• Asian Journal of Atmospheric Environment
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• 제5권4호
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• pp.263-277
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• 2011

Global atmospheric $CO_2$ distributions were simulated with a chemical transport model (GEOS-Chem) and compared with space-borne observations of $CO_2$ column density by GOSAT from April 2009 to January 2010. The GEOS-Chem model simulated 3-D global atmospheric $CO_2$ at $2^{\circ}{\times}2.5^{\circ}$ horizontal resolution using global $CO_2$ surface sources/sinks as well as 3-D emissions from aviation and the atmospheric oxidation of other carbon species. The seasonal cycle and spatial distribution of GEOS-Chem $CO_2$ columns were generally comparable with GOSAT columns over each continent with a systematic positive bias of ~1.0%. Data from the World Data Center for Greenhouse Gases (WDCGG) from twelve ground stations spanning $90^{\circ}S-82^{\circ}N$ were also compared with the modeled data for the period of 2004-2009 inclusive. The ground-based data show high correlations with the GEOS-Chem simulation ($0.66{\leq}R^2{\leq}0.99$) but the model data have a negative bias of ~1.0%, which is primarily due to the model initial conditions. Together these two comparisons can be used to infer that GOSAT $CO_2$ retrievals underestimate $CO_2$ column concentration by ~2.0%, as demonstrated in recent validation work using other methods. We further estimated individual source/sink contributions to the global atmospheric $CO_2$ budget and trends through 7 tagged $CO_2$ tracers (fossil fuels, ocean exchanges, biomass burning, biofuel burning, net terrestrial exchange, shipping, aviation, and CO oxidation) over 2004-2009. The global $CO_2$ trend over this period (2.1 ppmv/year) has been mainly driven by fossil fuel combustion and cement production (3.2 ppmv/year), reinforcing the fact that rigorous $CO_2$ reductions from human activities are necessary in order to stabilize atmospheric $CO_2$ levels.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.149-149
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• 2019

Gross Primary production (GPP) and evapotranspiration (ET) are the two critical components of carbon and water cycle respectively, linking the terrestrial surface and ecosystem with the atmosphere. The ratio between GPP to ET is called ecosystem water use efficiency (EWUE) and its quantification at the forest site helps to understand the impact of climate change due to large scale anthropogenic activities such as deforestation and irrigation. This study was conducted at the FLUXNET forest site CN-Qia (2003-2005) using Community land model (CLM 5.0). We simulated carbon and water fluxes including GPP, ecosystem respiration (ER), and ET using climatic variables as forcing dataset for 30 years (1981-2010). Model results were validated with the FLUXNET tower observations. The correlation showed better performance with values of 0.65, 0.77, and 0.63 for GPP, ER, and ET, respectively. The model underestimated the results with minimum bias of -0.04, -1.67, and -0.40 for GPP, ER, and ET, respectively. Effect of climate 'CLIM' and '$CO_2$' were analyzed based on EWUE and its trend was evaluated in the study period. The positive trend of EWUE was observed in the whole period from 1981-2010, and the trend showed further increase when simulated with rising $CO_2$. The time period were divided into two parts, from 1981-2000 and from 2001 to 2010, to identify the warming effect on EWUE. The first period showed the similar increasing trend of EWUE, but the second period showed slightly decreasing trend. This might be associated with the increase in ET in the wet temperate forest site due to increase in climate warming. Water use efficiency defined by transpiration (TR) (TWUE), and inherent-TR based WUE (IT-WUE) were also discussed. This research provides the evidence to climate warming and emphasized the importance of long term planning for management of water resources and evaporative demand in irrigation, deforestation and other anthropogenic activities.

