zooming in on emissions

UH team unlocks innovative approach to pinpoint pollution factors

A University of Houston team looked into what areas in Houston had the highest impact on emissions and how certain meteorological factors play into ozone formation. Photo via UH.edu

A team of researchers at the University of Houston are using machine learning to help guide pollution fighting strategies.

As reported in the journal Environmental Pollution last month, the team used the SHAP algorithm of machine learning (a game theory approach) and the Positive Matrix Factorization to pinpoint what areas in Houston had the highest impact on emissions and how certain meteorological factors play into ozone formation.

The paper was authored by Delaney Nelson, a doctoral student at the Department of Earth and Atmospheric Sciences of UH, and Yunsoo Choi, corresponding author and professor of atmospheric chemistry, AI deep learning, air quality modeling and satellite remote sensing.

The team's research closely tracked nitrogen-based compound and volatile organic compound measurements from Texas Commission on Environmental Quality's monitoring stations in the Houston area. After importing measurements from The Lynchburg Ferry station in Houston's ship channel and the urban Milby Park station, the machine learning and SHAP analysis showed a chemically definitive difference between the two areas.

For example, at the industrial station, the most impactful sources of pollution were from oil and gas flaring/production. At the urban site n_decane and industrial emissions/evaporation had the most impact on ozone.

According to Nelson and Choi, this shows that the machine learning and SHAP analysis approach can be used to tailor more precise air quality management strategies in different areas based on the site's unique characteristics.

“Once we know the specific emission sources and factors, we can develop targeted strategies to reduce emissions, which will in turn reduce ozone in the air and make it healthier for everyone," Choi said in a statement.

“Pollution is a critical issue in Houston, where you have extreme high heat and high concentration of ozone in the summers. The types of insights we got are very useful information for the local community to develop effective policies. That’s why we put our time, effort and technological expertise into this project," he continued.

Next the team envisions applying their approach in different cities and across the country.

“Austin, San Antonio and Dallas all have different characteristics, so I expect (volatile organic compound) sources will also be different,” Choi said. “Identifying VOC sources in different cities is very important because each city should have its own unique pollution fighting strategy.”

This summer, the City of Houston released an updated report on its major strategies to combat climate change and build a more resilient future for its residents.

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A View From HETI

SLB and Liberty Energy are working together to help solve the "bottleneck in AI infrastructure." Image courtesy SLB

Houston-headquartered SLB and Denver-based Liberty Energy Inc. announced a strategic agreement this month to support the rapid growth of new data center capacity.

Under the agreement, SLB will supply modular data center infrastructure and oversee large-scale execution, while Liberty will provide modular power generation systems and behind-the-meter power management technology for developers looking to add capacity. According to Reuters, the power will come from natural gas generation.

“The bottleneck in AI infrastructure is no longer just compute. It is the ability to deliver infrastructure and power on the timelines the market now demands,” Gavin Rennick, president of SLB’s New Energy and Industrial business, said in a news release. “By bringing together complementary infrastructure and power capabilities, we will help developers accelerate deployment of new data center capacity.”

The companies seek to specifically offer the modular technologies in areas without traditional grid connections or where grid capacity is limited.

They also aim to improve the "efficiency, flexibility and environmental performance of future data center energy systems," potentially through solutions like hybrid power systems and digital energy management, according to the news release.

Goldman Sachs estimates that U.S. data center capacity will more than double from 31 gigawatts in 2025 to 66 gigawatts in 2027. Other reports predict that Houston and Texas will be home to a significant portion of the data center boom, with capacity in the city and the state also expected to double in the next few years.

“The scale and complexity of AI energy infrastructure is fundamentally changing how power systems are built and deployed,” Ron Gusek, CEO of Liberty Energy, added in the release. “Liberty’s comprehensive power service platform is engineered to meet this transition, as customers increasingly prioritize tailored, integrated solutions. Building on our long-standing relationship with SLB, we are excited to bring power solutions that address immediate capacity constraints while supporting the next generation of energy systems.”

SLB sold its onshore hydraulic fracturing business in the United States and Canada to Liberty Energy in December 2020 in exchange for a 37 percent equity interest in the company.

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