We’re excited to announce our latest project, STACKS, a tool to visualize global power plant emissions. STACKS uses near-term weather forecast data to show where and how emissions from power plants spread for five different green house gasses (GHGs); CO2, NO2, SO2, Ozone and Methane.
Go To Stacks
You can customize STACKS to see specific emissions, power plant fuel types (coal, oil, gas or biomass) as well as emissions flows at different altitudes.
This project combines two areas we are increasingly focused on: advanced visualizations and synthetic data. It is a great example of how both of these emerging technologies can help us gain a better understanding of our impacts on the world.
Why did we build it?
Power plant emissions are largely invisible. Though the long-term impacts of GHGs are increasingly well understood, its hard to see and feel how emissions impact us on a daily basis. We have deep experience with climate data through our work with WRI, and are increasingly interested in large scale weather modeling through our work on synthetic data.
So it was obvious that combining these two areas would provide a unique perspective on power plant emissions.
Building a new synthetic data set
The first big challenge we ran into was the lack of global power plant emissions data. To create STACKS, we had to develop an entirely new data set. We used EPA’s eGRID emissions data for US power plants to develop a predictive model of emissions based on plant fuel type, age, capacity and annual generation. We then used WRI’s power plant database to provide the inputs into the model. Finally, we created a new data set of more than 10,500 power plants with estimated emissions for the five green house gases.
Open source, open data
To build STACKS, we leveraged a number of open sources tools, including D3, NodeJS, and the amazing Earth project by cambecc on GitHub. The data came from WRI and EPA, and we’re making the STACKS dataset freely available for those interested.
So take STACKS for a spin, we’d love to know what you think!