Research Seminars

Advances in carbon balance assessment in terrestrial ecosystems

The Research center «Smart technologies for sustainable development of the urban environment in the global change» (Smart Urban Nature) of Agrarian and Technological Institute of RUDN University organizes an international scientific seminar «Advances in carbon balance assessment in terrestrial ecosystems», which will be held on September 21, 2022. The event will take place online on the Microsoft Teams platform.

During the seminar, we will discuss existing approaches, as well as digital methods and tools for assessing carbon stocks in soils of various terrestrial ecosystems. We will talk about carbon stocks in technosols and urban soils in the north and south of Russia, in the polar and steppe climates. On the example of the Mukhrino Carbon Polygon, we will look at the soils of other ecosystems, mainly forests and swamps. Evgeny Zarov (Yugorsk State University) will also present the main activities of the Mukhrino Carbon Polygon in the Khanty-Mansiysk Autonomous Okrug – Yugra.

Event program:

17.30-18.00 Scientific research of the Mukhrino carbon poligon Evgeniy Zarov, Yugra University (Russia)
18.00-18.30 Technosols’ carbon stocks and CO2 emissions in Russian South Sergey Gorbov,

Southern Federal University (Russia)

 

18.30-19.00 Variability of urban soils’ carbon stocks in polar and steppe climates: challenges and opportunities for digital soil mapping
19.00-19.30 Discussion

Area: Science, Education, International cooperation

Subarea: Ecology and nature management

Format: Seminar

Type: International

Title: International Scientific Seminar «Advances in carbon balance assessment in terrestrial ecosystems»

Date: 21.09.2022

Location: Microsoft Teams

Contact person: Anastasia Konstantinova, konstantinova-av@rudn.ru

The link to the Seminar

https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZDFmYTVhOTQtZTUyMy00MjEwLThhMTYtZTY3MDM4Y2Y2YWRm%40thread.v2/0?context=%7b%22Tid%22%3a%222ae95c20-c675-4c48-88d3-f276b762bf52%22%2c%22Oid%22%3a%22f65a4194-298d-48a6-9141-2a62230f1670%22%7d

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