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Oxygenated volatile organic compounds (OVOC) have a significant impact on atmospheric oxidation capacity and climate. OVOCs are directly emitted from biogenic sources and are produced from the oxidation of hydrocarbons in the atmosphere. However, their budget remains poorly understood, due to incomplete representation of photochemical OVOC production and uncertainties in terrestrial emissions and ocean/atmosphere exchanges. In addition, OVOC atmospheric measurements are scarce in remote areas, in particular in tropical regions. In this work, we exploit a 2-year high-temporal resolution dataset of mass spectrometry (PTR-MS) measurements of OVOC compounds at a remote high-altitude tropical site, the Maïdo Observatory (2155m asl) on Reunion Island. More precisely, the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is used to provide an updated evaluation of the budget of OVOCs over Reunion Island, based on the PTR-MS dataset complemented with meteorological measurements and satellite (TROPOMI) retrievals of relevant compounds. The model is configured to allow three nested domains centred on Reunion Island, with spatial resolution from 12.5, 2.5 and 0.5 km. The finest resolution is needed due to the complex orography of the island and the spatially heterogeneous distribution of reactive species. For computational reasons, the focus is on two one-month simulations in January and July 2019, allowing analysis of seasonal differences and their impacts on model performance and chemical budget.The WRF-simulated meteorology is first evaluated against meteorological measurements at a remote site (Maïdo) and two urban sites (Saint Denis and Saint Pierre). The impact of physical parameterizations (i.e. planetary boundary layer parameterizations, surface scheme, etc.) is tested through sensitivity simulations. A high-resolution (1km2) anthropogenic emission inventory for Reunion is implemented, complemented with information from global inventories. Biogenic VOC emissions (primarily isoprene) are calculated on-line using the MEGAN algorithm and high-resolution distributions of standard emission factors and plant functional types (PFTs). The MOZART chemical mechanism is adopted. The chemical simulations are evaluated against (1) NO2 and HCHO vertical columns from TROPOMI, (2) the PTR-MS OVOC dataset at Maïdo, and (3) network air quality measurements at several sites. Those comparisons will provide new constraints on the emissions of NOx and VOCs, and will result in recommendations for further refinements. This work will lead to a better appraisal of OVOC sources and sinks over the island. The main unknowns and potential issues will be discussed.

The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is employed as an intercomparison tool for validating TROPOspheric Monitoring Instrument (TROPOMI) satellite NO2 retrievals against high-resolution Airborne Prism EXperiment (APEX) remote sensing observations performed in June 2019 in the region of Antwerp, a major hotspot of NO2 pollution in Europe. The model is first evaluated using meteorological and chemical observations in this area. Sensitivity simulations varying the model planetary layer boundary (PBL) parameterization were conducted for a 3 d period in June 2019, indicating a generally good performance of most parameterizations against meteorological data (namely ceilometer, surface meteorology, and balloon measurements), except for a moderate overestimation (∼ 1 m s−1) of near-surface wind speed. On average, all but one of the PBL schemes reproduce the surface NO2 measurements at stations of the Belgian Interregional Environmental Agency fairly well, although surface NO2 is generally underestimated during the day (between −4.3 % and −25.1 % on average) and overestimated at night (8.2 %–77.3 %). This discrepancy in the diurnal evolution arises despite (1) implementing a detailed representation of the diurnal cycle of emissions (Crippa et al., 2020) and (2) correcting the modeled concentrations to account for measurement interferences due to NOy reservoir species, which increases NO2 concentrations by about 20 % during the day. The model is further evaluated by comparing a 15 d simulation with surface NO2, NO, CO, and O3 data in the Antwerp region. The modeled daytime NO2 concentrations are more negatively biased during weekdays than during weekends, indicating a misrepresentation of the weekly temporal profile applied to the emissions obtained from Crippa et al. (2020). Using a mass balance approach, we determined a new weekly profile of NOx emissions, leading to a homogenization of the relative bias among the different weekdays. The ratio of weekend to weekday emissions is significantly lower in this updated profile (0.6) than in the profile based on Crippa et al. (2020; 0.84). Comparisons with remote sensing observations generally show a good reproduction of the spatial patterns of NO2 columns by the model. The model underestimated both APEX (by ca. −37 %) and TROPOMI columns (ca. −25 %) on 27 June, whereas no significant bias is found on 29 June. The two datasets are intercompared by using the model as an intermediate platform to account for differences in vertical sensitivity through the application of averaging kernels. The derived bias of TROPOMI v1.3.1 NO2 with respect to APEX is about −10 % for columns between (6–12) × 1015 molec. cm−2. The obtained bias for TROPOMI v1.3.1 increases with the NO2 column, following CAPEX=1.217Cv1.3-0.783 × 1015 molec. cm−2, in line with previous validation campaigns. The bias is slightly lower for the reprocessed TROPOMI v2.3.1, with CAPEX=1.055CPAL-0.437 × 1015 molec. cm−2 (PAL). Finally, a mass balance approach was used to perform a crude inversion of NOx emissions based on 15 d averaged TROPOMI columns. The emission correction is conducted only in regions with high columns and high sensitivity to emission changes in order to minimize the errors due to wind transport. The results suggest that emissions increase over Brussels–Antwerp (+20 %), the Ruhr Valley (13 %), and especially Paris (+39 %), and emissions decrease above a cluster of power plants in western Germany.

