融合卫星与地面数据，提升碳排放监测精度至3.92ppm
Enhancing Carbon Emission Reduction Strategies using OCO and ICOS data
- 用加权KNN与机器学习融合卫星、地面和气象数据
- 预测误差仅3.92ppm，显著提升局部碳排放监测精度
- 适合政策制定者和环境研究者用于精准碳减排规划
我们提出一种新方法，通过融合轨道碳观测器（OCO-2和OCO-3）的卫星数据、集成碳观测系统（ICOS）的地面观测数据以及欧洲中期天气预报中心再分析5版（ERA5）的气象数据，提升局部二氧化碳监测能力。相比传统全国尺度数据下采样方法，本方法采用多模态数据融合，实现高分辨率二氧化碳估算。利用加权K近邻（KNN）插值结合机器学习模型，从卫星测量预测地面二氧化碳浓度，取得3.92 ppm的均方根误差。结果表明，整合多源数据能有效捕捉局部排放特征，凸显高分辨率大气传输模型的价值。所建模型增强了二氧化碳监测的粒度，为制定针对性碳减排策略提供精确依据，是神经网络与KNN在环境监测中的一次创新应用，可适配不同区域与时间尺度。
原文摘要 · Abstract (English)
We propose a methodology to enhance local CO2 monitoring by integrating satellite data from the Orbiting Carbon Observatories (OCO-2 and OCO-3) with ground level observations from the Integrated Carbon Observation System (ICOS) and weather data from the ECMWF Reanalysis v5 (ERA5). Unlike traditional methods that downsample national data, our approach uses multimodal data fusion for high-resolution CO2 estimations. We employ weighted K-nearest neighbor (KNN) interpolation with machine learning models to predict ground level CO2 from satellite measurements, achieving a Root Mean Squared Error of 3.92 ppm. Our results show the effectiveness of integrating diverse data sources in capturing local emission patterns, highlighting the value of high-resolution atmospheric transport models. The developed model improves the granularity of CO2 monitoring, providing precise insights for targeted carbon mitigation strategies, and represents a novel application of neural networks and KNN in environmental monitoring, adaptable to various regions and temporal scales.
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