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High-time resolution PM2.5 source apportionment assisted by spectrum-based characteristics analysis
Jie Liu1, Fangjingxin Ma2, Tse-Lun Chen3
1School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China; Institute of Environmental Engineering (IfU), ETH Zürich, 8093 Zürich, Switzerland.
The Science of the Total Environment
|December 6, 2023
Summary
This study introduces a new framework for analyzing particulate matter (PM2.5) pollution by combining spectral analysis with source apportionment. The findings reveal secondary inorganic aerosols as the primary contributor to Beijing
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Data Analysis
Background:
- Particulate matter (PM2.5) pollution requires effective source apportionment for effective mitigation strategies.
- Frequency spectrum analysis offers potential for extracting characteristics of PM2.5 pollution events.
Purpose of the Study:
- To develop and apply an integrated framework for PM2.5 source apportionment using spectral analysis and receptor modeling.
- To extract spectrum characteristics of PM2.5 pollution anomalies for improved source identification.
Main Methods:
- Combined Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) for spectral analysis.
- Utilized Positive Matrix Factorization (PMF) receptor model for source contribution assessment.
- Applied the framework to hourly PM2.5 data from Beijing during the winter heating period.
Main Results:
- Successfully captured spectrum characteristics (frequency, location, duration, intensity) of PM2.5 pollution anomalies.
- Identified secondary inorganic aerosols as the dominant PM2.5 source (50.59%) during Beijing's winter heating period.
- Quantified contributions from other sources: biomass burning (15.01%), vehicle emissions (11.00%), coal combustion (10.70%), road dust (5.31%), industrial processes (3.88%), and fireworks (3.51%).
Conclusions:
- The integrated spectral analysis and PMF framework provides robust insights into PM2.5 pollution dynamics.
- This approach enhances understanding of temporal evolution, source identification, and contribution quantification of PM2.5.