We present a source apportionment study of a near-continuous 2-year dataset of volatile organic compounds (VOCs), recorded between October 2017 and November 2019 with a quadrupole-based high-sensitivity proton-transfer-reaction mass-spectrometry (hs-PTR-MS) instrument deployed at the Maïdo observatory (21.1∘ S, 55.4∘ E, 2160 m altitude). The observatory is located on La Réunion island in the southwest Indian Ocean. We discuss seasonal and diel profiles of six key VOC species unequivocally linked to specific sources – acetonitrile (CH3CN), isoprene (C5H8), isoprene oxidation products (Iox), benzene (C6H6), C8-aromatic compounds (C8H10), and dimethyl sulfide (DMS). The data are analyzed using the positive matrix factorization (PMF) method and back-trajectory calculations based on the Lagrangian mesoscale transport model FLEXPART-AROME to identify the impact of different sources on air masses sampled at the observatory. As opposed to the biomass burning tracer CH3CN, which does not exhibit a typical diel pattern consistently throughout the dataset, we identify pronounced diel profiles with a daytime maximum for the biogenic (C5H8 and Iox) and anthropogenic (C6H6, C8H10) tracers. The marine tracer DMS generally displays a daytime maximum except for the austral winter when the difference between daytime and nighttime mixing ratios vanishes. Four factors were identified by the PMF: background/biomass burning, anthropogenic, primary biogenic, and secondary biogenic. Despite human activity being concentrated in a few coastal areas, the PMF results indicate that the anthropogenic source factor is the dominant contributor to the VOC load (38 %), followed by the background/biomass burning source factor originating in the free troposphere (33 %), and by the primary (15 %) and secondary biogenic (14 %) source factors. FLEXPART-AROME simulations showed that the observatory was most sensitive to anthropogenic emissions west of Maïdo while the strongest biogenic contributions coincided with air masses passing over the northeastern part of La Réunion. At night, the observatory is often located in the free troposphere, while during the day, the measurements are influenced by mesoscale sources. Interquartile ranges of nighttime 30 min average mixing ratios of methanol (CH3OH), CH3CN, acetaldehyde (CH3CHO), formic acid (HCOOH), acetone (CH3COCH3), acetic acid (CH3COOH), and methyl ethyl ketone (MEK), representative for the atmospheric composition of the free troposphere, were found to be 525–887, 79–110, 61–101, 172–335, 259–379, 64–164, and 11–21 pptv, respectively.

