Research Papers using PurpleAir Data or PurpleAir Sensors

Welcome to our collection of research papers focused on air quality monitoring and the use of PurpleAir sensors and PurpleAir data in scientific studies. PurpleAir sensors have been utilized in various research projects around the globe, contributing to a deeper understanding of air pollution and its impacts. Below, you'll find a non-comprehensive list of peer-reviewed articles and reports that highlight the significance of PurpleAir technology in air quality research.


Studies on Precision and Corrections for PurpleAir Sensors

  1. Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke 

    • Citation: Barkjohn, K. K., Holder, A. L., Frederick, S. G., & Clements, A. L. (2022). Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke. Sensors, 22(24), 9669. 
    • Abstract: This study evaluates the accuracy of PurpleAir PM2.5 measurements during extreme wildfire smoke events. The authors propose a correction method to enhance the reliability of data from PurpleAir sensors under such conditions, demonstrating improved alignment with reference-grade instruments.
  2. Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor 

    • Citation: Barkjohn, K. K., Gantt, B., & Clements, A. L. (2021). Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor. Atmos. Meas. Tech., 14, 4617–4637. 
    • Abstract: This research develops a nationwide correction algorithm for PM2.5 data obtained from PurpleAir sensors across the United States. The correction enhances data accuracy, making PurpleAir a more robust tool for air quality monitoring on a national scale.

Studies on PurpleAir Sensor Performance

  1. PurpleAir PM2.5 Performance Across the U.S. #2 

    • Citation: Johnson, K., A. Holder, S. Frederick, G. Hagler, AND A. Clements. PurpleAir PM2.5 performance across the U.S.#2. Meeting between ORD, OAR/AirNow, and USFS, Research Triangle Park, NC, February 03, 2020. 
    • Abstract: PurpleAir particulate matter (PM) sensors are increasingly used in the United States and other countries by a variety of individuals and organizations for continuous monitoring of ambient air pollutant conditions, with additional sensors often deployed for monitoring during wildfire smoke episodes. The performance of these sensors must be evaluated during smoke impacted times, and nominally corrected for bias if necessary, to ensure accurate data are reported to inform appropriate health protective actions.
  2. Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece 

    • Citation: Stavroulas, I., Grivas, G., Michalopoulos, P., Liakakou, E., Bougiatioti, A., Kalkavouras, P., Fameli, K. M., Hatzianastassiou, N., Mihalopoulos, N., & Gerasopoulos, E. (2020). Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece. Atmosphere, 11(9), 926. 
    • Abstract: This study evaluates the performance of PurpleAir PA-II sensors in various urban air quality conditions in Greece. The results demonstrate the sensors' reliability and effectiveness in capturing particulate matter variations in an urban environment.
  3. Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments 

    • Citation: Heintzelman, A., Filippelli, G. M., J., M., Wilson, J. S., Wang, L., Druschel, G. K., & Lulla, V. O. (2023). Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments. International Journal of Environmental Research and Public Health, 20(3), 1934. 
    • Abstract: This research investigates the effectiveness of low-cost sensor networks in detecting fine-scale variations in particulate matter within urban settings. The findings highlight the capability of these sensors to provide detailed air quality data, aiding in urban environmental management and policy-making.
  4. The Present State of Low-Cost Air Quality Sensors in Japan and Their Accuracy 

    • Citation: Lassalle, M. W. (2024) The present state of low-cost air quality sensors in Japan and their accuracy, International Journal of Environmental Studies, pp. 1–19.
    • Abstract: This study evaluates the accuracy and deployment of low-cost air quality sensors in Japan, comparing pre-assembled sensors like PurpleAir with self-assembled sensors from the Citizen Science Project and highly accurate weather stations. The findings suggest that low-cost sensors, when properly calibrated, can effectively monitor air quality, particularly in rural areas where traditional monitoring stations are scarce. The research highlights the potential of low-cost sensors to enhance air quality data coverage and their role in supporting the Sustainable Development Goals (SDGs) related to clean air.

