Search

Tropospheric ozone results from in situ chemical formation and stratosphere–troposphere exchange (STE), with the latter being more important in the middle and upper troposphere than in the lower troposphere. Ozone photochemical formation is nonlinear and results from the oxidation of methane and non-methane hydrocarbons (NMHCs) in the presence of nitrogen oxide (NOx=NO+NO2). Previous studies showed that O3 short- and long-term trends are nonlinearly controlled by near-surface anthropogenic emissions of carbon monoxide (CO), volatile organic compounds (VOCs), and nitrogen oxides, which may also be impacted by the long-range transport (LRT) of O3 and its precursors. In addition, several studies have demonstrated the important role of STE in enhancing ozone levels, especially in the midlatitudes. In this article, we investigate tropospheric ozone spatial variability and trends from 2005 to 2019 and relate those to ozone precursors on global and regional scales. We also investigate the spatiotemporal characteristics of the ozone formation regime in relation to ozone chemical sources and sinks. Our analysis is based on remote sensing products of the tropospheric column of ozone (TrC-O3) and its precursors, nitrogen dioxide (TrC-NO2), formaldehyde (TrC-HCHO), and total column CO (TC-CO), as well as ozonesonde data and model simulations. Our results indicate a complex relationship between tropospheric ozone column levels, surface ozone levels, and ozone precursors. While the increasing trends of near-surface ozone concentrations can largely be explained by variations in VOC and NOx concentration under different regimes, TrC-O3 may also be affected by other variables such as tropopause height and STE as well as LRT. Decreasing or increasing trends in TrC-NO2 have varying effects on TrC-O3, which is related to the different local chemistry in each region. We also shed light on the contribution of NOx lightning and soil NO and nitrous acid (HONO) emissions to trends of tropospheric ozone on regional and global scales.

Volatile organic compounds (VOCs) significantly impact air quality and atmospheric chemistry, influencing ozone formation and secondary organic aerosol production. Despite their importance, the uncertainties associated with representing VOCs in atmospheric emission inventories are considerable. This work presents a spatiotemporal assessment and evaluation of benzene, toluene, and xylene (BTX) emissions and concentrations in Spain by combining bottom-up emissions, air quality modelling techniques, and ground-based observations. The emissions produced by High-Elective Resolution Modelling Emission System (HERMESv3) were used as input to the Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) chemical transport model to simulate surface concentrations across Spain. Comparing modelled and observed levels revealed uncertainty in the anthropogenic emissions, which were further explored through sensitivity tests. The largest levels of observed benzene and xylene were found in industrial sites near coke ovens, refineries, and car manufacturing facilities, where the modelling results show large underestimations. Official emissions reported for these facilities were replaced by alternative estimates, resulting in varied improvements in the model's performance across different stations. However, uncertainties associated with industrial emission processes persist, emphasising the need for further refinement. For toluene, consistent overestimations in background stations were mainly related to uncertainties in the spatial disaggregation of emissions from industrial-use solvent activities, mainly wood paint applications. Observed benzene levels in Barcelona's urban traffic areas were 5 times larger than the ones observed in Madrid. MONARCH failed to reproduce the observed gradient between the two cities due to uncertainties arising from estimating emissions from motorcycles and mopeds, as well as from different measurement methods and the model's capacity to accurately simulate meteorological conditions. Our results are constrained by the spatial and temporal coverage of available BTX observations, posing a key challenge in evaluating the spatial distribution of modelled levels and associated emissions.

The 2020 COVID-19 crisis caused an unprecedented drop in anthropogenic emissions of air pollutants and greenhouse gases. Given that emissions estimates from official national inventories for the year 2020 were not reported until 2 years later, new and non-traditional datasets to estimate near-real-time emissions became particularly relevant and widely used in international monitoring and modelling activities during the pandemic. This study investigates the impact of the COVID-19 pandemic on 2020 European (the 27 EU member states and the UK) emissions by comparing a selection of such near-real-time emission estimates, with the official inventories that were subsequently reported in 2022 under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) and the United Nations Framework Convention on Climate Change (UNFCCC). Results indicate that annual changes in total 2020 emissions reported by official and near-real-time estimates are fairly in line for most of the chemical species, with NOx and fossil fuel CO2 being reported as the ones that experienced the largest reduction in Europe in all cases. However, large discrepancies arise between the official and non-official datasets when comparing annual results at the sector and country level, indicating that caution should be exercised when estimating changes in emissions using specific near-real-time activity datasets, such as time mobility data derived from smartphones. The main examples of these differences are observed for the manufacturing industry NOx (relative changes ranging between −21.4 % and −5.4 %) and road transport CO2 (relative changes ranging between −29.3 % and −5.6 %) total European emissions. Additionally, significant discrepancies are observed between the quarterly and monthly distribution of emissions drops reported by the various near-real-time inventories, with differences of up to a factor of 1.5 for total NOx during April 2020, when restrictions were at their maximum. For residential combustion, shipping and the public energy industry, results indicate that changes in emissions that occurred between 2019 and 2020 were mainly dominated by non-COVID-19 factors, including meteorology, the implementation of the Global Sulphur Cap and the shutdown of coal-fired power plants as part of national decarbonization efforts, respectively. The potential increase in NMVOC emissions from the intensive use of personal protective equipment such as hand sanitizer gels is considered in a heterogeneous way across countries in officially reported inventories, indicating the need for some countries to base their calculations on more advanced methods. The findings of this study can be used to better understand the uncertainties in near-real-time emissions and how such emissions could be used in the future to provide timely updates to emission datasets that are critical for modelling and monitoring applications.

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.