Isoprene is a hydrocarbon emitted in large quantities by terrestrial vegetation. It is a precursor to several air quality and climate pollutants including ozone. Emission rates vary with plant species and environmental conditions. This variability can be modeled using the Model of Emissions of Gases and Aerosols from Nature (MEGAN). MEGAN parameterizes isoprene emission rates as a vegetation-specific standard rate which is modulated by scaling factors that depend on meteorological and environmental driving variables. Recent experiments have identified large uncertainties in the MEGAN temperature response parameterization, while the emission rates under standard conditions are poorly constrained in some regions due to a lack of representative measurements and uncertainties in landcover. In this study, we use Bayesian model-data fusion to optimize the MEGAN temperature response and standard emission rates using satellite- and ground-based observational constraints. Optimization of the standard emission rate with satellite constraints reduced model biases but was highly sensitive to model input errors and drought stress and was found to be inconsistent with ground-based constraints at an Amazonian field site, reflecting large uncertainties in the satellite-based emissions. Optimization of the temperature response with ground-based constraints increased the temperature sensitivity of the model by a factor of five at an Amazonian field site but had no impact at a UK field site, demonstrating significant ecosystem-dependent variability of the isoprene emission temperature sensitivity. Ground-based measurements of isoprene across a wide range of ecosystems will be key for obtaining an accurate representation of isoprene emission temperature sensitivity in global biogeochemical models., C.A. DiMaria acknowledges a Canada Graduate Scholarship—Doctoral (CGS D) Grant funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) (application no. PGSD3-546,721-2020). This work was also supported by Grant 16SUASEMIS from the Canadian Space Agency. R. Seco acknowledges a Ramón y Cajal Grant (RYC2020-029216-I) funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future.” IDAEA-CSIC is a Severo Ochoa Centre of Research Excellence (MCIN/AEI, Project CEX2018-000794-S). The BR-Sa1 field measurements were supported by Núcleo de Apoio à Pesquisa no Pará (NAPPA) em Santarém-Pa/Instituto Nacional de Pesquisas da Amazônia (INPA), Programa de Grande Escala Biosfera Atmosfera na Amazônia (LBA) and Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) em Santarém-Pa. V. Ferracci acknowledges funding from the Natural Environmental Research Council (NERC) project “Biodiversity and land-use impacts (BALI) on tropical ecosystems” (NE/K016377/1) in support of the Wytham Woods measurements. Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA).

Among the biogenic volatile organic compounds (BVOCs) emitted by plant foliage, isoprene is by far the most important in terms of both global emission and atmospheric impact. It is highly reactive in the air, and its degradation favours the generation of ozone (in the presence of NOx) and secondary organic aerosols. A critical aspect of BVOC emission modelling is the representation of land use and land cover (LULC). The current emission inventories are usually based on land cover maps that are either modelled and dynamic or satellite-based and static. In this study, we use the state-of-the-art Model of Emissions of Gases and Aerosols from Nature (MEGAN) model coupled with the canopy model MOHYCAN (Model for Hydrocarbon emissions by the CANopy) to generate and evaluate emission inventories relying on satellite-based LULC maps at annual time steps. To this purpose, we first intercompare the distribution and evolution (2001–2016) of tree coverage from three global satellite-based datasets, MODerate resolution Imaging Spectroradiometer (MODIS), ESA Climate Change Initiative Land Cover (ESA CCI-LC), and the Global Forest Watch (GFW), and from national inventories. Substantial differences are found between the datasets; e.g. the global areal coverage of trees ranges from 30 to 50×106 km2, with trends spanning from −0.26 to +0.03 % yr−1 between 2001 and 2016. At the national level, the increasing trends in forest cover reported by some national inventories (in particular for the US) are contradicted by all remotely sensed datasets. To a great extent, these discrepancies stem from the plurality of definitions of forest used. According to some local censuses, clear cut areas and seedling or young trees are classified as forest, while satellite-based mappings of trees rely on a minimum height. Three inventories of isoprene emissions are generated, differing only in their LULC datasets used as input: (i) the static distribution of the stand-alone version of MEGAN, (ii) the time-dependent MODIS land cover dataset, and (iii) the MODIS dataset modified to match the tree cover distribution from the GFW database. The mean annual isoprene emissions (350–520 Tg yr−1) span a wide range due to differences in tree distributions, especially in isoprene-rich regions. The impact of LULC changes is a mitigating effect ranging from 0.04 to 0.33 % yr−1 on the positive trends (0.94 % yr−1) mainly driven by temperature and solar radiation. This study highlights the uncertainty in spatial distributions of and temporal variability in isoprene associated with remotely sensed LULC datasets. The interannual variability in the emissions is evaluated against spaceborne observations of formaldehyde (HCHO), a major isoprene oxidation product, through simulations using the global chemistry transport model (CTM) IMAGESv2. A high correlation (R > 0.8) is found between the observed and simulated interannual variability in HCHO columns in most forested regions. The implementation of LULC change has little impact on this correlation due to the dominance of meteorology as a driver of short-term interannual variability. Nevertheless, the simulation accounting for the large tree cover declines of the GFW database over several regions, notably Indonesia and Mato Grosso in Brazil, provides the best agreement with the HCHO column trends observed by the Ozone Monitoring Instrument (OMI). Overall, our study indicates that the continuous tree cover fields at fine resolution provided by the GFW database are our preferred choice for constraining LULC (in combination with discrete LULC maps such as those of MODIS) in biogenic isoprene emission models.

