Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments
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 wit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4798 |
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| author | Kyan Kuo Shlipak Julian Probsdorfer Christian L’Orange |
| author_facet | Kyan Kuo Shlipak Julian Probsdorfer Christian L’Orange |
| author_sort | Kyan Kuo Shlipak |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-78d2b11a19ec4a1c83ea818d731bf440 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-78d2b11a19ec4a1c83ea818d731bf4402025-08-20T04:00:51ZengMDPI AGSensors1424-82202025-08-012515479810.3390/s25154798Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging EnvironmentsKyan Kuo Shlipak0Julian Probsdorfer1Christian L’Orange2Department of Mechanical Engineering, McCormick School of Engineering, Northwestern University, 633 Clark Street, Evanston, IL 60208, USADepartment of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, CO 80523, USADepartment of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, CO 80523, USAOutdoor 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.https://www.mdpi.com/1424-8220/25/15/4798air quality monitorair pollutionlow-cost sensorsnumerical modelingparticulate mattersimulation |
| spellingShingle | Kyan Kuo Shlipak Julian Probsdorfer Christian L’Orange Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments Sensors air quality monitor air pollution low-cost sensors numerical modeling particulate matter simulation |
| title | Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments |
| title_full | Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments |
| title_fullStr | Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments |
| title_full_unstemmed | Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments |
| title_short | Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments |
| title_sort | air storm informed decision making to improve the success of solar powered air quality samplers in challenging environments |
| topic | air quality monitor air pollution low-cost sensors numerical modeling particulate matter simulation |
| url | https://www.mdpi.com/1424-8220/25/15/4798 |
| work_keys_str_mv | AT kyankuoshlipak airstorminformeddecisionmakingtoimprovethesuccessofsolarpoweredairqualitysamplersinchallengingenvironments AT julianprobsdorfer airstorminformeddecisionmakingtoimprovethesuccessofsolarpoweredairqualitysamplersinchallengingenvironments AT christianlorange airstorminformeddecisionmakingtoimprovethesuccessofsolarpoweredairqualitysamplersinchallengingenvironments |