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INVERSE MODELLING FOR IDENTIFICATION OF POINT- SOURCE EMISSIONS IN ATMOSPHERE
SARVESH KUMAR SINGH
CENTRE FOR ATMOSPHERIC SCIENCES
INDIAN INSTITUTE OF TECHNOLOGY DELHI
NOVEMBER 2011
© Indian Institute of Technology Delhi (IITD), New Delhi, 2011
Inverse Modelling for Identification of Point-Source
Emissions in Atmosphere
by
Sarvesh Kumar Singh Centre for Atmospheric Sciences
Submitted
in fulfillment of the requirements of the degree of
Doctor of Philosophy
to the
Indian Institute of Technology Delhi November 2011
medicated to I7VIy Parents
Certficate
This is to certify that the thesis entitled "Inverse Modelling for Identification of Point Source
Emissions in Atmosphere" being submitted by Mr. Sarvesh Kumar Singh to the Indian
Institute of Technology Delhi for the award of the degree of DOCTOR OF PHILOSOPHY is a
record of the original bonafide research carried out by him. He has worked under my guidance
and supervision and has fulfilled the requirements for the submission of thesis. The results
presented in this thesis have not been submitted in part or full to any other University or Institute
for award of any degree or diploma.
New Delhi Professor Maithili Sharan
November 2011 Centre for Atmospheric Sciences
Indian Institute of Technology Delhi
Hauz Khas, New Delhi-110 016, India
Acknowfedgements
My PhD years at the Indian Institute of Technology (IIT) Delhi and this thesis had mentorship
from numerous outstanding individuals both from within the institute and outside of it. It is to
these individuals that took part in my study, that my heart felt gratitude and thanks; without their
help, this thesis would not have been a reality.
I would like to express my deepest gratitude to my thesis supervisor, Prof. Maithili
Sharan, Centre for Atmospheric Sciences (CAS), IIT Delhi, for his invaluable guidance,
suggestions and endless encouragement. Prof. Sharan always gave me the freedom to pursue my
own interests and provided me with insightful suggestions and support in developing
independent thinking and research skills. He has been an exceptional mentor and I appreciate
both our professional and personal conversations over the years. The knowledge and wisdom I
have gained from him will forever guide me in education and in life.
It is difficult to overstate my gratitude to Dr. Jean-Pierre Issartel, Centre d'Etudes du
Bouchet, France, for his enthusiasm, inspiration, crucial contribution and his great efforts to
explain things clearly and simply. Throughout my PhD, he provided sound advice, good
teaching, good company and lots of good ideas. I would have been lost without him.
I would like to thank Prof. S. K. Dash, Head, CAS, IIT Delhi, for providing all the
essential facilities in the Centre to carry out the work. I also wish to extend my deep appreciation
to my SRC members; Prof. (Ms.) P. Goyal, Chairman, CRC, Prof. R. P. Sharma, Center for
Energy Studies and Prof. 0. P. Sharma for their constant encouragement and generously sharing
their knowledge and time. I am thankful to Prof. S. K. Dube, Prof. G. Jayaraman, Prof. U. C.
Mohanty, Prof. A. D. Rao, Prof. H. C. Upadhyay, Prof. M. Mohan, Prof. K. AchutaRao, Dr. S.
Dey, Dr. R. C. Raghava and Dr. P. Agarwal for their fruitful suggestions, whole hearted support
and encouragement.
I humbly acknowledge the assistance of whole CAS staff especially L. S. Negi, V. K.
Kaushik, Krishan Kumar, Dataram, Kedari, Mrs. Kusum and Mrs. Saroj Gupta, for their help and
support. I want to acknowledge IITD and MHRD for providing me financial support in the form
of scholarship during my research.
In my daily work, I have been blessed with a friendly and cheerful group of fellow
students. I convey special thanks to my friends Dr. Pramod Kumar, Dr. Mukesh, Sunil and
Anikender with whom I shared my joy and sorrows. Their warm company, unwavering support
in my ups and downs, and helpful suggestions offered at various stages of my Ph.D. work, made
my stay at IIT pleasant and memorable. Now, it is pleasure to mention my colleagues with hearty
thanks: Drs. Jagabandhu, Subrat, Sankalp, Senthil and Swagata, Palash, Liby, Kanhu, Deepak,
Srinivas and Suraj for their support, nice company and sharing various thoughts during the
"Friday break" in the evening. I wish to express thanks to my recent friends Amit, Abhishek,
Dhirender, Himanshu, Ragi, Rati, Amit and Piyush, for their inseparable support and warm
company.
Words fail me to express my appreciation to my wife, Rani whose dedication, love and
persistent confidence in me, has taken the load off my shoulder. I owe her for being unselfishly
let her intelligence, passions, and ambitions collide with mine.
