Influence of graphene oxide nanosheets on the cotransport of cu-tetracycline multi-pollutants in saturated porous media
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Antibiotic-heavy metal multi-pollutants are produced by intensive livestock farming and become an increasingly prominent problem. In this study, the transport behavior of tetracycline (TC) and its chelate with copper ions (Cu-TC) in saturated sand column with and without graphene oxide (GO) prefilled was investigated by laboratory breakthrough experiments. The effects of pH, ionic strength, and the cotransport with GO were studied detailedly. The results showed that the prepared nano-GO had a single- or multilayered sheet structure with a diameter of several μm. The surface of GO contained abundant oxygen-containing functional groups, which imparted it strong hydrophilicity and electronegativity. Pollutant transport experiments showed that decrease of H+ weakened the transport ability of TC and Cu-TC. Both Na+ and Ca2+ promoted the transport of TC, with Ca2+ having a much greater effect. The presence of Na+ inhibited the transport of Cu-TC, while Ca2+ promoted Cu-TC transport. The addition of Cu2+ was more favorable for the transport of Cu-TC than TC alone. In the GO-prefilled column, the effluent concentrations of TC and Cu-TC greatly decreased due to adsorption onto GO surfaces. The transport of Cu-TC was more related to GO concentration than TC alone due to the high affinity between GO and Cu-TC. Moreover, the transport behavior of GO in the sand column was consistent with that of the corresponding TC or Cu-TC, indicating that GO could cotransport with TC and Cu-TC multi-pollutants. Our study showed that the GO would interact with TC and Cu-TC and thus have significant influences on the fate and transport of these pollutions in porous media.
KeywordsTetracycline (TC) Cu2 + Multi-pollutant Graphene oxide Porous media Transport
The authors would like to express appreciation to the anonymous reviewers and editors for their valuable comments and suggestions. The authors would like to acknowledge the National Natural Science Foundation (41703120), Shenyang Municipal Science and Technology (Z17-5-079), and the National Natural Science Foundation of Liaoning Province (2019-ZD-0560).
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