The spatial distribution characteristics and ground-level estimation of NO2 and SO2 over Huaihe River Basin and Shanghai based on satellite observations

People in Huaihe River Basin and Shanghai have been suffering from severe air pollution of nitrogen dioxide and sulfur dioxide due to the development of heavy industry. Traditional ambient monitoring station measurements can provide real-time accurate data, but it is limited due to the less number of monitoring sites. Satellite observation data from remote sensing can provide a wide range opollutants concentrations in long-time sequence. Top-down approaches based on satellite data can be effectively applied to estimate the ground concentrations of pollutants. In this paper, the tropospheric pollutants columns from the Ozone Monitoring Instrument(OMI) were used to analyse the seasonal variation of NO2 and SO2 in 2015. Moreover, the ground-level NO2 and SO2 concentrations of the Huaihe River Basin and Shanghai at this time were estimated by the data and meteorological data. The results show that: the concentrations of NO2 and SO2 are highest in winter, and high-value areas are mainly located in Shandong and Northern Henan. Estimating the ground-level NO2 and SO2 concentrations based on satellite observations is reliable with the validation R2 0.48 and 0.47 respectively. Finally, The spatial distribution of satellite-derived annual mean NO2 and SO2 has a similar characteristics to the satellite columns.

[1]  M. Miller,et al.  Attribution of modeled atmospheric sulfate and SO 2 in the Northern Hemisphere for June–July 1997 , 2006 .

[2]  Kees de Hoogh,et al.  Western European land use regression incorporating satellite- and ground-based measurements of NO2 and PM10. , 2013, Environmental science & technology.

[3]  F. Boersmaa,et al.  Validation of OMI Tropospheric NO2 Observations During INTEX-B and Application to Constrain NOx Emissions Over the Eastern United States and Mexico Please share how this access benefits you. Your story matters , 2008 .

[4]  Jian Xu,et al.  Estimating Ground Level NO2 Concentrations over Central-Eastern China Using a Satellite-Based Geographically and Temporally Weighted Regression Model , 2017, Remote. Sens..

[5]  Changqing Lin,et al.  Assessment of health burden caused by particulate matter in southern China using high-resolution satellite observation. , 2017, Environment international.

[6]  Zhao Chunsheng.  A Method of Inferring Ground Level NO_2 Using Satellite-Borne OMI Observations , 2011 .

[7]  Tao Yu,et al.  Assessment of human health impact from exposure to multiple air pollutants in China based on satellite observations , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[8]  James F. Gleason,et al.  An improved retrieval of tropospheric nitrogen dioxide from GOME , 2002 .

[9]  Joseph P. Pinto,et al.  Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument , 2008 .

[10]  P. Monks,et al.  Estimating daily surface NO 2 concentrations from satellite data - a case study over Hong Kong using land use regression models , 2016 .

[11]  T. Jiang,et al.  The Core Conclusions and Interpretation of Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , 2015 .

[12]  Yu Chao.  SO_2 long-term monitoring by satellite in the Pearl River Delta , 2012 .

[13]  Yuhang Wang,et al.  Seasonal budgets of reactive nitrogen species and ozone over the United States, and export fluxes to the global atmosphere , 1998 .

[14]  Han Nian-long.  Conversion on different dimensions of atmospheric ozone , 2010 .

[15]  Henk Eskes,et al.  Validation of urban NO 2 concentrations and their diurnal and seasonal variations observed from the SCIAMACHY and OMI sensors using in situ surface measurements in Israeli cities , 2009 .