Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July...Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July 23 of 2014, this article adopted Pearson correlation coefficient method to determine the relevance among each pollutant of these cities with the help of SPSS. The results showed that such three leading indexes as SO2, PM10 and PM2.5 had strong correlation in Beijing, Tianjin and main cities of Hebei. Finally, some suggestions and preventive measures for the cooperative governance of air pollution in Beijing-Tianjin-Hebei Region were put forward, hoping this can help them.展开更多
In this paper, the authers introduce certain entire exponential type interpolation operatots and study the convergence problem of these operatots in c(R) or Lp(R) (1≤p<∞)
Based on the statistical analysis of API （air pollution index）, the study improves the layout of the site in the downtown of Nanjing and the surroundings. Through selecting more relevant factors to establish the API...Based on the statistical analysis of API （air pollution index）, the study improves the layout of the site in the downtown of Nanjing and the surroundings. Through selecting more relevant factors to establish the API regression equation and making the inversion of API data in simulated sites, the interpolation values of API in both actual sites and simulated sites have been calculated. The methods include IDW （inverse distance weighting） interpolation, Spline interpolation, and Kriging interpolation Spherical model, Exponential model and the Gaussian model. Meanwhile, through the cross-validation to test the results of interpolation in different models or parameters, the study also obtains the best fit of the interpolation model or parameters. In addition, IDW p = 3, fitting coefficient of 0.644; Spline interpolation w = 1, the fitting coefficient of 0.972; Kriging interpolation, Gaussian, fitting coefficient of 0.684. The study indicates that in best fitting model, the parameters after in increasing the simulated site are not in line with the ones previous. The result shows that it is best to test different data separately and select the appropriate interpolation model, but not blindly use the same spatial interpolation. After the increasing of the stimulated site, the API estimated results in three interpolation methods are consistent with the spatial distribution trend. In the aspect of calculating the range, the improvement close the results between 3 interpolation methods and increase of the stimulated sites, and the values of Spline interpolation and Kriging interpolation is closer.展开更多
文摘Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July 23 of 2014, this article adopted Pearson correlation coefficient method to determine the relevance among each pollutant of these cities with the help of SPSS. The results showed that such three leading indexes as SO2, PM10 and PM2.5 had strong correlation in Beijing, Tianjin and main cities of Hebei. Finally, some suggestions and preventive measures for the cooperative governance of air pollution in Beijing-Tianjin-Hebei Region were put forward, hoping this can help them.
文摘In this paper, the authers introduce certain entire exponential type interpolation operatots and study the convergence problem of these operatots in c(R) or Lp(R) (1≤p<∞)
文摘Based on the statistical analysis of API （air pollution index）, the study improves the layout of the site in the downtown of Nanjing and the surroundings. Through selecting more relevant factors to establish the API regression equation and making the inversion of API data in simulated sites, the interpolation values of API in both actual sites and simulated sites have been calculated. The methods include IDW （inverse distance weighting） interpolation, Spline interpolation, and Kriging interpolation Spherical model, Exponential model and the Gaussian model. Meanwhile, through the cross-validation to test the results of interpolation in different models or parameters, the study also obtains the best fit of the interpolation model or parameters. In addition, IDW p = 3, fitting coefficient of 0.644; Spline interpolation w = 1, the fitting coefficient of 0.972; Kriging interpolation, Gaussian, fitting coefficient of 0.684. The study indicates that in best fitting model, the parameters after in increasing the simulated site are not in line with the ones previous. The result shows that it is best to test different data separately and select the appropriate interpolation model, but not blindly use the same spatial interpolation. After the increasing of the stimulated site, the API estimated results in three interpolation methods are consistent with the spatial distribution trend. In the aspect of calculating the range, the improvement close the results between 3 interpolation methods and increase of the stimulated sites, and the values of Spline interpolation and Kriging interpolation is closer.
