Analytical Modelling of the Advection, Dispersion, Deposition and Rain

Scavenging of Chemical Constituents emitted from the Stacks of NLC

Thermal Power Plant

A Project funded by NLC-CARD

(Grant No: CARD/ENVT/MAP/Project/1218/2009)

Thesis submitted in partial fulfilment of the requirements for the award of

Bachelor of Technology in Chemical Engineering

By

Jason Ryan Picardo (07BCH008)

Chemical Engineering Division

School Of Mechanical and Building Sciences

November 2010

2

School Of Mechanical and Building Sciences

Chemical Engineering Division

BONAFIDE CERTIFICATE

This is to certify that the thesis entitled “Analytical Modelling of the Advection, Dispersion,

Deposition and Rain Scavenging of Chemical Constituents emitted from the Stacks of NLC

Thermal Power Plant” being submitted by Mr. Jason Ryan Picardo, in partial fulfilment of the

requirements for the award of a Bachelor of Technology in Chemical Engineering, to the School

of Mechanical and Building Sciences, VIT University is a record of bonafide work done under

my guidance. The contents of this project work, in full or in part, have neither been taken from

any other source nor have been submitted to any other institute or university for the award of a

degree or diploma and the same is certified.

Dr. S. Ghosh (Sr. Prof) Dr. Anand Gurumoorthy

Project Guide Internal Guide

Dr. L. N. Muruganandam

Division Leader Chemical Engineering

School Seal

Internal Examiner External Examiner

3

Contents

Page No.

ACKNOWLEDGEMENTS 5

ABSTRACT 6

NOMENCLATURE 8

LIST OF FIGURES 10

LIST OF TABLES 11

Chapter 1 INTRODUCTION 12

Chapter 2 DISPERSION OF GASEOUS POLLUTANT FROM

MULTIPLE ELEVATED STACKS AT NLC

14

2.1 Atmospheric Diffusion Equation 14

2.2 Gaussian Plume Solution 14

2.3 Dispersion Parameters and Atmospheric Stability 16

2.4 Plume Rise and Effective Wind Speed 17

2.5 Adapting the Gaussian model for multiple point

emission sources- generation of monthly average

contours

20

2.6 Simulation for SO2, April 2009- Results and

Discussions

21

Chapter 3 ESTABLISHING THE EFFICACY OF THE

CLEANSING ACTION OF TROPICAL

EVERGREENS

23

3.1 Deposition Velocity- Theory of Resistances 23

3.2 Aerodynamic Resistance 25

3.3 Quasilaminar Resistance 28

3.4 Surface or Canopy Resistance 29

4

3.5 Deposition Velocity for SO2, April 2009 33

3.6 Removal from a continuous plume 35

3.7 Deposition of polluting species onto the canopy 36

3.8 Removal of residual pollution- Cleansing action of trees 37

3.9 Cleansing of Air by trees on 30th April 2009 39

3.10 Conclusions 40

Chapter 4 SCAVENGING EFFICACY OF THE NORTH EAST

MONSOONS

41

4.1 Mass Transfer of SO2 into a falling rain drop 41

4.2 pH of a drop when it reaches ground level 44

4.3 Quantification of Plume Washout - Scavenging

Coefficient

45

4.4 Investigation of Wet Scavenging and Rainfall Acidity at

NLC

48

4.5 Conclusions 50

Chapter 5 OVERALL CONCLUSIONS AND FUTURE WORK 51

REFERENCES 53

5

ACKNOWLEDGEMENTS

Firstly, I would like to thank our honourable Chancellor Dr. G. Viswanathan and the

administration of VIT University for providing excellent academic facilities and for the

opportunity to carry out this work at VIT University.

I owe not only the success of this project work but also a great deal of personal advancement,

academic and otherwise, to my project guide Dr. S Ghosh, Senior Professor, SMBS. This work

rode on his vision. His mentorship enriched and refined my work and his encouragement gave

me the strength to believe that our goals will be accomplished.

Dr. Anand Gurumoorthy, my internal department guide was supportive and encouraging. The

many discussions we had during the course of the semester have broadened my vision with

respect to research in general and chemical engineering research in particular.

I thank the Division Leader of the Chemical Engineering Division, Prof. Muruganandam for his

enthusiastic support during the course of this work.

I am extremely grateful to Dr. Jayasankar Variyar, Prof. VIT University-Chennai who ensured

that I would be able to work on this interdisciplinary project with Prof. Ghosh. His mentorship

and friendship remain invaluable to me.

During the course of this work a large number of research papers were referenced. Access to

these papers was kindly provided by the Library. In particular I thank Dr. Adhinarayanan,

Deputy Librarian, for his efforts in this regard. I extend my gratitude to Mr. Ankit Rai for his

help and cooperation during certain parts of this work.

Ultimately I extend a prayer of thanksgiving to my Heavenly Father and Jesus Christ, My Lord

for blessing me with this project, with wonderful professors and for essentially every element of

my life.

6

ABSTRACT

In this work we develop an analytical model, with codes for its simulation in FORTRAN 90,

which is capable of modelling three processes that are central to any environmental-air pollution

study:

1. Dispersion of gaseous pollutants emitted continuously from multiple elevated point

sources. In our study these sources are the elevated stack of the power plants at the

Neyveli Lignite Corporation (NLC).

2. Deposition of gaseous pollutants onto a forest canopy and the subsequent reduction in

pollution levels. This includes removal from plumes as well as scavenging of residual

pollution from the atmosphere.

3. Washout of pollution from the atmosphere during the heavy showers of the North East

Monsoons, which lash the coast of South East India from October to December, and the

resultant acidity of the rainfall.

The atmospheric dispersion model is a Gaussian based model which makes use of

meteorological inputs provided by NLC, namely the solar radiation, wind speed and wind

direction. The model considers the various stability states of the atmosphere via the Pasquil

Stability Classes (Turner 1969). Region specific calculations of plume rise and wind speed at

stack height are incorporated. The model is simulated for the case of SO2 and contours for the

monthly mean concentrations around NLC are obtained. This required a transformation of map

coordinates into a form suitable for the simple Gaussian equation of dispersion and a

superposition of the emission from the multiple sources as well as averaging over the entire

month.

Apart from the development of a dispersion model the other major aim of the NLC funded

project was to quantify the effect of the extensive evergreen tree cover at NLC. Towards this

objective, the dry deposition of gaseous pollutants is quantified in terms of the universal

deposition velocity parameterization via inferential modelling of the transport pathways. The

deposition to vegetation was given prime attention and the the methods of Wesely (1989) were

adopted to account for canopy resistance. The deposition velocity is incorporated into the

dispersion model to investigate the role of trees in abating air pollution. It was found that while

they do not affect ambient concentrations at locations directly in the path of a continuously

emitted plume, they do have a cleansing effect on the atmosphere under conditions of no-

replenishment. Such a case can occur when a moderate wind is followed by a low speed wind in

a different direction or when a moderate wind is followed by a period of calms. Both cases will

results in large regions where the residual pollution which is not being replenished is taken up by

the vegetative canopy, in the absence of which it would remain in the atmosphere till a

sufficiently strong wind advects it away.

7

In addition to the above two studies a detailed investigation of the mass transfer of gaseous

pollutants (SO2) into falling rain drops is presented with the aim of quantifying the wet

scavenging of pollutants during the NE Monsoons. We first analyse the interface mass transfer

phenomena occurring at the surface of a falling rain drop. We demonstrate that moderate to large

sized drops can fall through considerable distances without saturating with SO2. Recent studies

on the Drop Size Distribution of the NE Monsoonal rain over South India (SS Roy et al 2005,

Konwar et al 2006) describe the rain drop size spectrum as a modified gamma distribution.

Armed with this information we determine the scavenging coefficient- a parameterization

accounting for the spectrum of drop sizes and the mass transfer into these drops, for various rain

rates and then fit the results to a linear regression line. Coupling this information with our

dispersion model we demonstrate the scavenging efficacy of a typical NE Monsoonal shower

over NLC and determine the pH of the rain water in the surrounding area, which is home to

128,133 people.

This work apart from achieving the objectives of NLC will prove to be useful to EIA specialists,

policy planners at the decision making level, as well as by other power plants in the making.

8

NOMENCLATURE

Cl − Concentration of dissolved gas in the liquid drop (molL-1

)

Cg − Concentration of the gas species in the air surrounding the drop (molL-1

)

CL − Concentration of HSO3− in the drop (mol L

-1)

CLsat − Concentration of HSO3− in the drop at saturation (mol L

-1)

CT − Vertically integrated concentration of gas over a unit area (m. mol L-1

)

Cg* − Concentration predicted by Gaussian Dispersion Model (g m

-3)

W − Flux of gas species to the ground per unit height (mol L-1

s-1

)

F − Net Flux of gas species to the ground (m.mol L-1

s-1

)

h − Height above the ground (m)

T − Temperature of ambient air (K)

fg − Ventilation Coefficient (dimensionless)

KH − Henry‟s Law Constant (mol L-1

atm-1

)

Dg − Gas Phase Diffusivity of gas species (SO2) (mm2s

-1)

Dg* − Modified Gas Phase Diffusivity of gas species (SO2) (mm

2s

-1)

D − Diameter of the rain drop (mm)

R − Universal Gas Constant

α − Mass accommodation coefficient (dimensionless)

VG − Molecular Thermal Velocity (m/s)

K1 − Dissociation Constant for dissociation of SO2 to HSO3− (mol L

-1)

MM − Molecular Mass of SO2 (g/mol)

k − Average Mass Transfer Coefficient (s-1

)

Kc − Empirical Mass transfer coefficient (mm s-1

)

Re − Reynolds No. (dimensionless)

Sc − Schmidt No. (dimensionless)

Ut − Terminal Velocity of a falling drop (mm s-1

)

ν − kinematic viscosity of air (mm2s

-1)

Q − Empirical Constant to determine Ut (s-1

)

[H+]ini − H

+ ion concentration in a drop leaving the cloud base (mol L

-1)

[H+]abs − H

+ concentration in a drop due to dissociation of absorbed SO2 (mol L

-1)

pHini − pH of a drop leaving the cloud base (dimensionless)

pHground − pH of a drop at the ground (dimensionless)

[HSO3−]abs − HSO3

− ion concentration due to dissociation of absorbed SO2 (mol L

-1)

β − Scavenging Coefficient (s-1

)

N(D) − Rain Drop Size Distribution function

N0 − Parameter of the Modified Gamma distribution (m-3

mm-1

)

Λ − Parameter of the Modified Gamma distribution (mm-1

)

μ − Parameter of the Modified Gamma distribution (dimensionless)

p − Precipitation intensity/ rain rate (mmhr-1

)

q − Source Strength (g s-1

)

9

u − Horizontal wind velocity (m/s)

H − Effective Height of the stack in the Gaussian Plume Equation (m)

σy, σz − Dispersion Parameters of the Gaussian Plume Equation (m)

Δh − Plume rise

W0 − Velocity with which the effluent exits the stack upwards

Ds − The Stack diameter at the exit

xg, yg, zg − Coordinates to be inputted to the Gaussian plume Equation

10

LIST OF FIGURES

Page no.

