Journal of Geo-information Science ›› 2017, Vol. 19 ›› Issue (3): 382-389.doi: 10.3724/SP.J.1047.2017.00382

• Orginal Article • Previous Articles     Next Articles

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;


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