drwater/dwcl/CL1.tex

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% Author: Pedro Henrique Pereira Braga %
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Email: mingsu@rcees.ac.cn}
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Professor\\
Research Center for Eco-Environmental
Sciences\\ % Sender's department/institution
Chinese Academy of Sciences\\
P.O. Box 2871, Beijing % Sender's address
% , \\ % Sender's city, state or province,
% \\ % postal code
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Editor-in-Chief\\
\textit{JOURNAL}
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\begin{document}
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% TO ADDRESS
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\begin{letter}{\recipientdetails}
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\opening{Dear Editor,}
% \begin{doublespacing}
We are pleased to submit our manuscript, \textbf{``MANUSCRIPT TITLE''},
for consideration at \textbf{\emph{JOURNAL}}.
Among all algal-derived taste-and-odor compounds, 2-methylisoborneol
(MIB) stands out as the most notorious and widespread offender. Since
its initial identification, nearly 300 documented MIB-related odor
outbreaks have been reported globally. With an exceptionally low odor
threshold (10 ng L\textsuperscript{-1}), MIB imparts a distinct
earthy-musty smell that is immediately detectable in drinking
water---transforming it from a mere water quality parameter into a
critical public concern capable of triggering widespread complaints
during outbreaks.
The partitioning of MIB between intracellular and extracellular
compartments critically determines treatment strategy selection. When
MIB is primarily intracellular, conventional treatment processes such as
coagulation-sedimentation can achieve cost-effective removal. However,
when MIB becomes predominantly extracellular, utilities must implement
advanced treatment options like ozone-activated carbon - a significantly
more expensive approach that remains unavailable at many facilities.
This fundamental distinction creates a critical decision point for water
treatment optimization, where accurate prediction of MIB partitioning
directly impacts both operational efficiency and treatment costs.
Yet, current research and monitoring practices overlook a key
limitation: most studies and water treatment plants measure only total
MIB concentrations, assuming a fixed intracellular/extracellular ratio.
This oversimplification leads to suboptimal process selection, unstable
removal efficiency, and, in some cases, exacerbates MIB release through
inappropriate treatment.
Our study systematically investigates the dynamic partitioning of MIB
between intracellular and extracellular compartments through controlled
experiments with two major MIB-producing cyanobacteria
(\emph{Pseudanabaena} and \emph{Planktothricoides}). We demonstrate that
the extracellular fraction exhibits distinct growth phase-dependent
patterns, where crowding effects during rapid growth phases trigger
substantial MIB release. Building on these observations, we developed a
\textbf{mechanistic model} based on logistic growth dynamics that
quantitatively predicts MIB release patterns across the complete
cyanobacterial life cycle.
Our model's application to Lake Taihu's MIB outbreaks uncovered novel
spatiotemporal patterns in cyanobacterial populations responsible for
odorant production. More significantly, we established a predictive risk
assessment framework that pinpoints key environmental drivers
controlling both MIB occurrence and its release dynamics. This framework
enables proactive identification of high-risk conditions preceding major
release events.
Key Findings of this study include:
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
Crowding effects during cellular growth govern MIB partitioning.
\item
The model successfully forecasts large-scale MIB release events.
\item
It provides actionable guidance for optimizing water treatment
processes.
\end{enumerate}
To our knowledge, this is the first mechanistic framework explaining
growth-dependent MIB release dynamics. Importantly, our approach shows
promise for modeling other algal metabolites (e.g., geosmin,
cyanotoxins), offering broader implications for water quality
management.
We believe this study will interest researchers working on algal
metabolites, water treatment engineers, and resource managers addressing
cyanobacterial blooms. The combination of fundamental insights and
practical applications aligns well with \textbf{\emph{JOURNAL}}'s
mission to bridge scientific discovery and environmental technology.
To ensure a thorough and expert evaluation of our manuscript, we suggest
the following potential reviewers with specialized expertise in algal
metabolites, water treatment technologies, and cyanobacterial ecology.
Additional information about each candidate reviewer is available via
the \emph{Web link} provided.
\textbf{Potential Reviewers:}
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
\textbf{Tsair-Fuh Lin}: National Cheng Kung University. Email:
\href{mailto:tflin@mail.ncku.edu.tw}{\nolinkurl{tflin@mail.ncku.edu.tw}};
\emph{\href{https://researchoutput.ncku.edu.tw/en/persons/tsair-fuh-lin}{Web
Link}}
\item
\textbf{Arash Zamyadi}: Civil \& Environmental Engineering, Monash
University. Email:
\href{mailto:Arash.Zamyadi@monash.edu}{\nolinkurl{Arash.Zamyadi@monash.edu}};
\emph{\href{https://research.monash.edu/en/persons/arash-zamyadi}{Web
Link}}
\item
\textbf{Xuwei Deng}: Institute of hydrobiology, Chinese Academy of
Sciences. Email:
\href{mailto:dengxuwei@ihb.ac.cn}{\nolinkurl{dengxuwei@ihb.ac.cn}};
\emph{\href{https://www.researchgate.net/profile/Deng-Xuwei}{Web
Link}}
\item
\textbf{Shushi Peng}: Peking University. Email:
\href{mailto:speng@pku.edu.cn}{\nolinkurl{speng@pku.edu.cn}};
\emph{\href{https://www.researchgate.net/profile/Shushi-Peng}{Web
Link}}
\item
\textbf{Tom Andersen}: University of Oslo. Email:
\href{mailto:tom.andersen@ibv.uio.no}{\nolinkurl{tom.andersen@ibv.uio.no}};
\emph{\href{https://www.mn.uio.no/ibv/english/people/aca/tomand/}{Web
Link}}
\item
\textbf{Muhe Diao}: University of Calgray. Email:
\href{mailto:muhe.diao@ucalgary.ca}{\nolinkurl{muhe.diao@ucalgary.ca}};
\emph{\href{https://www.ucalgary.ca/labs/ebg/people/muhe-diao}{Web
Link}}
\end{enumerate}
Thank you for considering our work. We would be pleased to address any
questions or suggestions during the review process.
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\closing{Yours Sincerely,}
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\end{letter}
\end{document}