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A critical measure for our understanding of the complex non-linear processes which determine atmospheric composition is through the use of Chemical Transport Models and Earth System Models. In order to evaluate the veracity of these models, observations are required, however the availability and quality of these observations serves as a major impediment to this process. The most temporally consistent measurements have been made at the surface by established measurement networks, typically for the purpose of monitoring local exceedances of air quality limits. There are multiple networks which report this data, in disparate formats, requiring harmonisation to allow for synthesis.On the occasion that evaluation efforts use data from multiple networks, there is typically little to no detail given about the methodology used for the data synthesis across the different networks, or regarding the quality assurance (QA) or station classifications employed to subset the data. Therefore, evaluation efforts across different research groups are often incomparable.As a response to this common challenge, we established GHOST (Globally Harmonised Observational Surface Treatment). GHOST can be succinctly stated as an effort to standardise the data / metadata from the major public reporting networks which provide in situ atmospheric measurements at the surface. In total the dataset comprises of ~20 billion processed measurements, for ~200 components, across 32 networks, from 1970 to 2022. This represents the biggest collection of harmonised atmospheric composition surface measurements ever composed. Substantial efforts have been made towards standardising almost every facet of provided data / metadata from across the networks. On top of this, additional metadata was added by processing various commonly utilised globally gridded datasets (e.g. land use), as well as adding temporal classifications per measurement (e.g. weekday / weekend). As the dataset spans many decades, metadata is handled dynamically and allowed to vary through the record, important for instances when there are changes in measurement instrumentation or the measurement position.Major efforts were made for the standardisation of the numerous metadata fields detailing measurement procedures, with all measurements linked to a dictionary of standard measurement methods and standard instruments. Great effort was also spent in the standardisation of station classifications, providing large flexibility for the subsetting of stations. Rather than dropping any measurements which are labelled as potentially erroneous by the measurement provider, standardised data flags are associated with each individual measurement. On top of this, GHOST own QA flags are also associated per measurement.All data is now freely available to the community.

This study presents a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula over the period 2018-2021 (using the recently released PAL product to ensure consistency).A first exploration of the impact of cloud cover on the availability of TROPOMI TrC-NO2 observations indicates that data gaps range between 20-30% in summer to 55-70% in April and November, with substantial spatial differences between northern and southern and/or arid areas. The spatial distribution of TrC-NO2 highlights strong hotspots over urban areas (especially Madrid and Barcelona), with additional enhancements along international maritime routes and major highways. A reasonable correlation with surface NO2 mixing ratios is found, around 0.7-0.8 depending on the averaging time.The weekly and monthly variability of TrC-NO2 over the peninsula is then analyzed at the light of the urban cover fraction (taken from the Copernicus Land Monitoring Service). From least to most urbanized areas, the weekend reduction is found to range from -10 to -40%. A detailed analysis at the intra-agglomeration scale highlights that strongest weekend effects do not always peak in the center but sometimes in surrounding cities, which suggests a larger contribution of commuting to total NOx anthropogenic emissions. Similarly, the monthly profiles strongly change depending on the level of urbanization, from -40%/+26% in summer/winter in most urbanized areas, to -10%/+20% in least urbanized ones. Interestingly, the same analysis applied to cropland fraction highlight an enhancement in June-July that could be due to natural soil NO emissions that are known to peak during the warm season. Beyond some specific discrepancies, a generally good consistency is found between the variability of NO2 seen from space with TROPOMI and the one observed at the surface.Our study thus illustrates the potential of TROPOMI TrC-NO2 to provide a valuable complement to surface monitoring network, especially in agricultural and maritime areas where surface NO2 observations are missing but yet crucial for better understanding the impact of local NOx emissions, especially for the production of tropospheric ozone. Petetin, H., Guevara, M., Compernolle, S., Bowdalo, D., Bretonnière, P.-A., Enciso, S., Jorba, O., Lopez, F., Soret, A., and Pérez García-Pando, C.: Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1056, 2022.

