Journal of Geo-information Science >
A Method of Spatial Interpolation of Air Pollution Concentration Considering WindDirection and Speed
Received date: 2016-05-09
Request revised date: 2016-09-22
Online published: 2017-03-20
Copyright
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.
LI Jialin , FAN Zide , DENG Min . A Method of Spatial Interpolation of Air Pollution Concentration Considering WindDirection and Speed[J]. Journal of Geo-information 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/m3) | RMS/(μg/m3) | |
---|---|---|
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 PM2.5 concentration interpolationresults图7 PM2.5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/m3) | RMS/(μg/m3) | |
---|---|---|
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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