Dylan Jones , 多伦多大学物理系教授, 1998年获得哈佛大学地球和行星科学博士学位。他的研究重点是将大气成分观测与全球化学传输模型结合，以更好地了解污染如何影响大气的化学和动态状态，使用同化和反演建模技术量化痕量气体对于空气质量和全球碳循环的影响。

Dylan Jones is a professor in the Department of Physics at the University of Toronto. He received his B.A. in Physics and Astronomy and Astrophysics from Harvard University in 1990, his M.Sc. in Applied Physics from Harvard University in 1994, and his Ph.D. in Earth and Planetary Sciences from Harvard University in 1998. His research is focused on integrating measurements of atmospheric composition with global three-dimensional models of chemistry and transport to develop a better understanding of how pollution influences the chemical and dynamical state of the atmosphere. His research also uses chemical data assimilation and inverse modeling techniques to quantify surface fluxes of trace constituents that are important for air quality and the global carbon cycle.

Jason Blake Cohen, 杰森·布莱克·科恩，中国矿业大学教授，国家重大人才计划青年学者，麻省理工学院博士、新加坡麻省理工科研中心博士后，曾任新加坡国立大学助理教授、中山大学教授。致力于利用卫星遥感更好地理解与量化气溶胶以及其他污染物的影响。

Jason Blake Cohen, graduated from the Massachusetts Institute of Technology in 2010 with a Ph.D. degree. From July 2016 to June 2021, served as professor at Sun Yat-sen University. From July 2021 to present, served as professor in China University of Mining and Technology. Dr. Jason Blake Cohen was selected into the national “Thousand Young Talents” plan, served as an editorial board member of the SCI journal “Journal of Meteorological Research”, has long served as the convener and moderator of the Asian Oceanian Geoscience Society (AOGS), and published more than 40 papers in world-class academic journals article, presided two projects of the National Natural Science Foundation of China, the National Thousand Young Talents Program, and the Guangdong Talents Program.

朱丽叶， 中山大学“百人计划”引进人才，副教授。2014年博士毕业于美国科罗拉多大学博尔德分校（University of Colorado at Boulder）。主要研究方向为：1）数据同化，排放清单优化，全球污染物传输数值模拟，源解析；2）城市污染物扩散，超细颗粒物观测，城市规划与公共健康；3）大数据技术与机器学习方法在大气科学领域的运用。

Liye Zhu is an associate professor of Atmosphere Science at Sun Yat-sen University. She was recruited through the “Hundred-Talent Program” by School of Atmosphere Science in 2018 and was appointed as the Vice Chair of the Department of Atmospheric Chemistry and Physics. She obtained her B.S. from University of Science and Technology of China in 2009, and her Ph.D. from University of Colorado at Boulder in 2014. Then, she worked as a postdoctoral fellow in Colorado State University and University of California, Los Angeles. The research of Prof. Liye Zhu focuses on Air Quality, Data Assimilation, Satellite Remote Sensing, and Big data and AI Application. She founded the Satellite Data to Freshman (SDTF) data sharing platform. She has published more than 30 papers in SCI journals, including PNAS, ACP, GRL, JGR, ERL, GMD, EP, and AE. She teaches “Fundamentals of Atmospheric Science” and “Computational Methods” for undergraduates. Prof. Liye Zhu is the Principal Investigator of multiple research grants, including National Natural Science Foundation of China grant, Natural Science Foundation of Guangdong Province Grant, and Basic Research Program of Guangzhou City.

Abstract: With the rapid development of satellite observation technology, its application fields continue to expand and become an important strategic resource of the new generation of information infrastructure. In this lecture, we will give an introduction of atmospheric environmental remote sensing and data processing. We will introduce what satellite remote sensing is and what types of data products satellite will provide. We will also show examples of how to download, read and process satellite data. This lecture is a useful tutorial for beginners who want to use satellite remote sensing data but do not know where to start. Please mark the lecture time on your calendar and you will gain useful knowledge and skills to start your satellite data processing.

Oleg Dubovik，法国里尔大学教授、研究主任，美国地球物理联盟会士（AGU Fellow），1992年获得博士学位。专注于卫星、地面和机载遥感观测反演气溶胶特性。

Oleg Dubovik specializes in retrievals of aerosol properties from satellite, ground-based, and airborne remote sensing observations. Dr. Dubovik has received his PhD. from Institute of Physics, Minsk, Belarus in 1992. Then, he worked in Japan for two years, participating in ADEOS/ILAS algorithm developments, and nine years in USA at GSFC/NASA research center. Since 2006, Dr. Dubovik works in the Laboratoire d’Optique Atmosphérique, Université Lille, France as CNRS “Research Director”. Main scientific accomplishments of Dr. Dubovik include following developments: – aerosol retrieval algorithm for AERONET federated network of ground-based radiometers; – one of pioneering climatologies of absorption and optical properties of ambient tropospheric aerosol; – popular efficient software for modeling scattering by non-spherical aerosol particles. Dr. Dubovik focuses on refinement of methodological aspects of numerical inversion for needs of remote sensing by applying elaborated statistical optimization approach. The developed principles were recently realized GRASP open-source algorithm (https://www.grasp-open.com) that can be applied to retrieval detailed aerosol properties from diverse observations including both passive and active observations from satellite or ground. For example, GRASP has used for deriving extended set of parameters from observations by POLDER and MERIS satellite instruments, combination of ground-based observations by lidar and radiometer, etc.

