Increased ozone pollution alongside reduced nitrogen dioxide concentrations during Vienna's first COVID-19 lockdown: Significance for air quality management

Abstract

Background: Lockdowns amid the COVID-19 pandemic have offered a real-world opportunity to better understand air quality responses to previously unseen anthropogenic emission reductions.

Methods and main objective: This work examines the impact of Vienna's first lockdown on ground-level concentrations of nitrogen dioxide (NO₂), ozone (O₃) and total oxidant (O_x). The analysis runs over January to September 2020 and considers business as usual scenarios created with machine learning models to provide a baseline for robustly diagnosing lockdown-related air quality changes. Models were also developed to normalise the air pollutant time series, enabling facilitated intervention assessment.

Core findings: NO₂ concentrations were on average -20.1% [13.7-30.4%] lower during the lockdown. However, this benefit was offset by amplified O₃ pollution of +8.5% [3.7-11.0%] in the same period. The consistency in the direction of change indicates that the NO₂ reductions and O₃ increases were ubiquitous over Vienna. O_x concentrations increased slightly by +4.3% [1.8-6.4%], suggesting that a significant part of the drops in NO₂ was compensated by gains in O₃. Accordingly, 82% of lockdown days with lowered NO₂ were accompanied by 81% of days with amplified O₃. The recovery shapes of the pollutant concentrations were depicted and discussed. The business as usual-related outcomes were broadly consistent with the patterns outlined by the normalised time series. These findings allowed to argue further that the detected changes in air quality were of anthropogenic and not of meteorological reason. Pollutant changes on the machine learning baseline revealed that the impact of the lockdown on urban air quality were lower than the raw measurements show. Besides, measured traffic drops in major Austrian roads were more significant for light-duty than for heavy-duty vehicles. It was also noted that the use of mobility reports based on cell phone movement as activity data can overestimate the reduction of emissions for the road transport sector, particularly for heavy-duty vehicles. As heavy-duty vehicles can make up a large fraction of the fleet emissions of nitrogen oxides, the change in the volume of these vehicles on the roads may be the main driver to explain the change in NO₂ concentrations.

Interpretation and implications: A probable future with emissions of volatile organic compounds (VOCs) dropping slower than emissions of nitrogen oxides could risk worsened urban O₃ pollution under a VOC-limited photochemical regime. More holistic policies will be needed to achieve improved air quality levels across different regions and criteria pollutants.

Keywords: Air quality data; Atmospheric composition; COVID-19 lockdown; Machine learning; Meteorology.

Graphical abstract

Fig. 1

Locations of the air quality monitors and meteorological stations used in this study. Outer line represents the boundary of Vienna and inner lines represent the city’s districts. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article)

Fig. 2

Mobility changes (%) in the road transport sector for Vienna and Austria. Left: monthly-average daily traffic MADT counts for LDV and HDV. Right: 7-day rolling mean on the Google transit data and Apple driving data. See Methods for details on the baselines from which the percentage changes have been derived. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article)

Fig. 3

Wind roses for the five selected meteorological stations and different periods (see text for the definition of the periods). The annotations in green show mean wind speeds and calm wind frequencies of each period. Calm winds were defined as having hourly speeds <0.5 m s⁻¹. The radial scale denotes the frequency of counts by wind direction sector. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article)

Fig. 4

Daily mean of NO₂ concentration deltas Δ. The study period runs between February 16 and September 30, 2020. The deltas represent the differences between the measured pollutant concentrations and the business as usual predictions. Solid grey lines are 7-day rolling means of the concentration deltas. Dashed vertical lines indicate the LOCK-2020 period.

Fig. 5

Same as Fig. 4 but for O₃.

Fig. 6

Same as Fig. 4 but for O_x.

Fig. 7

Seven-day rolling mean of concentration deltas Δ for each pollutant between February 16 and September 30, 2020. Shaded areas are the standard deviation of the means. Dashed vertical lines indicate the LOCK-2020 period.

Fig. 8

Seven-day rolling mean of the normalised pollutant time series between January 1 and September 30, 2020. Dashed vertical lines indicate the LOCK-2020 period. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article)

Fig. 9

Percentage changes in NO₂, O₃ and O_x from the machine learning-derived business as usual (BAU) scenarios against the historical approach (HIST-2015-2019). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article)