Machine Learning-Driven FT-ICR MS Analysis of Leachate DOM Ozonation and Membrane Fouling
Abstract
Pre-ozonation
mitigates
forward
osmosis
membrane
fouling
by
transforming
dissolved
organic
matter
(DOM);
however,
the
dynamic
interplay
between
ozonation-induced
precursor-product
evolution
and
behavior
remains
unclear.
We
demonstrated
that
pre-ozonation
preferentially
oxidizes
fulvic
acids,
followed
soluble
proteins
(S-PN),
in
landfill
leachates,
whereas
excessive
ozone
increases
S-PN
aged
leachates.
Based
on
interpretable
machine
learning
linkage
analysis,
we
identified
key
molecular
properties
(O/C,
weight
[MW],
oxygen
count,
double
bond
equivalents
minus
oxygen)
governing
reactivity
unveiled
following
transformation
pathways:
addition,
dealkylation,
desulfonation,
collectively
convert
unsaturated
low-oxygen
compounds
into
saturated,
oxygen-rich
mid/small
molecules.
In
particular,
sulfur-containing
(CHOS
CHONS)
undergo
conversion
highly
oxidized
saturated
(CHO
CHON).
reduced
oxidizing
lignin/carboxyl-rich
alicyclic
(CRAM)-like
aliphatic/protein
structures,
notably
those
containing
sulfur,
while
lowering
DOM
hydrophobicity
zeta
potential.
Over-ozonation
leachates
converts
CHONS-lignin/CRAM
low-MW
CHON-aliphatic/proteins
enriched
with
carboxylic
aggravating
irreversible
fouling.
This
study
elucidates
novel
mechanisms
underlying
impact
of
ozone-driven
transformations
offers
critical
insights
for
optimizing
quantitative
treatment
strategies
recalcitrant
wastewater.
Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown
Published: April 22, 2025
Language: Английский