performance evaluation of air pollution models

PERFORMANCE EVALUATION OF AIR POLLUTION MODELS

A DISSERTATION Submitted in partial fulfilment of the

requirements for the award of the degree of

MASTER OF TECHNOLOGY in

CIVIL ENGINEERING (With Specialization in Transportation Engineering

with Diversification to Traffic En eenng

1 4G

By //

ASIHJ UII'®~IHf

, ~ ov tes,10

Xv ~X

DEPARTMENT OF CIVIL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY ROORKEE

ROORKEE-247 667 (INDIA)

JULY, 2003

CANDIDATE DECLARATION

I hereby declare that the work being presented in the dissertation titled "Performance

Evaluation of Air Pollution Models" in partial fulfillment of the requirements for the award of

the degree of Master of Technology in Civil Engineering with specialization in Transportation

Engineering with diversification in Traffic Engineering, submitted in the Department of Civil

Engineering, Indian Institute of Technology, Roorkee, Roorkee, is an authentic record of my own

work carried out for a period of seven months from September 2002 to November 2002 and from

March 2003 to July 2003 under the supervision of Prof.(Dr.) S.S.Jain, Professor and

co-ordinator, Centre of Transportation Engineering, Department of Civil Engineering, Indian

Institute of Technology, Roorkee, Roorkee, Dr. M.Parida Assistant Professor, Transportation

Engineering Section, Department of Civil Engineering, Indian Institute of Technology Roorkee,

Roorkee.

The matter embodied in this dissertation has not been submitted by me for the award of

any other degree or diploma.

r Roorkee

Dated:2A July, 2003

ASHUTOSH RASTOGI

This is to certify that the above statement made by the candidate is correct to the best of

our knowledge

Dr. M.Parida Assistant Professor Transportation Engineering Section Department of Civil Engineering Indian Institute of Technology, Roorkee, Roorkee-247667 (U.A.) India

Prof.(Dr.) S.S.Jain, Professor and Co-ordinator, Centre of Transportation Engineering(COTE), Department of Civil Engineering, Indian Institute of Technology, Roorkee, Roorkee-247667 (U.A.) India

ACKNOWLEDGEMENTS

I wish to express my most sincere appreciation and deep sense of gratitude to

Prof. S.S.Jain, Professor and Co-ordinator, Centre of Transportation Engineering (COTE),

Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee and

Dr. M. Panda, Assistant Professor, Transportation Engineering Section, Department of Civil

Engineering, Indian Institute of Technology Roorkee, Roorkee for their kind help, constant

encouragement and invaluable guidance throughout the course of this thesis work. I also wish to

express my sincere thanks to the entire faculty of Transportation Engineering section, Department

of Civil Engineering for their constant encouragement.

Thanks are also due to Ms. Namita Mittal, Junior Research Fellow, AICTE NCP Project;

Dr.M.P.S.Chauhan, Fellow-C; Mr. Ram Kumar, Project Technician and Mr. B.S.Karki Mobile

Lab Van Operator of the Centre of Transportation Engineering for the assistance and co-

operation extended by them during various stages of field studies and data collection.

Thanks are due to my friends especially Mr. Ritesh Kumar and Mr. Jitendra Kumar Yadav

and all my other batch mates for providing constant encouragement and unforgettable assistance

during various phases of my work.

It's my privilege to express my fathomless gratitude to my parents, elder brother and sister

and brother-in-law for their constant support and encouragement; without their blessing, this

thesis would not have reached to its present form. And always the Almighty GOD.

)-9, July2003

(Ashutosh Rastogi)

it

ABSTRACT

In a rapidly developing country like India, the transport sector is growing rapidly and

number of vehicles on Indian roads has increased from 0.3 million in 1951 to 37.2 million in

1997 i.e. a increase of almost 124 times. This has lead to overcrowded roads and a polluted

environment. These alarming increases in the pollution in our metropolitan cities cause various

health hazards.

To predict the vehicular pollutants various models have been developed abroad and the

most popular among them are the CALINE models and the Finite Line Source Models. However,

the suitability of these models for Indian conditions must be thoroughly investigated before they

are applied for prediction of pollutants concentration in India.

In this dissertation, an effort has been made to study the various air quality models and to

evaluate the performance of CALINE-4 and General Finite Line Source Model for eight locations

in Delhi in terms of carbon monoxide concentration.

The General Finite Line Source Model was developed for Indian conditions and CALINE-4

was developed for American conditions. Prediction of carbon monoxide concentration has been

done for all the eight selected locations of Delhi. The Comparison between model predicted

concentration and observed concentration was performed using statistical methods, like regression

analysis, significance test and Index of agreement, to evaluate model performance.

After doing statistical analysis both models gives satisfactory results. The t-test shows that

tcaicu lated value is always less than t,ab„iates value for degree of freedom 15 and level of significance

0.05 for all the eight locations for both models which imply that difference between observed and

predicted value is insignificant. Regression analysis shows good correlation between observed and

predicted values by both models with r2 value ranged from 0.575 to 0.9398 for CALINE-4 and

0.7006 to 0.8751 for GFLSM. The minimum value of Index of agreement for CALINE-4 is 0.51

and 0.61 for GFLSM, which implies that GFLSM predictions are more error free than CALINE-4

predictions. Hence evaluation of the performance of both models has been satisfactory in terms of

statistical analysis.

The application of CAL[NE-4 and GFLSM for prediction of CO concentrations shows that

both models generally under predicts in most cases. This means the predicted values will generally

be less than the observed values and therefore the modeled values can be safely adopted for

decision-making purpose.

iv

LIST OF TABLES

Serial Title Page

No.

1.1 Sources of Pollution 2

1.2 Tonnes of Emission per Day in Metropolitan Cities in India 3

1.3 Emission Characteristics of Different Vehicles in grams 3

per liter of Fuel Consumed

1.4 Summaries of the Results of Various Studies Conducted 4

in Delhi in 2000

1.5 Indian and Euro Norms for Petrol Driven Cars 5

1.6 Indian and EURO Norms for Diesel Light Duty Vehicles (3.5 Tonnes) 6

1.7 Major Vehicle-Emitted Pollutants and Their Adverse Effects 7

1.8 Distribution of the Population of Delhi vs the Distribution of NDMC 8

Deaths Among the Three Areas of Delhi by Place of Residence

2.1 Validation of CALINE-4 23

2.2 Quantitative Evaluation of GFLSM for CO 24

2.3 Model Evaluation: Statistical Analysis Parameters for CO 24

3.1 Location Chosen for Field Studies 27

3.2 Details of Locations Chosen for Field Studies 27

3.3 Summary of Traffic Volume for all the selected locations. 41

V

3.4 Observed Concentration of CO(pg/m3) 45

4.1 Parameters Used 61

5.1 Sample Calculation, Index of Agreement, LI (Safardarjung)

63

5.2 Sample Calculation oft —test: Location L1(Safdarjung) Using GFLSM 65

5.3 t-test for Location LI (CALINE-4) 67

5.4 t-test for Location L2 (CALINE-4) 69







5.11 Observed and Predicted Concentrations of Carbon Monoxide 82

in gg/m3 for eight different identified locations using CALINE-4

5.12 Index of Agreement d, for all Locations (CALINE-4) 84

5.13 t-test for Location L1(GFLSM) 87



5,16 t-test for Location L4(GFLSM) 93

v1



5.19 t-test for Location L7(GFLSM) gg


5.21 Observed and Predicted Concentrations of Carbon Monoxide 102

in µg/m3 for eight different identified locations using GFLSM

5.22 Index of Agreement d, for all Locations (GFLSM) 104

vii

LIST OF FIGURES

Serial Title Page

No. No.

2.1 Axis System of the Gaussian Plume Model 11

2.2 Parameters of BOX Model 13

2.3 Finite Line Source Axis System 16

2.4 Line Source and Receptor Relationship 18

3.1 Map of Delhi Showing Identified Locations of Field Studies 30

3.2 A View of Location L1 31








3.10 a Hourly observed concentration of CO at Safdarjung 48

3.10 b Hourly observed concentration of CO at Karol Bagh 48

3.10 c Hourly observed concentration of CO at India Gate 48

3.10 d Hourly observed concentration of CO at New Friends Colony 48

3.10 e Hourly observed concentration of CO at ITO 49

3.10 f Hourly observed concentration of CO at Laxmi Nagar 49

viii

3.10 g Hourly observed concentration of CO at AIIMS

49

3.10 h Hourly observed concentration of CO at Red Fort

49

4.1 Relationship between wind co-ordinate system 57

and line source co-ordinate system.

5.1 Location L1: Observed Vs Predicted concentration of CO in Using CALINE-4

66

5.2 Location L1: observed Vs Predicted (CO-Standard case)

67


68


69


70

5.6 Location [3: observed Vs Predicted (CO-Standard case)

71


72


73


74


75


76


77


78


79


80


81

5.17 Observed Vs Predicted concentration of CO (CALINE-4)

83

5.18 Location L1: Observed Vs Predicted concentration of CO in Using GFLSM

86

ix

5 1 Location L1: observed Vs Predicted (CO-Standard case)

87


88


89


90


91


92


93


94


95


96


97


98


99


100


101

5.34 Observed Vs Predicted concentration of CO (GFLSM)

103

x

LIST OF PHOTOGRAPHS

Serial Title o No. No.

3.1 A View of Location 1 (Safadarjung Hospital) 28

3.2 A View of Location 4 (New Friends Colony) 28

3.3 A View of Location 5 (I.T.O.) 28

3.4 A View of Location 6 ( Laxmi Nagar) 29

3.5 A View of Location 7 (AIIMS)

29

xi

CONTENTS Page No.

CANDIDATE'S DECLARATION i

ACKNOWLEDGEMENT ii

ABSTRACT iii

LIST OF TABLES iv

LIST OF FIGURES viii

LIST OF PHOTOGRAPHS xi

CHAPTER 1: INTRODUCTION

1.1 General

1.2 Air Pollution Scenario in India 2

1.3 Air Pollution Standards in India 4

1.4 Health Hazards Due to Vehicular Pollution 6

1.5 Study of Delhi 7

1.6 Objective and Scope of the Study 8

1.7 Composition of Dissertation 9

CHAPTER 2: REVIEW OF MODELS FOR AIR POLLUTION

2.1 General 10

2.2 Gaussian Plume Model 10

2.3 The BOX Model 12

2.4 Finite Line Source Models 14

2.4.1 Finite line source model 14

2.4.2 General finite line source model 17

2.4.3 EPA Hiway model 17

2.5 Infinite Line Source Models 19

2.5.1 California line source model 19

2.5.2 General Motors (GM) model 20

2.6 Softwares Available for Air Pollution Modeling 20

2.6.1 CALINE-4

20

2.6.2 HIWAY-2

21

2.7 Case Studies 21

2.7.1 Case study of Delhi

21

2.7.2 Case study of Madras 23

2.8 Selection of Models 25

CHAPTER 3: FIELD SURVEY, DATA COLLECTION AND LABORATORY

STUDIES

3.1 General

26

3.2 Selection of Sites 26

3.3 Equipments Used in Data Collection 39

3.4 Field Study and Data Collection Procedure 39

3.4.1 Traffic volume studies 39

3.4.2 Air pollution monitoring 40

3.5 Laboratory Studies 46

3.5.1 Monitoring of carbon monoxide 46

CHAPTER 4: MODELING OF AIR POLLUTANT CONCENTRATION

4.1 General 50

4.2 Modeling of Air Pollutant Concentration Using CALINE-4 50

4.2.1 Working with CALINE-4 model 50

4.2.2 Prediction of carbon monoxide Using CALINE-4 55

4.3 Modeling of Air Pollutant Concentration Using GFLSM 56

4.3.1 Working with GFLSM 56

4.3.2 Determination of input parameters to the model 58

CHAPTER 5: PERFORMANCE EVALUATION OF AIR POLLUTION MODELS

5.1 Overview 62

5.2 Site Wise Performance Evaluation 62

5.2.1 Index of agreement 63

5.2.2 Significance test 64

5.3 Performance of CALINE-4 65

5.3.1 CALINE-4 performance for L 1

66

5.3.2 CALINE-4 performance for L2

68

5.3.3 CALINE-4 performance for L3 70


72

5.3.5 CALINE-4 performance for L5 74


76


78


80

5.3.9 Validation of CALINE-4

84

5.4 Performance of General Finite Line Source Model (GFLSM)

85

5.4.1 GFLSM performance for L1

86


88


90


92


94


96


98

5,4.8 GFLSM performance for L8

100

5.4.9 Validation of GFLSM

104

CHAPTER 6: CONCLUSION AND RECOMMENDATIONS

6.1 Conclusions 105

6.2 Recommendations 106

REFERENCES

APPENDICES

CHAPTER 1

INTRODUCTION

1.1 General Air pollution can be defined as presence in atmosphere of one or more contaminants in such

quantities and for such duration that is injurious to human health or welfare, and animal or plant

life [1]. When the concentration of contaminants exceeds a level such that it causes the effects

mentioned above, it becomes a pollutant.

With tremendous growth in the urbanization as well as commercialization, the whole world is

in the grip of severe environmental crisis. The tremendous increase in the number of vehicles has

contributed significantly to the increase of petroleum products. Petroleum consumption has

increased by almost 400% in the last two decades [1]. It is clear that now a days due to increase in

number vehicles on the roads transport sector is the major source of air pollution. Hence it

becomes imperative that a study on transport related air pollution be carried out and various

strategies for the control of air pollution should be implemented. To predict the transport related

air pollution various models have been developed worldwide.

Vehicular pollution models can be defined as a mathematical technique or methodology

based on physical principles, for estimating pollutant concentration in space and time for a given

set of emissions and meteorological conditions. One of the important application of these models

is that they can be used for prediction of contaminants in the future [2].

1.2 Air Pollution Scenario in India

In India, air pollution is widespread in urban areas where vehicles are the major contributors

and in a few other areas with a high concentration of industrial and thermal power plants.

Vehicular emissions are of particular concern since these are ground level sources and thus have

the maximum impact on the general population. The number of motor vehicles has increased from

0.3 million in 1951 to 37.2 million in 1997 [2]. Out of these, 32% are concentrated in 23 metropolitan

cities i.e. a 124-fold increase. This has resulted in over crowding and increased pollution. Various

sources of pollution in India and their contributing percentage is shown in Tablel.l.

Table 1.1: Sources of Pollution

Source 1970-71 (in %)

1980-81 (in %)

1990-91 (in %)

2000-2001 (in %)

Industrial 56 40 29 20

Vehicular 23 42 64 72

Domestic 21 18 7 8 Source:[31

Under the National Ambient Air Quality Monitoring (NAAQM) network, three criteria air

pollutants, namely, SPM, SO and NOx have been identified for regular monitoring at all the 290

stations spread across the country. The most prevalent form of air pollution appears to be SPM

although there are many stations at which SO land NO2 levels exceeds permissible limits. The

following Table 1.2 indicates the daily vehicle emission load for the major metropolitan cities in

India.

Table 1.2: Tonnes of Emission per Day in Metropolitan Cities in India (CO+HC+NOx+SPM)

Year 2-W 3-W Cars MUVs CV Total

1986 1,134 912 1,112 1,059 5,772 9,992

1991 2,598 1,787 1,985 2,004 9,528 17,901

1998 4,486 2,923 2,377 2,099 11,943 23,828

2001 5,775 4,910 2,562 2,282 12,272 27,800

source: 141

The emission characteristics of different vehicles in grams per liter of fuel consumed are

presented in Table 1.3.

Table 1.3: Emission Characteristics of Different Vehicles in grams per liter of Fuel Consumed.

Vehicle Type CO HC NO.

Cars 240 31 16

Mopeds 304 215 1.8

Scooter/Motorcycles 257 161 1.8

Diesel Vehicles 19 8 37

Source:pl

The above data proves that two, three and four wheelers with petrol engines emit higher

levels of CO and HC (too injurious to health) and heavy vehicles with diesel engine emits

significantly higher level of NOR .

Central Pollution Control Board in 2000 had carried out air quality modeling studies to

predict long term averaged concentrations of S02, NOx and SPM in Delhi considering four

categories of sources viz; industrial point source, traffic, small industries, and domestic coal

consumption. Summary of the results of this study is shown in table 1.4.

