Abstract
The COVID-19 pandemic and ensuing lockdown of many US States resulted in rapid changes to motor vehicle traffic and their associated emissions. This presents a challenge for air quality modelling and forecasting during this period, in that transportation emission inventories need to be updated in near real-time. Here, we update the previously developed fuel-based inventory of vehicle emissions (FIVE) to account for changes due to COVID-19 lockdowns. We first construct a 2020 business-as-usual (BAU) case inventory and adjust the emissions for a COVID-19 case using monthly fuel sales information. We evaluate cellular phone-based mobility data products (Google COVID-19 Community Mobility, Apple COVID-19 Mobility Trends) in comparison to embedded traffic monitoring sites in four US cities. We find that mobility datasets tend to overestimate traffic reductions in April 2020 (i.e. lockdown period), while fuel sales adjustments are more similar to changes observed by traffic monitors; for example, mobility-based methods for scaling emissions result in an approximately two-times greater estimate of on-road nitrogen oxide (NO x ) reductions in April 2020 than we find using a fuel-based method. Overall, FIVE estimates a 20%–25% reduction in mobile source NO x emissions in April 2020 versus BAU, and a smaller 6%–7% drop by July. Reductions in April showed considerable spatial heterogeneity, ranging from 6% to 39% at the state level. Similar decreases are found for carbon monoxide (CO) and volatile organic compounds. Decreases to mobile source NO x emissions are expected to lower total US anthropogenic emissions by 9%–12% and 3%–4% in April and July, respectively, with larger relative impacts in urban areas. Changes to diurnal and day-of-week patterns of light- and heavy-duty vehicular traffic are evaluated and found to be relatively minor. Beyond the applicability to modelling air quality in 2020, this work also represents a methodology for quickly updating US transportation inventories and for calibrating mobility-based estimates of emissions.
Highlights
Beginning in January of 2020, the international spread of the COVID-19 virus incurred policy interventions on mobility (e.g. ‘lockdowns’) by governments throughout the world, causing unprecedented decreases to vehicle traffic (Parr et al 2020, FHWA 2020c)
We find that mobility datasets tend to overestimate traffic reductions in April 2020, while fuel sales adjustments are more similar to changes observed by traffic monitors; for example, mobility-based methods for scaling emissions result in an approximately two-times greater estimate of on-road nitrogen oxide (NOx) reductions in April 2020 than we find using a fuel-based method
The Apple and Google mobility datasets do not explicitly capture HD traffic trends, which are important to on-road trends due to differences in emission factors by engine type
Summary
Beginning in January of 2020, the international spread of the COVID-19 virus incurred policy interventions on mobility (e.g. ‘lockdowns’) by governments throughout the world, causing unprecedented decreases to vehicle traffic (Parr et al 2020, FHWA 2020c). Studies have indicated that these changes in traffic directly affected air pollution, as presently reported in China (Chen et al 2021) and the United States (Xiang et al 2020). Changes of similar magnitude have been observed in Europe (Barré et al 2021) and China (Wang et al 2020). Satellite-based retrievals of NO2 column concentrations have been used to observe declines across the US (19%–28%, Bauwens et al 2020; 30%, Blumberg 2020; 21.6%, Goldberg et al 2020) and Europe (Barré et al 2021). The response of secondary air pollutants such as fine particulate matter (PM2.5) and ozone has been smaller or even opposite that of the decreases in NO2 owing to atmospheric chemistry (Berman and Ebisu 2020, Chen et al 2020, Wang et al 2020)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