Figure 2.1: Gaussian Plume Model 15

Figure 2.2: Variation of p with surface roughness and 19

Monin Obhukov Length for ref. height of 10m

Figure 2.3: Transformation of Coordinates 20

Figure 2.4: Average contours SO2 for 08:30, April 2009 21

Figure 2.5: Average contours SO2 for 14:30, April 2009 22

Figure 3.1: Process of Dry Deposition [16] 24

Figure 3.2 Resistance Schematic for Dry Deposition

Model of Wesely (1989) 30

Figure 3.3: Vd calculation April 2009 08:30 33

Figure 3.4: Vd calculation April 2009 14:30 33

Figure 3.5: Vd calculation April 2009 02:30 34

Figure 3.6: Vd at different times during the month of April 2009 34

Figure 3.7: April 2009 14:30 Without Dry Deposition 36

Figure 3.8: April 2009 14:30 With Dry Depsition; Vd=0.006m/s 36

Figure 3.9: Amount of SO2 deposited; entire map vegetated 37

Figure 3.10: Amount of SO2 deposited; half map vegetated 37

Figure 3.11: Relatively higher concentrations of SO2 at 08:30

as compared with 14:30 38

Figure 3.12: Concentrations of SO2 after zero and two hours 39

Figure 3.13: Concentrations of SO2 after fours and eight hours 39

Figure 4.1: Concentration of bisulphite in a falling drop 44

Figure 4.1: pH of drops of various sizes at the ground level 45

Figure 4.3: Rain drop size spectrum for various

rain rates over Cuddalore [6] 47

Figure 4.4: Scavenging coefficient for various rain rates 47

Figure 4.5: Contours of SO2 with and without the shower 49

Figure 4.6: Contours of pH of rain water around NLC 49

11

LIST OF TABLES

Page No.

Table 2.1: Pasquill Stability Classes 16

Table 2.2: Briggs formula for dispersion parameters 17

Table 2.3: Surface Roughness 19

Table 2.4: Monin Obhukov Length and Atmospheric Stability 19

Table 3.1: Correlation parameters for the determination of

Monin Obhukov length from (3.7) 28

Table 3.2: Input Resistances (s m-1) for Computations of

Surface Resistances ( rc ) [19] 31

Table 3.3: Relevant Properties of Gases for Dry Deposition Calculations [2] 31

Table 4.1: Parameters for use in Equation (4.2) 43

Table 4.2: Details of the stacks emitting SO2 and the plume rise 49

12

CHAPTER 1: INTRODUCTION

Neyveli is located 160 km from Chennai in the Cuddalore district of Tamil Nadu. Neyveli

Lignite Corporation (NLC) is a company promoted by the government of India under the

Ministry of Coals. Neyveli Thermal Power Stations are South Asia's first and only lignite fired

Thermal Power Stations and also the first pit-head power stations in India. NLC covers an area

of about fifty-four square km, which includes Neyveli Township and temporary colonies such as

Mandarakuppam, Thedirkuppam, Thandavankuppam, and Block-21. It mines twenty-four

million metric tones per annum (MTPA) of lignite, and produces 2,490 megawatts per annum

(MW/year) of electricity from three open cast mines.

NLC operates the largest open-cast lignite mine in India and is listed on the Bombay Stock

Exchange and National Stock Exchange. The origin state Tamil Nadu gets 1,167 MW, while the

rest is distributed to other states namely, Andhra Pradesh, Karnataka, Kerala and the union

territory of Puducherry. NLC's Power Stations maintain a very high Plant Load Factor (PLF)

when compared to the National average. In this study we will be focusing on the two thermal

Power Stations, namely TPS1 and TPS2. TPS 1 has four stacks each emitting between 150-300

g/s of SO2. TPS2 has seven stacks with emissions of SO2 at around 300 g/s.

The Neyveli town ship is home to 128,133 people and naturally air quality is an important

environmental factor which must be monitored and controlled. With such large amounts of

emissions it is necessary to have continuous monitoring of ambient air pollution levels to ensure

that the norms of the Pollution Control Board are met. NLC has 13 monitoring stations located at

various points across the township which fulfil this requirement. However this data is discrete by

nature and cannot give a complete picture of the air quality, especially at intermediate locations.

This requires a detailed atmospheric dispersion modelling study which can provide a picture of

the spatially distributed concentrations of air pollutants.

The founding fathers of NLC began a massive afforestation program which has now resulted in

an extensive tree cover. Neyveli has a variety of species- many of them evergreens which

amount to 17 million in total. It is common belief that trees help reduce pollution and keep the

air cleaner. Naturally it was felt that these trees must be playing an important role and no doubt

similar thoughts were in the minds of the founding fathers. However, to quantify the role of this

huge canopy of trees a detailed modelling exercise is called for.

It was with these objectives that a joint project was initiated between NLC and VIT University in

2009 with Dr. S. Ghosh (Sr. Prof. SMBS) as the Principle Investigator. The funding agency is

the Centre for Applied Research and Development (CARD) which is the In-house Research and

Development Centre of NLC. It has been recognized by the Department of Science and

Technology since 1975. The work described in this thesis was done towards achieving the goals

of this project and was funded under the same.

13

There are three major sections in this work aimed at answering three important questions. What

are the ambient air concentrations around NLC? What is the role of the trees if any? What is the

extent of washout of pollutants during precipitation events and is the rain water acidic? The latter

question was asked primarily by the researchers since the NE Monsoon lashes the coast of Tamil

Nadu from October to December and understanding precipitation effects on air pollution will be

important during these months.

In the second chapter we focus on the development of the atmospheric dispersion model. This

model, apart from answering the primary question of this study, is an essential tool in the

research of the following sections. Next we present the quantification of dry deposition of

gaseous pollutants onto vegetative canopies. This process is responsible for the cleansing action

of the trees and is quantified in terms of a deposition velocity parameter. The calculation of the

same and its incorporation into a model, to answer our second question, is the subject of the third

chapter. The processes modeled make this Asia based study unique- the year round high solar

insolation, high temperatures, the convective nature of the atmospheric boundary layer, the Leaf

Area Indices, the leaf morphology and the overall robustness of these trees. The fourth chapter

presents a detailed investigation into the scavenging of gaseous pollutants by rain. The mass

transfer into falling rain drops and the drop size spectra of different rain rates are considered and

a parameterization in terms of a scavenging coefficient is achieved. This information when

coupled with our dispersion model can demonstrate the removal of air pollution by a shower as

well as predict the pH of the rain water due to the absorbed gas.

Codes for simulating the model for different situations and calculating various parameters were

written in FORTRAN 90. Visualizations of results were obtained using the plotting tools of

MATLAB®. The end result is a model which is portable and can serve as a decision making tool

in the screening, scoping and baseline analyses of Environmental Impact Assessment studies.

Finally we present the overall conclusions which can be drawn from the application of this

model to NLC with particular emphasis on the power plant and present a road map for future

research work.

14

CHAPTER 2: DISPERSION OF GASEOUS POLLUTANT FROM MULTIPLE ELEVATED

STACKS AT NLC

2.1 Atmospheric Diffusion Equation

The emissions from an elevated stack are advected by the wind along the wind direction as well

as dispersed due to the turbulent eddies in the flow. For the case of gases the assumption can be

made that the concentration of the gas does not affect the dynamics of the flow. Then the species

transport equation can be separated from the momentum transport equation. Once the flow field

is known this equation can be solved to give us the concentration distribution. Generally the

effect on the concentration field of molecular diffusion due to random molecular movement is

much less than that of turbulent dispersion due to eddies in a turbulent flow field, such as that

existing in the atmosphere. Thus the diffusion terms are justifiably neglected. The final equation

we arrive upon which describes the dispersion of a gas in a turbulent flow field is called the

Atmospheric Diffusion Equation (The terms „Dispersion‟ is more appropriate than „Diffusion‟

but we use it since it is prevalent in literature) [2].

tXSx

cK

xx

cu

t

c

i

ii

ii

j ,

(2.1)

Where the velocity and concentration are ensemble averages and K is the eddy diffusivity. X is a

position vector. The subscript < i > implies summation of terms with < i > going from 1 to 3.

