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Mills, S.A.* ; Maya-Manzano, J.M. ; Tummon, F.* ; MacKenzie, A.R.* ; Pope, F.D.*

Machine learning methods for low-cost pollen monitoring - Model optimisation and interpretability.

Sci. Total Environ. 903:165853 (2023)

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Pollen is a major issue globally, causing as much as 40 % of the population to suffer from hay fever and other allergic conditions. Current techniques for monitoring pollen are either laborious and slow, or expensive, thus alternative methods are needed to provide timely and more localised information on airborne pollen concentrations. We have demonstrated previously that low-cost Optical Particle Counter (OPC) sensors can be used to estimate pollen concentrations when machine learning methods are used to process the data and learn the relationships between OPC output data and conventionally measured pollen concentrations. This study demonstrates how methodical hyperparameter tuning can be employed to significantly improve model performance. We present the results of a range of models based on tuned hyperparameter configurations trained to predict Poaceae (Barnhart), Quercus (L.), Betula (L.), Pinus (L.) and total pollen concentrations. The results achieved here are a significant improvement on results we previously reported: the average R2 scores for the total pollen models have at least doubled compared to using previous parameter settings. Furthermore, we employ the explainable Artificial Intelligence (XAI) technique, SHAP, to interpret the models and understand how each of the input features (i.e. particle sizes) affect the estimated output concentration for each pollen type. In particular, we found that Quercus pollen has a strong positive correlation with particles of optical diameter 1.7-2.3 μm, which distinguishes it from other pollen types such as Poaceae and may suggest that type-specific subpollen particles are present in this size range. There is much further work to be done, especially in training and testing models on data obtained across different environments to evaluate the extent of generalisability. Nevertheless, this work demonstrates the potential this method can offer for low-cost monitoring of pollen and the valuable insight we can gain from what the model has learned.

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