The University of Houston’s Air-Quality Forecasting & Machine Learning group has been a center of excellence for Atmospheric Science & Chemistry, Air-Quality Forecasting and Artificial Intelligence research, teaching, and practice since its founding.
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.
A team of scientists at University of Houston Department of Earth and Atmospheric Sciences examined air movement outdoors and its effects on the spread of respiratory diseases like COVID-19. The findings published online in Science of The Total Environment.
Masoud Ghahremanloo’s Work Published in Journal of Geophysical Research: Atmospheres
A University of Houston doctoral student has developed a more accurate artificial intelligence (AI) method for reading amounts of ground-level nitrogen dioxide (NO2) with the help of satellite technology.
As vehicle traffic lightened and industry slowed during the COVID-19 stay-at-home period in 2020, a University of Houston study by the air quality forecasting group led by Yunsoo Choi, associate professor in the Department of Earth and Atmospheric Sciences, estimates levels of potentially-dangerous air pollutants simultaneously decreased in major cities across the country.