In Journal of hazardous materials
Bioaccumulation and adsorption are efficient methods for removing heavy metal ions (HMIs) from aqueous environments. However, methods to quantifiably characterize the removal selectivity for co-existing HMIs are limited. In this study, we applied Shapley additive explanations (SHAP) following extreme gradient boosting (XGBoost) modeling, to generate SHAP values. We used these values to create an affinity interference index (AII) that quantitatively represented the interference between metal ions in a multi-metal bioaccumulation system. The selectivity for simultaneous bioaccumulation of Pb2+, Cu2+, and Zn2+ by living Bacillus subtilis biomass was then characterized as a proof of concept. The AII indicated that the bioaccumulation of Zn2+ was more strongly inhibited by Pb2+/Cu2+ (AII = 1) than that of Pb2+/Cu2+ by Zn2+. Moreover, the presence of Zn2+ promoted the bioaccumulation of Pb2+ (AII = 0.39), which was confirmed in further experiments where the bioaccumulation of Pb2+ (300 μM) was increased by 38% with Zn2+ (300 μM). This study demonstrated that the combination of XGBoost and SHAP is effective in the quantifiable characterization of the antagonistic and synergistic effects in a multi-metal simultaneous bioaccumulation system. This method could also be generalized to similar tasks for analyzing the selectivity effects in a multi-component system.
Wang Sheng, Zhou Ying, You Xinxin, Wang Bing, Du Linna
2022-Dec-21
Heavy metals, Machine learning, Multi-component bioaccumulation, Shapley additive explanations
