Conference Agenda

Recently, with the rapid developments of economy and industry, how to reduce heavy air pollution has been a challenging task for Chinese people and the Chinese government. In order to study air pollutants and their chemical transformation in North China, variations of the concentrations of atmospheric constituents were analyzed for four representative sites in North China during 2005-2015. Satellite-derived vertical column densities (VCDs) of SO₂, NO₂, O₃, HCHO, and aerosol optical depth (AOD) over these four sites were used together with ground-based radiation and meteorological measurements at each site. On the base of the analysis, a photochemical mechanism relating the formation of PM_2.5 and O₃ in North China was given. In particular, the key role of volatile organic compounds (VOCs) in chemical and photochemical reactions is found to be prominent in the summer season. We make some suggestions for air pollution control in North China, especially to reduce anthropogenic VOC emissions and artificial biogenic VOC emissions.

Key words: Trace gases, particulate matter (PM), emission, solar radiation, air pollution control.

We study air quality over China using satellite observations, especially their spatial and temporal variability. For the period 2007-2017 we derived monthly SO₂ and NO_x emissions for China on a provincial level. To derive NO_x emission we applied the inversion algorithm DECSO v5.1 to OMI NO₂ retrievals of the newly developed QA4ECValgorithm. In DECSO modelled NO₂ concentrations are constraint by the NO₂ satellite observations using a Kalman filter technique. SO₂ is derived for each year by applying a correction factor to the MEIC SO₂ emissions based on the derived provincial trend in SO₂.
Both the SO₂ and NO_x emissions are used in the regional chemical-transport model CHIMERE. This model participates in an ensemble forecast of 9 operational models for East China. The ensemble forecast is compared to in-situ observations and performs better than each individual model.
The results will be validated using ground observations of the network of in-situ instruments in the cities and also using measurements at specific ground stations in different types of forests.

Using the inversion algorithm DECSO we derived monthly NO_x emissions on a 0.25 x 0.25 degree resolution over East Asia for an 11-year period (2007 to 2017) based on OMI observations. We used these emissions to analyse trends and seasonal cycle of maritime emissions over Chinese seas. No effective regulations on NO_x emissions have been implemented for ships in China, which is reflected in the trend analysis of maritime emissions. The effect of maritime emissions on the air quality over land will be discussed. Simulations by an atmospheric chemistry transport model show a notable influence of maritime emissions on air pollution over coastal areas, especially in summer. The satellite-derived spatial distribution and the magnitude of maritime emissions over Chinese seas are in good agreement with bottom-up studies based on the Automatic Identification System of ships.

Dose-Response Functions (DRFs) are a very important tool for estimating the deterioration – degradation of structural materials, used in both modern constructions and cultural heritage monuments, due to atmospheric pollution and climatological parameters. To date, the available in literature DRFs make use of ground based air pollution and climatological data in order to model materials’ deterioration. This limits the possibility of using DRFs only in areas where the necessary ground based data, are available. In this study are presented the first results of the attempt to develop new kind of DRFs that will model the deterioration – degradation of materials, in particular carbon steel and limestone, using only satellite data. This new kind of DRFs is expected to help in monitoring materials deterioration – degradation to areas where there are no available ground based data as well as expanding the usage of satellite data by introducing a totally new field of implementation. The term “Satellite Sensed Data-Dose Response Functions (SSD-DRFs)” is proposed for this new kind of DRFs.

