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北京大氣顆粒物污染特征及空間分布插值分析

Pollution characteristics of particulate matter and interpolation analysis of its spatial distribution in the Beijing area

  • 摘要: 為較好地表征當前北京整個區域大氣顆粒物質量濃度隨時間尺度的變化及區域分布污染特征,根據北京市35個監測站點獲得的2013年3—5月顆粒物質量濃度1 h均值,分析和研究PM2.5和PM10質量濃度的季節性變化并提高其空間分辨率,在此基礎上探討顆粒物可能的影響因素及污染來源.結果表明,3—5月顆粒物質量濃度具有周期性變化規律和顯著相關性,應用MATLAB空間插值算法實現的顆粒物質量濃度區域分布圖具有一定精度,可外推并揭示顆粒物區域污染特征.分析表明當前北京顆粒物污染的影響因素有冬末的冷鋒和降雪、春季的沙塵和大風、夏初的降雨和濕熱等;污染區域則呈現南高北低的特征,污染來源除了本地人為源以外,周邊區域傳輸也有較大影響.通過顆粒物污染的時間序列和空間插值的結合分析,可為進一步研究顆粒物時空關系及掌握區域污染特征提供方法.

     

    Abstract: The 1-hour average mass concentration of particulate matter from March to May 2013 obtained from monitoring stations was used to characterize the concentration variation of particulate matter with time scale and its regional distribution in the Beijing area. The mass concentrations of PM2.5 and PM10 were studied to find out their seasonal variation characteristics, and their spatial resolution was improved. Based on that, the possible factors and pollution sources of particulate matter were then preliminary discussed. The results show that there are a periodical variation and a significant correlation on the average mass concentration of particulate matter from March to May in the Beijing area. Interpolation results on the particulate concentration distribution by using MATLAB spatial interpolation tools have certain precision to extrapolate and reveal the regional pollution characteristics. According to analysis, the main factors affected particulate concentration in the Beijing area are cold front and snowfall in late winter, dust and wind in spring, rainfall and hot-humid weather in early summer, and so on. The particulate concentration distribution shows an overall trend of high in the south and low in the north, and the pollution sources are very likely caused by local anthropogenic sources as well as the transmission of surrounding area. The conjoint analysis on time series and spatial interpolation of particulate concentration has significance for further research of the time-space relationship of particulate matter, and it also provides a method for understanding regional pollution characteristics.

     

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