Characteristics and drivers of carbonaceous aerosol pollution in a coastal city of northern China

Abstract

Carbonaceous aerosols significantly impact air quality, human health and climate change, yet their concentration levels and influencing factors exhibit significant regional variability. This study examined the concentration levels and temporal variations of carbonaceous aerosols in Qingdao, a typical coastal city in China, using a year-long, high-time-resolution dataset of organic carbon (OC) and elemental carbon (EC) measurements. The impacts of meteorological conditions, primary emissions, atmospheric oxidants, and sea-land breezes were systematically analyzed by employing an interpretable machine learning model. The results revealed that atmospheric OC and EC concentration levels were relatively low in Qingdao, but secondary organic carbon (SOC) accounted for 43% of OC, emphasizing the substantial influence of secondary sources. SOC concentrations peaked in the evening, whereas primary organic carbon (POC) and EC concentrations peaked during morning rush hours. The elevated carbonaceous aerosol concentration observed in winter likely resulted from enhanced primary emissions coupled with unfavorable dispersion conditions, whereas intensive photochemical activities during summer facilitated SOC formation. Higher SOC levels were observed during sea-land breeze days than non-sea-land breeze days. The machine learning model indicated that atmospheric oxidants played an important role in SOC formation during sea-land breeze days, while combustion related emissions may be the key factor on non-sea-land breeze days. Furthermore, SOC levels were higher under land breezes compared to sea breezes, likely due to enhanced primary emissions from terrestrial sources coupled with confined pollutant dispersion. These findings revealed complex emission-meteorology-chemistry interactions affecting coastal air quality, informing targeted air pollution mitigation strategies.

Keywords

Carbonaceous aerosols / Secondary organic carbon / Temporal variations / Driving factors / Sea-land breezes / Machine learning

CRediT authorship contribution statement

Li Fang: Writing - original draft, Validation, Methodology, Investigation, Formal analysis. He Meng: Writing - review & edit-ing, Methodology, Conceptualization. Yanjun Yang: Methodology, Data curation. Ruiyu Li: Validation, Methodology. Yang Yue: Vali-dation, Methodology. Caiqing Yan: Writing - review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing finan-cial interests or personal relationsh ips that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the National Key research and Development Program of China (Grant Nos. 2023YFC3710200 and 2022YFC3701101), the Natural Science Foundation of China (No. 4225110), and Natural Science Foundation of Jiangsu Province (BK20220275).

Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102151.

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