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Assessing Air Quality Changes Before and During the Movement Control Order Using Stochastic Boosted Regression Trees
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Assessing Air Quality Changes Before and During the Movement Control Order Using Stochastic Boosted Regression Trees
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Journal Article
Assessing Air Quality Changes Before and During the Movement Control Order Using Stochastic Boosted Regression Trees
2025
Overview
This study utilizes stochastic boosted regression trees (BRT) to investigate the effects of the COVID-19 Movement Control Order (MCO) on air quality in Ipoh City, Malaysia. The model aims to explore the Strength of Interaction Effects (SIE) and Relative Variable Importance (RVI) of key pollutants and meteorological variables impacting PM2.5 concentrations. Hourly data on gaseous pollutants (SO₂, NO₂, CO, O₃) and meteorological conditions (wind direction, wind speed, relative humidity, and temperature) were obtained from the Department of Environment for the periods of January to June in both 2019 and 2020, resulting in 2,231 data points. The BRT model was constructed using R software, with the optimal number of trees (nt = 4,372) determined through Out-of-Bag (OOB) iterations. Model performance was evaluated using various statistical metrics, including a Factor of Two (FAC2) of 0.91, R² values exceeding 0.56 (R = 0.74), and an Index of Agreement (IOA) of 0.67, indicating the model’s robustness. The analysis revealed significant differences in the RVI during the MCO and non-MCO periods. In non-MCO data, PM2.5 concentrations were primarily influenced by CO (18.9%), SO₂ (14.6%), O₃ (12.9%), and wind direction (10.66%). During the MCO, the most important variables were CO (22.6%), RH (13.4%), SO₂ (14.7%), and O₃ (12.1%). Additionally, the SIE analysis highlighted interactions such as CO-wind direction (0.24), O₃-wind speed (0.19), and NO₂-CO (0.15). These findings demonstrate that the BRT model effectively captures the key factors influencing air pollution and their interactions. The results provide valuable insights for urban planners and local authorities, helping them design strategies to mitigate pollutant levels by addressing the most impactful variables. The model could guide policy decisions and optimize air quality management, particularly during periods of reduced human activity or emergency conditions like the MCO.
Publisher
IOP Publishing
