| Since the industrial revolution,the use of fossil fuels has become the main power of industrial and agricultural production,and People’s Daily life cannot do without chemical fuels.With the development of industrial and agricultural production as well as cities,haze phenomenon occurs frequently,and the word "haze" has become a word often mentioned by Chinese people.Haze contains a variety of aerosol particles,which do harm to human and affect people’s normal production and life.The most obvious observation is the change of visibility.The Beijing-Tianjin-Hebei region is one of the regions with the most serious air pollution in China.In this paper,the Southern Central Hebei Plain(SCHP)region is selected as the study area,which is of typical significance and can further provide guidance and suggestions for other plains regions in China.For air pollution,we need an accurate,simple and practical model to analyze its spatial and temporal distribution.On the research of aerosol,domestic and foreign research on satellite inversion is very extensive,the use of satellite remote sensing to carry out aerosol inversion research has been recognized by all countries.Among them,numerous studies have been carried out by MODIS,a medium-resolution sensor.The data obtained from this sensor are free of charge,and the time-spatial range and various resolution data are very comprehensive.Surface meteorological data is an important index to characterize the weather conditions of a region.Therefore,the inversion model of the relationship between aerosol optical thickness and surface meteorological data can be established through satellite remote sensing data to obtain the spatial and temporal distribution of AOD in a large scale,thus making up for the deficiency of ground-based monitoring and providing the possibility to understand the characteristics of regional air pollution.In this paper,based on the climate center in Hebei province from 2016 to 2017,hours of SCHP meteorological monitoring data and MODIS AOD data,use the empirical model in Elterman AOD inversion model based on a new simple and practical,and high accuracy of AOD inversion model,this article notes for R-ERM＿AOD relation model,using correlation analysis and root mean square error(RMSE)and relative prediction error statistics index to evaluate the accuracy of the model fitting,then analysis in 2017 SCHP of the spatial distribution characteristics of AOD and the changing rule of the different time scales.The main research conclusions are as follows:(1)The empirical model was used to re-select ground meteorological data and aerosol elevation for research.The R-E model obtained from 1km data in 2016,after fitting,R was 0.78,which was much higher than the 0.13 and 0.29 of E model and Q model.The RMSE of R-E model was 0.21,while both E model and Q model were about 0.40.After data verification in 2017,the R after model fitting of R-E model is0.70,and the RMSE is 0.20.The R of E model and Q model are 0.11 and 0.35,respectively,and the RMSE is 0.33 and 0.25.From the results of model verification,the fitting accuracy of the model proposed in this paper is good,and the RMSE is small,which indicates that the new AOD inversion mode proposed by improving the relationship model between meteorological elements and aerosol elevation can be better applicable to the inversion of AOD changes within a certain range.(2)Comparing the relative error(RE)between AOD calculated from each model and AOD calculated from satellite remote sensing data,the RE of E model of 1km resolution data is 39%,the standard deviation is 22%,and the maximum value is151%;The RE of Q model is 46%,the standard deviation is 46% and the maximum is299%.The RE of R-E model is 23%,the standard deviation is 16% and the maximum is 114%.Both of them are lower than the values of E model and Q,which further proves that R-E model has better fitting effect.Through further research and analysis,it is found that the E model underestimates AOD,the summer E model has a poor fitting effect,the model fitting effect is better in the low AOD value areas in the SCHP,and the Q model has a poor fitting effect in winter,and there is an overestimation of AOD value,but in high AOD value areas such as Shijiazhuang,the fitting effect is better.The R-E model optimizes this extreme phenomenon and is greatly improved in summer.In July,the relative error of E model is 60-70%,Q model is 30-40% and R-E model is 10-20%.It works best on average.(3)The mean value,minimum value and maximum value of 1km resolution AOD data are 0.67,0.02 and 3.4,while the mean value,minimum value and maximum value of 10 km resolution AOD data are 0.87,0.04 and maximum value are3.81.Overall 10 km resolution data from AOD value higher than 1 km,10 km after through the model validation data E model fitting correlation in 0.23,0.36 Q model,and R-E model is 0.72,but the root mean square error is higher 0.28,10 km resolution data of relative error of the mean,standard deviation,maximum E model is 41%,21%,97%,Q model is 33%,22%,213%,R-E model was 23%,16%,101%.R-E model is still the best model,but the maximum value is lower than 1km resolution data,and the maximum value of Q mode decreases the most,which also indicates that Q mode overestimates AOD.In summary,the average AOD value obtained from 10 km resolution inversion is higher than that of 1km resolution inversion,and the overall model fitting effect of 1km resolution data is better than that of 10 km resolution data.(4)The spatial and temporal distribution of AOD.According to the spatial distribution diagram,AOD value in the study area decreased from southwest to northeast.Shijiazhuang,Xingtai and Handan are areas with high AOD values,while in the SCHP,Langfang Cangzhou has low AOD values.The AOD was the highest in summer,followed by autumn and winter,and the lowest in spring.The AOD value in July and August was the highest,within the range of 0.8～1.1,followed by October.The spatial variation was the most complex.The lowest month was in March,and AOD in the region was mostly between 0.2 and 0.3. |
PDF Full Text Request