UP2.2

To hinder the circulation of the COVID-19 virus, European governments implemented emergency measures going from light social distancing to strict lockdowns, depending on the country. As a consequence, many industries, businesses and transport networks were forced to either close down or drastically reduce their activity, which resulted in an unprecedented drop of anthropogenic emissions. This work presents the Copernicus Atmosphere Monitoring Service (CAMS) European regional emission adjustment factors associated to the COVID-19 mobility restrictions, an open source dataset of daily-, sector-, pollutant- and country-dependent emission adjustment factors for Europe linked to the COVID-19 pandemic. The resulting dataset covers a total of six emission sectors, including: road transport, energy industry, manufacturing industry, residential and commercial combustion, aviation and shipping. The time period covered by the dataset includes the first and second waves of the disease ocurred during 2020, starting from 21 February, when the first European localised lockdown was implemented in the region of Lombardy (Italy), until 31 December, when COVID-19 transmission remained widespread and several countries had nationwide restrictions still in place. The adjustment factor dataset is based on a wide range of information sources and approaches, including open access and measured activity data and meteorological data, as well as the use of machine learning techniques. We combined the computed emission adjustment factors with the CAMS European gridded emission inventory to spatially (0.1x0.05 degrees) and temporally (daily) quantify reductions in 2020 emissions from both criteria pollutants (NO_x, SO₂, NMVOC, NH₃, CO, PM10 and PM2.5) and greenhouse gases (CO₂ fossil fuel, CO₂ biofuel and CH₄) as compared to a business-as-usual scenario, as well as to assess the contribution of each sector and country to the overall reductions. The resulting gridded and time-resolved emission reductions suggest an heterogeneous impact of the COVID-19 restrictions across pollutants, sectors and countries.

How to cite: Guevara, M., Jorba, O., Petetin, H., Denier Van Der Gon, H., Kuenen, J., Super, I., Peuch, V.-H., and Pérez García-Pando, C.: Quantification of the Emission Changes in Europe During 2020 Due to the COVID-19 Mobility Restrictions, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-427, https://doi.org/10.5194/ems2021-427, 2021.

The COVID-19 pandemic led to the lockdown of over one-third of Chinese cities in early 2020. Observations have shown significant reductions of atmospheric abundances of NO₂ over China during this period. This change in atmospheric NO₂ implies a dramatic change in emission of NO_x, which provides a unique opportunity to study the response of the chemistry of the atmospheric to large reductions in anthropogenic emissions. We use a deep learning (DL) model to quantify the change in surface emissions of NO_x in China that are associated with the observed changes in atmospheric NO₂ during the lockdown period. Compared to conventional data assimilation systems, deep neural networks are free of the potential errors associated with parameterized subgrid-scale processes. Furthermore, they are not susceptible to the chemical errors typically found in atmospheric chemical transport models. The neural-network-based approach also offers a more computationally efficient means of inverse modeling of NO_x emissions at high spatial resolutions. Our DL model is trained using meteorological predictors and reanalysis data of surface NO₂ from 2005 to 2017. The evaluation is conducted using in-situ measurements of NO₂ in 2019 and 2020. The Baidu 'Qianxi' migration data sets are used to evaluate the model's performance in capturing the typical variation in Chinese NOx emissions during the Chinese New Year holidays. The TROPOMI-derived TCR-2 chemical reanalysis is used to evaluate the DL analysis in 2020. We show that the DL-based approach is able to better reproduce the variation in anthropogenic NO_x emissions and capture the reduction in Chinese NO_x emissions during the period of the COVID-19 pandemic.

How to cite: He, T.-L., Jones, D., Miyazaki, K., Bowman, K., Jiang, Z., and Li, R.: Deep learning for Chinese NOx emission inversion and the integration of in situ observations: a case study on the COVID-19 pandemic, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-391, https://doi.org/10.5194/ems2021-391, 2021.

We use the global Community Earth System Model to investigate the response of secondary pollutants (ozone O₃, secondary organic aerosols SOA) in different parts of the world in response to modified emissions of primary pollutants during the COVID‐19 pandemic. We quantify the respective effects of the reductions in anthropogenic emissions and meteorological anomalies, including a discussion on long-term changes from the chemical climatology. We show that the level of NOx has been reduced by typically 40 % in China during February 2020 and by similar amounts in many areas of Europe and North America in mid-March to mid-April 2020. Relative to a situation in which the emission reductions are ignored and despite the calculated increase in hydroxyl and peroxy radicals, the ozone concentration increased only in a few NOx‐saturated regions during the winter months of the pandemic when the titration of this molecule by NOx was reduced. In other regions, where ozone is NOx‐controlled, the concentration of ozone decreased. Zonally averaged ozone concentrations in the free troposphere during Northern Hemisphere spring and summer were 5 to 15 % lower than 19-year climatological values, in good quantitative agreement with observations from ozonesondes and ground-based remote sensing from the Network for the Detection of Atmospheric Composition Change (NDACC). About one third of this anomaly is attributed to the drastic reduction in air traffic during the pandemic, another third to reductions in surface emissions, the remainder to 2020 meteorological conditions, including the exceptional springtime Arctic stratospheric ozone depletion. The overall COVID-19 reduction in mean northern hemisphere tropospheric ozone in June is less than 5 ppb below 400 hPa, but reaches 8 ppb at 250 hPa.