We introduce two new drought stress algorithms designed to simulate isoprene emission with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) model. The two approaches include the representation of the impact of drought on isoprene emission with a simple empirical approach for offline MEGAN applications and a more process-based approach for online MEGAN in Community Land Model (CLM) simulations. The two versions differ in their implementation of leaf-temperature impacts of mild drought. For the online version of MEGAN that is coupled to CLM, the impact of drought on leaf temperature is simulated directly and the calculated leaf temperature is considered for the estimation of isoprene emission. For the offline version, we apply an empirical algorithm derived from whole-canopy flux measurements for simulating the impact of drought ranging from mild to severe stage. In addition, the offline approach adopts the ratio (fPET) of actual evapotranspiration to potential evapotranspiration to quantify the severity of drought instead of using soil moisture. We applied the two algorithms in the CLM-CAM-chem (the Community Atmosphere Model with Chemistry) model to simulate the impact of drought on isoprene emission and found that drought can decrease isoprene emission globally by 11% in 2012. We further compared the formaldehyde (HCHO) vertical column density simulated by CAM-chem to satellite HCHO observations. We found that the proposed drought algorithm can improve the match with the HCHO observations during droughts, but the performance of the drought algorithm is limited by the capacity of the model to capture the severity of drought., H. Wang, X. Jiang and A. Guenther were supported by the U.S. National Science Foundation (NSF) Atmospheric Chemistry program award AGS-1643042, the National Aeronautics and Space Administration (NASA) and the Atmospheric Composition Modeling and Analysis Program (ACMAP) program award 80NSSC19K0986. R. Seco acknowledges Grants RYC2020-029216-I and CEX2018-000794-S funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future.” T. Stavrakou and A. Guenther were partly supported by the ALBERI project, funded by the Belgian Science Policy Office via the STEREO II programme (SR/00/373). T. Karl received support from projects FWF P30600-NBL and FWF P33701. The authors would like to acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation.

Biogenic volatile organic compounds (BVOCs) play important roles at cellular, foliar, ecosystem and atmospheric levels. The Amazonian rainforest represents one of the major global sources of BVOCs, so its study is essential for understanding BVOC dynamics. It also provides insights into the role of such large and biodiverse forest ecosystem in regional and global atmospheric chemistry and climate. We review the current information on Amazonian BVOCs and identify future research priorities exploring biogenic emissions and drivers, ecological interactions, atmospheric impacts, depositional processes and modifications to BVOC dynamics due to changes in climate and land cover. A feedback loop between Amazonian BVOCs and the trends of climate and land-use changes in Amazonia is then constructed. Satellite observations and model simulation time series demonstrate the validity of the proposed loop showing a combined effect of climate change and deforestation on BVOC emission in Amazonia. A decreasing trend of isoprene during the wet season, most likely due to forest biomass loss, and an increasing trend of the sesquiterpene to isoprene ratio during the dry season suggest increasing temperature stress-induced emissions due to climate change.

Wildfires release in the atmosphere large amounts of aerosols and ozone precursors and have a significant impact on air quality and the climate. Global warming leads to more frequent and intense wildfires such as those that occurred in Australia in January 2020, in Siberia in 2021 or in California in the last few years. The spaceborne TROPOspheric Monitoring Instrument (TROPOMI) launched in October 2017 onboard the Sentinel-5 Precursor platform provides invaluable information on a series of key trace gases (NO2, HONO, CO, HCHO, CHOCHO)and on aerosols. Its daily global coverage and high spatial resolution are ideal to monitor wildfire emissions and to characterize their spatial extent and temporal evolution.In this work, we present an analysis of the trace gas distributions observed by TROPOMI for various selected intense wildfire events, with a specific focus on glyoxal (CHOCHO). Primarily emitted gases such as NO2 show intense signals near the fire sources with limited spatial extent. On contrary, other species like formaldehyde, carbon monoxide and glyoxal are mostly produced via secondary processes, which contribute to extend significantly their spatial spread. Those TROPOMI spatial patterns are consistent with the current knowledge of the different production mechanisms. However, we report here the identification of a very strong reduction of glyoxal slant columns in presence of very high clouds or aerosols, while other gases do not show such behavior. Uptake on aerosols or cloud droplets is a known destruction mechanism for glyoxal and is the most likely cause for this signal reduction. We hypothesize that, owing to its high solubility in water, glyoxal is transferred into the liquid phase within the convective cells of (pyro)cumulus clouds and that (contrary to formaldehyde) it is not degassed upon freezing and therefore remains in the condensed phase in the upper troposphere. We investigate the conditions in which this process is observed by correlating the glyoxal level with e.g. the fire intensity, the presence of high clouds and their altitude (pyrocumulonimbus), atmospheric conditions (temperature and humidity), the nature of the burning eco-system, etc.