In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time. We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from −10 % to −40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from −40 % in summer to +60 % in winter in the most urbanized areas, and from −10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting; this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets. Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.

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.

Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with model output statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate the extent to which AQ forecasts can be improved using a variety of MOS methods, including moving average, quantile mapping, Kalman filter, analogs and gradient boosting machine methods, and consider as well the persistence method as a reference. We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a comprehensive set of continuous and categorical metrics at various timescales, along different lead times and using different meteorological input datasets. Our results show that O3 forecasts can be substantially improved using such MOS corrections and that improvements go well beyond the correction of the systematic bias. Depending on the timescale and lead time, root mean square errors decreased from 20 %–40 % to 10 %–30 %, while Pearson correlation coefficients increased from 0.7–0.8 to 0.8–0.9. Although the improvement typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. The MOS methods relying on meteorological data were found to provide relatively similar performance with two different meteorological inputs. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with the lowest errors and highest correlations. However, they are not necessarily the best in predicting the peak O3 episodes, for which simpler MOS methods can achieve better results. Although the complex impact of MOS methods on the distribution of and variability in raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest. This research has been funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433, as well as the MITIGATE project (PID2020-116324RA-I00/AEI/10.13039/501100011033) from the Agencia Estatal de Investigacion (AEI). We also acknowledge support by the AXA Research Fund and Red Temática ACTRIS España (CGL2017-90884-REDT), the BSC-CNS “Centro de Excelencia Severo Ochoa 2015-2019” program (SEV-2015-0493), PRACE, and RES for awarding us access to the MareNostrum supercomputer in the Barcelona Supercomputing Center as well as H2020 ACTRIS IMP (no. 871115). We also acknowledge support from the VITALISE project (PID2019-108086RA-I00) funded by MCIN/AEI/10.13039/501100011033.

Over the last century, our societies have experienced a sharp increase in urban population and fossil-fuelled transportation, turning air pollution into a critical issue. It is therefore key to accurately characterize the spatiotemporal variability of surface air pollution in order to understand its effects upon the environment, knowledge that can then be used to design effective pollution reduction policies. Global atmospheric composition reanalyses offer great capabilities towards this characterization through assimilation of satellite measurements. However, they generally do not integrate surface measurements and thus remain affected by significant biases at ground level. In this study, we thoroughly evaluate two global atmospheric composition reanalyses, the Copernicus Atmosphere Monitoring Service (CAMSRA) and the Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA-2), between 2003 and 2020, against independent surface measurements of O3, NO2, CO, SO2 and particulate matter (PM; both PM10 and PM2.5) over the European continent. Overall, both reanalyses present significant and persistent biases for almost all examined pollutants. CAMSRA clearly outperforms MERRA-2 in capturing the spatiotemporal variability of most pollutants, as shown by generally lower biases (all pollutants except for PM2.5), lower errors (all pollutants) and higher correlations (all pollutants except SO2). CAMSRA also outperforms MERRA-2 in capturing the annual trends found in all pollutants (except for SO2). Overall, CAMSRA tends to perform best for O3 and CO, followed by NO2 and PM10, while poorer results are typically found for SO2 and PM2.5. Higher correlations are generally found in autumn and/or winter for reactive gases. Compared to MERRA-2, CAMSRA assimilates a wider range of satellite products which, while enhancing the performance of the reanalysis in the troposphere (as shown by other studies), has a limited impact on the surface. The biases found in both reanalyses are likely explained by a combination of factors, including errors in emission inventories and/or sinks, a lack of surface data assimilation, and their relatively coarse resolution. Our results highlight the current limitations of reanalyses to represent surface pollution, which limits their applicability for health and environmental impact studies. When applied to reanalysis data, bias-correction methodologies based on surface observations should help to constrain the spatiotemporal variability of surface pollution and its associated impacts.

