Data-Driven Detection of Nocturnal Pollen Fragmentation Triggered by High Humidity in an Urban Environment

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Public Health

Background:

• Biological particulate matter (BioPM) impacts urban health and climate.
• Pollen fragmentation into smaller particles poses public health risks.
• Meteorological triggers for pollen fragmentation are poorly understood.

Purpose of the Study:

• To develop a data-driven method for detecting pollen fragmentation.
• To identify meteorological thresholds for pollen fragmentation.
• To quantify environmental drivers of BioPM components.

Main Methods:

• Integrated deep learning, clustering, and automated machine learning.
• Utilized meteorological and online BioPM spectral data.
• Applied explainable methods for component identification and driver quantification.

Main Results:

• Routine detection of pollen fragmentation is now possible.
• Pollen fragmentation is triggered by relative humidity exceeding 90%.
• Fragmentation predominantly occurs at night, increasing daily pollen concentrations.

Conclusions:

• Humidity above 90% is a critical threshold for pollen fragmentation.
• Nighttime fragmentation is a significant, previously overlooked factor in urban pollen exposure.
• This phenomenon has potential implications for urban public health.