Atmospheric particulate matter (PM) from both natural and anthropogenic emission sources can bring adverse effects on public health. Long-term exposure to particular matter with aerodynamic diameters less than 2.5 μm (PM_2.5) can cause lung and respiratory diseases and even premature death. PM_2.5 not only threatens people's health, but also causes the decrease of atmospheric visibility and the degradation of the city scenery. In recent years, with the rapid development of industrialization and urbanization, PM_2.5 has become the primary air pollutant in China, especially in most major cities, such as Beijing, Shanghai and Guangzhou, where the fastest economic growth has occurred. To understand the effects of PM_2.5 on the Earth’s environmental system and human health, it is necessary to routinely monitor PM_2.5. Given the considerable advantages of satellite remote sensing, especially the large coverage provided at the spatial scale and stable continuity at the time scale, aerosol optical depth (AOD) retrieved from satellite sensors has been widely considered to be a good method for atmospheric PM monitoring. There was a distinct spatial pattern of correlation between AOD retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based in situ PM concentrations. In this study we established three types of AOD-PM_2.5 retrieving model based on Physical and Artificial Neural Network technology. Firstly, we using satellite retrieved AOD and other meteorological parameters such as the planetary boundary layer height (PBLH), temperature (TEMP), relative humidity (RH), U wind component (U), V wind component (V), surface pressure (SP), and large-scale precipitation (LSP), to establish GA-BP ANN AOD-PM_2.5 retrieve model. The test correlation coefficient R reached 0.83. This model is seasonally and regionally stable. The satellite AOD and ANN retrieved PM_2.5 has the similar trend and distribution, and the trained model have practical as well as theoretical value. Then, a physically-based model is developed to estimate the concentration of PM_2.5, in which, fine mode aerosol optical depth (AOD) at 440, 550 and 675nm, Effective Radius of the Fine particles, ground-based fine particulate matter (PM_2.5) data, relative humidity (RH) and boundary layer height (BLH) data are used. We proposed a new way to derive the integrated extinction efficiency <Qext> by using aerosol parameters in AERONET together with PM_2.5observations. The results show that R²can reach to 0.70 and Root Mean Square Error (RMSE) is 33.67 μg/m³ at Beijing site at 440 nm. The results are comparable with other findings based on physically-based methods. Finally, we also tested the performance of Specific Particle Swarm Extinction Mass Conversion Algorithm using remotely sensed data. Ground-level observed PM_2.5, Planetary Boundary Layer Height (PBLH) and relative humidity (RH) reanalyzed by European Centre for Medium-Range Weather Forecasts (ECMWF), aerosol optical depth (AOD), Fine Mode Fraction (FMF), particle size distribution, refractive indices from AERONET of Beijing area are used. The validation of PM_2.5 from SPSEMCA algorithm to AERONET observation data and MODIS monitoring data achieve acceptable results, R = 0.70, RMSE = 58.75μg/m³ for AERONET data, R = 0.6, RMSE = 48.36 μg/m³ for MODIS data, respectively. Then the trend of temporal and spatial distribution of Beijing and surrounding areas has been revealed. On the whole, this study will provide practical method for PM_2.5 estimation based on satellite data.

The present study aims to investigate spatio-temporal evolution and trend in the aerosol optical properties (aerosol optical depth, AOD; Ångström exponent, AE), qualitatively identify different aerosol types and sources over an urban city, Nanjing in the Yangtze River Delta, East China. For this purpose, the Collection 5.1 Level-2 data obtained from the MODIS sensor onboard Terra and Aqua satellites, the MISR, and the OMI for the period between 2002 and 2015 have been analyzed. A notable spatiotemporal heterogeneity was observed in the optical properties of aerosols on the seasonal scale over East China. The seasonal mean AOD₅₅₀ (AE_470-660) was found to be maximum with 0.97 ± 0.48 during summer (summer) (1.16 ± 0.33) and a minimum of 0.61 ± 0.28 during the winter (spring) season (0.80 ± 0.28). AE_470-660 found higher in summer indicate relative abundance of fine mode aerosols over the coarse mode. Annual mean Terra AOD₅₅₀ showed a strong decreasing trend (–0.70% year^-1), while the Aqua exhibited a slight increasing trend (+0.01 year^-1) during the study period. We also used the HYSPLIT model for presenting cluster trajectory analysis which revealed that the airmasses from different source regions contributed greatly to aerosol loading. Using the AOD-AE method (hereafter called as Technique-I), five major aerosol types were identified. In all the seasons, the mixed (MX) type of aerosol is dominant followed by the biomass burning/urban-industrial (BU) and desert dust (DD) aerosol types during summer and spring seasons, respectively. Further, the sub-classification of aerosol types was carried out considering into account of the characteristics of absorbing aerosol index (AAI) (hereafter called as Technique-II). The two clustering techniques showed reasonable consistency in the obtained results. The various aerosol types (absorbing and non-absorbing) and their change over a region are highly helpful in fine tuning the models to decrease the uncertainty in the radiative and climatic effects of aerosols.

