Deciphering performance and potential mechanism of anammox-based nitrogen removal process responding to nanoparticulate and ionic forms of different heavy metals through big data analysis

Abstract

As a promising process to remove nitrogen, anaerobic ammonium oxidation (anammox) is sensitive to environmental factors and toxic substances (antibiotics, heavy metals, sulfides, etc.). Especially, the nitrogen removal efficiency and biological activity of the anammox system were greatly inhibited by heavy metals. Few studies have been devoted to systematically investigating the effects and quantitatively deciphering the mechanism of different heavy metals on anammox-based systems. Based on the big data analysis of previous publications, the ionic heavy metals contributed to the greater decrease of specific anammox activity (SAA) than their nanoparticulate forms. Especially the inhibition ratio of SAA severely deteriorated under Cu2+ and CuO NPs, reaching 85 % and 81 %, respectively. This inhibition effect of heavy metals on nitrogen removal was also verified by the correlation analysis. Moreover, Logistic kinetic model was suitable to fit the inhibition periods of heavy metals, and Gaussian model could accurately predict nitrogen removal rate (R2 over 0.95) better than artificial neural network model. Based on quantitatively calculation of three independent nitrogen removal processes, nano heavy metals had a slight promotion effect on the ammonia oxidation process that proliferated amoA, while Ni2+ and CuO nanoparticulate inhibited nitrite oxidation process severely accompanying by the augmentation of resistance genes. Importantly, the anammox process was restrained by all heavy metals, verifying that anammox played a key role in the whole nitrogen removal process. This big data analysis provides a useful direction that it should give priority to ionic heavy metal and the copper element for anammox-based process under stress of heavy metals.