A CUTTING-EDGE APPROACH TO TACKLING POLLUTION IN HOUSTON AND BEYOND: University of Houston Researchers Use Machine Learning and SHAP Analysis to Pinpoint Air Pollution Sources

With its notoriously hot and humid climate and robust industrial environment, Houston is one of the most ozone-polluted cities in the United States. Now, a University of Houston research team is integrating the power of machine learning (ML) with innovative analysis techniques to pinpoint the city’s air pollution sources more accurately.

While the ozone layer in the stratosphere protects the Earth, and us, from the harmful rays of the sun, it’s also a major pollutant that can be harmful to human health when it’s closer to the ground. Long-term exposure to surface ozone can cause difficulty breathing, worsen asthma and increase the risk of heart disease, according to the Environmental Protection Agency.

The research team integrated the Positive Matrix Factorization (PMF) model with the SHAP algorithm of machine learning, which helps explain why ML models make certain decisions while also making the data more understandable. Their analysis revealed that in industrial areas, Houston’s oil and gas industry had the highest impact on emissions, while shortwave radiation and relative humidity were the two most important influencing factors for overall ozone concentration. The work is published in the journal Environmental Pollution.

Check out the full article here

Prof. Yunsoo Choi
Prof. Yunsoo Choi
Professor of Atmospheric Chemistry, Air-Quality Modeling, AI (Deep Learning/Machine Learning), Satellite Remote Sensing

My research interests include Atmospheric Chemistry, Air-Quality Modeling and AI (Deep Learning/ Machine Learning).