最新刊期
卷 26 ， 期 5 ， 2022
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摘要：Climate change is the most critical issue related to human survival and economic development currently being faced by the whole world. Greenhouse gases (GHGs) and aerosol are the main factors contributing to global warming and atmospheric environmental degradation caused by anthropogenic emissions; thus, they are the core detection targets of satellite remote sensing platforms. Compared with traditional single-target satellites, the collaborative monitoring of GHGs and aerosol on the same airborne platform, “Greenhouse gases and Aerosol Collaborative Observation Constellation” (GACOC), could significantly improve the accuracy of CO2 and CH4 retrieval. This way could improve the ability to estimate the carbon source and sink via the “top-down” method, as well as the ability to distinguish anthropogenic/natural sources of CO2, CH4, and atmospheric particulate matters. The GACOC has become an important spatial detection approach actively developed by aerospace agencies of various countries.This study introduces the satellites launched by the European Union, Japan, China, and the United States that can monitor GHGs and aerosol in one space-borne platform. These satellites are further divided into two categories according to their missions. The first one is the comprehensive atmospheric sounding satellites that independently detect GHGs and aerosol. These satellites can provide the temporal and spatial distribution of columnar CO2 or CH4 concentration and aerosol properties in the global context. The representative satellites of this category include ENVISAT, Sentinel-5P, FY-3D, and GF-5, as well as GF-5(02), DQ-1, DQ-2, and MetOp-SG-A that are about to launch in 1-3 years. The second category is the GHG monitoring satellites. Synchronous aerosol and cloud observations on the same platform provide necessary information for high-precision inversion of GHGs. The typical GHG satellites include GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat, and the ESA-planned CO2M series.Focusing on the significant national demands such as assessment of carbon neutrality pathways and atmospheric environmental governance, this study also discusses the development tendencies of monitoring GHGs and aerosol within the framework of a collaborative observation constellation.(1) Identification and quantitative monitoring of large anthropogenic emission sources. The anthropogenic CO2/CH4 and aerosol particles (and other tracers such as NO2) emitted from large-scale industrial areas or cities have some similarities in source, environment, and meteorological condition. Therefore, the high-resolution GHGs and aerosol observation by collaborative satellites can be employed to improve the ability to identify, track, and monitor large-scale, fixed, anthropogenic sources more efficiently.(2) High-precision joint inversion of atmospheric GHGs and aerosol. The scattering of aerosol and cloud greatly impact the inversion accuracy of CO2/CH4 satellite products. The advanced spaceborne technology that combines multi-angle, multi-band, and polarimetric measurements obtain high-precision aerosol optical and microphysical parameters. These parameters can be used to generate observation-based aerosol models when dealing with aerosol scattering during GHG’s inversion, and these models are more appropriate than the traditional models from modeling data.(3) Active–passive satellite networking. No single satellite can acquire a daily, global-coverage GHG or aerosol product due to the issues such as limited swath width, large number of cloudy pixels, and strict data quality criteria. Therefore, active–passive satellite networking is an essential approach to satisfy the demands of operationally observing the earth. The GACOC could fill in the data gap effectively and generate a spatially–temporally continuous global dataset of GHGs and aerosol. These data can provide a solid foundation for scientific research such as accurate assessments of climate change and dynamic monitoring of the atmospheric environment.关键词：greenhouse gases;aerosol;satellite remote sensing;carbon dioxide;collaborative observation constellation- 2759
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发布时间：2022-05-30 -
摘要：Ozone is an important trace gas which absorbs UV radiation and protects life on earth from its potentially harmful effects. It is an important greenhouse gas in the troposphere. On average, approximately 90% of the atmospheric ozone is found in the stratosphere, and 10% is found in the troposphere. After the PM2.5 reduction, the increasing surface ozone concentration has become the primary concern in China, which is a significant pollution control task in the Chinese “14th Five-Year Plan.” This study reviews the substantial development of international ozone satellite observations in the past 60 years, including the satellite payloads, ozone retrieval, and monitoring application. The development of satellite can be divided into three stages with three viewing geometries (limb, occultation, and nadir), the limb and occultation observations are mainly focused on the middle and upper atmospheres, while the nadir observation can provide effective information on the troposphere, with a better horizontal resolution and the capability to derive the ozone in low middle layer of troposphere by retrieval method. The ozone retrieval methods and monitoring applications are constantly updated with the development of the satellite. This study focuses on the important progress in the satellite remote sensing retrieval algorithm for total ozone column and vertical ozone profiles, the observation of surface ozone concentration and its precursors, the observation and regional transmission of stratospheric ozone intrusion, and the validation of ozone satellite observation data. The algorithm of the total ozone column retrieval has high accuracy (up to about 90%—95%) while the ozone profile retrieval algorithm limited by satellite payloads, clouds and method, its accuracy is relatively low (up to about 70%—75%). There are some problems in the retrieval of the near surface ozone concentration by machine learning method, which has poor robustness and easy to overfitting. Satellite remote sensing combined with other data can monitor stratospheric ozone