Comprehensive monitoring of NO2 exceedances is imperative for protecting human health, especially in urban areas with traffic. However, an accurate spatial characterization of the exceedances is challenging due to the typically low density of air quality monitoring stations and the inherent uncertainties in urban air quality models. We study how observational data from different sources and timescales can be combined with a dispersion air quality model to obtain bias-corrected NO2 hourly maps at the street scale. We present a kriging-based data fusion workflow that merges dispersion model output with continuous hourly observations and uses a machine-learning-based land use regression (LUR) model constrained with past short intensive passive dosimeter campaign measurements. While the hourly observations allow the bias adjustment of the temporal variability in the dispersion model, the microscale LUR model adds information on the NO2 spatial patterns. Our method includes an uncertainty calculation based on the estimated error variance of the universal kriging technique, which is subsequently used to produce urban maps of probability of exceeding the 200 µg m−3 hourly and the 40 µg m−3 annual NO2 average limits. We assess the statistical performance of this approach in the city of Barcelona for the year 2019. Our results show that simply merging the monitoring stations with the model output already significantly increases the correlation coefficient (r) by +29 % and decreases the root mean square error (RMSE) by −32 %. When adding the time-invariant microscale LUR model in the data fusion workflow, the improvement is even more remarkable, with +46 % and −48 % for the r and RMSE, respectively. Our work highlights the usefulness of high-resolution spatial information in data fusion methods to better estimate exceedances at the street scale.

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.

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.

We present a European dataset of daily sector-, pollutant- and country-dependent emission adjustment factors associated with the COVID-19 mobility restrictions for the year 2020. We considered metrics traditionally used to estimate emissions, such as energy statistics or traffic counts, as well as information derived from new mobility indicators and machine learning techniques. The resulting dataset covers a total of nine emission sectors, including road transport, the energy industry, the manufacturing industry, residential and commercial combustion, aviation, shipping, off-road transport, use of solvents, and fugitive emissions from transportation and distribution of fossil fuels. The dataset was produced to be combined with the Copernicus CAMS-REG_v5.1 2020 business-as-usual (BAU) inventory, which provides high-resolution (0.1∘×0.05∘) emission estimates for 2020 omitting the impact of the COVID-19 restrictions. The combination of both datasets allows quantifying spatially and temporally resolved reductions in primary emissions from both criteria pollutants (NOx, SO2, non-methane volatile organic compounds – NMVOCs, NH3, CO, PM10 and PM2.5) and greenhouse gases (CO2 fossil fuel, CO2 biofuel and CH4), as well as assessing the contribution of each emission sector and European country to the overall emission changes. Estimated overall emission changes in 2020 relative to BAU emissions were as follows: −10.5 % for NOx (−602 kt), −7.8 % (−260.2 Mt) for CO2 from fossil fuels, −4.7 % (−808.5 kt) for CO, −4.6 % (−80 kt) for SO2, −3.3 % (−19.1 Mt) for CO2 from biofuels, −3.0 % (−56.3 kt) for PM10, −2.5 % (−173.3 kt) for NMVOCs, −2.1 % (−24.3 kt) for PM2.5, −0.9 % (−156.1 kt) for CH4 and −0.2 % (−8.6 kt) for NH3. The most pronounced drop in emissions occurred in April (up to −32.8 % on average for NOx) when mobility restrictions were at their maxima. The emission reductions during the second epidemic wave between October and December were 3 to 4 times lower than those occurred during the spring lockdown, as mobility restrictions were generally softer (e.g. curfews, limited social gatherings). Italy, France, Spain, the United Kingdom and Germany were, together, the largest contributors to the total EU27 + UK (27 member states of the European Union and the UK) absolute emission decreases. At the sectoral level, the largest emission declines were found for aviation (−51 % to −56 %), followed by road transport (−15.5 % to −18.8 %), the latter being the main driver of the estimated reductions for the majority of pollutants. The collection of COVID-19 emission adjustment factors (https://doi.org/10.24380/k966-3957, Guevara et al., 2022) and the CAMS-REG_v5.1 2020 BAU gridded inventory (https://doi.org/10.24380/eptm-kn40, Kuenen et al., 2022b) have been produced in support of air quality modelling studies.

Between December 2005 and 2013, the In-service Aircraft for a Global Observing System (IAGOS) program produced almost daily in situ measurements of CO and O3 between Europe and southern Africa. IAGOS data combined with measurements from the Infrared Atmospheric Sounding Interferometer (IASI) instrument aboard the Metop-A satellite (2008–2013) are used to characterize meridional distributions and seasonality of CO and O3 in the African upper troposphere (UT). The FLEXPART particle dispersion model and the SOFT-IO model which combines the FLEXPART model with CO emission inventories are used to explore the sources and origins of the observed transects of CO and O3. We focus our analysis on two main seasons: December to March (DJFM) and June to October (JJASO). These seasons have been defined according to the position of Intertropical Convergence Zone (ITCZ), determined using in situ measurements from IAGOS. During both seasons, the UT CO meridional transects are characterized by maximum mixing ratios located 10∘ from the position of the ITCZ above the dry regions inside the hemisphere of the strongest Hadley cell (132 to 165 ppb at 0–5∘ N in DJFM and 128 to 149 ppb at 3–7∘ S in JJASO) and decreasing values southward and northward. The O3 meridional transects are characterized by mixing ratio minima of ∼42–54 ppb at the ITCZ (10–16∘ S in DJFM and 5–8∘ N in JJASO) framed by local maxima (∼53–71 ppb) coincident with the wind shear zones north and south of the ITCZ. O3 gradients are strongest in the hemisphere of the strongest Hadley cell. IASI UT O3 distributions in DJFM have revealed that the maxima are a part of a crescent-shaped O3 plume above the Atlantic Ocean around the Gulf of Guinea. CO emitted at the surface is transported towards the ITCZ by the trade winds and then convectively uplifted. Once in the upper troposphere, CO-enriched air masses are transported away from the ITCZ by the upper branches of the Hadley cells and accumulate within the zonal wind shear zones where the maximum CO mixing ratios are found. Anthropogenic and fires both contribute, by the same order of magnitude, to the CO budget of the African upper troposphere. Local fires have the highest contribution and drive the location of the observed UT CO maxima. Anthropogenic CO contribution is mostly from Africa during the entire year, with a low seasonal variability. There is also a large contribution from Asia in JJASO related to the fast convective uplift of polluted air masses in the Asian monsoon region which are further westward transported by the tropical easterly jet (TEJ) and the Asian monsoon anticyclone (AMA). O3 minima correspond to air masses that were recently uplifted from the surface where mixing ratios are low at the ITCZ. The O3 maxima correspond to old high-altitude air masses uplifted from either local or long-distance area of high O3 precursor emissions (Africa and South America during all the year, South Asia mainly in JJASO) and must be created during transport by photochemistry.

