地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (3): 382-389.doi: 10.3724/SP.J.1047.2017.00382

• 遥感科学与应用技术 • 上一篇    下一篇

顾及风向和风速的空气污染物浓度插值方法

李佳霖1(), 樊子德1,2, 邓敏1,*()   

  1. 1. 中南大学地理信息系,长沙 410083
    2. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
  • 收稿日期:2016-05-09 修回日期:2016-09-22 出版日期:2017-03-20 发布日期:2017-03-20
  • 通讯作者: 邓敏 E-mail:garlic_lee@csu.edu.cn;dengmin208@tom.com
  • 作者简介:

    作者简介:李佳霖(1992-),男,山东滨州人,硕士生,主要从事时空插值方法的研究工作。E-mail:garlic_lee@csu.edu.cn

  • 基金资助:
    国家“863”计划(2013AA122301);高等学校博士点专项科研基金(20110162110056);湖南省博士生优秀学位论文资助项目(CX2014B050)

A Method of Spatial Interpolation of Air Pollution Concentration Considering WindDirection and Speed

LI Jialin1(), FAN Zide1,2, DENG Min1,*()   

  1. 1. Department of Geo-informatics, Central South University, Changsha 410083, China
    2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2016-05-09 Revised:2016-09-22 Online:2017-03-20 Published:2017-03-20
  • Contact: DENG Min E-mail:garlic_lee@csu.edu.cn;dengmin208@tom.com

摘要:

随着经济的快速发展,中国大部分地区空气污染状况日趋严重。空气污染物浓度插值对于进一步分析污染物时空分布情况,估计不同地区人群的暴露风险,制定防范措施具有重要作用。然而,现有空间插值方法由于没有充分考虑风向和风速因素对于污染物扩散的影响,故直接应用于空气污染物浓度插值,会对插值结果造成不利的影响。因此,本文提出一种顾及风向和风速的空气污染物浓度插值方法(Direction-Velocity IDW,DVIDW)。该方法首先根据离散气象站点处的风向和风速数据建立风场表面,然后利用风场数据计算空气污染物的扩散距离,根据扩散距离计算风场中待求点与采样点间的最短路径距离,最后由最短路径距离替代欧式距离进行反距离加权插值。本文分别采用2组实际空气污染物浓度数据,对DVIDW方法和其他常用的空间插值方法进行实验对比分析,验证了本文方法的可行性和优越性。

关键词: 空间插值, 风向风速, 空气污染物浓度, 风场, 最短路径距离

Abstract:

With the rapid development of economy, air pollution becomes more and more serious in China. The quality of the interpolation results of air pollutant concentration is very significant for analyzing the spatial-temporal distribution of the air pollutant, estimating the exposure risk of people in different areas, and making precaution. However, there are some problems when applying the existing spatial interpolation methods directly to the interpolation of air pollutant concentration. One of the most important problems is that the existing spatial interpolation methods cannot fully consider the influence of wind direction and speed on the air pollutant diffusion. We proposed a method (Direction-Velocity IDW) of spatial interpolation of air pollutant concentration taking wind direction and speed into account. First, we constructed a wind-field surface based on the discrete wind direction and speed data and the diffusion distance is computed in the wind-field. Then, we used Dijkstra algorithm to obtain the shortest path in wind-field. Finally, we interpolated the attribute value using IDW by the shortest path distance instead of the Euclidean distance. In the experiment, we verified the effectiveness of the method we proposed by comparing DVIDW and the commonly used spatial interpolation methods. We concluded that the proposed method (DVIDW) can produce interpolation results with higher precision.

Key words: spatial interpolation, wind direction and speed, air pollutant concentration, wind field, shortest path distance