Abstract
Microbial action in SWIS is one of the main ways to remove contaminants. Studying the metabolic processes and pathways of microorganisms is helpful to reveal the mechanism of pollutant removal in the "black box" process of SWIS. In this study, based on metabolomics and UPLC-MS, partial least squares (PLS-DA), principal component analysis (PCA) pattern recognition and cluster analysis were used to classify the microbial samples. According to the model's variable importance factor (VIP value) being greater than 1.5, a total of 53 potential biomarkers were screened out. There was a significant correlation between the microbial metabolites and soil profile. Most microbial metabolites were concentrated in the H2 layer (subsurface layer of SWIS), while there were relatively few in the H4 and H6 layers (middle and lower layers of SWIS); organic acids and alcohol metabolites mainly existed in the anoxic environment (H4 layer); antibiotics, growth hormones and pigments and other small molecule metabolites mainly existed under anaerobic conditions (H6 layer). The results of RDA analysis indicated that environmental factors had an effect on the microbial metabolites. With the variation of different height profiles, the metabolites were significantly affected by ORP and NO3 -, which were negatively correlated. The above conclusions indicated that metabolomics is a reliable, accurate and effective method to quantitatively characterize the stability of SWIS.