Journal
JOURNAL OF HAZARDOUS MATERIALS
Volume 408, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jhazmat.2020.124845
Keywords
Chemical oxidation; TPHs; Soil properties; Availability; Complex effects
Categories
Funding
- National Key Research and Development Program of China [2019YFC1803803, 2017YFD0800703]
- National Natural Science Foundation of China [21976025, 21677024]
- Key Research and Development Program of Liaoning [2019JH2/10300047]
- Science & Technology Innovation Fund of Dalian [2019J12SN73]
- National key laboratory of urban water resources and water environment (Harbin Institute of Technology) [QA201531]
- Fundamental Research Funds for the Central Universities
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This study provides empirical evidence for the interrelated effects of natural soils components and target TPHs on persulfate oxidation of TPHs, demonstrating that such interrelation forms the basis of a predictive model with high accuracy for persulfate oxidation.
Persulfate-based chemical oxidation of petroleum hydrocarbons (TPHs) in soils usually varies drastically with soil sites. Complex effects of soil components on persulfate oxidation of TPHs remains poorly understood, impeding the understanding of persulfate oxidation in practical systems. Here we provided empirical evidence for the interrelated effects of natural soils components and target TPHs on persulfate oxidation of TPHs. Inputs of TPHs led to notable alterations of organic matter, minerals and pH of soils, which in turn influenced distributions and availability of TPHs in soils. These soil/TPH properties and oxidant dose constituted five interrelated terms that were used to develop a predictive model of persulfate oxidation of TPHs. Such interrelation accounted for ilmenite-base coupling activation of persulfate oxidation, Fe/Mn mineral activation of persulfate oxidation, chemical oxidant demand of soils, mass transfer-reactivity limiting of TPHs, and applicable parameters of per sulfate oxidation, respectively. The interrelation-based model of persulfate oxidation of TPHs displayed high predictive accuracy of 43% for a factor of 0.3 above and below the ideal fit, despite large differences in contaminated sites and applicable parameters. This finding may have practical interests in the optimization of persulfate oxidation.
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