Precursor concentration and solar radiation intensity determine the dynamic equilibrium between ozone creation and destruction. Tropospheric ozone is a dangerous pollutant that can lead to a number of health problems as well as environmental difficulties. In contrast, stratospheric ozone creates a protective ozone layer. Exposure to high levels of tropospheric ozone can cause a range of respiratory problems, including coughing, throat irritation, and worsening of asthma symptoms. Long-term exposure can lead to more severe health issues such as chronic respiratory diseases, reduced lung function, and increased mortality rates. Children, the elderly, and individuals with pre-existing health conditions are particularly vulnerable to the adverse effects of ozone. Ground-level ozone can also damage flora, which can result in decreased agricultural production, damage to forests, and a decline in biodiversity. It prevents plants from photosynthesizing, which slows down their growth and increases their vulnerability to pests, illnesses, and harsh weather condition.

Scientists have recorded major breakthroughs in the application of Artificial Intelligence (AI) in health, weather forecasting and other areas of science. Scientists at the University of Houston's (UH's) Air Quality Forecasting and Modeling Lab have developed a new artificial intelligence system that could lead to improved ways to control high ozone problems and even contribute to solutions for climate change issues. The breakthrough, published online in the scientific journal, Scientific Reports-Nature, showed ozone levels in the earth's troposphere (the lowest level of the atmosphere) can now be forecast with accuracy up to two weeks in advance, a remarkable improvement over current systems that can accurately predict ozone levels only three days ahead. Professor of atmospheric chemistry and AI deep learning at UH's College of Natural Sciences and Mathematics, Yunsoo Choi, said: "This was very challenging. Nobody had done this previously. I believe we are the first to try to forecast surface ozone levels two weeks in advance."

Ozone levels in the earth's troposphere (the lowest level of our atmosphere) can now be forecasted with accuracy up to two weeks in advance, a remarkable improvement over current systems that can accurately predict ozone levels only three days ahead. The new artificial intelligence system developed in the University of Houston's Air Quality Forecasting and Modeling Lab could lead to improved ways to control high ozone problems and even contribute to solutions for climate change issues. Nobody had done this previously. I believe we are the first to try to forecast surface ozone levels two weeks in advance." The findings are published online in the scientific journal, Scientific Reports-Nature. Ozone, a colorless gas, is helpful in the right place and amount. As a part of the earth's stratosphere ("the ozone layer"), it protects by filtering out UV radiation from the sun. But when there are high concentrations of ozone near earth's surface, it is toxic to lungs and hearts.

That would improve health alerts for people at heightened risk of developing problems because of high ozone levels. Yunsoo Choi, associate professor in the Department of Earth and Atmospheric Sciences and corresponding author for a paper explaining the work, said they built an artificially intelligent model using a convolutional neural network, which is able to take information from current conditions and accurately predict ozone levels for the next day. The work was published in the journal Neural Networks. "If we know the conditions of today, we can predict the conditions of tomorrow," Choi said. Ozone is an unstable gas, formed by a chemical reaction when sunlight combines with nitrogen oxides (NOx) and volatile organic compounds, both of which are found in automobile and industrial emissions.

That would improve health alerts for people at heightened risk of developing problems because of high ozone levels. Yunsoo Choi, associate professor in the Department of Earth and Atmospheric Sciences and corresponding author for a paper explaining the work, said they built an artificially intelligent model using a convolutional neural network, which is able to take information from current conditions and accurately predict ozone levels for the next day. The work was published in the journal Neural Networks. "If we know the conditions of today, we can predict the conditions of tomorrow," Choi said. Ozone is an unstable gas, formed by a chemical reaction when sunlight combines with nitrogen oxides (NOx) and volatile organic compounds, both of which are found in automobile and industrial emissions.