We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21 February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26 April 2020. This period includes 5 weeks (23 March until 26 April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be −33 % for NOx, −8 % for non-methane volatile organic compounds (NMVOCs), −7 % for SOx and −7 % for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85 % of the total reduction is attributable to road transport, except SOx. The reductions reached −50 % (NOx), −14 % (NMVOCs), −12 % (SOx) and −15 % (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to −58 % at urban background locations (Madrid, Spain) and −44 % at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86 % and 93 %, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90 %. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement. 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 Agencia Estatal de Investigacion (AEI) as part of the VITALISE project (PID2019-108086RA-I00/AEI/0.13039/501100011033), the AXA Research Fund, and the 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 Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. Carlos Pérez García-Pando also acknowledges support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the MICINN.

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 compared with urban background sites. Our results are consistent with foreseen (although still uncertain) changes in anthropogenic emissions induced by the lockdown. We also show the importance of taking the meteorological variability into account for accurately assessing the impact of the lockdown on NO2 levels, in particular at fine spatial and temporal scales. Meteorology-normalized estimates such as those presented here are crucial to reliably quantify the health implications of the lockdown due to reduced air pollution.

In the last two decades reanalyses have become a powerful tool for modern geosciences as they combine both model- and observation-based (mostly from remote-sensing sources) information to provide physically-consistent data of land, ocean and atmospheric fields with continuous spatial and temporal coverage. In the frame of the ERC project EARLY-ADAPT (https://early-adapt.eu/), a pioneer health dataset is currently being collected over Europe to investigate the time-varying health effects of climate and air pollution, which will shed light into the early adaptation response to climate change in the area of human health. This impact will be quantified by fitting epidemiological models on historical local health, climate and air pollution data, which thus requires a long-term air quality database at daily-scale over all of Europe. In this context, atmospheric composition reanalyses provide highly valuable information, but remain subject to biases and errors both in terms of spatiotemporal variability and long-term trends. It is therefore key to determine whether these reanalyses correctly capture the values, trends and cyclic processes of the different aforementioned fields.Our work aims to evaluate how the Copernicus Atmosphere Monitoring Service global reanalysis (CAMSRA), developed by the ECMWF, and the Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA-2), produced by NASA, perform against independent ground-level in-situ observations over Europe for a period of 18 years (2003 – 2020). We analyse these reanalyses products considering the most harmful pollutants for human health, namely O3, NO2, SO2, CO, PM2.5 and PM10. A careful quality-assurance filtering of the surface observations is performed using GHOST, which stands for Globally Harmonised Observational Surface Treatment, a BSC in-house project dedicated to the harmonisation of global air pollution surface observations and its metadata, with the purpose of facilitating a greater quality of observational/model comparison in the atmospheric chemistry community. This study considers a domain extending from 25°W to 45°E in longitude, and from 27°N to 72°N in latitude, thus covering all continental Europe as well as the Canary Islands, Iceland, Western/European Russia, North Africa and the westernmost regions of the Middle East and the Caucasus.CAMSRA and MERRA-2 reproduce the observational values, trends and seasonal cycles with a varying degree of accuracy, depending on the pollutant considered, though significant and persistent biases are found in almost all cases. As the computed statistics present strong spatiotemporal dependencies, given the long-term scope of the evaluation, a regional and country-level analysis has been performed in order to provide a more exhaustive and complete evaluation. An intercomparison between CAMSRA and MERRA-2 has also been conducted for the pollutants available in both reanalyses. The obtained results highlight the necessity of applying bias correction schemes when working with air pollution reanalysis data, and open the door for improved continental-wide, regional-scale, environmental epidemiological analyses of the health impacts of air pollutants.

Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with Model Output Statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate to what extent AQ forecasts can be improved using a variety of MOS methods, including persistence (PERS), moving average (MA), quantile mapping (QM), Kalman Filter (KF), analogs (AN), and gradient boosting machine (GBM). We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a very comprehensive set of continuous and categorical metrics at various time scales (hourly to daily), along different lead times (1 to 4 days), and using different meteorological input data (forecast vs reanalyzed).Our results show that O3 forecasts can be substantially improved using such MOS corrections and that this improvement goes much beyond the correction of the systematic bias. Although it typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. When considering MOS methods relying on meteorological information and comparing the results obtained with IFS forecasts and ERA5 reanalysis, the relative deterioration brought by the use of IFS is minor, which paves the way for their use in operational MOS applications. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with lowest errors and highest correlations. However, they are not necessarily the best in predicting the highest O3 episodes, for which simpler MOS methods can give better results. Although the complex impact of MOS methods on the distribution and variability of raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.Petetin, H., Bowdalo, D., Bretonnière, P.-A., Guevara, M., Jorba, O., Armengol, J. M., Samso Cabre, M., Serradell, K., Soret, A., and Pérez Garcia-Pando, C.: Model Output Statistics (MOS) applied to CAMS O3 forecasts: trade-offs between continuous and categorical skill scores, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-864, in review, 2021.

Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with Model Output Statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate to what extent AQ forecasts can be improved using a variety of MOS methods, including persistence (PERS), moving average (MA), quantile mapping (QM), Kalman Filter (KF), analogs (AN), and gradient boosting machine (GBM). We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a very comprehensive set of continuous and categorical metrics at various time scales (hourly to daily), along different lead times (1 to 4 days), and using different meteorological input data (forecast vs reanalyzed). Our results show that O3 forecasts can be substantially improved using such MOS corrections and that this improvement goes much beyond the correction of the systematic bias. Although it typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. When considering MOS methods relying on meteorological information and comparing the results obtained with IFS forecasts and ERA5 reanalysis, the relative deterioration brought by the use of IFS is minor, which paves the way for their use in operational MOS applications. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with lowest errors and highest correlations. However, they are not necessarily the best in predicting the highest O3 episodes, for which simpler MOS methods can give better results. Although the complex impact of MOS methods on the distribution and variability of raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.

The emergence of the COVID-19 pandemic forced most countries to put in place lockdown measures to slow down the transmission of the virus. These lockdowns have led to temporal improvements in air quality. Here, we evaluate the changes in NO2 and O3 levels along with the associated impact upon premature mortality during the COVID-19 lockdown and deconfinement periods along the first epidemic wave across the provincial capital cities of Spain. We first quantify the change in pollutants solely due to the lockdown as the difference between business-as-usual (BAU) pollution levels, estimated with a machine learning-based meteorological normalization technique, and observed concentrations. Second, instead of using exposure-response functions between the pollutants and mortality reported in the literature, we fit conditional quasi-Poisson regression models to estimate city-specific associations between daily pollutant levels and non-accidental mortality during the period 2010–2018. Significant relative risk values are observed at lag 1 for NO2 (1.0047 [95% CI: 1.0014 to 1.0081]) and at lag 0 for O3 (1.0039 [1.0013 to 1.0065]). On average NO2 changed by −51% (intercity range −65.7 to −30.9%) and −36.4% (−53.7 to −11.6%), and O3 by −1.1% (−20.2 to 23.8%) and 0.6% (−12.4 to 23.0%), during the lockdown (57 days) and deconfinement (42 days) periods, respectively. We obtain a reduction in attributable mortality associated with NO2 changes of −119 (95% CI: -273 to −24) deaths over the lockdown, and of −53 (−114 to −10) deaths over the deconfinement. This was partially compensated by an increase in the attributable number of deaths, 14 (−72 to 99) during the lockdown, and 8 (−27 to 50) during the deconfinement, associated with the rise in O3 levels in the most populous cities during the analysed period, despite the overall small average reductions. Our study shows that the potential trade-offs between multiple air pollutants should be taken into account when evaluating the health impacts of environmental exposures., Graphical abstract Image 1

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 14th 2020. Over the following days and weeks, strong reductions of nitrogen dioxide (NO2) 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 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 business-as-usual with the actually 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. During the period of study, going from the enforcement of the state of alarm in Spain on March 14th to April 23rd, we found the lockdown measures to be responsible for a 50 % reduction of NO2 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 NO2 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 NO2 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 NO2 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.