I am indebted to my IIT Roorkee friends especially Amioy, Amit, Nilesh, Navin,
Sandhya, Shailender and Vikas, for providing a stimulating and fun environment in which to
learn and grow. Lastly, I offer my regards and blessings to all of those who supported me in any
respect during the completion of the thesis.
Despite the geographical distance, my family was always nearby. My father made sure I
felt his confidence and encouragement. His advice was consistently timely and useful. Words
cannot completely express my love and gratitude to my family who have supported and
encouraged me through this journey. I would like to thank my parents, sister, Shalini and brother,
Mukesh for their life-long support, everlasting love, and sacrifices, which sustained my interest
in research and motivated me towards the successful completion of this study.
Finally, I thank the almighty God for the passion, strength, perseverance and the
resources to complete this study.
New Delhi Sarvesh Kumar Singh
Abstract Identification of unknown releases in atmosphere is an exigent, practically important and
challenging problem due to national security, industrial hazards, CBR (Chemical, Biological and
Radiological) releases and emergency strategic planning concerns. Since such releases are
unexpected, highly poisonous and impossible to observe or measure directly on site; a direct
identification is not feasible. Therefore, it is required to develop the inverse modelling
techniques for an accurate and fast preliminary identification of the releases from the limited set
of atmospheric concentration measurements.
The primary objective of the thesis is to develop and evaluate the inverse modelling
techniques for retrieval of point source emissions from a set of limited atmospheric concentration
measurements at local scale. The thesis is divided into seven chapters. The first chapter is an
introductory chapter which reviews the earlier works on the inverse modelling techniques along
with their feasibility, applicability, stability and limitations in identifying the releases.
An inversion technique is developed in the second chapter within least square framework
to identify a ground level point emission. An adjoint modelling approach is described to establish
the source-receptor correspondence. This concept is followed in the subsequent chapters for
describing the backward transport of the pollutant. An alternative optimization technique, free
from initialization is proposed for the estimation of release parameters. The technique is
evaluated with the concentration measurements obtained from Indian Institute of Technology
(IIT) Delhi, diffusion experiment in low wind convective conditions.
In chapter 3, the identification of a ground level point emission is addressed within the
assimilative framework of renormalization theory. This theory has been extended for the
identification of a point source based on the property that these are associated with the maximum
of the renormalized estimate computed from the observations. The theory introduces a weight
function and a weight matrix in order to include the natural informations retrieved from the
geometry of the monitoring network and overcomes the limitations encountered in chapter 2. The
theoretical comparisons are highlighted between theories of renormalization and least square.
In general, near surface releases are treated as ground level emissions. Sometimes the
concentrations distribution released from such near surface releases may be sensitive even to a
small height of source and receptor above the ground. Therefore, a renormalization inversion
technique is proposed in chapter 4 for identification of an elevated point release and its
application is investigated in low wind stable conditions. An improved formulation is proposed
for the estimation of source intensity and an estimate is derived for the retrieval errors. The
source reconstruction is carried out using the observations from Idaho diffusion experiment in
low wind stable conditions. The sensitivity studies are carried out to (i) analyze the sensitivity of
the source estimation with respect to signal perturbation caused by the background concentration
of the species in the ambient air and (ii) optimize the number of receptors in a fixed monitoring
network.
The source reconstruction is highly sensitive to the noise in the observations and
representativity errors associated with the dispersion model. In view of this, the minimization of
the model representativity error is addressed in chapter 5 utilizing the modeled concentrations
predicted from an analytical dispersion model and the corresponding measured concentrations
released from an elevated point source in Idaho diffusion experiment in low wind stable
conditions. In general, a linear regression methodology is described for minimizing the model
representativity errors. In view of experimental considerations, continuous functions of
regression coefficients in terms of radial distance from the source are evolved for modifying the
model predicted adjoint functions. A comparison is discussed between source estimates retrieved
in this chapter and in chapter 4 without accounting for model errors.
The identification of single point release is extended to multiple-point releases in chapter
6. The inverse modelling methodology is developed for identifying the multiple-point emissions
releasing similar tracer, in which influence from the various emissions are merged in each
detector's measurement. The identification is addressed from a limited merged set of
atmospheric concentration measurements. The source estimation method is based on two-step
minimization of objective function within the least square framework. A retrieval algorithm is
proposed, free from initialization, for identifying release parameters simultaneously. The
algorithm is further modified and improved by introducing the natural informations retrieved
from the geometry of the monitoring network in terms of weight functions. The methodology has
been successfully applied to identify the two and three simultaneous point emissions from
synthetic measurements generated from the model without noise or with some controlled noise
artificially added and from pseudo real measurements generated from IIT Delhi diffusion
experiment in low wind convective conditions by combining several of single point release runs.
