Comparative study of the spatial interpolation methods for the Shanghai regional air quality evaluation

Xuanyi Zhang; Runhe Shi; Maosi Chen

doi:10.1117/12.2320402

18 September 2018 Comparative study of the spatial interpolation methods for the Shanghai regional air quality evaluation

Xuanyi Zhang, Runhe Shi, Maosi Chen

Author Affiliations +

Proceedings Volume 10767, Remote Sensing and Modeling of Ecosystems for Sustainability XV; 107670Q (2018) https://doi.org/10.1117/12.2320402
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

For the past few years, the aerosol pollution in Shanghai is getting worse, leading to the haze weather and air quality deterioration as well. This paper is a comparative study on reliability and applicability of the spatial interpolation methods for the regional air quality evaluation, the daily data of the air quality indices (AQI, PM_2.5 and PM₁₀) comes from the Shanghai automatic monitoring stations, which helps us to compare the different interpolation methods in testing and measuring various air pollutants in Shanghai. Inverse Distance Weighted (IDW), Spline and Kriging were respectively used for the calculation of spatial interpolation. With the aforementioned methods we can compare the interpolation methods and gain the four indices, such as the mean error (ME), the mean relative error (MRE), the root mean squared error (RMSIE), and the correlation coefficient (R²) , which help us make a comprehensive comparative analysis of the spatial interpolation methods for the Shanghai regional air quality. The result shows that the IDW method is optimal for PM_2.5 concentration and AQI, while Kriging Method is the Best for the concentration of PM₁₀. We can also find that Seasonal characteristics and different spatial aggregation characteristics have a significant impact on the interpolated results of air pollutants.

Citation Download Citation

Xuanyi Zhang, Runhe Shi, and Maosi Chen "Comparative study of the spatial interpolation methods for the Shanghai regional air quality evaluation", Proc. SPIE 10767, Remote Sensing and Modeling of Ecosystems for Sustainability XV, 107670Q (18 September 2018); https://doi.org/10.1117/12.2320402

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