RESUMEN
Appropriate mixed carbon sources have great potential to enhance denitrification efficiency and reduce operational costs in municipal wastewater treatment plants (WWTPs). However, traditional methods struggle to efficiently select the optimal mixture due to the variety of compositions. Herein, we developed a machine learning-assisted high-throughput method enabling WWTPs to rapidly identify and optimize mixed carbon sources. Taking a local WWTP as an example, a mixed carbon source denitrification data set was established via a high-throughput method and employed to train a machine learning model. The composition of carbon sources and the types of inoculated sludge served as input variables. The XGBoost algorithm was employed to predict the total nitrogen removal rate and microbial growth, thereby aiding in the assessment of the denitrification potential. The predicted carbon sources exhibited an enhanced denitrification potential over single carbon sources in both kinetic experiments and long-term reactor operations. Model feature analysis shows that the cumulative effect and interaction among individual carbon sources in a mixture significantly enhance the overall denitrification potential. Metagenomic analysis reveals that the mixed carbon sources increased the diversity and complexity of denitrifying bacterial ecological networks in WWTPs. This work offers an efficient method for WWTPs to optimize mixed carbon source compositions and provides new insights into the mechanism behind enhanced denitrification under a supply of multiple carbon sources.
RESUMEN
As one of the extensively used feed additives in livestock and poultry breeding, p-arsanilic acid (p-ASA) has become an organoarsenic pollutant with great concern. For the efficient removal of p-ASA from water, the combination of chemical oxidation and adsorption is recognized as a promising process. Herein, hollow/porous Mn-Fe-mixed oxide (MnFeO) nanocubes were synthesized and used in coupling with peroxymonosulfate (PMS) to oxidize p-ASA and remove the total arsenic (As). Under acidic conditions, both p-ASA and total As could be completely removed in the PMS/MnFeO process and the overall performance was substantially better than that of the Mn/Fe monometallic system. More importantly, an interface-promoted direct oxidation mechanism was found in the p-ASA-involved PMS/MnFeO system. Rather than activate PMS to generate reactive oxygen species (i.e., SO4·-, ·OH, and 1O2), the MnFeO nanocubes first adsorbed p-ASA to form a ligand-oxide interface, which improved the oxidation of the adsorbed p-ASA by PMS and ultimately enhanced the removal of the total As. Such a direct oxidation process achieved selective oxidation of p-ASA and avoidance of severe interference from the commonly present constituents in real water samples. After facile elution with dilute alkali solution, the used MnFeO nanocubes exhibited superior recyclability in the repeated p-ASA removal experiments. Therefore, this work provides a promising approach for efficient abatement of phenylarsenical-caused water pollution based on the PMS/MnFeO oxidation process.
