Regulated Traffic Emission Influence on Urban Air Quality - A Coherent Analysis using Sequential Covid-19 Pandemic Lockdown Episodes in Lucknow, India
DOI:
https://doi.org/10.56042/jsir.v85i3.19430Keywords:
COVID-19 lockdowns, Dispersion modelling, Particle pollution, Traffic volume, Vehicular emissionAbstract
Air quality deterioration has been a major concern due to the increased road transportation and other urban activities to fulfill the requirements of the growing population in the cities. A relative difference between the impact of stationary sources and road traffic on urban air quality was rarely addressed. Further, dispersion extent of traffic emissions during the regulated road vehicular density and movement was underexplored. The study quantitatively assessed the influence of regulated road traffic emissions on atmospheric particle pollution using the episodic COVID-19 traffic restrictions in Lucknow, India. Concurrent VKT emissions and near-road measurements of PM10 were evaluated for 9 sampling sites covering residential, commercial, and industrial areas in the city. An incremental trend is observed in ambient PM10 by 1.9 and 1.7 times with the sequentially increased load of vehicular emission from the complete lockdown to partial lockdown and the following partial lockdown to unlock phases of COVID-19, respectively, in the city. USEPA AERMOD-model predictions concerning traffic emission alone provided the impact of air dispersion ranging from 0.2 km to 1.5 km distance from the city roads to nearby surrounding areas. Predictions and observations of PM10 differed maximally during the complete lockdown, and by the unlock phase of COVID-19, the difference was reduced, which indicates the significant influence of stationary and non-stationary sources in the city. Further, the variations between the predictions and observations of PM10 for residential, commercial, and industrial sites in the city give a comprehensive understanding of emission source distribution impact under urban land use settings in Lucknow
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