intrusion, but it is still difficult to quantify the impact of surface ozone concentration. Compared with that of the international ozone satellite remote sensing monitoring, the development of China’s ozone monitoring satellites lags behind. Although the hyperspectral observation satellites and atmospheric environment monitoring satellites to be launched in succession in the national civil space infrastructure planning have preliminary ozone monitoring capabilities, a large gap exists in the function and performance of satellite payloads, such as spatial resolution and signal-to-noise ratio. In terms of retrieval algorithm and monitoring application, the retrieval accuracy of the total amount of ozone column at present is relatively high. Retrieval accuracy of ozone concentration in the middle and lower troposphere and near surface is insufficient. Evaluation and cause analysis of ozone pollution, such as the migration and transformation process of near surface ozone pollution and the stratospheric ozone intrusion identification, are also inadequate. These are the key problems that need to be solved in the next step.关键词：Ozone;remote sensing;total colum and profile retrieval;stratosphere;satellite payloads;Retrieval;monitoring application- 2124
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发布时间：2022-05-30 -
摘要：Aerosols, solid or liquid particles suspended in the atmosphere, are an important component in the troposphere. It is well known that atmospheric aerosols have significant impacts on environment, climate and ecosystem. Thus, the knowledge of the spatial-temporal distribution and evolution of aerosol physical-chemical-optical properties with high resolution is of great importance to quantitatively and accurately assess their climate and environmental effects. As an advanced remote sensing technology, lidar has been widely used to observe aerosol properties around the world, which is mainly attributed as its unique advantages in large detection range and high spatial-temporal resolutions. The basic principle of lidar remote sensing is that after sending lasers to the atmosphere backscattering signals from aerosols can be detected and further analyzed. This paper summarizes the research progress of lidar for detecting atmospheric aerosol over the past decades from three aspects: Firstly, the main types of lidar that can be used for atmospheric aerosol detection are briefly introduced, such as Mie scattering lidar, polarized lidar, Raman lidar, high spectral resolution lidar, fluorescent lidar, etc. They usually employ several principles of physics, such as Mie scattering, Raman scattering and fluorescence scattering. Secondly, the lidar-based research progress of aerosol properties at home and aboard, such as optical properties (e.g., extinction/backscattering coefficient, lidar ratio, aerosol optical depth, Ångström exponent), size (e.g., color ratio), shape (e.g., depolarization ratio), composition (e.g., dust, smoke, sulfate, etc.), and concentration (e.g., mass concentration), are individually introduced. Finally, with the advances of photoelectric technology, artificial intelligence, and precision machining technology in recent years, the future development of aerosol lidar is prospected in this review paper. Lidar will be more miniaturized and intelligent, making it easier to carry on Unmanned Aerial Vehicle platforms. Abundant aerosol parameter inversion algorithms will be established. More ground-based lidar observation network and space-borne lidar projects will be established and improved successively.关键词：lidar;aerosol;atmospheric remote sensing;remote sensing- 1685
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发布时间：2022-05-30 -
摘要：Clouds are collections of water droplets or ice crystal colloids suspended in the atmosphere. They are a visible manifestation of the Earth’s massive water cycle, which play an important role in global climate. Since the radiation intensity signal cannot accurately detect the internal physical characteristics of thick clouds, especially convective clouds, high-quality cloud observations cannot be achieved only by using this signal. Polarimetric remote sensing can describe the spectral characteristics of intensity, directions, phase positions, and polarization states of light， so as to expand the volumes and dimensions of the information observed. It can detect the size, shape, and other microphysical parameters, showing unique application advantages in cloud remote sensing.A wealth of literature on the development of polarimetric sensors can be found, for example, POLDER launched by France, ASP designed by the United States, and DPC developed by China. This paper summarizes the characteristics of internationally developed landmark polarimetric sensors and introduces the polarimetric sensor that will be launched soon. It is found that the development of polarimetric sensors underwent three main periods. During the first period, polarimetric sensors had low spatial resolution, fewer polarization spectrums, fewer angles, and low polarimetric accuracy. During the second period, the four elements mentioned above have been improved. In the third period, the sensors were developed into products with high spatial resolution, more polarization spectrum, large angles, and high polarimetric accuracy. The accessible spatial resolution, polarization data, and polarimetric accuracy were all greatly improved.In addition, this paper discusses the research on polarization data of cloud detection, physical characteristics, and optical characteristics of clouds. Starting from a series of problems existing in traditional remote sensing observation methods in cloud research, including poor cloud detection of accuracy, physical and optical characteristics, etc., the advantages of polarization detection are revealed through a detailed introduction to the classical polarization cloud parameter algorithms. Moreover, the development history of cloud polarimetric remote sensing research and the critical role played by the application of polarization data in cloud-related studies are explored.By reviewing the development of polarimetric sensors and the evolution of cloud remote sensing algorithms in the past three decades, we found that the polarimetric accuracy, and spatial and time resolution were improved as the number of polarimetric sensors increased. However, the acquired polarimetric signal is increasingly complex. Therefore, when designing the algorithm of cloud remote sensing, the factors we should consider will be fewer. In contrast, the algorithm will be more rigorous, and the inversion accuracy of cloud parameters will be higher. Considering that with the accumulation of experience and the development of instruments, it is believed that the polarization data will make significant progress in optimizing cloud parameters.关键词：atmospheric remote sensing;polarization;POLDER;APS;DPC;cloud;vector radiative transfer;inversion algorithm- 1065
