The COVID-19 pandemic strongly affected global logistics and supply chains, and hence shipping activity. This challenge asks you to target the characterization of dynamic shipping activities as seen from space.
Our team of high school students developed a visualization tool that tracks atmospheric air stagnation caused by Nitrogen Dioxide (NO2) emissions from global shipping routing through port activity using satellite imaging and accumulation risk assessments in order to prevent ground and oceanic body and ozone concentrations.
We have developed a web-hosted NO2 concentration and port NO2 accumulation imaging system that cleanly displays empirical world-wide data. As global warming follows its gradual and ever-increasing course, NO2 and other nitrogen oxides have been strongly linked to heavy machinery exhaust like industrial machinery, large transportation trucks, and most of all, watercraft like shipping. Nitrogen Oxide an its family of equally poisonous and reactive gases are producing smog, increased hazardous particulate matter, and contributed to acid rain. Although shipping emissions temporarily dropped during international lockdown measures, as ports start opening back up in 2021, these affects are on track to come back at an accelerated rate. Having the ability to properly and easily forecast NO2 port accumulations in real-time allows for informed decisions on emission curb plans. For example, with this modeling tool, the EPA's National Ambient Air Quality Standards (NAAQS) could be better adapted to localities and zones in order to boost efficiency as seen in low level operations with the firm state standards in place on top of tailored plans. Our team chose to look at the problem through a meteorological lens - how do atmospheric phenomena influence NO2 pollution? Using lower level atmospheric data from the Global Forecast System (GFS) weather prediction model, including wind speed and vertical velocity at 1000hPa, 925hPa, and 850 hPa, we were able to develop risk levels for NO2 accumulation and stagnation. We hope to use this technology to arm those politically involved in the formation and passage of lasting emission-curbing projects and legislation; displaying vital climate data in an easy-to-navigate manner is the missing link that bridges the rigorous benefits of scientific data and the ease of use to make fast and precise decisions in law.
Our team was inspired to choose the challenge: Ship Traffic and the Impact on Air Quality, because we have recognized the affects of climate change on society and humanity itself as likely to be an extinctionary event that could end the lineage of humanity if not properly addressed; in other words, this is the most important issue we've faced since the Cold War. Climate Change is going to impact teens and children in our generation, and we want to do the best we can to help. Our approach to addressing the challenge was to build a system that facilitates data to information conversion in order to aid those with governmental/political power to make the right and more accurate decisions. Because we feel that a black and white line graph can't convey a complete message of current climate/warming conditions —especially if done in real-time—we believed that an interactive, user-engaging way of presenting data as information rather than meaningless numbers is the correct way of educating leaders who would act on behalf of up-to-date/convenient heat maps of NO2 accumulation risks and its general concentrations. We used Python to access forecasts as well as estimate risk levels. The Python files were run on an Azure Virtual Machine, to keep the forecasts updating frequently. Our scripts estimated risk using the Global Forecast System (GFS) weather model's wind speed and vertical velocity data. We used Carto to display our results - the risk levels for US ports and NO2 concentrations globally. A common problem we experienced was our reduced experience using some tools, including MetPy and Azure, which slowed down our progress. We also did not have access to many paid tools, originally, we thought of using ESRI's ArcGIS Online. Since we're high school students, we didn't have the financial resources nor academic access to use the tool. Our team also considered the idea of using Machine Learning/Artificial Intelligence to estimate risk levels. However, after extensive research, we decided that numerical weather models are currently more successful at simulating atmospheric processes than ML/AI. Even though we experienced issues during development, we were able to successfully develop a visualization of NO2 stagnation risk and concentration using our tools.
Our team used NASA Earth Observation Data for Nitrogen Dioxide pollution. The data had an important influence on our project, by displaying it on our web application we are able to show users NO2 concentrations across the world. This augments the user's ability to see high NO2 accumulation risk ports in the US - If an area with a high risk has high concentrations, the user is aware of the increased NO2 pollution.
The link to our demo is below.
If you'd like to view our project, here is the link to access it live. The data is updated daily.
https://axeinator.carto.com/builder/c23b5acb-96f6-4c57-b22e-7eb23160bb57/embed
Our solution uses open weather models and data and can be easily integrated into the Earth Observing Dashboard. Using the World Bank's International Ports dataset, we accessed forecasts for each port. The forecasts are from the Global Forecast System (GFS), a weather model produced by the National Centers for Environmental Prediction. Integrating our solution would require a daily job to be run that develops risk levels for each global port. Then, each port could be clicked on by users, and show its risk.
Note: Because of computing power constraints, the solution we provided only displays risks for a selection of US ports. However, this solution can easily be scaled up for all global ports.
#air-quality #climatechange #cc #no2 #shipping #emissions #visualization
This project has been submitted for consideration during the Judging process.