耦合卡尔曼滤波和多层次聚类的中国PM_(2.5)时空分布分析  

Analyzing Spatiotemporal Distribution of PM_(2.5) in China by Integrating Kalman Filter and Multilevel Clustering

作　　者：刘鹏华[1] 姚尧[1,2] 梁昊[3] 梁兆堂 张亚涛[1] 王昊松 

机构地区：[1]中山大学地理科学与规划学院,广州510275 [2]中山大学广东省城市化与地理环境空间模拟重点实验室,广州510275 [3]南京大学江苏省地理信息技术重点实验室,南京210023

出　　处：《地球信息科学学报》2017年第4期475-485,共11页Journal of Geo-information Science

基　　金：国家自然科学重点基金项目(41531176);国家自然科学基金项目(41671398;41601420)

摘　　要：近年来,细颗粒物污染尤其是PM_(2.5)受到人们越来越多的关注,研究PM_(2.5)的时空分布规律也具有越来越重大的意义。传统的遥感反演方法模型复杂,且不能揭示近地表面的PM_(2.5)分布规律。地面监测站的建设为PM_(2.5)的研究提供了更实时的观测数据,但由于测量噪声的影响,观测数据存在不准确的极端异常值。为了揭示中国PM_(2.5)的时空分布特征,本研究采用Kalman滤波对2015年中国338个城市的空气质量监测网络大数据进行最佳估计,并分析其时空特征。同时,根据中国各城市的PM_(2.5)浓度的时序分布,采用基于DTW的K-Medoids聚类方法将其分为4个等级,并采用q统计量来评估PM_(2.5)浓度分布的空间分层异质性。结果表明,采用Kalman滤波能有效去除数据噪声,峰值信噪比(PSNR)明显增大。在时空分布上,中国PM_(2.5)时间分布曲线呈现"U"形,冬季PM_(2.5)浓度明显高于夏季,且日变化曲线呈现"W"形;秋冬季PM_(2.5)浓度的空间分层异质性非常显著,且空间分布呈现"双核分布",重污染区主要分布在华北平原、新疆等地,西藏、广东、云南等地是稳定的空气质量优良区。Serious air pollution has recently aroused wide public concerns in China. The traditional method of quantitative remote sensing model is not only sophisticated but also inaccurate to fetch the exact PM2.5 data near the ground. Though the built-up ground monitoring stations can now provide sufficient PM2.5 observation data with high sampling frequency, there still exist many extreme outliers due to inevitable observation noise.Therefore, in this study, we adopted Kalman filter for optimal estimation of time-series of air quality data in 338 cities of China and comprehensively analyzed the spatiotemporal distribution pattern during the period of 2015.In our detailed analysis, we used DTW based K-Medoids clustering to classify cities into 4 levels according to their contamination degree, and utilized q statistic technique to evaluate the spatial stratified heterogeneity of PM2.5. The results show that by using Kalman filter, noise can be effectively reduced and value of PSNR can be significantly improved. In the study of temporal distribution, we found that PM2.5 followed a‘U’curve in yearly temporal distributions while daily temporal distributions obeyed a‘W’curve. PM2.5density is much higher in winter than in summer in China, and spatial stratified heterogeneity is even more pronounced during the fallwinter stage. In the study of spatial distribution, it can be clearly seen that PM2.5 appears a‘Dual-core’pattern across China where concentration of PM2.5 spiked at Xinjiang and North China plain. In contrast, Xizang,Guangdong and Yunnan are more stable areas with excellent air quality, ranking first-tier nationwide.

关 键 词：PM2.5 大数据 卡尔曼滤波 时空分析 K-Medoids 

分 类 号：X513[环境科学与工程—环境工程] X87