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Quantifying the impact of urban road traffic on air quality: activity pre-pandemic and during partial and full lockdowns

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

The impact of partial and full COVID lockdowns in 2020 on vehicle miles traveled (VMT) in Kuwait was estimated using data extracted from the Directions API of Google Maps and a Python script running as a cronjob. This approach was validated by comparing the predictions based on the app to measuring traffic flows for 1 week across four road segments considered in this study. VMT during lockdown periods were compared to VMT for the same calendar weeks before the pandemic. NOx emissions were estimated based on VMT and were used to simulate the spatial patterns of NOx concentrations using an air quality model (AERMOD). Compared to pre-pandemic periods, VMT was reduced by up to 25.5% and 42.6% during the 2-week partial and full lockdown episodes, respectively. The largest reduction in the traffic flow rate occurred during the middle of these 2-week periods, when the traffic flow rate decreased by 35% and 49% during the partial and full lockdown periods, respectively. The AERMOD simulation results predicted a reduction in the average maximum concentration of emissions directly related to VMT across the region by up to 38%, with the maximum concentration shifting to less populous residential areas as a result of the lockdown. Graphical Abstract: (Figure presented.).

Original languageEnglish
Article number418
JournalEnvironmental Monitoring and Assessment
Volume196
Issue number5
DOIs
StatePublished - May 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Air quality
  • COVID-19 lockdown
  • Dispersion modeling
  • Transportation emission
  • Urban road traffic

Funding Agency

  • Kuwait Foundation for the Advancement of Sciences

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