3

Table 1.4: Summaries of the Results of Various Studies Conducted in Delhi in 2000

Pollutants Sector Range

CO Transport 76% to 90%

Industrial 37% to 13%

Domestic & other sources 10% to 16.3%

NOx Transport 66% to 74%


Domestic & other sources 1% to 2%

SO2 Transport 5% to 12%


Domestic & other sources Nil to 4%

PM Transport 3% to 22%


Domestic & other sources 2% to 4%

Source: [6l

Hence from the above data it is clear that transport sector is happening to be the major source

of air pollution in terms of carbon monoxide and NOx emissions.

1.3 Emission Standards in India

An ambient air quality standard is a national target for an acceptable concentration of a

specific pollutant in air. Under the clean air act, EPA develops two standards for each pollutant of

concern:

• A primary standard to protect public health. The clean Air Act mandates that primary

standards be based entirely on heath related information, with out considering the cost of

attaining the standard.

• A secondary standard to protect public welfare. Public welfare effects on soils, water,

crops, vegetation, buildings, property, animals, wild life, weather, visibility, transportation

and other economic values, as well as personal comfort and well-being.

The Indian and Euro norms for petrol driven passenger cars are presented in table 1.5 and the

Indian and Euro norms for diesel light duty vehicles (3.5 tonnes) are presented in Table 1.6.

Table 1.5: Indian and Euro Norms for Petrol Driven Cars

1991/92 1996 1998 2000 2005

INDIA EURO -I INDIA EURO-I INDIA INDIA EURO -III EURO -IV

CO 14.3 to 8.68 to 4.34 to 2.72 2.2 2.72 2.3 1.0

g/km 27.1 12.4 6.20

HC 2.0 to NA NA NA NA NA 0.20 0.1

g/km 2.9

NO NA NA NA NA NA NA 0.15 0.08

g/km

HC + 3.4 to 1.5 to

NO, NA 0.97 0.57 0.97 NA NA 4.36 2.18

g/km

Source [7]

For petrol vehicles

Euro I was effective from 1 June 1999 and Euro II from 1 April 2000 for private (non-

commercial) vehicles in the National Capital Region [8]

5

• Diesel vehicles with gross vehicle weight >3.5 tonnes: Euro I for diesel vehicles is

effective from 1 June 1999 and Euro 11 from 1 April 2000 for private (non-

commercial) vehicles in the National Capital Region.

• Diesel vehicles with gross vehicle weight >3.5 tonnes: A 10% relaxation is given in

the conformity of production standards for CO and combined HC + NOx for all

categories of vehicles. However, the relaxation limit is reduced to 9% for CO, 12% for

HC and NOx, and 11% for PM with effect from 1 April 2000.[8]

Table 1.6 Indian and EURO Norms for Diesel Light Duty Vehicles (3.5 Tonnes)

Pollutant 1991/92 1996 2000

INDIA EURO —I INDIA EURO —I INDIA

2.72 to 11.2 or 4.5 or 2.75 to CO g/l m 14.0 1.0 to l.5

6.90 5.0 to 9.0 6.90

2.4 or 1.1 or HC +

iiC g/xwh 3.5 HC+1NO NO norms

norms

NO, g/kWh 18 14.4

HC + NOx g/km 0.97 to 1.7 2,0 to 4.3 0.7 to 1.3 0.97 to 1.70

0.61 kWh or SPMg/km 0.14to0.7

0.14 to 0.25

Source [9j

1.4 Health Hazards Due to Vehicular Pollution

It has been proved beyond doubt that in almost all the large cities of the world, air pollution is

a major problem for physical and mental health of the people. The primary pollutants, generally

6

present in the exhaust emissions are Nitrogen Oxides (NO), Carbon Monoxide(CO),

Hydrocarbon(HC), Lead(Pb) and Suspended Particulate Matter(SPM). Major adverse effects of

these pollutants on environment and human health are listed in Table-1.7.

Table 1.7 Major Vehicle-Emitted Pollutants and Their Adverse Effects

Typical urban Typical

Adverse Concentration Pollutants emission rate

effects (ground level) (g/km/vehicle)

(µg/m3)

Carbon Reduces the 02 carrying capacity Petrol- 10 10,000

Monoxide of the blood Diesel-1

Hydrocarbon Contribute to photochemical Petrol-1 2,000

reactions, some are carcinogenic. Diesel-0.3

Contribute to photochemical Nitrogen Petrol-4.5

reactions, acid rains and possible 900 Oxides Diesel-4.5

association with respiratory illness

Particulate Causes bronchial problems, Petrol-. 13 40

Matter deteriorating visibility. Diesel-4.3

Sulphur Contributes to acid rains and Petrol-0.09 60

Dioxide respiratory illness. Diesel-0.4

Source[10j

1.5 Study of Delhi Mortality data for years 1991 through 1994 were obtained from the New Delhi Municipal

Committee (NDMC), one of the three distinct regions, which comprise the National Capital

Territory. Because the NDMC houses a large concentration of Delhi's hospitals, approximately

one-fourth of the 60,000 deaths in Delhi each year occur in the NDMC, in spite of the fact that

only 3.6 percent of the population resides there. Although the NDMC data represent only

25percent of all deaths occurring in Delhi, the geographic distribution of the Delhi residents who 7

died due to non-traumatic causes in the NDMC mirrors the geographic distribution of the

population, as shown in Table 1.8.

Table 1.8 Distribution of the Population of Delhi vs the Distribution of NDMC Deaths Among the Three Areas of Delhi by Place of Residence

Region Census population,1991 NDMC NmrTrauma deaths

1991-1994

Municipal Corporation of Delhi -Urban 8,075,935 34,455

New Delhi municipal committee 301,297 1,999

Delhi cantonment Board 94,393 49

Total 8,471,625 36,503

Source [ I 1 ]

1.6 Objectives and Scope of the Study

The objectives of this study are:

1. To compare the measured carbon monoxide concentration values with those predicted

using CALINE-4 and General finite line source model (GFLSM).

2. To evaluate the performance of CALINE-4 and GFLSM.

Although scope of the present study is limited to various locations of Delhi only but the

performance evaluation of CALINE-4 and GFLSM are very helpful for carrying out further

studies in any part of India.

1.7 Composition of Thesis This thesis report has been divided in to seven chapters. The First Chapter introduces the

topic. An overview of some of the models, prediction software and selection of models for the

present study are presented in Chapter Two. Chapter Three deals with the procedures of data

collection, field studies and laboratory experimentation for determination of pollutant

concentrations. Chapter Four deals with modelling of air pollutant concentration using

CALINE-4 and GFLSM.. Fifth chapter deals with Performance Evaluation, of selected Air

Pollution prediction models, through statistical analysis. For this, CALINE-4 and General Finite

Line Source Model (GFLSM) have been used. Sixth Chapter lists the conclusions and

recommendations for further study.

9

CHAPTER 2

REVIEW OF MODELS FOR AIR POLLUTION

2.1 General

Environment planning for any urban area can be achieved by means of theoretical

mathematical models, which requires the knowledge of emission inventory and meteorology.

Rapid progress has been made in studies on mathematical models of urban air pollution during

past 10 years [12]. The models have become more accurate and complex in parallel with

computer software development. Numerous air pollution prediction models have been developed

which are for finite line source as well as for infinite line source. Line source models are used to

simulate dispersion from roadways where vehicles are continually emitting pollutants. These

models are based on Gaussian dispersion phenomenon. Line source models can be for finite

length or for infinite length of roadway. Some of the most popular air quality prediction models

are discussed in the following sections.

2.2 Gaussian Plume Model This is a generic mathematical model developed and widely used to describe the

dispersion phenomenon. This distribution of material with in the plum of pollutants is assumed to

be Gaussian in both the vertical and horizontal direction [13].

The axis system shown in Figure 21 is utilized and the following assumptions are

made [14].

i). The plume has a Gaussian distribution in both horizontal and vertical planes with ay , az as the

standard deviation across horizontal and vertical dimension of plume at downwind direction.

10

ii). Uniform and continuous emission of Q g/s of pollutant takes place.

iii). The mean speed affecting the plume is U, which is wind speed at the source level

i.e. at the point where dispersion starts.

iv). Material diffused remains suspended in air for long periods of time.

v). Steady state conditions prevail.

(x,-Y,z)

(x,-Y,0)

Fig 2.1 Axis System of the Gaussian Plume Model

Gaussian model equation is given by zz

X(x,y,z) = 1 exp(- y ~_) [exp(- (Z — H)2 ) +

exp(- (z +

2iru o- o- , lay 2Q_ 26__ Y

..........(2.1)

Where,

X(x,y,z) = Concentration of pollutants at the point (x,y,z) in

space (g/m3).

Q = Emission rate (g/m2/s)

u = Horizontal wind speed at the source level (m/s).ss

H = Height of emission (m).

ay , 6z =Standard deviation across horizontal and vertical dimension of plume at down

wind direction.

The disadvantage of using Gaussian plume models in field lies in specifying the n's

and the mean wind direction over an extended period of time, which makes it impractical.

Gaussian plume model works best for averaging times between 10 minutes and a few hours[ l31.

2.3 The BOX Model This is the simplest form of a dispersion model, which is often used to estimate air

pollution due to area sources [14]. This model may be considered as derived from the idea of the

continuity of mass of a volume element.

From Fig 2.2 the box represents a 3-D volume with in which air pollutants are

assumes to be thoroughly mixed. The box is usually defined as having unit width and so oriented

that its length, S, lies in the direction of mean wind, U. Pollutants are assumed to be emitted at a

constant rate (Unit time per unit area Q(g/m2/s)). Z equals the height of box, to which pollutants

are assumed to h dispersed.

12

Top of Mixing Layer Z

ind !locity, U

E uilibrium Concentration, Ce

/A\ GROUND Fig 2.2 Parameters of BOX Model

QS is equal to total emission rate per unit width when divided by ventilation rate, UZ, gives

the upwind edge of the area source.

Area source strength, Q = Mass * Area (2.2)

Time

Equilibrium Concentration, Cr =2 ............... (2.3) UZ

Box models can be used to obtain order of magnitude estimates of ambient pollution level.

However, the simplifying assumption of the model (uniform mixing to a constant level, Z) lead to

results that do not simulate true atmosphere conditions accurately. Special application of the box

model is an elevated inversion above a point source in valley.

Then,

Q .................(2.4) C= HWC

Q: Area source strength (g/m2/s)

W: Width of the valley (m)

H: Mixing height (m)

U: Mean wind speed (m/s)

N V

13

2.4 Finite Line Source Models

Various finite line source models are available to predict the vehicular pollutants discussed in

this section.

2.4.1 Finite line source model

This model was presented by Casandy (1972).The emission from a differential length dy'

of a line source will be Q*dy'. The line source will be located on the Y-axis (see Fig 2.4) and the

receptor is at the arbitrarily positioned point (x, y) downwind.

The ground level concentration at the receptor caused by emissions from the element dY

located at (0, Y'), which is effectively a point source, is found from Equation 2.5 as (for

perpendicular wind),

dC = QdY

ex — ''11 J (2.5) L rruQY6. 20

The concentration at the receptor caused by the entire line source is therefore found by integrating

over Y to be,

C= QdY f lex Y —Y Y

r u o v_ -L12 p 2cr; (2.6)

which gives,

C'— Q [er~LQ Y)+er rL/2+Y

IJ

Some interesting cases of Equation 2.7 can be observed. When the receptor is downwind

of the centerline of the source, y0 and we get,

'~2 Q r~ L ........................ (2.8) C Y n u e 2 F2

14

For large values of L - Equation 2.5 reduces to (since crf(mo) I from the property of error 2J2c,

function),

c=fcL Q u . (2.9)

This is the expression for the concentration downwind of an infinite line source normal to the

mean wind vector.

From the tables of the error function we find erf(t) = 0.95 when t = 1.38, and therefore, a

line source can be approximated as an infinite line source to an accuracy of 5% or better if

L >1.38 or L > 3.90 c, where receptor is at the plume centerline. The concentration at this 2', 2o.

receptor is only 5% less than the concentration that would be produced by an infinite line source

of equal strength.

15

anrr

Fig. 2.3 Finite Line Source Axis System

L

2.4.2 General finite line source model

Most of the models available for prediction of transportation related air pollutant works

on the principle of Gaussian plume theory and most of these models have been developed for the

conditions prevailing in America. To overcome this problem Luhar and Patil [2] developed

General Finite Line Source Model in 1986. This model also works on the methodology of

Gaussian Plume theory. This model also overcomes the infinite line source constraints. A lot of

work for prediction of air pollutant concentrations using this model had already been done

in India as well as abroad. This model gives quiet satisfactory results.

2.4.3 EPA HIWAY model

This is a short term (one hour) line source dispersion model. It was developed by Zimmerman and

Thompson in Feb.1975. This model simulates a highway with a finite number of point sources

and the total contribution of all points is calculates by numerical integration of the Gaussian point

source equation over a finite length. The co-ordinates (m.) of the end points of a line source of

length L (m), representing a single lane extending from point A to point B as shown in Fig2.4.[2]

Because of the physical significance of mechanical mixing above the roadway, some

initial values of the vertical and horizontal dispersion parameters are assumed. To accomplish

this, the point source is displaced by a virtual distance upwind such that cr, and a. have initial

values at roadside [15].

17

B (RR,S°)

Line source

Source

Fig 2.4 Line Source and Receptor Relationship

From figure 2.3 the co-ordinates of the end points of line source of length D (m), representing

a single line extending from point A to point B are ERA, SA) and (RB , SB).the direction of line

source from A to B from the north is 13°.The co-ordinates R and S of any point along the line at

an arbitrary distance L (m) from A is given by:

R=RA+L sin (3

S=SA +L cos 13

Given receptor at (Rk, SL) , the down wind distance X (m) and cross wind distance Y (m) of the

receptor from the point (R , S) for any wind direction 9° is given by

X = (S - SK) cosO + (R — RK) sin 0

Y= (S - SK) sin A - (R — RK) cos o

Where,

Rand S are the function of L , X and Y.

After breaking the line source in number of points and finding out the respective co-ordinates

the concentration C from the line source is given by:

1R

C=OffdL .(2.10) ua

Where

F = Point source dispersion function (m'2)

u = Average wind speed measured at the height of 2 m.

for stable condition : If mixing height is >5000m

z z z f =

c 2Q exp(-

rz) [exp(- ( 2~ ) ) + exp( (~2Q ) )~ (2.11)

2~c u

If mixing height is <5000m

yz

f = 1

exp(- ) ..............(2.12) z 2~uor 2 2o-r

2.5 Infinite Line Source Models

2.5.1 California line source model This model was developed by California Department of Transportation. It uses separate

equations for calculating pollutant concentrations under crosswind and parallel wind conditions.

This is based on Gaussian Infinite line source diffusion equation. In the crosswind case the

mixing cell concentration is determined by the wind speed and pollutant emission rate of the

vehicles. Dispersion downwind is dependant on the atmospheric stability classification. In the

parallel wind case, the California model accumulates pollutant within the mixing cell, to account

for downwind buildup. Pollutants are then dispersed laterally at a rate dominated by the stability

class [161

CALINE-4 is the latest version in a series of line source air quality model developed by

California Department of Transportation in contrast to CALINE-3 which is used for CO only,

CALINE-4 is used to predict concentrations of Nitrogen Oxide (NOx) and suspended particulate

concentration along with CO.

2.5.2 General Motors (GM) model

This model describes the downwind dispersion of pollutants near the roadway. This model

was developed by Chock (1978), on the basis of experimental data obtained in the General

Motors dispersing study on a test track. This experiment and experiment reported by Dabberdt

(1976) showed the mechanical mixing plays an important role in dispersing pollutants near

roadways while plume rise due to heated exhaust couldn't be ignored at low crosswinds. The

model avoids the cumbersome integration necessary for conventional Gaussian models, based on

infinite line source approach, the model specifies the dispersion parameters as a function of wind

road orientation angle and distance from the source to the receptor and includes plume rise over

the highway under very stable and light wind condition [ 17].