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- 275
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发布时间：2022-05-30 -
摘要：Air pollution, as important environmental problem, directly affects daily life and physical health of public. The gradual maturity of polluted gas and particulate matter observation technology has rapidly developed the monitoring of air pollutants near the surface based on satellite platforms. This study aims to clarify the collaborative observation’s history for aerosols and gases and then provide a reference for future satellite platform design.In this study, the popular remote sensing methods for trace gases and atmospheric particulates that are concerned on atmospheric environment are first described, and the applicable scenarios, advantages, and disadvantages of each method are discussed. Next, satellite platforms for collaborative observations of trace gases and aerosols are reviewed. According to the characteristics of remote sensing principle for the trace gases, the satellite platform is divided into ultraviolet and infrared bands, and the development course of sensors and satellite platforms are discussed and analyzed. Finally, we discuss the issues to be solved urgently by satellite platforms and remote sensing algorithms aiming to monitor air pollutants near the ground, as well as possible future development directions.For various trace gases, the good universal remote sensing methods are differential absorption spectrometry method and optimal estimation algorithm, which can fully utilize the absorption spectrum lines to achieve inversion of gases. The differential absorption spectroscopy method is effective for the monitoring of trace gases. However, the optimized estimation algorithm can further extract the layered information of trace gases from the hyperspectral information, which is helpful for obtaining a more detailed vertical distribution of trace gases in the atmospheric column. The band residual method and linear fitting method have strong pertinence to specific pollutant gases (such as sulfur dioxide). These simplified algorithms also have great advantages and application value. The core issue of the aerosol inversion algorithm is the signal decoupling of ground and atmosphere. Adding the information from spectrum, angle, polarization, and time series can effectively increase the decoupling capabilities. The algorithms derived from these principles include dark target algorithm, deep blue algorithm, empirical orthogonal function algorithm, polarization algorithm, and time series algorithm. Since the launch of NOAA-9 carrying SBUV/2 and AVHRR/2 in 1984, the collaborative detection of polluted gases and particulate matter has begun. Subsequently, Europe, the United States, South Korea, and China have launched satellites carrying advanced sensors, from the polar orbit to geostationary orbit. In the future, FY-4A of China, Geo-kompsat-2b of South Korea, Sentinel-4 of Europe, and TEMPO of the United States can be forming a global geostationary satellite constellation with high spatial resolution and hourly monitoring capability to achieve collaborative monitoring of polluted gases and particulate matter.On the basis of the summary of trace gas and atmospheric aerosol inversion algorithms, the development history of satellite platforms and sensors is combined from the perspective of cooperative observation of gas and particulate matter. The advantages of cooperative observation of sensors in the ultraviolet, visible, and infrared bands are discussed. The high temporal and spatial resolution air pollution monitoring capabilities of the geostationary satellite constellation in the future and the contribution of Chinese satellites are prospected.关键词：satellite;trace gas;particle;atmospheric environment;remote sensing;collaborative observation- 3023
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发布时间：2022-05-30
Review
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摘要：Quantitative retrieval of aerosol Single Scattering Albedo (SSA) from satellite remote sensing is important for climate assessment and air pollution control. This study developed a preliminary SSA retrieval algorithm based on S5p/TROPOMI and Aqua/MODIS in eastern China.The optical absorption of aerosol is highly sensitive in the near-ultraviolet bands, which is significantly correlated with the aerosol model, vertical profile, and corresponding aerosol loading. Considering the actual situation of aerosols in eastern China, the Optical Properties of Aerosols and Clouds aerosol model is constrained using AERONET data in eastern China to produce a more suitable aerosol type, and the corresponding vertical structure of different aerosol types is predefined using ground-based Lidar. Then, the radiative transfer model SCIATRAN is used for sensitivity analysis to further adjust the aerosol model and establish a series of look-up tables (LUTs) for different aerosol subtypes. The SSA can retrieve individual LUT at pixels with the collection of TROPOMI Ultraviolet Absorbing Index (UVAI) and MODIS AOD together. In the process, the Ångström Index values from MODIS and UVAI are used in combination for preliminary classification of aerosol type to improve the accuracy and efficiency.Compared with the ground-based observations, the coefficient of determination (R2) is 0.61, and the root mean square error is 0.05. Compared with OMI instantaneous inversion and monthly average SSA images, the distributions of TROPOMI SSA show a consistent overall trend and have better spatial continuity and larger inter-pixel variation. Further site-by-site analysis shows that the SSA and AOD are highly correlated with the type of aerosols where the site is located. The SSA in Shanghai with more sea salt aerosols is stable above 0.95, while the Beijing area is affected by multiple factors and the SSA varies greatly with time series from 0.85 to 0.98.In conclusion, the preliminary SSA retrieval algorithm based on TROPOMI in this study has good verification accuracy. Using MODIS aerosol products as input can effectively improve the accuracy of SSA inversion. The algorithm still has some uncertainties and needs to be further improved from the aspects of aerosol type and aerosol vertical profile. Nevertheless, the algorithm is helpful for the classification of aerosol types, aerosol microphysical, and optical properties of aerosol in small- and medium-scale regions.关键词：remote sensing;TROPOMI;MODIS;SSA;absorbing aerosol;UVAI- 1055
