인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
As urban areas account for more than 70% of global anthropogenic GHG emissions, there is growing need to establish observation networks for identification of GHG emission characteristics. However, due to the high variability of observed CO₂, urban monitoring networks require structured data management standards to improve the understanding of complex concentration patterns. This study proposed new data selection criteria suitable for urban monitoring networks. Data variability was analyzed based on these criteria and compared to those of global background station Mauna Loa (MLO). For MLO data, 44% of observed data were considered as background concentrations, and the differences based on the selection criteria were less than 1 ppm. In contrast, for urban stations, less than 20% of the total data was considered as background concentrations, and differences were as large as 13.5 ppm depending on data selection. MLO showed a steady annual growth rate of 2.25 ppm/yr despite the data selection criteria. However, for the urban monitoring network, annual growth rates at single sites ranged from 2.08 to 2.22 ppm/yr depending on the data selection criteria. Furthermore, data selection can sometimes result in opposite outcomes. For example, the ΔCO/ΔCO₂ emission ratio in winter was 6.97±0.25 ppb/ppm with urban data but was 15.59±1.23 ppb/ppm with background data. Additionally, unequal numbers of data points resulting from data selection can lead to unreliable inter-seasonal comparison results. This study suggests the importance of the data selection method in urban GHG monitoring to avoid significant variation and potential biases
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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