基于 OCO-2 卫星 XCO2 数据和随机森林模型的中国各集群地区碳排放估算

IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Yibing Tan , Shanshan Wang , Ruibin Xue , Sanbao Zhang , Tianyu Wang , Jiaqi Liu , Bin Zhou
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引用次数: 0

摘要

大气中的二氧化碳(CO2)是最重要的温室气体之一,其浓度因人类活动而稳步上升。为了实现碳峰值和碳中和目标,量化碳排放和评估碳减排战略至关重要。为了建立具有全时间序列和空间覆盖的高精度观测,我们开发了一种时空插值方法,以 0.5° × 0.5° 的分辨率获得了 2015-2021 年中国大陆的 XCO2 数据。结果表明,中国大陆的 XCO2 呈东西梯度变化,东部水平较高,西部水平较低,并呈现出春季升高、夏季降低的季节性规律。随后,根据时间序列 XCO2 异常值(ΔXCO2)和 ODIAC(人为二氧化碳开放源数据清单)碳排放数据,将研究区域划分为七个组群。这种分类旨在强调碳排放的空间异质性差异,其结果突出表明,ΔXCO2 高的地区反映了较高的碳排放量。最后,利用随机森林模型估算了每个集群的碳排放量,R2 约为 0.6。为评估影响碳排放预测的变量,计算了每个变量的重要性。具体而言,代表人类生产活动的夜间照明数据(NTL)成为影响大多数集群碳排放预测的关键变量。相比之下,初级生产力总值(GPP)在中国西南部(SWC)被认为是一个更关键的变量,这主要是由于该地区错综复杂的植被碳汇系统。温度(T)是影响华东(EC)某些发达城市碳排放量估算的关键变量,其驱动力是城市热岛效应,该效应放大了能源消耗,改变了土地利用,影响了城市系统,从而影响了碳排放的空间模式。通过利用遥感数据建立机器学习模型,对不同特征区域的碳排放量进行量化,可为精细化碳监测和管理策略提供新的见解和支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of carbon emissions in various clustered regions of China based on OCO-2 satellite XCO2 data and random forest modelling
Atmospheric carbon dioxide (CO2) stands as one of the most important greenhouse gasses, with steadily increasing concentrations attributable to human activities. In the pursuit of reaching peak carbon and carbon neutrality goals, it is essential to quantify carbon emissions and evaluate carbon reduction strategies. To establish a high-precision observation with full time series and spatial coverage, a spatio-temporal interpolation method was developed to obtain XCO2 data over mainland China at a resolution of 0.5° × 0.5° for the years 2015–2021. An east-west gradient, higher levels in the east and lower levels in the west, was observed, exhibiting a seasonal pattern of elevation in spring and reduction in summer. Subsequently, the research area is classified into seven clusters based on time-series XCO2 anomalies (ΔXCO2) and ODIAC (Open Source Data Inventory of Anthropogenic Carbon Dioxide) carbon emission data. This classification aims to emphasize the differentiation of spatial heterogeneity in carbon emissions and the results highlight that regions with high ΔXCO2 reflect higher carbon emission. Finally, the carbon emissions of each cluster were estimated by using a random forest model individually yielding an R2 of approximately 0.6. For assessing the variables influencing carbon emission predictions, the importance of each variable was calculated. Specifically, NightTime Lighting data (NTL), representing human production activities, emerged as a crucial variable influencing carbon emission predictions in most clusters. In comparison, Gross Primary Productivity (GPP) is considered a more critical variable in Southwest China (SWC), primarily owing to the intricate vegetation carbon sink system in this region. Temperature (T) emerges as a key variable influencing the estimation of carbon emissions in certain developed cities in Eastern China (EC), driven by the urban heat island effect which amplifies energy consumption, modifies land use, and impacts urban systems, influencing the spatial patterns of carbon emissions. Carbon emissions in different characteristic regions was quantified by establishing machine learning models with remote sensing data, which can provide new insights and support for refined carbon monitoring and management strategy.
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来源期刊
Atmospheric Environment
Atmospheric Environment 环境科学-环境科学
CiteScore
9.40
自引率
8.00%
发文量
458
审稿时长
53 days
期刊介绍: Atmospheric Environment has an open access mirror journal Atmospheric Environment: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. Atmospheric Environment is the international journal for scientists in different disciplines related to atmospheric composition and its impacts. The journal publishes scientific articles with atmospheric relevance of emissions and depositions of gaseous and particulate compounds, chemical processes and physical effects in the atmosphere, as well as impacts of the changing atmospheric composition on human health, air quality, climate change, and ecosystems.
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