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发布时间：2022-05-30 -
摘要：FY-4A, as the latest generation of China’s geostationary meteorological satellite, has been launched on December 11, 2016. The Advanced Geosynchronous Radiation Imager (AGRI) is the primary payload onboard FY-4A, and it can image China and its surrounding areas 205 times per day. The AGRI with high-frequency observation capabilities can provide sufficient data support for aerosol monitoring, but few aerosol products are developed using AGRI. The accuracy of the available FY-4A AOD datasets is also inferior to that of the Himawari-8 aerosol product, which can also cover China. In this study, an aerosol optical depth (AOD) retrieval algorithm based on the database of ratio of surface reflectance of different channels is proposed to develop high-accuracy FY-4A AOD dataset.This algorithm involves four steps: (1) detect and remove cloud pixels in FY-4A L1 data; (2) perform gas absorption correction on FY-4A L1 data using the reanalysis data released by European Center for Medium-Range Weather Forecasts; (3) select the sub-dark pixels for each month from FY-4A data after gas absorption correction, and perform atmospheric correction on these sub-dark pixels using the background AOD (i.e., AOD at 550 nm is 0.02) to obtain the surface reflectance of VIS06 and NIR08 channels, obtain the ratio of surface reflectance of these two channels, and perform the abovementioned operations for all the pixels in the study area to complete the construction of surface reflectance ratio database of VIS06 and NIR08 channels; (4) retrieve AOD using FY-4A L1 data after gas absorption correction based on the constructed surface reflectance ratio database.The algorithm has been applied to aerosol retrieval over Beijing–Tianjin–Hebei region from May 2019 to October 2019. Comparison of FY-4A AOD retrieval results with MODIS AOD dataset released by NASA shows that the two AOD datasets have a consistent spatial distribution trend. Validation result of MODIS AOD dataset, the official Himawari-8 AOD dataset released by the Japan Meteorological Agency, and FY-4A AOD dataset against ground-based AOD data provided by Aerosol Robotic Network shows that the accuracy of FY-4A AOD dataset is better than that of Himawari-8 AOD dataset and MODIS AOD data. The root mean square error, mean absolute error, correlation coefficient with ground-based data, and percentage of retrieval results with error within ±(0.05+0.15AODAERONET) of FY-4A AOD dataset are 0.12, 0.09, 0.91, and 65.86%, respectively.Although the signal-to-noise ratio of FY-4A/AGRI is lower than that of Himawari-8/AHI, the absolute and relative errors of FY-4A AOD dataset are better than those of Himawari-8 AOD dataset. The statistical parameters of FY-4A AOD dataset are also slightly better than those of MODIS AOD dataset, which is one of the widely used AOD datasets with high accuracy. Therefore, the FY-4A AOD dataset developed in this study has high accuracy.关键词：aerosol optical depth;remote sensing retrieval;FY-4A;advanced geosynchronous radiation imager;geostationary satellite- 3109
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发布时间：2022-05-30 -
摘要：The MEdium-Resolution Spectral Imager (MERSI) carried by the Chinese Fengyun-3 (FY-3) satellite belongs to the same type of sensor as MODIS of NASA. Most channels of MERSI are similar in design as MODIS and are capable for aerosol retrieval. However, no reliable, stable, and globally applicable operational products are available for MERSI.On the basis of MODIS Dark Target (DT) algorithm, this paper constructs a globally applicable land aerosol retrieval algorithm for the new generation MERSI-II sensor onboard the newly launched FY-3D satellite. The aim of this paper is to test the quantitative capability of the sensor; hence, the algorithm design is consistent with DT algorithm as much as possible. The improvements of this algorithm from the MODIS DT algorithm are mainly in two aspects: surface estimation model and pixel screen. Considering the difference in channel settings between MERSI and MODIS, a surface reflectance estimation model is proposed for MERSI-II. Moreover, the method of inland water mask is improved to solve the defect of DT algorithm in haze leakage retrieval.By comparing granule retrieval of MERSI-II with aerosol products of MODIS, the spatial distribution and magnitude of AOD value show good consistency with a correlation coefficient above 0.9. After improving the identification method of inland water mask, the high aerosol loading of haze region which was missing in MODIS aerosol product, has been successfully retrieved from MERSI-II in this paper. Finally, we conducted the retrieval test with three-month global observation of MERSI-II. A comparison of the retrieval results with ground-based observation of AERONET shows that the overall accuracy of validation is good, and the correlation coefficient of scatterplots reaches 0.866. Moreover, the number of collocated points falling into the expected error EE = ± (0.05 + 0.15 τ) reaches 65.14%, which is close to the requirement of 2/3. The larger width of MERSI, in addition to its improvement of pixel mask, increases the number of MERSI-retrieved pixels. The proportion of matched collocation points is approximately 20% more than that of MODIS. Furthermore, a comparison between the monthly average results of MERSI-II and MODIS shows that the consistency of their spatial distribution is good. The magnitude of AOD value has good correlation with a coefficient of approximately 0.93.In conclusion, aerosol retrieval from MERSI-II using the proposed algorithm is close to the present similar products. Such performance is an important supplement to the global time series aerosol observation. Therefore, MERSI has good quantitative application ability, and the sensor performance and calibration are gradually becoming mature.关键词：FY-3;MERSI;aerosol optical depth;Dark Target (DT);Haze;inland water mask;surface reflectance estimation;ground-based validation- 5250