Apart from this Eulerian approach to describe atmospheric dispersion many workers use a

Lagrangian approach as well [7,8]. However for our study we used a simpler Eulerian based

model the merits of which will become apparent, especially for our objectives. A complete

description of the concentration in the atmosphere will require a solution of the equations of fluid

flow as well as equation 2.1. This is not practical for our purpose of large scale dispersion

modelling and is way too expensive. Instead several analytical models are available in which

suitable parameters are experimentally determined to ensure a better match of model results with

reality. CFD simulation of environmental flows are carried out for small cases mainly to verify

the simpler models or help develop better descriptions of critical parameters.

The simplest and most widely used analytical models is the Gaussian plume equation for a fully

reflecting earth with will be described in the next section.

2.2 Gaussian Plume Equation

The Gaussian plume equation was derived from the atmospheric diffusion equation for a case of

homogeneous stationary turbulence with fixed wind speed along a specific direction which is

invariant with height. The major assumptions made are listed below [2]:

15

1. Continuous emission or emission times equal to or greater than travel times to the

downwind position of interest so that diffusion in the direction of travel can be ignored.

2. The material diffused is a stable gas or aerosol (<20 mm diameter), which remains

suspended in the air over long periods of time.

3. Mass is conserved through reflection at surfaces.

4. Steady-state conditions during the time interval for which the model is used, usually one

hour.

5. Constant wind speed, u, with height is assumed.

6. Constant wind direction with height is assumed.

7. The wind shear effect on horizontal diffusion is not considered (effect becomes large

after ~10 km).

8. The dispersion parameters are assumed to be independent of z and functions of x (and

hence U alone).

9. The averaging time of all quantities are assumed to be the same.

Even if the turbulence is assumed to be homogeneous as a first approximation, the presence of a

solid boundary at the ground level must be allowed for to conserve mass. Usually, the ground is

assumed to be a perfect reflector and its presence is represented by a mirror image source placed

below the ground. If the effective source height is assumed to be at elevation „h‟, the

concentration can be estimated through superposition.

If the x axis is taken to be along the centerline of the plume in the direction of the mean wind,

with y (the horizontal axis) and z (the vertical axis), it can be assumed that a plume traveling

horizontally at a mean speed u, disperses horizontally (y) and vertically (z) so that the

concentration of a pollutant at any cross section of the plume follows the normal Gaussian

Probability distribution. σy ,σz are the standard deviations of the dispersions in the y and z

directions. S is the source strength. Thus, for any point (x,y,z) in the plume the concentration C

of pollutant at that point is such that [2]

Cg* (x,y,z) = q/(2πuσyσz)×exp(-y

2/(2σy)

2)×[exp(-(z-H)

2/(2σz)

2)-exp(-(z+H)

2/(2σz)

2)] (2.2)

Figure 2.1: Gaussian Plume Model

16

2.3 Dispersion Parameters and Atmospheric Stability

The amount by which the plume spreads depends on the turbulence of the atmosphere i.e. the

state of mixing. This is characterized by the stability of the atmosphere which is dependent on

the temperature profile of the atmosphere and its deviation from the adiabatic neutral profile.

Thus the values of the dispersion parameters σy and σz depend on the stability of the atmosphere.

A neutral atmosphere neither enhances nor inhibits mechanical turbulence. An unstable

atmosphere enhances turbulence, whereas a stable atmosphere inhibits mechanical turbulence.

The turbulence of the atmosphere is by far the most important parameter affecting dilution of a

pollutant. The more unstable the atmosphere, the greater is the dilution.

The stability is characterized by a parameter called the Monin-Obhukov length which accounts

for the buoyancy effects with respect to the inertial effects to characterize stability. Its

determination is a non trivial matter and so most studies use a discrete set of classes called the

Pasquill Classes. These were first proposed by Pasquill (1961) and later modified by Turner

(1969). They make use of routine meteorological measurements to classify the state of stability

of the atmosphere [2].

Table 2.1: Pasquill Stability Classes

As mentioned before, the dispersion parameters should depend on the state of stability. The

sigma values should also increase with downwind distance from the plume according to Taylor‟s

statistical description of homogeneous turbulence. Briggs proposed empirical formula for the

estimation of these parameters of the gaussian dispersion model for urban and open country

conditions. These have been used in this model as well. They are applicable to bent over plumes.

17

A comprehensive handbook describing the standard methods employed in atmospheric

dispersion modelling is reference [18].

Table 2.2: Briggs formula for dispersion parameters

2.4 Plume Rise and Effective Wind Speed

The Gaussian air pollutant dispersion equation requires the input of H which is the pollutant

plume's centerline height above ground level. H is the sum of the actual physical height of the

pollutant plume's emission source point and ΔH-the plume rise due the plume's buoyancy. Most

of the air dispersion models developed between the late 1960s and the early 2000s used what are

known as "the Briggs‟ equations." Briggs‟ divided air pollution plumes into these four general

categories:

1. Cold jet plumes in calm ambient air conditions

2. Cold jet plumes in windy ambient air conditions

3. Hot, buoyant plumes in calm ambient air conditions

4. Hot, buoyant plumes in windy ambient air conditions

Briggs‟ considered the trajectory of cold jet plumes to be dominated by their initial velocity

momentum, and the trajectory of hot, buoyant plumes to be dominated by their buoyant

momentum to the extent that their initial velocity momentum was relatively unimportant.

18

Although Briggs‟ proposed plume rise equations for each of the above plume categories, it is

important to emphasize that "the Briggs‟ equations" which become widely used are those that he

proposed for bent-over, hot buoyant plumes. In general, Briggs' equations for bent-over, hot

buoyant plumes are based on observations and data involving plumes from typical combustion

sources such as the flue gas stacks from steam-generating boilers burning fossil fuels in large

power plants. Therefore the stack exit velocities were probably in the range of 20 to 100 ft/s (6 to

30 m/s) with exit temperatures ranging from 250 to 500 °F (120 to 260 °C).

To determine ΔH in this model, the formula proposed by the Bureau of Indian Standards (BIS)

which are the Modified Briggs Equations have been used.

1. For hot effluents with heat release (QH) of106 cal/s or more

Δh = 0.84(12.4+0.09h)QH0.25

/u (2.3)

2. For not very hot releases

Δh = 3WoD/u (2.4)

The exit flue gas temperature for Thermal Power Station I in NLC is 158 C. This has to be

treated as a cold discharge (Briggs, G.A., 1971) and hence the formula for not very hot releases

specified by the Bureau of Indian Standards has been used (2.4).

The wind speed at the effective stack height is required in the Gaussian plume equation.

Moreover the calculation of the plume rise requires the wind speed at the stack height.

Measurements of wind speed are generally made much closer to ground level (10 m) and thus a

method of calculating the wind speed at any height given a ground measurement is necessary.

The variation of wind speed with height follows a logarithmic relationship. This has been

confirmed by measurements and can be explained by similarity theory [2]. A simple-to-use

empirical formulation relates the wind speed at any height to that at a reference height in terms

of a power law:

p

r

rxxz

zzuzu

)()(

(2.5)

The exponent (p) is dependent on the surface roughness and the stability class of the atmosphere.

The surface roughness parameter for different surfaces is given by Voldner (1985).

19

Table 2.3: Surface Roughness

For our case we take its value to be 1 because of the extensive tree cover over NLC. The value

of p can be determined once the stability is known using the following figure:

Figure 2.2: Variation of p with surface roughness and Monin Obhukov Length for ref. height of 10m

The Monin-Obhukov length is related to stability in the following manner:

Table 2.4: Monin Obhukov Length and Atmospheric Stability

20

2.5 Adapting the Gaussian model for multiple point emission sources- generation of

monthly average contours

The Gaussian plume model is suited for a single elevated source. Building a model for a multiple

point source case can be done by superimposing the predicted concentrations from all sources at

any point. Thus any point of a grid, laid over the region of interest, can be fed into the equation

(2.2) and the concentration calculated. This is then repeated for all other sources and added to

obtain the overall concentration at that point. However the coordinates to be inputted to eq. 2.2

are different from the global map coordinates. Thus a transformation of coordinates is required.

This involves both translation and rotation. The transformation is depicted in the figure below

and the equations of transformation are presented. This development was an independent

achievement which however can also be found in current work [9].

Figure 2.3: Transformation of Coordinates

The next consideration is the adaptation of the model to predict monthly mean concentrations.

NLC has provided us with data on the monthly average ambient air concentrations at the 13

measurement stations. Hence we need our model to deliver similar meaningful average results.

In order to achieve this, the concentration at a point due to all sources is calculated once for

every wind (magnitude and direction). These concentrations are weighted by the frequency of the

particular wind and averaged to get a monthly mean. If we are averaging at a particular time then

21

it is possible to use the wind data at that particular time for all the days of the month, in which

case the weighting factor would be the inverse of the number of days of the month. If however

we are interested in an average of a different period then the wind data will have to be divided

into classes based on magnitude and directions. Each combination of magnitude and direction

would be taken as a wind in the model with an appropriate frequency. Such a classification of

data as well as useful visualization of the raw data as wind roses can be obtained with the

freeware WRPlot VIEW® from Lakes Environmental Software

®.

2.6 Simulation for April 2009- Results and Discussions

Meteorological data was provided by NLC for the months of 2008-2009. This data included

measurements of solar radiation, wind speed, and wind direction among others. The month of

April 2009 was chosen for presentation in this report as it is a dry month and thus useful for our

study of dry deposition in the next chapter. For the sake of brevity the other results have not been

included here but will be similar in nature differing due to the changing wind patterns and solar

insolation received.

Meteorological data was provided for three times of the day. Average concentration contours

were calculated for 08:30 and 14:30.

Figure 2.4: Average contours SO2 for 08:30, April 2009

22

Figure 2.5: Average contours SO2 for 14:30, April 2009

From the above figures the role of the wind in driving the pollution over different areas of the

Neyveli township is clear. The rose like diagram in the top corner represents the wind directions

and magnitude experienced during April 2009. They are blowing from the end of the arms and

towards the centre. The colors give the magnitude.