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 In-service Aircraft for a Global Observing System (IAGOS) is a European research infrastructure that equips the Airbus A340/330 with a system for monitoring atmospheric composition. The IAGOS instruments have three different configurations: IAGOS-Core, IAGOS – Measurement of Ozone and Water Vapor by Airbus In Service Aircraft (IAGOS-MOZAIC) and IAGOS – Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container (IAGOS-CARIBIC). Since 1994, there have been a total of 17 aircraft equipped. In this study, we perform an intercomparison of about 8000 landing and takeoff profiles to compare the O3 and CO measurements performed from these different configurations. The collocated profiles used in the study met various selection criteria. The first was a maximal 1 h time difference between an ascent or descent by two different aircraft at the same airport and the second was a selection based on the similarity of air masses based on the meteorological data acquired by the aircraft. We provide here an evaluation of the internal consistency of the O3 and CO measurements since 1994. For both O3 and CO, we find no drift in the bias amongst the different instrument units (six O3 and six CO IAGOS-MOZAIC instruments, nine IAGOS-Core Package1 and the two instruments used in the IAGOS-CARIBIC aircraft). This result gives us confidence that the entire IAGOS database can be treated as one continuous program and is therefore appropriate for studies of long-term trends.

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 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.

This study investigates the role of biomass burning and long-range transport in the anomalies of carbon monoxide (CO) regularly observed along the tropospheric vertical profiles measured in the framework of the In-service Aircraft for a Global Observing System (IAGOS). Considering the high interannual variability of biomass burning emissions and the episodic nature of long-range pollution transport, one strength of this study is the amount of data taken into account, namely 30 000 vertical profiles at nine clusters of airports in Europe, North America, Asia, India and southern Africa over the period 2002–2017.As a preliminary, a brief overview of the spatiotemporal variability, latitudinal distribution, interannual variability and trends of biomass burning CO emissions from 14 regions is provided. The distribution of CO mixing ratios at different levels of the troposphere is also provided based on the entire IAGOS database (125 million CO observations).This study focuses on the free troposphere (altitudes above 2 km) where the long-range transport of pollution is favoured. Anomalies at a given airport cluster are here defined as departures from the local seasonally averaged climatological vertical profile. The intensity of these anomalies varies significantly depending on the airport, with maximum (minimum) CO anomalies of 110–150 (48) ppbv in Asia (Europe). Looking at the seasonal variation of the frequency of occurrence, the 25 % strongest CO anomalies appear reasonably well distributed throughout the year, in contrast to the 5 % or 1 % strongest anomalies that exhibit a strong seasonality with, for instance, more frequent anomalies during summertime in the northern United States, during winter/spring in Japan, during spring in south-east China, during the non-monsoon seasons in south-east Asia and south India, and during summer/fall in Windhoek, Namibia. Depending on the location, these strong anomalies are observed in different parts of the free troposphere.In order to investigate the role of biomass burning emissions in these anomalies, we used the SOFT-IO (SOft attribution using FlexparT and carbon monoxide emission inventories for In-situ Observation database) v1.0 IAGOS added-value products that consist of FLEXible PARTicle dispersion model (FLEXPART) 20-day backward simulations along all IAGOS aircraft trajectories, coupled with anthropogenic Monitoring Atmospheric Composition and Climate (MACC)/CityZEN EU projects (MACCity) and biomass burning Global Fire Assimilation System (GFAS) CO emission inventories and vertical injections. SOFT-IO estimates the contribution (in ppbv) of the recent (less than 20 days) primary worldwide CO emissions, tagged per source region. Biomass burning emissions are found to play an important role in the strongest CO anomalies observed at most airport clusters. The regional tags indicate a large contribution from boreal regions at airport clusters in Europe and North America during the summer season. In both Japan and south India, the anthropogenic emissions dominate all throughout the year, except for the strongest summertime anomalies observed in Japan that are due to Siberian fires. The strongest CO anomalies at airport clusters located in south-east Asia are induced by fires burning during spring in south-east Asia and during fall in equatorial Asia. In southern Africa, the Windhoek airport was mainly impacted by fires in Southern Hemisphere Africa and South America.To our knowledge, no other studies have used such a large dataset of in situ vertical profiles for deriving a climatology of the impact of biomass burning versus anthropogenic emissions on the strongest CO anomalies observed in the troposphere, in combination with information on the source regions. This study therefore provides both qualitative and quantitative information for interpreting the highly variable CO vertical distribution in several regions of interest.