With the rapid development of China's economy and high rate of industrialization, environmental pollution has become a major challenge for the country. The present study is aimed at analyzing spatiotemporal heterogeneities and changes in trends of different aerosol optical properties observed over China. To achieve this, Collection 6 Level 3 data retrieved from the MODerate resolution Imaging Spectroradiometer (MODIS; 2002-2016) and Ozone Monitoring Instrument (OMI; 2005-2016) sensors were used to investigate aerosol optical depth (AOD₅₅₀), Ångstrӧm exponent (AE_470-660) and absorption aerosol Index (AAI). The spatial distribution of annual mean AOD₅₅₀ was noticed to be high over economically and industrialized regions of east, south and northeast regions of China; while low aerosol loadings were located over rural and less developed areas of west and northeast of China. High AE_470-660 (>1.0) values were characterized by the abundance of fine-mode particles and vice-versa, likely attributed to large anthropogenic activities. Similarly, high AOD with corresponding high AE and low AAI were characterized over the urban-industrialized regions of central, east and south of China during most of the months; being more pronounced in June and July. On seasonal scale, AOD values were found to be high during spring followed by the summer and autumn, and low during the winter season. It is also evident that all aerosol parameters showed a single peak frequency distribution in all seasons over entire China. Further, the annual, monthly and seasonal spatial trends revealed a decreasing trend in AOD over most regions of China, except in the southwest of China which showed a positive increasing trend. Significant increasing trends were noted in AAI for all the seasons, particularly during autumn and winter, resulted in a large amount of absorbing type of aerosols produced from biomass burning and desert dust.

Cloud and aerosol are the important part of the gas system and plays an important role in the radiation budget. The Changes in aerosols and associated radiative forcing, and their impact on the climate system have been highly valued by the scientific community in the recent years due their large uncertainty factor. In recent decades, the rapid development of social economy in East China, aerosol particles emissions and production of secondary aerosol pollutants by photochemical reactions are also increasing, caused serious environmental and climate problems. Hence, it is important to study the temporal and spatial distribution of aerosols in this region, and understand the aerosol-cloud interactions.

In the present study, we examined the spatial and temporal variations in aerosol optical depth (AOD) at 550 nm and its relationship with various cloud parameters derived from the Moderate resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra satellite during 2000-2016. High mean AOD values were observed in almost all regions during the summer season, with low values in autumn/winter seasons. The Angstrom exponent that increases with AOD is opposite; to what would be the case if swelling of particles due to hygroscopic growth near cloudy areas played a major role in the MODIS data. We then analyzed the relationships between AOD and four other cloud parameters, namely water vapor (WV), cloud fraction (CF), cloud top temperature (CTT), and cloud top pressure (CTP). The correlation between AOD and CF was greater than 0.5 in Shandong province, and in the northern part of the study area, it is lower than 0.2. The analyses showed strong positive correlations between AOD and WV over Fujian and Zhejiang Provinces. The correlation between AOD and CF was positive for urban and desert regions; and negative over coastal locations of East China. AOD showed a similar correlation with CTP and CTT in all regions with negative correlation coefficient which also follows the second indirect effect of aerosols; but positive correlations were found in some parts of the southern region.

Thirteen years of continuous tropospheric NO₂ observations from OMI provide insight into the air quality levels in China. The use of these daily NO₂ concentrations for emission estimates of NO_x attempts to fill in for inaccuracies and temporal discontinuities of the bottom-up emission inventories, keeping the NO_x emission estimates up-to-date. Furthermore, this top-down approach inherently takes into account rapid changes in emissions caused by economic activity, political regulations or even events such as fires.

Within the framework of the newly developed EU QA4ECV project and the GlobEmission project, algorithms have been developed to derive NO₂ concentrations and NO_x emissions, respectively, from space with more accuracy. We demonstrate the detection of spatially and temporally heterogeneous changes in NO_x emissions derived from the DECSO v5.1 algorithm after it is applied to OMI NO₂ observations derived with the DOMINO v2 and QA4ECV algorithms over China from 2004 up to 2017. QA4ECV provides improved OMI NO₂ retrieval products with detailed uncertainty estimates and quality indicators. We investigate to what extent it is now possible to also improve trend detection limit as compared to the previous DOMINO v2 datasets. We observe a distinct increasing trend until about 2012 and a distinct decreasing trend from about 2012 onwards.