It can be seen that there is a significant change in the wind direction from morning to evening.

This means that there will be a good deal of residual concentration left at regions which will not

be receive direct emissions from the stacks at 1430 but which were recipients of the pollution in

the morning at 08:30. In the next chapter it will be shown that the removal of such residual

pollution is a major benefit of having a large tree cover near sources of atmospheric pollutants.

It is seen that during this month the receptors 5, 9, 10 and 11 do not receive any emissions. This

result is borne out in the data provided by NLC. Further receptor 6 receives the highest

concentration in the afternoon. This result is also apparent form NLC data.

The usefulness of such a model will be realised in Environmental Impact Assessment Studies

which aim to identify the impact of setting up a new power plant, or in our case the impact of an

expansion. It will also be useful in determining the intermittent pollutant levels between

measuring stations and will give a clear indication if there is a possiblity of a considerable rise in

the level of pollutant between stations.

23

CHAPTER 3: ESTABLISHING THE EFFICACY OF THE CLEANSING ACTION OF

TROPICAL EVERGREENS

Once pollutants are released into the atmosphere they can leave it either by removal or by

undergoing a chemical reaction. Removal mechanisms are of two types-dry deposition and wet

deposition. In this chapter we will look at dry deposition in some detail while rain washout, a

mechanism which is part the set of wet removal mechanisms is discussed in the next chapter.

3.1 Deposition Velocity- Theory of Resistances

Dry deposition is of considerable importance in calculating the overall budget of a species in the

atmosphere. Furthermore, the substance which gets deposited can affect the deposition surface in

many ways, often adversely. Hence the quantification of dry deposition is important from a dual

perspective and much work has been done on the subject. Experimental measurements of dry

deposition fluxes via techniques such as eddy correlation and accumulation, the gradient method

as well as analysis of the depositing surface-natural or surrogate, have been carried out in

different parts of the world. These studies, apart from giving local information, form a basis for

the development of modelling techniques to predict the dry deposition flux when measurements

cannot be made or are inconvenient. In our case it is indeed not convenient to make such

measurements and thus we will be focusing on the modelling of dry deposition. For more

information on experimental techniques the interested reader may refer [2,10-13]. Work has also

been done to verify the various models available and generally it is found that one or the other is

better depending on the conditions (wet or dry) and climate (mid-latitude or tropical) [13-15].

For a good review of the state of the science the reader may refer to the references [2,16,17].

When the flux of a species which is irreversibly taken up by a surface is being modelled then the

process is simpler since the surface can be considered as a perfect sink. When there is no flux of

gas away from the surface then the deposition can be modeled by the inferential resistance

method in which it is assumed that the concentration of the gas at the depositing surface is zero.

If not then the flux will have to be written in terms of the concentration difference at the

reference height and at the surface. In the case of SO2 the inferential method is suitable and thus

the flux to the ground is taken to be directly related to the concentration in the atmosphere.

Another assumption made is that in the surface layer (100-10 m above the ground) the flux is

constant. Thus at any reference height the flux can be represented by a first order relationship as:

F= −Vd×C (3.1)

C is the concentration measured at the reference height. Vd is a parameter called the deposition

velocity as it has the units of ms-1. It varies with height, as it must since the flux is assumed to

be constant. Thus the problem of determining the flux of a species is transformed into the

determination of the deposition velocity. Such a formulation is widely used since it is easy to

incorporate into dispersion models, especially analytical ones. It is also easily incorporated as a

surface boundary condition to the atmospheric diffusion equation.

24

The process of dry deposition is usually divided into three stages:

1. Transportation from the free atmosphere to the receptor surface (turbulent layer transport)

2. Transport through the quasi-laminar layer near the receptor surface

3. Capture or absorption by the surface.

Figure 3.1: Process of Dry Deposition [16]

The stages in the dry deposition process are treated analogously to the flow of electrical current

through a network of resistances in series. This approach is called resistance modelling of dry

deposition (Garland, 1977). In this analogy, the aerodynamic resistance (ra) refers to turbulent

transport from the free atmosphere down to the receptor surface, the boundary layer resistance

(rb) applies to transport across the quasi-laminar layer near the receptor surface and the surface

resistance (rc) refers to the interaction of the gas with the surface. This resistance is called the

canopy resistance for deposition onto canopies. The inverse of the total resistance is the dry

deposition velocity (Vd).

Vd = (ra+rb+rc)-1

(3.2)

An advantage of the resistance analogy is that processes are separated and related to

„measurable‟ quantities. They allow the lumping of complex micro-physical processes into a

25

single parameter. The disadvantage of this simplicity is that Vd is difficult to specify and may

lead to significant deviation from measured values especially under conditions which are not

accounted for in the model. This could occur if that particular condition was a rare occurrence at

the location where the model was developed and hence it was not given much attention. For eg.

the parameterization of Wesely (1989) [19] shows errors of 60% under wet conditions [14]. In

the next sections we will describe the methods of calculating the resistances for gases. Methods

for calculation of resistances for particles can be found in [2].

The final step in the dry deposition process is actual uptake of the vapor molecules or

particles by the surface. Gaseous species may absorbed irreversibly into the surface; particles

simply adhere. The amount of moisture on the surface and its stickiness are important

factors at this step. For moderately soluble gases, such as SO2 and O3, the presence of

surface moisture can have a marked effect on whether or not the molecule is actually

removed. For highly soluble and chemically reactive gases, such as HNO3, deposition is

rapid and irreversible on almost any surface. Solid particles may bounce off a smooth

surface; liquid particles are more likely to adhere upon contact.

3.2 Aerodynamic Resistance

Turbulent transport is the mechanism that brings material from the bulk atmosphere down

to the surface and therefore determines the aerodynamic resistance. The turbulence intensity

is principally dependent on the lower atmospheric stability and the surface roughness and

can be determined from micrometeorological measurements and surface characteristics such

as wind speed, temperature, and radiation and the surface roughness length. During daytime

conditions, the turbulence intensity is typically large over a reasonably thick layer (i.e., the well-

mixed layer), thus exposing a correspondingly ample reservoir of material to potential surface

deposition. During the night, stable stratification of the atmosphere near the surface often

reduces the intensity and vertical extent of the turbulence, effectively diminishing the

overall dry deposition flux. The aerodynamic resistance is independent of species or

whether a gas or particle is involved except that gravitational settling must be taken into

account for large particles.

The aerodynamic component of the overall dry deposition resistance is typically based

on gradient-transport theory and mass-transfer/momentum-transfer similarity (or mass-

transfer/heat-transfer similarity). It is presumed that turbulent transport of species through

the surface layer (i.e., constant-flux layer) is expressible in terms of an eddy diffusivity

multiplied by a concentration gradient, that turbulent transport of material occurs by

mechanisms that are similar to those for turbulent heat and/or momentum transport, that

measurements obtained for one of these entities thus can be applied, using scaling

parameters, to calculate the corresponding behavior of another. Expressions for the

aerodynamic resistance are most easily obtained by integrating the micrometeorological

26

flux-gradient relationships. Applications of similarity theory to turbulent transfer through

the surface layer suggest that the eddy diffusivity should be proportional to the friction

velocity and the height above the ground. Under diabatic conditions the eddy diffusivity

is modified from its neutral form by a function dependent on the dimensionless height

scale, , where L is the Monin-Obukhov length.

The vertical turbulent flux of a quantity, say, C, through the (constant-flux) surface layer

is expressed as

(3.3)

where K is the appropriate eddy diffusivity and Fa is, by definition, constant across the

layer. From dimensional analysis and micrometeorological measurements, the eddy

momentum (KM ) and heat diffusivity (KT) can be expressed by

(3.4)

(3.5)

where k is the von Karman constant, is the friction velocity, and and ,

respectively, are empirically determined dimensionless momentum and temperature profile

functions.

If Eq. (3.3) is integrated across the depth of the constant-flux (i.e., surface ) layer from

down to , the flux may be written as

(3.6)

where, as above, and refer to concentrations at the top and bottom of the constant-

flux layer and denotes either or , whichever is deemed analogous to

the species profile function. The aerodynamic resistance is thus given by

(3.7)

The integral in Eq.(3.6) is evaluated from the bottom of the constant-flux layer (at ,

the roughness length) to the top ( , the reference height implicit in the definition of

). If suitable empirical forms of the stability dependent temperature profile are assumed then

the above equation (3.6) can be integrated to yield explicit expressions for ra. These expressions

can be found in [2] and are given below:

27

)__(01_

)_____(0_

)___(10_

151

1

7.41

)(

41 unstablefor

neutralfor

stablefor

T

(3.8)

)(

)(

)(

tantan211

11lnln

1

ln1

7.4ln1

0

11

22

2

0

2

0

0

unstable

neutral

stable

z

z

u

z

z

u

z

z

u

r

r

rro

o

o

a

(3.9)

Where 0 = 41

0151 and 0 = Lzr 00

41,151

The theory is applicable only in the surface layer where the flux in non divergent and can be

assumed constant, that is the Richardson No. should be between -3 and 2. An approximate

maximum extent is 100m.

In order to use the above equations it is necessary to determine the friction velocity and the

Monin- Obhukov length. The friction velocity can be calculated by

0

*ln

)(

zdz

zkuu

(3.10)

where u(z) is the velocity at a certain height z. The displacement length d can be defined

(Panofsky and Dutton 1984) as typically 70%–80% of the height of the large roughness

elements. Thus, d can be neglected in this equation when z is much greater than d. The roughness

length z0 is generally a function of surface roughness, even though it may be affected by the

wind speed (when the roughness elements bend with the wind) and by the wind direction when

different terrain features surrounds the region. The roughness length can be developed as a

function of season and surface type. The values of the surface roughness z0 (cm) for each surface

type are presented in the Table 2.3 (Voldner et al. 1986).

