Atmospheric emissions characteristic ratio (NO2/CO2)

Atmospheric emissions characteristic ratio (NO2/CO2)

Description

The NO2/CO2 ratio is defined as the ratio of air pollutant (NO2) data from GEMS to greenhouse gas (CO2) data from the Orbiting Carbon Observatory (OCO-2 and OCO-3). This can serve as a proxy indicator to compare the influences of emissions of air pollutants and greenhouse gases.

Assuming that emission signal can be detected from satellite data, the areas with NO2/CO2 ratios higher than the national (or regional) average* suggest that they are more influenced by the emission of atmospheric pollutants compared to greenhouse gas emissions. Conversely, if the ratio is lower than the average, it indicates the influence of greenhouse gas emissions is more dominant.

* Number in the figure

Applications

This information is particularly useful in understanding the atmospheric characteristics based on emissions in regions with high population density, high gross domestic product (GDP), or high vehicle usage rates, where the values tend to be higher. As the values are derived from regions where both atmospheric pollutants and greenhouse gases are concurrently observed, it can also be used for devising integrated strategies to tackle climate change and air pollution.

Note: data interpretation

○ Considering variations in fuel types, combustion conditions, and environmental policies (emission regulations), comparisons between countries with similar economic development rates may be more suitable.

○ For carbon dioxide (a greenhouse gas), emissions during spring to fall seasons may be underestimated due to the influence of vegetation (a CO2 sink), making winter the most suitable period for analyzing anthropogenic emissions.

○ With long-term data accumulation, it is possible to track the temporal trends of the ratio between atmospheric pollutants and greenhouse gases for specific regions. This can be useful in determining policy priorities.

※ As complex influences are involved, diverse information and comparisons should be taken into account for thorough analysis.