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<p>Using hourly PM<sub>10</sub> and PM<sub>2.5</sub> concentration data from three provincial environmental monitoring stations in Yong’an City, Fujian Province, China, along with synchronous precipitation and wind field data from 2017 to 2023, this study systematically investigates the scavenging mechanism of precipitation on aerosols, quantifies concentration thresholds for the first time, and reveals the scientific connotations of scavenging differences between PM<sub>10</sub> and PM<sub>2.5</sub>. Precipitation significantly scavenges both particle types, with scavenging efficiency increasing with process rainfall (proportion exceeding 70% when &gt; 1&#xa0;mm), maximum rainfall intensity (significantly improved when &gt; 1&#xa0;mm/h), and precipitation duration (significantly enhanced when &gt; 4&#xa0;h). The cumulative effect of frequent light rain should not be underestimated. For PM<sub>10</sub>, the scavenging amount reaches a saturation point at initial concentrations of 120–140&#xa0;μg/m<sup>3</sup>, which defines this interval as the threshold. When initial concentrations exceed this threshold, the growth rate of scavenging amount stabilizes, reflecting the physical upper limit of raindrop capture capacity. In contrast, PM<sub>2.5</sub> scavenging is dominated by precipitation. There is no obvious saturation point or corresponding concentration threshold observed, likely due to its small particle size and hygroscopic components reducing collision efficiency. ENE/NE wind directions at 850&#xa0;hPa are significantly correlated with negative scavenging, indicating that cross-regional pollutant transport may offset the wet scavenging effect of precipitation. This study is the first to quantify concentration thresholds, refining the framework of precipitation-aerosol interactions and providing critical scientific evidence for atmospheric self-purification capacity assessment, regional pollution joint prevention, and numerical model optimization.</p>

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Impact of Precipitation on Threshold Differentiation of PM10 and PM2.5 Concentrations and Cross-Scale Effects of Wind Field Negative Scavenging in Yong’an City

  • 期刊论文

  • 发表日期：

    2025年6月26日

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  • Xi Yang,
  • Liang Zhuang,
  • Shengcan Wu

摘要

Using hourly PM10 and PM2.5 concentration data from three provincial environmental monitoring stations in Yong’an City, Fujian Province, China, along with synchronous precipitation and wind field data from 2017 to 2023, this study systematically investigates the scavenging mechanism of precipitation on aerosols, quantifies concentration thresholds for the first time, and reveals the scientific connotations of scavenging differences between PM10 and PM2.5. Precipitation significantly scavenges both particle types, with scavenging efficiency increasing with process rainfall (proportion exceeding 70% when > 1 mm), maximum rainfall intensity (significantly improved when > 1 mm/h), and precipitation duration (significantly enhanced when > 4 h). The cumulative effect of frequent light rain should not be underestimated. For PM10, the scavenging amount reaches a saturation point at initial concentrations of 120–140 μg/m3, which defines this interval as the threshold. When initial concentrations exceed this threshold, the growth rate of scavenging amount stabilizes, reflecting the physical upper limit of raindrop capture capacity. In contrast, PM2.5 scavenging is dominated by precipitation. There is no obvious saturation point or corresponding concentration threshold observed, likely due to its small particle size and hygroscopic components reducing collision efficiency. ENE/NE wind directions at 850 hPa are significantly correlated with negative scavenging, indicating that cross-regional pollutant transport may offset the wet scavenging effect of precipitation. This study is the first to quantify concentration thresholds, refining the framework of precipitation-aerosol interactions and providing critical scientific evidence for atmospheric self-purification capacity assessment, regional pollution joint prevention, and numerical model optimization.