XGBoost-based analysis of short- and long-term COVID-19 air quality impacts in Tehran, Iran.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: NO₂: -30 to -36%, PM₂.₅: -18 to -22%, CO: -20%, O₃: +20-30% during lockdown

• Primary Outcome: Air pollutant concentration changes across COVID-19 phases

• Nnt Or Sensitivity: Model R² values: PM (0.78-0.88), O₃ (<0.75); IOA: 0.68-0.88 for particulates

• Confidence Interval: Not clearly reported

Clinical Application

Methodology requires sophisticated air quality monitoring networks and advanced statistical modeling capabilities Limited direct applicability to Australian cities due to different emission sources, geography, and meteorology. However, methodology could inform Australian air quality research and policy development, particularly relevant for NHMRC environmental health guidelines Environmental health researchers, public health officials, urban planners in cities with similar pollution profiles

Abstract

This study examines the air quality trends associated with COVID-19 lockdowns and the subsequent recovery on air quality in Tehran, employing an established XGBoost framework for de-weathering with daily data from 22 monitoring stations from 2019 to 2025. A comprehensive analysis was conducted on six principal pollutants (PM₂.₅, PM₁₀, NO₂, O₃, SO₂, and CO) across four distinct temporal phases: Pre-pandemic, Pandemic, Post-COVID, and After Termination. While the model effectively reduced meteorological bias, exhibiting strong efficacy for particulate matter (R = 0.78-0.88; IOA = 0.68-0.88; FAC2 > 0.95), we explicitly acknowledge less consistent outcomes for reactive gases such as O₃ (R < 0.75; NMGE > 0.30). The imposition of lockdown measures coincided with significant reductions in pollutants associated with vehicular traffic: NO₂ exhibited a decline of - 30 to - 36%, PM₂.₅ decreased by - 18 to - 22%, and CO diminished by - 20%. In contrast, O₃ experienced an increase of + 20-30%, indicative of diminished NO titration. Principal Component Analysis (PCA) corroborated these trends, with PC1 (NO₂, PM₂.₅, CO) accounting for approximately 60% of the variance throughout the pandemic. In subsequent phases, partial recoveries were noted: PM₂.₅ escalated by + 15-20%, NO₂ stabilized at around -10%, SO₂ consistently decreased (-30% by 2025), whereas O₃ surged by + 20% by 2025, clustering with PM₁₀ in PC2, thereby highlighting the impacts of dust and secondary formation processes. Lockdowns yielded transient enhancements in air quality, with restricted long-term advantages. Sustainable progress necessitates the adoption of comprehensive policies targeting traffic, industrial emissions, VOCs, and dust management.

References

1. 1.Akbarzadeh Lalehlou, N., & Vizvari, B. (2024). XGBoost-based analysis of short- and long-term COVID-19 air quality impacts in Tehran, Iran. Environmental Monitoring and Assessment. https://doi.org/10.3389/fmicb.2022.853775