Satellite Data for Emission Measurement

Summary Notes on Satellite-Based Monitoring of Greenhouse Gases in India

Research Overview:

• Conducted by researchers at Indian Institute of Technology, Bombay.
• Focused on using satellite data to measure carbon dioxide (CO2) and methane (CH4) levels accurately in Indian metropolitan areas (e.g., Mumbai, Delhi).
• Key outcomes included identifying emission hotspots linked to wastewater, landfills, and industrial activities.

Methodology:

• A statistical model named SARIMA (Seasonal Autoregressive Integrated Moving Average) was developed to forecast greenhouse gas levels.
• SARIMA functions similarly to weather forecasts, utilizing recent readings and historical data to predict future emissions while accounting for seasonal variations.

Data Sources:

• Utilized data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) for CO2 and European Space Agency’s Sentinel-5P for CH4.
• Satellite data validated against Total Carbon Column Observing Network (TCCON) for accuracy.

Significance:

• The research addresses the lack of a comprehensive ground monitoring network for GHGs in India.
• Satellite-derived data allows for targeting the worst emission sources, aiding in the formulation of effective policies.
• Emphasizes the importance of combining satellite and ground data for improved emission estimates.

Global Context:

• India is one of 195 signatories to the 2016 Paris Accord, which aims to limit global warming to below 1.5°C.
• Accurate measurement of GHG emissions is critical for monitoring compliance with nationally determined contributions (NDCs).

Recommendations:

• Calls for expanding ground-based monitoring sites in India.
• Suggests that integrating machine learning with physics-based models and advanced satellite sensors can enhance future monitoring systems.

Environmental Impact:

• Informs policy measures aimed at reducing emissions through landfill gas capture and improved traffic management.
• Contributes to broader climate policy and environmental management strategies.

Technical Insights:

• The study highlights the potential of machine learning (ML) as a tool to refine emission measurement systems while advocating for a mixed approach that includes physics-based modeling and ground data.

Key Facts:

• SARIMA model used for forecasting emissions.
• Emission hotspots linked to specific urban features (landfills, industrial areas).
• Emphasizes both satellite data for coverage and ground data for precision in GHG emissions measurement.

Importance for Policy Makers:

• Data-driven insights for devising strategies to mitigate urban emissions.
• Monitoring and evaluation of existing policies' effectiveness regarding emission reductions.

This study presents a critical advancement in using science and technology for environmental monitoring, with implications for public policy and climate action initiatives in India.