| With the development of industrialization and urbanization,haze and fine mode particulate pollution(PM2.5)are becoming more and more serious in China.PM2.5has a sustained impact on the environment,climate change and human health.The real-time monitoring of near-surface PM2.5 concentration is urgently necessary.Because of the limited spatial coverage of ground-level PM2.5 monitoring systems,ground-based PM2.5 concentration measurement is insufficient under many circumstances.Therefore,this study retrieves the ground-level PM2.5 concentration based on satellite remote sensing technology.Specific Particle Swarm Extinction Mass Conversion Algorithms(SPSEMCA)based on MODIS/MAIAC Aerosol Optical Depth(AOD)data was constructed,and a geographically weighted regression model of PM2.5 concentration in Central and Eastern China was established based on Himawari-8/AHI data.Besides,in order to clarify the historical characteristics of haze in China,the distribution,influencing factors and health risks of haze in China over the past 40 years since 1973 were comprehensively analyzed.Firstly,an AOD-PM2.5 Specific Particle Swarm Extinction Mass Conversion Algorithm(SPSEMCA)using MODIS/MAIAC 1km x 1km aerosol optical depth data is introduced.Ground-level observed PM2.5;planetary boundary layer height and relative humidity in Beijing area in 2015 were used to establish this algorithm,and the same datasets for 2016 were used to test the performance of SPSEMCA.The SPSEMCA involves four steps to obtain PM2.5 values from AOD datasets.In particle correction,we use?2.5(the extinction fraction caused by particles with diameter less than 2.5?m)to make accurate simulation of AOD2.5,which is contributed by the specific particle swarm PM2.5.In vertical correction,we compare the performance of PBLH retrieved by satellite Lidar CALIPSO data and Planetary Boundary Layer Height(PBLH)reanalyzed by ECMWF and make a systematic correction for PBLH.For extinction to volume conversion,relative humidity and FMF were used together to simulate the AVEC(averaged volume extinction coefficient,?m2/?m3).MODIS LUT-SAD FMF was used to avoid the large uncertainties caused by the MODIS/FMF product.The validation of PM2.5 from the SPSEMCA algorithm to AERONET observation data and MODIS monitoring data achieved acceptable results,R=0.70,RMSE(root mean square error)=58.75?g/m3 for AERONET data,R=0.75,RMSE=43.38?g/m3 for MODIS data,respectively.The results of SPSEMCA during haze pollution period show that the algorithm can present PM2.5accurately.The algorithm can be applied to PM2.5 retrieval under extreme weather conditions such as haze.Subsequently,based on Himawari-8/AHI AOD,a Geographically Temporally and Land use Weighted Regression(GTLWR)model for hourly PM2.5 retrieval in central and Eastern China is established.The weights are constructed using longitude,latitude,day,hour and land use type data.The weighted regression model was constructed using AHI hourly AOD,surface relative humidity and boundary layer height data.The PM2.5 concentration is retrieved at eight hours per day(UTC 1:00,2:00,3:00,4:00,5:00,6:00,7:00,8:00)in 2017.The performance of cross validation were excellent.For all 437,642 sample points,in the modeling set,R2 is 0.886,RMSE is only 12.180 g/m3,in 10-fold cross validation set,R2 is 0.784,RMSE is20.104 g/m3,and RMSE is only 8 g/m3 higher than the error of the modeling set.It shows that the model built in this study has high predictability and reliability,and can be used in PM2.5 retrieval.The spatial and temporal distribution characteristics of PM2.5 concentration per hour in 2017 are so obvious.PM2.5 concentration in the middle and east of China is obviously higher in the morning and lower in the afternoon,and it tends to decrease gradually with the time.In order to test the stability of the model,the performance PM2.5 model for extreme weather processes such as haze and dust are tested.The results show that the model can monitor the occurrence,development and regression of extreme weather processes.The PM2.5retrieval result is accuracy,which plays an important role in forecasting and early warning of extreme weather.In addition,the long time series analysis of haze in China from 1973 to 2017was also carried out.The haze was identified by visibility and meteorological data from 1973 to 2017,and the number of haze days and the visibility in haze days were also calculated.By using standard deviation ellipse analysis and principal component regression,the variation of haze gravity center and the contribution rate of main influencing factors in nine typical areas were calculated.Seasonal variations of haze and health risk assessment were also carried out.The results show that the changesin haze in China present as five periods:1973-1979,1980-1989,1990-2001,2002-2010,and 2011-2017.Haze days in the majority of typical cities in central and eastern China show a tendency of growth or fluctuating growth,and the variation intensified after the 1990s.In most areas,the visibility on haze days wasapproximately 7km until 2017.The main body of the haze area in China is approximatelya diamond-shaped pentagon with BEIJING,CHENGDU,NANNING,GUANGZHOU,and SHANGHAI as the apexes.For most regions,pollutant emissions were the main contributing factors.The haze health risk increased sharply and expanded from 1973 to 2017.High-and extremely high-risk areas are still concentrated in the diamond region ofthe haze area and have expanded along or surround main traffic lines and major cities.Through analysis of the contributions of haze factors in various regions of China,this study suggests that the most important means of haze governance comprise four aspects:I)the reduction of exhaust emissions,II)the implementation of green consumption,travel,and behaviour concepts,III)enhanced governance,and IV)urbanization that is as balanced as possible;furthermore,the development of cities such as enclaves should be promoted to avoid health risks to large cities. |
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