Nitrogen oxides (NOx = NO + NO2) play a major role in tropospheric chemistry through their impact on ozone and hydroxyl radical (OH) distributions, and therefore on the oxidizing capacity of the atmosphere. Whereas anthropogenic NOx emissions are dominant globally, natural sources are responsible for ca. 30 % of the total emissions into the atmosphere. These sources include soil emissions (due to microbial nitrification and denitrification) and lightning (due to thermal dissociation of O2 followed by recombination with N2). Chemistry-transport models (CTMs) rely on bottom-up (BU) inventories, the uncertainty of which is acknowledged, especially for natural sources. Soil NOx is mainly emitted as nitric oxide (NO) and current global BU estimates range from 4 to 15 Tg N yr-1 with nearly 70% occurring in the tropics. Satellite retrievals of tropospheric NO2 columns are used as top-down constraints in CTMs to derive NOx emissions from various sources (anthropogenic, biomass burning, soil, lightning) such as illustrated in Martin et al. (2003), Jaeglé et al. (2005), Müller et Stavrakou (2005), Stavrakou et al. (2008) or Vinken et al. (2014). This is realized through the method of source inverse modelling, which consists in the optimization of emissions in a CTM in order to minimize the discrepancy between observed and simulated NO2 columns.In this study, we present top-down monthly soil NOemissions at 0.5° resolution over Africa and South America for 2019 based on spaceborne tropospheric NO2 columns from the TROPOspheric Monitoring Instrument (TROPOMI). In a first step, we evaluate three global BU inventories against each other and against flux observations over the Tropics. The following BU estimates are considered: (1) YL-MAG, based on the Yienger and Levy parameterization (1995), (2) CAMS, provided by the Copernicus Atmosphere Monitoring Service (Granier et al., 2019; Simpson and Darras, 2021), and (3) HEMCO, calculated using Harvard–NASA Emission Component software (Weng et al., 2020). The last two estimates rely on the parameterization of Hudman et al. (2012). We assess YL-MAG, CAMS and HEMCO inventories against in situ measurements of biogenic soil NO fluxes compiled from literature distinguishing between seasons (dry/wet) and biomes. Based on this evaluation, the best BU inventory is selected and further used as a priori information in the regional MAGRITTE CTM (Müller et al., 2019) run at 0.5°×0.5° resolution for the year 2019. Monthly top-down NOx fluxes (from the anthropogenic, biomass burning, soil and lightning categories) are inferred from TROPOMI NO2 columns using an inversion framework based on the adjoint of MAGRITTE. The top-down soil NO fluxes and NOx abundances are subsequently validated against in situ measurements over the two tropical regions.

The aim of this paper is two-fold: to provide guidance on how to best interpret TROPOMI trace gas retrievals and to highlight how TROPOMI trace gas data can be used to understand event-based impacts on air quality from regional to city-scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO and CHOCHO) detected by the Sentinel-5P TROPOMI instrument, driven by reductions of anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in NO2 concentrations on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in two-week averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short time scale are detectable from space, is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China which is in concert with the other trace gas reductions observed during lockdown, however large, interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19 lockdown-driven reductions in satellite observed trace gas column amounts, using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction of anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future, the combined use of inverse modelling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here, will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

