Alagade Adarsh, Sahu Manoranjan
Environmental Science and Engineering Department, Indian Institute of Technology Bombay, 507, Aerosol and Nanoparticle Technology Laboratory, Mumbai, 400076, India.
Inter-Disciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai, 400076, India.
Environ Sci Pollut Res Int. 2025 May;32(24):15006-15024. doi: 10.1007/s11356-025-36583-1. Epub 2025 Jun 4.
The urgent challenge of climate change, driven by greenhouse gas (GHG) emissions, highlights the need for reliable monitoring to inform effective mitigation strategies. However, the lack of monitoring stations in India for direct GHG measurements poses a challenge. This study explores the potential of satellite data for monitoring CO and CH concentrations in Indian megacities, specifically for Mumbai and Delhi. We assessed the reliability of Orbiting Carbon Observatory-2 (OCO-2) satellite data for CO and Sentinel-5 Precursor data for CH by comparing them with ground-based Total Carbon Column Observing Network (TCCON) measurements. Additionally, we applied the Seasonal Autoregressive Integrated Moving Average (SARIMA) model for CO₂ forecasting in Mumbai, achieving high accuracy (mean absolute error = 2.1 ppm, root mean square error = 2.72 ppm). While data limitations restrict CO analysis for Delhi, the findings for Mumbai significantly contribute to understanding urban CO dynamics. The SARIMA model also shows promise for CH forecasting with MAE of 8.3 ppb (Mumbai) and 7.78 ppb (Delhi) and RMSE of 10.37 ppb (Mumbai) and 11.92 ppb (Delhi). These findings underscore the utility of satellite data and forecasting models in monitoring urban GHG emissions. The observed rise in CO and CH concentrations highlights the urgency of implementing serious actions to address climate change.
由温室气体（GHG）排放驱动的气候变化这一紧迫挑战凸显了进行可靠监测以制定有效缓解策略的必要性。然而，印度缺乏用于直接测量温室气体的监测站，这构成了一项挑战。本研究探讨了卫星数据用于监测印度特大城市（特别是孟买和德里）一氧化碳（CO）和甲烷（CH）浓度的潜力。我们通过将轨道碳观测站 - 2（OCO - 2）卫星数据用于CO监测以及哨兵 - 5前体数据用于CH监测，并与地面总碳柱观测网络（TCCON）的测量数据进行比较，评估了这些数据的可靠性。此外，我们应用季节性自回归积分移动平均（SARIMA）模型对孟买的二氧化碳（CO₂）进行预测，取得了高精度（平均绝对误差 = 2.1 ppm，均方根误差 = 2.72 ppm）。虽然数据限制使得无法对德里进行CO分析，但孟买的研究结果对理解城市CO动态有显著贡献。SARIMA模型在CH预测方面也显示出前景，孟买的平均绝对误差为8.3 ppb，德里为7.78 ppb；孟买的均方根误差为10.37 ppb，德里为11.92 ppb。这些发现强调了卫星数据和预测模型在监测城市温室气体排放方面的实用性。观测到的CO和CH浓度上升凸显了采取严肃行动应对气候变化的紧迫性。