When a direct measurement of L is not feasible the Monin Obhukov Length can be determined if

the Pasquill Stability class is known according to the formula (L. Golder 1972)

)(log1

010 zbaL

(3.11)

28

The coefficients in the above equation are given for different Pasquill classes in the table below:

Table 3.1: Correlation parameters for the determination of Monin Obhukov length from (3.7)

3.3 Quasi Laminar Resistance

The resistance model for dry deposition postulates that adjacent to the surface exists a

quasi-laminar layer, across which the resistance to transfer depends on molecular properties

of the substance and surface characteristics. This layer does not usually correspond to a

laminar boundary layer in the classical sense; rather it is the consequence of many

viscous layers adjacent to the obstacles comprising the overall, effective surface seen by

the atmosphere. The depth of this layer constantly changes in response to turbulent shear

stresses adjacent to the surface or surface elements. In fact, the layer may only exist

intermittently on such surfaces as plant leaves, which are often in continuous motion.

Whether a quasi-laminar layer actually exists, physically depends on the smoothness and

the shape of the surface elements, and to some extent, the variability of the near-surface

turbulence, but, in terms of the theory, it is considered to exist.

A viscous boundary layer adjacent to the surface of some obstacle on which deposition

is occurring is an impediment to all depositing species, regardless of the orientation of

the target surface. Molecular and Brownian diffusion occur independently of direction;

molecular diffusion can occur to the underside of a leaf just as easily as it can to the

top surface. The flux across the quasi-laminar sublayer adjacent to the surface is

expressed in terms of a dimensionless transfer coefficient, B, multiplying the concentration

difference across the layer, C2-C1. Since, under steady-state conditions, this flux is equal to

that across the surface layer, we write

(3.12)

where C1 is the concentration at the surface, and, by convention, the transfer coefficient is

dimensionalized by . The quasi-laminar layer resistance is then given by

(3.13)

29

The quasi-laminar resistance depends on the molecular (for gases) or Brownian (for

particles) diffusivity of the material being considered. This dependence can be accounted for

through the dimensionless Schmidt number, Sc = v/D, where v is the kinematic viscosity of

air and D is the molecular diffusivity of the species. Measurements over canopies have

shown to be relatively insensitive to the canopy roughness length . A useful expression

for for gases in terms of the Schmidt number is (Wesely, 1989),

(3.14)

3.4 Surface or Canopy Resistance

Surface properties greatly influence the rates of particle and gas dry deposition, either

directly through chemical reactions with the surface or indirectly through perturbation of

the quasi-laminar layer. The capture of gases by vegetation depends primarily on the

accessibility of the gas to reaction sites within the plant. Numerous field studies show

that rc is the dominant resistance for SO2 deposition to a plant canopy (Matt et al., 1987).

The resistance offered by soil, building materials, water, and snow surfaces to gaseous dry

deposition generally depends on the moisture level and pH of the surface, and the

solubility and reactivity of the gas. For SO2, the solubility is pH dependent, and, as a

result, a moist surface may offer greater resistance with repeated exposure.

The canopy resistance rc is usually the most difficult of the three flux resistances to

evaluate theoretically. Under ideal conditions, rc can be related to surface conditions, time

of day, season, and so on. In field studies providing independent measurements of the dry

deposition flux, rc is sometimes determined as simply the difference after the aerodynamic and

quasi-laminar resistances have been subtracted from the measured inverse deposition velocity , rc

= vd-1

– ra – rb. There is a sizable body of work devoted to estimating rc as a function of

chemical species, canopy type, and meteorological conditions. It is usually presumed that,

once deposited at the surface, the gas or particle is captured irreversibly and cannot

reenter the atmosphere.

The surface or canopy resistance rc poses the complexity in specifying a quantitative

model. rc is assumed to be zero for particles and thus in developing a model for rc we need

consider only gases. The approach adopted here is based primarily on the methodology

developed by Wesely (1989) [19] for regional-scale modeling over a range of species, land-

use types, and seasons. The surface resistance is calculated from the individual resistance by

(Figure 3.3)

=

(3.15)

30

Cz

ra

rb

rst rm

Cm

rlu Clu Vegetation

rc rdc rcl Ccl Lower

“Canopy”

rac rgs Cg

Cc “Ground”

Figure 3.2 Resistance Schematic for Dry Deposition Model of Wesely (1989)

where the first term includes the leaf stomatal ( rst ) and mesophyll ( rm ) resistances, the

second term is outer surface resistance in the upper canopy ( rlu ), which includes the leaf

cuticular resistance in healthy vegetation and the other outer surface resistances; the third term is

resistance in the lower canopy, which includes the resistance to transfer by buoyant convection

(rdc) and the resistance to uptake by leaves, twigs, and other exposed surfaces ( rcl ); and the

fourth term is resistance at the ground, which includes a transfer resistance ( rac ) for processes

that depend only on canopy height and a resistance for uptake by the soil, leaf litter, and so on at

the ground surface ( rgs ).

The bulk canopy stomatal resistance is calculated from tabulated values of rj (where rj is the

minimum bulk canopy stomatal resistance for water vapor) , the solar radiation ( G in W m-2

),

and surface air temperature ( Ts in oC between 0 and 40

oC ) using

=

(3.16)

Outside this range, the stomata are assumed to be closed and rst is set to a large value. The

combined minimum stomatal and mesophyll resistance is calculated from

31

=

+ =

(3.17)

where is the ratio of the molecular diffusivity of water to that of the specific

gas, is the effective Henry‟s law constant (M atm

-1 ) for gas, and

is a normalized (0 to

1) reactivity factor for the dissolved gas (Table 3.3). The second term on the R.H.S of (4.32)

is the mesophyll resistance for the gas of interest.

The resistance of the outer surfaces in the upper canopy for a specific gas is computed from

=

(3.18)

Resistance

Component

rj rlu rac rgsS rgsO rclS rclO

Seasonal Category 1: Midsummer with Lush

Vegetation

100 2000 2000 100 300 2000 1000

Seasonal Category 2: Autumn with Unharvested

Cropland

500 8000 1700 100 300 4000 600

Seasonal Category 3: Late Autumn After Frost,

No Snow

500 8000 1500 200 300 6000 600

Seasonal Category 4: Winter, Snow on Ground

and Subfreezing

800 9000 1500 100 3500 400 600

Seasonal Category 5: Transitional Spring with

Partially Green Short Annuals

190 3000 1500 200 300 3000 700

Table 3.2: Input Resistances (s m-1) for Computations of Surface Resistances ( rc ) [19]

Species Rate of

Molecular

Diffusivities

Henry‟s Law

Constantb (H* )

( M atm-1

)

Henry‟s Law

Exponenta

(A)

Normalized

Reactivity

( fo )

Sulfur dioxide 1.89 -3020 0

aThe exponent A is used in the expression H(T) = H exp{A[1/298 – 1/T]} to calculate H at the surface

temperature. bEffective Henry‟s law constant assuming a pH of about 6.5.

Table 3.3: Relevant Properties of Gases for Dry Deposition Calculations [2]

32

The resistance rdc is determined by the effects of mixing forced by buoyant convection

(as a result of surface heating of the ground and/or lower canopy) and by penetration of

winds into canopies on the sides of hills. The resistance (in s m-1

) is estimated from

(3.19)

where is the slope of the local terrain in radians. The resistance of the exposed surfaces

in the lower portions of structures ( canopies or buildings) is computed from

(3.20)

where rclS and rclO are given for each season and mixed forest including wetland in

Table 4.1. Similarly, at the ground, the resistances are computed from

(3.21)

The values of the parameters needed for computation were tabulated be Wesely [19] for different

seasons and different land use types. Caution must be exercised while selecting values. The

importance of the seasonal type is that the amount of tree cover varies. A more modern

parameterization by Zhang et. al (2003) [20] incorporates a LAI (leaf Area Index) which varies

with season differently for different vegetative types. This provides for a better description

especially in parts of the world which are different in vegetation and climate from the place for

which the model was originally developed. For this study we have used the Wesely

parameterization and selected the values for deciduous forest since NLC has a large tree cover.

The values for various seasons are given below. For April the obvious choice is mid-summer

with lush vegetation. For the months of November December seasonal category 3 may be used.

For more information the reader is referred to the original work [19]. Values from Wesely are

presented in Table 3.2 [19]

Table 3.3 lists relevant properties needed to calculate gaseous deposition layer and surface

resistances for this model. It is important to recognize that the reactivity factors assigned

to the depositing species are approximate and may vary significantly with the vegetation

type or state. The Henry‟s law constants can be adjusted for temperature using the

expression given in the footnote to the table and adjusted for pH on the leaf surface.

33

3.5 Deposition Velocity for SO2, April 2009

The above computations were performed for the month of April 2009. Data was provided by

NLC. The values of ra, rb and rc were computed at three times of the day- 02:30, 08:30 and

14:30. The reference height was taken arbitrarily as 90m. The results are presented below along

with the calculated deposition velocities.

Figure 3.3: Vd calculation April 2009 08:30

Figure 3.4: Vd calculation April 2009 14:30

From a comparison of figures 3.4 and 3.5 we see that the aerodynamic resistance is higher in the

morning.This is primarily due to the lower wind speed at 08:30 as well as due to the more

convective nature of the afternoons at NLC. The surface resistance remains nearly the same due

to the healthy sunshine received right from morning to afternoon in April. The deposition

velocity is generally higher in the afternoon. In the night the stomata closes due to the absence of

34

stimulation by light. Hence no gas uptake through the stomatal pathway is possible and the value

of surface resistance rises appreciably leading to low values of deposition velocity. The large flat

portions of fig. 3.6 are due to the threshold values set for friction velocity and velocity when the

measurements are too low and attributable to errors in measurement or anomalies.