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发布时间：2022-05-30 -
摘要：This study aims to verify the effect of the aerosol optical thickness data of the Fengyun-3D satellite MERSI sensor on the pollution process prediction of PM2.5.This study was based on WRF-Chem (Weather Research and Forecasting Model Coupled with) Atmospheric Chemistry model and three-dimensional variational assimilation method, which were used to study the assimilation and prediction of a PM2.5 pollution process in northern China from February 10 to 13, 2020.The assimilation data were derived from PM2.5 concentration data from conventional ground stations and Aerosol Optical Depth (AOD) data from the MERSI sensor on the FY-3D satellite. The control experiment did not assimilate any data. The three groups of assimilation experiments were to assimilate ground PM2.5, satellite AOD, and PM2.5 and AOD data at the same time.Results show that the three groups of assimilation experiments can effectively improve the accuracy of the initial field. With ground PM2.5 as the test standard, compared with the control experiment, assimilating PM2.5 data, AOD data, and PM2.5 and AOD data at the same time, the average mean deviation of the initial field was decreased by 54.9%, 21.9%, and 49.0%, the average correlation coefficient was increased by 51.4%, 16.0%, and 34.0%, and the average root mean square error was decreased by 50.6%, 17.2%, and 42.3%. With AOD as the test standard, compared with the control experiment, the average mean deviation of the initial field in three assimilation experiments was decreased by 37.6%, 78.4%, and 83%, and the average root mean square error was decreased by 31.6%, 62.2%, and 65.2%. The initial field after assimilation can significantly improve the prediction, and the improvement lasted for more than 24 h. In general, the experiment of assimilating two kinds of data at the same time had the best improvement effect on the 24 h prediction. With ground PM2.5 as the test standard, the average mean deviation of the 24 h forecast was decreased by 19.7%, the correlation coefficient was increased by 8.8%, and the root mean square error was decreased by 17.2. With AOD as the test standard, the average mean deviation of 24 h forecast was decreased by 40.1%, the correlation coefficient was increased by 25.9%, and the root mean square error was decreased by 34.7%.The experiment also found that the assimilation of FY-3D satellite AOD data had a better lasting effect on the late prediction than only assimilating ground PM2.5 data.关键词：remote sensing;WRF-Chem Model;three-dimensional Variation;data assimilation;FY-3 satellite;AOD- 2711
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发布时间：2022-05-30 -
摘要：The study of atmospheric aerosols lays the foundation of global climate change, air quality, and public health research. Different from those in the Chinese mainland, the special atmospheric circulation and local emission sources in Taiwan, china from its seagirt terrain have led to the difference in aerosols’ characteristics. Lack of information on aerosol temporal and spatial characteristics might cause errors in the retrieval of satellite aerosol parameters.On the basis of the historical data of several representative Aerosol Robotic NETwork (AERONET) sites in Taiwan, this study explored the temporal and spatial variation characteristics and differences of aerosol parameters and types of typical sites in Taiwan, China. First, the change trend of aerosol optical parameters was analyzed. The observation sample points of AERONET were divided into six categories, namely, maritime, continental, desert dust, sub-continental, urban industry, and biomass burning aerosols, using the graphical classification method. The differences in aerosol types at different sites and the effects of wind direction and speed on Aerosol Optical Depth (AOD) and aerosol types were explored, and they were comparing with the aerosol optical parameters of Beijing. Second, the MODIS data were validated against the AERONET data.The annual average of AOD at each station is decreasing annually, which suggests the highest seasonal variation in spring (0.5257) and the diurnal variation of bimodal structure. The dominant aerosol type is urban industrial, and only Lulin station is maritime type. The northeast wind prevails in Taiwan, China, and the AOD is lower and the maritime aerosol type occupies larger proportion when the wind speed is higher. Conversely, urban industrial aerosols dominate. The average values of Ångström Exponent, Single Scattering Albedo, Refractive Index-Imaginary Part, and Asymmetry Factor are 1.3283, 0.9564, 0.0054, and 0.7292, respectively. Compared with those in the Beijing site (39.9768°N, 116.3813°E), the annual average of AOD, seasonal variation, and dominant aerosol types in the “Central University” shows a dramatic difference. For the remote retrieval products, MODIS AOD has higher verification accuracy at different sites, and only the Lulin site is slightly lower (R2=0.5925). As for the verification results of different aerosol types, urban industry (R2=0.7238), biomass burning (R2=0.6161), and sub-continental (R2=0.5116) have higher accuracy, while maritime (R2=0.1585) and continental (R2=0.1111) have significantly lower accuracy.The types of aerosols in Taiwan, China show differences in temporal and spatial characteristics. The proportion of sub-continental types in the southwest coastal sites increases in autumn and winter, while the continental types in the northwest coast increase. Refining the characteristic changes of aerosol parameters plays an important role in guiding the aerosol satellite retrieval algorithm for island regions with distinct features of circulation.关键词：atmospheric remote sensing;aerosol;AERONET;Aerosol Optical Properties;temporal and spatial analysis- 1154
