Key drivers and source mechanisms of oxidative potential in fine particles from an industrial city of Northern China Plain.

Abstract

The oxidative potential (OP) of particulate matter (PM) is crucial for understanding its ability to generate reactive oxygen species. However, the major chemical drivers influencing OP still need to be better understood. This study investigated the seasonal variations of OP and identified key drivers and source mechanisms in the industrial city of Zibo, located in North China Plain. We used the XGBoost model and Positive Matrix Factorization (PMF) to identify key drivers and source mechanisms. In 2022, PM_2.5 samples were collected from an urban site in Zibo, and major chemical components were analyzed. OP was quantified using the dithiothreitol (DTT) method. The results revealed that the annual average DTTv in Zibo City for 2022 was 1.1 nmol/min/m³, with the highest DTTv levels observed in autumn, followed by spring, summer, and winter. Using the XGBoost model, we identified that metal elements such as Pb, Ba, and Cu, along with water-soluble ions NO₃^- and SO₄^2-, significantly contributed to DTTv. Source apportionment analysis via PMF identified five major sources of PM_2.5. Throughout the study period, secondary particles were the predominant contributors to PM_2.5 (49 %), while coal combustion had the lowest contribution (7 %). To further elucidate the sources of OP in PM_2.5, we integrated the measured OP with source contributions derived from PMF. The findings indicated that secondary particles and industrial sources contributed the most to DTTv, accounting for 40 % and 21 %, respectively. The OP sources exhibited seasonal variations: secondary particles were the primary contributors in winter, while dust sources dominated in spring. In summer, vehicle emissions increased substantially, and industrial emissions became the major source in autumn. This study highlighted the critical drivers and source mechanisms of OP in industrial cities and would be beneficial for future air quality control and risk reduction.