2.6 Softwares Available For Air Pollution Modeling

2.6.1 CALINE-4

The California Department of Transportation (CALTRANS) has been the leader in the

development of dispersion models for highways. The first line source model, CALINE was

published in 1972, for predicting CO concentrations.

In 1975, a revised version of original model, CALINE-2 was developed. This model could

compute concentration for depressed sections and for wind parallel to the road ways. Subsequent

20

studies indicated that CALINE-2 seriously over-predicted the concentration for stable, parallel

wind conditions.

In 1979, a third version, CALINE-3 was developed. CALINE-3 retained the basic

Gaussian dispersion methodology but used new horizontal and vertical dispersion curves

modified for the effects of surface roughness, averaging time and vehicle induced turbulence.

CALINE-4 is the latest version and the concentration of CO, NO2 and aerosols can be

predicted using this model. Software for this model is freely available and it is very helpful as it

saves time as respect to hand-calculations. [ 18]

2.6.2 HIWAY models

HIWAY is a short term (I hour) line source dispersion model. It was developed in

February, 1975 by EPA. The model is written in FORTRAN. HIWAY-2 is the upgraded version

of the model and was released by the EPA in 1980, and gives more realistic concentration

estimates due to an upgrade dispersion algorithm. The main disadvantage of the model is that it

still does not acknowledge the existence of 3-lane and 5-lane highways [19].

2.7 Case Studies

2.7.1 Case study of Delhi

Vivian Robert [20] conducted a study at Delhi in 2001 to predict the vehicular pollution in

eight selected location in Delhi in terms of the concentrations of CO with the help of CALINE-4

model.

The carbon monoxide concentrations for the selected location in the city have been

predicted using CALINE-4 software for two different conditions: Standard and Worst Case Wind

Angle

21

Using the inputs, hourly predictions of carbon monoxide have been done of all the unit oil

for both the run conditions using CALINE-4. The following regression analysis results between

predicted CALINE-4 outputs and observed concentration are derived for both these cases and t-

test is applied to check the reliability of inter-relationship between predicted and observed values

Case 1- Standard case:

Y = 0.6858 X + 1682.9 ....................(2.13)

R2 = 0.66

Case-2 Worst case wind angle

Y = 0.5849X + 1881.3 ....................(2.14)

R2 = 0.62

Where,

Y = Measured concentration

X = Predicted concentration

Using the above regression equations, the validation of predicted concentration of carbon

monoxide has been carried out for observed concentration at km. 174.0 on NH-58.The predicted

values are corrected by applying the calibration equation i.e.

Y = 0.6858 X+ 1682.9

Where,

Y = Corrected value of CO concentration.

X = Predicted value of CO concentration.

The corrected values are compared with the observed values and the difference between

the observed and predicted values are examined.

The comparison between corrected values of the predicted concentrations of carbon

monoxide and the observed values are given in table 2.1.

22

Table 2.1 Validation of CALINE-4

Predicted

(µg/m')

Corrected (µg/m')

Measured

(µg/m') Difference

(ttg/m') %

Difference

2645 2703.34 2120 583.341 21.58

2760 2747.71 2450 297.708 10.83

2760 2747.71 2300 447,08 16.29

2760 2747.71 2800 -52.292 -1.90

2645 2703.34 2700 3.341 0.12

2645 2703.34 2700 3.341 0.12.

2645 2703.34 2780 -76.659 -2.84

2645 2703.34 2780 -76.659 -2.84

Summary

The comparison of calibrated concentrations and observed concentrations show that the

values fall with in close ranges and as such the results are encouraging. The percentage variation

between predicted and measured values lies between 0.12% and 21.58%. Out of eight values,

only one value exceeds 20%.The higher value may be due to some error in measurement of CO

concentration. However, in such complex field experimentations, 20% variation between

observed and predicted concentration can be accepted hence therefore this checks the

transferability of CALINE-4 model.

2.7.2 Case study of Madras

To check the transferability of GFLSM model Sivocumar et al [21] conducted a study at

Madras in 1999 to predict the vehicular pollution near roadways.

The model was run with estimated emission, meteorological conditions, geometry of

the road and receptors. The model predictions were carried out for 1 h average time, since the CO

monitoring was done in the field for 1 h averaging time. Comparisons between model outputs and

23

measurements were performed using both quantitative data analysis technique and statistical

methods to evaluate model performance.

Quantitative and statistical data analysis of the model output is given in Table 2.2

and Table 2.3.

Table 2.2 Quantitative Evaluation of GFLSM for CO

Sample size 168

Prediction within a factor of 2 (%) 82

Exact prediction (%) 1

Over prediction (%) 25

Under prediction (%) 46

Table 2.3 Model Evaluation: Statistical Analysis Parameters for CO:mg/s

SI No. Parameters GFLSM

Summary measures

observed mean 5.51

ii Predicted mean 4.21

iii Observed deviation 0.90

iv Predicted deviation 0.64

2 Linear regression

i Intercept (a) 2.64

ii Slope(b) 0.28

iii co-relation coefficient (a) 0.40

3 Difference measure

i Total root mean square error 1.67

4 Index of agreement(d) 0.51

24

From the Table 2.3 it is seen that GFLSM is able to describe the observed variability

closely. The measures which are next important are the regression coefficients a and b. The value

of b is nearer to 1 and also the value of a is close to zero, indicating better prediction.

The relatively comprehensive difference measure of the degree to which the observed

variate is accurately estimated by the simulated variate, the index agreement (d), gives the degree

to which model prediction are error free. In other words, it suggests that the model has explained

the percentage of the potential for error. Thus, 51 % of the potential for error has been explained

by the model, which implies that there is a lower error in GFLSM results.

It can be observed from the analysis presented that GFLSM which is derived for the

Bombay conditions perform well for Madras also. Hence, it is transferable to other locations also.

However, there is slight over prediction when the wind direction is parallel to the roadway.

2.8 Selection of Models

In this study to evaluate the performance of air pollution models, two models have been

selected and these are CALINE-4 and General Finite Line Source Model (GFLSM).

CALINE-4 is the model developed by California transportation Department

(CALTRANS) and this is the most popular model, which has been used worldwide. CALINE-4 is

the latest model available at present. Also software of CALINE-4 is also available which is user

friendly and saves the time from very lengthy hand calculations.

General Finite Line Source Model (GFLSM) is the model developed by Luhar et al in

1986 for Indian conditions, which is based on Gaussian plume model. A lot of work for

prediction of air pollutant concentrations using this model had already been done.

25

CHAPTER 3

FIELD SURVEY, DATA COLLECTION

AND LABORATORY STUDIES

3.1 General To evaluate the performance of the selected models the following database has been

collected at Delhi during the period of November 2002 and March 2003.

(i) Classified traffic volume.

(ii) Air samples of CO

(iii) Meteorological parameters like wind speed, wind direction, Temperature, Mixing height

and Stability class.

(iv) Geometric parameters like road width, number of lanes, lane width, shoulder width,

presence of medians and its width.

(v) Longitudinal section parameters like the distance of receptor point from the intersection.

Except the data given in (iii) all other data has been collected from the field.

3.2 Selection of Sites

Keeping in view the objective of the study, the sites were so selected as to represent the

whole urban area in different land use zones like, Residential area, Commercial Zone, Silence

Zone and Heavy Traffic zone. Total eight locations have been selected and categorized in

different zones as indicated in table 3.1.The locations are shown in photo 3.1 to 3.5 and the map

of Delhi showing the sites selected for field studies is shown in fig.3.1 and the site diagrams of all

locations are shown in fig 3.2 to 3.9.

26

Table 3.1 Location Chosen for Field Studies

Type of Zone Location

Residential Zone • New Friends colony • Laxmi Nagar

Commercial • Karol Bagh Zone • Redfort

Silence Zone • Safdarjung Hospital • AIIMS Hospital

Heavy Traffic • India Gate Zone • ITO

Table3.2 Details of Locations Chosen for Field Studies

Location Data Name of the No of Details of Type of Type of

Code and Duration place Lanes median Land use Road

Number (day/night)

Safadarjung LI 6 Present Silence Bituminous 8/8Hours

Hospital

Karol L2 I 6 Present Commercial Bituminous 8/81-tours

Bagh

U India

6 Present Heavy

Bituminous 8I8Hours Gate Traffic

New friends I, L4 4 Present Residential Bituminous 8/8Hours

Colony

Heavy L5 I.T.O. 7 Present Bituminous 8/8Hours

Traffic

Laxmi L6 7 Present Residential Bituminous 8/8Hours

Nagar

AIIMS L7 6 Present Silence Bituminous 8/8Hours

Hospital

Red L8 7 Present Commercial Bituminous 8/8Hours

fort

27

Photo 3.1 A View of Location 1(Safdarjung Hospital)

Photo 3.2 A View of Location 4(New Friends Colony)

Photo 3.3 A View of Location 5 (I.T.0)

i

28

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38

3.3 Equipments Used In Data Collection

In addition to manual data recording of classified traffic volume count, a number of field

instrument with all the accessories are required for this job. Following are the instruments used in

air pollution monitoring.

• Air sample collection tubes: In these tubes air samples of Carbon monoxide and

hydrocarbon were collected using water replacement method.

• Other instruments: Measuring wheel and measuring tape were used to measure the

geometric parameters of locations. Generator was also engaged in field for permanent

power supply to run high volume sampler.

3.4 Field Study and Data Collection Procedure

Delhi is one of the major metropolitan cities of India where transportation facilities are

improving every year to meet increase demand due to excessive population growth. Following are

the studies conducted for data collection.

3.4.1 Traffic volume studies

At all the selected locations, Traffic volume studies were conducted continuously for a

period of 8 hours day and night. Directional classified traffic volume data were manually

recorded Proforma shown in AppendixAl.This Performa covers the all categories of vehicles.

The summarized traffic volume is given in table3.3.

39

3.4.2 Air pollution monitoring

The air pollution parameter carbon Monoxide was monitored at the selected locations

shown in table4.1. The major vehicular pollutant considered in this study is Carbon monoxide

(CO). The Proforma used is shown in Appendix A2. The observed concentration of Carbon

monoxide is given in table 3.4.

40

Table 3.3 Summary of Traffic Volume for all the selected locations.

~~mm~mmo~~om~mm~ ® ©m~mm~~om~mm ®om~mmom~oomME ®ommmmom~MEEMIN ' m

C/J: Car/Jeep, MR: Mini Bus, S/M: Scooter/Motor Cycle, Tr.: Truck, TT: Tractor Trailer, AR: AutoRickshaw

!II

Summary of Traffic Volume for all the selected locations(Cont.)

•

i • •

~0~000m~ommom ©® ; IMME Nom® ©om©m©m

, moom000mm © ©v ©boo m~ao~■oomm0000 vv C/J: Car/Jeep, MB: Mini Bus, S/M: Scooter/Motor Cycle, Tr.: Truck, TT: Tractor Trailer, AR: Auto Rickshaw

42


ku

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mmm ®mom ® ®mm

a m~mmom~o~m ®~mo~ ■~ammm o mmmam ©I ©mmo°© ©mmom mom ®~m~om~omamman C/J: Car/Jeep, M6: Mini Bus, S/M: Scooter/Motor Cycle, Tr.: 't ruck, TT: Tractor'[ railer, AK: Auto Rickshaw

43


m mm ®mom ® ®mmm

C/J: Car/Jeep, MB: Mini Bus, S/M: Scooter/Motor Cycle, Tr.: Truck, TT: Tractor Trailer, AR: Auto Rickshaw

44

Table 3.4: Observed Concentration of CO(µg/m3)

Location—► L1 L2 L3 L4 L5 L6 L7 L8 Timed _ 7:00-8:00 AM 4982 3876 4936 2896 4896 3825 5241 5916

8:00-9:00 5101 4103 6013 3615 4253 4521 5463 5825

9:00-10:00 6531 4712 6921 5105 4365 5040 5825 4986

10:00-11:00 6118 4804 7216 4818 5040 3791 4537 5135

11:00-1200 PM

5918 5213 7038 4021 4921 4653 4819 5421

12:00-1:00 4826 5538 6113 4598 4240 5121 3720 4991

1:00-2:00 5403 5065 5890 3792 4121 5294 3815 5216

2:00-3:00 5613 6018 6420 3815 4991 5013 3653 4815

48255621 6:00-7:00

7:00-8:00

4298

4973 4208 5103

5270

5094

5012

4812

5507

5294

5261

5413

5013

a21`

8:00-9:00 4589 4056 4986 4873 4729 5143 4789 4316

9:00-10:00 4960 4840 5895 4848 4893 4896 4961 4513

10:00-11:00 3985 3987 5420 4373 ' 4521 4537 4815 3987

11:00 -12:00 3302 3417 4876 3789 3987 4527 3719 3983

12_00-1:00 AM

1:00-2:00

2:00-3:00

3427

2678

3169

4271

4128

3765

5026

4569

3956

3468

3259

2928

4329

3895

3756

2869

2130

2420

3869

2679

3710

3147

4221

3785

3:00-4:OO AM 3701 3824 3826 2763 4201 2263 3015 3942

45

3.5 Laboratory Studies

3.5.1 Monitoring of carbon monoxide

Indian standard code IS: 5152(Part X)-1976[22(i)] has suggested the following methods

for monitoring the ambient carbon monoxide concentration.

1. Iodine Peroxide Method

2. Indicator Tube Method

3. Non-Dispersive Infrared Absorption Method

4. Gas Chromatography Method

The ambient air samples at all the identified locations were collected using the glass air sample

collection tubes by water replacement method.

The gas chromatograph (AIMIt, NUCON, Series 5700) was used for the analysis of the

air samples. A sample of the air containing carbon monoxide is injected in to the gas

chromatograph where it is carried from one end of the column to other. During its movement, the

constituents of the sample undergo distribution at different rates and ultimately get separated from

another. The separated constituents emerge from the end of the column one after the other and are

detected by suitable means whose response is related to the amount of a specific component

leaving the column. The concentration of the different constituents is plotted in the form of

inverted "V" shape curve(s) one after the other by the recorder. The peaks of the curves depend

upon the concentration of the different gases detected by the instrument. Concentrations of the

constitutes is calculated on the basis of the peak areas on the chromatograph obtained known

amount of constituent, namely, carbon monoxide, hydrocarbon etc. using the same apparatus

under identical conditions.

The samples were tested under the following testing conditions as suggested by Indian

standard IS: 5182(Part X)-1976

• Temperature - 50°C

46

• Carrier Gas - Hydrogen

• Carrier Gas Flow — 5 liters/hour

• Sample loop — .10 ml

• Bridge Current — 350 mA

• Chart Speed — 30 cm/Hour

Results:

The hourly average concentrations of carbon monoxide were determined using the

procedure explained above and the results for all the locations are presented in the form of bar

charts in fig 3.10(a) to 3.10(h).The Carbon Monoxide Concentrations exceed the safe limit of

4000 pg/m3[23] prescribed for Indian conditions in most cases.

47

III Karol Bagh

r0i II,

ri

Q S o 8 o S o S 8 0 °0 8 8 S 53 sa

g o d o o 0 o do o d d o o g g roo~~, .n r o

Time

~8o8n g_so88aSS. 8,~8.85a

o $ f Time

Fig 3.10(a) Hourly observed concentration Fig 3.10(b) Hourly observed concentration of CO at Safdarjung of CO at Karol Bagh

India Gate 6000 .. _._ .. ._.. _.

7000

6000 5000

4000 3000

0 2000 1000

~so~ a gssaa gseq

Time

Fig 3.10(c) Hourly observed concentration of CO at India Gate

Note: Y Axis: Observed Concentration in µg/m3

New Friends Colony 6000........ _..... ._._. .._.

5000

4000 j z 3000

1000 iiHiUiILUiiiii 0

Time

Fig 3.10(d) Hourly observed concentration of CO at New Friends Colony

48

ITO

Time

Fig 310(e) Hourly observed concentration of CO at ITO

Laxmi Nagar 6000

5000

4000

0 2000 1 000 ilfiftilhilfi iftikui

Time

Fig 3.10(f) Hourly observed concentration of CO at Laxmi Nagar

AIIMS

7000i 6000

5000

40

O IILUi1~~l J rUulililE 2000

1000 oil

Time

Fig 3.10(g) Hourly observed concentration of CO at AIIMS

Time

Fig 3.10(h) Hourly observed concentration of CO at Red Fort

Note: Y Axis: Observed Concentration in jig/rn3

49

CHAPTER 4

Modeling of Air Pollutant Concentration

4.1 General In the present study, total eight sites have been selected as given in table 4.1 In this total

eight hours day and eight hours night data had been collected for all the eight locations selected.

