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Article

Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments

by
Kyan Kuo Shlipak
1,*,
Julian Probsdorfer
2 and
Christian L’Orange
2
1
Department of Mechanical Engineering, McCormick School of Engineering, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
2
Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, CO 80523, USA
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(15), 4798; https://doi.org/10.3390/s25154798
Submission received: 20 June 2025 / Revised: 17 July 2025 / Accepted: 1 August 2025 / Published: 4 August 2025
(This article belongs to the Special Issue Recent Trends in Air Quality Sensing)

Abstract

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure.
Keywords: air quality monitor; air pollution; low-cost sensors; numerical modeling; particulate matter; simulation; heat transfer; solar powered sensors air quality monitor; air pollution; low-cost sensors; numerical modeling; particulate matter; simulation; heat transfer; solar powered sensors

Share and Cite

MDPI and ACS Style

Shlipak, K.K.; Probsdorfer, J.; L’Orange, C. Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments. Sensors 2025, 25, 4798. https://doi.org/10.3390/s25154798

AMA Style

Shlipak KK, Probsdorfer J, L’Orange C. Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments. Sensors. 2025; 25(15):4798. https://doi.org/10.3390/s25154798

Chicago/Turabian Style

Shlipak, Kyan Kuo, Julian Probsdorfer, and Christian L’Orange. 2025. "Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments" Sensors 25, no. 15: 4798. https://doi.org/10.3390/s25154798

APA Style

Shlipak, K. K., Probsdorfer, J., & L’Orange, C. (2025). Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments. Sensors, 25(15), 4798. https://doi.org/10.3390/s25154798

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