This paper investigates in an innovative way the climatological vertical stratification of relative humidity (RH), ozone (O3) and carbon monoxide (CO) mixing ratios within the planetary boundary layer (PBL) and at the interface with the free troposphere (FT). The climatology includes all vertical profiles available at northern mid-latitudes over the period 1994–2016 in both the IAGOS (In-service Aircraft for a Global Observing System) and WOUDC (World Ozone and Ultraviolet Radiation Data Centre) databases, which represents more than 90 000 vertical profiles. For all individual profiles, apart from the specific case of surface-based temperature inversions (SBIs), the PBL height is estimated following the elevated temperature inversion (EI) method. Several features of both SBIs and EIs are analysed, including their diurnal and seasonal variations. Based on these PBL height estimates (denoted h), the novel approach introduced in this paper consists of building a so-called PBL-referenced vertical distribution of O3, CO and RH by averaging all individual profiles beforehand expressed as a function of z∕h rather than z (with z the altitude). Using this vertical coordinate system allows us to highlight the features existing at the PBL–FT interface that would have been smoothed otherwise.Results demonstrate that the frequently assumed well-mixed PBL remains an exception for both chemical species. Within the PBL, CO profiles are characterized by a mean vertical stratification (here defined as the standard deviation of the CO profile between the surface and the PBL top, normalized by the mean) of 11 %, with moderate seasonal and diurnal variations. A higher vertical stratification is observed for O3 mixing ratios (18 %), with stronger seasonal and diurnal variability (from ∼ 10 % in spring–summer midday–afternoon to ∼ 25 % in winter–fall night). This vertical stratification is distributed heterogeneously in the PBL with stronger vertical gradients observed at both the surface (due to dry deposition and titration by NO for O3 and due to surface emissions for CO) and the PBL–FT interface. These gradients vary with the season from the lowest values in summer to the highest ones in winter. In contrast to CO, the O3 vertical stratification was found to vary with the surface potential temperature following an interesting bell shape with the weakest stratification for both the lowest (typically negative) and highest temperatures, which could be due to much lower O3 dry deposition in the presence of snow.Therefore, results demonstrate that EIs act as a geophysical interface separating air masses of distinct chemical composition and/or chemical regime. This is further supported by the analysis of the correlation of O3 and CO mixing ratios between the different altitude levels in the PBL and FT (the so-called vertical autocorrelation). Results indeed highlight lower correlations apart from the PBL–FT interface and higher correlations within each of the two atmospheric compartments (PBL and FT).The mean climatological O3 and CO PBL-referenced profiles analysed in this study are freely available on the IAGOS portal for all seasons and times of day (https://doi.org/10.25326/4).

In situ measurements in the upper troposphere–lower stratosphere (UTLS) have been performed in the framework of the European research infrastructure IAGOS (In-service Aircraft for a Global Observing System) for ozone since 1994 and for carbon monoxide (CO) since 2002. The flight tracks cover a wide range of longitudes in the northern extratropics, extending from the North American western coast (125° W) to the eastern Asian coast (135° E) and more recently over the northern Pacific Ocean. Several tropical regions are also sampled frequently, such as the Brazilian coast, central and southern Africa, southeastern Asia, and the western half of the Maritime Continent. As a result, a new set of climatologies for O3 (August 1994–December 2013) and CO (December 2001–December 2013) in the upper troposphere (UT), tropopause layer, and lower stratosphere (LS) are made available, including gridded horizontal distributions on a semi-global scale and seasonal cycles over eight well-sampled regions of interest in the northern extratropics. The seasonal cycles generally show a summertime maximum in O3 and a springtime maximum in CO in the UT, in contrast to the systematic springtime maximum in O3 and the quasi-absence of a seasonal cycle of CO in the LS. This study highlights some regional variabilities in the UT, notably (i) a west–east difference of O3 in boreal summer with up to 15 ppb more O3 over central Russia compared with northeast America, (ii) a systematic west–east gradient of CO from 60 to 140° E, especially noticeable in spring and summer with about 5 ppb by 10 degrees longitude, (iii) a broad spring/summer maximum of CO over northeast Asia, and (iv) a spring maximum of O3 over western North America. Thanks to almost 20 years of O3 and 12 years of CO measurements, the IAGOS database is a unique data set to derive trends in the UTLS at northern midlatitudes. Trends in O3 in the UT are positive and statistically significant in most regions, ranging from +0.25 to +0.45 ppb yr−1, characterized by the significant increase in the lowest values of the distribution. No significant trends of O3 are detected in the LS. Trends of CO in the UT, tropopause, and LS are almost all negative and statistically significant. The estimated slopes range from −1.37 to −0.59 ppb yr−1, with a nearly homogeneous decrease in the lowest values of the monthly distribution (5th percentile) contrasting with the high interregional variability in the decrease in the highest values (95th percentile).

Since 1994, the In-service Aircraft for a Global Observing System (IAGOS) program has produced in situ measurements of the atmospheric composition during more than 51 000 commercial flights. In order to help analyze these observations and understand the processes driving the observed concentration distribution and variability, we developed the SOFT-IO tool to quantify source–receptor links for all measured data. Based on the FLEXPART particle dispersion model (Stohl et al., 2005), SOFT-IO simulates the contributions of anthropogenic and biomass burning emissions from the ECCAD emission inventory database for all locations and times corresponding to the measured carbon monoxide mixing ratios along each IAGOS flight. Contributions are simulated from emissions occurring during the last 20 days before an observation, separating individual contributions from the different source regions. The main goal is to supply added-value products to the IAGOS database by evincing the geographical origin and emission sources driving the CO enhancements observed in the troposphere and lower stratosphere. This requires a good match between observed and modeled CO enhancements. Indeed, SOFT-IO detects more than 95 % of the observed CO anomalies over most of the regions sampled by IAGOS in the troposphere. In the majority of cases, SOFT-IO simulates CO pollution plumes with biases lower than 10–15 ppbv. Differences between the model and observations are larger for very low or very high observed CO values. The added-value products will help in the understanding of the trace-gas distribution and seasonal variability. They are available in the IAGOS database via http://www.iagos.org. The SOFT-IO tool could also be applied to similar data sets of CO observations (e.g., ground-based measurements, satellite observations). SOFT-IO could also be used for statistical validation as well as for intercomparisons of emission inventories using large amounts of data.

