Abstract：
Conventional environmental health research is primarily focused on isolated chemical exposures, neglecting the complex interactions between multiple pollutants that may synergistically or antagonistically influence toxicity, thereby posing unexpected health risks. In this study, we address this knowledge gap by introducing an explainable machine learning (ML) approach with Feature Localized Intercept Transformed-Shapley Additive Explanations (FLIT-SHAP) designed to extract the dose-response relationships of specific pollutants in mixtures. In contrast to traditional SHAP, FLIT-SHAP can localize the global intercept to elucidate mixture effects, which is crucial for understanding the oxidative potential (OP) of ambient particulate matter (PM). Assessing multi-pollutant OP using FLIT-SHAP revealed both synergistic (55-63 %) and antagonistic (25-42 %) effects in laboratory-controlled OP data, but an antagonistic (33-66 %; lower OP) effect in ambient PM. Notably, the FLIT-SHAP approach demonstrated higher prediction accuracy (R2 = 0.99) compared to the additive model (R2 = 0.89) when evaluated against real-world PM samples. Quinones, such as phenanthrenequinone, play a more significant role in PM2.5 than previously recognized. Through this study, we highlighted the potential of FLIT-SHAP to enhance toxicity predictions and aid decision-making in the field of environmental health.
摘要：
传统的环境健康研究主要集中在孤立的化学物质暴露上，忽略多种污染物之间可能协同或拮抗影响毒性的复杂相互作用，从而带来意想不到的健康风险。在这项研究中,我们通过引入可解释的机器学习（ML）方法来解决这一知识差距，该方法具有特征局部截距转换-Shapley加法解释（FLIT-SHAP），旨在提取混合物中特定污染物的剂量-反应关系。与传统的SHAP相比，FLIT-SHAP可以定位全局截距以阐明混合效应，这对于理解环境颗粒物（PM）的氧化电势（OP）至关重要。使用FLIT-SHAP评估多污染物OP在实验室控制的OP数据中显示出协同作用（55-63％）和拮抗作用（25-42％），但在环境PM中具有拮抗作用（33-66％；降低OP）。值得注意的是,当针对真实世界PM样本进行评估时,FLIT-SHAP方法显示出比加性模型(R2=0.89)更高的预测准确度(R2=0.99).Quinones,如菲醌，在PM2.5中发挥的作用比以前认识到的更重要。通过这项研究,我们强调了FLIT-SHAP在环境卫生领域增强毒性预测和辅助决策的潜力.