4.2 Modeling of Air Pollutant Concentration Using CALINE-4

4.2.1 CALINE 4 model

The various input parameters in the five different input screens of CALINE-4 are briefly

described in the following sections [24]:

Job Parameters

The Job Parameters Screen contains general information that identifies the job, defines

general modelling parameters, and sets the units (feet or meters) that will be used to input data on

the Link Geometry and Receptor Positions Screens.

File Name: Display only, not editable. Displays the name of the file where the current job is

stored.

Job Title: Optional, Provides a space for the user to enter a brief job description, up to 40

characters in length.

Run Type: Different choices determine averaging times (for CO concentrations) and how the

hourly average wind angle(s) will be determined. (Wind angle is the angle between the roadway

link and the wind direction. CALINE-4 calculates the angles based on data in the Link Geometry

and Run Conditio Sc Most users should invoke the "worst-case wind angle" run type

and apply a persistence factor of 0.6 to 0.7 in order to estimate an 8-hour average CO

concentration.

• Standard - Calculates 1-hour average CO concentrations at the receptors.

• Multi-Run - Calculates 8-hour average CO concentrations at the receptors.

• Worst-case wind angle - Calculates 1-hour average CO concentrations at the receptors. The

model selects the wind angles that produce the highest CO concentrations at each of the

receptors.

• Multi-Run/Worst-Case hybrid - Calculates 8-hour average CO concentrations at the

receptors. The model selects the wind angles that produce the highest CO concentrations at

each of the receptors.

Aerodynamic Roughness Coefficient: Also known as the Davenport-Wieringa roughness-

length. These choices determine the amount of local air turbulence that affects plume spreading.

CL4 offers the following 4 choices for aerodynamic roughness coefficient:

• Rural: Roughness Coefficient = 10 cm

• Suburban: Roughness Coefficient = 100 cm

• Central Business District: Roughness Coefficient = 400 cm

• Other: Appropriate values as specified by CALINE-4.

Model Information: Provides summary information for convenience and quality assurance.

• Link/Receptor Geometry Units: Select whether meters or feet will be used to define the

geometry of the roadway links and receptor positions.

• Altitude above Sea Level: Define the altitude above mean sea level. This input is used to

determine the rate of plume spreading

• Number of Links: The sum total number of links that the user has defined on the Link

Geometry Page.

51

• Number of Receptors: The sum total number of receptors that the user has defined on the

Receptor Positions page.

• Averaging Interval: Indicates whether the user has opted to calculate 1-hour or 8-hour

average CO concentrations at the receptors.

"Run" - Clickthis button to run the job as specified. First, be sure that the information on all

five pages of CL4 is complete: Job Parameters, Link Geometry, Link Activity, Run Conditions,

and Receptor Conditions.

"Exit" - Click this button to exit the CL4 program. CL4 issues a warning if changes or new user

inputs might be lost.

Link Geometry

Fill in the matrix to define the roadway network to be modelled. Each row in the matrix

defines a single link. Up to 20 links may be entered. Links are defined as straight-line segments.

Link Name: Optional. The user may define a 12-character description for the link.

Link Type: The user must select one of the following 5 choices to define the type of roadway

that each link represents.

• At-Grade - For at-grade sections, CALINE does not permit the plume to mix below ground

level, which is assumed to be at a height of zero. The height of the link above the ground is

defined in the Link Height cell.

• Fill - For fill sections, CALINE-4 automatically resets the link height to zero, and assumes

that air flow follows the surface terrain, undisturbed

52

• Depressed - For depressed sections, CALINL-4 increases the residence time of an air parcel

in the mixing zone. The residence time increases in relation to the depth of the roadway

depression.

• Bridge - For bridge sections, CALINE-4 allows air to flow above and below the link. The

plume is permitted to mix downward from the link, until it reaches the distance defined in the

Link Height cell.

• Parking Lot - Parking lot links should be defined to be coincident with the parking lot access

ways.

Endpoint Coordinates: Links are defined as straight-line segments. The entire length of each

link should deviate no further than 3 meters from the centreline of the actual roadway. The

endpoint coordinates, (xl, yl) and (x2, y2), defines the positions of link endpoints. A map of the

link geometry is shown on the Receptor Positions Page.

Link Height: For all link types except bridges, Link Height represents the height of the link

above the surrounding terrain. Ground level is defined at 0 meters or feet (z=0).

Mixing Zone Width: Mixing zone is defined as the width of the roadway, plus 3 meters on

either side. The minimum allowable value is 10 meters, or 32.81 feet.

Canyon/Bluff Mix: The Canyon/Bluff Mix feature has not been validated with field

measurements. Only very rare circumstances warrant its use. This feature must be used with

extreme caution.

Link Activity

The Link Activity screen defines the level of traffic and auto emission rate observed at

each link.

53

Traffic Volume: The hourly traffic volume anticipated to travel on each link, in units of vehicles

per hour. If a multi-run scenario is selected, traffic volume must be defined for 8 hours.

Emission Factor: The weighted average emission rate of the local vehicle fleet, expressed in

terms of grams per mile per vehicle. Emission rates vary by time of day. Therefore, if a multi-run

scenario is selected, emission factors must be defined for 8 hours.

Run Conditions

The Run Conditions screen contains the meteorological parameters needed to run

CALINE-4. The worst-case meteorological conditions that can be anticipated at the project

location must be employed.

Wind Speed - Expressed in meters per second. The minimum choice available for CALINE-4 is

0.5 m/s. Alternatively, EPA recommends a value of 1 m/s as the worst-case wind speed.

Wind Direction - The direction the wind is blowing from, measured clockwise in degrees from

the north (0 = north, 90 = east, 180 = south, 270 = west). Most users should opt for the "Worst-

Case Wind Angle" choice on the Job Parameters screen. If "Worst-Case" is selected, CALINE-4

does not use this input.

Wind Direction Standard Deviation - The statistical standard deviation of the Wind Direction,

sometimes termed "sigma theta".

Atmospheric Stability Class - A measure of the turbulence of the atmosphere. Values 1 through

7 correspond to the standard definitions for stability class A through E. Stability class E (or 7)

represents the most stable conditions.

Mixing Height - The altitude to which thermal turbulence occurs due to solar heating of the

ground. Reasonable values for the worst-case mixing height rarely have a significant impact on

CALINE-4 model results.

54

Ambient Pollutant Concentration - This measure reflects the pre-existing background level of

carbon monoxide, expressed in parts per million. CALINE-4 adds the pre-existing and modelled

CO concentrations together to determine the total impact at each receptor.

Ambient Temperature - The ambient air temperature significantly affects vehicle CO emissions.

A temperature that reflects wintertime conditions should be selected, expressed in degrees Celsius

Receptor Position

The Receptor Positions Screen contains the data inputs for all receptor positions, and also

displays a diagram of the link geometry and receptor positions. Receptors should be defined with

the same Cartesian coordinate system and units of measure as the link geometry. For each

receptor (maximum no. of receptors = 20), space is provided for an 8-character description, the

X-coordinate, the Y-coordinate, and the height (Z).

4.2.2 Prediction of carbon monoxide using CALINE-4

The sample weighted emission factors and meteorological data used for the prediction

have been given in APPENDIX B and APPENDIX C respectively. The total traffic volume for

both directions has been used for prediction and has been presented in Table 3.3

Using the inputs mentioned above, hourly predictions of Carbon monoxide have been

done at all the locations for standard wind run condition. Sample run screens and output have

been shown in APPENDIX D1 and APPENDIX D2.

55

4.3 Modeling of Air Pollutant Concentration Using GFLSM

4.3.1 Working with GFLSM

A simple general finite line source model was developed be Luhar and Patil [2] in 1986,

which overcomes the infinite line source constraints. The basic methodology used in the

developing this model includes co-ordinate transformation between the wind co-ordinate system

(x i ,y i ,z i ) and the line source coordinate system (x,y,z).The middle point of the line source can be

assumed as the origin for both coordinate systems, which also have the same z-axis. The position

of the receptor R in the line source coordinate system is (x,y,z) and that in wind co-ordinate

system is(xj,yi,zi)as shown in fig4.l.In the line source co-ordination system all the parameters

viz. x ,y ,z can be evaluated from road receptor geometry. This model specifies the dispersion

parameter as a function of wind-road orientation angle and distance from the source.

56

Fig 4.1 Relationship between wind co-ordinate system and line source

co-ordinate system.

57

Pollutant concentrations (jig/m}) are computed from the equation:

z—H z z C= Q [exp( ( 2~ z) )texp(-(~2a

H) )]* 257ru v_ sing

lerfl sin O(L l 2 — y) — x cos 9 +er fl sin O(L / 2 + y) + x cos 9 I]

20 y. 2-y

(4.1)

where,

Q = emission rate per unit length ( g/m-s).

u = mean ambient wind speed (m/s).

= horizontal, vertical dispersion parameter (m).

x = distance from the receptor fro the line source (m).

y = receptor distance from the roadway centre line along the line source (m)..

z = height of the receptor relative to the ground (1,8m).

H = plume centre height relative to the ground (m).

L = length of the source (m).

B = angle between the ambient wind and the road.

erfO = error function

Error function table is given in APPENDIX E.

4.3.2Determination of input parameters to the model:

Input parameters to the model can be determined based on the following information

(i) Mobile source emission: The emission rate per unit length Q(g/m-s) of the roadway is use as

the input for the models and the mobile source emission factor is used to calculate it. The mobile

58

source emission factor is defined as the quantity of a pollutant emitted when a vehicle runs a unit

length of road and depends upon the type, speed, age, etc. , of the vehicle. Hence Q is given by :

Q = Ef* Vh ..... (4.2) ................... Where,

Ef= pollutant emission factor

= vehicle density (vehicles/hour)

The emission factors Ef determined by Luhar and Patil (1986) were used to determine mobile

source emission.

For Carbon monoxide

Ef= 956.4 S-o.ss ..........................(4.3)

Where

S =Vehicle Speed (miles/hour)

Ef= 53.1 gm/mile (at vehicle speed=30 mph)

(ii) Meteorological conditions: The most important meteorological parameters affecting the

transport and dispersion of air pollutants are wind speed, wind direction, and atmospheric

stability. All the meteorological data was collected from the Indian Meteorological Department,

Delhi presented in APPENDIX C.

(iii) Geometry of road: Based on the information on the geometry of the road and receptor

location the input parameters x, y, z, L and B were measured from the map.

(iv) Dispersion Parameters: Horizontal dispersion parameter o and vertical dispersion

parameter o. is calculated, as suggested by Luhar and Patil, as follows.

59

Horizontal dispersion parameter a,: It is calculated by using modified EPA HI WAY model

horizontal dispersion parameter and given by:

z z

= 2Q., ....................(4.5)

Where

o-,,= 3.57 -0.53 Uo ....................(4.6)

U0= mean wind speed (m(s)

sinA I o .... (4.7) —-- ...................

2.15cosA

1. depends on stability class and given by

A. = 18.333 - 1.8096 ln(x/l 000)/57.2958 for unstable (A to C) ....................(4.8)

= 14.333 - 1.7706 in(x/1000)/57.2958 for neutral (D) ....................(4.9)

= 12.500 - 1.0857 ln(x/1000)/57.2958 for stable (E to F) ...................(4.10)

Here X in meters and /, in radians.

Vertical dispersion parameters, : It is calculated by using General Motors (GM) model

vertical dispersion parameter and given by:

6~=(a+ sin B

when B= 0°

then I/sing = 1/0.2242 for stability class (A-D)

= 1/0.1466 for stability class (E)

B = angle between the ambient wind and the road.

a, b, c depends upon stability class and taken from the Table 4.1.

...................(4.11)

60

Table 4.1 Parameters Used

Parameter Stable (A to C) Neutral (D) Unstable(E to F)

a 1.49 1.14 1.14

b 0.15 0.10 0.05

c 0.77 0.97 1.33

a 20.7 11.1 11.1

Ui(m/s) 0.18 0.27 0.27

U°(m/s) 0.23 0.38 0.63

(v) Plume centre height H: Luhar and Patil (1986) suggested that Plume center height can be

taken as given in General Motors model and is given by:

H = h°+hp ....................(4.12)

h°=Line source height(m) taken as 0.5m.

hp=Plume rise= (4.13)

( a*U .3 11/2 *,x ....................

F, =Buoyancy flux = g * 465 ....................(4.14) T*380

g = acceleration due to gravity (m/s2)

T = Temperature in °K

U' = U° sin0+UI ....................(4.15)

a,U° and U1 depends on stability class and taken from table 4.1

Sample calculation has been shown in APPENDIX F.

61

CHAPTER 5

PERFORMANCE EVALUATION OF AIR POLLUTION MODELS

5.1 Overview

The evaluation of model performance is a matter of great interest and it becomes

particularly important in all those fields in which modeling is used as a decision making tool.

Evaluation of the performance of an air-quality model generally focuses on assessing the

accuracy of the model prediction relative to observed concentrations [25]. In this study model

evaluation is carried out by comparing the observed data from the field with predicted data. A

significance test is also applied to check the consistency of the observed data with the predicted

data to have confidence in prediction exercise.

5.2 Site-wise Performance Evaluation

Performance evaluation of both models has been done for all locations. The concentration

of Carbon Monoxide, which was monitored in the field, compared with predicted concentration

using CALINE-4 and GFLSM. The index agreement (d) for each location is also calculated which

is explained in section 5.2.1.

Statistical test to determine the reliability of inter-relationship between predicted and

observed values like t-test has also been performed and found to give acceptable results at a 95%

confidence level. A line of regression between the predicted and the observed values has also

been developed and a good co-relation has been obtained for CALINE-4 as well as GFLSM.

62

5.2.1 Index of agreement (d)

It gives the degree to which model predictions are error free. It suggests that the

percentage of the potential for error has been explained by model. If d=0.51 it means that 51% of

the potential for error has been explained by the model which implies that there is lower error in

model result[16].

Y ( P —Or )2

d=1— "-' 0<_d_<l

—°+° —°U 2 Where,

Pr Predicted concentration

0, Observed concentration

O = Average observed concentration

Table 5.1 Sample Calculation, Index of Agreement, L1 (Safardarjung)

Observed (0)

Predicted (P) P-O

2

(P-O) -

(P-O) -

(O-O) I 5H61 (7)2

si 1 2 3 4 5 6 7 8 1. 4982 4600 -382 145924 -43 339 382 145924 2. 5101 4945 -156 24336 302 458 760 577600 3. 6531 6440 -91 8281 1797 1888 3685 13579225 4. 6118 5750 -368 135424 1107 1475 2582 6666724 5. 5918 6325 407 165649 1682 1275 2957 8743849 6. 4826 4945 119 14161 302 183 485 235225 7. 5403 5290 -113 12769 647 760 1407 1979649 8. 5613 5290 -323 104329 647 970 1617 2614689 9. 4298 4600 302 91204 -43 -345 388 150544 10, 4973 4485 -488 238144 -158 330 488 238144 11. 4589 4485 -104 10816 -158 -54 212 44944 12. 4960 4830 -130 16900 187 317 504 254016 13. 3985 3795 -190 36100 -848 -658 1506 2268036 14. 3302 3795 493 243049 -848 -1341 2189 4791721 15 3427 3220 -207 42849 -1423 -1216 2639 6964321 16 2678 2645 -33 1089 -1998 -1965 3963 15705369 17 3169 2990 -179 32041 -1653 -1474 3127 9778129 18 3701 3450 -251 63001 1193 942 2135 4558225

0=4643 µg/m3 IB

1_O)2 = 1386066

63

O+O,—O ~)' =79296334

_> d=1— 1386066 = 0.98 79296334

This means that 98% of the potential for error at location L1 has been explained by

CALINE-4 model which implies that there is lower error in model result.

5.2.2 Significance test:

A comparative test is applied to check the consistency of the observed data with the

predicted model by using the paired t-test. In t-test the observed data sets were compared with the

predicted value of CO concentration.

