Impacts of Meteorological Trapping on Air Quality in Greater Beirut Area by an Iterative Gaussian Model

Abstract

Urban air quality in the Greater Beirut Area (GBA) is strongly influenced by the interaction between emission sources and meteorological dispersion conditions. This study develops an integrated modeling framework to quantify and project air pollution dynamics in GBA by coupling emission reconstruction, machine learning–based (ML) meteorological forecasting, and an iterative Gaussian urban box model. Wind speed (WS10) and planetary boundary layer height (PBLH) were derived from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data for the period 2015–2025 and extended to 2050 using nonlinear regression and stochastic forecasting techniques to preserve seasonal variability and atmospheric dynamics. Emission inventories were reconstructed for two dominant sectors: diesel standby generators and road transport. Generator emissions were derived through a calibrated reverse-engineering approach based on Beirut-specific fuel consumption and operating hours, while transport emissions were estimated using national inventory data by the Lebanese 4th National Communication on Climate Change. Historical trends (2016 2025) capture the structural shift induced by the 2019 electricity crisis, during which diesel generator usage significantly increased. Results show that Nitrogen Dioxide (NO2) is the most critical pollutant, with daily concentrations exceeding World Health Organization (WHO) guidelines on approximately 78% of days in 2025. Particulate Matters (PM2.5) and Sulfur Dioxide (SO2) exhibit lower but still significant exceedance frequencies, with strong dependence on meteorological conditions. The analysis demonstrates that pollutant concentrations vary by more than an order of magnitude due to fluctuations in PBLH highlighting the dominant role of meteorological trapping. Future projections (2026-2050) under three scenarios reveal a strong divergence in air quality outcomes. Under a Business-as-Usual (BAU) scenario, exceedances remain persistently high, while moderate mitigation leads to gradual improvements. Only a combined electrification and renewable energy transition scenario achieves substantial reductions, eliminating SO2 and PM2.5 exceedances and reducing NO2 exceedance days to fewer than 10 by 2050. The results demonstrate that air quality in Beirut responds nonlinearly to emission reductions and that big structural changes in the energy and transport sectors are required to achieve sustained compliance with international air quality standards.