![A land use regression model to predict emissions from oil and gas production using machine learning](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBb01OIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--cfe63fa7c2d8c2b88fc3592dedf28a461222e4d5/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--33b2b080106a274a4ca568f8742d366d42f20c14/JEI-22-159_Fig6.png)
Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.
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