The null hypothesis of t-test was that the mean value of difference between pair of

observed data and predicted ones is zero.

Therefore, formulation of this test is as follows:

d ~ halt —

d * V n ............................(5.1)

s

where,

d :Mean of the difference between observed data and predicted data

sd: standard deviation of the difference.

n: no. of paired samples

tcaic =Calculated value oft.

Sample calculation has been shown in table no. 5.2.

64

Table 5.2 Sample Calculation oft —test: Location I,1(Sa Ida r,jung) Using GFLSM

SI no.

Observed concentration

_gittL.tiL_1

Predicted concentration (}Lt;/m3) dl

Difference dl d2

1 4982 4515 467 2 5101 4635 466 3 6531 5125 1406 4 6118 5233 885 5 5918 5512 406 6 4826 5535 -709 7 5403 5675 -272 8 5613 5435 178 9 4298 4360 -62

10 4589 4648 -59 11 4960 4194 766 12 3302 3516 -214 13 3427 3219 208 14 2678 2964 -286 15 3169 3018 151 16 3701 3501 200

d= 220.6875

sd=417.5928

n=16

from above formula

220.6875 tcalc=

* 16 =1.70581 417.5928

ttabulated = 2.13 for degree of freedom = 15 and level of significance=0.05

Hence tcalc < ttabulated

this implies that there is insignificant difference between observed data and predicted value

therefore our model is correct

5.3 Performance of CALINE-4 Performance evaluation of CALINE-4 is carried by using statistical analysis and following

are the bar charts and scatter plots which show the variation of predicted concentration with the

observed one for all locations.

65

5.3.1 CALINE-4 performance for LI (Safdarjung)

Location Safdarjung comes under silence zone. From the bar chart shown in fig 5.1 it is

clear that observed concentration of CO is greater than the prescribed limit of 4000 µg/m3 mostly

in day time and CALINE-4 prediction is quite close to the observed one.

Regression analysis, shown in fig5.2, between predicted and observed one gives

satisfactory results with coefficient of correlation R2=0.9398 which implies that predicted values

is quite close to observed one.

Significance test between observed and predicted values gives t,,,,=1.12 which is less than

t,at ui„< ;= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference

between observed and predicted values is insignificant.

M

G000.

soonl

v ~

L0014000

E

rn ~

° ]000

2000

U 1000 ,

709-. 900 now 9:00

Time

Fig 5.1 Location L1: Observed Vs Predicted concentration of CO in Using CALINE-4

66

1000 2000 3000 4000 5000 6000

Predicted Concentration (microgram/cu.m.)

Fig 5.2 Location LI: observed Vs Predicted (CO-Standard case)

Table 5.3 t-test for Location Li (CALINE-4)

77.1875

Sd 2765302

N 16

D.O.F. 15

tcaic 1.116514

a 0.05

tniaird 2.13

7000

6000 ,

E 5000

E m

0 4000

° 3000 R i C

2000: U

7000 I O

0.,

0 WA

67

5.3.2 CALINE-4 performance for i,2 (Karolbagh)

Location Karolbagh comes under Commercial Zone. From the bar chart shown in fig 5.3

the observed concentration of CO is far greater than the prescribed limit of 4000 µg/m3 in day &

night time both and the CALINE-4 prediction is quite close to the observed one.

Regression analysis, shown in fig5.4, between predicted and observed one gives very

satisfactory results with coefficient of correlation R2"0.709 which implies that predicted values is

quite close to observed one.

Significance test between observed and predicted values gives t,aIc= l .98 which is less than

[tabulated — 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


7000

6000

5000

E 4000

o 0000 }

2000 t

c ~

V 1000

700 8:00. 9.00. 1000- 1100- 1200- I:00-

80055 9D0 1000 11 00 1200 100 2:00

PM

Time


68

6000

._5000 E

%4000

0

EE'S000

92000 U

oi-a

y=0.815x+590.57

R'=0.709

1000 2000 3000 6000 5000 6000

Predicted(microgram per cu,mj

Fig 5.4 Location L2: observed Vs Predicted (CO-Standard

Table 5.4 t-test for Location L2 (Caline-4)

158.1875

Sd 319.1518

N 16

D.O.F. 15

tcaic 1.982599

a 0.05

toabulated 2.13

tE

69


7000

6500

E

fl }

i

900- 1005- 1100- 1260- 100 200- 000-

1000 1107 1200 1:00!4.1 7:00 000 50004 1:00- 200- 600- 760- 020-

0:00 3:00 0:00 8:00 9:00

5000

E

of 4000 o

E ]000

0 04

2000

C ~ 0

V 1000

100- 800- 900. 0000M 900 1000

5.3.3 CALINE-4 performance for L3 (India Gate)

Location India Gate comes under Heavy Traffic Zone. From the bar chart shown in fig 5.5

the observed concentration of CO is far greater than the prescribed limit of 4000 1g/m3 in day &

night time both and the CALINE-4 prediction is quite close to the observed one.


satisfactory results with coefficient of correlation R2 0.7533 which implies that predicted values


Significance test between observed and predicted values gives t4.4.1 4.=2.05 which is less than

t,,,h„i„oDci= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference

Time


70

7000 1

epos i .

E ~

~saao i • • •

.4000

j y = 0.8923x + 213.58

aoo { R2 = 0.7533

ozwo~

1000

0 1000 2000 3000 4000 5000 6090 7000 9000

Predicted(microgram per cu.m)

Fig 5.6 Location L3: observed Vs Predicted (CO-Standard case)


164.3571

Sd 320.3142

N 16

D.O.F. 15

tcalc 2.052449

a 0.05

tmbwued 2.13

71

5.3.4 CALINE-4 performance for L4 (New friends colony)

Location New friends colony comes under residential area. From the bar chart shown in

fig 5.7 the observed concentration of CO is far greater than the prescribed limit of 4000 .sg/m3

mostly in peak hours.




Significance test between observed and predicted values gives tcuu~ 4.57 which is more

than ttabu ated= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference

between observed and predicted values is significant.

.11

5000

E

i" IIITllhII IlIIIl E

m 3000

0

E 2000

10001. .

i ■ oe~:~ei im aree~nedl

00. 900- t000- ttno 1200 f9a 2:00- 200-

00 1100 1100 1200 1:00 M1 200 ] oa 4.00 AM

Time


72

5000

4500

' y = 0.733x + 673.26 RZ = 0.8441

1000 2000 3000 4000 5000 6000

Predicted(microgram per cum)


Table 5.6 t-test for Location L4 (Catine-4)

d 360.1875

Sd 315.1728

N 16

D.O.F. 15

tc41c 4.571302

a 0.05

ttabulatcd 2.13

4000 j

3500

3 ~ 3000 1

2 2500

E 2000

1500

a ~

0 1000

500

0?-

0

73


6000

5000 m d E

:0 4000

0 n E 122 3000

0 L0

E

S 2000

0 a)

~aoo

700- 8:00- 800)0 9:00

290- 8100 700- 0:00 700 8 00

5.3.5 CALINE-4 performance for L5(ITO)

Location ITO comes under Heavy Traffic Zone. From the bar chart shown in fig 5.9 the

observed concentration of CO is far greater than the prescribed limit of 4000 µg/m3 for all

24hours.


satisfactory results with coefficient of correlation R2=0.575 which implies that predicted values is


Significance test between observed and predicted values gives tcai,=1.84 which is less than

tz ~ ul ztcd= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference

i oo~o~e. n wea~mea ~,

809- 900 10.00 11.00 1200- 100- 200- 000- 900 1000 1100 1200 1 DOOM 200 300 480001

Time


74

5000

0000

a° E

~ 3000 -I

O 1

E c I 0 2000

CM

a

M Observed

5.3.6 CALINE-4 performance for L6(Laxmi Nagar)

Location Laxmi Nagar comes under residential zone. From the bar chart shown in fig5.l 1

the observed concentration of CO is far greater than the prescribed limit of 4000 µg/m3 mostly in

peak hours.

Regression analysis, shown in fig 5.12, between predicted and observed one gives very



Significance test between observed and predicted values gives t391 3= 2.03 which is less than

ttahu jalcd= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


RM

700 0:0 9.00- 10:00. 1100- 1200- 1:00- 200- 600- 7:00- 500- 900- 1000- 11.00- 1200- I: 00- 2:00- 300.

890 644 900 10:00 11:00 1200 1:00 200 300 790 8.00 0:00 1000 1190 1200 100 AM 200 300 4:00964

PM

Time


76

6000

00°°

E dc°o

O. I' E m 3000 l rn y = 0.6551 x + 1149.1

2000

2

a 0

1200

0 1000 2000 7000 1000 5000 5000

Predicted (microgram per cu.m)



d 183.125

Sd 359.6214

N 16

D.O.F. 15

tca c 2.036864

a 0.05

ttabulated 2.13

77

5.3.7 CALINE-4 performance for L7(AIIMS)

Location AIIMS comes under silence zone. From the bar chart shown in fig 5.13 it is clear

that observed concentration of CO is greater than the prescribed limit of 4000 4g/m3 mostly in

day time and CALINE-4 prediction is quite close to the observed one. The CALINE-4 predictions

exceed the observed values for some hours it may be due to the error in taking observations.




Significance test between observed and predicted values gives talc 1.32 which is less than

ttab,hL,d= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference

between observed and predicted values is insignificant

1000

6000

E j 0007

a E 4000

T 0

V_ E 0006

C

2 2000

C

CO

L) 1000

:

100- 600- 900. 1000- 1100- 1200- 100- 200.

B 00 M1 900 1090 1120 12:00 100 200 000

PM

900- 100- 900- 900. 1000- 11.00 rzao- Ias 700 800 900 1000 1100 1200 102P 100

106286

1

000-

4 -0 2 206

Time


78

MI

4100 1

4000 1

4500

y = 0.4287x + 2518.2

R0 = 0.5882 3000

E 2500

Z000

Z 1500

0 1020

in

-------

0 1000 2000 3000 4000 5000 5000




254.5625

Sd 768.3668

N 16

D.O.F. 15

tc0~0 1.325213

a 0.05

ttabulated 2.13

79

5.3.8 CALINE-4 performance for L8 (Red Fort)

Location Red Fort comes under Commercial zone. From the bar chart shown in fig 5.15 it

is clear that observed concentration of CO is greater than the prescribed limit of 4000 jig/m3

through out the day and CALINE-4 prediction is quite close to the observed one.




Significance test between observed and predicted values gives t,=2.02 which is less than

t b„iaicd= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


ass

5000 E V

N o_ 4000

CM E rn 0

3000 a C 0

2000

C C

7000

0

j I obtuse: ® PreeAted

700- 890 900- 1000. 1100- 1200- 1:00- 2:00- 600- 700- 600. 900- 10.00- 1190- 12:00- 1:00- 200. 0:00- 8;00964 8:00 10.00 1100 1200 100 2.00 5:00 0.00 0:00 900 1000 11.00 1200 150969 2:00 3:00 40005

PM

Time


80

6000

5000

j 4000

a 3000

E d 2000 Z N 0

1000

y = 0.8793x + 304.16

R2 = 0.7523

1000 2000 3000 4000 5000 6000 7000




166.5

Sd 329.2033

N 16

D.O.F. 15

tea l0 2.023066

a 0.05

ttabulatcd j

2.13

81

o O O N O O N r Ul

0O N

0O r

45 to Y') N

to 01 N 01

~O N

O ~

O O to

O tO N t0

LL W J

O

tO to O to 0 tO O 0 N N

to O to N U) i0 O O M a a to d O d t0 Q to

V V M d Q Q to to to V N M

Q c0 N M N ~O t0 W fD r

O

to O N O O N N t0 0 O 4'l O to to m

to to N

O N d d

O h M

d to

W Q Q

N M

V Q

N O O

N to

d W Q

O (D Q

d O1 to

V O) to M

V N to

(D N N N

N N N N

Z

N ~O O M C M r a M W r QI M O O M

O [0 — O O tO N O O W

O W N to to O

O uo F- J

to M O O N N O) O

O) N N

.- W M O O7 N

A O Io W W N W N O V N

O N V Q C< N O V V' to d M N M to Q Q

to O N N N tO O O O O N to O N to to O O G ZS W N

CC N

N O N

O O W N

W (O M

N O ID N

N N f0

M M

M M d

N M N ~O

O d ON Q to V O M M to Q V V M M M N N

U J

LL y to N N W W N N O Q M W M r W to W M Z to '- 0 O> O) N to N Q r W to i[] N to

O N W8 d d M M ~O N O O V M M N N

M O V N O7 N r O fD N K

00 tO J

C a t0 M.

M ~-- N

W N

M r 0 to O N N

M O

to to N m O

O W r

to N to

tD ~O

to N

O Q O c0 to N

r O r <D ~O

y t0

to O u1

Oo Q

W ~O N

W Q

O u~

to N M

N M

-p O ~O O O ~1 O A O O L N •- Q W 0 O r N r M 0) r r O fp iD l0 i0 Q n ~fl Q d

Q M? d M M M M M IL N O J

• t0 N M O F to O M to M

to t0 W to

N to O to

to O Q to r N

W N

N tO to N

O

O U) O N N to 0 0 0 to to O 0) U) O V

O O C ~1 l0 W r M O) N N (D d V W r N t0 OI Q

(t) d• W O

~ O~ N

` Q O Q O O 00 O 00

O O I N c']

..

OlA W { N M

pF p o o a It! 0 00

00

o

!iI O

0

I

° a 00 o ~ O ~- cV 10 f` O ~ ~- N

n

82

Fig 5.17 Observed Vs Predicted Concentration of CO (CALINE-4)

7000

6000 1

j x

S • X X ~ ,

~.~ 560 i • ~ '

d aaao ~ x

o aaoo -I

1 1

2000 y = 0.9607x + 88.372

R2 =09398 0

1000

0 1000 2007 3000 4000 5000 6000

Predicted(microgram per cu.m)

83

5.3.9 Validation of CALINE-4

The observed concentrations and predicted concentrations for all eight locations is

presented in Table 5.11 By visual examination of the predicted and observed results, certain

differences were observed between the values. Therefore a scatter plot has been drawn to

compare the measured and predicted values of carbon monoxide and to check the co-relation

coefficient R2 value, as shown in Fig 5.17. Also index of agreement of all locations has been

calculated presented in table 5.12.The calibrated equation for CALINE-4 is as follows:

Using Caline-4 Y=0.9607 X + 88.372 R2 0.9398

Where, Where, Y=Observed CO concentration

X= Predicted CO concentration

Table 5.12 Index of Agreement d, for all Locations

Location Index of Agreement, d L 1 0.98 L2 0.88 L3 0.89 L4 0.87 L5 J 0.51 L6 0.98 L7 0.61 L8 0.98

From table 5.12 the variation of index of agreement lies between values 0.98 to 0.51

which is very satisfactory and statistical analysis presented in table 5.3 to table 5.10 indicates that

performance of CALINE-4 model is very good.

84

5.4 Performance of General Finite Line Source Model (GFLSM) Performance evaluation of GFLSM is carried out in the same manner as completed for

CALINE-4. Following are the bar charts and scatter plots, which show the variation of predicted

concentration with the observed one.