毕磊, 浙江大学“百人计划”研究员，国家优秀青年科学基金获得者。长期从事大气非球形粒子散射研究及其在辐射传输和遥感中的应用，系统地发展了非球形粒子电磁散射计算新技术，推进了气溶胶和冰云的光学特性研究，取得的研究成果在大气遥感、数值天气预报以及全球气候变化研究等方面具有重要的应用价值。

Lei Bi, a “Hundred Talents” professor at Zhejiang University since 2015. Dr. Lei Bi’s primary research interest focuses on electromagnetic wave scattering by non-spherical and inhomogeneous particles such as ice crystals and aerosols in the atmosphere. Such fundamental research work has a wide variety of applications in atmospheric radiation and remote sensing, weather and climate studies. He has published 73 refereed journal papers, which have been cited 1868 times (web of science) and 2547 times (google scholar). He obtained his Ph.D degree in 2011 in Physics Department at Texas A&M University (TAMU), USA, and then conducted research as a Postdoctoral Research Associate (2011-2014) and an Assistant Research Scientist (2014-2015) at the Department of Atmospheric Sciences at TAMU. In 2015, he received Richard Goody award for his contribution to atmospheric radiation research. In 2016, he received Xie Yibing young scientist award. In 2019, he received the IAMAS Early Career Scientist Medal. In 2020, he received an NSF grant for excellent young research scientists. He serves as an International Radiation Commission (IRC) commissioner, an associate editor of Journal of Quantitative Spectroscopy & Radiative Transfer, an editor of Advances in Atmospheric Sciences, and an editor of Asia-Pacific Journal of Atmospheric Sciences.

梅林露，德国不来梅大学环境物理研究所高级研究科学家。研究方向包括辐射传输理论、冰冻圈、气溶胶遥感、云与地表特性、长期数据创建与应用，XBEAR（可扩展不来梅气溶胶/云和地表参数反演）算法的PI，快速辐射传输模型FASMAR的PI，负责辐射传输软件包SCIATRAN中的气溶胶、云和地表部分。

Linlu Mei is a senior research scientist at Institute of Environmental Physics, University of Bremen. His main research topics include radiative transfer theory, cryosphere, remote sensing of aerosol, cloud and surface properties, long-term data creation and applications. Dr. Mei is the PI of the XBEAR (eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval) algorithm, which is providing global aerosol, cloud and surface data products for MERIS and OLCI instruments. Dr. Mei is also the PI of the fast radiative transfer model- FASMAR (Fast and Accurate Semi-analytical Model of Atmosphere-surface Reflectance) and responsible for the aerosol, cloud and surface parts in the radiative transfer software package –SCIATRAN, which is widely used in 58 countries.

Abstract: This lecture will present three major aspects for surface and atmospheric remote sensing. The first aspect is an introduction of basic knowledge for radiative transfer theory and corresponding radiative transfer models, including the fast radiative transfer model – FASMAR and full radiative transfer package –SCIATRAN. The second aspect will focus on the retrieval theory by introducing classical inverse problems, 2 – 3 examples using the XBAER algorithm to derive global aerosol, cloud and surface properties will be detailed presented. The last part of this lecture is to introduce a few examples of how to use the satellite products to obtain new understanding for climate change.

Steve Hung Lam Yim，新加坡南洋理工大学亚洲环境学院的副教授，他的研究致力于提高对气候变化和空气质量的认识，以及对人类健康和地球健康的影响。除了研究之外，他还努力为各个政府委员会和专业协会做出贡献，旨在将他研究积累的知识传递给社会。现任世界卫生组织全球空气污染与健康技术咨询组（GAPH-TAG）成员，并担任中国教育部高等学校大气科学学科咨询委员会特邀委员。

Steve Hung Lam Yim is an Associate Professor of the Asian School of the Environment at Nanyang Technological University in Singapore. By training, he is an atmospheric scientist and a modeler. To develop a sustainable environment, his research devotes to improving knowledge of climate change and air quality, and the influences on human health and planetary health. Besides research, he strives to contribute to various government committees and professional associations, aiming at transferring his developed knowledge through research to the society. He serves as the member of the Global Air Pollution and Health – Technical Advisory Group (GAPH-TAG) of WHO, and also serves as the invited committee member of the National Advisory Board on Atmospheric Science Discipline in Higher Education, the Ministry of Education of China. Additionally, he contributes to the editorial boards of international peer-review journals including Scientific Reports (Nature) and Atmosphere. His research focuses on the interactions between climate and air quality, and the resultant impacts on public health and ecosystem. His on-going research is to develop, advance and utilize the state-of-the-art multi-scale atmospheric models, along with measurement and statistical approaches, to understand the mechanisms that drive the climate-air quality interactions, and evaluate the impacts on human exposure, mosquito abundance and soil acidity. Particular interests are investigating air quality impacts of sectoral emissions and transboundary air pollution at multiple spatial scales, studying regional climate change due to land-use changes and global climate change, and evaluating and quantifying the effects of air pollution on respiratory and cardiovascular diseases. His research also assesses and develops emission control scenarios to provide scientific-based policy solutions for a sustainable environment in present and future years.