Studies on Spatial Variation and Indoor-Outdoor Comparison

  1. Spatial Variation of PM2.5 Indoors and Outdoors: Results from 261 Regulatory Monitors Compared to 14,000 Low-Cost Monitors in Three Western States over 4.7 Years 

    • Citation: Wallace, L., & Zhao, T. (2023). Spatial Variation of PM2.5 Indoors and Outdoors: Results from 261 Regulatory Monitors Compared to 14,000 Low-Cost Monitors in Three Western States over 4.7 Years. Sensors, 23(9), 4387. 
    • Abstract: This study compares PM2.5 measurements from 261 regulatory monitors and 14,000 low-cost monitors, including PurpleAir, over nearly five years in three western U.S. states. The analysis reveals significant spatial variation and highlights the value of widespread low-cost sensors in complementing regulatory networks for comprehensive air quality assessment.
  2. Indoor Contribution to PM2.5 Exposure Using All PurpleAir Sites in Washington, Oregon, and California 

    • Citation: Wallace, L. A., Zhao, T., & Klepeis, N. E. (2022). Indoor Contribution to PM2.5 Exposure Using All PurpleAir Sites in Washington, Oregon, and California. Indoor Air, 32(9), e13105. 
    • Abstract: This research investigates the indoor contribution to PM2.5 exposure utilizing data from PurpleAir sensors across Washington, Oregon, and California. The study provides insights into indoor air quality patterns and their implications for overall exposure to particulate matter.

Studies on Sensor Algorithms and Performance

  1. Testing a New “Decrypted” Algorithm for Plantower Sensors Measuring PM2.5: Comparison with an Alternative Algorithm 

    • Citation: Wallace, L. (2023). Testing a New “Decrypted” Algorithm for Plantower Sensors Measuring PM2.5: Comparison with an Alternative Algorithm. Algorithms, 16(8), 392. 
    • Abstract: This paper evaluates a new decrypted algorithm for Plantower sensors, which are widely used in low-cost PM2.5 monitors like PurpleAir. The study compares this algorithm with an existing alternative, assessing improvements in accuracy and reliability of PM2.5 measurements.
  2. Cracking the Code—Matching a Proprietary Algorithm for a Low-Cost Sensor Measuring PM1 and PM2.5 

    • Citation: Wallace, L. (2023). Cracking the Code—Matching a Proprietary Algorithm for a Low-Cost Sensor Measuring PM1 and PM2.5. Science of The Total Environment, 893, 164874. 
    • Abstract: This study attempts to replicate a proprietary algorithm for low-cost sensors to enhance their PM1 and PM2.5 measurement accuracy. Results demonstrate the potential for significant improvements in data quality, making these sensors more effective for both research and practical applications.
  3. Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5 

    • Citation: Wallace, L. (2022). Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5. Sensors, 22(7), 2755. 
    • Abstract: Over a three-year period, this study evaluates the performance of PurpleAir sensors in both indoor and outdoor settings. The research focuses on bias, precision, and detection limits, employing an improved algorithm to enhance the accuracy of PM2.5 measurements.
  4. Development and Evaluation of Correction Models for a Low-Cost Fine Particulate Matter Monitor

    • Citation: Nilson, B., Jackson, P. L., Schiller, C. L., & Parsons, M. T. (2022). Development and evaluation of correction models for a low-cost fine particulate matter monitor. Atmos. Meas. Tech., 15, 3315–3328.
    • Abstract: This study develops and evaluates several correction models to improve the accuracy of low-cost PM2.5 monitors, specifically focusing on PurpleAir sensors. The research compares these new models with existing ones, demonstrating significant improvements in data accuracy, which makes these sensors more reliable for air quality monitoring.

Studies on Air Quality and Exposure

  1. Secondhand Exposure from Vaping Marijuana: Concentrations, Emissions, and Exposures Determined Using Both Research-Grade and Low-Cost Monitors 

    • Citation: Wallace, L., Ott, W., Zhao, T., Cheng, K., & Hildemann, L. (2020). Secondhand Exposure from Vaping Marijuana: Concentrations, Emissions, and Exposures Determined Using Both Research-Grade and Low-Cost Monitors. Atmospheric Environment: X, 8, 100093. 
    • Abstract: This paper examines secondhand exposure to marijuana vapor, analyzing concentrations and emissions using both research-grade and low-cost monitors, including PurpleAir. The study provides important data on exposure levels and the efficacy of low-cost sensors in such measurements.

