Identifying urban air pollution hot-spots by dispersion modeling when data are scarce: application to diesel generators in Beirut, Lebanon

TL;DR

This study uses dispersion modeling to identify urban air pollution hot-spots from diesel generators in Beirut, providing a cost-effective way to assess pollution distribution and mitigation strategies in data-scarce environments.

Contribution

It introduces a Lagrangian transport model to predict PM2.5 dispersion from diesel generators in Beirut, accounting for topography, meteorology, and urban features, in the absence of sensor data.

Findings

Elevating stack height reduces pollution exposure under various conditions.

Dispersion patterns are significantly affected by topography and atmospheric stability.

Modeling results inform mitigation strategies for urban air quality management.

Abstract

Diesel generators are emerging as community-initiated solutions to compensate for electricity shortage in cities marred by economical crisis and/or conflict. The resulting pollution distribution in dense urban environments is a major source of concern to the population. In the absence of periodic observations from properly distributed sensors, as is the case in Beirut, physically based computational modeling stand out as an effective tool for predicting the pollutant distribution in complex environments, and a cost-effective framework for investigating what-if scenarios and assessing mitigation strategies. Here, we present a Lagrangian transport model-based study of PM2.5 dispersion originating from a large number of diesel generators in Beirut. We explore large and small scale dispersion patterns in selected smalls domains and over the entire city. The scenarios considered investigate the impact of topography, atmospheric stability, presence of buildings, diesel generators distribution, and stacks elevations for representative meteorological conditions. Assessment of these scenarios is carried out in terms of small and large scale dispersion patterns and the mean concentration at street level and population exposure proxy indicators. We also report on the efficacy of elevating the stack height as a mitigation measure at different representative wind and atmospheric stability conditions.