We determined NOx emissions from Paris in summer 2009 and winter 2009/2010 by applying the closed integral method (CIM) to a large set of car multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements performed within the framework of the MEGAPOLI project (http://megapoli.dmi.dk/). MAX-DOAS measurements of the tropospheric NO2 vertical column density (VCD) were performed in large circles around Paris. From the combination of the observed NO2 VCDs with wind fields, the NO2 influx into and the outflux from the encircled area was determined. The difference between the influx and outflux represents the total emission. Compared to previous applications of the CIM, the large number of measurements during the MEGAPOLI campaign allowed the investigation of important aspects of the CIM. In particular, the applicability of the CIM under various atmospheric conditions could be tested. Another important advantage of the measurements during MEGAPOLI is that simultaneous atmospheric model simulations with a high spatial resolution (3 × 3 km2) are available for all days. Based on these model data, it was possible to test the consistency of the CIM and to derive information about favourable or non-favourable conditions for the application of the CIM. We found that in most situations the uncertainties and the variability in the wind data dominate the total error budget, which typically ranges between 30 and 50 %. Also, measurement gaps and uncertainties in the partitioning ratio between NO and NO2 are important error sources. Based on a consistency check, we deduced a set of criteria on whether measurement conditions are suitable or not for the application of the CIM. We also developed a method for the calculation of the total error budget of the derived NOx emissions. Typical errors are between ±30 and ±50 % for individual days (with one full circle around Paris). From the application of the CIM to car MAX-DOAS observations we derive daily average NOx emissions for Paris of 4.0 × 1025 molec s−1 for summer and of 6.9 × 1025 molec s−1 in winter. These values are a factor of about 1.4 and 2.0 larger than the corresponding emissions derived from the application of the CIM to the model data, using the Toegepast Natuurwetenschappelijk Onderzoek (TNO) MEGAPOLI emission inventory, in summer and winter, respectively. Similar ratios (1.5 and 2.3 for summer and winter, respectively) were found for the comparison with the Monitoring Atmospheric composition and climate III (MACC-III) emission inventory. The highest NOx emissions were found during some cold days in February. Enhanced domestic heating and a reduced conversion efficiency of catalytic converters might contribute to these enhanced NOx emissions.

In the framework of the MOZAIC–IAGOS programme, vertical profiles of ozone (O3) and carbon monoxide (CO) have been available since 1994 and 2002, respectively. This study investigates the variability and trend of both species in three tropospheric layers above the German airports Frankfurt and Munich. About 21 300 flights have taken place over the period 1994–2012, which represents the worldwide densest vertical in situ data set of O3 and CO (with ∼ 96 flights per month on average). The mean vertical profile of ozone shows a strong gradient in the first kilometre during the whole year and in the tropopause region in spring and summer. The mean vertical profile of CO is characterised by high mixing ratios at the ground, a strong decrease in the first kilometre, in particular in winter and autumn, and a moderate one in the free troposphere. O3 minimises in November–December and shows a broad spring/summer maximum in the lower and mid-troposphere and a sharp maximum in summer in the upper troposphere. The seasonal variation of CO shows a broad minimum in July–October close to the surface and in September–October it occurs higher in the troposphere, while the maximum occurs in February–April in the whole troposphere. Over the period 1994–2012, O3 has changed insignificantly (at a 95 % confidence level), except in winter where a slightly significant increase (from +0.83 [+0.13;+1.67] % yr−1 in the LT to +0.62 [+0.02;+1.22] % yr−1 in the UT, relative to the reference year 2000) is found. The O3 5th percentile shows similar upward trends at the annual scale in all three tropospheric layers. All trends remain insignificant for the O3 95th percentile. In contrast, for CO the mean as well as its 5th and 95th percentiles decrease both at the annual scale and at the seasonal scale in winter, spring and summer (although not always in all three tropospheric layers) with trends ranging between −1.22 [−2.27;−0.47] and −2.63 [−4.54;−1.42] % yr−1, relative to the reference year 2004. However, all CO trends remain insignificant in autumn. The phase of the seasonal variation of O3 was found to change in the troposphere. The O3 maxima moves forward in time at a rate of −17.8 ± 11.5 days decade−1 in the lower troposphere, in general agreement with previous studies. Interestingly, this seasonal shift is shown to persist in the mid-troposphere (−7.8 ± 4.2 days decade−1) but turns insignificant in the upper troposphere.

Secondary inorganic compounds represent a major fraction of fine aerosol in the Paris megacity. The thermodynamics behind their formation is now relatively well constrained but, due to sparse direct measurements of their precursors (in particular NH3 and HNO3), uncertainties remain on their concentrations and variability as well as the formation regime of ammonium nitrate (in terms of limited species among NH3 and HNO3) in urban environments such as Paris. This study presents the first urban background measurements of both inorganic aerosol compounds and their gaseous precursors during several months within the city of Paris. Intense agriculture-related NH3 episodes are observed in spring/summer while HNO3 concentrations remain relatively low, even during summer, which leads to a NH3-rich regime in Paris. The local formation of ammonium nitrate within the city appears low, despite high NOx emissions. The data set also allows evaluating the CHIMERE chemistry-transport model (CTM). Interestingly, the rather good results obtained on ammonium nitrates hide significant errors on gaseous precursors (e.g., mean bias of −75 and +195 % for NH3 and HNO3, respectively). This leads to a misrepresentation of the nitrate formation regime through a highly underestimated gas ratio metric (introduced by Ansari and Pandis, 1998) and a much higher sensitivity of nitrate concentrations to ammonia changes. Several uncertainty sources are investigated, pointing out the importance of better assessing both NH3 agricultural emissions and OH concentrations in the future. These results remind us of the caution required when using of CTMs for emission scenario analysis, highlighting the importance of prior diagnostic and dynamic evaluations.

Chemistry transport models (CTMs) are an indispensable tool for studying and predicting atmospheric and climate effects associated with carbonaceous aerosol from open biomass burning (BB); this type of aerosol is known to contribute significantly to both global radiative forcing and to episodes of air pollution in regions affected by wildfires. Improving model performance requires systematic comparison of simulation results with measurements of BB aerosol and elucidation of possible reasons for discrepancies between them, which, by default, are frequently attributed in the literature to uncertainties in emission data. Based on published laboratory data on the atmospheric evolution of BB aerosol and using the volatility basis set (VBS) framework for organic aerosol modeling, we examined the importance of taking gas-particle partitioning and oxidation of semi-volatile organic compounds (SVOCs) into account in simulations of the mesoscale evolution of smoke plumes from intense wildfires that occurred in western Russia in 2010. Biomass burning emissions of primary aerosol components were constrained with PM10 and CO data from the air pollution monitoring network in the Moscow region. The results of the simulations performed with the CHIMERE CTM were evaluated by considering, in particular, the ratio of smoke-related enhancements in PM10 and CO concentrations (ΔPM10 and ΔCO) measured in Finland (in the city of Kuopio), nearly 1000 km downstream of the fire emission sources. It is found that while the simulations based on a "conventional" approach to BB aerosol modeling (disregarding oxidation of SVOCs and assuming organic aerosol material to be non-volatile) strongly underestimated values of ΔPM10/ΔCO observed in Kuopio (by a factor of 2), employing the "advanced" representation of atmospheric processing of organic aerosol material resulted in bringing the simulations to a much closer agreement with the ground measurements. Furthermore, taking gas-particle partitioning and oxidation of SVOCs into account is found to result in a major improvement of the agreement of simulations and satellite measurements of aerosol optical depth, as well as in considerable changes in predicted aerosol composition and top-down BB aerosol emission estimates derived from AOD measurements.