Figure 3.5: Vd calculation April 2009 02:30

From the above figures we find that during the day time in April the average Vd value can be

taken as 0.006 ms-1. We will use this value in the further studies in this chapter where we will

investigate the role of the extensive tree cover in controlling air pollution at NLC. The

computations described above can be easily carried out for other months. For the reader who is

interested in more examples of such computations we suggest he refer [21-23].

Figure 3.6: Vd at different times during the month of April 2009

35

3.6 Removal from a Continuous Plume

The stacks at NLC emit polluting gases round the clock throughout the year unceasingly. These

emissions can affect human life and the environment adversely if they are concentrated to high

levels at any location. Thankfully nature has an amazing capacity to absorb anthropogenic

stresses such as polluting emissions. While this cannot be seen as a green signal to continue to

harming the environment, it is heartening since any switch over to green fuels will take a

considerable time. Until then we must rely on fossil fuels. Under such circumstances it is indeed

important to understand the mechanisms by which we can naturally reduce the harmful effects of

these emissions. Cleansing of air by trees has long since been viewed as once such mechanism.

This is one of the reasons behind afforestations and greening of urban areas. However, what

affect would 17 million trees have on a power plant which emits around 3000g/s of SO2 alone

everyday of the year 24×7? No study on this scale has been conducted thus far- especially not in

India where the unique conditions of climate and vegetation make borrowing results from mid-

latitude regions unsatisfactory.

In this work we systematically investigate the role of tress at NLC. The first step is to establish

wether the trees are capable of sucking out pollution from the plumes which are being

continuously emitted. Do they act as a type of anti-pollution system-counter acting the stacks and

fighting to keep the air clean? To answer this question the deposition velocity calculated in the

previous section is incorporated into the dispersion model to account for the depletion of the

plume due to the dry deposition flux of SO2 onto the trees. The new reduced concentrations are

then computed and compared with the old. This computation is not a trivial task and complicates

the coding task considerably. Furthermore there is not much literature available which describes

a model/methodology suitable for our task. This proved to be the most challenging task of the

modelling exercise. The results were however far from spectacular from an environmentalist

point of view. Instead as one should expect, the source strenghts are far to high and the pollution

is carried over the trees too quickly for the canopy to have any affect on the ambient air

concentration at a location directly in the path of a plume. While the flux of SO2 is considerable,

the replenishment from the source is much higher. Below, two plots of the average

concentrations of SO2, with and without including depostion, are presented for comparison. The

month chosen is again April 2009. There is hardly andy change-indicating the incapability of the

trees to clean the air in regions directly affected by such strong emitting sources as we have at

NLC.

36

Figure 3.7: April 2009 14:30 Without Dry Deposition

Figure 3.8: April 2009 14:30 With Dry Depsition; Vd=0.006m/s

Do these results mean that planting 17 million trees is a wasted effort as far as keeping the air

clean is concerned? The answer is a definite No and section 3.8 tells us why. Before we proceed

it is important to take a look at things from the point of view of the trees.

3.7 Deposition of polluting species onto the canopy

In the previous section, it was mentioned that the flux of SO2 is considerable. While this may not

affect the ambient air concentration at regions along the plume path, it is important in terms of

quantifying the amount of substance deposited at a vegetative surface over time. The deposition

of acidic substances such as SO2 can have various harmful impacts upon the trees. For these

reasons and to demonstrate the validity of the opening statement of the section we present below

the mass of SO2 deposited over NLC if the entire region were afforested. The value at any point

represents the mass collected per hour over a 200×230m rectangular area about the point. These

computations, performed for a fully vegetated NLC, are for illustrative purposes. To demonstrate

the ability of the model to handle situations with varying extents of green cover, we present a

simulation for the case where only half of the map is covered by forest (Vd=0.006m/s) while the

rest is not (Vd=0.002m/s).

37

Figure 3.9: Amount of SO2 deposited; entire map vegetated

Figure 3.10: Amount of SO2 deposited; half map vegetated

3.8 Removal of residual pollution- Cleansing action of trees

While trees do not have much impact on direct pollution levels they however have a major

impact on the level of residual pollution. Residual pollutants are those which are not being

directly emitted at the time of analysis. These pollutants were emitted earlier and are no longer

being emitted by the source. Such pollutants will remain in the atmosphere unless they are

advected by wind, transformed by chemical reaction or removed by either dry or wet deposition

mechanisms. It is here that the trees play a big role.

We can also refer to such conditions, when the pollution is being removed without any further

addition, as conditions of no-replenishment- a self coined term. Under such conditions the trees,

over several hours, can scavenge much of the pollution- dramatically reducing air pollution

levels. These conditions would occur over large areas of NLC every day. A comparison of fig.

2.4 and 2.5 will show that the wind changes direct appreciably during the day. This will leave

behind residual pollution over large areas of NLC. Moreover when a moderate morning wind is

followed by a mild afternoon breeze, as was the case on 30th

April, then removal of these

residual pollutants is left to the tree cover. In the absence of trees the pollution levels would

remain considerably higher until a strong wind picks up and advects the gases away. Of course it

38

would only pollute another region! In the figure below the regions which on an average are left

with residual pollution is shown. This figure depicts the relatively higher concentrations during

08:30 as compared to 14:30 for the month of April 2009.

Figure 3.11: Relatively higher concentrations of SO2 at 08:30 as compared with 14:30

Under the conditions of low winds the pollution will remain over the trees and be gradually

removed from the atmosphere. The air above the trees will be in vertical motion depending on

the stability of the atmosphere. Under stable conditions the pollution remains trapped at the

surface while at unstable conditions strong mixing can dilute the pollution throughout the

atmosphere.

Consider a column of mixed air containing the pollutants reaching form the surface of the

vegetative canopy up to the top of the well mixed layer of the atmosphere. This is the region of

the atmosphere in which the air is churned about and can be considered as a stirred tank. During

the night this height will be low- about 100m due to the formation of an inversion layer.

However during a summer afternoon it can extend beyond 1000m. For this case we have taken

the height to be a 1000m.

Thus we have a scenario in which species are being removed at the bottom surface of the

atmosphere while it is being mixed by the turbulent eddies of the flow. Such a situation can be

represented by the following mass balance on the species (SO2) for a column of air of height

equal to the mixing layer height, Hmix.

mix

d

H

CV

dt

dC

(3.22)

39

This equation when integrated yields the following expression for the time dependent

concentration in the mixed zone of the atmosphere, where C0 is the initial residual concentration.

mix

d

H

tVC

dt

dCexp0

(3.23)

3.9 Cleansing of Air by trees on 30th

April 2009

In order to study the removal of residual pollutants under the conditions of no-replenishment

described above, it is necessary to consider a particular day when such conditions prevailed. 30th

April 2009 was such a day when a moderate NW wind of 1.7 m/s swept NLC at 08:30 while a

mild breeze (0.3m/s) blew from the east at 14:30. Under such conditions practically all the

morning pollution levels would remain as residual levels during the afternoon. These pollutant

gases will not be carried away by the mild wind, however there will be strong turbulent vertical

mixing during the afternoon since the conditions will be unstable. The height of the mixing layer

is taken to be 1000m. In the figures which follow contours of the residual concentration are

presented at various time intervals after 08:30. The purpose of this study is to give an

appreciation for this role of trees and not to demonstrate the exact transient concentration

profiles. Thus although the variation of wind between 08:30 and 14:30 are not considered and the

advection is neglected we feel this case study still serves to bear out the discussion of the

preceding paragraphs.

Figure 3.12: Concentrations of SO2 after zero and two hours

Figure 3.13: Concentrations of SO2 after fours and eight hours

40

The removal of pollution is clearly seen at receptor thirteen, where the concentration decreases

by about 70μg/m3 over 8 hours.

3.10 Conclusions

When viewing these results it must be remembered that the atmosphere is most unstable at

afternoon, during the summer. During winter days the mixing layer can be a few hundreds of

meters high while at night it can be just a 100 meters due to the formation of an inversion layer.

Under such conditions pollution emitted during the earlier hours can get trapped during the night

and create harmfully high levels of pollution. This would be prevented by the removal action of

the trees which would be much more pronounced when the mixing height in eqn. (3.19) is

reduced from 1000m to 100m. Of course ground level pollution would be removed to a greater

extent. This is why trees should be planted in urban areas where the major pollution source is at

ground level and the residual pollution often is trapped during the night. The presence of trees

would then definitely improve the air quality.

It is important to note that for the above simulations the entire study area was taken to be covered

by trees. Although this is not the case, we do not expect any deviation in our ultimate conclusion

since NLC has such a large tree cover. In future work the data on the location of forest cover will

be obtained from NLC and the study repeated, as well as further investigation carried out into

other cases wherein the trees can play an important role.

41

CHAPTER 4: SCAVENGING EFFICACY OF THE NORTH EAST MONSOONS

India receives heavy rainfall from two distinct monsoons. The South East Monsoon, bearing

moisture from the Indian Ocean, holds sway over most of the Subcontinent from June to

September. The North East Monsoon (NE Monsoon) or Retreating Monsoon brings moisture

from the Bay of Bengal and empties itself over the South Eastern coast of India during the

months of October to December. It is the latter which forms the basis of this study-this study is

therefore timely as some of the world‟s mega cities (i.e. Chennai and Kolkata) are along this

coastline.

The State of Tamil Nadu, located along the South East coastline, is most affected by the NE

Monsoon and receives rain rates almost thrice that of mid-latitude precipitation (>150mm/hr).

