可吸入颗粒物(PM10)浓度时空变异性及影响因素分析

本文以厦门市为例，首先对PM10平均浓度数据进行趋势性、周期性和空间变异性进行分析，然后分析了气温、降水强度、降水日数、风向等气候因子对PM10浓度变化在时间上的影响，同时使用了交叉相关分析的方法对月平均降水日数和PM10大气污染指数(API)为优(PM10浓度<0.05mg/m3)的月内天数百分比的相关性进行分析，最后利用从遥感影像获取的地表覆盖数据，分析了土地利用类型与局部PM10年平均浓度的关系。分析结果表明：厦门市PM10浓度年内变化无明显的上升和下降的趋势，但有显著的周期性，分别为3、7和29天，气候因素对PM10浓度的变化有显著的影响，其中月平均降水日数对API为优的月内天数百分比的影响有明显的滞后性，滞后周期大约为3个月，局部PM10年均浓度与该地区的土地覆盖类型有明显的相关性，并且植被覆盖的比例越大，该地区的PM10年平均浓度就越小。

Temporal and Spatial Variation of PM10 Concentration and Its Influence Factor Analysis

The temporal changing trend, periodicity and spatial variation of PM10 concentration in Xiamen City measured in four different monitoring sites have been analyzed by using time series and simple statistics approaches including correlation analysis, principle component analysis and cross-correlation analysis. The results show the PM10 concentration had no significant increasing and decreasing trend from 2001 to 2002, but had significant periodicity (the most significant periods are 14 and 7 days for different monitoring sites). The spatial distributions of PM10 concentrations, however, are different with monitoring sites. The second PCA can describe the distribution difference exactly. The influences of meteorological factors, including air temperature, rainfall intensity, wind direction and rainfall days, on PM10 concentration have also been analyzed. Especially, the cross correlation analysis (CCA) was applied to estimate the influence of monthly rainfall days (MRD) on PM10 monthly average concentration (MAC). Meteorological factors can change the variation of PM10 concentration directly. The influence of MRD on MAC lags for about 2 or 3 months. Moreover, land cover types are also important to the spatial distribution of PM10 concentration. The local regions where the vegetation cover ratio (VCR) extracted from relatively remote sense data is high have relative low PM10 annual average concentration.