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Development and application of a method to classify airborne pollen taxa concentration using light scattering data
Kenji Miki1,2, Toshio Fujita3, Norio Sahashi4
1Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan. mikik@keio.jp.
Scientific Reports
|November 17, 2021
Summary
A new method accurately distinguishes between Japanese cypress and Japanese cedar pollen counts, even when mixed. This advance enables development of simple, automated pollen monitoring networks for key allergens.
Area of Science:
- Environmental Science
- Botany
- Aerobiology
Background:
- Automated pollen monitoring networks using laser optics are established in Japan.
- Current methods struggle to differentiate pollen counts for various species.
- A novel method for distinguishing two pollen taxa has recently been developed.
Purpose of the Study:
- To field-evaluate a new method for distinguishing pollen counts of two major Japanese allergens.
- To assess the method's efficacy in differentiating Chamaecyparis obtusa and Cryptomeria japonica pollen.
- To determine the feasibility of developing an automated, high-density monitoring system for specific pollen types.
Main Methods:
- Application of a recently developed distinguishing method to field data.
- Analysis of atmospheric pollen data for Chamaecyparis obtusa and Cryptomeria japonica.
- Evaluation of the method's performance during simultaneous presence of both pollen types.
Main Results:
- The applied method successfully distinguished between Chamaecyparis obtusa and Cryptomeria japonica pollen counts.
- Accurate differentiation was achieved even when both pollen types were present simultaneously in the atmosphere.
- The study demonstrated the method's effectiveness in real-world atmospheric conditions.
Conclusions:
- The developed method can accurately differentiate pollen counts of specific allergenic species.
- This technology can be integrated into existing pollen monitoring networks for enhanced specificity.
- It paves the way for automated, simple, and high-spatial-density monitoring of key Japanese allergens like Chamaecyparis obtusa and Cryptomeria japonica.
