Source-specific fingerprints and machine learning-driven apportionment of lead-containing fine particles from typical industrial emissions in China

Lead-containing fine particles (Pb-FPs) from industrial emissions pose significant health risks, but their source-specific characteristics remain poorly characterized. This study presents a comprehensive investigation of Pb-FPs derived from four major industrial sectors in China, i.e. coal-fired power (CFP), iron and steel smelting (ISS), waste incineration power (WIP), and biomass power generation (BP), through systematic analysis of 134 PM samples collected nationwide using single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOF-MS). Our results showed that WIP (5 ×10⁷ particles/mg) and ISS (3.9 ×10⁷ particles/mg) activities emitted significantly higher number concentrations of Pb-FPs compared to CFP and BP sources. Pb-multi-metal FPs accounted for 66.7–81.2 % of total Pb-FPs number concentrations across all sources, with the mass fraction of Pb was predominantly ≤ 10 %. Distinct elemental fingerprints were identified for each source type, particularly metal-rich matrices associated with Pb. We developed a source apportionment approach by evaluating five machine learning algorithms, with XGBoost emerging as the optimal classifier (F1 score = 0.76, accuracy = 0.77) after Bayesian optimization and 10-fold cross-validation. Application of the model to PM_2.5 samples from Beijing and Shanghai revealed persistent and substantial contributions from ISS-derived Pb-FPs (6.7–38.1 % in Beijing, 10.5–33.7 % in Shanghai), with additional average inputs from CFP (7.4 %), WIP (5.8 %), and BP (12.1 %). These results highlight the dominant role of ISS in atmospheric Pb pollution across industrialized regions of China and emphasize the need for targeted mitigation strategies.