Fi * EFi * No. of. Bakeries
= (2*14.2*12/1000) * 1.89 * 1
= 0.64 kg/yr (PM 2.5)
4.4.5 Emissions From Building Construction
The Particulate matter emissions from construction sector are estimated on the basis of total
building construction activities in the region. The data and statistics on building construction
activities in study area is compiled from databases available from Vizianagaram Municipal
Corporation.
E = AC * EF 4.7
Where,
E = Emission load in MT/yr
AC = Base area of construction in Acre – Month
EF = Emission Factor in MT /Ac – M
1m2
= 0.000247105 Acre
If Construction period is less than 7 months then the Area Disturbed Factor is 0.2.
If Construction period is greater than or equal to 7 months then the Area Disturbed Factor is
0.5.

60. 48
Table 4.7 Building construction Emission load for Lanka Veedhi grid
Building Construction ( EF PM 10 - 0.009884 Kg/Day –m2
& PM 2.5 - 0.0019768 Kg/Day
– m2
Building construction ( EF PM 10 - 1.2 MT/acre/month & PM 2.5 - 0.24 MT/acre/month)
S.No
Base area
in (m2
)
Duration
of activity
per month
Base area in
(acre/month
/year)
Emission factor
(MT/acre-
month)
Emission Load
(MT / yr)
PM10 PM 2.5 PM10 PM 2.5
1 1946.5 12 0.481 1.2 0.24 3.463 0.693
2 1122.49 4 0.277 1.2 0.24 0.266 0.053
3 831.29 4 0.205 1.2 0.24 0.197 0.039
4 1145.0 9 0.283 1.2 0.24 1.528 0.306
5 824.59 2 0.204 1.2 0.24 0.098 0.020
5.552 1.110
Acre Disturbed = 0.5 * 0.481 = 0.240
Acre - Months of activity = Duration * Acre Disturbed
= 12 * 0.240 = 2.886
Emission Loads = EF * Acre Months of activity
E = 1.2 * 2.886 = 3.463 MT / Ac – M (PM 10)
E = 0.24 * 2.886 = 0.693 MT / Ac – M (PM 2.5)
4.4.6 Emissions From Crematoria
Cremating the bodies of dead people is an ancient ritual and practice in India. The total
emissions from cremation calculated using
n
E =∑ (Fi*EFw) + (Bi *EFb) 4.8
i=1
Where,
E = Total city emission (kg/y)
F= Wood consumption (e.g. MT/y)
EFw= Relevant emission factor for wood (e.g. kg/MT)
B= Body burnt (number)
EFb= Relevant emission factor for dead body (e.g. kg/body)
i= ith
crematoria