多尺度PM_2.5分布特征的空间插值与遥感反演对比

精確識別污染物濃度的空間分布是進行區域大氣污染防治的重要基礎。利用MODIS衛星數據,采用基于地面氣象和環境空氣質量監測站點觀測數據為基礎的反演模型,反演獲取2013年12月珠三角地區典型大氣污染過程1 km分辨率的PM2.5濃度數據,對比分析遙感反演及基于環境空氣質量監測站點觀測數據的空間插值方法對區域、城市和鄉鎮尺度PM2.5濃度空間分布特征的再現效果差異。結果表明,珠三角地區PM2.5遙感反演結果與地面觀測數據的相關性達到0.74,相關性水平較好,遙感反演結果可描述區域、城市和鄉鎮尺度上PM2.5污染濃度的空間分布特征,識別不同空間位置的污染程度差異;基于站點觀測數據的空間插值方法對PM2.5濃度空間分布特征的再現能力有限,在區域尺度PM2.5濃度空間分布特征分析時效果尚可,在站點有限的城市和鄉鎮尺度分析中效果不佳,容易產生對高濃度污染地區的誤判;在需要利用站點觀測數據分析區域尺度PM2.5濃度空間分布特征時,析取克里金、反距離權重或徑向基函數插值方法的效果相對較好。Identification of the pollutant concentration spatial distribution is an important foundation of regional atmospheric pollution control. 1km resolution PM2.5 concentration maps of Pearl River Delta region (PRD) are prepared through MODIS satellite data inversion. Taking a typical pollution day in December 2013 as a study case, multi -scale contrastive analysis between remote sensing inversion method and different spatial interpolation methods based on site observation data are carried out to compare the ability and applicability in representing the spatial distribution characteristics of atmospheric PM2.5 concentration. Results show that remote sensing inversion method could effectively characterize the spatial distribution of atmospheric PM2.5 concentration in township, city and regional scales, identifying spatial pollution variations. The spatial distribution characteristic representing ability and applicability of spatial interpolation methods based on site observation data are limited. Some of the spatial interpolation methods can be effective in regional scale analysis. But no spatial interpolation methods are effective in city and township scale analysis, or could effectively estimate the pollutant concentration level where the monitoring sites are sparse. When the spatial interpolation methods have to be used to estimate the spatial distribution pattern of regional atmospheric PM2.5 concentration, disjunctive kriging, inverse distance weighting or radial basis function method could be better choices.