Application of machine learning and statistical modeling to identify sources of air pollutant levels in Kitchener, Ontario, Canada

Mohammed, Wisam; Adamescu, Adrian; Neil, Lucas; Shantz, N. C.; Townend, Tom; Lysy, Martin; Al‐Abadleh, Hind A.

doi:10.1039/d2ea00084a

Cited by 1 publication

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“…Once the correlation analysis was performed, we carried out an analysis using ML techniques in order to predict the Alnus pollen concentration. In recent years, ML models have been successfully implemented to predict air quality indices in Smart cities [77,78]. In the present work, four different classifiers were tested: Random Forests (RF), Support Vector Machines (SVM), Gaussian Naïve Bayes (GNB) and Multi-Layer Perceptron (MLP).…”

Section: Discussionmentioning

confidence: 99%

Alnus Airborne Pollen Trends during the Last 26 Years for Improving Machine Learning-Based Forecasting Methods

Novo-Lourés

Fernández-González

Pavón

et al. 2023

Forests

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Black alder (Alnus glutinosa (L.) Gaertn.) is a species of tree widespread along Europe and belongs to mixed hardwood forests. In urban environments, the tree is usually located along watercourses, as is the case in the city of Ourense. This taxon belongs to the betulaceae family, so it has a high allergenic potential in sensitive people. Due to the high allergenic capacity of this pollen type and the increase in global temperature produced by climate change, which induces a greater allergenicity, the present study proposes the implementation of a Machine Learning (ML) model capable of accurately predicting high-risk periods for allergies among sensitive people. The study was carried out in the city of Ourense for 28 years and pollen data were collected by means of the Hirst trap model Lanzoni VPPS-2000. During the same period, meteorological data were obtained from the meteorological station of METEOGALICIA in Ourense. We observed that Alnus airborne pollen was present in the study area during winter months, mainly in January and February. We found statistically significant trends for the end of the main pollen season with a lag trend of 0.68 days per year, and an increase in the annual pollen integral of 112 pollen grains per year and approximately 12 pollen grains/m3 per year during the pollen peak. A Spearman correlation test was carried out in order to select the variables for the ML model. The best ML model was Random Forest, which was able to detect those days with medium and high labels.

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Section: Discussionmentioning

confidence: 99%

Alnus Airborne Pollen Trends during the Last 26 Years for Improving Machine Learning-Based Forecasting Methods

Novo-Lourés

Fernández-González

Pavón

et al. 2023

Forests

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Application of machine learning and statistical modeling to identify sources of air pollutant levels in Kitchener, Ontario, Canada

Abstract: Machine learning is used across many disciplines to identify complex relations between outcomes and several potential predictors. In the case of air quality research in heavily populated urban centers, such...

Cited by 1 publication

References 43 publications

Alnus Airborne Pollen Trends during the Last 26 Years for Improving Machine Learning-Based Forecasting Methods

Alnus Airborne Pollen Trends during the Last 26 Years for Improving Machine Learning-Based Forecasting Methods

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