Document Type
Article
Disciplines
1.4 CHEMICAL SCIENCES
Abstract
The real-time monitoring of primary biological aerosol particles (PBAP) such as pollen and fungal spores has receivedmuch attention in recent years as a result of their health and climatic effects. In this study, theWideband Integrated Bioaerosol Sensor (WIBS) 4+ model was evaluated for its ability to sample and detect ambient fungal spore and pollen concentrations, compared to the traditional Hirst volumetric method. Although the determination of total pollen and fungal spore ambient concentrations are of interest, the selective detection of individual pollen/fungal spore types are often of greater allergenic/agricultural concern. To aid in this endeavour, modifications were made to the WIBS-4 instrument to target chlorophyll fluorescence. Two additional fluorescence channels (FL4 and FL5 channels) were combined with the standard WIBS channels (FL1, FL2, FL3). The purpose of this modification is to help discriminate between grass and herb pollen from other pollen. The WIBS-4+ was able to successfully detect and differentiate between different bioaerosol classes. The addition of the FL4 and FL5 channels also allowed for the improved differentiation between tree (R2 = 0.8), herbaceous (R2 = 0.6) and grass (R2 = 0.4) pollen and fungal spores (R2 = 0.8). Both grass and herbaceous pollen types showed a high correlation with D type particles, showing strong fluorescence in the FL4 channel. The additional fluorescent data that were introduced also improved clustering attempts, making k-means clustering a comparable solution for this high-resolution data.
DOI
https://doi.org/ 10.3390/s22228747
Recommended Citation
Markey, E., Clancy, H. & Martínez, J. (2022). A Modified Spectroscopic Approach for the Real-Time Detection of Pollen and Fungal Spores at a Semi-Urban Site Using the WIBS-4+, Part I. Sensors, vol. 22, no. 8747. doi:10.3390/s22228747
Funder
This research was funded by Environmental Protection Agency of Ireland under the projects: Pollen Monitoring and Modeling (POMMEL), grant number 2017-CCRP-FS.35 and Fungal mOnitoring NeTwork ANd Algorithm (FONTANA), grant number 2018-CCRP-MS.53. E.M. acknowledges support from the Irish Research/EPA, grant number GOIPG/2019/4195
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.