85

5.4.1 GFLSM performance for Li (Safdarjung)

Location Safdarjung comes under silence zone. From the bar chart shown in fig 5.18 it is

clear that observed concentration of CO is greater than the prescribed limit of 4000 µg/m3 mostly

in day time and GFLSM prediction is quite close to the observed one.


satisfactory results with coefficient of correlation R2=0.7748which implies that predicted values


Significance test between observed and predicted values gives t,at~ 1.71 which is less than

ttabMtatud° 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


7000

6000

5000

E

4000

E n ~a 3000 rn 0

-2000

E N

1000

0 U

700. 000. 900- 1000. 11'00- 1200- 1:00. 200. 6:00- 700- 8.00 900- 10:00- 1100- 12:00- 100, 200- 0:00-

800494 500 1600 1120 1200 1:00 2:00 300 700 000 900 10'.00 11:00 1200 1:00 AM 200 390 400 AM PM

Time

Fig 5.18 Location Li: Observed Vs Predicted concentration of CO in Using GFLSM

86

7000

4000 I

5200 ' E

4000

2000

rn 0

2000

1000

0

i . y = 0.754x + 860.75

R2 = 0.7748

1000 2000 3000 4000 5000 6000

7000

Predicted(Microgram per cubic meter)

Fig 5.19 Location LI: observed Vs Predicted (CO-Standard case)

Table 5.13 t-test for Location LI (GFLSM)

220.6875

Sd 517.4928

N 16

D.O.F. 15

teals 1.71

a 0.05

ttabulatcd 2.13

87

u06served LPredialedJ

6:00- 9:00- 10D0. 11:00- 1200- 1:00- 900 1000 1100 12:00 1:00 200

AM

5.4.2 GFLSM performance for L2 (Karolbagh)

Location Karolbagh comes under Commercial Zone. From the bar chart shown in fig 5.20

the observed concentration of CO is far greater than the prescribed limit of 4000 µg/m3 in day &

night time both and the GFLSM prediction is quite close to the observed one.




Significance test between observed and predicted values gives tca,c 1.16 which is less than

t,abuiaLed= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


Time

Fig 5.20 Location L2 Observed Vs Predicted concentration of CO in Using GFLSM

88

6000 i

5000

yI • •

E 4000 u a

y=0.7562x+645.24

E~ a R'=0.801

0 o u g_ 2000 J 00 d

N

0 1000

0 1000 2000 ]000 4000 5000 6000

Predicted (Microgram per cubic meter)

Fig 5.21 Location L2: Observed Vs Predicted (CO-Standard case)

Table 5.14 t-test for Location L2

d 68.6875

Sd 235.3728

N 16

D.O.F. 15

talc 1.16

a 0.05

ttahulalal 2.13

7000

89

loss

S 6002

a a 5000

.4002

5.4.3 GFLSM performance for L3 (India Gate)

Location India Gate comes under Heavy Traffic Zone. From the bar chart shown in fig

5.22 the observed concentration of CO is far greater than the prescribed limit of 4000 µg/m3 in

day & night time both and the GFLSM prediction is quite close to the observed one.




Significance test between observed and predicted values gives t,ai~ 1.90 which is less than

t(abojated= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


700. 800 8.00- 1000- 1100- +zoo- too- zoo- 8:00- 700 9:00- BAO. M00- 11 DO- 1200- 100. 2:00- 200- 8.00 Rd 800 1000 1100 1200 too 2:00 0,00 Loo 800 800 1090 1100 12:00 .001614 200 000 4:00444

PM

Time

Fig 5.22 Location L3: Observed Vs Predicted concentration of CO in Using GFLSM

90

7000

d 5000 E E

4000

a E @ 3WO

D

y = 0.8B94x + 97.5 R' = 0.8751

2000

100D 2000 3000 400 5000 6000 7O




99.5625

Sd 209.2746

N 16

D.O.F. 15

tcalc 1.903002

a 0.05

ttabulated 2.13

91

5.4.4 GFLSM performance for L4 (New Friends Colony)

Location New friends colony comes under residential area. From the bar chart shown in

fig 5.24 the observed concentration of CO is far greater than the prescribed limit of 4000 pg/m3

mostly in peak hours.


satisfactory results with coefficient of correlation R2 0.8685 which implies that predicted values


Significance test between observed and predicted values gives team1.34 which is less than

ttabulated° 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


6000

5000

4000

3000

E

Z3 2000

0

1000

U

700- 000- 900- 10:00- 1100- 1200- 100- 200- 6:00- 790. 0:02- 900- 1000- 11:00- 1200- 100- 200- 3:00- 900004 900 1000 1100 1200 100 2:00 300 700 0:00 900 1000 1100 1200 100041 2:00 300 410016

PM

Time


92

M

a

E40 j

0000

E ~ I 0 `u

iaoo

0

y = 0.8886x + 427.07 R2 0.8685

1000 2000 0000 4000 5000 6000




d 148.0625

Sd 443.5004

N 16

D.O.F. 15

teak 1.335399

a 0.05

ttabulatcd 2.13

93

5.4.5 GFLSM performance for L5 (ITO)


observed concentration of CO is far greater than the prescribed limit of 4000 µg/m3 for all

24hours.




Significance test between observed and predicted values gives t,1 =1 .49 which is less than

tcanuiaeed= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


6000

M3

4000

a ci v

3000 C-

2 rn

2000

C 0

1000 c c 0 U

7.00- 890- 000- 1000- 11:00- 1200- 1D0- 2:00- 600- 700- 8:00- 9:00- 1000- 1100. 1200- 100- 200- 200-

800AM 9:00 1000 1100 1200 100 2:00 100 7:00 8.00 900 10:00 1100 1200 1:00AM 2:00 300 490 AM

PM

Time


94

LM

U I

U

°' I Y' 8.7361x. 1126.5 a a0001

00 0 0

2000

a

4130 4200 1700 4400 4500 4600 ~----~-~-- ~_ ...o 4600 --

Predicted (Microgram per cubic meter) shoo 00

Fig 5.27 Location L5; observed Vs Predicted (CO- Standard case)


d 146.375

Sd 392.9056

N 16

D.O.F. 15

tcaic 1.49018

a 0.05

ttabulared 2.13

95

5.4.6 GFLSM performance for L6 (Laxmi Nagar)

Location Laxmi Nagar comes under residential zone. From the bar chart shown in fig5.28

the observed concentration of CO is far greater than the prescribed limit of 4000 µg/m3 mostly in

peak hours.




Significance test between observed and predicted values gives t,,i~ 1.40 which is less than

tn,b„iated= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


6000

5000

4000

i,

47000

E

0

E 2000

0 0

loco

a. U

700- 800. 900- 1000- 11:00- 1200- 1:00- 2:00- 0:00- 7:00- 800- 9:00. 7020- 1100- 1200. 7:00- 200- 2.00- 920PM 900 10.00 1100 1200 100 2:00 020 700 0:00 990 10:00 11:00 12.00 120712 200 390 400701

PM

Time


96

6000

5000

4000

a

p 3000

C

0

3~ 2000 1

v m

y = 0.6372x + 1584.6 R'=0.8279

0 1000 2000 3000

00 40

5000 6000




120.375

Sd 343.5047

N 16

D.O.F. 15

tcalc 1.401728

a 0.05

ttabulated 2.13

97

5.4.7 GFLSM performance for L7 (AIIMS)

Location AIIMS comes under silence zone. From the bar chart shown in fig 5.30 it is clear

that observed concentration of CO is greater than the prescribed limit of 4000 sg/m3 mostly in

day time and GFLSM prediction is quite close to the observed one. The GFLSM prediction

exceeds the observed values for some hours it may be due to the error in taking observations.




Significance test between observed and predicted values gives t0010 0.989 which is less

than tabulated= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


M

6000

E 5000

.n V

4000 Oa

3000

O 2000

moo 0 U

100- 820- 9Q3. 1000. 1100- 1153- 135- 200- 0:00- 700- 802- 900- 1000. 1100- 1200. 100- 200- 000- 00/01 900 1500 1190 200 1:cc 200 300 700 300 9.00 10.00 1150 1200 00/014 200 ).50 400 AM

PM

Time


98

M

5000

£ 4000

y= 0.8133x+757.9

W 0.8592 a 3000

E

0 2000

V

1000

O

0 4 -• _

0 1000 2000 3000 4000 5000




M

173.4375

Sd 482.631

N 16

D.O.F. 15

tease 1.437

a 0.05

ttabulated 2.13

99

5.4.8 GFLSM performance for L8 (Red Fort)

Location Red Fort comes under Commercial zone. From the bar chart shown in fig 5.32 it

is clear that observed concentration of CO is greater than the prescribed limit of 4000 pg/m3

through out the day and GFLSM prediction is quite close to the observed one.




Significance test between observed and predicted values gives tcaiG 0.80 which is less than

ttabu laled= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


7000

6000

E aoo a

4000 n E

o coo

020,

Toro 0 U

700. eoo. 904. 1090. Iloo. 1100. loo. 233. 600. loo- Boo- 900. 7000- 1100. 1000- loo. dmo- 300. 803 761 900 1000 1100 1200 100 200 100 700 ego 900 1000 1100 1000 100761 too 000 400761

PM

Time


100

6000

5000

v Z 4000 E a

0.3000

rn O

2000

Z 01000{

0 1000

y = 0.7628x t 1101.5

R' = 0.7006

2000 3000 4000 5000 6000




d 115.375

Sd 574.9108

N 16

D.O.F. 15

tcatc 0.802733

a 0.05

ttabutated 2.13

101

(I)

00

0

N N NN NN NNNNN

0 - o NNNNOONN N

c N

LD N N N N

NNOONNNOOr

NNNNNN

-N 00 00

N

0

N

'0 N

N

N

N 00

N Q\ Q\ 0 N

o N N N N NNNNNN N 41 11

2 S

5 S

5 S

S - -

5252 - - -

52222525; - - -

S 5555

N

o S NNNNNNNNN N

-

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'ONNNNNNNNNN

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00 5250 '0 o

00 -N --NOON\ONNN NN'0'0NNNNNNNNNNNNNN N N N N '0 N

5gO.o5g5

881

5555o555<

102

Fig 5.34 Observed Vs Predicted Concentration of CO (GFLSM)

8000

70001

6000

E 5000

U 4000

Q ~

E To

C7 30001 o

E 20001

c ,000 0

x c ,

r • ,

i t ,

y = 0.754x + 860.75

R2 = 0.7748

I

0 1000 2000 3000 4000 3000 6000 7000

Predicted(microgram per cub. m.)

103

5.4.9 Validation of GFLSM

The observed concentrations and predicted concentrations for all eight locations are

presented in Table 5.21. By visual examination of the predicted and observed results, certain

differences were observed between the values. Therefore a scatter plot has been drawn to

compare the measured and predicted values of carbon monoxide and to check the co-relation

coefficient R2 value, as shown in Fig 5,34. The calibrated equation for GFLSM is as follows:

Using GFLSM Y = 0.754 X + 860.75 R2=0.7748

Where, Y = Observed CO concentration

X = Predicted CO concentration

Table 5.22 Index of Agreement d, for all Locations (GFLSM)

Location Index of Agreement, d L l 0.91 L2 0.98 L3 0.79 L4 0.89 L5 0.61 L6 0.94 L7 0.84 L8 0.74

From table 5.22 the variation of index of agreement lies between values 0.98 to 0.61. So

maximum 98% of the potential for error has been explained by this model, which implies that

there is lower error in GFLSM results, which is very satisfactory, and statistical analysis

presented in table 5.13 to table 5.20 indicates that performance of GFLSM is very good.

104

CHAPTER 6

CONCLUSIONS AND RECOMMENDATIONS

6.1 CONCLUSIONS

Based on the results of field, laboratories studies and statistical analysis results

following conclusions have been drawn:

1. The observed CO concentrations exceed the safe permissible one hour concentration of

4000µg/m3 at all the locations during day and night times both. The observed CO

Concentration was maximum of 7216pg/m3 in morning peak at India Gate and minimum

of 2896µg/m3 at New Friends Colony at night.

2. Location wise statistical analysis on the results of CALINE-4 indicating a very good

correlation with observed values with r2 values ranged from 0.9398 to 0.575.

3. Location wise statistical analysis on the results of GFLSM prediction indicates a very

good correlation with observed values with r2 values ranged from 0.8751 to 0.7006.

4. Significance test (t-test) done on the results of CALINE-4 gives satisfactory results and

shows tcaiculaled value less than ttahulated for degree of freedom 15 and level of significance

0.05 for seven out of eight locations. Hence performance of CALINE-4 is satisfactory.

5. Significance test (t-test) done on the results of GFLSM gives very satisfactory results and

shows tc;,ic„i;,«d value less than ttah„i,«d for degree of freedom 15 and level of significance

0.05 for all the eight locations. Hence performance of GFLSM is very satisfactory.

105

6. Index of agreement (d) value for CALINE-4 varies from 0.51 to 0.98, which implies that

minimum 51% of the potential for error has been explained by the model. Hence there is

lower error in CALINE-4 results.

7. Index of agreement (d) value for GFLSM varies from 0.61 to 0.98.Hence, which implies

that minimum 51% of the potential for error has been explained by the model. Hence most

of GFLSM predictions are error free.

8. On the basis of statistical analysis GFLSM performance is better than CALINE-4 because

minimum value of r2 for GFLSM and CALINE-4 are 0.7006 and 0.575 respectively,

Minimum values of Index of agreement for GFLSM and CALINE-4 are 0.61 and 0.51

respectively and t-test shows that there is insignificant difference between observed and

predicted values of GFLSM for all the eight locations while for CALINE-4 there are

seven, out of eight, locations where insignificant difference between observed and

predicted values is obtained. This may be due to that CALINE-4 predictions may not be

suitable for Indian conditions.

6.2 RECOMMENDATIONS

The recommendations for further work in this topic may be as follows:

1. Since GFLSM was developed for Indian conditions only hence there is a need to carry out

geographical transferability of this model.

2. GFLSM predictions involve a very lengthy hand calculation and are very time taking

hence there is a need for user-friendly software for this model like CALINE-4.

106

REFERENCES

1. htty:llwww.teriin.org

2. Luhar, A. K. (1985), "Vehicular pollution modeling" M.Tech Thesis, IIT-Bombay.

3. "White paper on Delhi with an action Plan", (2001) Govt. of India, Ministry of Environment

and Forests, New Delhi

4. SIAM(2001) ," I and M for in use vehicle in India",GITE Regional Workshop on I and M

Maintenance Policy in Asia, Bangkok.

5. Vinod Sibal and Yash Sachdeva (2001)," Urban transport scenario in India and its linkages

with energy and environment." Urban Transport Journal, pp 34-55.

6. http:l\www.petrolium.nic.in

7. Chitra Rajgopal, Kapoor, J.C. (2001), "Environment pollution from mobile sources- An

important consideration for long-term transport policy." Proc., Seminar of Indian National

Academy of Engineering, Hyderabad, pp234-264.

8. http://www.teriin.org/urban/fuelfm.htm

9. Renu Jain (2001)," Dispersion modeling of gaseous pollutants in Jaipur city and assessment

of their health effects on residents" Ph.D Thesis, University of Rajasthan.

10. http://www.worldbank.ori(nipr/work paper/ 1860/delhipap.pdf.

11. http:// www. defra.gov.uk

12. Pickett, E. E.(1986), "Atmosphere pollution" Second Edition, Published by John Wiley and

Sons, New York

13. Benarie, M.M.(1980), "Urban air pollution modeling" Published by The M.I.T. Press,

London.

14. Yeh, J.T. (1977), "Modelling atmospheric dispersion of pollutants." Air Pollution Control

and Design Handbook, Part 1, Published by: Marcel Dekker, pp-167-178.

15. M. L. Terry and N. E. Kennith (1976), "Measured vs predicted air quality near highways",

Journal of the Environmental Engineering Division, Vol. 102, Nos. EE 1-6.

16. Benson, P. E. (1988), "Development and verification of California line source model",

Transport Research Record No. 1176, pp. 69-76.

17. Chock D.P. (1977a)" General motors sulfate dispersion experiment: An overview of the

wind, temperature and concentration fields" Atmospheric Environment, (11) pp553-559.

18. Benson, P.E., Nokes, W.A. and Cramer R.L. (1986), "Evaluation of the CALINE-4 line

source dispersion model for complex terrains" Transport Research Record No. 1058,pp 7-13.

19. Cohn, L. F. and Mevoy, G.R. (1982), "Environmental analysis of transportation systems."

Published by John Wiley and Sons, New York.

20. Robert,V.(2001), "A Study on transport related pollution" M.Tech Thesis, Department of

Civil Engineering,IIT-Roorkee,Roorkee.

21. Sivacoumar, R. and Thansekaran, K.(1999), "Line source model for vehicular pollution

prediction near roadways and model evaluation through statistical analysis" Journal of

Environment Pollution, Vol. 104,pp 389-395.

22. Indian Standard Code Related to Air Pollution Measurement:

i). IS:5182(PartX)-1976, "Indian standard method for measurement of air pollution,

carbon monoxide"

23. "Air quality status and trends in India" National Ambient Air Quality Monitoring

Series" CPCB, Ministry of environment and Forests, NAAQMS/14/1999-2000.

