thesis.tex

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The University of York, Chemistry Department

Submitted in accordance with the requirements\\
    For the degree of Doctor of Philosophy\\ in Atmospheric Chemistry

\vspace{1.3cm}


\textbf{I declare that this thesis is a presentation of original work and I am the sole author. This work has not previously been presented for an award at this, or any other, University. All sources are acknowledged as References.}

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\title{Understanding Atmospheric Chemistry Using Graph-Theory, Visualisation and Machine Learning}
\author{Daniel Ellis}

\date{March 2020}


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\begin{center}
\begin{quotation}
    \centering
  \large{\emph{\textbf{Veritatem inquirenti, semel in vita de omnibus,\\ quantum fieri potest, esse dubitandu:}\\
 In order to seek truth, it is necessary once in the course of our life, to doubt, as far as possible, of all things. }  }
\vspace{\baselineskip}\linebreak
  \begin{flushright}\small{
  - Descartes, Rene, \textit{Principles of Philosophy}
  }
\end{flushright}
 \end{quotation}
\end{center}


\newpage


\chapter*{Abstract}
\parbox{.75\textwidth}{
Atmospheric chemistry mechanisms play a pivotal role in our understanding of societal problems such as air pollution, climate change and stratospheric ozone loss. This thesis explores the benefits of representing these mechanisms in terms of a mathematical graph (or network) which connects species (nodes) through reactions (edges). We use the Dynamically Simple Model of Atmospheric Chemical Complexity and the Master Chemical Mechanism to explore the a number of real world senarios - using graph theory and machine learning to visualise, understand and analyse the underlying chemistry of the lower atmosphere.\\

We begin by exploring different visualisation techniques to depict chemistry within the atmosphere. It is found that the sociograph framework provides the most (visually) intuitive delineation of the species and their reactions. For large, complex systems, this type of qseudo-qualitative analysis has its limitations - physical and cognitive. Instead, the relationships between species in the network are quantified using graph centrality metrics and then compared against well-established methods such as the Jacobian and Rate Of Production Analysis. Further development of graph theory allows us to couple natural language processing, network decomposition, and clustering to identify species with similar lifetimes, reaction styles, or temporal profiles. \\

Having explored aspects of mechanism analysis, visualisation and reduction, we examine how varying representations of species structure can affect the patterns highlighted by unsupervised machine learning models. This is done by visualising them in 2D space and serves as a precursor to potential future work involving Graph Convoluted Neural Networks - thus consolidating the contents of this thesis.\\

Ultimately it is found that using a graph-theory approach can prove highly beneficial in the understanding and explanation of chemical mechanisms, but should not (as of yet) be used in substitution of existing investigation and reduction methods.\\

}





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    \chapter*{Acknowlegements}


\parbox{.75\textwidth}{
 First and foremost, I would like to thank my family, for which I wish I had spent more time with rather than being squirrelled away in an office. I could have gotten here only with your support for which I am grateful.\\

 Next, are my supervisors Andrew and Mat - mainly putting up with me but also being highly supportive throughout the PhD.\\

 A special mention also needs to go out to all my friends and colleagues at (\href{www.westminsterboatingbase.co.uk} {Westminster Boating Base}) who helped keep me sane irrespective to the severe watersport withdrawal, kept me employed and invited me to the annual France whitewater trip each year.\\

 On a similar note, thanks to all my university friends and collegues  (Rosie, Tomas, Ben, Mike, Killian, Pete, Peter etc.) with whom I could bounce ideas and discuss random work adjacent projects when needed.\\

 Additionally the Earth0 HPC cluster, for not failing on me, even after it was no longer officially in use (or supported).\\

 Finally, I am not sure whether to thank or apologise to those unfortunate enough to have to read any part of this work, but I am grateful anyway.\\

}
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\tableofcontents
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\include{./glossary}
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\listoftables
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\mainmatter



% Introduction 0
\include{./0_intro}
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% Ch1 1
 \include{./1_visual}
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% Chapter 2
\include{./2_graphs}
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% Chapter 3
\include{./3_centrality}
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 \include{./4_lumping}
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% Chapter 5
 \include{./5_DR}
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% Conclusion
 \include{./6_conclusion}


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