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Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

High-time resolution PM_2.5 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

Keywords:

High-time resolution PM(2.5) pollution Source apportionment Spectral analysis Spectrum characteristics extraction

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Last Updated: Jul 9, 2025

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

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Published on: January 7, 2019

Production and Measurement of Organic Particulate Matter in a Flow Tube Reactor

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Published on: December 15, 2018

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Published on: March 9, 2018

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.