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

• Coastal secondary organic aerosol (OA) exhibits lower concentrations but higher fractional contributions to total OA. • Sea-land breezes enhanced secondary organic carbon (SOC) mass concentrations. • Interpretable machine learning shows SOC drivers vary significantly by season. • O x drives SOC concentration during sea breezes, and CO dominates under land breeze conditions. 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.

Authors

Institutions

Publication Details

Journal
Geoscience Frontiers
Published
2025-09-10
DOI
https://doi.org/10.1016/j.gsf.2025.102151
Citations
1
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
1.30
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Ruiyu Li, Meng He, Fang Li, Yanjun Yang et al.
1 citations
Geoscience Frontiers
Atmospheric chemistry and aerosols
1.30
article

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

Ruiyu Li, Meng He, Fang Li, Yanjun Yang, Yang Yue, Caiqing Yan
article en
1 citations

Abstract

• Coastal secondary organic aerosol (OA) exhibits lower concentrations but higher fractional contributions to total OA. • Sea-land breezes enhanced secondary organic carbon (SOC) mass concentrations. • Interpretable machine learning shows SOC drivers vary significantly by season. • O x drives SOC concentration during sea breezes, and CO dominates under land breeze conditions. 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.

Geoscience FrontiersVol. 16(6)
Shandong University (CN), Chinese Academy of Sciences (CN), Suzhou Research Institute (CN), State Key Joint Laboratory of Environment Simulation and Pollution Control (CN), Research Center for Eco-Environmental Sciences (CN)
Openalex Percentile: Top 18%
Atmospheric chemistry and aerosols
1.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.