Contagious Intelligence - Space Apps Challenge

Contagious Intelligence| A New Perspective

A New Perspective

Due to the COVID-19 pandemic, protected areas and other forms of wilderness areas (e.g., arboretums, beaches, parks, marine monuments) have been closed worldwide. Your challenge is to lead the effort to examine any potential impacts of reduced human traffic in such local protected natural environments.

An Analytic Approach to Addressing Air Pollution in Donghu National Park

Summary

Using the data from the Aura satellite regarding Nitrogen Dioxide, we used data derived from Wuhan, China to prove the effects of CoViD-19 on the air pollutant concentration. We found a direct correlation between the concentration of NO2 and CoVid-19 cases in the area, and attributed this to the government restrictions on travel. The correlation between NO2 and CoVid-19 cases became the baseline of our algorithm; the algorithm uses this baseline data of the change in other air pollutant concentrations to determine which pollutants were more effected by the quarantine measures taken place. It can than be deduced from these conclusions which sources of pollution more more easily be reduced.

How We Addressed This Challenge

We used the Aura satellite to identify how the levels of nitrogen dioxide and other air pollutants fluctuated in the same period as CoVid-19 cases increased. Using the density of COVID-19 cases in the Wuhan region published by Johns Hopkins University, we created an algorithm that proved a correlation between the density of CoVid-19 cases and the levels of nitrogen dioxide and sulfur dioxide. Although we specifically analyze the biomes around Wuhan China, including the Donghu National Park, in the demo our algorithm could just as easily be applied to any other biome or environment effected by CoVid-19 as long as the data is present. With the government restriction imposed on this area, there was a significant decrease in human traffic. We proved how this decrease in human traffic resulted in changes to the air quality in the Wuhan region. Additionally we used our conclusions to help further the understanding of pollutants. We conclude that certain sources of air pollution are more "preventable" than others.

How We Developed This Project

We used the resources available to us on various NASA websites and other resources to accomplish our goals. Our primary resource were different compilations gas concentration averages from satellites such as Aura and Copernicus Two. We choose to use NO2 as our baseline gas because it is almost entirely produced by industry and transport (as opposed to methane); additionally NO2 decomposes into other gasses relatively quickly which allows for more precise results when compared to other pollutants which remain in the atmosphere for longer before decomposing. Upon examining this data, we saw a distinct correlation between the gas levels and the timeline of which Covid-19 occurred. From this data, we were able to create an algorithm that relates the levels of Nitrogen Dioxide and other pollutants to the number of Covid-19 cases derived from Wuhan, China. We used methane (CH4) as our example other pollutant in the demo because we knew that it would have decreased much less (because transport and the burning of fossil fuels does not play as large of a role in its production). We than arrived at an intuitive answer that CH4 is less preventable, this algorithm can be used to find more intricate complications with the global air pollution crisis.