How to cite: Gaubert, B., Bouarar, I., P. Brasseur, G., Doumbia, T., Darras, S., Deroubaix, A., Granier, C., Hannigan, J., Jo, D., Lacey, F., Liu, Y., Müller, J.-F., Ortega, I., Shi, X., Stavrakou, T., Steinbrecht, W., Tilmes, S., and Wang, T.: What are drivers of the tropospheric ozone reduction during spring 2020?, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-447, https://doi.org/10.5194/ems2021-447, 2021.

During the COVID-19 pandemic, the first lockdown period (March-May 2020) has led to an unprecedented reduction in pollutant emissions. For 3⁄4 of the more than 1,100 available monitoring stations in Europe, the average NO2 concentrations decreased by at least 25% (2.7 μg.m-3) compared to the average concentrations recorded during the same period of the previous seven years. The relative reduction was of similar magnitude in both urban and rural areas.

We further investigate the spatial distribution of the O3 change. Relative to the seven years average, positive anomalies were observed in northern Europe and negative anomalies in southwestern Europe. However, the level of total oxidant (Ox = O3 + NO2) remained unchanged except in southwestern Europe where it decreased.

At the global scale, the ozone concentration increased only in a few NOx-saturated regions. After presenting data from monitoring stations in Europe, we analyze the drivers of the change in surface ozone concentrations using the global Community Earth System Model. We contrast global simulations of the atmospheric composition with and without lockdown adjusted anthropogenic emissions for the COVID-19 period.

By comparing the situation in Europe with that of the United States and China, we show that the reduced cloudiness in northern Europe played a significant role by shifting the photochemical partitioning between NO2 and O3 toward more ozone, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants.

These results illustrate the complexity of the processes affecting ozone in the troposphere and hence the difficulty of implementing efficient regulations targeting air quality impacts.

How to cite: Deroubaix, A., Gaubert, B., Bouarar, I., Doumbia, T., Liu, Y., Stavrakou, T., Darras, S., Elguindi, N., Granier, C., Lacey, F., Müller, J.-F., Shi, X., Tilmes, S., Wang, T., and Brasseur, G.: Response of surface ozone concentration to emission reduction and meteorology during the COVID-19 lockdown, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-469, https://doi.org/10.5194/ems2021-469, 2021.

Ozone (O₃) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O₃ have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surface O₃ across China for periods before and during the lockdown. We find that daytime O₃ decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the O₃ changes, with a larger impact from the former especially in central China. The southward-shifted wind with increased temperature, enhanced planetary boundary layer height, decreased cloud fraction and precipitation favored the O₃ increase in north and central China, while the northward-shifted wind with decreased temperature and then biogenic volatile organic compounds (VOCs) emissions, increased cloud fraction and precipitation reduced O₃ in south China. As for the emission reduction, the drop in nitrogen oxide (NO_x) emission contributed to O₃ increases in populated regions, whereas the reduction in VOCs contributed to O₃ decreases across the country. Due to a decreasing level of NO_x saturation from north to south, the emission reduction in NO_x (46%) and VOC (32%) contributed to net O₃ increases in north China; the opposite effects of NO_x decrease (49%) and VOC decrease (24%) balanced out in central China, whereas the comparable decreases (45-55%) in the two precursors contributed to net O₃ declines in south China. Our study highlights the complex dependence of O₃ on its precursors and the importance of meteorology in the short-term O₃ variability.

How to cite: Liu, Y., Wang, T., Stavrakou, T., Elguindi, N., Doumbia, T., Granier, C., Bouarar, I., Gaubert, B., and Brasseur, G. P.: Diverse response of surface ozone to COVID-19 lockdown in China, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-436, https://doi.org/10.5194/ems2021-436, 2021.