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发布时间：2022-05-30
Aerosols Remote Sensing and Application
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摘要：Nitrogen dioxide (NO2) is both an important primary trace gaseous pollutant and a precursor to ozone and fine particulate matter production. There exist three widely used and publically available tropospheric NO2 Vertical Column Density (VCD) products based on OMI over East Asia, including QA4ECV from KNMI, OMNO2 from NASA and POMINO from Peking University. The spatiotemporal characteristics of tropospheric NO2 VCDs in each product have been extensively studied. However, quantitative knowledge of the differences between the three products is still inadequate.This research firstly updates the POMINO product developed by our group to version 2.1, including bug fixes and algorithm improvement, and expanding the spatial domain to East Asia, much of Southeast Asia and most of South Asia. Compared with QA4ECV and OMNO2 v4, POMINO v2.1 takes into account the anisotropy of surface reflectance and complex radiative effects of aerosols in the process of tropospheric NO2 AMF calculation. Then we quantitatively compare the NO2 data of QA4ECV, OMNO2 v4 and POMINO v2.1 in 2015—2020 based on either POMINO v2.1 or each product’s valid pixels.Results show that updates of POMINO do not significantly affect the retrieved NO2 VCDs (within 10% averaged over the spatial domain, dependent on seasons). When valid satellite pixels of three products are sampled consistently based on cloud radiation fraction of POMINO v2.1, the relative differences between the three products are about 10% averaged over Asia, although the maximum difference can reach 40% or more in severely polluted areas like Beijing-Tianjin-Hebei. A sensitivity test based on POMINO algorithm shows that tropospheric NO2 VCDs with implicit aerosol correction (as QA4ECV and OMNO2 v4) in December 2017 are lower than those with explicit correction by about 26.4% over Beijing-Tianjin-Hebei, and more than 11% over the whole North China Plain. As far as the long-term trend is concerned, all the three products show a nearly 30% decrease of annual mean tropospheric NO2 VCDs in Beijing-Tianjin-Hebei in 2015—2020, in contrast to relatively small VCD changes over the Yangtze River Delta. When valid satellite pixels are sampled based on each product’s own cloud screening, POMINO v2.1 provides much more valid pixels in polluted situations by 11%—44% and reduces the sampling bias, as a result of its explicit representation of aerosol optical effects in the NO2 and prerequisite cloud retrieval process.This research provides a basis for using and interpreting the three products, including their differences, effects of sampling and impacts of aerosol representation. Our results offer insight for better understanding of the pollution of nitrogen oxides and influences of current emission reductions.关键词：satellite remote sensing;nitrogen oxides;OMI;tropospheric NO2 VCDs;aerosol;data sampling;air pollution- 1055
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发布时间：2022-05-30 -
摘要：Determining Ozone Formation Sensitivity (OFS) and its driving factors is conducive to formulating effective ozone pollution control strategies. This study characterizes spatial and temporal variations in OFS by calculating the ratio of formaldehyde (HCHO, a maker of VOCs) to nitrogen dioxide (NO2) in Henan Province from 2005 to 2016. The relationships of OFS with precursor emissions and meteorological factors are also analyzed.The Level 3 gridded retrievals from the Ozone Monitoring Instrument (OMI) were adopted to calculate Formaldehyde Nitrogen Ratio (FNR). Then, we took FNR<2.3 to indicate VOC-limited regime, FNR>4.2 to indicate NOx-limited regime, and FNR between 2.3 and 4.2 to indicate transitional regime. Finally, the geographic detector model (GeoDetector) was used to quantify the influence of meteorological factors, anthropogenic emission precursors, and their interactions on OFS.The OFS in Henan Province changes in time and space. Most cities are transitional regimes in summer, where the O3 concentrations are relatively higher. The drastic change in tropospheric NO2 concentration determines the increase or decrease in VOC-limited regime. From 2005 to 2015, the FNR values decreased due to NOx emission reduction, and OFS tended to be a transitional regime. After 2016, the FNR values increased, and OFS tended to be NOx-limited. Anthropogenic emissions are the main driving factor of OFS in summer, which explains 40.5% of FNR variation on average. With the increase in CO (q = 0.46), PM2.5 (q = 0.41), NOx (q = 0.38), and NMVOC (q = 0.37) emissions, the FNR value decreases, which makes OFS more sensitive to VOC emissions in summer. Its sensitivity to NOx emission reduction decreases. Surface net solar radiation (SSR, q = 0.321) and total column water (TCW, q = 0.302) are the top two meteorological factors influencing OFS in summer in Henan Province. As SSR increases, FNR decreases, which makes ozone formation more sensitive to VOCs. TCW has a complex effect on OFS. When TCW is less than 40 kg/m2, FNR decreases with the increase in TCW, and ozone formation becomes more sensitive to VOCs. When TCW is larger than 40 kg/m2, FNR increases with the rise in TCW, and ozone formation becomes more sensitive to NOx. Interaction among factors enhances the ability to explain the change in OFS. In other words, each pair of factors has a greater influence on OFS than either. The interactions between precursors and meteorological factors have the most significant influence on OFS.Research results can enhance understanding of the photochemical process of ozone formation and provide a basis for formulating reasonable pollution reduction measures. However, the applicability of the OMI FNR indicator for the classification of ground-level ozone sensitivity in various regions still needs to be strengthened. Understanding the influence of driving factors and their interactions on changes in ozone sensitivity remains a challenge due to the nonlinear relationship between ozone sensitivity and its precursors and the complex reactions between meteorological conditions and precursors.关键词：air pollution;ozone formation sensitivity;GeoDetector model;meteorological factors;anthropogenic emissions;interactive effects- 1213
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发布时间：2022-05-30