• Ocean and Polar Research
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• 제36권1호
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• pp.25-37
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• 2014

To understand the degradation processes of organic matter related to sulfate reduction by Sulfate Reduction Bacteria (SRB) in the tidal flat sediments of Hwang-do and Sogeun-ri, Tae-an Peninsula in Chungnam-do, biogeochemical characteristics were analyzed and highlighted using specific microbial biomarkers. The organic geochemical parameters (TOC, ${\delta}^{13}C_{org}$, C/N ratio, long-chain-n-alkane) indicate that most of the organic matter has been derived from marine phytoplankton and bacteria in the fine-grained sediment of Sogeun-ri, although terrestrial plant components have occasionally been incorporated to a significant degree in the coarse-grained sediment of Hwang-do. The concentration of sulfate in pore water is a constant tendency with regard to depth profile, while methane concentration appears to be slightly different with regard to depth profile at the two sites. Especially, the sum of bacteria fatty acid (a-C15:0 + i-C15:0 + C16:1w5) confirms that the these concentrations in Sogeun-ri are related to the degradation of Benzene, Toluene, Ethylbenzene and Xylene (BTEX) compounds from the crude oil retained in the sediments as a result of the Hebei Spirit oil-spill accident in 2007. The methane-related microbial communities as shown by lipid biomarkers (crocetane, PMI) are larger in some sedimentary sections of Hwang-do than in the Sogeunri tidal flat. These findings suggest that methane production by microbiological processes is clearly governed by SRB activity along the vertical succession in organic-enriched tidal flats.

• Journal of Microbiology and Biotechnology
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• 제26권11호
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• pp.1891-1907
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• 2016

Yeasts that are present in marine environments have evolved to survive hostile environments that are characterized by high exogenous salt content, high concentrations of inhibitory compounds, and low soluble carbon and nitrogen levels. Therefore, yeasts isolated from marine environments could have interesting characteristics for industrial applications. However, the application of marine yeast in research or industry is currently very limited owing to the lack of a suitable isolation method. Current methods for isolation suffer from fungal interference and/or low number of yeast isolates. In this paper, an efficient and non-laborious isolation method has been developed and successfully isolated large numbers of yeasts without bacterial or fungal growth. The new method includes a three-cycle enrichment step followed by an isolation step and a confirmation step. Using this method, 116 marine yeast strains were isolated from 14 marine samples collected in the UK, Egypt, and the USA. These strains were further evaluated for the utilization of fermentable sugars (glucose, xylose, mannitol, and galactose) using a phenotypic microarray assay. Seventeen strains with higher sugar utilization capacity than the reference terrestrial yeast Saccharomyces cerevisiae NCYC 2592 were selected for identification by sequencing of the ITS and D1/D2 domains. These strains belonged to six species: S. cerevisiae, Candida tropicalis, Candida viswanathii, Wickerhamomyces anomalus, Candida glabrata, and Pichia kudriavzevii. The ability of these strains for improved sugar utilization using seawater-based media was confirmed and, therefore, they could potentially be utilized in fermentations using marine biomass in seawater media, particularly for the production of bioethanol and other biochemical products.

• 대한원격탐사학회지
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• 제35권6_2호
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• pp.1117-1132
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• 2019

