What am I Reading? Measuring Indoor Air Pollution
If you’re interested in contributing a short What Am I Reading post, we’d love to hear from you! Email us at info@agingclimatehealth.org.
Written by Elizabeth Sorensen Montoya, Ph.D. University of Colorado Boulder
www.elizabethsorensenmontoya.com
In a previous post, I explored the different methods for measuring outdoor air pollution and its impact on cognitive aging. In this post, I turn to the importance of considering other major sources of air pollution exposure—primarily indoor air pollution (IAP), which constitutes a significant and relatively understudied component of total exposure.
In developed nations, people spend nearly 90% of their time indoors (Klepeis et al., 2001). For adults aged 65 and older, approximately 80% of that time is spent within their own homes (Spalt et al., 2016). This high proportion of time spent indoors, coupled with estimates that IAP levels are two to five times higher than outdoor levels (Wallace et al., 1986), suggests that IAP (whether at home, school, or the workplace) is a meaningful channel of exposure. Yet until fairly recently, this channel had been severely underexplored in the academic literature due to a lack of sufficient monitoring capabilities. As technological advancements have increased the validity, reliability, and affordability of IAP monitors (Wang, Delp, and Singer, 2020), understanding the health impacts of IAP exposure has become increasingly feasible.
This issue is particularly significant in developing countries, where many households depend on solid fuels such as wood or coal for cooking and heating purposes, contributing to higher levels of IAP. Studies analyzing the impacts of such exposure have found significant associations between solid fuel use and reduced cognitive performance, as well as increased risk of cognitive decline (Peng et al., 2025). These studies also find that switching to cleaner fuel (such as electricity or natural gas) is associated with a lower risk of cognitive decline. However, few of these studies directly measure IAP, and instead rely on fuel type as a proxy for IAP. Though still indirect, Chen et al. (2023) estimate IAP exposure among older adults in Taiwan based on home ventilation status and daily indoor time, finding that even low-level IAP exposure is associated with cognitive impairment. As monitoring technology continues to improve, incorporating direct measures of IAP could build on this research and help clarify the pathways through which exposure affects cognitive performance.
Recent studies using real-time indoor air quality data offer further evidence of these cognitive effects. Using indoor sensors at a large chess tournament in Germany, Künn, Palacios, and Pestel (2023) find that higher levels of particulate matter (PM2.5) increase the likelihood of errors, suggesting that lower indoor air quality can harm one’s strategic decision making.
While observational studies like this offer valuable insight, it’s difficult to truly randomize exposure to IAP, making it difficult to draw firm causal conclusions. Xu et al. (2024) address this challenge by conducting an experiment in which college students took standardized tests on two consecutive weekends, with in-room air purifiers set to different filtration modes across the two test days. They find that air filtration is significantly associated with improved test scores—further supporting the idea that lower indoor air quality can harm cognitive function.
In another randomized experiment, Metcalfe and Roth (2025) explore the role of information and awareness. The harms of IAP are not very salient among the general population, and individual monitoring is rather uncommon. Arguing that recent technological advancements have made indoor monitors more accessible, and could thus lead to increased awareness, Metcalfe and Roth implement a field experiment in which IAP is monitored in all participating households, but IAP levels are revealed only to a randomly selected treatment group. They find that presenting households with information about their own IAP levels leads to a 17% overall reduction in pollution and a 34% reduction during periods of occupancy, suggesting that improved awareness alone can drive meaningful change.
While much of the current research on IAP has focused on the home, exposure also occurs in schools, workplaces, and during commutes. Using ambient air pollution exposure, de Souza et al. (2023) find large disparities in exposure between the home and workplace. Understanding whether similar disparities exist for IAP seems crucial, especially given that indoor air quality may vary substantially by workplace. While previous studies have used personal samplers or stationary sensors to examine workplace exposure to various hazards (e.g., particulate matter, chemicals, radiation), less is known about how IAP levels compare across the different indoor environments individuals occupy in a typical day, such as the home (particularly relevant for the retired population), workplace, and transit settings. How can such variation be accurately measured?
Wearable personal monitors present a potential solution to this challenge by allowing researchers to track individual-level exposure in real time as people move through different settings. Wako et al. (2025) provide a helpful review of the validity, reliability, and acceptability of these devices for exposure assessment. The reviewed studies suggest that these devices are generally reliable, but more accurate indoors than outdoors. They also highlight important limitations, including frequent malfunctions and user concerns regarding device size, noise, and ease of use.
