Modelling and prediction of air pollution using hybrid tree–LASSO approach
Journal
Mineral Processing and Extractive Metallurgy: Transactions of the Institutions of Mining and Metallurgy
Date Issued
2025-09-17
Author(s)
Dimovski, Pavel
Krstevska, Maja Celeska
Mateska, Aleksandra Krkoleva
Pechkova, Sijche
Pechkov, Aleksandar
DOI
10.1177/25726641251376762
Abstract
This study employs hybrid tree–Least Absolute Shrinkage and Selection Operator approach to forecast pollutant concentrations
(PM2.5, PM10, NO2, and CO) in Skopje, using data from 2018 to 2022, which includes meteorological variables and
pollution measurements from three sensor nodes. Models were trained on pre-COVID-19 data and then tested on post-
COVID-19 observations to assess the pandemic’s impact on air quality. Results show that models consistently overpredicted
pollution levels during the pandemic, suggesting a positive effect of COVID-19 restrictions on air quality.
Applications and research directions of the models in the context of metallurgy, mining, and mineral processing are
discussed.
(PM2.5, PM10, NO2, and CO) in Skopje, using data from 2018 to 2022, which includes meteorological variables and
pollution measurements from three sensor nodes. Models were trained on pre-COVID-19 data and then tested on post-
COVID-19 observations to assess the pandemic’s impact on air quality. Results show that models consistently overpredicted
pollution levels during the pandemic, suggesting a positive effect of COVID-19 restrictions on air quality.
Applications and research directions of the models in the context of metallurgy, mining, and mineral processing are
discussed.
Subjects
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