The state and neighbouring regions are home to several industries and power plants which spew

SO2 laden emission throughout the year. During heavy precipitation events, much of the air

borne pollution is washed away by the rain leaving the air fresher- a common experience for

locals living in industrialized areas. However, the downside is a reduction in the pH of the rain

water reaching the earth‟s surface. In order to quantify these phenomena it is necessary to study

the mass transfer of the trace pollutant gas (SO2 in this study) into falling rain drops, against a

backdrop of rain rates and rain drop size spectra. In order to identify areas of particular concern it

is necessary to develop need-based atmospheric dispersion models. While many such studies

have been carried out for the mid-latitudes, there are hardly any definitive quantitative studies

over the tropical regions. We believe that ours is a first study which deals with precipitation

scavenging of intense showers of the NE Monsoon over a sensitive region in tropical India.

4.1 Transient Interface Mass Transfer of SO2 into a falling Rain Drop

The well mixed model of liquid gas interface mass transfer is used in this section. A liquid drop

falling at its terminal velocity through the atmosphere experiences considerable shear at its

surface which induces internal circulations inside the drop. This allows us to make the

assumption that there are no concentration gradients inside the drop (well mixed assumption).

Hence the resistance to mass transfer is considered to exist only in the gaseous film surrounding

the drop.

For a gas which dissolves in water according to Henry‟s Law we can write the following

expression for the time rate of change of concentration [1]

dCl /dt = (12fgDg*/D

2) [Cg – (Cl / KHRT)] (4.1)

fg is the ventilation coefficient which is the ratio of the mass transfer of the gas for a drop falling

at its terminal velocity to that of a stationary drop.

Dg* is the modified diffusivity which is obtained from the binary diffusivity of SO2 (Dg) in air by

the following expression:

42

Dg* = Dg/ [1 + (8Dg/D αVG)] (4.2)

α, the mass accommodation coefficient is assumed to be 0.5 for this study [1] and VG, the

molecular thermal velocity can be computed by [2]

VG = [ 8RT/(πM.M) ]1/2

(4.3)

The SO2 in the liquid phase is present in a dissociated form. The prominent species present at

moderately acidic pH levels is the bisulphite ion. For the sake of simplicity and keeping the goal

of this section in mind, we decided to consider the dissociation of SO2 into bisulphite (HSO3-)

alone without considering any further oxidation reaction of bisulphite-the time of fall required

for the saturation of a drop will increase if we were to consider these additional aqueous

reactions. Thus by the current approach we can get a lower limit on the time to saturation which

serves our purpose. In order to write equation (3.20) in terms of HSO3- concentration we must

introduce the dissociation constant (K1) of the reaction of formation of HSO3-.

The equation for the transient concentration of HSO3- is:

dCL/dt = (12fgDg*/D

2) [Cg – (CL

2/ KHK1RT)] (4.4)

In this study we are concerned with wet scavenging in a local the region around the source of

emissions. It is unlikely that the emissions can travel up to the cloud base for there to be any

significant in cloud scavenging. Thus, we can assume that the concentration of HSO3- in the rain

droplets as they fall from the cloud base will be negligible. Then the initial condition for

equation (2) is dCL/ dt = 0; CL = 0. This first order differential equation can be solved to yield an

expression for the transient concentration of HSO3- in the drop.

CL (t) = CLsat tanh[ (k Cg t) / CLsat)] (4.5)

CLsat is the concentration in a saturated droplet which should be in equilibrium with the ambient

air concentration of SO2:

CLsat = [CgKHK1RT]1/2

(4.6)

k, the average mass transfer coefficient accounts for both collisional as well as diffusional

uptake:

k = (12Dg* fg) / D

2 (4.7)

The value of the ventilation coefficient can be determined from the following empirical relation

[1] in terms of the Reynolds (Re) and Schmidt Numbers (Sc).

fg = 0.78+0.308 Sc1/3

Re1/2

(4.8)

The dimensionless numbers are defined as:

43

Sc = Dg/ν (4.9)

Re = DUt/ ν (4.10)

We have used the following relation for calculating the terminal velocity [3]

Ut = Q(D/2) ; the constant Q = 8630 s-1

(4.11)

It is found that the concentration follows a tan-hyperbolic curve, finally attaining saturation after

a certain time of fall. The values for the parameters used in the above equations are given in table

4.1 [1].

Dg , Gas Phase diffusivity 14.1 mm2 s

-1

M.M, Molecular Mass SO2 64 gmol-1

, Kinematic viscosity of air 14.1 mm2 s

-1

KHRT, Henry‟s law constant product 30 (dimensionless)

K1 , Dissociation Constant 1.23×10-2 mol L

-1

Table 4.3: Parameters for use in Equation (4.2)

Equation (4.2) was used to plot the transient concentration of a drop as it falls through the

atmosphere. Here the time of fall was expressed in terms of the distance of fall after evaluating

the terminal velocity for each drop. The ambient air concentration was taken to be 150μg/m3.

This is the ambient concentration one can expect around NLC due to the emissions of Thermal

Power Station 1 (TPS1) which is based on observations as well from model results. The profiles

of concentration with time are plotted in figure 4.1.

44

Figure 4.1: Concentration of bisulphite in a falling drop

It is observed that the distance for which the drop falls before becoming saturated increases with

the drop diameter. While the small drops (D~0.5 mm) saturate in less than 50 meters, the

moderate sized drops (D~ 2mm) fall for 200m before attaining saturation. It is observed and

discussed in a following section that the average diameter of rain drops increases as the

precipitation rates increase. For the high intensity rainfall of the NE Monsoons, there is a

propensity of moderate to large sized rain drops and thus we can safely assume that the drops do

not get saturated as they fall through a plume of emissions from the NLC. Hence the rain will

wash out SO2 from the upper reaches of the emitted plume to the ground level. NLC is one of the

largest power plants in Asia-we expect that this expectation will obtain for most power plant

emissions over the Indian Subcontinent as well as over other countries which receive NE

monsoon rains.

4.2 pH of a drop when it reaches ground level

Apart from washout of emissions we are also concerned with the acidity of the rain water. Thus

it is useful at this stage to compute the pH of a rain drop which reaches the ground. We assume

that the pH of a rain drop as it leaves the cloud base is 5.6 due to absorption of CO2 in the upper

atmosphere. Applying the principle of electro-neutrality, we can find the new pH of a drop due to

the absorption of SO2 and its subsequent dissociation to HSO3−.

The concentration of H+ ions in a drop leaving the cloud base is given by

[H+]ini = 10^(− pHini) ; { pHini = 5.6} (4.12)

[H+]abs=[HSO3

−]abs (4.13)

pHground= − log10 ( [H+]ini + [H

+]abs) (4.14)

The pH of drops of different sizes when they reach the ground is depicted in figure 4.2.

45

Figure 4.2: pH of drops of various sizes at the ground level

The lower flat part of the curve represents drops which get saturated prior to their reaching the

ground- they attain a minimum pH of 4.5 for drop diameters up to 1 mm. The larger sized drops

(those exceeding diameters over 1mm) are unsaturated. The ground level pH of these drops

varies with their diameter. When the concentration of SO2 in the air remains constant during a

rain event, a preponderance of large drops will ensure a higher rainwater pH. Thus we can

conclude that the heavier rains of the NE Monsoons will have less acidic content that the milder

showers, at mid latitude locations, since the number of large drops increases with rain rate.

4.3 Quantification of plume washout –Scavenging Coefficient

The flux of a gas species from the air into rain drops per unit height of the atmosphere (W), due

to washout by rain, can be approximated by a linear relationship [2].

W= β Cg (4.15)

Where: β is the scavenging coefficient with units of time-1

.

The flux (F) of a gas species to the ground from a column of the atmosphere of unit cross-

sectional area (where the concentration of the gas is horizontally constant) is given by:

F = ∫ β Cg dh (4.16)

Thus if we represent the integrated concentration of the species from the ground to the upper

reaches of the emissions as CT, then for a value of β which is constant at all spatial positions, we

get the flux at the ground at any point (x,y) as:

F(x,y) = β CT(x,y) (4.17)

46

Thus, if we have the spatially distributed concentrations around an emission source we can

integrate the concentrations with height at every point to evaluate CT. With a suitable value of β

one can easily compute the flux of species to the ground due to washout by rain. The above

formulation is valid only if the gas is irreversibly soluble. This is not always the case for SO2.

However, our results in section 2 show that drops with diameter above 1mm do not become

saturated as they fall through an industrial plume. Thus for high rain rates of the NE Monsoons,

where the majority of the drops are of a moderate to large size, it is safe to proceed with the

assumption of irreversible absorption of SO2 without significant error for our subsequent

computations of the scavenging coefficient.

The scavenging coefficient, at a particular rain rate, for a size distributed spectra of rain drops is

given by [2].

β = ∫π Kc D2 N(D) d(D) (4.18)

Where: N(D) is the drop size distribution function which represents the number concentration of

drops of a given size (units: m-3

mm-1

)

Kc is the empirically determined mass transfer coefficient of a gaseous species to falling drops

and can be evaluated by the correlation proposed by [4].

Kc = (Dg/D) [2+0.6(Re)1/2

(Sc)1/3

] (4.19)

The drop size distribution varies with rain rate and is determined by fitting experimental drop

size distribution (DSD) data to established mathematical distributions. Many different

distributions have been used to represent the DSD of rain. These include the Marshal Palmer,

Lognormal and the Modified Gamma Distributions. In the work of Konwar and coworkers [5] it

is shown that the Lognormal and Modified Gamma distributions are similar and that they both

provide a better fit of the DSD than the Marshal Palmer distribution. They also show that the

Modified Gamma distribution gives the best fit for the DSD data for rain samples over Gadanki

located in Tamil Nadu, India. S.S. Roy and coworkers [6] have studied the DSD for rain over the

Cuddalore district of Tamil Nadu and have fitted the data to a gamma distribution. Fortunately,

NLC the target area for the application of our work is located in the district of Cuddalore.

