Satellite observations provide information on the sources of SO₂, an important pollutant that affects both air quality and climate. However, these observations suffer from relatively poor data quality due to weak signals of SO₂. Here, we use a machine learning technique to analyze satellite SO₂ observations in order to reduce the noise and artifacts over relatively clean areas while keeping the signals near pollution sources. This leads to significant improvement in satellite SO₂ data.