China was the first country to undergo large-scale lockdowns in response to the pandemic in early 2020 and a progressive return to normalization after April 2020. Spaceborne observations of atmospheric nitrogen dioxide (NO2) and oxygenated volatile organic compounds (OVOCs), including formaldehyde (HCHO), glyoxal (CHOCHO), and peroxyacetyl nitrate (PAN), reveal important changes over China in 2020, relative to 2019, in response to the pandemic-induced shutdown and the subsequent drop in pollutant emissions. In February, at the peak of the shutdown, the observed declines in OVOC levels were generally weaker (less than 20%) compared to the observed NO2 reductions (-40%). In May 2020, the observations reveal moderate decreases in NO2 (-15%) and PAN (-21%), small changes in CHOCHO (-3%) and HCHO (6%). Model simulations using the regional model MAGRITTEv1.1 with anthropogenic emissions accounting for the reductions due to the pandemic explain to a large extent the observed changes in lockdown-affected regions. The model results suggest that meteorological variability accounts for a minor but non-negligible part (~-5%) of the observed changes for NO2, whereas it is negligible for CHOCHO but plays a more substantial role for HCHO and PAN, especially in May. The interannual variability of biogenic and biomass burning emissions also contribute to the observed variations, explaining e.g. the important column increases of NO2 and OVOCs in February 2020, relative to 2019. These changes are well captured by the model simulations., SCOPUS: ar.j, info:eu-repo/semantics/published

In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over North-Western Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on year 2015 is used as the a priori in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. The high biases found between the in situ NO2 measurements and the base run surface NO2 decrease in the assimilated run in most cases. The bias in the station near the largest power plant decreases to 2.0 μg/m3 (2.83 μg/m3) from 10.5 μg/m3 (8.46 μg/m3) in 2019 (2018 respectively). Concerning the estimated annual a posteriori NOx emissions it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40-50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (-35% and -38% in 2018, -62% and -72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about -35% and-63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~-30% and -70%, respectively).

During the COVID-19 pandemic, the first lockdown period (March-May 2020) has led to an unprecedented reduction in pollutant emissions. For 3⁄4 of the more than 1,100 available monitoring stations in Europe, the average NO2 concentrations decreased by at least 25% (2.7 μg.m-3) compared to the average concentrations recorded during the same period of the previous seven years. The relative reduction was of similar magnitude in both urban and rural areas.We further investigate the spatial distribution of the O3 change. Relative to the seven years average, positive anomalies were observed in northern Europe and negative anomalies in southwestern Europe. However, the level of total oxidant (Ox = O3 + NO2) remained unchanged except in southwestern Europe where it decreased.At the global scale, the ozone concentration increased only in a few NOx-saturated regions. After presenting data from monitoring stations in Europe, we analyze the drivers of the change in surface ozone concentrations using the global Community Earth System Model. We contrast global simulations of the atmospheric composition with and without lockdown adjusted anthropogenic emissions for the COVID-19 period.By comparing the situation in Europe with that of the United States and China, we show that the reduced cloudiness in northern Europe played a significant role by shifting the photochemical partitioning between NO2 and O3 toward more ozone, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants.These results illustrate the complexity of the processes affecting ozone in the troposphere and hence the difficulty of implementing efficient regulations targeting air quality impacts.

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

Accurate monitoring of vegetation stress is required for better modelling and forecasting of primary production, in a world where heatwaves and droughts are expected to become increasingly prevalent. Variability in formaldehyde (HCHO) concentrations in the troposphere is dominated by local emissions of short-lived biogenic (BVOC) and pyrogenic volatile organic compounds. BVOCs are emitted by plants in a rapid protective response to abiotic stress, mediated by the energetic status of leaves (the excess of reducing power when photosynthetic light and dark reactions are decoupled, as occurs when stomata close in response to water stress). Emissions also increase exponentially with leaf temperature. New analytical methods for the detection of spatiotemporally contiguous extremes in remote-sensing data are applied here to satellite-derived atmospheric HCHO columns. BVOC emissions are shown to play a central role in the formation of the largest positive HCHO anomalies. Although vegetation stress can be captured by various remotely sensed quantities, spaceborne HCHO emerges as the most consistent recorder of vegetation responses to the largest climate extremes, especially in forested regions.