Pollutant Gases Remote Sensing and Application
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摘要：Monitoring concentrations of atmospheric particulate matters is essential to regional haze pollution prevention and control. Satellite-based Aerosol Optical Depth (AOD) data have been frequently used to map regional PM2.5 concentrations. However, the resultant PM2.5 concentration maps are always spatially incomplete due to significant data gaps in satellite-based AOD retrievals. This study aims to fill data gaps in AOD imageries to support spatially contiguous PM2.5 concentration mapping on an hourly basis in the Yangtze River Delta.An integrated data fusion approach was developed to seamlessly gear up the missing AOD imputation and multimodal data fusion approaches. Specifically, all available Himawari-8 AOD observations during the daytime were fused to maximize hourly AOD coverage in each single snapshot. To further tackle data gaps in fused AOD maps, a virtual AOD monitoring network was constructed by estimating AOD at each state-controlled air quality monitoring station based on ground measured air pollutant concentration. This way enables us to extend the sparsely distributed aerosol monitoring network nationwide, which significantly improves the spatial coverage of AOD. Subsequently, the reconstructed satellite AOD and PM inferred AOD were fused with AOD simulations from MERRA-2 using the optimal interpolation method to generate spatially contiguous yet far more accurate AOD reanalysis. Spatially complete PM2.5 concentration maps were finally generated on hourly basis over the study region using the random forest method.Ground validation results indicate that AOD values inferred from air quality measurements agree well with in situ AOD measurements, with R of 0.90 and RMSE of 0.13. The analyzed spatially complete AOD dataset has a correlation of 0.86 and RMSE of 0.16 compared with in situ AOD data, which is much higher than that of raw Himawari-8 AOD. The estimated PM2.5 concentration data also have a promising accuracy, with R of 0.9 and mean absolute error of 9.87 μg m-3 compared with in situ PM2.5 measurements.Compared with sparsely distributed in situ PM2.5 measurements, this spatially contiguous PM2.5 concentration dataset has great advantages in assessing PM2.5 variations in space and time in the Yangtze River Delta. Statistically significant decreasing trend over the whole study area also highlights the effectiveness of clean air actions in reducing PM2.5 loadings across China. Overall, the proposed method can be practically used for future PM2.5 mapping practices and the generated spatially contiguous PM2.5 concentration dataset is a promising data source for the assessment of the human exposure risk to haze pollution.关键词：remote sensing;PM2.5 concentration mapping;aerosol optical depth;multimodal data fusion;missing value imputation;air quality;Yangtze River Delta- 3034
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发布时间：2022-05-30 -
摘要：The dense gridded air quality monitoring sites established by local governments in China have laid the foundation for fine-tuned monitoring of urban air quality. However, whether it can help improve the capabilities of satellite-based surface PM2.5 concentration estimation and mapping remain to be tested. To this end, we conducted a case study with Xuzhou as an example.In order to describe the spatial distribution of PM2.5 in detail at urban scale , this study used the PM2.5 concentration data from 172 grid monitoring stations in Xuzhou, the apparent reflectance and aerosol optical thickness data from the Himawari-8/AHI and COMS/GOCI, meteorological data from ERA5 and other auxiliary data to carry out fine mapping study of PM2.5 concentration on the 0.005° spatial resolution grid. The machine learning and geostatistical algorithms of eXtreme Gradient Boosting (XGBoost), Random Forest (RF) and Geographically and Temporally Weight Regression (GTWR) are applied, and a variety of characteristic parameter combinations (listed in the body of the paper) were selected for comparative analysis.Compared with other two prediction models, the XGBoost model performed the best, in terms of model accuracy and degree of overfitting, with high correlation coefficient (0.90) and low root mean squard error (11.65 μg/m3). Meanwhile, the best parameter combination includes satellite data from Himawari-8 as well as GOCI and meteorological data from ERA5. These retrieved results were then compared with the measurements obtained from the national grid monitoring stations. For peer comparison, in addition, the TAP (Tracking Air Pollution in China) dataset of Tsinghua University and the CHAP (China High AirPollutants) dataset of the University of Maryland are also involved in this comparison, in the aspect of daily mean value and capacity of fine mapping of PM2.5 in Xuzhou. Besides, these two datasets use the data from national grid monitoring stations as the validation data..The following conclusions can be drawn:(1) Due to its higher temporal resolution, geostationary satellite measurements can better fit hourly urban grid monitoring station data in model prediction, and is more suitable for urban PM2.5 fine mapping than polar orbit satellite;(2) The estimation results based on urban grid monitoring stations and satellite measurements can make up for the lack of sparse national control stations to a certain extent, better reflect the difference of PM2.5 concentration distribution between different regions within a city, and better serve the accurate control of air pollution.关键词：remote sensing;PM2.5;grid site;fine drawing;Aerosol Optical Depth (AOD);apparent reflectance- 1020
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发布时间：2022-05-30 -