To put these trends into perspective we compare them with public data on energy consumption, traffic records, shipping and economic activity, and the environmental policies of China and we study their impact on the emission trends. The emissions are also compared to bottom-up emissions and their inconsistencies are discussed.

Tropospheric vertical column densities (VCDs) of NO2, HCHO, O3 and SO2 retrieved from MAX-DOAS network can be used to study temporal and spatial evolution of atmospheric pollution, and its transport and regional characteristics. Besides, MAX-DOAS network measurements can also be used to validate results from WRF-Chem model and products derived from spaced-based instrument, such as OMI and OMPS. We apply the MAX-DOAS profiles to the satellite retrievals and find that the accuracy of these modified satellite products has been improved. These modified satellite products can be performed every day with high spatial resolution and can be used to monitor air pollutants in real time and analysis chemical processes. The combination of results from ground-based and space-based measurements assists government in establishing appropriate emission control strategies.

The Environmental trace gas Monitoring Instrument (EMI) onboard Chinese high-resolution remote sensing satellite GaoFen-5 is an UV-VIS imaging spectrometer. The primary objective of EMI is to quantitatively measure global distribution of tropospheric and stratospheric trace gas. EMI is a nadir-viewing push broom spectrometer with a moderate resolution of 0.3 to 0.5 nm in the range 240 to 710 nm. The oxygen A-band is the best suited to retrieve cloud information. But the oxygen A-band lies outside the spectral range of EMI. So we retrieve the effective cloud fraction and effective cloud altitude using the O₂-O₂ absorption band at 477nm and 360nm. Similar to OMI cloud algorithm, from the measured radiance and irradiance spectra a reflectance spectrum is made, and a DOAS fit is applied to this reflectance spectrum, yielding the continuum reflectance and O₂-O₂ slant column density. Then these parameters are converted into cloud fraction and cloud height by interpolating in the look-up table. The look-up tables are generated by DOAS fit on spectra simulated using the VLIDORT, in the forward and inverse model, the cloud is replaced by a Lambertian surface with the albedo A_L=0.8. For fixed values of geometry, ground surface albedo, and ground surface altitude, which is obtained from the DOAS fit continuum reflectance as of function of cloud fraction and cloud height, the O₂-O₂ SCD is also a function with these parameters. So look-up tables are created for all relevant geometries, ground surface albedo and altitudes. Simultaneously, we use the O₂-O₂absorption bandaround 360nm to retrieve the cloud fraction and cloud height. The results of the two methods are integrated as the final cloud parameters. The effective cloud fraction and effective cloud height are used for cloud correction in the retrieval of trace gas like NO₂ and O₃.

The stationary satellite of Himawari-8 has high spatial and temporal resolution, so it can be monitored in real time. In the visible(VIS) band ,assuming different surface cover type, solar and the satellite angle and the aerosol type ,using discrete ordinate radiative transfer model (vlidort), combined with the historical data of aerosol profile and singe scattering albedo of ground-based instruments and calipso lidar ,to establish a simulation of the albedo of the top of the atmosphere .In the inversion process, it can assimilate modis high-precision surface reflectivity products, and adopt different inversion method and cloud removal algorithm according to different surface characteristics.Through the spring test in Beijing, the absorption of aerosol has a major influence on the inversion of aerosol optical thickness, and the wind and sand weather in Beijing spring has high scattering characteristics.

China Ozone pollution caused by photochemical reaction becomes a serious problem in China in the recent years. In this study, ozone profiles retrieved from the Ozone Monitoring Instrument (OMI) and the Ozone Mapper and Profiler Suite (OMPS) using the Optimal Estimation method (OEM) over China from 2013 to 2017. We apply soft calibration to OMI and OMPS radiance to eliminate the systematic component of fitting residuals. Tropospheric Ozone Columns (TOCs) is directly derived from the total column using the known tropopause. Hyper-spectral resolution Fourier transform infrared spectrometry (FTS) data at Hefei (31.86˚N, 117.27˚E), ozonesonde data at Hong Kong (22.20˚N, 114.10˚E) and Beijing (39.92˚N, 116.46˚E) are used to validate the tropospheric ozone column (TOC). The monthly variation of tropospheric ozone column (TOC) from 2013 to 2017 are also evaluated. In addition, we characterize the ozone and aerosol concentrations in the troposphere and surface UV irradiance to quantify the effects of aerosol particles and surface UV irradiance on the variability of tropospheric ozone.

