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2015~2024年安徽省近地面污染的时空变化特征及与气象影响要素的相关分析

Spatiotemporal Variation Characteristics of Near-Surface Pollutants and Their Meteorological Influencing Factors in Anhui Province from 2015 to 2024

  • 摘要: 基于安徽省9个市县38个空气质量监测站2015年至2024年逐小时空气质量监测数据和气象数据,探讨了细颗粒物(PM2.5)、可吸入颗粒物(PM10)、二氧化硫(SO2)、二氧化氮(NO2)、臭氧(O3)、一氧化碳(CO)及空气质量指数(AQI)的时空变化特征、长期趋势、季节变化及气象因素的季节变化对污染物和空气质量的影响。结果表明, 2015年~2024年,安徽AQI及主要污染物年均浓度普遍下降,空气质量整体改善,唯O?浓度呈上升趋势。O3整体呈现出南高北低的纬度分布特征,平均升幅随纬度降低(南移)而增大。六种主要空气污染物呈现明显不同的季节变化规律,颗粒物(PM2.5、PM10)和气态污染物(SO2、NO2、CO)均表现出冬季浓度最高,夏季最低的特征;而O3则相反,夏季浓度最高,冬季最低。具体表现为PM2.5、PM10和SO2浓度为冬季>春季>秋季>夏季,NO2和CO为冬季>秋季>春季>夏季。而O3浓度表现为夏季(82.3 μg/m3) > 春季(77.2 μg/m3) > 秋季(64.3 μg/m3) > 冬季(43.2 μg/m3)。颗粒物污染物的日变化呈双峰型, SO?、NO? 等气态污染物呈单峰型特征。日均值的相关分析表明,MDA8-O?与所有一次污染物均呈负相关,尤其与 CO(r = -0.256)、PM?.?(r = -0.233)和 NO?(r = -0.208)之间的负相关关系达到显著水平(p < 0.01),这可能说明O3在高前体物浓度下生成受抑,并与PM和NO?存在光化学消耗或竞争关系。所有污染物均与风速呈现显著负相关关系(p < 0.01),其中NO2表现出最强的负相关性(r = -0.338),芜湖、铜陵和安庆的风速与NO2相关系数最高,分别为-0.50、-0.47和-0.41,可能因为地处长江沿岸,风道通畅,风速较大时更易形成良好的扩散条件,对风速变化更敏感。大多数污染物与RH均表现出中等强度的显著负相关(所有p<0.01),年际尺度上O3在RH>75%随着RH增加迅速下降。

     

    Abstract: Based on hourly observational data from 38 air quality monitoring stations in 9 cities and counties in Anhui Province from 2015 to 2024, along with concurrent meteorological data, this study systematically analyzed the spatiotemporal evolution characteristics of PM?.?, PM??, SO?, NO?, O?, CO, and AQI, as well as their meteorological influencing factors. The results show that over the past decade, air quality in Anhui Province has significantly improved, with annual average concentrations of AQI and most pollutants continuously declining, except for O?, which exhibited an upward trend. Further analysis revealed that O? displays a latitudinal distribution pattern of "higher in the south and lower in the north," with the magnitude of increase gradually growing as latitude decreases.In terms of seasonal variations, different pollutants exhibited distinct patterns. Specifically, particulate matter such as PM?.? and PM??, as well as gaseous pollutants like SO?, NO?, and CO, all showed higher concentrations in winter and lower concentrations in summer, primarily due to increased heating emissions and worsened atmospheric diffusion conditions in winter. In contrast, O? concentrations followed an opposite seasonal pattern, peaking in summer (82.3 μg/m3) and reaching their lowest levels in winter (43.2 μg/m3), reflecting the significant influence of photochemical reactions on O? formation. Regarding diurnal variations, particulate matter exhibited a typical "bimodal" distribution, closely linked to traffic peaks during morning and evening rush hours and boundary layer changes, whereas gaseous pollutants mostly displayed a "unimodal" pattern. Analysis of meteorological factors revealed significant correlations between pollutant concentrations and weather conditions. First, MDA8-O? showed significant negative correlations with CO (r = -0.256), PM?.? (r = -0.233), and NO? (r = -0.208) (p < 0.01). Second, all pollutants exhibited significant negative correlations with wind speed (p < 0.01), with NO? showing the strongest correlation (r = -0.338). This relationship was particularly pronounced in cities along the Yangtze River, such as Wuhu (r = -0.50), Tongling (r = -0.47), and Anqing (r = -0.41), likely due to the local terrain"s wind channel effect, where higher wind speeds significantly improved pollutant dispersion. Additionally, relative humidity (RH) also had a notable impact on pollutant concentrations. Most pollutants showed moderate negative correlations with RH (p < 0.01). Notably, when RH exceeded 75%, O? concentrations decreased rapidly with increasing humidity.

     

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