Journal of Geoinformation Science >
A Method of Spatial Interpolation of Air Pollution Concentration Considering WindDirection and Speed
Received date: 20160509
Request revised date: 20160922
Online published: 20170320
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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 spatialtemporal 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 (DirectionVelocity IDW) of spatial interpolation of air pollutant concentration taking wind direction and speed into account. First, we constructed a windfield surface based on the discrete wind direction and speed data and the diffusion distance is computed in the windfield. Then, we used Dijkstra algorithm to obtain the shortest path in windfield. 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.
LI Jialin , FAN Zide , DENG Min . A Method of Spatial Interpolation of Air Pollution Concentration Considering WindDirection and Speed[J]. Journal of Geoinformation Science, 2017 , 19(3) : 382 389 . DOI: 10.3724/SP.J.1047.2017.00382
Fig. 1 Flow chart of the DVIDW algorithm图1 DVIDW算法流程图 
Fig. 2 Decomposition of vector data图2 矢量数据的分解 
Fig. 3 The calculation of the shortest path in the wind field图3 风场最短路径计算 
Fig. 4 The distribution map of air pollutant observation stations and meteorological observation stations图4 空气污染物观测站点和气象观测站点分布图 
Tab. 1 Results of experiments by the four different spatial interpolation methods (Test 1)表1 4种空间插值方法实验结果（实验1） 
MAE/(μg/m^{3})  RMS/(μg/m^{3})  

IDW_1  7.2732  15.2666 
IDW_2  7.4573  16.0977 
IDW_3  7.6951  17.0742 
DIDW_1  6.6000  14.5153 
DIDW_2  6.8691  14.8112 
DIDW_3  7.0291  15.0007 
Kriging  7.1302  15.0958 
DVIDW_1  6.2557  14.5614 
DVIDW_2  6.4666  14.6923 
DVIDW_3  6.5208  14.7595 
Fig. 5 Results of experiments by the four differentspatial interpolation methods (Test 1)图5 4种空间插值方法实验结果柱状图（实验1） 
Fig. 6 The spatial distribution of results of four interpolation methods (Test 1)图6 4种方法插值结果误差空间分布（实验1） 
Fig. 7 The map of PM_{2.5} concentration interpolationresults图7 PM_{2.5}浓度插值结果图 
Tab. 2 Results of experiments by the four different spatial interpolation methods表2 4种空间插值方法的实验结果（实验2） 
MAE/(μg/m^{3})  RMS/(μg/m^{3})  

IDW_1  15.6575  20.2964 
IDW_2  14.5205  19.5501 
IDW_3  13.8425  19.1654 
DIDW_1  10.4757  14.5905 
DIDW_2  10.6549  14.6874 
DIDW_3  10.8367  14.8179 
Kriging  15.1735  19.8676 
DVIDW_1  10.1501  14.6334 
DVIDW_2  10.2418  14.6044 
DVIDW_3  10.2829  14.5789 
Fig. 8 Results of experiments by the six differentspatial interpolation methods (Test 2)图8 4种空间插值方法的实验结果柱状图（实验2） 
Fig. 9 The spatial distribution of results of four interpolation methods (Test 2)图9 4种方法插值结果误差的空间分布（实验2） 
Fig. 10 The map of PM2.5 concentration interpolation results图10 PM2.5浓度插值结果图 
The authors have declared that no competing interests exist.
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