Microbially derived extracellular
polymeric substances (EPSs) occupy
a large portion of dissolved organic matter (DOM) in surface waters,
but the understanding of the photochemical behaviors of EPS is still
very limited. In this study, the photochemical characteristics of
EPS from different microbial sources (Shewanella oneidensis, Escherichia coli, and sewage sludge
flocs) were investigated in terms of the production of reactive species
(RS), such as triplet intermediates (3EPS*), hydroxyl radicals
(•OH), and singlet oxygen (1O2). The steady-state concentrations of •OH, 3EPS*, and 1O2 varied in the ranges of
2.55–8.73 × 10–17, 3.01–4.56
× 10–15, and 2.08–2.66 × 10–13 M, respectively, which were within the range reported
for DOM from other sources. The steady-state concentrations of RS
varied among different EPS isolates due to the diversity of their
composition. A strong photochemical degradation of the protein-like
components in EPS isolates was identified by excitation emission matrix
fluorescence with parallel factor analysis, but relatively, humic-like
components remained stable. Fourier-transform ion cyclotron resonance
mass spectrometry further revealed that the aliphatic portion of EPS
was resistant to irradiation, while other portions with lower H/C
ratios and higher O/C ratios were more susceptible to photolysis,
leading to the phototransformation of EPS to higher saturation and
lower aromaticity. With the phototransformation of EPS, the RS derived
from EPS could effectively promote the degradation of antibiotic tetracycline.
The findings of this study provide new insights into the photoinduced
self-evolution of EPS and the interrelated photochemical fate of contaminants
in the aquatic environment.
NBC exhibit significant efficiency in mediating MO or minerals reduction by accelerating electron transfer. NBC-700 has higher SSA, ETC and stronger redox property than NBC-400.
Apparent quantum yields (Φ) of photochemically produced reactive intermediates (PPRIs) formed by dissolved organic matter (DOM) are vital to element cycles and contaminant fates in surface water. Simultaneous determination of Φ PPRI values from numerous water samples through existing experimental methods is time consuming and ineffective. Herein, machine learning models were developed with a systematic data set including 1329 data points to predict the values of three Φ PPRIs (Φ 3DOM* , Φ 1O2 , and Φ •OH ) based on DOM spectral parameters, experimental conditions, and calculation parameters. The best predictive performances for Φ 3DOM* , Φ 1O2 , and Φ •OH were achieved using the CatBoost model, which outperformed the traditional linear regression models. The significances of the wavelength range and spectral parameters on the three Φ PPRI predictions were revealed, suggesting that DOM with lower molecular weight, lower aromatic content, and a more autochthonous portion possessed higher Φ PPRIs . Chain models were constructed by adding the predicted Φ 3DOM* as a new feature into the Φ 1O2 and Φ •OH models, which consequently improved the predictive performance of Φ 1O2 but worsened the Φ •OH prediction likely due to the complex formation pathways of •OH. Overall, this study offered robust Φ PPRI prediction across interlaboratory differences and provided new insights into the relationship between PPRIs formation and DOM properties.
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