The worldwide spread of Covid-19 pandemic caused a dramatic cutback of human activities and triggered a large-scale atmospheric composition experiment. This unfortunate situation provides the opportunity to investigate the response of atmospheric composition to the shutdown measures. Our focus will be on China, where the pandemic emerged in January 2020, and thence strict lockdowns were enforced. Substantial, large-scale decreases in pollutants levels over China and subsequent recovery were revealed by spaceborne observations from TROPOMI instrument on board Sentinel-5 Precursor, as well as by in situ measurements. Most published work on this topic relied on observed changes in column abundances of nitrogen dioxide (NO2), a predominantly anthropogenic compound and an important precursor for ozone production and secondary aerosol formation. Our work adds to this picture by studing the evolution of two other satellite-derived compounds, formaldehyde (HCHO) and peroxyacylnitrate (PAN), observed by TROPOMI and IASI, respectively. HCHO is an intermediate product in the chemical processing of volatile organic compounds (VOCs) of anthropogenic and natural origin. PAN is formed in the oxidation of anthropogenic and biogenic VOCs, and constitute the principal tropospheric NOx reservoir, enabling the transport and release of NOx away from the sources. Chemistry-transport simulations of PAN are challenging due to large uncertainties in formation mechanisms and precursor emissions. We will evaluate and analyze the observed variability of NO2, HCHO, and PAN columns using model simulations with the MAGRITTE v1.1 regional CTM run at 0.5ox0.5o resolution over China for 2019 and 2020. The model uses updated anthropogenic emissions from the CONFORM dataset, which takes into account the reductions during the shutdowns based on traffic and other economic activity data.

Biogenic volatile organic compounds (BVOCs) are emitted globally at about 1,100 Tg per year, with almost half of the share entailed by isoprene. Isoprene is highly reactive in the atmosphere, and its degradation impacts the atmospheric composition through the generation of ozone (in presence of NOx typical of polluted areas) and secondary organic aerosols, which both pose a risk to human health. Extreme weather conditions like heatwaves and droughts can substantially affect the emissions of isoprene in ways that are largely unknown. This limited knowledge is owed to the scarcity of isoprene flux measurements under drought stress conditions. The Missouri Ozarks AmeriFlux (MOFLUX) site is located in a high isoprene-emitting oak-hickory forested region with recurring drought occurrences. Until today, it is the only site with isoprene flux measurements that capture drought behaviour.In this study, we use the state-of-the-art MEGAN biogenic emission model (Guenther et al., 2006; 2012) coupled with the canopy model MOHYCAN (Müller et al., 2008) to estimate isoprene emissions and evaluate two different parameterizations of the soil moisture stress factor (γSM): (a) the one used in MEGANv2.1, which consists of a simple dependence on soil water content and the permanent wilting point with inputs either from ERA-Interim or the GLEAMv3 reanalysis (Martens et al., 2017), and (b) the parameterization available in MEGANv3 (Jiang et al., 2018), which considers the physiological effects of drought stress on plant photosynthesis as defined in the Community Land Model (CLM4.5), which embeds the MEGAN model. The effect of γSM on isoprene estimates is assessed against measurements of isoprene fluxes at the MOFLUX field site collected during the mild summer drought in 2011 (Potosnak et al., 2014) and the severe drought in 2012 (Seco et al., 2015). Based on the comparisons at the MOFLUX site, we perform an optimization of the empirical parameters of the MEGANv2.1 soil moisture stress parameterization. In addition, the parameterization is further evaluated using spaceborne formaldehyde (HCHO) columns observed by the OMI sounder. To this end, we perform multiyear simulations (2005-2016) of atmospheric composition with the IMAGES global chemistry-transport model (Müller et al., 2019) using isoprene emission datasets obtained for several variants of the parameterization. We evaluate the resulting HCHO column distributions and their interannual variability against OMI HCHO columns over drought-prone regions.This work is conducted in the frame of the ALBERI project, funded by the Belgian Science Policy Office through the STEREO III programme.

