Suchergebnisse

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. ; The research leading to these results has received funding from the Copernicus Atmosphere Monitoring Service (CAMS), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. We acknowledge support from the Ministerio de Ciencia, Innovación y Universidades (MICINN), as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R; the AXA Research Fund; and the 620 European Research Council (grant no. 773051, FRAGMENT). We also acknowledge PRACE and RES for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center through the eFRAGMENT2 and AECT-2020-1-0007 projects. This project has also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. Carlos Pérez García-Pando also acknowledges the support received through the Ramón y Cajal programme (grant no. RYC-2015-18690) of the MICINN. Modelling and satellite data were produced by the Copernicus Atmosphere Monitoring Service. We thank the three anonymous reviewers for their helpful comments that improved this paper. ; Peer Reviewed ; "Article signat per 27 autors/es: Jérôme Barré, Hervé Petetin, Augustin Colette, Marc Guevara, Vincent-Henri Peuch, Laurence Rouil, Richard Engelen, Antje Inness, Johannes Flemming, Carlos Pérez García-Pando, Dene Bowdalo, Frederik Meleux, Camilla Geels, Jesper H. Christensen, Michael Gauss, Anna Benedictow, Svetlana Tsyro, Elmar Friese, Joanna Struzewska, Jacek W. Kaminski, John Douros, Renske Timmermans, Lennart Robertson, Mario Adani, Oriol Jorba, Mathieu Joly, and Rostislav Kouznetsov" ; Postprint (published version)

The spread of the new coronavirus (COVID-19) forced the Spanish Government to implement extensive lockdown measures to reduce the number of hospital admissions, starting on March 14 th 2020. Over the following days and weeks, strong reductions of nitrogen dioxide (NO 2 ) pollution were reported in many regions of Spain. A substantial part of these reductions is obviously due to decreased local and regional anthropogenic emissions. Yet, the confounding effect of meteorological variability hinders a reliable quantification of the lockdown impact upon the observed pollution levels. Our study uses machine learning (ML) models fed by meteorological data along with other time features to estimate the business-as-usual NO 2 mixing ratios that would have been observed in the absence of the lockdown. We then quantify the so-called meteorology-normalized NO 2 reductions induced by the lockdown measures by comparing the business-as-usual with the actually observed NO 2 mixing ratios. We applied this analysis for a selection of urban background and traffic stations covering the more than 50 Spanish provinces and islands. The ML predictive models were found to perform remarkably well in most locations. During the period of study, going from the enforcement of the state of alarm in Spain on March 14 th to April 23 rd , we found the lockdown measures to be responsible for a 50 % reduction of NO 2 levels on average over all provinces and islands. The lockdown in Spain has gone through several phases with different levels of severity in the mobility restrictions. As expected the meteorology-normalized change of NO 2 was found to be stronger during the phases II (the most stringent one) and III than during phase I. In the largest agglomerations where both urban background and traffic stations were available, a stronger meteorology-normalized NO 2 change is highlighted at traffic stations compared to urban background ones. Our results are consistent with foreseen (although still uncertain) changes in anthropogenic emissions induced by the lockdown. We also show the importance of taking into account meteorological variability for accurately assessing the impact of the lockdown on NO 2 levels, in particular at fine spatial and temporal scales. Meteorology-normalized estimates such as the ones presented here are crucial to reliably quantify the health implications of the lockdown due to reduced air pollution.

The spread of the new coronavirus SARS-CoV-2 that causes COVID-19 forced the Spanish Government to implement extensive lockdown measures to reduce the number of hospital admissions, starting on 14 March 2020. Over the following days and weeks, strong reductions in nitrogen dioxide (NO2) pollution were reported in many regions of Spain. A substantial part of these reductions was obviously due to decreased local and regional anthropogenic emissions. Yet, the confounding effect of meteorological variability hinders a reliable quantification of the lockdown's impact upon the observed pollution levels. Our study uses machine-learning (ML) models fed by meteorological data along with other time features to estimate the "business-as-usual" NO2 mixing ratios that would have been observed in the absence of the lockdown. We then quantify the so-called meteorology-normalized NO2 reductions induced by the lockdown measures by comparing the estimated business-as-usual values with the observed NO2 mixing ratios. We applied this analysis for a selection of urban background and traffic stations covering the more than 50 Spanish provinces and islands. The ML predictive models were found to perform remarkably well in most locations, with an overall bias, root mean square error and correlation of +4 %, 29 % and 0.86, respectively. During the period of study, from the enforcement of the state of alarm in Spain on 14 March to 23 April, we found the lockdown measures to be responsible for a 50 % reduction in NO2 levels on average over all provinces and islands. The lockdown in Spain has gone through several phases with different levels of severity with respect to mobility restrictions. As expected, the meteorology-normalized change in NO2 was found to be stronger during phase II (the most stringent phase) and phase III of the lockdown than during phase I. In the largest agglomerations, where both urban background and traffic stations were available, a stronger meteorology-normalized NO2 change is highlighted at traffic stations ...

The research into the human health impacts from air pollution has advanced substantially over the last decade; in Europe ∼500 000 premature deaths are attributed annually to air pollution exposure [1], with adverse health effects beginning at much lower concentrations than previously understood [2]. The latest epidemiological evidence is now reflected in the new 2021 World Health Organization (WHO) Air Quality Guidelines (AQGs$^{\textrm{v2021}}$) [3], which represent a major revision over the previous ones set in 2005 (AQGs$^{\textrm{v2005}}$) [4]. Ten new AQGs have been set for the major health-damaging air pollutants, across different averaging times (annual$^{\textrm{(AN)}}$, peak season$^{\textrm{(PS)}}$, 24 h$^{\textrm{(24H)}}$, daily maximum 8 h average$^{\textrm{(8H)}})$, namely: particulate matter (PM$_{2.5}^{\textrm{AN, 24H}}$ and PM$_{10}^{\textrm{AN, 24H}})$, ozone (O$_3^{\textrm{PS, 8H}})$, nitrogen dioxide (NO$_2^{\textrm{AN, 24H}})$, sulphur dioxide (SO$_2^{\textrm{24H}})$ and carbon monoxide (CO$^{\textrm{24H}})$. The AQGs$^{\textrm{v2021}}$ are significantly more stringent, except for a relaxation in SO$_2^{\textrm{24H}}$, and O$_3^{\textrm{8H} }$ staying the same (supplementary table 1 (available online at stacks.iop.org/ERL/17/021002/mmedia)). This article quantifies the increase in non-compliance across all European measurement stations with AQGs$^{\textrm{v2021}}$ compared with AQGs$^{\textrm{v2005}}$, during business-as-usual (BAU) conditions (section 2); evaluates the impact that emission reductions during COVID-19 lockdowns had on non-compliance (section 3); and finally discusses the implications of our findings for any new European Union (EU) standards (section 4). ; This research has received funding from the Agencia Estatal de Investigacion (AEI) (MITIGATE, PID2020-116324RA-I00/AEI/10.13039/501100011033), European Research Council (#773051, FRAGMENT), and the AXA Research Fund. We also acknowledge Red Temática ACTRIS España (CGL2017-90884-REDT), H2020 ACTRIS IMP (#871115), and PRACE and RES for awarding access to Marenostrum4 Supercomputer based at the Barcelona Supercomputing Center. ; Peer reviewed