Methodologies for Measuring Microbial Methane Production and Emission from Soils—A Review

甲烷 土壤水分 环境科学 生产(经济) 环境化学 土壤科学 化学 宏观经济学 经济 有机化学
作者
М. В. Глаголев,O. R. Kotsyurbenko,А. Ф. Сабреков,Yu. V. Litti,Irina Terentieva
出处
期刊:Microbiology [Springer Nature]
卷期号:90 (1): 1-19 被引量:3
标识
DOI:10.1134/s0026261721010057
摘要

Methane is an important greenhouse gas. Prediction of its long-term effect on climate changes requires understanding of the sources of its release into the atmosphere. The present work aimed to review the most widely used modern methods for measuring microbial production and emission of methane in soils, and to assess their applicability to various microbial ecosystems. A classification of methods for measuring methane production in microbial soil ecosystems is proposed, based on the basic physical and mathematical approaches to data processing for each method. Under laboratory conditions, methane production is determined by soil samples incubation and layered mass balance methods, which makes it possible to investigate the methanogenic potential of a microbial community for subsequent simulation of microbial processes; these methods, however, cannot be directly extrapolated to natural ecosystems. To determine methane production in field experiments, gradient methods are used, as well as a deep soil chamber technique developed for wetlands. Methods for measuring methane emission are described, as well as their advantages and limitations. While traditional chamber techniques are simple and therefore widely used, their implementation is limited by such factors as changes in environmental conditions caused by using the chamber, small spatial scales, and difficulties in chamber installation in some ecosystems. More recently developed micrometeorological methods are free of these limitations and provide for proper estimations of gas emissions from ecosystems even if these emissions are heterogeneous in space and time. To estimate emissions from such specific objects as landfills and livestock complexes or at large scales (regional or even global), the inverse modeling method is used.
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