• Have you ever wondered about the quality of air you breathe at home, at work, and in between?
  • Do you bicycle to work for the exercise benefits (or want to!), but worry that it increases your exposure to air pollution?
  • Are you a teacher interested in exposing your students to real-world data?
The CMU RAMP network can help you answer these questions, and more!

The Real-time, Affordable, Multi-Pollutant (RAMP) air quality monitor measures carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and fine particulate mass (PM2.5). It was developed by CMU's Center for Atmospheric Particle Studies (CAPS) and SenSevere, a local start-up. Several RAMPs have been deployed in and around Pittsburgh (see map and images below.)

We are no longer seeking volunteers to host RAMP sites in Pittsburgh. We've published several peer-reviewed papers (listed below) using all the data we've collected. We make periodic reports on the data through our Google Drive.

On A rooftop pole

Businesses that have a pole on their roof can easily install the RAMP!

on a tripod in a yard

A RAMP sensor deployed on a tripod in a volunteer's backyard. We bring the tripod!

on your patio!

We can also provide the RAMP on a base plate that easily attaches to a patio frame.
Peer-reviewed papers using RAMP data
  • Jain, S.; Presto, A. A.; Zimmerman, N. Spatial Modeling of Daily PM₂.₅ , NO₂ , and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models. Environ. Sci. Technol. 2021, 55 (13), 8631–8641. https://doi.org/10.1021/acs.est.1c02653.
  • Li, J.; Hauryliuk, A.; Malings, C.; Eilenberg, S. R.; Subramanian, R.; Presto, A. A. Characterizing the Aging of Alphasense NO₂ Sensors in Long-Term Field Deployments. ACS Sensors 2021, 6 (8), 2952–2959. https://doi.org/10.1021/acssensors.1c00729.
  • Song, R.; Presto, A. A.; Saha, P.; Zimmerman, N.; Ellis, A.; Subramanian, R. Spatial Variations in Urban Air Pollution: Impacts of Diesel Bus Traffic and Restaurant Cooking at Small Scales. Air Qual. Atmos. Heal. 2021. https://doi.org/10.1007/s11869-021-01078-8.
  • Giordano, M. R.; Malings, C.; Pandis, S. N.; Presto, A. A.; McNeill, V. F.; Westervelt, D. M.; Beekmann, M.; Subramanian, R. From Low-Cost Sensors to High-Quality Data: A Summary of Challenges and Best Practices for Effectively Calibrating Low-Cost Particulate Matter Mass Sensors. J. Aerosol Sci. 2021, 158, 105833. https://doi.org/10.1016/j.jaerosci.2021.105833.
  • Tanzer-Gruener, R.; Li, J.; Eilenberg, S. R.; Robinson, A. L.; Presto, A. A. Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns. Environ. Sci. Technol. Lett. 2020, 7 (8), 554–559. https://doi.org/10.1021/acs.estlett.0c00365.
  •  Rose Eilenberg, S.; Subramanian, R.; Malings, C.; Hauryliuk, A.; Presto, A. A.; Robinson, A. L. Using a Network of Lower-Cost Monitors to Identify the Influence of Modifiable Factors Driving Spatial Patterns in Fine Particulate Matter Concentrations in an Urban Environment. J. Expo. Sci. Environ. Epidemiol. 2020, 30 (6), 949–961. https://doi.org/10.1038/s41370-020-0255-x.
  • Malings, C.; Westervelt, D. M.; Hauryliuk, A.; Presto, A. A.; Grieshop, A.; Bittner, A.; Beekmann, M. Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth to Surface Concentrations in North America and Africa. Atmos. Meas. Tech. 2020, 13 (7), 3873–3892. https://doi.org/10.5194/amt-13-3873-2020.
  • Zimmerman, N.; Li, H. Z.; Ellis, A.; Hauryliuk, A.; Robinson, E. S.; Gu, P.; Shah, R. U.; Ye, Q.; Snell, L.; Subramanian, R.; Robinson, A. L.; Apte, J. S.; Presto, A. A. Improving Correlations between Land Use and Air Pollutant Concentrations Using Wavelet Analysis: Insights from a Low-Cost Sensor Network. Aerosol Air Qual. Res. 2020, 20 (2), 314–328. https://doi.org/10.4209/aaqr.2019.03.0124.
  • Malings, C.; Tanzer, R.; Hauryliuk, A.; Saha, P. K.; Robinson, A. L.; Presto, A. A.; Subramanian, R. Fine Particle Mass Monitoring with Low-Cost Sensors: Corrections and Long-Term Performance Evaluation. Aerosol Sci. Technol. 2020, 54 (2), 160–174. https://doi.org/10.1080/02786826.2019.1623863.
  • Williams, R.; Duvall, R.; Kilaru, V.; Hagler, G.; Hassinger, L.; Benedict, K.; Rice, J.; Kaufman, A.; Judge, R.; Pierce, G.; Allen, G.; Bergin, M.; Cohen, R. C.; Fransioli, P.; Gerboles, M.; Habre, R.; Hannigan, M.; Jack, D.; Louie, P.; Martin, N. A.; Penza, M.; Polidori, A.; Subramanian, R.; Ray, K.; Schauer, J.; Seto, E.; Thurston, G.; Turner, J.; Wexler, A. S.; Ning, Z. Deliberating Performance Targets Workshop: Potential Paths for Emerging PM₂.₅ and O₃ Air Sensor Progress. Atmos. Environ. X 2019, 2, 100031. https://doi.org/10.1016/j.aeaoa.2019.100031.
  • Li, H. Z.; Gu, P.; Ye, Q.; Zimmerman, N.; Robinson, E. S.; Subramanian, R.; Apte, J. S.; Robinson, A. L.; Presto, A. A. Spatially Dense Air Pollutant Sampling: Implications of Spatial Variability on the Representativeness of Stationary Air Pollutant Monitors. Atmos. Environ. X 2019, 2, 100012. https://doi.org/10.1016/j.aeaoa.2019.100012.
  • Saha, P. K.; Zimmerman, N.; Malings, C.; Hauryliuk, A.; Li, Z.; Snell, L.; Subramanian, R.; Lipsky, E.; Apte, J. S.; Robinson, A. L.; Presto, A. A. Quantifying High-Resolution Spatial Variations and Local Source Impacts of Urban Ultrafine Particle Concentrations. Sci. Total Environ. 2019, 655, 473–481. https://doi.org/10.1016/j.scitotenv.2018.11.197.
  • Tanzer, R.; Malings, C.; Hauryliuk, A.; Subramanian, R.; Presto, A. A. Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice. Int. J. Environ. Res. Public Health 2019, 16 (14), 2523. https://doi.org/10.3390/ijerph16142523.
  • Malings, C.; Tanzer, R.; Hauryliuk, A.; Kumar, S. P. N.; Zimmerman, N.; Kara, L. B.; Presto, A. A. Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring. Atmos. Meas. Tech. 2019, 12 (2), 903–920. https://doi.org/10.5194/amt-12-903-2019.
  • Zimmerman, N.; Presto, A. A.; Kumar, S. P. N.; Gu, J.; Hauryliuk, A.; Robinson, E. S.; Robinson, A. L. A Machine Learning Calibration Model Using Random Forests to Improve Sensor Performance for Lower-Cost Air Quality Monitoring. Atmos. Meas. Tech. 2018, 11 (1), 291–313. https://doi.org/10.5194/amt-11-291-2018.