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Journal of environmental management. 2025 Apr 16:382:125243. doi: 10.1016/j.jenvman.2025.125243 Q18.42025

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

利用大数据阐明重金属纳米颗粒对污水中厌氧氨氧化过程的影响 翻译改进

Yiqun Hong  1, Zhenguo Chen  2, Zehua Huang  3, Chunying Zheng  4, Junxing Liu  1, Chenxi Zeng  1, Xiangfa Kong  1, Chao Zhang  1, Mingzhi Huang  5

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作者单位

  • 1 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.
  • 2 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. Electronic address: zhenguo.chen@m.scnu.edu.cn.
  • 3 SCNU (NAN'AN) Green and Low-carbon Innovation Center, Nan'an SCNU Institute of Green and Low-carbon Research, Quanzhou, 362300, PR China.
  • 4 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.
  • 5 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. Electronic address: mingzhi.huang@m.scnu.edu.cn.

    • DOI: 10.1016/j.jenvman.2025.125243 PMID: 40245740

    摘要 中英对照阅读

    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.

    Keywords: Anammox; Big data; Machine learning; Nanoparticles.

    Keywords：big data; heavy metal nanoparticles; anammox processes; wastewater treatment

    厌氧氨氧化是一种高效的氮去除过程，然而金属/金属氧化物纳米颗粒（M/MONPs）对这些系统的影响仍然研究不足。本研究调查了各种M/MONPs对氮去除率（NRR）的影响。皮尔森相关性分析和统计评估表明，银和氧化铜纳米颗粒表现出最高的抑制作用，抑制率分别为83.4%和73.7%。此外，机器学习模型，特别是极端梯度提升算法（XGBoost），表现出色，R²值超过0.91。SHapley Additive exPlanations (SHAP) 特征重要性分析显示纳米颗粒浓度以及进水氨氮浓度是最具影响力的因素。同时，关键特征的部分依赖图（PDP）分析进一步明确了这些关键变量的最优范围。本研究为在M/MONPs压力下提高厌氧氨氧化系统的NRR提供了一种新的预测方法和优化策略，并通过全面的大数据分析提供了依据。

    关键词： 厌氧氨氧化；大数据；机器学习；纳米颗粒。

    关键词：大数据; 重金属纳米粒子; 厌氧氨氧化过程; 废水处理

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    期刊名：Journal of environmental management

    缩写：J ENVIRON MANAGE

    ISSN：0301-4797

    e-ISSN：1095-8630

    IF/分区：8.4/Q1

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