Research On Satellite Renote Sensing Method Of PM_2.5 And Black Carbon Components

PM_2.5 is the main pollutant causing haze pollution events.Numerous studies at home and abroad have shown that PM_2.5 and its components have significant negative effects on human health,ecological environment,and climate.Among them,black carbon（BC）components have strong absorbance,and Its warming effect is second only to CO2.As one of the six largest BC emission zones in the world,China’s BC emissions account for approximately 14%of the global radiation forcing caused by global radiation balance.However,the current distribution of PM_2.5 monitoring stations in China is uneven,and BC monitoring stations are scarce,making it difficult to obtain large-scale,high-precision,and high-resolution monitoring data.At present,the method of retrieving aerosol optical thickness based on satellites is relatively mature,but there are still many challenges in satellite retrieval of different aerosol components.Therefore,large-scale and high-resolution accurate monitoring of PM_2.5 and BC component concentrations is of great significance for understanding their spatiotemporal distribution,diffusion pathways,formation mechanisms,air pollution prevention and control,and climate effects.Based on MODIS satellite data,a new method（CSEN）for retrieving high-resolution PM_2.5 from coupled semi empirical model（SEM）and numerical model（Chemical Transport Model（CTM））is developed in this paper.In this paper,the moisture absorption growth effect of aerosol chemical components is fully considered.By optimizing the air quality model to simulate the particulate matter components and aerosol extinction coefficient,a two parameter AOD-PM_2.5 model suitable for the central and eastern regions of China in 2019 is constructed,and the accuracy of the inversion of two AOD-PM_2.5 algorithms（SEM and CSEN）is evaluated.CSEN model combines the advantages of conventional ground monitoring data and numerical model and provides a new method for obtaining high-precision and high-resolution PM_2.5 data in other regions.The monthly averaged correlation coefficients（R）by CSEN were 0.92,0.82,0.84,and 0.83 in January,April,July,and October,respectively,whereas those of the SEM were0.80,0.77,0.72,and 0.72,respectively.Through the research and analysis of the seasonal spatial distribution characteristics of PM_2.5 and the seasonal change characteristics of PM_2.5 in major urban agglomerations,it is found that the statistical indicators verified by the CSEN method model show significant improvement in all seasons,especially in winter.In this study,we further focus on the bottleneck that it is difficult to directly retrieve aerosol components from satellite remote sensing data.By coupling the mixing assumption of BC aerosol and background aerosol in the atmosphere in the traditional radiative transfer equation,the microphysical characteristics of BC aerosol are parameterized to reveal the physical and chemical characteristics of the aerosol model and the sensitivity of the mixing mode in the radiative transfer process;The influence of BC aerosol concentration on the atmospheric top radiation is discussed,and the influence of other non-strongly absorbent aerosols（e.g.,dust,organic carbon,etc.）on the uncertainty of BC concentration retrieval results and the spectral dependence of BC aerosol are analyzed;Based on the sensitivity characteristics of different bands,the framework and scheme of BC aerosol concentration satellite retrieval over China’s land are constructed.This method was applied to MODIS sensors to invert the BC column concentration,aerosol absorption parameters,and near ground BC concentration in China in2016.The national average estimated BC column concentration was 1.82 mg/m²,with a spatial variance of±1.35 mg/m².Through sufficient verification with ground monitoring results（annual average R²=0.835,monthly average R²=0.776）.Proved the ability of the algorithm model to characterize the spatial pattern and characteristic distribution of BC aerosol pollution.To further explore the response ability of the MODIS satellite retrieval algorithm to respond to the BC generated by biomass combustion sources and its applicability in other regions,this method was improved and applied to the Amazon rainforest in 2019.Through a quantitative study on BC aerosols generated by Amazon rainforest fires,the absorption related parameters such as BC aerosol volume fraction(f_bc),single scattering albedo（SSA）,and absorbable aerosol optical thickness（AAOD）were obtained from the MODIS aerosol data.In addition,a quantitative study was conducted on the changes in absorbable aerosols during the event,the changes in aerosol characteristics during the transport of polluted air masses,and the direct radiation forcing effect of the fire.Through analysis,it was found that the pollutants generated by the event contained many BC aerosols,and the BC component significantly enhanced the radiative forcing effect of aerosols in the region.The feasibility of a BC retrieval algorithm based on single angle multispectral satellite for analyzing and responding to major biomass combustion events is proved.In this paper,a new method of coupled physical model and numerical model inversion for large range and high precision PM_2.5 pollution is proposed,which improves the precision of PM_2.5;By coupling the atmospheric aerosol mixing model in the traditional radiative transfer equation and a series of optimizations in the earth atmosphere decoupling method,a column concentration method based on the MODIS satellite data to inverse BC is established.The new method promotes the international quantitative research on the concentration of PM_2.5 and BC components based on satellite remote sensing,theoretically provides a new idea for the quantitative retrieval of other aerosol components,improves the application ability of satellite remote sensing in atmospheric environment monitoring,and provides scientific basis and data support for exploring the environmental health benefits of atmospheric pollutants PM_2.5 and BC components and serving the dual carbon strategy.