We retrieve sulfur dioxide (SO2) vertical columns from the Ozone Monitoring instrument (OMI) and Ozone Mapping and Profiler Suomi National Polar-orbiting Partnership spacecraft (OMPS) using optimal estimation method (OEM) over China from 2013 to 2017. Comparison between OEM retrievals and the principal component analysis (PCA) product shows that a general good agreement between using the different algorithms is obtained with a correlation coefficient of 0.7249 and a slope of 0.8789 over eastern China. Validations with ground-based Multi Axis Differential Optical Absorption Spectroscope (MAX-DOAS) measurements show that a monthly averaged ground-based SO2 results and coincident OMI SO2 results using OEM agree very well. The seasonal cycle of SO2 is consistent in both data sets with a maximum in winter, on average, 6×10e16 molecules*cm−2 in Xianghe (a key pollution area), and a minimum in summer, which has a mean value of 2×10e16 molecules*cm−2 there. Winter is the domestic heating season. Both show that SO2 originates mainly form human sources rather than natural ones. The spatio-temporal distribution over China shows that the pollution is mainly concentrated in Beijing-Tianjin-Hebei area and Sichuan Basin. And the yearly averaged SO2 results show that the SO2 vertical columns are decreasing from 2013, 4.57×10e16 molecules*cm−2, to 2017, 0.89×10e16 molecules*cm−2 in those key pollution areas. SO2 and NO2 are major aerosol precursors, and SO2 and NO2 respectively are sources of pollution mainly from coal- fired power plants and motor vehicle emissions. We also investigate the relationship between SO2 emission and aerosol production in Beijing. A stronger correlation between the SO2 concentrations and aerosol optical depths (AODs) measured by the MODIS satellite instrument than NO2 concentrations with AODs obtained in winter suggests that anthropogenic SO2 is the major contributor to the aerosol content during the period of the year.

The TROPOspheric Monitoring Instrument (TROPOMI) is a passive nadir-viewing satellite borne imaging spectrometer on board the Sentinel-5 Precursor (S5P) satellite which was launched on 13^th October 2017. Compared to previous satellite instruments such as SCIAMACHY, GOME-2 and OMI, TROPOMI provides much higher spatial resolution with a ground pixel size of ~25km² (3.5km × 7km) at nadir. TROPOMI provides global observation of cloud, aerosol and multiple atmospheric trace gases and greenhouse gases. The operational NO₂, HCHO and O₃ products of TROPOMI are compared to ground based MAX-DOAS and FTS observations in China. In addition, the influence of aerosol and trace gases vertical distribution profiles on TROPOMI retrieval are estimated by using MAX-DOAS derived aerosol and trace gases profiles in the satellite retrieval. The Environmental Monitor Instrument (EMI) is one of the hyper-spectral payloads on board on the Chinese Gao Fen 5 (GF-5) satellite. The satellite is expected to be launch in May 2018. EMI provides global observations of atmospheric trace gases with a moderate resolution of ~624km² (48km × 13km). Combing with the higher spatial resolution TROPOMI data, we could estimate the spatial averaging effect over pollution hotspot of the EMI observations.

This paper presents formaldehyde (HCHO) column observed by Ozone Mapping and Profiler Suite (OMPS) and the TROPOspheric Monitoring (TROPOMI) instrument. OMPS-NM on board the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite was launched successfully on 28 October 2011, and TROPOMI on board the Copernicus Sentinel-5 Precursor satellite was launched successfully on 13 October 2017. Both of them cross the equator each afternoon at about 13:30 local time (LT), thus provides a great opportunity to compare HCHO column observed by OMPS and TROPOMI instruments. Besides, tropospheric HCHO columns observed by OMPS was compared with that measured by the high resolution Fourier transform infrared spectrometry (FTS) which is located in the western suburbs of Hefei (117.17E, 31.9N) in this paper, and they show the similar trend with the correlation coefficient (R) of 0.78. Tropospheric HCHO column observed by OMPS and TROPOMI both keeps good agreement with that measured by MAX-DOAS with the correlation coefficient (R) of 0.76.