Studies on Community-Based and Low-Cost Air Quality Monitoring

  1. Low-cost PM2.5 Sensors Can Help Identify Driving Factors of Poor Air Quality and Benefit Communities 

    • Citation: Keyes, T., Domingo, R., Dynowski, S., Graves, R., Klein, M., Leonard, M., Pilgrim, J., Sanchirico, A., & Trinkaus, K. (2023). Low-cost PM2.5 sensors can help identify driving factors of poor air quality and benefit communities. Heliyon, 9(9), E19876. 
    • Abstract: This study highlights the potential of low-cost PM2.5 sensors to pinpoint the causes of poor air quality and provide valuable benefits to communities. The research demonstrates how these sensors can be utilized to empower local populations by identifying and addressing environmental health issues.
  2. Community-Based Participatory Research for Low-Cost Air Pollution Monitoring in the Wake of Unconventional Oil and Gas Development in the Ohio River Valley: Empowering Impacted Residents Through Community Science 

    • Citation: Raheja, G., Harper, L., Hoffman, A., Gorby, Y., Freese, L., O'Leary, B., Deron, N., Smith, S., Auch, T., Goodwin, M., & Westervelt, D. M. (2022). Community-based participatory research for low-cost air pollution monitoring in the wake of unconventional oil and gas development in the Ohio River Valley: Empowering impacted residents through community science. Environmental Research Letters, 17(6), 065006. 
    • Abstract: This paper discusses the use of community-based participatory research to monitor air pollution in areas affected by unconventional oil and gas development. The study emphasizes the role of low-cost sensors in empowering residents through community science to better understand and address local air quality concerns.

Studies on Algorithm Development and Application

  1. Contribution of Singular Spectral Analysis to Forecasting and Anomalies Detection of Indoors Air Quality 

    • Citation: Espinosa, F., BartolomĂ©, A. B., Hernández, P. V., & C., M. (2022). Contribution of Singular Spectral Analysis to Forecasting and Anomalies Detection of Indoors Air Quality. Sensors, 22(8), 3054. 
    • Abstract: This paper explores the use of Singular Spectral Analysis (SSA) for forecasting and detecting anomalies in indoor air quality. The study demonstrates how SSA can enhance the performance of low-cost air quality monitors by improving data analysis and prediction accuracy.
  2. Location Verification of Crowd-Sourced Sensors 

    • Citation: Kitras, C., Pollan, C., Myers, K., Tischner, C. W., & Lundrigan, P. (2023). Location Verification of Crowd-Sourced Sensors. 32nd International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, USA, 2023, pp. 1-7. 
    • Abstract: This study addresses the challenge of verifying the location of crowd-sourced sensors used for air quality monitoring. The authors present methods to ensure accurate geolocation of sensors, which is crucial for reliable data collection and analysis.

Studies on Indoor Air Quality Monitoring and Health Impacts

  1. Feasibility of Multi-dimensional Remote Monitoring of Indoor Air Quality and Asthma Control Among High-risk Urban Pregnant Women 

    • Citation: Wang, A., Meislin, R., Hsu, H., Brereton, N., Abdurrahman, N., Wharton, R., Bianco, A., Wang, J. G., Hanson, C., Bose, S. (2023). Feasibility of Multi-dimensional Remote Monitoring of Indoor Air Quality and Asthma Control Among High-risk Urban Pregnant Women. American Journal of Respiratory and Critical Care Medicine
    • Abstract: This study explores the feasibility of using low-cost, low-touch methods to remotely monitor indoor PM2.5 levels and asthma symptoms in high-risk urban pregnant women. The findings indicate high correlation between low-cost monitors and traditional ones, demonstrating the potential for these methods to enhance respiratory health surveillance among vulnerable populations. The study highlights significant variability in indoor PM2.5 levels and the frequent use of rescue medications by participants, suggesting a need for improved indoor air quality management in this demographic.
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