산림생태계 내의 총일차생산량은 산림 자원 생산량과 직결되고, 산림생태계의 건강성, 산림식물계절 및 생태계 서비스의 중요한 지표가 된다. 이 연구에서는 인공위성 자료와 기계학습 알고리즘을 활용하여 우리 나라의 산림유역의 총일차생산량을 연구하였다. 에디공분산 타워가 있는 6개 지점에서의 MODIS (Moderate Resolution Imaging Spectroradiometer) 산출물과 에디공분산타워의 총일차생산성으로 연구기간의 75%-80%에 해당하는 자료로 기계학습 알고리즘을 훈련하고 나머지 기간으로 구축된 모델의 총일차생산성 예측 결과를 검증하였다. 모델을 구축할 때 MODIS 지상 산출물과 대기 산출물을 조합하여 새로운 입력자료(e.g., 포화수증기압차)를 모델의 입력자료(Processed MODIS)로 사용하였을 때와 이러한 과정 없이 QC(Quality control)만 거친 MODIS 산출물을 그대로 입력자료(Unprocessed MODIS)로 사용하였을 때의 총일차생산량을 비교해 보고 그 활용 가능성에 대해 고찰하였다. 추가로 MODIS 총일차생산량 산출물(MYD17)과 에디공분산 총일차생산성 및 기계학습 알고리즘 기반의 총일차생산성과의 상관관계를 보고 그 적합성에 대해 논의하였다. 이 연구에서 사용된 기계학습 알고리즘은 Support Vector Machine (SVM)으로 산림생태계 연구에서 가장 많이 사용되고 있는 기계학습 알고리즘 중 하나이다. 기계학습 알고리즘 기반(SVM 모델)의 총일차생산량 예측 결과는 MODIS 총일차생산량 산출물(MYD17)보다 에디공분산 총일차생산량과 전반적으로 높은 상관관계를 보였고 특히 식생 성장을 시작하는 시점의 값을 좀더잘 예측하는 결과를 보였다. 단일 지역에서 Unprocessed MODIS 입력자료로 훈련된 SVM 모델 결과는 피어슨 상관계수 0.75 - 0.95 (p < 0.001), 6개의 연구 지점에서 훈련된 SVM 모델 결과는 피어슨 상관계수 0.77 - 0.94 (p < 0.001) 사이를 보였다. 이 결과는 훈련 자료에 다양한 이벤트들이 포함되면 모델의 예측력이 향상되는 가능성을 보여주었고 위성영상의 산출물을 재계산하여 새로운 산출물을 내는 과정을 거친 위성 자료가 아니어도 그 예측력에는 크게 문제가 없음을 보여주었다.

• 대한원격탐사학회지
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• 제27권4호
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• pp.481-494
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• 2011

다양한 공간규모에 대해서 식생의 생산성을 추정하기 위해 대기와 생태계내의 탄소순환 과정을 모니터링 하는 것은 탄소순환과정과 원격탐사기법을 결합함으로써 기능하다. 그 대표적인 예로서 미국 항공우주국(NASA)에서 개발한 지구관측시스템(Earth Observing System, EOS)은 총 일차생산성 (Gross Primary Productivity, GPP), 순 일차생산성 (Net Primary Productivity, NPP)을 전 지구에 대해 1km의 공간해상도로 제공한다. 그러나 MODIS의 생산성 추정에 기상입력자료로 이용되는 The NASA Data Assimilation Office (DAO)자료는 $1{\circ}\;{\times}\;1.25{\circ}$의 거친 해상도를 가지기 때문에 지역규모에서 지표기상의 상세한 공간적인 이질성을 반영하기 어렵다. 본 연구에서는 MODIS의 Aqua와 Terra 위성의 영상만을 이용하여 생산성을 추정하기 위한 입력자료로 사용되는 일 단위 일사량(MJ $day^{-1}$), 열 최저기온($^{\circ}C$, 낮 시간 평균 대기수증기압 포차(Yapor Pressure Deficit, Pa)을 5 km 해상도로 각각 추정하였다. 각각의 입력자료의 평가를 위해 미국 중서부 Corn Belt 지역 내에 위치하고 있는 7 개소의 지상기반의 관측자료를 수집하였다. MODIS 위성영상을 이용하여 추정한 기상입력자료와 관측자료를 비교한 결과, 일 최저기온은 ME와 상관계수가 각각 0.83에서 0.98, $-0.9^{\circ}C$ 에서 $+5.2^{\circ}C$ 의 범위로 양호한 상관성을 보였다. VPD는 약간 산개하는 경향을 보인다(ME = -183.8 Pa ~ +382.1 Pa; R = 0.51 ~ 0.92). 반면에, 일 단위 일사량은 MODIS가 약간 과대평가하는 경향을 보였지만 대부분의 지역에서 관측자료와 유의한 일치도를 보였다(ME = -0.4 MJ $day^{-1}$ +7.9 MJ $day^{-1}$; R = 0 67 ~ 0.97). 본 연구에서는 맑은 날에 대해서 MODIS 위성영상으로부터 추정된 기상입력자료가 MODIS가 제공하는 기상입력자료보다 상세한 지표 기상의 이질성을 반영한 자료를 생산할 수 있음을 확인하였다.