'The Tropospheric Ozone Assessment Report' (TOAR) is an activity of the International Global Atmospheric Chemistry Project. This paper is a component of the report, focusing on the present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation. Utilizing the TOAR surface ozone database, several figures present the global distribution and trends of daytime average ozone at 2702 non-urban monitoring sites, highlighting the regions and seasons of the world with the greatest ozone levels. Similarly, ozonesonde and commercial aircraft observations reveal ozone’s distribution throughout the depth of the free troposphere. Long-term surface observations are limited in their global spatial coverage, but data from remote locations indicate that ozone in the 21st century is greater than during the 1970s and 1980s. While some remote sites and many sites in the heavily polluted regions of East Asia show ozone increases since 2000, many others show decreases and there is no clear global pattern for surface ozone changes since 2000. Two new satellite products provide detailed views of ozone in the lower troposphere across East Asia and Europe, revealing the full spatial extent of the spring and summer ozone enhancements across eastern China that cannot be assessed from limited surface observations. Sufficient data are now available (ozonesondes, satellite, aircraft) across the tropics from South America eastwards to the western Pacific Ocean, to indicate a likely tropospheric column ozone increase since the 1990s. The 2014–2016 mean tropospheric ozone burden (TOB) between 60°N–60°S from five satellite products is 300 Tg ± 4%. While this agreement is excellent, the products differ in their quantification of TOB trends and further work is required to reconcile the differences. Satellites can now estimate ozone’s global long-wave radiative effect, but evaluation is difficult due to limited in situ observations where the radiative effect is greatest.

In the framework of the In Service Aircraft for Global Observing System (IAGOS) program, airborne in-situ O3 and CO measurements are performed routinely using in-service aircraft, providing vertical profiles from the surface to about 10–12 km. Due to the specificity of IAGOS measurements (measurements around busy international airports), uncertainties exist on their representativeness in the lower troposphere as they may be impacted by emissions related to airport activities and/or other aircraft. In this study, we thus investigate how the IAGOS measurements in the lower troposphere compare with nearby surface stations (from the local Air Quality monitoring network (AQN)) and more distant regional surface stations (from the Global Atmospheric Watch (GAW) network). The study focuses on Frankfurt but some results at other European airports (Vienna, Paris) are also discussed. Results indicate that the IAGOS observations close to the surface do not appear to be strongly impacted by local emissions related to airport activities. In terms of mixing ratio distribution, seasonal variations and trends, the CO and O3 mixing ratios measured by IAGOS in the first few hundred metres above the surface have similar characteristics to the mixing ratios measured at surrounding urban background stations. Higher in altitude, both the difference with data from the local AQN and the consistency with the GAW regional stations are higher, which indicates a larger representativeness of the IAGOS data. Despite few quantitative differences with Frankfurt, consistent results are obtained in the two other cities Vienna and Paris. Based on 11 years of data (2002–2012), this study thus demonstrates that IAGOS observations in the lowest troposphere can be used as a complement to surface stations to study the air quality in/around the agglomeration, providing important information on the vertical distribution of pollution.

High uncertainties affect black carbon (BC) emissions, and, despite its important impact on air pollution and climate, very few BC emissions evaluations are found in the literature. This paper presents a novel approach, based on airborne measurements across the Paris, France, plume, developed in order to evaluate BC and NOx emissions at the scale of a whole agglomeration. The methodology consists in integrating, for each transect, across the plume observed and simulated concentrations above background. This allows for several error sources (e.g., representativeness, chemistry, plume lateral dispersion) to be minimized in the model used. The procedure is applied with the CHIMERE chemistry-transport model to three inventories – the EMEP inventory and the so-called TNO and TNO-MP inventories – over the month of July 2009. Various systematic uncertainty sources both in the model (e.g., boundary layer height, vertical mixing, deposition) and in observations (e.g., BC nature) are discussed and quantified, notably through sensitivity tests. Large uncertainty values are determined in our results, which limits the usefulness of the method to rather strongly erroneous emission inventories. A statistically significant (but moderate) overestimation is obtained for the TNO BC emissions and the EMEP and TNO-MP NOx emissions, as well as for the BC / NOx emission ratio in TNO-MP. The benefit of the airborne approach is discussed through a comparison with the BC / NOx ratio at a ground site in Paris, which additionally suggests a spatially heterogeneous error in BC emissions over the agglomeration.

A detailed characterization of air quality in the megacity of Paris (France) during two 1-month intensive campaigns and from additional 1-year observations revealed that about 70 % of the urban background fine particulate matter (PM) is transported on average into the megacity from upwind regions. This dominant influence of regional sources was confirmed by in situ measurements during short intensive and longer-term campaigns, aerosol optical depth (AOD) measurements from ENVISAT, and modeling results from PMCAMx and CHIMERE chemistry transport models. While advection of sulfate is well documented for other megacities, there was surprisingly high contribution from long-range transport for both nitrate and organic aerosol. The origin of organic PM was investigated by comprehensive analysis of aerosol mass spectrometer (AMS), radiocarbon and tracer measurements during two intensive campaigns. Primary fossil fuel combustion emissions constituted less than 20 % in winter and 40 % in summer of carbonaceous fine PM, unexpectedly small for a megacity. Cooking activities and, during winter, residential wood burning are the major primary organic PM sources. This analysis suggests that the major part of secondary organic aerosol is of modern origin, i.e., from biogenic precursors and from wood burning. Black carbon concentrations are on the lower end of values encountered in megacities worldwide, but still represent an issue for air quality. These comparatively low air pollution levels are due to a combination of low emissions per inhabitant, flat terrain, and a meteorology that is in general not conducive to local pollution build-up. This revised picture of a megacity only being partially responsible for its own average and peak PM levels has important implications for air pollution regulation policies.

