UM-Dearborn Study Links Air Quality to Human Happiness and Well-being

A UM-Dearborn study found poor air quality negatively impacts both bird songs and human happiness, emphasizing the need for interventions.

March 30, 2026

A recent study in Biological Conservation found that bird singing and chirping were negatively impacted by poor air quality and smoke during the 2023 Canadian wildfires. This raised questions among UM-Dearborn researchers about the effects of air quality on human well-being. Social sciences and computer science faculty collaborated to explore this, examining the relationship between air quality and happiness.

“We want people to feel happy. We want people to be productive so they can accomplish their goals and contribute in a way they find to be meaningful,” said Natalia Czap, professor of economics and Department of Social Sciences chair. “Air quality is extremely important in that.”

Zheng Song computer and information science assistant professor Shashank Chauhan a CECS graduate student and Natalia Czap an economics professor and Department of Social Sciences chair all at UM Dearborn worked on a research team last term that explored air quality and wellbeing A U M Boost grant supported their work Photo courtesy of UM Dearborn

Czap led the study titled “Air Quality and Human Wellbeing: Assessing Emotional Impact of Lower Air Quality Using Autonomous Artificial Intelligence-Based Distributed Sensing Systems.” The study connected air quality with mood and developed an algorithm for high engagement in self-reporting studies. Funded by a U-M Bold Challenges Boost grant, over 120 participants used Atmotube Pro air quality sensors, tracking happiness four times daily for three weeks.

To optimize data collection, Czap collaborated with computer science experts Zheng Song and Qiang Zhu. “All researchers want to have high-quality data. But it can be challenging to motivate participants because they have other things to focus on in daily life,” Natalia Czap said. Their innovative approach resulted in a 90% response rate, exceeding expectations.

Song and Zhu adapted an explore and exploit (E&E) algorithm to determine the best times for data entry prompts. This method, similar to choosing restaurants by exploring or exploiting options, was explained by graduate student Shashank Chauhan. “Based on participant behavior, the E&E algorithm helped us learn the best time to prompt people to enter their data.”

Participants wore portable Atmotube Pro air quality sensors during the study like the one shown here Photo courtesy of UM Dearborn

Participants received four daily text messages through a smart survey system, inquiring about well-being and sunlight exposure. Sunshine, an established mood determinant, was a key variable. Data from air quality monitors and survey responses were analyzed to find correlations.

The SMS schedule evolved based on response patterns, refining prompt times. The E&E algorithm detected user habits, optimizing message timing. Air quality data also triggered messages, with gamification elements like a leaderboard boosting engagement.

Early 2024 data analysis revealed higher response rates with algorithmic interventions and a link between poor air quality and reduced happiness, particularly with higher PM2.5 levels. This study emphasizes the need for policies addressing air quality to enhance public health and cognitive performance.

“Let’s say there is a high-stakes test and the air quality near you is poor. If you know how you and people around you are likely to be affected, you might wait and do the test on another day,” she said. “Even when something seems obvious because of anecdotal evidence, that isn’t enough. You need data — and high-quality data — to help create policies.”

This is an abbreviated version of a story that can be found online at myumi.ch/A1q4b.