地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (12): 1717-1724.doi: 10.3724/SP.J.1047.2016.01717

• 遥感科学与应用技术 • 上一篇    

集成多源遥感数据的PM2.5浓度空间分布制图

蒲强1(), 邹滨1, 翟亮2,*(), 郭宇1, 桑会勇2, BILAL Muhammad3   

  1. 1. 中南大学地球科学与信息物理学院,长沙 41008
    2. 中国测绘科学研究院 地理空间信息工程国家测绘地理信息局重点实验室,北京 100039
    3. The Hong Kong Polytechnic University, Department of Land Surveying and Geo-Infomatics, Hongkong 999077
  • 收稿日期:2015-12-14 修回日期:2016-01-25 出版日期:2016-12-27 发布日期:2016-12-20
  • 通讯作者: 翟亮 E-mail:145011073@.csu.edu.cn;zhailiang@casm.ac.cn
  • 作者简介:

    作者简介:蒲 强(1990-),男,硕士生,主要从事遥感气溶胶反演与空气污染时空分布模拟研究。E-mail:145011073@.csu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41201384);湖南省高校开放平台创新基金项目(15K132);国家测绘局地理国情监测重点实验室开放基金项目(2014NGCM01);地理国情监测项目(HNGQJC2015-02, 2015JC03);资源与环境信息系统国家重点实验室开放基金项目(A类);中南大学研究生自主探索创新项目(2016zzts429)

Mapping of PM2.5 Fine Particulates Using Multi-source Remote Sensing Data

PU Qiang1, ZOU Bin1, ZHAI Liang2,*(), GUO Yu1, SANG Huiyong2, BILAL Muhammad3   

  1. 1. School of Geosciences and Info-physics, Central South University, Changsha 410083 China
    2. The Key Laboratory of Geo-Informatics ofState Bureau of Surveying and Mapping, Chinese Academy of Surveying & Mapping, Beijing 100039, China;
    3. Department of Land Surveying and Geo-Infomatics, Hong Kong Polytechnic University, Hongkong 999077, China
  • Received:2015-12-14 Revised:2016-01-25 Online:2016-12-27 Published:2016-12-20
  • Contact: ZHAI Liang E-mail:145011073@.csu.edu.cn;zhailiang@casm.ac.cn

摘要:

针对传统地面稀疏站点监测PM2.5浓度以点带面的缺陷,本研究拟借助多源遥感数据开展了地面大气细颗粒物PM2.5浓度空间分布模拟研究。以京津冀地区2013年的年均、季均PM2.5浓度模拟图为例,用简化的气溶胶反演算法(SARA)反演了 1 km高分辨率AOD,并结合高分辨率遥感提取污染相关地理要素,对研究区PM2.5浓度空间分布进行地理统计模拟及优选。结果表明:① SARA算法反演的AOD与地基AERONET相关性达0.99,能准确地反映研究区AOD的时空分布特征;② 集成多源遥感数据的地理加权回归模型拟合度高(平均R2-0.66),其空间模拟显示研究区平均PM2.5污染南部城镇最重,中东部城区次之,西北山区较轻;③ 研究区PM2.5污染程度高,全年平均模拟浓度高达75 μg/m3,在气候环境及主要污染源季节性差异驱动下,浓度分布季节性特征显著,冬季污染最严重,而夏、秋季相对较轻。该成果对于精细把握PM2.5污染特征,指导污染防控具有重要意义。

关键词: AOD反演, PM2.5, 地理加权回归, 遥感制图

Abstract:

Chronic exposure to fine particulate matter with the aerodynamic diameter less than 2.5 μm (PM2.5) is a major risk factor for cardiovascular and respiratory morbidity. Environmental problems of PM2.5 in China have aroused great concerns within both the government and the public. However, studies of this subject have been limited due to the spare distribution of national PM2.5 monitoring network. Satellite-derived aerosol optical depth (AOD) measurements can provide the spatiotemporally resolved monitoring and have been widely applied to estimate the ground level PM2.5 concentration. So far, the AOD measurements only have moderate spatial resolutions (3 to 17.6 km) which are insufficient for resolving the city level aerosol features. In order to address this issue, a 1 km resolution AOD retrieval algorithm-SARA (for MODIS instruments) was applied. Additionally, based on the high-spatial resolution fusion images, the PM2.5 related factors (such as the land use data, dusty area and major roads information) had been extracted. This study then proposed a novel geostatistical modeling method that integrates the fine resolution retrieved AOD and the high resolution area-source emission data to investigate the spatial variability of PM2.5 levels. Results show that the SARA retrieved AOD can obtain an average correlation coefficient R of 0.99 and an RMSE of 0.08, when comparing with AERONET measurements, which indicates that SARA has good accuracy and reliability in representing the aerosol conditions over the study area. Results show that, the annual average value of PM2.5 even exceeded 75μg/m3 and a discrepant spatial heterogeneity occurred between seasons. The worst conditions occurred in the winter, and then in the autumn. The GWR-predicted PM2.5 concentrations (with an average R2 of 0.66) are strongly correlated with the in situ observations. The spatial patterns revealed in the mapping results show that the high-level PM2.5 pollution clustered in southern parts of the study domain, while the low-level mainly appears in the rural or mountain land within the northern areas. Furthermore, these findings recommend the combination superiority of GWR model and multi-source remote sensing data, as well as introduced the great potential of fine-scale PM2.5 simulation in distinguishing the spatial patterns of PM2.5 and guiding the air pollution control strategies.

Key words: AOD retrieval, PM2.5, GWR, remote sensing mapping