We propose a new method to estimate surface-level particulate matter(PM)concentrations by using satellite-retrieved Aerosol Optical Thickness(AOT).This method considers the distribution and variation of Planetary Boun...We propose a new method to estimate surface-level particulate matter(PM)concentrations by using satellite-retrieved Aerosol Optical Thickness(AOT).This method considers the distribution and variation of Planetary Boundary Layer(PBL)height and relative humidity(RH)at the regional scale.The method estimates surface-level particulate matter concentrations using the data simulated by an atmospheric boundary layer model RAMS and satellite-retrieved AOT.By incorporation MODIS AOT,PBL height and RH simulated by RAMS,this method is applied to estimate the surface-level PM 2.5 concentrations in North China region.The result is evaluated by using 16 ground-based observations deployed in the research region,and the result shows a good agreement between estimated PM 2.5 concentrations and observations,and the coefficient of determination R2 is 0.61 between the estimated PM 2.5 concentrations and the observations.In addition,surface-level PM 2.5 concentrations are also estimated by using MODIS AOT,ground-based LIDAR observations and RH measurements.A comparison between the two estimated PM 2.5 concentrations shows that the new method proposed in this paper is better than the traditional method.The coefficient of determination R2 is improved from 0.32 to 0.62.展开更多
Mapping the mass concentration of near-surface atmospheric particulate matter(PM)using satellite observations has become a popular research niche,leading to the development of a variety of instruments,algorithms,and d...Mapping the mass concentration of near-surface atmospheric particulate matter(PM)using satellite observations has become a popular research niche,leading to the development of a variety of instruments,algorithms,and datasets over the past two decades.In this study,we conducted a holistic review of the major advances and challenges in quantifying PM,with a specific focus on instruments,algorithms,datasets,and modeling methods that have been developed over the past 20 years.The aim of this study is to provide a general guide for future satellite-based PM concentration mapping practices and to better support air quality monitoring and management of environmental health.Specifically,we review the evolution of satellite platforms,sensors,inversion algorithms,and datasets that can be used for monitoring aerosol properties.We then compare various practical methods and techniques that have been used to estimate PM mass concentrations and group them into four primary categories:(1)univariate regression,(2)chemical transport models(CTM),(3)multivariate regression,and(4)empirical physical approaches.Considering the main challenges encountered in PM mapping practices,for example,data gaps and discontinuity,a hybrid method is proposed with the aim of generating PM concentration maps that are both spatially continuous and have high precision.展开更多
基金supported by National Department Public Benefit Research Foundation (Ministry of Environmental Protection of the People’s Republic of China) (Grant No. 201009001)National Natural Science Foundation of China (Grant No. 41101327)
文摘We propose a new method to estimate surface-level particulate matter(PM)concentrations by using satellite-retrieved Aerosol Optical Thickness(AOT).This method considers the distribution and variation of Planetary Boundary Layer(PBL)height and relative humidity(RH)at the regional scale.The method estimates surface-level particulate matter concentrations using the data simulated by an atmospheric boundary layer model RAMS and satellite-retrieved AOT.By incorporation MODIS AOT,PBL height and RH simulated by RAMS,this method is applied to estimate the surface-level PM 2.5 concentrations in North China region.The result is evaluated by using 16 ground-based observations deployed in the research region,and the result shows a good agreement between estimated PM 2.5 concentrations and observations,and the coefficient of determination R2 is 0.61 between the estimated PM 2.5 concentrations and the observations.In addition,surface-level PM 2.5 concentrations are also estimated by using MODIS AOT,ground-based LIDAR observations and RH measurements.A comparison between the two estimated PM 2.5 concentrations shows that the new method proposed in this paper is better than the traditional method.The coefficient of determination R2 is improved from 0.32 to 0.62.
基金This study was supported by the National Outstanding Youth Foundation of China(41925019)the National Key R&D Program of China(2016YFE0201400)+1 种基金the National Natural Science Foundation of China(41701413,41671367)We also acknowledge the support of the Labex CaPPA project,which is funded by the French National Research Agency under contract"ANR-11-LABX-0005-01".
文摘Mapping the mass concentration of near-surface atmospheric particulate matter(PM)using satellite observations has become a popular research niche,leading to the development of a variety of instruments,algorithms,and datasets over the past two decades.In this study,we conducted a holistic review of the major advances and challenges in quantifying PM,with a specific focus on instruments,algorithms,datasets,and modeling methods that have been developed over the past 20 years.The aim of this study is to provide a general guide for future satellite-based PM concentration mapping practices and to better support air quality monitoring and management of environmental health.Specifically,we review the evolution of satellite platforms,sensors,inversion algorithms,and datasets that can be used for monitoring aerosol properties.We then compare various practical methods and techniques that have been used to estimate PM mass concentrations and group them into four primary categories:(1)univariate regression,(2)chemical transport models(CTM),(3)multivariate regression,and(4)empirical physical approaches.Considering the main challenges encountered in PM mapping practices,for example,data gaps and discontinuity,a hybrid method is proposed with the aim of generating PM concentration maps that are both spatially continuous and have high precision.
