As improved and accumulated satellite records become available,it is significant to provide up-to-date perspectives on the spatiotemporal signatures of tropospheric nitrogen dioxide(NO2)over China,the knowledge of which is helpful for air pollution control.In this study,the Ozone Monitoring Instrument NO2 dataset for the last 10 years(2005–14)was retrieved to examine multiple aspects of NO2 columns,including distributions,trends,and seasonal cycle.The pattern of average NO2suggests five hotspots with column density higher than 20×1015 molec cm-2:Jing-Jin-Tang;combined southern Hebei and northern Henan;Jinan;the Yangtze River Delta;and the Pearl River Delta.Furthermore,substantial and widespread NO2 growths are distributed over the North China Plain.By contrast,downward trends in NO2 amounts prevail in the megacities of Beijing,Shanghai,and Guangzhou,despite generally high loading levels.Except for the Pearl River Delta,there appears to be temporally consistent behaviors across all regions considered,where NO2 had an abrupt decline during 2008 to 2009,then a drastic increase up to 2013,before beginning to reduce again after 2013.However,the NO2 over the Pearl River Delta is not coevolving with the rest,having experienced a moderate rise from 2005 to 2007,followed by a reduction thereafter.A marked seasonality is apparent,with a maximum in winter and a minimum in summer,regardless of the region.The annual amplitude of NO2 is less pronounced over the Pearl River Delta,whereas the largest range is observed over the combined Southern Hebei and Northern Henan region,induced by enhanced NO2emission in wintertime due to intense domestic heating.
WANG TingWANG Pu-CaiFrancois HENDRICKYU HuanMichel VAN ROOZENDAEL
Measurements of aerosol optical characteristics were carried out with a Photoelectric Aerosol Nephelometer(PhAN)in Beijing and at Xinglong Observatory,which is located 150 km northeast of Beijing.Aerosol size distributions were retrieved by means of the inverse problem solution.Mean volume size distributions of the fine aerosol fraction were unimodal with the maximum radius in the range 0.11–0.15μm.Accumulation of aerosol matter in the air basin of Beijing takes place mainly due to the growth of particle size,but not their number.A simple optical method to detect aerosol nonsphericity is proposed.
This paper presents an empirical model for estimating the zonal mean aerosol extinction profiles in the stratosphere over 10°-wide latitude bands between 60°S and 60°N, on the basis of Stratospheric Aerosol and Gas Experiment(SAGE) II aerosol extinction measurements at 1.02, 0.525, and 0.452 μm during the volcanically quiescent period between 1998–2004. First, an empirical model is developed for calculating the stratospheric aerosol extinction profiles at 1.02 μm. Then, starting from the 1.02 μm extinction profile and an exponential spectral dependence, an empirical algorithm is developed that allows the aerosol extinction profiles at other wavelengths to be calculated. Comparisons of the model-calculated aerosol extinction profiles at the wavelengths of 1.02, 0.525, and 0.452 μm and the SAGE II measurements show that the model-calculated aerosol extinction coefficients conform well with the SAGE II values, with the relative differences generally being within 15% from 2 km above the tropopause to 40 km. The model-calculated stratospheric aerosol optical depths at the three wavelengths are also in good agreement with the corresponding optical depths derived from the SAGE II measurements, with the relative differences being within 0.9% for all latitude bands. This paper provides a useful tool in simulating zonal mean aerosol extinction profiles, which can be used as representative background stratospheric aerosols in view of atmospheric modeling and remote sensing retrievals.
Based on the optimal estimation method, a satellite XCO2 retrieval algorithm was constructed by combining LBLRTM with VLIDORT. One-year GOSAT/TANSO observations over four TCCON stations were tested by our algorithm, and retrieval results were compared with GOSAT L2 B products and ground-based FTS measurements. Meanwhile, the influence of CO2 line mixing effect on retrieval was estimated, and the research showed that neglecting CO2 line mixing effect could result in approximately 0.25% XCO2 underestimation. The accuracy of XCO2 retrievals was similar to GOSAT L2 B products at cloud-free footprints with aerosol optical depth less than 0.3, and 1% accuracy of XCO2 retrievals can be reached based on the validation result with TCCON measurements.
PARASOL(Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) multi-channel and multi-directional polarized data for different aerosol types were compared.The PARASOL polarized radiance data at 490 nm,670 nm,and 865 nm increased with aerosol optical thickness(AOT) for fine-mode aerosols;however,the polarized radiances at 490 nm and 670 nm decreased as AOT increased for coarse dust aerosols.Thus,the variation of the polarized radiance with AOT can be used to identify fine or coarse particle-dominated aerosols.Polarized radiances at three wavelengths for fine-and coarse-mode aerosols were analyzed and fitted by linear regression.The slope of the line for 670 nm and 490 nm wavelength pairs is less than 0.35 for dust aerosols.However,the value for fine-mode aerosols is greater than 0.60.The Support Vector Machine method(SVM) based on 12 vector features was used to discriminate clear sky,coarse dust aerosols,fine-mode aerosols,and cloud.Two cases were given and validated by AErosol RObotic NETwork(AERONET) measurements,MODIS(Moderate Resolution Imaging Spectroradiometer) FMF(Fine Mode Fraction at 550 nm) images,PARASOL RGB(Red Green Blue) images,and CALIOP(Cloud-Aerosol Lidar with Orthogonal Polarization) VFM(Vertical Feature Mask) data.
The anthropogenic CO column content in the atmosphere is derived from measurements with infrared grating spectrometers in Beijing,China,and Moscow,Russia,during 1992–2012.Some specific variation characteristics and long-term variation trends of the CO column content in the atmosphere in these regions are discussed.An evident variation trend of anthropogenic CO in the atmosphere for the Beijing region is not observed during 1992–2012,while for the Moscow region,it decreases yearly by about 1.4% for the same period.High CO concentrations appear quite frequently in Beijing,but much less frequently in Moscow,except during the natural fire events in summer 2010.From back trajectory analysis,the high CO concentration observed in Beijing can be attributed to the intensive CO emission sources in its surrounding areas.
WANG Pu-CaiGeorgy S.GOLITSYNWANG Geng-ChenEvgeny I.GRECHKOVadim S.RAKITINEkaterina V.FOKEEVAAnatoly V.DZHOLA
Characterization of aerosols is required to reduce uncertainties in satellite retrievals of global aerosols and for modeling the effects of these aerosols on climate.Aerosols in the North China Plain(NCP) are complex,which provides a good opportunity to study key aerosol optical properties for various aerosol types.A cluster analysis of key optical properties obtained from Aerosol Robotic Network(AERONET) data in Beijing and Xianghe during 2001-11 was performed to identify dominant aerosol types and their associated optical properties.Five dominant aerosol types were identified.The results show that the urban/industrial aerosol of moderate absorption was dominant in the region and that this type varied little with season.Urban/industrial aerosol of weak absorption was the next most common type and mainly occurs in summer,followed by that strong aerosols occurring mainly in winter.All were predominantly fine mode particles.Mineral dust(MD) and polluted dust(PD) occurred mainly in spring,followed by winter,and their absorption decreased with wavelength.In addition,aerosol dynamics and optical parameters such as refractive index and asymmetry factor were examined.Results show that the size of coarse mode particles decreased with AOD indicating the domination of external mixing between aerosols.
SUN LiXIA Xiang-AoWANG Pu-CaiCHEN Hong-BinPhilippe GOLOUBZHANG Wen-Xing
