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Diesel generators are emerging as community-initiated solutions to compensatefor electricity shortage in cities marred by economical crisis and/or conflict.The resulting pollution distribution in dense urban environments is a majorsource of concern to the population. In the absence of periodic observationsfrom properly distributed sensors, as is the case in Beirut, physically basedcomputational modeling stand out as an effective tool for predicting thepollutant distribution in complex environments, and a cost-effective frameworkfor investigating what-if scenarios and assessing mitigation strategies. Here,we present a Lagrangian transport model-based study of PM2.5 dispersionoriginating from a large number of diesel generators in Beirut. We explorelarge and small scale dispersion patterns in selected smalls domains and overthe 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. Assessmentof these scenarios is carried out in terms of small and large scale dispersionpatterns and the mean concentration at street level and population exposureproxy indicators. We also report on the efficacy of elevating the stack heightas a mitigation measure at different representative wind and atmosphericstability conditions.

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