According to the EPA, the transportation sector accounts for 27% of all greenhouse gas emissions in the United States, and emissions from road transportation account for a large majority of that.
Researchers have tried to measure these emissions more closely, but they are often incomplete and limited.
The scientists at the APL used artificial intelligence and machine learning to estimate the road transportation emissions of the top500 emitting cities.
The assistant program manager for Environmental resilience in APL's Research and Exploratory Development said that they were able to estimate the average daily traffic on individual road segments in urban areas.
According to Hughes, the ability to calculate emissions per road segment provides an unprecedented level of detail and global coverage. An automated, accurate, global, timely and actionable road transportation greenhouse gas emissions estimates can be created.
Hughes said that actionable is the key word. It's a challenge to tackle climate change because you can't change what you can't measure. We need to know where the greenhouse gas emissions are coming from, but we also need to account for a lot of tiny contributions across time and geography.
It is possible to make the invisible invisible.
More than one billion motor vehicles are in the world. An APL team of experts thought they could measure the greenhouse gases from these vehicles by pulling signatures of emissions from satellite data. They looked at emissions data and correlated it with images to see if they could use neural networks.
The team realized there was a way to address the challenge. APL was involved in a project called Functional Map of the World, where experts mapped out land use and structures, and used satellite data from different times of day to understand and differentiate between similar looking structures.
Instead of focusing on buildings, we looked at networks of roads that connected them. Satellite imagery, road network data and population are some of the inputs that a deep neural network takes.
"We were able to train our machine learning models with this ground-truth data on vehicle activity there, and then apply the model predictions first at the country level, and most recently for the top 500 cities globally," he said.
Climate TRACE is an international coalition formed to make meaningful climate action faster and easier by independently tracking greenhouse gas emissions with unprecedented detail and speed.
APL is making sustainable contributions to the global challenges of climate change.
"It is a great example of how our unique skills and capabilities can be applied to understand and one day solve complex problems," said Bobby Armiger, who along with Hughes co-leads the Labwide focus on climate change research.
APL's transportation data was included in the inventory of greenhouse gas emissions released by Climate TRACE. In addition to releasing facility-level information, Climate TRACE updated its independent inventory of emissions from every country to include data from 2021.
Radical transparency to global emissions is brought about.
A group of people in the Climate TRACE coalition have a goal.
"It's about knowing where all the greenhouse gases are, where the emissions are coming from, and being very open about how we are calculating those emissions." That is a huge challenge. If you have that radical transparency, you can begin to do some analysis and comparisons to figure out what's working and what's not in terms of mitigated emissions.
The coalition wants to understand how to bring different data sets and emissions inventories together to create a new best estimate of what's really going on.
"That dream of radical transparency and knowing where all of the emissions are coming from in real time with every new satellite image is still a ways away," Hughes said. It feels close now.
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