Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference.

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The urban heat island effect causes increased heat stress in urban areas. Cool roofs and urban greening have been promoted as mitigation strategies to reduce this effect. However, evaluating their efficacy remains a challenge, as potential temperature reductions depend on local characteristics. Existing methods to characterize their efficacy, such as computational fluid dynamics and urban canopy models, are computationally burdensome and require a high degree of expertise to employ. We propose a data-driven approach to overcome these hurdles, inspired by recent innovations in spatial causal inference. This approach allows for estimates of hypothetical interventions to reduce the urban heat island effect. We demonstrate this approach by modeling evening temperature in Durham, North Carolina, using readily retrieved air temperature, land cover, and satellite data. Hypothetical interventions such as lining streets with trees, cool roofs, and changing parking lots to green space are estimated to decrease evening temperatures by a maximum of 0.7-0.9   [Formula: see text], with reduced effects on temperature as a function of distance from the intervention. Because of the ease of data access, this approach may be applied to other cities in the U.S. to help them come up with city-specific solutions for reducing urban heat stress.





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Calhoun, Zachary D, Frank Willard, Chenhao Ge, Claudia Rodriguez, Mike Bergin and David Carlson (2024). Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference. Scientific reports, 14(1). p. 540. 10.1038/s41598-023-50981-w Retrieved from

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Michael Howard Bergin

Sternberg Family Professor of Civil & Environmental Engineering

My general research focus is on the influence of air pollution on both climate and human health. My specific interest is particulate matter (PM), and I have done a wide range of studies on the emission, formation, deposition and impacts of PM. I am particularly interested in how PM impacts climate by modifying the radiation balance of the atmosphere and I have done studies in both pristine regions of the world (Greenland and the Himalaya), as well as hazy regions (the Southeastern US, China, and India). More recently I have been studying the influence of PM on human health with emphasis on determining the relative contributions of sources (such as biomass burning and vehicular emissions) to acute health impacts. I am also involved in developing and deploying the next generation of air quality sensors to inform citizens on the quality of the air they are breathing so that they can make informed decisions to improve their air. My vision involves combining a multidisciplinary, multicultural approach to research and education that brings together researchers from around the world to collectively work together to make the air cleaner.


David Carlson

Associate Professor of Civil and Environmental Engineering

My general research focus is on developing novel machine learning and artificial intelligence techniques that can be used to accelerate scientific discovery.  I work extensively both on the fundamental theory and algorithms as well as translating them into scientific applications.  I have extensive partnerships deploying machine learning techniques in environmental health, mental health, and neuroscience.  

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