The Marine Ecosystem Observation and Research Station on the Yangtze River Estuary, MNR, held the Se
Author:sio
Date:2022-04-21
Hits:3241

Recently, the Marine Ecosystem Observation and Research Station on the Yangtze River Estuary, MNR, held the Second Meeting of the First Academic Committee. The meeting was held online and offline. Committee members and experts from Sun Yat-sen University, Shanghai Jiaotong University, South China Sea Institute of Oceanography, CAS and other units attended the meeting. Academician Su Jilan, Director of the Academic Committee, presided over the expert meeting.

At the meeting, Chen Jianfang, Deputy Director of SIO, introduced the construction background and main tasks of the Station on behalf of SIO. Researcher Zhou Feng, Head of the Station, reported the work progress in 2021 and the five-year construction and development plan. The participating experts affirmed the achievements of the Station in ecological environment monitoring, scientific research and team building of the Yangtze River Estuary in the past year. They also put forward many constructive comments and suggestions on the daily operation, development direction, talent team construction, data sharing and demonstration service of the Station. Researcher Chai Fei, Director of the State Key Laboratory of Satellite Ocean Environment Dynamics, said that the Marine Ecosystem Observation and Research Station on the Yangtze River Estuary, MNR is an important support platform for the SIO, MNR and the State Key Laboratory to independently obtain coastal ecological observation data, and that support for the Station will be strengthened.

Associate Researcher Li Dewang and Dr. Meng Qicheng, the backbones of the Station, respectively made academic reports on the “Research Progress on Carbon Sources and Sinks and  Oxygen Deficiency in the Yangtze Estuary Based on Anchor System Observations”, and “Research Progress on the Driving Factors of Interannual Variation in Oxygen-Starved Areas Outside the Yangtze Estuary Based on Numerical Models and Long-Term Monitoring”.