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Temporal evolution characteristics of PM2.5 concentration based on continuous wavelet transform.

Xiaobing Chen1, Lirong Yin2, Yulin Fan3

  • 1School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China.

The Science of the Total Environment
|November 3, 2019
PubMed
Summary
This summary is machine-generated.

Fine particulate matter (PM2.5) evolution shows time-varying oscillation periods. Analyzing PM2.5 concentration reveals multiscale features crucial for accurate haze prediction.

Keywords:
Continuous wavelet transformMorlet waveletPM2.5Temporal evolution characteristic

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Data Analysis

Background:

  • Fine particulate matter (PM2.5) is a key air quality indicator.
  • Previous studies often overlooked localized temporal features in PM2.5 evolution.
  • Nonstationary nature of PM2.5 concentration necessitates advanced analytical methods.

Purpose of the Study:

  • To investigate the localized, intermittent oscillations in PM2.5 concentration.
  • To identify predominant periodicities at various scales within PM2.5 data.
  • To assess the significance of multiscale temporal features for haze prediction.

Main Methods:

  • Application of wavelet transform for analyzing localized oscillations.
  • Analysis of daily average PM2.5 concentration data from monitoring stations.
  • Examination of hourly average PM2.5 concentration during heavy haze events.

Main Results:

  • Daily PM2.5 data exhibited time-varying predominant oscillation periods (e.g., 14-32 d, 62-104 d, 105-178 d, 216-389 d), with 298 days being dominant initially.
  • Hourly PM2.5 during heavy haze showed abrupt shifts in principal periods and strong energy at a 63-hour scale.
  • Both analyses confirmed the presence of significant multiscale temporal features.

Conclusions:

  • The temporal evolution of PM2.5 concentration is characterized by multiscale features.
  • These localized, intermittent periodicities are critical and should not be disregarded in haze prediction models.
  • Understanding these dynamic patterns can significantly improve the accuracy of future haze forecasting.