We compare tropospheric column densities (vertically integrated concentrations) of NO2 from three data sets for the metropolitan area of Paris during two extensive measurement campaigns (25 days in summer 2009 and 29 days in winter 2010) within the European research project MEGAPOLI. The selected data sets comprise a regional chemical transport model (CHIMERE) as well as two observational data sets: ground-based mobile Multi-AXis-Differential Optical Absorption Spectroscopy (car-MAX-DOAS) measurements and satellite measurements from the Ozone Monitoring Instrument (OMI). On most days, car-MAX-DOAS measurements were carried out along large circles (diameter ~ 35 km) around Paris. The car-MAX-DOAS results are compared to coincident data from CHIMERE and OMI. All three data sets have their specific strengths and weaknesses, especially with respect to their spatiotemporal resolution and coverage as well as their uncertainties. Thus we compare them in two different ways: first, we simply consider the original data sets. Second, we compare modified versions making synergistic use of the complementary information from different data sets. For example, profile information from the regional model is used to improve the satellite data, observations of the horizontal trace gas distribution are used to adjust the respective spatial patterns of the model simulations, or the model is used as a transfer tool to bridge the spatial scales between car-MAX-DOAS and satellite observations. Using the modified versions of the data sets, the comparison results substantially improve compared to the original versions. In general, good agreement between the data sets is found outside the emission plume, but inside the emission plumes the tropospheric NO2 vertical column densities (VCDs). are systematically underestimated by the CHIMERE model and the satellite observations (compared to the car-MAX-DOAS observations). One major result from our study is that for satellite validation close to strong emission sources (like power plants or megacities), detailed information about the intra-pixel heterogeneity is essential. Such information may be gained from simultaneous car-MAX-DOAS measurements using multiple instruments or by combining (car-) MAX-DOAS measurements with results from regional model simulations.

Aerosol simulations in chemistry transport models (CTMs) still suffer from numerous uncertainties, and diagnostic evaluations are required to point out major error sources. This paper presents an original approach to evaluate CTMs based on local and imported contributions in a large megacity rather than urban background concentrations. The study is applied to the CHIMERE model in the Paris region (France) and considers the fine particulate matter (PM2.5) and its main chemical constituents (elemental and organic carbon, nitrate, sulfate and ammonium), for which daily measurements are available during a whole year at various stations (PARTICULES project). Back-trajectory data are used to locate the upwind station, from which the concentration is identified as the import, the local production being deduced from the urban concentration by subtraction. Uncertainties on these contributions are quantified. Small biases in urban background PM2.5 simulations (bias of +16%) hide significant error compensations between local and advected contributions, as well as in PM2.5 chemical compounds. In particular, winter time organic matter (OM) imports appear strongly underestimated while local OM and elemental carbon (EC) production is overestimated all along the year. Erroneous continental wood burning emissions and missing secondary organic aerosol (SOA) pathways may explain errors on advected OM, while the carbonaceous compounds is likely to be related to errors in emissions and dynamics. A statistically significant local formation of nitrate is also highlighted from observations, but missed by the model. Together with the overestimation of nitrate imports, it leads to a bias of +51% on the local PM2.5 contribution. Such an evaluation finally gives more detailed insights on major gaps in current CTMs on which future efforts are needed.

Simulations with the chemistry transport model CHIMERE are compared to measurements performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer campaign in the Greater Paris region in July 2009. The volatility-basis-set approach (VBS) is implemented into this model, taking into account the volatility of primary organic aerosol (POA) and the chemical aging of semi-volatile organic species. Organic aerosol is the main focus and is simulated with three different configurations with a modified treatment of POA volatility and modified secondary organic aerosol (SOA) formation schemes. In addition, two types of emission inventories are used as model input in order to test the uncertainty related to the emissions. Predictions of basic meteorological parameters and primary and secondary pollutant concentrations are evaluated, and four pollution regimes are defined according to the air mass origin. Primary pollutants are generally overestimated, while ozone is consistent with observations. Sulfate is generally overestimated, while ammonium and nitrate levels are well simulated with the refined emission data set. As expected, the simulation with non-volatile POA and a single-step SOA formation mechanism largely overestimates POA and underestimates SOA. Simulation of organic aerosol with the VBS approach taking into account the aging of semi-volatile organic compounds (SVOC) shows the best correlation with measurements. High-concentration events observed mostly after long-range transport are well reproduced by the model. Depending on the emission inventory used, simulated POA levels are either reasonable or underestimated, while SOA levels tend to be overestimated. Several uncertainties related to the VBS scheme (POA volatility, SOA yields, the aging parameterization), to emission input data, and to simulated OH levels can be responsible for this behavior. Despite these uncertainties, the implementation of the VBS scheme into the CHIMERE model allowed for much more realistic organic aerosol simulations for Paris during summertime. The advection of SOA from outside Paris is mostly responsible for the highest OA concentration levels. During advection of polluted air masses from northeast (Benelux and Central Europe), simulations indicate high levels of both anthropogenic and biogenic SOA fractions, while biogenic SOA dominates during periods with advection from Southern France and Spain.

