耦合卡尔曼滤波和多层次聚类的中国PM2.5时空分布分析

doi:10.3724/SP.J.1047.2017.00475

地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (4): 475-485.doi: 10.3724/SP.J.1047.2017.00475

• 地理空间分析综合应用 • 上一篇下一篇

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

刘鹏华¹(), 姚尧^1,^2,^*(), 梁昊³, 梁兆堂¹, 张亚涛¹, 王昊松¹

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

收稿日期:2016-07-01 修回日期:2016-11-01 出版日期:2017-04-20 发布日期:2017-04-20
作者简介:
作者简介：刘鹏华（1995-）,男,本科生,研究方向为遥感与地理信息系统。E-mail:liuphhhh@foxmail.com
基金资助:
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）

Analyzing Spatiotemporal Distribution of PM_2.5 in China by Integrating Kalman Filter and Multi-level Clustering

LIU Penghua¹(), YAO Yao^1,^2,^*(), LIANG Hao³, LIANG Zhaotang¹, ZHANG Yatao¹, WANG Haosong¹

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Key Laboratory for Urbanization and Geo-simulation of Guangdong Province, Sun Yat-sen University, Guangzhou 510275, China
3. Key Laboratory for Geographical Information Science and Technology of Jiangsu Province, Nanjing University, Nanjing 210023, China

Received:2016-07-01 Revised:2016-11-01 Online:2017-04-20 Published:2017-04-20
Contact: YAO Yao

摘要/Abstract

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。

关键词: PM_2.5, 大数据, 卡尔曼滤波, 时空分析, K-Medoids

Abstract:

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 PM_2.5 data near the ground. Though the built-up ground monitoring stations can now provide sufficient PM_2.5 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 PM_2.5. 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 PM_2.5 followed a ‘U’ curve in yearly temporal distributions while daily temporal distributions obeyed a ‘W’ curve. PM_2.5 density is much higher in winter than in summer in China, and spatial stratified heterogeneity is even more pronounced during the fall-winter stage. In the study of spatial distribution, it can be clearly seen that PM_2.5 appears a ‘Dual-core’ pattern across China where concentration of PM_2.5 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.

Key words: PM_2.5, big Data, kalman filter, spatiotemporal analysis, K-Medoids

刘鹏华, 姚尧, 梁昊, 梁兆堂, 张亚涛, 王昊松. 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[J]. 地球信息科学学报, 2017, 19(4): 475-485.DOI:10.3724/SP.J.1047.2017.00475

LIU Penghua,YAO Yao,LIANG Hao,LIANG Zhaotang,ZHANG Yatao,WANG Haosong. Analyzing Spatiotemporal Distribution of PM_2.5 in China by Integrating Kalman Filter and Multi-level Clustering[J]. Journal of Geo-information Science, 2017, 19(4): 475-485.DOI:10.3724/SP.J.1047.2017.00475

图/表 12

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地区	滤波前	滤波后
北京	12.8132	21.0545
上海	9.7523	14.9660
广州	13.2474	19.1636
南京	17.9053	27.0554

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 Multi-level Clustering

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耦合卡尔曼滤波和多层次聚类的中国PM2.5时空分布分析

Analyzing Spatiotemporal Distribution of PM2.5 in China by Integrating Kalman Filter and Multi-level Clustering

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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 Multi-level Clustering