SNUCO2M 관측 네트워크를 활용한 도시 CO2 농도 분석 방법론 제안

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.