Correspondence

The concentration of ozone at the Earth’s surface is measured at many locations across the globe for the purposes of air quality monitoring and atmospheric chemistry research.We have brought together all publicly available surface ozone observations from online databases from the modern era to build a consistent data set for the evaluation of chemical transport and chemistry-climate (Earth System) models for projects such as the Chemistry-Climate Model Initiative and Aer-Chem-MIP. From a total data set of approximately 6600 sites and 500 million hourly observations from 1971–2015, approximately 2200 sites and 200 million hourly observations pass screening as high-quality sites in regionally representative locations that are appropriate for use in global model evaluation. There is generally good data volume since the start of air quality monitoring networks in 1990 through 2013. Ozone observations are biased heavily toward North America and Europe with sparse coverage over the rest of the globe. This data set is made available for the purposes of model evaluation as a set of gridded metrics intended to describe the distribution of ozone concentrations on monthly and annual timescales. Metrics include the moments of the distribution, percentiles, maximum daily 8-hour average (MDA8), sum of means over 35 ppb (daily maximum 8-h; SOMO35), accumulated ozone exposure above a threshold of 40 ppbv (AOT40), and metrics related to air quality regulatory thresholds. Gridded data sets are stored as netCDF-4 files and are available to download from the British Atmospheric Data Centre (doi:10.5285/08fbe63d-fa6d-4a7ab952- 5932e3ab0452). We provide recommendations to the ozone measurement community regarding improving metadata reporting to simplify ongoing and future efforts in working with ozone data from disparate networks in a consistent manner., peer-reviewed

Halogens in the troposphere are increasingly recognized as playing an important role for atmospheric chemistry, and possibly climate. Bromine and iodine react catalytically to destroy ozone (O3), oxidize mercury, and modify oxidative capacity that is relevant for the lifetime of greenhouse gases. Most of the tropospheric O3 and methane (CH4) loss occurs at tropical latitudes. Here we report simultaneous measurements of vertical profiles of bromine oxide (BrO) and iodine oxide (IO) in the tropical and subtropical free troposphere (10 °N to 40 °S), and show that these halogens are responsible for 34% of the column-integrated loss of tropospheric O3. The observed BrO concentrations increase strongly with altitude (∼ 3.4 pptv at 13.5 km), and are 2-4 times higher than predicted in the tropical free troposphere. BrO resembles model predictions more closely in stratospheric air. The largest model low bias is observed in the lower tropical transition layer (TTL) over the tropical eastern Pacific Ocean, and may reflect a missing inorganic bromine source supplying an additional 2.5-6.4 pptv total inorganic bromine (Bry), or model overestimated Bry wet scavenging. Our results highlight the importance of heterogeneous chemistry on ice clouds, and imply an additional Bry source from the debromination of sea salt residue in the lower TTL. The observed levels of bromine oxidize mercury up to 3.5 times faster than models predict, possibly increasing mercury deposition to the ocean. The halogen-catalyzed loss of tropospheric O3 needs to be considered when estimating past and future ozone radiative effects.

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by the COVID-19 lockdown using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI), surface site measurements and simulations from the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of NO2 changes 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 NO2 column changes with both weather-normalized surface NO2 concentration changes and simulated changes by the CAMS regional ensemble, composed of 11 models, using recently published emission reductions induced by the lockdown. We show that all estimates show the same tendency on NO2 reductions. Locations where the lockdown was stricter show stronger reductions and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Regarding average reductions, 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 the model-based estimates.