技術論文
PMFモデルとECトレーサー法を併用したPM2.5発生源の長期変動解析—近畿地方の都市部と郊外部—
キーワード:
Positive Matrix Factorization,
Elemental Carbon Tracer Method,
PM2.5,
COVID-19,
Vessel Fuel,
Road Traffic
2026 年 41 巻 1 号 p. 51-64
詳細
2026 年 41 巻 1 号 p. 51-64
We used the elemental carbon (EC) tracer method to identify primary organic carbon (POC) and secondary organic carbon (SOC) from the organic carbon dataset and applied both POC and SOC data for the Positive Matrix Factorization (PMF) model to estimate PM2.5 source contributions at the urban and suburban sites in the Kinki region, during 2016–2022. The PMF model extracted seven sources: heavy oil combustion, road traffic, biomass burning, soil and industry, SOC formation, secondary sulfate, and secondary nitrate. The result indicated PM2.5 contributions from heavy oil combustion suddenly dropped from 2020 due to the vessel fuel regulations issued in 2020. Additionally, other primary PM2.5 sources, such as road traffic, showed decreasing trends. Meanwhile, PM2.5 contributions from SOC formation exhibited increasing trends after 2020 and were higher in spring and summer. We also analyzed the impact of COVID-19 movement restrictions in spring 2020 on PM2.5 from road traffic. The PMF-estimated PM2.5 concentrations from road traffic in suburban sites dropped compared to those in 2019 and 2021, whereas those in urban sites didn’t show similar trends. The PMF with EC tracer method demonstrated an effective estimate of the primary and secondary PM2.5 source contributions.
