Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space

TL;DR

This paper introduces a deep learning method that uses remote sensing data to accurately estimate and monitor air pollution and greenhouse gas emissions at high spatial and temporal resolutions globally.

Contribution

The work presents a novel deep learning approach that combines optical satellite imagery with atmospheric measurements to estimate air pollution with high spatial and temporal detail.

Findings

High accuracy in NO₂ estimation with mean absolute error <6 μg/m³

Enables high-resolution, global monitoring of air pollution sources

Provides a scalable method for real-time air quality assessment

Abstract

Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse Gases (GHGs). Despite the importance of limiting GHG emissions to mitigate climate change, detailed information about the spatial and temporal distribution of GHG and other air pollutants is difficult to obtain. Existing models for surface-level air pollution rely on extensive land-use datasets which are often locally restricted and temporally static. This work proposes a deep learning approach for the prediction of ambient air pollution that only relies on remote sensing data that is globally available and frequently updated. Combining optical satellite imagery with satellite-based atmospheric column density air pollution measurements enables the scaling of air pollution estimates (in this case NO$_2$) to high spatial resolution (up to $\sim$10m) at arbitrary locations and adds a temporal component to these estimates. The proposed model performs with high accuracy when evaluated against air quality measurements from ground stations (mean absolute error $<$6$~\mu g/m^3$). Our results enable the identification and temporal monitoring of major sources of air pollution and GHGs.