Team: 1
School: Cleveland High
Area of Science: Environmental Sciences
Proposal: Air pollution is a leading cause of premature deaths, responsible for about 5 million deaths annually worldwide due to lung cancer, heart disease, respiratory diseases, and more. One of these harmful pollutants includes ozone (O3). Natural ozone in the stratosphere, extending about 32 miles above the Earth’s surface, is necessary for protecting us from the sun’s harmful ultraviolet rays. On the other hand, ozone in the troposphere, the layer closest to the Earth, is a harmful pollutant created when nitrogen oxides and volatile organic compounds (both manmade pollutants) chemically react with sunlight. This “bad†ozone is the main component of photochemical smog and causes a plethora of health and environmental issues. Since sunlight is necessary for the reaction, ground ozone concentration is highest during warmer months.
The goal of this project will be to create a Python code that uses machine learning to analyze previous levels of ground-level ozone and meteorological data of cities in India, which has generally had high levels of air pollution. This will be used to make a program that can predict ozone levels days in advance for cities worldwide.
By predicting the days with high ozone levels, vulnerable populations (the elderly, people with respiratory diseases) can take precautions to avoid extended exposure outside. People can also help by driving less, avoiding idling, conserving electricity by setting air conditioners to higher temperatures, and more. By identifying when ozone levels are highest, people will be able to take the most effective precautions to reduce its negative impact.
Mentor: Mark Petersen
Team Members:
Eliana Juarez
Sofia Juarez
Graciela Rodriguez
Sponsoring Teacher: Ashli Johnston