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<p>To quantify the spatiotemporal variations of PM<sub>2.5</sub> and its response relationship with the driving factors (including meteorological factors and other air pollutants) in the multi-scale time–frequency domain, the monitoring data of PM<sub>2.5</sub> and its driving factors in Hunan Province from 2017 to 2021 were researched via four wavelet transform methods including continuous wavelet transform (CWT), discrete wavelet transform (DWT), wavelet transform coherence (WTC), and multiple wavelet coherence (MWC). Results revealed that: (1) The annual average PM<sub>2.5</sub> concentration exhibited a decreasing trend, with a cumulative decrease of 27.2%. The seasonal distribution pattern of PM<sub>2.5</sub> was winter &gt; fall &gt; spring &gt; summer. (2) The mutation of PM<sub>2.5</sub> mainly occurred in winter and was particularly concentrated in the Chang-Zhu-Tan urban agglomeration. The periodicity of 250–280&#xa0;days was the dominant cycle in the time series. In addition, 30–40- and 70–80-day cycles were observed in winter and from autumn to winter, respectively. (3) The response of PM<sub>2.5</sub> to its driving factors depended on the time–frequency scale and the combination of factors. For the meteorological factors, temperature (TEM) was the strongest single factor that affected PM<sub>2.5</sub> at all time–frequency scale. Meanwhile, the coherence increased with an increasing number of meteorological factors. The other air pollutants had higher abilities to explain PM<sub>2.5</sub> variations than the meteorological factors. Among them, PM<sub>10</sub> was the strongest single factor that affected the PM<sub>2.5</sub> at all time–frequency scale, with a significant positive coherence between the two. The tetravariate combination of PM<sub>10</sub>-SO<sub>2</sub>-O<sub>3</sub>-CO at the large time–frequency scales showed the highest degree of explanation of PM<sub>2.5</sub> concentration variations among all combinations. (4) Combining meteorological and pollutant factors significantly improves PM<sub>2.5</sub> variation explanation, but more factors do not guarantee better results. The research results of this paper may provide a reference for more precise identification of the influencing factors of PM<sub>2.5</sub> and the formulation of related air pollution control policies.</p>

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Wavelet Transform-based Scaling Response of PM2.5 with Meteorological Factors and Other Air Pollutants

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Yuyao Liu,
  • Yongjun Ye,
  • Zanchao Xu,
  • Hanqing Wang

摘要

To quantify the spatiotemporal variations of PM2.5 and its response relationship with the driving factors (including meteorological factors and other air pollutants) in the multi-scale time–frequency domain, the monitoring data of PM2.5 and its driving factors in Hunan Province from 2017 to 2021 were researched via four wavelet transform methods including continuous wavelet transform (CWT), discrete wavelet transform (DWT), wavelet transform coherence (WTC), and multiple wavelet coherence (MWC). Results revealed that: (1) The annual average PM2.5 concentration exhibited a decreasing trend, with a cumulative decrease of 27.2%. The seasonal distribution pattern of PM2.5 was winter > fall > spring > summer. (2) The mutation of PM2.5 mainly occurred in winter and was particularly concentrated in the Chang-Zhu-Tan urban agglomeration. The periodicity of 250–280 days was the dominant cycle in the time series. In addition, 30–40- and 70–80-day cycles were observed in winter and from autumn to winter, respectively. (3) The response of PM2.5 to its driving factors depended on the time–frequency scale and the combination of factors. For the meteorological factors, temperature (TEM) was the strongest single factor that affected PM2.5 at all time–frequency scale. Meanwhile, the coherence increased with an increasing number of meteorological factors. The other air pollutants had higher abilities to explain PM2.5 variations than the meteorological factors. Among them, PM10 was the strongest single factor that affected the PM2.5 at all time–frequency scale, with a significant positive coherence between the two. The tetravariate combination of PM10-SO2-O3-CO at the large time–frequency scales showed the highest degree of explanation of PM2.5 concentration variations among all combinations. (4) Combining meteorological and pollutant factors significantly improves PM2.5 variation explanation, but more factors do not guarantee better results. The research results of this paper may provide a reference for more precise identification of the influencing factors of PM2.5 and the formulation of related air pollution control policies.