Circulation Of Carbon Dioxide Research Articles

Microbial Communities Affected by Hydraulic Fracturing and Environmental Factors within an In Situ Coal Reservoir.

The rise of coalbed methane bioengineering enables the conversion and utilization of carbon dioxide through microbial action and the carbon cycle. The environment of underground coal reservoirs is the result of a comprehensive effort by microorganisms. Some studies on reservoir microorganisms have progressed in laboratory conditions. However, it does not replicate the interaction between microorganisms and the environment on site. Hydraulic fracturing is an engineering technology to improve the natural permeability of tight reservoirs and is also a prerequisite for increasing biomethane production. In addition to expanding the pore and fracture systems of coal reservoirs, hydraulic fracturing also improves the living conditions of microbial communities in underground space. The characteristics of microbial communities in the reservoir after hydraulic fracturing are unclear. To this end, we applied the 16S rRNA sequencing technique to coalbed methane production water after hydraulic fracturing south of the Qinshui Basin to analyze the microbial response of the hydraulic fracturing process in the coal reservoir. The diversity of microbial communities associated with organic degradation was improved after hydraulic fracturing in the coal reservoir. The proportion of Actinobacteria in the reservoir water of the study area increased significantly, and the abundance of Aminicenantes and Planctomycetes increased, which do not exist in non-fracturing coalbed methane wells or exist at very low abundance. There are different types of methanogens in the study area, especially in fracturing wells. Ecological factors also determine the metabolic pathway of methanogens in coal seams. After hydraulic fracturing, the impact on the reservoir's microbial communities remains within months. Hydraulic fracturing can strengthen the carbon circulation process, thereby enhancing the block's methane and carbon dioxide circulation. The study provides a unique theoretical basis for microbially enhanced coalbed methane.

Measurement of degradation on under-utilized natural turf

The role of turf serving animal husbandry is significantly declining with the decreasing number of grazing livestock in Hungary. Accordingly, the area of under-utilized or non-utilized turfs is increasing. At the University of Debrecen, Institutes for Agricultural Research and Educational Farm, Karcag Research Institute we studied four types of turf utilization in three repetitions on a salt meadow with Alopecurus pratensis. As a result of the performed examinations, we identified the composition of the flora structure on the investigated area and we measured carbon-dioxide circulation and soil moisture.

東シベリア・カラマツ林における水・エネルギー・炭素循環−GAME，CREST，RIHNの成果より−

東シベリアにおける1998年～2013年にわたる水・エネルギー・炭素（WEC）循環の結果を総括する．スパスカヤパッド観測サイトでは，カラマツ林での暖候期におけるWEC循環と様々な環境因子との関係が明らかになった．また，スパスカヤパッドとエルゲイでの観測では，同じカラマツ林でほぼ等しい気象条件であっても下層構成樹種によりWEC循環が異なっていた．レナ河流域における流出量の観測では，永久凍土帯における年々の活動層深は100 cm year-1 で深くなっており，近年はその速度が増加していた．WEC循環のモデル研究では，陸面モデルで推定された蒸発散量は渦相関法から導かれた蒸発散量の実測値とほぼ同じ範囲にあった．流出モデルでは，年間の流出量のうち約30 ％が非常に遅い流出成分であり，それは実測値ともほぼ一致した．今後は，地球温暖化が予測される中で，東シベリアに存在する北方林が環境の変化にどのように応答するのかを明らかにすることが課題である．東シベリアにおける湿潤化のWEC循環への影響は，流域全体へと広がっていくであろう．WEC循環の研究は，乾燥化ばかりでなく湿潤化にも目を向けなければならない．

Computational Thinking in High School Science Classrooms

[ILLUSTRATION OMITTED] Computational thinking is a fundamental skill for everyone, not just for computer scientists. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability (Wing 2006, p. 33). A Framework for K-12 Science Education identified eight practices as essential elements of the K-12 science and engineering curriculum (NRC 2012, p. 49). These practices are deeply embedded within the Next Generation Science Standards (NGSS Lead States 2013a), where they are wedded closely to core ideas in the science disciplines. Most of the practices, such as Developing and Using Models, Planning and Carrying Out Investigations, and Analyzing and Interpreting Data, are well known among science educators. In contrast, the practice of Using Mathematics and Computational Thinking raises questions in the minds of many educators. As mathematics has long been integral to science teaching, the questions tend to revolve around the meaning of computational thinking. The Framework envisions computational thinking as a powerful intellectual tool: Since the mid-20th century, computational theories, information and computer technologies, and algorithms have revolutionized virtually all scientific and engineering fields. These tools and strategies allow scientists and engineers to collect and analyze large data sets, search for distinctive patterns, and identify relationships and significant features in ways that were previously impossible. They also provide powerful new techniques for employing mathematics to model complex phenomena---for example, the circulation of carbon dioxide in the atmosphere and ocean (NRC 2012, p. 64). and computational thinking We (the authors) asked ourselves how the different forms of computational thinking suggested in this definition differed from mathematical thinking. Mathematics, it's important to note, is not just a skill but also a way of thinking about the world. The Framework distinguishes mathematics from data analysis in the use of symbols to express relationships. So, for example, a student might graph data from an experiment and notice a pattern. Mathematics comes in when the student expresses the pattern as an equation that can predict additional data points. Students develop mathematical thinking when they approach a new situation with a range of mathematical skills in mind. Similarly, they develop computational thinking when they approach a new situation with an awareness of the many ways that computers can help them visualize systems and solve problems. The Venn diagram in Figure 1 shows how we see the relationship between mathematical and computational thinking. The diagram (itself a mathematical tool) shows which capabilities can be considered part of mathematical thinking, which are part of computational thinking, and which are part of both. As the diagram illustrates, analyzing and interpreting data is common to both mathematical and computational thinking, as are problem solving, mathematical modeling, and statistics and probability. Figure 1 also lists capabilities unique to computational thinking. In the remainder of this article we will illustrate how simulation, data mining, and automated data collection are important in today's science classroom. As you read these examples, keep in mind that simply using computers is not enough; students must be encouraged to re-orient and deepen their understanding about science using computational thinking. Your goal should be to help students build learning skills by recognizing the ways they can use computers to carry out investigations and solve practical problems. [FIGURE 1 OMITTED] The NGSS describes the practice of mathematics and computational thinking for high school as follows: Mathematical and computational thinking in 9-12 builds on K-8 experiences and progresses to using algebraic thinking and analysis, a range of linear and nonlinear functions including trigonometric functions, exponentials and logarithms, and computational tools for statistical analysis to analyze, represent, and model data. …

新生代における熱水活動とグローバル二酸化炭素循環

新生代における熱水活動とグローバル二酸化炭素循環

中国半乾燥農地における水と二酸化炭素の循環に関する日中共同研究

中国半乾燥農地における水と二酸化炭素の循環に関する日中共同研究

Carbon cycle: Carbon dioxide circulation through ocean and atmosphere

Carbon cycle: Carbon dioxide circulation through ocean and atmosphere