摘要：PM2.5, as the primary indicator of environmental quality, not only affects the occurrence of haze but also is closely related to public health and has raised great attention recently. Although PM2.5 ground monitoring stations are expanding, they are still on the sparse side to identify the spatiotemporal heterogeneity of PM2.5 concentrations. With the development of remote sensing technology, satellite-based Aerosol Optical Depth (AOD) data provide an effective way to estimate large-scale PM2.5 concentrations. This study aims to develop a novel deep neural network model for estimating PM2.5 concentrations in the Yangtze River Delta (YRD).In addition to satellite remote sensing AOD data, meteorological factors, digital elevation model data, normalized different vegetation index data, and the lunar calendar day representing Chinese production and living habits were integrated into the proposed attention-based Self-Adaptive Deep Neural Network (SADNN) in this study to estimate PM2.5 concentrations in the YRD region from 2015 to 2020. Five-fold cross-validation was executed to evaluate the estimation accuracy of the SADNN. The multiple linear regression and random forest models were applied to compare with the SADNN.The cross-validation results showed the proposed SADNN model had a high coefficient of determination value of 0.85 and a slope of 0.86, which were highly consistent with ground-level observations. The results also showed better performance than those of multiple linear regression and random forest models. The results for the spring festival in 2016 demonstrated the effectiveness of integrating the lunar calendar day and attention module into the model. The spatiotemporal patterns of PM2.5 in the YRD were as follows: PM2.5 concentrations were high in the north and low in the south, and the coastal and mountainous areas were better than inland and plain areas, respectively. On seasonal scales, winter was the most polluted season, while summer was the best. The overall PM2.5 concentration in the YRD showed a decreasing trend from 2015 to 2020, especially in Shanghai Municipality, with the decreasing speed of 3.30 μg/(m3·a), following the Jiangsu Province (2.65 μg/(m3·a)). Zhejiang Province and Anhui Province had a lower decreasing speed of less than 2 μg/(m3·a), and Anhui Province needed more effort and attention to improve the air quality due to its overall high PM2.5 concentrations.In conclusion, applying satellite remote sensing data and the proposed SADNN model to accurately estimate spatially continuous PM2.5 concentrations can greatly make up for the lack of ground monitoring stations and scientific guide for environmental policy planning and implementation.关键词：remote sensing;aerosol optical depth (AOD);deep learning;Attention module;the Yangtze River Delta;air quality- 956
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发布时间：2022-05-30
Satellite-Based PM2.5 Estimation and Mapping
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摘要：The high-resolution multi-mode satellite (GFDM) is equipped with China’s first civil-using Synchronization Monitoring Atmosphere Corrector (SMAC). This satellite can acquire the atmospheric parameters of the same observation area of the high-resolution camera and achieve accurate correction for the image taken by the high-resolution camera. This study discusses the overall design ideas of synchronization atmospheric correction for high-resolution multi-mode satellites. The design schemes of time-space synchronization, multi-spectrum, multi-polarization channel detection system, and the verification results on ground and on orbit are presented.GFDM satellite has the capability of rapid attitude maneuvering. Thus, it can realize efficient and flexible observation of ground targets, which greatly improves its imaging efficiency. However, this feature also brings new requirement for the assurance of the quality of the high-resolution images obtained by the satellite. When the satellite takes photograph at a larger angle, the atmospheric transmission path of light from ground observing target increases. This condition will cause greater impact on the image’s modulation transfer function and different adjacent pixel effect compared with the situation of sub-satellite point viewing. GFDM satellite adopts a space-ground integrated atmospheric synchronization correction solution to obtain remote sensing image data products with high radiation accuracy and high commercial value. SMAC is equipped on GFDM to obtain atmospheric detecting data that strictly match the image obtained by the high-resolution camera temporally and spatially. The spectrum band and polarization channel design and the key performance control measures of SMAC during its manufacturing process fully consider the subsequent ground atmospheric retrieval requirements. The ground atmospheric retrieval algorithm also considers the design properties of SMAC. Through this cooperation between the two important processes, more accurate atmospheric parameters can be provided for the atmospheric correction of high-resolution images of GFDM. This study gives the scheme design, main technical properties, and ground test results of SMAC. A radiation comparison between SMAC and ground-based solar/sky radiometer was conducted on ground, and good experimental results were achieved.GFDM satellite was launched to orbit on July 3, 2020. On the first day (the 6th orbit circle) after launch, an initial status check was performed. On the second day, the SMAC started the atmosphere detection and the detected data were downloaded to ground. The ground application system used these detection data to perform atmospheric parameter inversion and image atmospheric correction. The atmospheric parameter inversion results and the effect of atmospheric correction of high-resolution images were examined. The comparison between the measurement data from the global AERONET site shows that the inversion results of atmospheric aerosol optical thickness and atmospheric water vapor content based on SMAC detection data are credible. Using the inverted atmospheric parameter, a good atmospheric correction effect on high-resolution images can be achieved, detailed information of the satellite images is significantly restored, and the ground object reflectance is effectively improved. These results all effectively support the subsequent quantification application of GFDM high-resolution image data. The successful application of SMAC in-orbit also provides a reference for the subsequent satellites which need to improve their quantitative application level.关键词：remote sensing;atmosphere corrector;overall design;on-orbit verification;polarization;Aerosol Optical Depth (AOD);Columnar Water Vapor (CWV)- 970
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发布时间：2022-05-30
Atmospheric Correction
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