Research On The Relationship Between OMI Satellite Data In Sichuan Province And Near-ground NO₂ Concentration

NO₂ is a trace gas that plays an important role in tropospheric and stratospheric chemistry.A large number of studies have shown that the Beijing-Tianjin-Hebei,Yangtze River Delta,Pearl River Delta,and Sichuan Basins are high-value areas of NO₂.In recent years,the resident population of Sichuan Province has continued to grow,and the industrial level has developed rapidly.By the end of 2018,the resident population of Sichuan Province reached 83.41 million,ranking fourth in the country,leading to the increasingly prominent air pollution in Sichuan Province.The means of analyzing the temporal and spatial distribution characteristics of tropospheric NO₂column concentration by satellite remote sensing method is relatively mature,but the research on the temporal and spatial distribution of NO₂ in Sichuan is relatively rare and the related researches on the retrieval of the NO₂ column concentration to the ground are also scarce.Therefore,this thesis analyzes the spatial and temporal distribution characteristics of NO₂ in Sichuan Province from 2005 to 2018 through OMI satellite data and establishes relationship between satellite data from 2015 to2018 and the contemporary ground-level NO₂ monitoring data.The study found:?1?During the study period,the annual average tropospheric NO₂ column concentration in Sichuan Province is 1.66×10¹⁵ mol cm^-2,and the annual increase rate is 0.02 mol cm^-2 yr^-1.The seasonal characteristics are obvious and the seasonal variation trend is winter(2.15×10¹⁵ mol cm^-2)>spring(1.72×10¹⁵ molcm^-2)>autumn(1.65×10¹⁵ mol cm^-2)>summer(1.49×10¹⁵ mol cm^-2);?2?The spatial distribution trend of tropospheric NO₂ column concentration in Sichuan Province is closely related to topography.The high value area is concentrated in the basin with altitude below 800m.From the average value of tropospheric NO₂ column concentration in Sichuan Province from 2005 to 2018 we can discover that Chengdu,Deyang and Guang'an City are high-value areas (>4×10¹⁵ mol cm^-2),and Zigong City,Meishan City,Luzhou City,Neijiang City, Ziyang City,Suining City and Yibin City are the second highest value areas (3×10¹⁵ mol cm^-2—4×10¹⁵ mol cm^-2).Leshan City,Nanchong City,Dazhou City, Mianyang City,Guangyuan City,Bazhong City,Panzhihua City,Ya'an City are grouped as the median area(1×10¹⁵ mol cm^-2—3×10¹⁵ mol cm^-2)while Liangshan,Aba,and Ganzi are low-value areas(<1×10¹⁵ mol cm^-2);?3?The correlation between NO₂ monitoring mean and OMI remote sensing data of the national control station in Sichuan Province from 13:00 to 14:00 and from 13:00 to 15:00 reached 0.92,indicating that the NO₂ monitoring mean of the national control station can effectively match the OMI remote sensing data.And the OMI satellite inversion results can effectively manifest the ground NO₂ emission capacity in Sichuan Province;?4?Using the stepwise regression and BP neural network to establish the relationship between the monthly NO₂ monitoring of the national control station from 13:00 to 15:00 in 2015 to 2017,the monthly mean value of OMI remote sensing data, temperature,precipitation and the monthly mean value of hours of sunshine using 2018 date to test,it was found that the stepwise regression and BP neural network fitting effects are both good,which applicable to the retrieval of OMI remote sensing tropospheric NO₂ column concentration to the ground in Sichuan Province.From the comparision of the simulation curves,the stepwise regression simulation effect is better.The multiple stepwise regression equation:Ground_NO2=5.835+4.373*OMI_NO2-0.542*T+0.162*RH?A stepwise regression and BP neural network model were used to establish the relationship between the tropospheric NO₂ column concentration and the ground-level NO₂ concentration in Sichuan Province,and the feasibility of the algorithm was verified,which provided a method for the ground-level NO₂ concentration inversion.