Texas A&M University researchers are using artificial intelligence to study how severe weather events affect environmental pollution. The project focuses on how heavy rainfall and lightning influence pollution levels.
The team from the Environmental Engineering Department uses AI algorithms to analyze data from satellite imagery, weather stations, and pollution monitors. Dr. John Smith, the lead researcher, said the goal is to identify pollution patterns linked to extreme weather.
Early findings show pollution levels often rise after severe weather. Rainfall causes runoff that carries pollutants into waterways, increasing water pollution. Lightning produces nitrogen oxides, which can lead to ozone formation, affecting air quality.
The researchers use machine learning to process data and find correlations. They work with local authorities and environmental agencies to gather information and offer insights for policy development.
The project aims to improve pollution forecasting models by adding AI capabilities. This could help authorities better respond to pollution following storms.
The team plans to expand the dataset and include more variables, such as wind and temperature changes, to improve analysis. They hope the study will support practical solutions for pollution control.
Researchers believe their work will inform policy decisions and contribute to managing the environmental effects of severe weather through technology.