We looked at the change in air pollution as an indicator of UN SDGs. Using earth observation data we produced a heat map showing carbon monoxide levels world-wide in 2019, before COVID-19 and in 2020, after and during the COVID-19 outbreak. The webpage aims to inform the public on how pollution levels have changed since COVID-19 and the need to reduce pollution levels in the long term to achieve the SDGs.
The Sustainable Development Goals (SDGs) are an important part of global society and it is essential that we keep them at the forefront of science and policy, especially with a view to moving out of the COVID-19 crisis. We elected to look into SDG 11, Sustainable Cities and Communities, because it is important to link the impact of coronavirus to global air pollution and then link this back to the impact it has on communities. This will enable us to produce a more interdisciplinary, integrated outlook on the future and how best to move forward following the pandemic, ensuring to keep the environmental crisis we also face in mind.
Using data from the Canadian Space Agency on Carbon Monoxide (CO) concentration and JavaScript’s mapbox library, we have plotted a heat map of CO concentration in atmospheric columns (mol/m^3) across the world. There are 2 layers to the map, one for April 2019, and one for April 2020, allowing us to compare the CO concentration before and during the COVID-19 outbreak. The layers are able to be turned on and off individually for clearer comparisons. On zooming in and out of the world map, the resolution of the data will change. For example, zooming in very close will allow the user to see individual data points, and zooming out very far will show a more general trend for that location. The code that was produced to create the heatmap could also be used in the future to plot more data (perhaps other SDGs) relatively easily, by pointing to a new geojson file containing the data to be plotted.
The project was developed in Microsoft’s Visual Studio Code, and a GitHub repository was used throughout.
In order to produce the air pollution heat map, we had to consider many issues, including the size of numbers the program was handling, and the number of data entries that it had to handle. In JavaScript, the maximum size for an integer is 2^53, which is around 10^16, but the data the program was processing was in the region of 10^18, so we had to convert the units from mol/cm^3 to mol/m^3. This created a new problem, because it is very difficult to change 250,000 data entries by hand. We wrote some python scripts to quickly convert all of the data so that our program could process it. Another problem we came across was making the layers on the heatmap easier to load. To do this, we used clusters of data on the map so that not all of the data entries have to be shown at once. We used the mean value of close-by points to determine the colour of the clusters.
After the size of the numbers was adjusted so that the program could handle it, we were able to produce a detailed heatmap of the CO concentration data, which illustrates the difference in air quality before and during COVID-19.
We had also planned to make the link between air pollution and crop yield, thereby addressing another important aspect of the SDGs: global food security. Pollution has varied but predominantly negative impacts on crop yield (NASA, n.d.). Therefore, we thought it important to explore this issue because theoretically the coronavirus outbreak and thus lockdown has led to a reduction in air pollution, which in turn would improve crop yield. This would have been useful to look at the effects of COVID-19 in a holistic manner but due to time constraints and limited expertise/knowledge, we opted to limit our research to air pollution alone.
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NASA (n.d.) Food Security from Space [online] (Accessed: 30 May 2020) Available at <https://airquality.gsfc.nasa.gov/sites/default/files/airquality/AirQuality_FoodSecurity-WEB.pdf>
Trabucco, A., and Zomer, R.J. 2018. Global Aridity Index and Potential Evapo-Transpiration (ET0) Climate Database v2. CGIAR Consortium for Spatial Information (CGIAR-CSI). Published online, available from the CGIAR-CSI GeoPortal at https://cgiarcsi.community
CSA 2020. MOPITT – Carbon monoxide concentrations measurements subset for March and April 2019 and 2020 [online] (Accessed: 30 May 2020) Available at ftp://ftp.asc-csa.gc.ca/users/OpenData_DonneesOuvertes/pub/MOPITT/
World Health Organisation (2020) Air pollution [online] (Accessed: 31 May 2020). Available at https://www.who.int/health-topics/air-pollution#tab=tab_2