贾鹏，武汉大学教授，海外高层次引进人才，武汉大学空间全生命周期健康国际研究中心主任（International Institute of Spatial Lifecourse Health）。主要研究方向包括健康地理学、生态环境健康、传染病预测与防控、医学图像处理、医疗资源配置规划等；开创了交叉研究方向“空间全生命周期流行病学”，应用地理信息系统、测绘遥感、移动设备、人工智能、统计、大数据等技术于健康相关领域，尤其是大型人群队列研究、真实世界医疗健康数据和人居环境数据挖掘。

Peng Jia is a professor in the School of Resource and Environmental Sciences at Wuhan University, and the founding director of the International Institute of Spatial Lifecourse Health (ISLE). His primary interest is health geography and spatial epidemiology, mainly focusing on the applications of GIS, statistics, remote sensing, big data, mobile devices, and artificial intelligence in environmental health areas, particularly in chronic disease (e.g., obesity, cardiovascular diseases) and emerging infectious disease (e.g., COVID-19, HIV). His other interests include the association between climate and health, construction and optimization of national hierarchical healthcare systems, allocation and equality of healthcare resources, etc. He was the recipient for the Outstanding Article of the Year Award from the U.S. Agency for Healthcare Research and Quality, and the 2019 IJERPH Young Investigator Award. He has published more than 170 papers in SCI journals, including Nature Medicine, Lancet Global Health, and Environmental Health Perspectives.

Abstract: This lecture will present an empirical study which estimated and compared the Hans’ and minorities’ risks for cardiometabolic abnormalities (CAs) associated with long-term exposure to ambient air pollution in Southwest China, on the basis of the China Multi-Ethnic Cohort. The concentrations of ambient air pollutants, including particulate matters (PM1, PM2.5, and PM10) and nitrogen dioxide (NO2), were generated from random forest models on the basis of multi-source data. The analysis sample included 51,037 Hans and 28,702 minority participants. The prevalence of CAs was 25.0%, slightly higher in the minorities (25.5%) than the Hans (24.4%). The higher risks for CAs in the overall population were associated with each 10 μg/m3 increase in the exposure to PM1 (OR = 1.07 [1.05–1.09]), PM2.5 (OR = 1.11 [1.06–1.17]), PM10 (OR = 1.04 [1.03–1.06]), and NO2 (OR = 1.04 [1.03–1.07]). Compared to the Hans, the higher risks for CAs were observed in the minorities for PM1 (OR = 1.35 [1.18–1.53]), PM2.5 (OR = 1.61 [1.34–1.93]), and PM10 (OR = 1.15 [1.07–1.23]). The findings provide a better understanding of ethnic disparities in CA risks when being exposed to ambient air pollution in China, which also have important implications for other low- and middle-income countries where less health resources are available to conduct such studies.

Benoît Tournadre，国立巴黎高等矿业学院观测、影响和能源 (O.I.E) 中心的博士后研究员，他的研究方向是利用卫星图像估算地表太阳辐射。基于在不同气候和环境主题（海洋学、古气候学和空气污染）方面的经验，他还致力于通过各种行动（会议、教育科学研讨会、学校参观实验室、开发网络工具来方便获取科学数据等）提高公众对这些问题的认识。

Benoît Tournadre is a post-doctoral researcher at the Observation, Impacts, Energy (O.I.E) center of Mines ParisTech, where he works on the estimation of solar radiation on the Earth’s surface from satellite imagery. Based on experience in different climate and environmental themes (oceanography, paleoclimatology and air pollution), he also devotes his time to raising public awareness of these issues through various actions (conferences, educational scientific workshops, school visits to laboratories, development of web tools to facilitate access to scientific data, etc.).

Abstract: This lecture will present key elements to understand past, present and expected changes of the Earth’s climate. We will explore how climate is measured and what has been observed over the last century. An extension of these observations in the past is possible with the study of paleoclimate proxies that inform us on many million years. Our understanding of the climate machine is also based on models describing the physics and interactions between atmosphere, land, oceans and ice sheets. These models can also help us in making projections of how the Earth’s climate will evolve in the future. For the lecture to be the most adapted to your interests, we ask to all participants to fill the short survey at the link below. The questions do not require any investigation from your side, it is even better if you answer just from what you already know (or don’t know). https://form.dragnsurvey.com/survey/r/839d2f29