Identification of primary effecters of N2O emissions from full-scale biological nitrogen removal systems using random forest approach

Wastewater treatment plants (WWTPs) have long been recognized as point sources of N₂O, a potent greenhouse gas and ozone-depleting agent. Multiple mechanisms, both biotic and abiotic, have been suggested to be responsible for N₂O production from WWTPs, with basis on extrapolation from laboratory results and statistical analyses of metadata collected from operational full-scale plants. In this study, random forest (RF) analysis, a machine-learning approach for feature selection from highly multivariate datasets, was adopted to investigate N₂O production mechanism in activated sludge tanks of WWTPs from a novel perspective. Standardized measurements of N₂O effluxes coupled with exhaustive metadata collection were performed at activated sludge tanks of three biological nitrogen removal WWTPs at different times of the year. The multivariate datasets were used as inputs for RF analyses. Computation of the permutation variable importance measures returned biomass-normalized dissolved inorganic carbon concentration (DIC·VSS⁻¹) and specific ammonia oxidation activity (sOURAOB) as the most influential parameters determining N₂O emissions from the aerated zones (or phases) of activated sludge bioreactors. For the anoxic tanks, dissolved-organic-carbon-to-NO₂⁻/NO₃⁻ ratio (DOC·(NO₂⁻-N + NO₃⁻-N)⁻¹) was singled out as the most influential. These data analysis results clearly indicate disparate mechanisms for N₂O generation in the oxic and anoxic activated sludge bioreactors, and provide evidences against significant contributions of N₂O carryover across different zones or phases or niche-specific microbial reactions, with aerobic NH₃/NH₄⁺ oxidation to NO₂⁻ and anoxic denitrification predominantly responsible from aerated and anoxic zones or phases of activated sludge bioreactors, respectively.