Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models

Abstract

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

Impact of the COVID-19 pandemic related to lockdown measures on tropospheric NO<sub>2</sub> columns over Île-de-France

“…The tropospheric NO 2 reduction obtained from the SAOZ data is about 50 % (56 % at the Paris site and 46 % at the southwestern suburban site). These values are close to the literature data found for Europe within the estimated error bars (Barré et al, 2021;Prunet et al, 2020). This work highlights the ability of satellite TROPOMI measurements to distinguish between the tropospheric columns of urban and suburban sites, showing higher mean values at an urban station compared to a suburban one.…”

“…Many studies have considered specific techniques to limit the effect of meteorological conditions in their data. In the case of Paris, a 45 %-52 % reduction in NO 2 concentration was estimated by Collivignarelli et al (2021), using equivalent temperature and wind speed days, and ∼ 50 % was estimated by Barré et al (2021), using a gradient boosting machine learning (GBML) technique. In the case of tropospheric NO 2 columns measured by satellite instruments, Prunet et al ( 2020) estimated a 2-weekaveraged reduction of NO 2 varying between 52 % and 86 %, using the city-scale NO 2 plume mass method for 16 March-26 April.…”

Air Quality in the Italian Northwestern Alps during Year 2020: Assessment of the COVID-19 «Lockdown Effect» from Multi-Technique Observations and Models

“…Similar to other countries [3][4][5][6][7][8][9][10][11][12][13][14][15][16], the «lockdown effect» on air quality in Italy has been observed and profusely studied with special regard to the urban areas in the northern regions. Indeed, these latter were not only the first to introduce the new regulations and disrupt their business-as-usual activities, but they are also the most densely populated and industrialised, and-due to the orographical conformation of the Alps and the Apennines enclosing the Po basin and limiting ventilation-one of the European areas mostly impacted by atmospheric pollution.…”

Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

“…For quantitative estimates of the COVID-19 measures, these factors should be carefully taken into account. This can be done through (i) daily-based analysis of the NO2 plumes from cities using wind speed fields from meteorological models and subsequent emission derivation (Lorente et al, 2019;Goldberg et al, 2019); (ii) regression models to estimate the impact of natural variability and emission trends in the observations (Diamond and Wood, 2020); (iii) chemistrytransport modelling (Chang et al, 2020;Liu et al, 2020;Barré et al, 2021); and (iv) inverse modelling and data assimilation approaches (Ding et al, 2020;Miyazaki et al, 2020).…”