24. Nirjar R.S., Jain S.S., Parida M., Sharma N., Robert V., Mittal N. (Sept. 2002).

"Development of transport related air pollutants modeling for an urban area" Journal of IRC

(Discussion Paper No.487), Vol. 63-2, Sept. 2002, pp 289-324.

25. Jain S.S., Parida M., Mittal N.(2003),"A Modelling framework for transport related air

pollution prediction for urban areas" Journal of Institute of Urban Transport (India),Special

Issue, Vol.4, No.1, March(2003).

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Appendix B

Emission Factors of CO for Different Categories of Vehicles

Vehicle type C/J (2-stroke) MIS

(4 -stroke) LCV BUS Truck A.R.

Fuel Petrol Petrol Petrol Diesel CNG Diesel CNG Mass

Emission 9.5 8.3 2.4 9.3 5.35 12.7 4.8 Factors(g/km) for CO

Sample Weighted Emission Factors (CO)

India Gate Emission factor (CO)

Total vehicles

%CIJ %MB %BUS %S/M M.T. %Tr. %AR glkm g/mile

1859 0.548 0.018 0.071 0.169 0.041 0.002 0.151 8.287 13.29 5358 0.588 0.008 0.034 0.232 0.015 0.002 0.122 8.511 13.65

10279 0.429 0.016 0.02 0.457 0.004 0.004 0.07 8.541 13.7 13765 0.565 0.003 0.018 0.332 0.003 0.003 0.075 8.671 13.91 12419 0.603 0.006 0.018 0.28 0.009 0.002 0.082 8.709 13.97 9001 0.457 0.005 0.019 0.446 0.014 0.002 0.057 8.621 13.83 8893 0.418 0.01 0.023 0.435 0.011 9E-04 0.102 8.396 13.47 9204 0.499 0.008 0.016 0.341 0.016 0.002 0.118 8.472 13.59

27447 0.282 0.002 0.006 0.585 0.001 4E-04 0.123 8.195 13.15 11811 0.623 0.002 0.006 0.284 3E-04 3E-04 0.085 8.738 14.02 7652 0.561 0.001 0.011 0.308 4E-04 8E-04 0.117 8.532 13.69 7354 0.6 0.002 0.01 0.259 0.027 0.001 0.102 8.667 13.9 4566 0.628 2E-04 0.004 0.231 0.02 0.009 0.107 8.728 14 3736 0.846 0 0.006 0.049 0.009 0.005 0.085 9.032 14.49

888 0.607 0.003 0.003 0.054 0.084 0.063 0.185 8.737 14.01 424 0.427 0.002 0.002 0.146 0.137 0.064 0.222 8A449 13.55 384 0.49 0.005 0.034 0.133 0.102 0.107 0.13 8.908 14.29 246 0.301 0 0.004 0.195 0.163 0.199 0.138 9.204 14.76

C/J: Car/Jccp, MR: Mini Bus, S/M: Scooter/Motor Cycle,'Fr.: Truck, AR: Auto Rickshaw

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APPENDIX D1

Sample Run Screen of CALINE-4

Input Screen: Job Parameters

Input Screen: Link Geometry

Input Screen: Link Activilty

11I File Edit View Help -

[ Job Parameters I Link Geometry Link Activity 11 Rum Conditions Receptor Positions

hun hours

Wind Speed (m/s) 1 7

Wind Direction (degrees) - Wind Direction Std. Den (degrees) 1St

Atmospheric Stability Class (1 -7) 2 - Mixing Height (m) SEE

Ambient Temperature (degrees C) 22

Ambient Pollutant Concentration (ppm) 1

Input Screen: Run Conditions

Input Screen: Receptor position

Appendix E Error Function Table

x 1e erfe x x 1p erfe x x to erfe x x Io erfc x 0.01 0.988717 0.51 0.470756 * 10 1.01 0153,90*100 1.51 0.327233 * 0" 0.02 0.97734 1022 0.462!01 *10 1.02 0.149162*l0 1.52 0.3 15865 *l0' 0.03 0.966159 0.53 0.453536 *10 1.03 0.145216 *10 1.53 0.304838 *10" 0.04 0.954889 0.54 0.445061 *100 1.04 0.144350 *10 1.54 0.294143*10' 0.05 0.943628 0.55 0.436667 * 10 1.05 0134564*100 1.55 _ 0.283773 * 10" 0.06 0.932376 0.56 0.428384 *10 1.66 0.133856 *10 1.56 0.273719*10 0.07 0.921142 0.57 0.420184 *10 1.07 0.130227 *10 1.57 0.263974*10" 0.08 0.909922 0.58 0.412077 * 10 1.08 0.126674 * 10 1.58 0.254530* 10" 0.09 0.898719 0.59 0.440406* 10 1.09 0,123197*100 1.59 0. 245380* 10" 0.10 0.887537 0.60 0.396144 * 10 1.10 0.119795 *100 1.60 0.236516* 10 0.11 0.876377 0.61 0.388319 * 10 III _V.1164 77 * 10 1.61 0. 227932* 10" 0.12 0,865242 0.62 0.380589 * 10 1.12 - 0.113212 I0 1.62 0. 'i'61'10' 0.13 0.85433 0.63 0.327954 * 10 1.13 0.110029 * 10 1.63 0. 211572* 10" 0.14 0.83053 0.64 0.365414 *10 1.14 0.106918 *10 1.64 0.203782 *10" 0.15 0.832004 0.65 0.357971 *10 1.15 0.103876 *10 1.65 0.196244 *i0" 0.16 0.820988 0.66 0.350623 *100 1.16 0.100904 * 10 1.66 0. 198951 * 10" 0.17 0.810008 0.67 0.343372 * I0 1.17 0.979996 * 10" 1.67 0. 181896* l0" 0.18 0.799064 0.68 0.336218 *10 1.18 0.951626 *10' 1.68 0.175072 *10" 0.19 0.788 (60 0.69 0.329160 * I0 1.19 0.923917 * 10" 1.69 0.168474 * l0" 0.20 0.777297 031 0.322199 * 10 1.20 0.896860 * 10" 1.70 0.162095 * 10 0.21 0.766478 0.72 0.308567 *100 1.21 0.870445 * 10' 1.71 0.155930 * 10 022 0.755704 0.73 0.301896 * 10 1.22 0.844661 *10" 1.72 0.149972 *10" 0.23 0.744977 0.74 0295322* 0.295322*100 1.23 6.81949-9* 10- 1.73 0.144215 * 10" 0.24 0.734300 0.75 0.288844 *100 1.24 0.794948 *10' 1.74 0.138654 *10- 0.25 0.723674 0.76 0.282463 * 10 1.25 0.770999 * 10" 1.75 0.133283 * 1 T 0.26 0.713100 0.77 0.276178 *100 1.26 0.747641 *0" 1.76 0.128097 * 10" 0.27 0.702582 0.88 0,269990*100 1.27 0.724864 * 10- 1.77 0.123091 *10" 0.28 0.692120 0.79 0.2638'"'i' 1.28 0.702658 *10' 1.78 0.118258 *10- 0.29 0.681717 0.80 0.257899 *10 1.29 0.681014 *10' 1.79 0.113594 *10" 0.30 0.671373 0.81 0.251997 *l0 1.30 0.659921 * 10.1 1.80 'i1091"101'" 0.31 0.661092 0.82 0.246 0246189*100 1.31 0.639369 * 10- 1.81 0.104755 * t 0" 0.32 0.650874 0.83 0.240476 *100 1.32 0.619348 * 10- 1.82 0.100568 * 0.33 0.640721 0.84 0.234857 0,00 1.33 0.599050*10" 1.83 0.965319*101 0.34 0.630635 0.85 0.229332 *10 1.34 0.580863 *10' 1.84 0.926405 *10 0.35 0.620618 0.86 111"l0 1.35 0.562378 *10" 1.85 0.888897 *10 0.36 0.610670 0.87 0.218560*100 1.36 0.544386 *10 1.86 0.852751 *10 0.37 0.600794 0.88 0.213313 * 10 1.37 0.526876 * 10' 1.87 0.817925 * l0' 0.38 0.590991 0.89 0.208157 *10 138 0.509840 * 10" 1.88 0.784378 * 10 0.39 0.581261 0.90 0.203092 *100 .39 0.493267 *10" 1.89 0.752068 * l0' 0.40 0.571608 0.91 0.1981 17 * 10 1.40 0.477149 * 10" 1.90 "111951''l0' ~I 0.4] 0.562031 0.92 0.193232 * 10 1.41 0.461476 * 10' 1.91 0.691006 * 10" 0.42 0.552532 0.93 0.188436 *10 1.42 0.446238 *l0" 1.92 0.662177 *10' 0.43 0.543113 0.94 0.183729 *10 1.43 0.431427 *l0" 1.93 0.634435 *10 0.44 0.533675 0.95 0,1791090100 1.44 0.417034 * 10- 1.94 0.607743 * 10'2 0.45 0.524518 0.96 0.174576 * 10 1.45 0.403050 * 10- 1.95 0.582066 *10.2 0.46 0.515345 0.96 -6.170130 * 10 1.46 0.389465 * 10- 1.96 0,557372*10.2 0.47 0.506255 0.97 0.170008 0,00 1.47 0.376271 *lo" 1.97 0,553627 *10 2 0.48 0.497250 0.98 0.165768 *10 1.48 0.363459 *10 1.98 0.510800 *10" 0.49 0.488332 0.99 0.161492 *100 1-49 0.351021 * 10" 1.99 0.488859 * 10 0.50 0.479500 1.00 0.157299 *10 (.50 0.338949 * 10" 2.0 0.467773 * l0"

Note: Io = 1, erf (x) = 1- erfe (x)

Appendix F

Sample Calculation Using GFLSM

Location: New friends colony

Time: 8-9 pm

u=0 Wind direction = 00

Temperature, T = 286.6 °k

Volume = 1797 vph

Using Eicher map of Delhi

L = 426.67m

X=25m

Y=87.5m

Stability class = 3

a= 1.49

b=0.15

c = 0.77

a=20.7

U0= 0.23

U1 = 0.18

Speed, S= 18.8 miles/hr

Calculation of Emission rate(g/mile-s):

Q=Ef * un Ef= 956.4 S"o.ss

= 956.5 * 18.8-0.85

= 78.8

Q = 78.8 * 1797/3600 = 39.33 g/mile-s

Input Screen: Receptor position

APPENDIX D2

Output Screen of Caline-4

CALINE4: CALIFORNIA LINE SOURCE DISPERSION MODEL JUNE 1989 VERSION PAGE I

JOB: India Gate 10-I l am RUN: Hour I

POLLUTANT: Carbon Monoxide

I. SITE VARIABLES

U= 1.7 MIS Z0= 100. CM ALT= 210. (M) BRG= .0 DEGREES VD= .0 CM/S CLAS= 2 (B) VS= .0 CM/S MIXH= 966. M AMB= 1.5 PPM

SIGTH= 15. DEGREES TEMP= 22.0 DEGREE (C)

II. LINK VARIABLES

LINK * LINK COORDINATES (M) * EF H W DESCRIPTION * X1 Yl X2 Y2 * TYPE VPH (G/MI) (M) (M)

A. Link A * 20025 15030 20025 15435 * AG 13765 13.9 .0 16.5

III. RECEPTOR LOCATIONS AND MODEL RESULTS

* * PRED COORDINATES (M) * CONC

RECEPTOR * X Y Z * (PPM) * * ------------ ---------------------- ------

1. Recpt 1 * 20015 15235 1.8 * 6.1


x 10 erfc x x Io erfe x x IQ erfc x x Io erfc x 1101 0.988717 0.51 0.470756 * 10 1.01 0.153190*100 1.51 0.327233 10" 0.02 0.97734 0.52 6.462101 * 10 1.02 0.149162 * 10 1.52 0315865 * 10 0.03 0.966159 0.53 0.453536 *10 1.03 0.1452 66 *10 1.53 0.304838 *10 0.04 0.954889 0.54 0.445061 *10 1.04 0.144350*10 1.54 0.294143*10 0.05 0.943628 0.55 0.436667 *10 1.05 0.134564 *10 1.55 0.283773*10' 0.06 0.932376 0.56 0.428384*10 1.06 0.133856*10 .56 0.273719*10 0.07 0.921142 0.57 0.420184*100 1.07 0.130227 *10 1.57 0. 263974*10- 0.08 0.909922 0.58 0.412077 * 10 1.08 0.126674 * 10 1.58 0. 254530* 10 0.09 0.898719 0.59 0.440406* 10 1.09 0.123197 * 10 1.59 0. 245380* 10 0.10 0.887537 0.60 0.396144 *10 1.10 0.119795 *I0 1.60 0.236516*10 0. l I 0.876377 0.61 0.3883 19 * 10 1.11 1 16467 * 10 1.61 0. 227932* 10- 0. 12 0.865242 0.62 0.380589 * 10 1.12 0.113212 * 10 1.62 9619* 10 0.13 0.85433 0.63 0.327954 * 10 1.13 0. 0.1l0029*100 1.63 0.211572* 10 0.14 0.83053 0.64 0.365414 *10 1.14 0.106918 *10 1.64 *10' 0203782 0.15 0.832004 0.65 0357971 *100 1.15 0.103876 *10 1.65 0.196244 *10" 0.16 0.820988 0.66 0.350623 * 10 1.16 0. 0.100904*100 1.66 0. 183951 * I0 0.17 0.810008 0.67 0.343372 * 10 1_ 17 0.979996 *10'T 1.67 0. 181896* 10" 0.18 0.799064 0.68 0.336218 *10 1.18 0.951626 *10- 1.68 0.175072 *10 0.19 0.788160 0.69 0.329160*100 1.19 0.923917 *10" 1.69 1 88474 *10 0.20 0.777297 0.71 0.322199 * 10 1.20 0.896860 * 10- 1.70 0.162095 * I0' 0.21 0.766478 0.72 0.308567 *100 1.21 0.870445 *10" 1.71 0.155930 * 10 0.22 0.755704 0.73 0.301896 *10 1.22 0.844661 *10- 1.72 0.149972 *10- 0.23 0.744977 0.74 0.295322 *100 1.23 0.819499 * 10- 1.73 0.144215 * 10 0.24 0.734300 0.75 0.288844 * 10 1.24 0.794948 *10" 1.74 0.138654 *101 0.25 0.723674 0.76 0.282463 *100 1.25 0.770999 31 1.75 0.133283 * 1" 0.26 0.713100 0.77 0.276178 *100 1.26 0.747641 * 10- 1.76 0.128097 0.27 0.702582 0.78 0.269990 * 10 1.27 0.724864 * 10 1.77 0.123091 *I0'1

0.28 0.692120 0.79 0.263897 *10 1.28 0.702658 *10" 1.78 0.1 18258 *10- 0.29 0.681717 0.80 0.257899 *10 1.29 0.681014 *10" 1.79 0.113594 *10 0.30 0.671373 0.81 0.251997 * 10 1.30 0.659921 *10" 1.80 0.109095 * l0 0.31 0.661092 0.82 0.246189 *100 1.31 0.639369 * 10 1.81 0.104755 * 10 0.32 0.650874 0.83 0.240476 * 10 1.32 0.619348 *10" 1.82 0.100568 1 0.33 0.640721 0.84 0.234857 *100 1.33 0,599050 * l 1.83 0.965 1913 0.34 0.630635 0.85 0.229332 *10 1.34 0.580863 *10" 1.84 0.926405 *10 0.35 0.620618 0.86 0.223900 *00 1.35 0.562378 *10- 1.85 0.888897 *10 0.36 0.610670 0.87 0.218560 *10 1.36 0.544386 *10" 1.86 0.852751 *10- 0.37 0.600794 0.88 0.213313 *100 1.37 0.526876 *10" 1.87 0.817925 * 10 0.38 0.590991 0.89 0.2081 S7 10 1.38 0.509840 * 10 1.88 0.784378 * 10 2 0839 0.581261 0.90 0.203092 *10 L39 0.493267 *l0" 1.89 0.752068 *10 0.40 0.571608 0.91 0. 0.198117*100 1.40 0.477149 * 10- 1.90 0.720957 * l0 0.41 0.562031 0.92 0. 0,193232*100 1.41 0,461476'108' 1.91 0.691006 * 10" 0.42 0.552532 0.93 0.188436 * 10 1.42 0.446238 * 10" 1.92 0.662177 * 10" 0.43 0.543113 0.94 0.183729 * l0 1.43 0.431427 *10' 1.93 0.634435 *10.2 0.44 0.533675 0.95 0.179109*10 1.44 0,417034 '10" 1,94 0.607743 *102 0.45 0.524518 0.96 0.174576*100 1.45 0.403050 * 10 1.95 --6.582066 * 10 0.46 0.515345 0.96 0.170130 *10 1.46 0.389465 *10- 1.96 0.557372 *10- 0.47 0.506255 0.97 0.170008 * 10 1.47 0.376271 * 10- 1.97 0.553627 * 10 0.48 0.497250 0.98 0.165768 *100 1.48 0.363459 * 10- .98 0.510800 ,101r" 0.49 0.488332 0.99 0.161492 *10 1.49 0.351021 *10- 1.99 0.488859 *10 0.50 0.479500 1.00 0.157299 * 10 1.50 0.338949 * 10" 2.0 0.467773 * 10"