A unique feature of this study is that all the proposed inverse modelling techniques are
evaluated with the real observations. The thesis explores new concepts associated with the
geometry of the monitoring network and emphasizes on a further understanding about the effect
of observation and model representativity errors. The point source reconstruction is exhibited in
the form of source estimate isopleths and further analyzed quantitatively by comparing them
with their corresponding original prescribed source parameters.
Contents
Certificate Acknowledgments Abstract Contents List of Figures List of Tables
1 General Introduction ...........................................................................................................1 1.1 Introduction ................................................................................................................... 2 1.2 Inverse Modelling .......................................................................................................... 5
1.2.1 Source-Receptor Correspondence .....................................................................5 1.2.2 Mathematical Formulation ................................................................................6 1.2.3 Parametric Estimation .......................................................................................7
1.3 Inverse Modelling Techniques ......................................................................................7 1.3.1 Least Square Technique ....................................................................................8 1.3.2 Regularization ...................................................................................................9 1.3.3 Genetic Algorithm ...........................................................................................11 1.3.4 Bayesian Method .............................................................................................13 1 .3.5 Kalman Filter ...................................................................................................16 1.3.6 Concept of Adjoint Modelling .........................................................................19 1.3.7 Back Trajectory Models ...................................................................................21 1.3.8 Renormalization Technique .............................................................................25 1.3.9 Markov Chain Monte Carlo Method ................................................................28 1.3.10 Maximum Entropy on Mean ............................................................................29 1.3.11 Variational Assimilation ..................................................................................32
1.4 Identification of Multiple Point Releases ....................................................................36 1.5 Issues and Limitations .................................................................................................38
1.5.1 Noise in Measurements ...................................................................................39 1.5.2 Selecting a Goodness-of-Fit Measure .............................................................39
1.5.3 Number of Source Parameters ........................................... .............................40
1.5.4 Prior Initialization .............................................................. .............................40
1.5.5 Non-Uniqueness of Solution ...........................................................................40 1.5.6 High Resolution ................................................................. .............................40
1.5.7 Sequential Estimation ........................................................ .............................41
1.5.8 Atmospheric Dispersion Model ......................................... .............................41
1.5.9 Model Representativity Error ............................................. .............................42
1.6 Organization of the Thesis ..........................................................................................42
2 Identification of Single-Point Emission using Classical Least Square Technique .......46 2.1 Introduction .................................................................................................................47 2.2 Methodology ...............................................................................................................49
2.2.1 Source-Receptor Correspondence ...................................................................49
2.2.2 Least Square Formulation ...............................................................................51 2.2.3 Atmospheric Dispersion Model ......................................................................54
2.3 Diffusion Experiment ..................................................................................................56 2.4 Numerical Computations ............................................................................................58
2.5 Results and Discussion ...............................................................................................59
2.5.1 Synthetic Measurements .................................................................................60
2.5.2 Real Measurements .........................................................................................61
2.5.3 Errors in the Retrieval .....................................................................................63
2.6 Advantages and Limitations ........................................................................................64
2.6.1 Advantages ......................................................................................................64
2.6.2 Assumptions/Limitations ................................................................................65
2.6.3 Vertical and Temporal Dimensions ................................................................66
2.6.4 Experimental Data ..........................................................................................66
2.7 Conclusions .................................................................................................................67
3 Identification of Single-Point Emission using Renormalization Technique .................69
3.1 Introduction ................................................................................................................. 70
3.2 Inverse Modelling ........................................................................................................71
3.2.1 Normed Space of Continuous Ground Sources ...............................................72 3.2.2 Computation of the Adjoint Functions ............................................................73
3.2.3 Geometric Weights: The Renormalization ......................................................74
3.2.4 Retrieval of a Point Source ..............................................................................76
3.2.5 Accuracy of Point Source Retrieval ................................................................77
3.3 Diffusion Experiment ..................................................................................................78
3.4 Description of the Computations .................................................................................79
3.5 Results and Discussion ................................................................................................82
3.5.1 Renormalizing Weights and Renormalized Representation ............................84
3.5.2 Synthetic Data ..................................................................................................86
3.5.2.1 Identification of a Point Source ........................................................86
3.5.2.2 Extension of the Estimate Upwind ...................................................86
3.5.3 Real Data .........................................................................................................87
3.5.2.1 Identification of a Point Source ........................................................88
3.5.2.2 Separation of Noise and Signal ........................................................89
3.5.4 Comparison with Least Square Estimation .....................................................91
3.6 Issues and Limitations .................................................................................................93
3.6.1 Renormalized Framework ...............................................................................93
3.6.2 Intensity of the Point Source ...........................................................................95
3.6.3 Feasibility of Source Identification .................................................................96
