Team: 1
School: Cleveland High
Area of Science: Environmental Sciences
Interim: What is the project about? (the definition of the problem)
This project is about the issue that is the recalcitrant behavior of ozone in the troposphere. Ozone is a harmful secondary pollutant affecting billions of people (Shaddick, 2020), formed as a result of many chemical interactions between volatile organic compounds (VOCs), nitrous oxides (NOx), and ultraviolet radiation. Ozone is known to have had a variety of negative impacts on human life, responsible for 1 in 5 premature deaths (“Air pollution- World Health Organization,†n.d.), and crop health, and is the main constituent of visible photochemical smog. Currently, the acceptable limit of ozone is 70 ppb (EPA, 2015), but since ozone is a secondary pollutant, this can only be done by regulating the primary pollutants it is formed by.
In the past few years and especially with the pandemic lockdowns, there have been numerous instances of sudden decreases in precursor chemicals leading to an increase in surface ozone. The questions we seek to answer are:
Why might this occur?
What are the limiting factors of ozone?
Since air pollution is most severe in developing countries that lack proper air pollution regulation, this is an issue relevant to public health and environmental justice (“Air pollution hurts the poorest most,†n.d.). Additionally, more developed countries may also greatly benefit from this sort of project by improving air pollution policy to avoid increasing surface ozone.
These questions are essential to better policymaking in air pollution regulation and may prevent us from causing even more severe ozone events, and lead the way to more effective air pollution regulation practices in the future.
How are you/do you plan to solve this problem computationally?
To solve this issue, we are going to develop from the ground up a Python program that uses differential equations and inverse probability theory to estimate the rate and amount of ozone formed over time with different pollutant concentration combinations. Photochemical models are effective ways to model, simulate, and test different air quality scenarios (Shaw, 2021).
Thousands of chemical processes will be simplified down to a few dozen of the most essential steps, and inputs to the model will be used to calculate ozone formation rates over time at different levels of VOCs, NOx, and ultraviolet radiation.
What progress have you made up to this point? (research, code, etc.)
At this point, we are determining the specific equations that will be a part of the model. After choosing these equations, we will test them out by solving some of these equations by hand, comparing them to real-life values. Next, we will create a Python program to solve these differential equations and use discretization to make a photochemical box model to simulate ozone formation in urban areas.
What results are you expecting?
We are expecting for there to be different limiting factors in different areas, depending on the abundance of the relative precursors- for example, VOC-limited in city environments and NOx-limited in rural environments. We also might find a significant impact of differing ultraviolet radiation wavelengths on ozone production, which could be a cause for the anomalous increase in ozone despite decreases in precursors.
Sources
Air pollution- World Health Organization. (n.d.). Retrieved from https://www.who.int/health-topics/air-pollution
Shaddick, G., Thomas, M.L., Mudu, P. et al. Half the world’s population are exposed to increasing air pollution. npj Clim Atmos Sci 3, 23 (2020). https://doi.org/10.1038/s41612-020-0124-2
Environmental Protection Agency. (n.d.). Ozone National Ambient Air Quality Standards (NAAQS). EPA. Retrieved February 2, 2022, from https://www.epa.gov/ground-level-ozone-pollution/ozone-national-ambient-air-quality-standards-naaqs
Air pollution hurts the poorest most - UNEP. UN Environment Programme. (n.d.). Retrieved October 14, 2021, from https://www.unep.org/news-and-stories/story/air-pollution-hurts-poorest-most
Shaw, David & Carslaw, Nicola. (2021). INCHEM-Py: An open source Python box model for indoor air chemistry. Journal of Open Source Software. 6. 3224. 10.21105/joss.03224.
Team Members:
Sponsoring Teacher: Ashli Knoell