Governments were enforced to respond to SARS-CoV-2 virus spread by taking a wide range of policy measures. Several studies have reported a decrease in air pollution following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference, and did not assess the role of different policy interventions. These responses offered an unprecedented opportunity to assess the effectiveness of several interventions to reduce air pollution levels worldwide. Using an accurate representation of business-as-usual and lockdown air pollution scenarios, provided by Copernicus Atmosphere Monitoring Service (CAMS), we quantitatively evaluated the association between policies responses to the COVID-19 pandemic with changes in NO₂, O₃, PM_2.5, and PM₁₀ levels in 47 European cities. We also estimated the short-term mortality in the period of February-July 2020. An advanced spatio-temporal Bayesian non-linear mixed effect model was performed to determine the association between air pollutant levels and stringency indices as well as individual policy measures. The results indicate non-linear relationships, with a stronger decrease in NO₂ and to a lesser extent PM₁₀ and PM_2.5 at very strict policy levels. Differences across interventions were also identified, actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements had strong effects, while restrictions on internal movement and international travels showed little impact. The observed decrease in pollution potentially resulted in hundreds of avoided deaths across the European cities. This project provides information that can help inform future policies on air pollution reduction.

How to cite: Schneider, R., Masselot, P., Vicedo-Cabrera, A. M., Sera, F., Blangiardo, M., Forlani, C., Douros, J., Jorba, O., Adani, M., Kouznetsov, R., Couvidat, F., Arteta, J., Raux, B., Guevara, M., Colette, A., Barré, J., Peuch, V.-H., and Gasparrini, A.: Association between COVID-19 lockdown policies and air pollution with associated mortality reduction in Europe, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-431, https://doi.org/10.5194/ems2021-431, 2021.

Brazil is the country with the highest number of COVID-19 cases and deaths in the southern hemisphere, third behind India and U.S globally. Some studies have analyzed the relationship between mobility, meteorology and air pollution, finding that staying out-of-home increases cases about 5 days and deaths about two weeks after the exposure (Ibarra-Espinosa, et al., 2021). In this work we will extend the analyses presented by Ibarra-Espinosa et al., (2021), by including more Brazilian cities. Specifically, the metropolitan region of Rio de Janeiro is considered a Megacity and monitors meteorology and air pollution, necessary to the analyses. The metropolitan regions of Porto Alegre, Belo Horizonte and Curitiba as well. The method consists in applying a semiparametric model (Dominici et al, 2004), but in this case, controllying all the environmental factors and their interactions and the parameter consists in the mobility alone. We will compare local mobility index, as Google Residential Mobility Index (RMI), as done by Ibarra-Espinosa et al., (2021). Due to the high dispersion of the data, COVID-19 will be modeled by quasi-poisson and negative binomial distribution, with generalized additive models (Wood., 2017; Zeileis et al., 2008; R Core Team, 2021). 

Ibarra-Espinosa, S., de Freitas, E.D., Ropkins, K., Dominici, F., Rehbein, A., 2021. Association between COVID-19, mobility and environment in São Paulo, Brazil. medRxiv. https://doi.org/10.1101/2021.02.08.21250113 

Dominici F, McDermott A, Hastie TJ. 2004. Improved semiparametric time series models of air pollution and mortality. J Am Stat Assoc 99: 938–948. R Core Team. 2021. R: A Language and Environment for Statistical Computing. 

Wood S. 2017. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC. 

Zeileis A, Kleiber C, Jackman S. 2008. Regression Models for Count Data in R. J Stat Software, Artic 27:1–25; doi:10.18637/jss.v027.i08. 

How to cite: Ibarra, S. and Dias de Freitas, E.: Association between COVID-19, mobility and environment in Brazilian capitals, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-110, https://doi.org/10.5194/ems2021-110, 2021.

Recent studies have shown that increased outdoor concentrations of particulate matter (PM) enhances the transmission of the novel Coronavirus (COVID-19). Although the viability of this causal relationship has been established indoors, outdoor correlations are contested based on potential confounding effects, such as urban mobility. Testing the hypothesis of PM-assisted airborne viral transmission is important to support the decision-making process and mitigation of future airborne epidemics. In a recent study we have shown that Granger causality analysis supports a causal relationship between outdoor PM concentrations and COVID-19 new cases. In this study, we aim to further explore this causal link by considering urban mobility as a common driver and a mediator in a set of causal networks based on lagged multivariate linear regressions. Causal networks are graphical models designed to help distinguishing and quantifying correlation and causation relationships. We quantify the strength at which PM increases COVID-19 new cases directly and the strength of urban mobility as a driver of both PM and COVID-19 new cases. We also quantify the effect of COVID-19 new cases in urban mobility that causes the PM concentration. We employ a dataset of daily air quality measurements in 52 cities in the United States of America (USA) considering PM concentrations in two particle size ranges, smaller than 2.5 μm (PM2.5), and between 10 and 2.5 μm (PMC). PMC is related to soil dust resuspension in most cities. So, we used the PMC as an urban mobility proxy. We also employ carbon monoxide (CO) along with the Apple dataset of IPhone users mobility which shows the relative volume of satellite navigation requests by city in the USA.

How to cite: Kamigauti, L. Y., Martins Palma Perez, G., and Andrade, M. D. F.: Causal relationships between particulate matter and COVID-19 cases in USA cities, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-186, https://doi.org/10.5194/ems2021-186, 2021.