By capturing individual-level time- and location-specific data, wearable monitors offer a potential method for improving the accuracy of air pollution measurement—particularly for IAP, where their performance is the strongest. However, the use of such devices for large-scale assessments is resource-intensive and likely not feasible for every researcher. Further, device uptake and proper use may be correlated with unobserved factors like health awareness or technological savvy, and in studies analyzing cognition, may be directly related to the outcome of interest. While these devices offer a promising tool for providing more granular measures of exposure, their use must carefully account for these limitations.
All of the cited studies point to the importance of taking IAP seriously; the existing research suggests it is a meaningful contributor to overall exposure and an important factor in cognitive decline. Advances in monitoring technology are making it easier to move beyond proxies and estimated exposure and toward direct, individual-level exposure. There are certainly still real challenges, particularly when it comes to large-scale implementation, as this research often demands significant resources both in cost and personnel. But a growing range of methods—from experimental interventions to personal monitoring—are helping shed light on when and where IAP matters most.
References:
Chen, Yen-Ching, Pei-Iun Hsieh, Jia-Kun Chen, Emily Kuo, Hwa-Lung Yu, Jeng-Min Chiou, and Jen-Hau Chen. “Effect of indoor air quality on the association of long-term exposure to low-level air pollutants with cognition in older adults.” Environmental Research 233 (2023): 115483.
de Souza, Priyanka, Susan Anenberg, Carrie Makarewicz, Manish Shirgaokar, Fabio Duarte, Carlo Ratti, John L. Durant, Patrick L. Kinney, and Deb Niemeier. “Quantifying disparities in air pollution exposures across the United States using home and work addresses.” Environmental science & technology 58, no. 1 (2023): 280-290.
Klepeis, Neil E., William C. Nelson, Wayne R. Ott, John P. Robinson, Andy M. Tsang, Paul Switzer, Joseph V. Behar, Stephen C. Hern, and William H. Engelmann. “The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants.” Journal of Exposure Science & Environmental Epidemiology 11, no. 3 (2001): 231-252
Künn, Steffen, Juan Palacios, and Nico Pestel. “Indoor air quality and strategic decision making.” Management Science 69, no. 9 (2023): 5354-5377.
Metcalfe, Robert D., and Sefi Roth. Making the Invisible Visible: The Impact of Revealing Indoor Air Pollution on Behavior and Welfare. No. w33510. National Bureau of Economic Research, 2025.
Peng, Hongye, Miyuan Wang, Yichong Wang, Zuohu Niu, Feiya Suo, Jixiang Liu, Tianhui Zhou, and Shukun Yao. “The association between indoor air pollution from solid fuels and cognitive impairment: a systematic review and meta-analysis.” Reviews on environmental health 40, no. 1 (2025): 85-96.
Spalt, Elizabeth W., Cynthia L. Curl, Ryan W. Allen, Martin Cohen, Sara D. Adar, Karen H. Stukovsky, Ed Avol et al. “Time–location patterns of a diverse population of older adults: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).” Journal of exposure science & environmental epidemiology 26, no. 4 (2016): 349-355.
Wako, Wako Golicha, Tom Clemens, Scott Ogletree, Andrew James Williams, and Ruth Jepson. “Validity, Reliability and Acceptability of Wearable Sensor Devices to Monitor Personal Exposure to Air Pollution and Pollen: A Systematic Review of Mobility Based Exposure Studies.” Building and Environment (2025): 112931.
Wallace, Lance A., Edo D. Pellizzari, Tyler D. Hartwell, Roy Whitmore, Charles Sparacino, and Harvey Zelon. “Total Exposure Assessment Methodology (TEAM) Study: personal exposures, indoor-outdoor relationships, and breath levels of volatile organic compounds in New Jersey.” Environment International 12, no. 1-4 (1986): 369-387.
Wang, Zhiqiang, William W. Delp, and Brett C. Singer. “Performance of low-cost indoor air quality monitors for PM2. 5 and PM10 from residential sources.” Building and Environment 171 (2020): 106654.
Xu, Jia, Hong Zhao, Yujuan Zhang, Wen Yang, Xinhua Wang, Chunmei Geng, Yan Li et al. “Reducing indoor particulate air pollution improves student test scores: a randomized double-blind crossover study.” Environmental Science & Technology 58, no. 19 (2024): 8207-8214.