3.7 Conclusions .................................................................................................................97
4 Identification of an Elevated Point Release: Application to Low Wind Stable Conditions .........................................................................................................................99 4.1 Introduction ...............................................................................................................100 4.2 Inversion Technique ..................................................................................................103
4.2.1 Adjoint Functions ..........................................................................................104 4.2.2 Renormalization ............................................................................................106 4.2.3 Identification of a Point Release from Measurements ..................................108 4.2.4 Estimation of Intensity ..................................................................................110
4.2.5 Estimation of Errors ......................................................................................111
4.3 Diffusion Experiment .................................................................................................112
4.4 Numerical Computations ...........................................................................................115
4.5 Results and Discussion ..............................................................................................118
4.5.1 Synthetic Data ................................................................................................119
4.5.2 Real Data ........................................................................................................121
4.5.3 Extension of the Estimate Upwind .................................................................127
4.5.4 Errors in the Retrieval ....................................................................................128
4.5.5 Sensitivity Analysis ........................................................................................128
4.5.5.1 Background Concentration ..............................................................129
4.5.5.2 Optimizing the Network Design ......................................................131
4.5.6 Issues of Surface/Elevated Releases ...............................................................134
4.6 Limitations ..................................................................................................................135
4.6.1 Data .................................................................................................................135
4.6.2 Representativity Error .....................................................................................135
4.7 Conclusions .................................................................................................................136
5 Minimization of Model Representativity Errors in a Point Source Reconstruction ....138 5.1 Introduction .................................................................................................................13 9
5.2 Methodology ...............................................................................................................141 5.2.1 Representativity Errors Minimization .............................................................141 5.2.2 Estimation of Regression Coefficients ............................................................143
5.2.3 Inverse Modelling ............................................................................................144 5.2.4 Modification of Adjoint Functions ..................................................................144
5.2.5 Experimental Consideration ............................................................................145
5.3 Numerical Computations .............................................................................................146
5.4 Results and Discussion ................................................................................................147
5.5.1 Real Data .........................................................................................................148
5.5.2 Quantitative Comparison of Results with Chapter 4 .......................................151
5.5 Assumptions and Limitations ......................................................................................153
5.6 Conclusions .................................................................................................................154
6 Inverse Modelling for Identification of Multiple-Point Emission Sources ...................156
6.1 Introduction .................................................................................................................157
6.2 Inverse Modelling .......................................................................................................161
6.2.1 Correspondence Between Source and Measurements ....................................161 6.2.2 Identification of Two-Point Sources ...............................................................162
6.2.3 Identification of m-Point Sources ...................................................................165
6.2.4 Weighted Source-Receptor Correspondence ..................................................167
6.2.5 Weighted Approach for Identifying m-point Emission ..................................169
6.3 Preparation of Pseudo Real Measurements ................................................................171
6.4 Numerical Computations ............................................................................................174
6.5 Results and Discussion ...............................................................................................176
6.5.1 Synthetic Measurements .................................................................................176
6.5.2 Pseudo-Real Measurements ............................................................................177
6.5.1.1 Non-Weighted Formulation .............................................................177
6.5.1.2 Weighted Formulation .....................................................................179
6.5.3 Distribution of Weight Function .....................................................................183
6.5.4 Reduction in Computational Time ..................................................................184
6.5.5 Comparative Features of Weighted and Non-Weighted Formulations ...........185
6.5.6 Noisy Synthetic Measurements .......................................................................186
6.5.7 Estimation of Noise Proportion in Pseudo Real Measurements ......................191
6.5.8 Identification of Four Point Sources ................................................................192
6.6 Advantages and Limitations ........................................................................................193
6.6.1 Advantages ......................................................................................................193
6.6.2 Assumptions/Limitations .................................................................................194
6.6.3 Data Limitations ..............................................................................................194
6.7 Conclusions .................................................................................................................195
7 Conclusions and Future Perspectives ...............................................................................197 7.1 Conclusions ...................................................... ...........................................................198
7.2 Limitations and Future Perspectives ................ ...........................................................202
7.2.1 Non-Reactive Tracer Gas ..................... ...........................................................202
7.2.2 Linear Adjoint Modelling ................................................................................202
7.2.3 Known Number of Releases ............................................................................203 7.2.4 Local Scale Point Emission .................. ...........................................................203
7.2.5 Height of Source and Receptors ......................................................................203
7.2.6 Data Availability .............................................................................................203 References .................................................................................................................................205 Bio-Data ....................................................................................................................................235