Multi-source Remote Sensing Detection Of Urban Air Pollution In Shaanxi Section Of The Yellow River Basin

In recent years,with the rapid development of China’s economy,the problem of air pollution has become increasingly serious.Air pollution has a serious impact on human health,ecological environment and atmospheric climate.At present,most of the relevant research at home and abroad focus on fine particulate matter pollution(mainly PM2.5 pollution),focusing on its source and sink distribution,temporal and spatial patterns and health effects.The traditional air pollution monitoring method mainly realizes the monitoring work by laying ground monitoring stations,but this method has certain limitations,such as the limited number of monitoring stations and uneven distribution,resulting in the inability to meet the needs of real-time monitoring of air pollution in large areas.Multi-source remote sensing technology can effectively improve this problem in this direction,which can better reflect the spatial characteristics,source and sink distribution and transmission path of air pollution by virtue of the significant advantages of diverse sensor and data products,short observation period and high resolution.As the mother river of the Chinese nation,it is of great significance for the Yellow River to carry out ecological environmental protection and pollution prevention and control in its basin.In this paper,some cities in the middle section of the Yellow River Basin(namely the three cities in Shaanxi Province)were selected as the study area,mainly due to the distinctive topographic and geomorphological characteristics of the region,the obvious difference in altitude between the north and the south,and the prominent seasonal and climatic characteristics,and it is one of the areas with serious air pollution in the Yellow River Basin.Therefore,studying the urban air pollution in the Shaanxi section of the Yellow River Basin is of great significance for protecting the ecological environment quality of the Yellow River Basin and promoting the prevention and control of air pollution in the Yellow River Basin.This paper uses a variety of remote sensing data and meteorological data products,and fully combines multisource remote sensing technology to study the air pollution in this region.On the basis of data acquisition and processing,three types of algorithm models were constructed to explore and analyze the spatial and temporal situation,distribution characteristics and influencing factors of PM2.5 concentration in the study area.The main research contents are as follows:(1)A correlation analysis model was constructed to study the correlation between Aerosol Optical Depth(AOD),Normalized Difference Vegetation Index(NDVI)and Precipitation(PRCP)and PM2.5 concentration in the region in 2020.The model results show that there is a significant correlation between the respective variables and the dependent variable,and there is a strong spatial difference.(2)A multiple linear regression model with PM2.5 concentration as the dependent variable and NDVI,PRCP,AOD and other meteorological factors as independent variables was constructed.The obtained model parameters are as follows: the coefficient of determination R2 value is 0.799,the model Sig value is 0,the model Durbin-Watson value is 1.710,and the rmse value of the model fitting value is 7.709.The model results and related parameters indicate that the fitting accuracy of the multiple regression model is up to standard and the results obtained are reliable.(3)Ordinary Least Squares(OLS)and Geographically Weighted Regression(GWR)were selected for modeling,and the better class was selected according to the AICc value of the parameter used to evaluate the performance of the model.The comparative results show that the GWR model is significantly better than the OLS model.Combined with the main results of this paper,it can be concluded that the use of multisource remote sensing technology to study the temporal and spatial changes and influencing laws of PM2.5 concentration in cities in Shaanxi section of the Yellow River Basin is reliable in the model results and feasible in terms of ideas and methods.In the study area,the correlation between the respective variables of the model and PM2.5 concentration was significant,and the regularity of PM2.5 concentration affected by various factors was prominent,and there were obvious differences between them.Finally,by comprehensively evaluating the parameters and performance of each model,the GWR model is the optimal model for studying the spatial and temporal distribution pattern of PM2.5 concentration in this region based on the premise of fully considering the three types of remote sensing data,meteorological data and geographical location information.