Alcibahy Mariam, Gafoor Fahim Abdul, Mustafa Farhan, El Fadel Mutasem, Al Hashemi Hamed, Al Hammadi Ali, Al Shehhi Maryam R
Civil and Environmental Engineering Department, Khalifa University, Abu Dhabi, UAE.
Hong Kong University of Science and Technology, Hong Kong, China.
Sci Rep. 2025 Jan 4;15(1):766. doi: 10.1038/s41598-024-84593-9.
Estimating spatiotemporal maps of greenhouse gases (GHGs) is important for understanding climate change and developing mitigation strategies. However, current methods face challenges, including the coarse resolution of numerical models, and gaps in satellite data, making it essential to improve the spatiotemporal estimation of GHGs. This study aims to develop an advanced technique to produce high-fidelity (1 km) maps of CO and CH over the Arabian Peninsula, a highly vulnerable region to climate change. Using XGBoost, columnar carbon dioxide (XCO) and methane (XCH) concentrations using satellite data from OCO-2 and Sentinel-5P (the target variables) were downscaled, with ancillary data from CarbonTracker, MODIS Terra, and ERA-5 (the input variables). The model is trained and validated against these datasets, achieving high performance for XCO (R = 0.98, RMSE = 0.58 ppm) and moderate accuracy for XCH (R = 0.63, RMSE = 13.26 ppb). Seasonal cycles and long-term trends were identified, with higher concentrations observed in summer, and emission hotspots in urban and industrial areas. Comparisons with the EDGAR inventory highlighted the significant contributions of the power, oil, and transportation sectors to GHG emissions. These results demonstrate the value of high-resolution data for local-scale monitoring, supporting targeted mitigation strategies and sustainable policymaking in the region. Future work could integrate ground-based observations to further enhance GHG monitoring accuracy.
估算温室气体（GHGs）的时空分布图对于理解气候变化和制定缓解策略至关重要。然而，当前的方法面临诸多挑战，包括数值模型分辨率粗糙以及卫星数据存在 gaps，这使得改进温室气体的时空估算变得至关重要。本研究旨在开发一种先进技术，以生成阿拉伯半岛（一个气候变化高度脆弱的地区）一氧化碳（CO）和甲烷（CH）的高保真（1公里）地图。利用XGBoost，使用来自OCO - 2和哨兵 - 5P的卫星数据（目标变量）对柱状二氧化碳（XCO）和甲烷（XCH）浓度进行了降尺度处理，并结合了来自CarbonTracker、MODIS Terra和ERA - 5的辅助数据（输入变量）。该模型针对这些数据集进行了训练和验证，XCO取得了高性能（R = 0.98，RMSE = 0.58 ppm），XCH取得了中等精度（R = 0.63，RMSE = 13.26 ppb）。识别出了季节性周期和长期趋势，夏季观测到更高的浓度，城市和工业区存在排放热点。与EDGAR清单的比较突出了电力、石油和交通部门对温室气体排放的重大贡献。这些结果证明了高分辨率数据在地方尺度监测中的价值，为该地区的针对性缓解策略和可持续政策制定提供了支持。未来的工作可以整合地面观测，以进一步提高温室气体监测的准确性。