Abstract Ozone is generally assumed to have weak diurnal variations in the free troposphere due to lower production rates than in the boundary layer, in addition to a much lower NO titration and the absence of dry deposition at the surface. However, this hypothesis has not been proven due to a lack of high frequency observations at multiple times per day. For the first time, we take benefit from the frequent O3 vertical profiles measured above Frankfurt in the framework of the MOZAIC-IAGOS program to investigate the diurnal variations of O3 mixing ratios at multiple pressure levels throughout the troposphere. With about 21,000 aircraft profiles between 1994 and 2012 (98 per month on average), distributed throughout the day, this is the only dataset that can allow such a study. As expected, strong diurnal variations are observed close to the surface, in particular during spring and summer (enhanced photochemistry and surface deposition). Higher in altitude, our observations show a decrease of the diurnal cycle, with no diurnal cycle discernible above 750 hPa, whatever the season. Similar results are observed for the different percentiles of the O3 distribution (5th, 25th, 50th, 75th, 95th). An insight of the changes of the diurnal cycles between 1994–2003 and 2004–2012 is also given. We found higher O3 mixing ratios during the latter period, particularly on the lowest pressure levels, despite lower mixing ratios during summer.

No abstract available.

Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003-2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R 2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23-92 %, 11-13 % and 115-132 % higher than the corresponding satellite or reanalysis products, respectively. Over 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Air pollution remains as a substantial health problem, particularly regarding the combined health risks arising from simultaneous exposure to multiple air pollutants. However, understanding these combined exposure events over long periods has been hindered by sparse and temporally inconsistent monitoring data. Here we analyze daily ambient PM 2.5 , PM 10 , NO 2 and O 3 concentrations at a 0.1-degree resolution during 2003-2019 across 1426 contiguous regions in 35 European countries, representing 543 million people. We find that PM10 levels decline by 2.72% annually, followed by NO 2 (2.45%) and PM 2.5 (1.72%). In contrast, O 3 increase by 0.58% in southern Europe, leading to a surge in unclean air days. Despite air quality advances, 86.3% of Europeans experience at least one compound event day per year, especially for PM 2.5 -NO 2 and PM 2.5 -O 3 . We highlight the improvements in air quality control but emphasize the need for targeted measures addressing specific pollutants and their compound events, particularly amidst rising temperatures., (© 2024. The Author(s).)

Tropospheric ozone (O 3 ) is a secondary air pollutant that affects human health, vegetation and climate, especially in Mediterranean countries such as Spain. In order to tackle this long-standing issue, the Spanish government recently started to design the Spanish O 3 Mitigation Plan. To support this initiative and ultimately provide recommendations, we performed a first ambitious emission and air quality modeling exercise. This study presents the development of different emission scenarios - aligned with or beyond the measures planned for 2030 in Spain - and the modeling of their respective impact on the O 3 pollution across Spain (in July 2019) with both MONARCH and WRF-CMAQ air quality models. The modeling experiments include a base case scenario, a so-called planned emission (PE) scenario integrating the expected emission changes related to 2030, and a set of specific emission scenarios in which additional emission changes are applied to specific sectors (on e.g., road transport, maritime traffic) on top of the PE scenario. The planned emission scenario considerably reduces daily 8-h maximum O 3 concentrations (-4 μg/m 3 on average), with strongest reductions in Madrid region, north of Catalonia, Valencia region, Galicia and Andalusia. The frequency of observed daily exceedances of the 120 μg/m 3 daily 8-h maximum target value and 180 μg/m 3 hourly information threshold could be reduced by -37 and -77 %, respectively. The results of the specific scenarios highlight road transport and maritime traffic as two key emission sectors contributing to O 3 pollution, over the entire country and the Mediterranean coast, respectively, while solvent use and industry emissions have a more limited and localized impact on O 3 . In any case, even with the implementation of all the emission scenarios, daily exceedances of the aforementioned thresholds will still be recorded over the country., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hervé Petetin reports financial support was provided by State Agency of Research. Marc Guevara reports financial support was provided by State Agency of Research. Carlos Pérez García-Pando reports financial support was provided by Spain Ministry for the Ecological Transition and Demographic Challenge. Carlos Pérez García-Pando reports financial support was provided by AXA Research Fund., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

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 NO 2 and O 3 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 NO 2 (1.0047 [95% CI: 1.0014 to 1.0081]) and at lag 0 for O 3 (1.0039 [1.0013 to 1.0065]). On average NO 2 changed by -51% (intercity range -65.7 to -30.9%) and -36.4% (-53.7 to -11.6%), and O 3 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 NO 2 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 O 3 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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

We offer an overview of the COVID-19 -driven air quality changes across 11 metropolises in Spain with the focus on lessons learned on how continuing abating pollution. Traffic flow decreased by up to 80% during the lockdown and remained relatively low during the full relaxation (June and July). After the lockdown a significant shift from public transport to private vehicles (+21% in Barcelona) persisted due to the pervasive fear that using public transport might increase the risk of SARS-CoV-2 infection, which need to be reverted as soon as possible. NO 2 levels fell below 50% of the WHO annual air quality guidelines (WHOAQGs), but those of PM 2.5 were reduced less than expected due to the lower contributions from traffic, increased contributions from agricultural and domestic biomass burning, or meteorological conditions favoring high secondary aerosol formation yields. Even during the lockdown, the annual PM 2.5 WHOAQG was exceeded in cities within the NE and E regions with high NH 3 emissions from farming and agriculture. Decreases in PM 10 levels were greater than in PM 2.5 due to reduced emissions from road dust, vehicle wear, and construction/demolition. Averaged O 3 daily maximum 8-h (8hDM) experienced a generalized decrease in the rural receptor sites in the relaxation (June-July) with -20% reduced mobility. For urban areas O 3 8hDM responses were heterogeneous, with increases or decreases depending on the period and location. Thus, after canceling out the effect of meteorology, 5 out of 11 cities experienced O 3 decreases during the lockdown, while the remaining 6 either did not experience relevant reductions or increased. During the relaxation period and coinciding with the growing O 3 season (June-July), most cities experienced decreases. However, the O 3 WHOAQG was still exceeded during the lockdown and full relaxation periods in several cities. For secondary pollutants, such as O 3 and PM 2.5 , further chemical and dispersion modeling along with source apportionment techniques to identify major precursor reduction targets are required to evaluate their abatement potential., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)