Assured by the work of Konwar et. al [5] we decided to adopt the gamma distribution of S.S Roy

et al [6] for our study. Curves of the gamma distribution for various rain rates are shown in

figure 3. It can be seen that the peak diameter increases as the rain rate increases. The modified

gamma distribution function is:

N(D)=N0Dμexp(-λD) (4.20)

47

Figure 4.3: Rain drop size spectrum for various rain rates over Cuddalore [6]

Using the modified gamma distribution function (4.20) in equation (4.18) we numerically

integrated the resulting expression to yield the value of the scavenging coefficient. This was

repeated for all the rain rates studied by S.S. Roy et al [6] to generate the curve shown in figure

4. Since we require rain rates greater than 100mmhr-1

in our study it was necessary to extrapolate

the data by fitting a regression line to the same.

β = (2.1961×10-5

) p + 1.9244×10-4

(4.21)

Figure 4.4: Scavenging coefficient for various rain rates

In order to determine the spatially distributed concentration of a pollutant around an elevated

emission source such as an industrial stack, it is necessary to employ an air dispersion model. In

our study of NLC we developed a Gaussian model which accounts for plume rise, the convective

nature of the boundary layer using solar insolation data, the Pasquill Stability classes, wind speed

48

and direction to yield Cg* (x,y,z) , the gas-phase SO2 plume concentration . A detailed exposition

of Gaussian dispersion models can be found in [2].

During a rain event, the effect of plume washout can be included into a Gaussian dispersion

model by reducing the source strength by an exponential factor which involves the product of the

scavenging coefficient and the distance from the stack along the prevailing wind direction.

Hence the washed out concentration is given by [2].

Cg*

.washout (x,y,z) = exp(-βx/u)× Cg* (x,y,z) (4.22)

Thus as a parcel of air travels through the rain and away from the stack, the concentration of the

soluble species will be exponentially depleted by the rain. Equation (4.22) can be used to

simulate plume washout only if the rain drops do not become saturated with SO2 as they falls

through the plume. The results of Section 4.1 allow us to make this assumption.

The rain which washes out SO2 from the atmosphere will reach the ground with a reduced pH.

We consider the increase in H+ ions due to the dissociation of dissolved SO2 into HSO3

-. First we

compute the amount of SO2 brought to the ground per unit area by the rain, for a unit time using

equation (4.17). The volume of rain water over that surface for a unit time is simply the rain rate.

Thus the concentration of HSO3- ions can be estimated at any location which in turn allows a

computation of the pH at that location.

4.4 Investigation of Wet Scavenging and Rainfall Acidity at NLC

Meteorological data was provided for the month of October by NLC as well as data on their

Thermal Power Station and its emissions. This data along with plume rise calculations are shown

in Table 2. This work is concerned with the emissions from the first thermal power station

(TPS1). We apply the model developed in Section 3 for a typical October day which received

heavy showers to the tune of 122.4mmhr-1

. A horizontal wind of magnitude 1m/s blew in from

the North East at the time of the shower. The concentration contours of SO2 surrounding the

Thermal Power Station in the absence of rain is first shown for the sake of comparison. Then wet

scavenging is accounted for and the new contours are shown (Figure 4.5). It is clear that the large

drops of sharp NE monsoon showers rapidly cleans the ambient air-for an hour of rain fall with

an intensity of 122 mm/h, the plume concentration depletes considerably. Finally contours of the

rain water pH are displayed in Figure 4.6-pH values are acceptable over almost all the 13

receptors. This was possible only because of the preponderance of very large drops during

monsoon showers. The text markers represent the receptor locations where data collection

facilities are located.

49

Stack SO2

emitted

(g/s)

Height of

Stack (m)

Top Inner

Dia. (m)

Flue Gas

Velocity

(m/s)

Horizontal Wind

Speed (m/s)

Speed at Stack

height(m/s)

Plume

Rise (m)

Effective Stack

Height (m)

1 227.8 60 5.1 20.2 1 1.9 165.1 225.1

2 271.4 60 5.1 20.2 1 1.9 165.1 225.1

3 153.2 60 5.1 13.5 1 1.9 110.3 170.3

4 306 120 5.1 27.2 1 2.4 174.4 294.4

Table 4.4: Details of the stacks emitting SO2 and the plume rise

Figure 4.5: Contours of SO2 with and without the shower

Figure 4.6: Contours of pH of rain water around NLC

50

4.5 Conclusions

Rapidly developing economies like China and India are also among the most populous countries

in the world. This entails spiralling energy requirements for billions of people becoming

increasingly affluent. Renewable sources of energy and nuclear power will require another three

decades to match power production through conventional methods. Coal based power plants still

powers most of India‟s cities- inevitably spewing out enormous amounts of acidic pollutants. If

one were to quantify ambient pollution levels over an industrialized region in India, one would

expect alarming levels of noxious pollutants. However, it must be borne in mind that the

subcontinent experiences an enormous amount of received precipitation through the North East

as well as the South West Monsoons.

Environmental Impact Assessment studies in developing countries address the issue of power

plant induced pollution. However, they borrow and apply models developed for more temperate

climes relevant to the mid latitudes- tropical climes receive precipitation intensities often 10

times higher than their mid latitude counterparts. In addition, the receptors of such pollutants are

located over urban conurbations where the populations are several orders of magnitudes higher

than over the mid latitudes.

In this carefully selected case study we have shown results for the first time how the intense NE

monsoonal rains cleanse the atmosphere of a large township centered on Asia‟s largest lignite

based power plant. We have placed a particular emphasis on the characterization of the drop size

spectrum emanating from showers over the South Eastern Coast of India covering Asia‟s

megacities of Calcutta and Chennai. To our pleasant surprise we find that although the source

strengths from the NLC stacks are of the order of 200 g/s, the ambient air quality standards are

seldom exceeded over 13 receptor locations of the township, particularly during October to

December every year. Clearly, ambient air pollution is partitioned into the dispersed phase which

is received at the ground. This brings out the issue of the quality of the rain water received. Our

study reveals that owing to the peculiar nature of the NE Monsoon precipitations there is a

preponderance of large drops exceeding 1mm in diameter. These drops will fall through

industrial plumes without becoming saturated. It was further observed that the larger the drop

size, the higher will be its pH when it reaches the ground. These large drops would be non-

existent or would be fewer in numbers for the milder mid latitude precipitation intensities. For

the first time we have established an empirical relationship between the scavenging coefficients

for the rain rates expected during NE Monsoonal showers. The dilution effects in these large

droplets raise the pH to almost acceptable levels.

We expect that the results from this first study can be easily adapted to other monsoon dominated

regions in many other developing nations. The mathematical analysis presented can be used by

EIA specialists, policy planners at the decision making level, as well as by other power plants in

the making.

51

CHAPTER 5: OVERALL CONCLUSIONS AND FUTURE WORK

The air dispersion model developed in this work is able to predict the patterns of concentration

distribution about the NLC stacks. This model will be useful to NLC in their routine meetings

with the Pollution Control Board as well as when they plan for any expansion. However before it

can be used by NLC some more work will have to be done on the code. At the top of the agenda

is the incorporation of a better treatment of calm winds. The Gaussian equation over predicts

concentrations when the winds are low (below 0.5 m/s). Moreover when the wind speed at the

stack height is less than 1m/s, the plume may not bend over at all. This will lead to lower

concentrations than currently predicted. A simple effective method of treating such a situation is

by using a puff type model in which dispersion alone causes the spread of the emissions in all

directions, radially outwards. This will be the approach used by us in our future work.

In addition the codes for the dispersion model will have to be suitable packaged in a user friendly

system- possibly a GUI framework, before it can be used easily by NLC or any other interested

party. A comparison of our model results with standard software would be enlightening. It would

demonstrate the benefit of a tailor made model as well as reveal any shortcomings. AERMOD is

one such software widely used around the world and a comparison of our model with the same is

definitely on the cards.

With regard to our investigation of the role of the 17 million odd trees at NLC the work is far

from over. Next we plan to get data on tree cover from NLC and carry out more detailed

simulations under various conditions of no-replenishment. The deposition velocity

parameterization can be upgraded to that of Zhang et. al. [20] and a NLC specific LAI

incorporated.

The wet deposition studies were carried out for the case of SO2 alone. This study will have to be

extended to NOx as well so as to give a complete picture of the rain water acidity. Apart from

washout, the extension of the entire model, including both dispersion and dry and wet deposition

to NOx is a part of the NLC agenda and the work will be commenced shortly.

Large amounts of Suspended Particulate matter is emitted from NLC each year. The

development of parallel models for SPM is a major task since the mechanisms involved in

dispersion and deposition of particles are different from those involved in the case of gases.

However, the exercise will be simpler, now that we have developed a model for gases. This work

is also one of the project objectives.

Air pollution is an evil which we are not going to be rid off any time soon. Especially in India we

will remain heavily reliant on coal for years to come. In such a situation a detailed study such as

the one described in this thesis is important to capture a glimpse of the overall picture. The

emissions from the stacks not only reach the lungs of living beings but have several other

destinations mediated by complex microphysical pathways. The understanding of these

processes is far from complete and more work must be done especially in developing regions of

the world. NLC, Asia‟s Largest Lignite based Power Plant, is an ideal study area.

52

This work indicates that in spite of large amounts of emissions the environment may have some

respite during the monsoons (October-December) because of the heavy rains which wash away

the pollutants, at the same time ensuring that the rainwater is sufficiently dilute. Dry deposition

round the year also helps to keep pollution levels in check by reducing residual pollution levels.

Of course, deposition only removes pollutants from the air to transport them somewhere else.

Whether their new destinations results in them being less harmful due to some chemical

transformation, as within leaves of plants or by any other mechanism, deserves close attention.

This calls for a detailed analysis of micro physical processes such as the transport of SO2 through

the stomata and its transformation to sulphate, via numerical 3D modelling. Such a study is also

on the author‟s agenda.

53

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