Leveraging big data to elucidate the impact of heavy metal nanoparticles on anammox processes in wastewater treatment

Anammox is a highly efficient nitrogen removal process, yet the effects of metal/metal-oxide nanoparticles (M/MONPs) on these systems remain underexplored. This study investigates the impact of various M/MONPs on the nitrogen removal rate (NRR). Pearson correlation analysis and statistical evaluation indicates that silver and copper oxide nanoparticles exhibit the highest inhibitory effect, with an inhibition rate of 83.4 % and 73.7 %, respectively. Furthermore, Machine learning models, particularly extreme gradient boost (XGBoost), demonstrate superior performance, with R² values exceeding 0.91. SHapley Additive exPlanations (SHAP) feature importance analysis highlighted nanoparticles concentration, influent ammonia nitrogen concentration as the most influential factors. Additionally, Partial Dependence Plots (PDP) analysis of key features provided further clarity on the optimal ranges for these critical variables. The present study provides a novel predictive methodology and optimization strategies for enhancing the NRR of anammox system under M/MONPs stress, informed by comprehensive big data analysis.