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

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

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  • NETL
  • NSTL
  • Elsevier
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 R~2 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.

AnammoxNanoparticlesBig dataMachine learning

Yiqun Hong、Zhenguo Chen、Zehua Huang、Chunying Zheng、Junxing Liu、Chenxi Zeng、Xiangfa Kong、Chao Zhang、Mingzhi Huang

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Guangdong Provincial Engineering Research Center of Intelligent Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, PR China||SCNU (NAN'AN) Green and Low-carbon Innovation Center, Nan'an SCNU Institute of Green and Low-carbon Research, Quanzhou, 362300, PR China

SCNU (NAN'AN) Green and Low-carbon Innovation Center, Nan'an SCNU Institute of Green and Low-carbon Research, Quanzhou, 362300, PR China

Guangdong Provincial Engineering Research Center of Intelligent Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, PR China

2025

10.1016/j.jenvman.2025.125243

Journal of environmental management

Journal of environmental management

ISSN：0301-4797
年,卷(期)：2025.382(May)
  • 43