Note: 1p = 1, erf (x) = 1- erfc (x)


x to erfc x x to erfc x x Io erfc x x L0 erfc x 0.01 0.988717 0.51 0.470756 *I0 1.01 0.153190 * 10 1.51 0.327233 * 10 0.02 0.97734 0.52 0.462101 * 10 1.02 0.149162 * 10 1.52 0.315865 *10" 0.03 0.966159 0.53 0,453536*10 1.03 0. 0.l45216*100 1.53 0.304838 * 10 0.04 0.954889 0.54 0.445061 * 10 1.04 0.144350 * 10 1.54 0.294143* 10 0.05 0.943628 0.55 0.436667 *10 1.05 0.134564 *10 1.55 0.283773*10 0.06 0.932376 0.56 0.428384*100 1.06 0.133856 *10 1,56 0.273719*10 0.07 0.921142 0.57 0.420184 *10 1.07 0.130227 *10 1,57 0.263974*10' 0.08 0.909922 0.58 0.412077 *10 1.08 0.126674 *10 1.58 0.254530*10 0.09 0.898719 0.59 0.440406*10 1.09 -0.123197 *10 1.59 0.245380*10 0.10 0.887537 0.60 0.396144*10 1.10 0.119795*10 1.60 0.236516*10" 0.11 0.876377 0.61 0.388319 * 10 1.1] 0.116467 * 10 1.61 0. 227932* 10- 0.12 0.865242 0.62 0.380589 *10 1.12 0.113212 *10 1.62 0. 219619*10 0.13 0.85433 0.63 0.327954*10 1.13 0.110029*l0 1.63 0.211572*10 0.14 0.83053 0.64 0.365414 *10 1,14 0.106918 *10 1.64 0.203782 *l0 0.15 0.832004 0.65 0.357971 *10 1.15 0.103876 *10 1.65 0.196244 *10 0.16 0.820988 0.66 0.350623 *10° 1.16 0.100904 * 1 O 1.66 0. 188951 * 10" 0.17 0.810008 0.67 0.343372 *10 1.17 0.979996 *10 1.67 0.181896*10 0.18 0.799064 0.68 0.336218 * 10 1.18 .951626 * 10 1.68 0.175072 * 10 0.19 0.788160 0.69 0.329160 * l0 1.19 -C9-2-3917 * 10 1.69 0.168474 * 10 0.20 0.777297 0.71 0.322199 * 10 1.20 0.896860 *10" 1.70 0.162095 * I o' 0.21 0.766478 0.72 0.308567 * 10 1.21 0.870445 10' 1.71 0.155930 *10" 0.22 0.755704 0.73 0.301896 * 10 1.22 0.844661 * 10" 1.72 0.149972 * 10 0.23 0.744977 0.74 0.295322 * 10 1.23 0.819499'10" 1.73 0.144215 *10" 0.24 0.734300 0.75 0.288844 *10 1.24 0.794948 *10 1.74 0.138654 *10 0.25 0,723674 0.76 0.282463 *10 1.25 0,770999 *10 1.75 0.133283 10 0.26 0.713100 0.77 0.276178 *10 1.26 0.747641 *10 .76 .1280 77 *10' 0.27 0.702582 0.78 0.269990 * 10 1.27 0.724864 * 10 1.77 0.123091 *10" 0.28 0.692120 0.79 0.263897 *10 1.28 0.702658 *10 1.78 0.118258 *10 0.29 0.681717 0.80 0.257899 *10 1.29. 0.681014 *10 1.79 0.113594 *t0 0.30 0.671373 0.81 0.251997 *10 1.30 0.659921 *10 1.80 0.109095 *10" 0.31 0.661092 0.82 0.246189 *10 1.31 0,639369 *10 1.81 0.104755 *10 0.32 0.650874 0.83 0.240476 * 10 1.32 0.619348 * 10 1.82 0.100568 * l0 0.33 0.640721 0.84 0.234857 *10 1.33 0.599050 *10 1.83 0.965319*10-. 0.34 0.630635 0.85 0229332 *10 1.34 0.580863 *10 1.84 0.926405 *10" 0.35 0.620618 0.86 0.223900 *10 1.35 0.562378*l'ö21' 1.85 0.888897 *10 0.36 0.610670 0.87 0.218560 *10 1.36 0.544386 *10" 1.86 0.852751 *10 0.37 0.600794 0.88 0.213313 * 10 1.37 0.526876 * 10 1.87 0.817925 * 10' 0.38 0.590991 0.89 0.208157 * 10 1.38 0.509840 *10" 1.88 0.784378 * l0 0.39 0.581261 0.90 0.203092 *10 1.39 0.493267 *10' 1.89 0.752068* 10. 0.40 0.571608 0.91 0.1981 17 * 10 1.40 0.477149°10" 1.90 0.720957 * 10 0.41 0.562031 0.92 0.193232 * 10 1.41 0.461476 * 10 1.91 0.691006 * 10 0.42 0.552532 0.93 0.188436 * 10 1.42 0.446238 * 10 1.92 0.662177 * 10 0.43 0.543113 0.94 0.183729*100 1.43 0.431427 * 10 1.93 0.634435 * 10 0.44 0.533675 0.95 0.179109 *10 1.44 0.417034 *10 1.94 0.607743 *10' 0.45 0.524518 0.96 0.174576 * 10 1.45 0.403050 * 10' 1.95 0.5820 6 0.46 0.515345 0.96 0.170130 *10 1.46 0.389465 *10 1.96 0.557372 *10' 0.47 0.506255 0.97 0.170008 *100 1.47 0.376271 * 10 1.97 0.553627 * 10 0.48 0.497250 0.98 0.165768 *100 1.48 0.363459 * 10 1.98 0.510800 *10.2 0.49 0.488332 0.99 0.161492 *10 1.49 0.351021 *10" 1.99 0.488859 *10 0.50 0.479500 1.00 0,157299 *10 1.50 0.338949 *10 2.0 0.467773 *10

Note: I9 = 1, erf (x) = 1- erfc (x)

Error Function Table (Cont.)

x to erfc x x Io erfc x x Io erfc x 2.01 0.447515 *10 2.51 0.385705 *l0-' 3.01 0.207384 2O2t 0.428055 *10.2 2.52 0.365499 * 10' 3.02 0.194658 * iö' 03t 0.409365 * 10 2.53 0.346285 *I' 3.03 0.182678 * i 0"

2.041 0.391419*102 2.54 0.328020 * 10- 3.04 0.171403 * 10 205t 0.374190 *10" 2.55 0310660.*10 3.05 0.160792 *10 2.06 0.357654 *I0' 2.56 0.294162 *10' 3.06 0.150810 *10' 2.07 0.341785 *10.2 2.57 0.248488 *10-3 3.07 0.141420 * 10 2.08 0.326559 * 10 2.58 0.263600 *T 3.08 0.132589 * 10" 2.09 0.311954 * 10 2.59 0.249460 *RyS 3.09 0.124286 *l 2.10 0.297956 * 10 2.60 0.236034 * 10 3.10 0.116481 *10-4 2.11 0.284515 *j'2 0.223288 *10 3 3.11 0.109144 *10 2.12 0.271639 * 10- 2.62 0.211 190 *13 5 3.12 0.102250 * 10" 2.13 0.259397 *102 2.63 0.199710 * 10- 3.13 0257734 *10-5 2.14 0.247471 *10.2 2.64 0.188819 *10.5 3.14 0.896896 *10-5 2.15 0.236139 * l0 2.65 0.178487 * 10" 3.15 0.839760 * 10" 2.16 0.225285 *102 2.66 0.168689 * 10' 3.16 0.7861 12 * 10 2.17 0.214889 *l0 2.67 0.159398 *10-3 3.17 0.735751 *10'* 2.18 0.204935*10.2 2.68 0.150591 *10' 3.18 0.688482 *10' 2.19 0.195405 *104 2.69 0.142243 * 10- 3.19 0.644126 * 10 2.20 0.186284 * 10 2.70 0.134332 * 10" 3.20 0.602513 * 10 2.21 0.177556 * 10- 2.71 0.126837 * 10 3.21 0.563479*10-5 2.22 0.169205 * 10 2.72 0.119738 * 10' 3.22 0.526872 * I0' 2.23 0. U.161217*102 2.73 0.113014 * 10 3.23 0.492549 * 10-' 2.24 0.153577 * 10- 2.74 0.106648 * 10' 3.24 0.460373 *j0" 2.25 0.146271 *10.2 2.75 0.100621 *10.3 3.25 0.430216 * 10 2.26 0,139288*10.2 2.76 0.949170 *10 3.26 0.401956 *10" 2.27 0.132613 *'2 2.77 0.895191 * 10' 3.27 0.375482 *10.5 2.28 0. 0,126234*10.2 2.78 0. 844121 *10 3.28 0.350682 *10' 2.29 0.120139 *103 2.79 0.795812 *10 3.29 0.327457 *10 5 2.30 0.114317*10.2 2.80 0.750126 *10" 3.30 0.305711 *10" 2,31 0.108757 * 10.2 2.81 0.706927 *10 3.31 0.285353 *10' 2.32 0.103449 *102 2.82 0.666089 * 10 3.32 0.266299 *10.5 2.33 0.983803 * 10" 2.83 0.627491 * 10- 3.33 0. 248470* 10' 2.34 0,935428*10.3 2.84 0.591017 * 10- 3.34 0.231789 * 10' 2.35 0.889265 *10.3 2.85 0.556557 * 10" 3.35 0.216186 *10-5 2.36 0.845221 *10-3 2.86 0.524006 * 10 3.36 0.201595 *10 5 2.37 0,803208 *10.3 2.87 0.493264 * 10" 3.37 0.187953 *10-5 2.38 0.763140 * 10- 188 0.464238 * l0 3.38 0.175199 * l0 2.39 0.724934 *10' 2.89 0.436836 *10 3.39 0.163278 *10 2.40 0.688512 *10-3 2.90 0.410973 *10.4 3.40 0.152139 *10 2.41 0.653796 *103 2.91 0.386567 *10 4 3.41 0.141731 *10-5 142 0.620714 * 10 2.92 0.363541 * 10 3.42 0.132011 *10 5 2.43 0.589196 * 10; 2.93 0.341821 * 10' 3.43 0.122932 * 10 2.44 0.559172 *10' 2.94 0.321338 *-4 3.44 0.144455 *10' 2.45 0.530578 *103 2.95 0.302024 *10" 3.45 0.106543 *10 5 2.46 0.503353 * 10 2.96 0.283817 *10 4 3.46 0.099157 * 00" 2.47 0.477432 *103 2.97 0.266656 * 10' 3.47 0.092266 *10.5 2.48 0.452762 *10" 2.98 0.250485 *10-4 3.48 0.085837 *10 5 2.49 0.429586 *10.3 2.99 0.235250 *10" 3.49 0.079841 *10' 2.50 0.406950*10- 3.0 0.220899 *10 3.50 0.074248 *10-

Note: Io =1, erf (x) = 1- erfc (x)

Appendix F

Sample Calculation Using GFLSM

Location: New friends colony

Time: 8-9 pm

u=0 Wind direction = 00

Temperature, T = 286.6 °k

Volume = 1797 vph

Using Eicher map of Delhi

L = 426.67m

X=25m

Y=87.5m

Stability class = 3

a= 1.49

b=0.15

c = 0.77

a=20.7

U° = 0.23

U, =0.18

Speed, S= 18.8 miles/hr

Calculation of Emission rate(g/mile-s):

Q=Ef * Vh

E f = 956.4 S °85

=956.5*18.8-085

= 78.8

Q = 78.8 * 1797/3600 = 39.33 g/mile-s

Sample Calculation Using GFLSM (Cont.)

Calculation of Plume centre height H(m):

H = h0 +h

hP Plume rise = ( a FU3 )112 * X

Fi =Buoyancy flux = T*380 * 465 =10*465/(286.6*380) = 0.043

U'=Uosin(0)+UI=0.18m/s

ht,= 14.8 m

H=0.5+14.8=15.3m

Calculation of dispersion parameters a v ,o Z

2 z o.„_

cr =2o- ,

Where

o- ,= 3.57 -0.53 Uo

Uo= mean wind speed (m/s)

X sin 2 via = 2.15cosA

d = 18,333 - 1.8096 ln(x/1000)/57.2958 for stability class 5

= 18.333 - 1.8096 ln(25/1000)/57.2958 = 0.436

cr = 5.41

6., = 3.57 -0.53 * 0.23 = 3.44

ay, = 6.88

Q~,= 8.75 m

Sample Calculation Using GFLSM (Cont.)

o-' —(a+ 12.714 m sing

*X)` =(1.49+0.1460*

25)°"=

Prediction of CO concentration z z

C=-- Q Lexp( (Z2, ) )+exp( (Z2a ) )]* 2\/ru 6_

[erfl sin9(L/2—y)—xcos0

I+erfl sin9(L/2+y)+xcos0 I ~ 2Q,, 2a y

C= 39.33

2 J r*0.18 *12.714

ex (1.8 15.3)z

)+exp(- (1.8+15.3)2

p(~ 2 * 12.1742) p( 2 * 12.174 2 )~

[ergsin0*(L/2—y)-25* cos 0 I+erfl sin 0(L/2+y)+25* cos 0,) ~2*8.75 2*8.75

= 6.912 g/mz-mile = 6.912/1609 = 0.003852 g/m3 = 3852 µg/m3

5000 d E U -0 4000

0

3000 01 0

E 2000

0 C

1000

U

5.3.5 CALINE-4 performance for L5(TI'O)


observed concentration of CO is far greater than the prescribed limit of 4000 .tg/m3 for all

24hours.




Significance test between observed and predicted values gives tc,ic=1.84 which is less than

t,abulated= 2.13, for degree of freedom = 15 and level of significance =0.05, hence difference


6000

700. 800. 90U f 000. 1100. 1200. 1:00. 200. 600. 7:00- 800- 800A41 900 1000 11.00 1200 100 200 700 7:00 800 960

Time

900. 1000. 1100- 1200. 100- 200.

UflH 300.

1000 100 1200 10001 200 300 400PM

n Pr¢d61,d I


74

E 4000

el U

3900

E

0 3800

3700 C

o

3600

O U 3500

~ ii'

y = 0.4492x + 1926.5

Rt = 0.575

2000 3000 4000 5000 6000

Predicted

Fig 5.10 Location L5observed Vs Predicted (CO-Standard case)


d 110.25

Sd 239.0455

N 16

D.O.F. 15

tcalc 1.844837

a 0.05

tabulated 2.13

75

Photo 3.1 A Vie" of Location 1(Safdarjung Hospital)

Photo 3.2 A View of Location 4(New Friends Colony)

Photo 3.3 A View of Location 5 (I.T.0)

~s 0

28

Photo 3.4 A View of Location 6(Laxmi Nagar)

Photo 3.5 A View of Location 7(AIIMS)

29

u, w 0

Cr)

Lu L- Lu 0

U) z O

a u Cr

30

performance evaluation of air pollution models

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