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Field Evaluation of an Automated Pollen Sensor

Author

Listed:
  • Chenyang Jiang

    (Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA)

  • Wenhao Wang

    (Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA)

  • Linlin Du

    (Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA)

  • Guanyu Huang

    (Department of Environmental and Health Sciences, Spelman College, Atlanta, GA 30314, USA)

  • Caitlin McConaghy

    (Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA)

  • Stanley Fineman

    (Atlanta Allergy and Asthma Clinic, Department of Pediatrics, Emory University School of Medicine, Marietta, GA 30060, USA)

  • Yang Liu

    (Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA)

Abstract

Background: Seasonal pollen is a common cause of allergic respiratory disease. In the United States, pollen monitoring occurs via manual counting, a method which is both labor-intensive and has a considerable time delay. In this paper, we report the field-testing results of a new, automated, real-time pollen imaging sensor in Atlanta, GA. Methods: We first compared the pollen concentrations measured by an automated real-time pollen sensor (APS-300, Pollen Sense LLC) collocated with a Rotorod M40 sampler in 2020 at an allergy clinic in northwest Atlanta. An internal consistency assessment was then conducted with two collocated APS-300 sensors in downtown Atlanta during the 2021 pollen season. We also investigated the spatial heterogeneity of pollen concentrations using the APS-300 measurements. Results: Overall, the daily pollen concentrations reported by the APS-300 and the Rotorod M40 sampler with manual counting were strongly correlated (r = 0.85) during the peak pollen season. The APS-300 reported fewer tree pollen taxa, resulting in a slight underestimation of total pollen counts. Both the APS-300 and Rotorod M40 reported Quercus ( Oak ) and Pinus ( Pine ) as dominant pollen taxa during the peak tree pollen season. Pollen concentrations reported by APS-300 in the summer and fall were less accurate. The daily total and speciated pollen concentrations reported by two collocated APS-300 sensors were highly correlated (r = 0.93–0.99). Pollen concentrations showed substantial spatial and temporal heterogeneity in terms of peak levels at three locations in Atlanta. Conclusions: The APS-300 sensor was able to provide internally consistent, real-time pollen concentrations that are strongly correlated with the current gold-standard measurements during the peak pollen season. When compared with manual counting approaches, the fully automated sensor has the significant advantage of being mobile with the ability to provide real-time pollen data. However, the sensor’s weed and grass pollen identification algorithms require further improvement.

Suggested Citation

  • Chenyang Jiang & Wenhao Wang & Linlin Du & Guanyu Huang & Caitlin McConaghy & Stanley Fineman & Yang Liu, 2022. "Field Evaluation of an Automated Pollen Sensor," IJERPH, MDPI, vol. 19(11), pages 1-14, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6444-:d:824270
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    References listed on IDEAS

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    1. Maria Pilar Plaza & Franziska Kolek & Vivien Leier-Wirtz & Jens Otto Brunner & Claudia Traidl-Hoffmann & Athanasios Damialis, 2022. "Detecting Airborne Pollen Using an Automatic, Real-Time Monitoring System: Evidence from Two Sites," IJERPH, MDPI, vol. 19(4), pages 1-17, February.
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    1. Campana, P.E. & Lastanao, P. & Zainali, S. & Zhang, J. & Landelius, T. & Melton, F., 2022. "Towards an operational irrigation management system for Sweden with a water–food–energy nexus perspective," Agricultural Water Management, Elsevier, vol. 271(C).

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