Volatile organic compounds (VOCs) are key precursors for the formation of surface level ozone (O3) and secondary organic aerosols, and therefore, they have a significant impact on air quality and climate. In addition, through their interaction with the hydroxyl radical (OH), they impact the atmospheric lifetime of methane, further affecting climate. Among non-methane VOCs, the oxygenated species (OVOCs) are especially relevant in remote regions where they constitute the largest OH sink. Due to the paucity of data at these locations, OVOCs sources and sinks are poorly constrained in models. This work addresses the critical need for OVOC observations at remote locations. A high-sensitivity quadrupole-based proton-transfer-reaction mass-spectrometry VOC analyzer (hs-PTR-MS) was deployed at La Réunion --- a remote tropical island located in the south west Indian Ocean, home to the Maïdo observatory --- between October 2017 and November 2019. As the observatory is located near the top of the planetary boundary layer (PBL), pristine marine boundary layer air masses, enriched with compounds emitted by mesoscale sources, reach the observatory during the day. At night, the observatory is located in the free troposphere. The variability in PBL development drives the diel concentration profiles of a variety of biogenic and anthropogenic tracers recorded with the hs-PTR-MS instrument. The seasonal variability of biogenic tracers is driven by the hot and wet versus the cold and dry seasons. Every year, biomass burning plumes originating from African and Madagascan fires reach the observatory between August and November, significantly impacting local air quality at La Réunion.We will present both the diel and seasonal variability using the 2-year near-continuous (O)VOC dataset recorded with the hs-PTR-MS instrument. The analysis of the complete dataset is performed using the positive matrix factorization approach, complemented by back-trajectory calculations using the Lagrangian transport model FLEXPART-AROME to identify mesoscale sources.

Atmospheric chemistry is critical in determining air quality and thus impacts climate change. Anthropogenic species are released into the atmosphere, and undergo complex photochemical transformations leading to the production of secondary pollutants, among which ozone and particulate matter. This can induce adverse effects on human health, visibility, ecosystems and local meteorology. The combination of state-of-the-art atmospheric models with accurate atmospheric measurements of atmospheric species abundances is needed to evaluate whether atmospheric models can successfully simulate the chemical and physical processes occurring, and hopefully monitor the emissions of anthropogenic compounds and help in the implementation and verification of abatement policies.In this work, ground-based, airborne and spaceborne measuring techniques are used to evaluate the performance of the full chemistry on-line WRF-Chem model over Antwerp in Flanders, Belgium, one of the areas with the highest NO2 pollution in the world. The model is configured to allow two nested domains with spatial resolution changing from 5 to 1km, so as to pinpoint the most pollutant sources in the region, and applied to simulate the urban air quality over the Antwerp agglomeration.We will briefly discuss the choices and adaptations made regarding the physical parameterizations, emission inventories and chemical mechanism. The model performance is evaluated through comparison with various observation types. The physics parameterizations in WRF model are evaluated through comparison against ground-based data from two meteorological stations in the Antwerp region. The WRF-Chem NO2 distributions are evaluated against (1) hourly measured concentration values from monitoring stations in Flanders, (2) vertical columns measured by an airborne hyperspectral imager APEX, providing a 2-dimensional spatial mapping, on 27 and 29 June 2019, and (3) spaceborne NO2 columns over Belgium obtained from the high-resolution TROPOMI instrument aboard S5p. The consistency of the model biases across the three datasets will be discussed, and recommendations will be made for improving model performance in this region.

In order to fight the spread of the global COVID-19 pandemic, most of the world countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to global and regional emission inventories has been developed. This dataset provides, for the January–August 2020 period, gridded AFs at a 0.1 × 0.1 latitude/longitude degree resolution, on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs is provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first six months of 2020. Maximum decreases in the emissions are found in February in Eastern China, with an average reduction of 20–30 % in NOx, NMVOCs and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30–50 %) in South America. In India and African regions, NOx and NMVOCs emissions are reduced by 15–30 %. For the others species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC are estimated. As discussed in the paper, reductions vary highly across regions and sectors, due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid adjustmeNt Factor fOR eMissions) (https://doi.org/10.25326/88). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/).

Using the CAM-chem Model, we simulate the response of chemical species in the free troposphere to scenarios of primary pollutant emission reductions during the COVID-19 pandemic. Zonally averaged ozone in the free troposphere during Northern Hemisphere spring and summer is found to be 5%–15% lower than 19-yr climatological values, in good agreement with observations. About one third of this anomaly is attributed to the reduction scenario of air traffic during the pandemic, another third to the reduction scenario of surface emissions, the remainder to 2020 meteorological conditions, including the exceptional springtime Arctic stratospheric ozone depletion. For the combined emission reductions, the overall COVID-19 reduction in northern hemisphere tropospheric ozone in June is less than 5ppb below 400hPa, but reaches 8ppb at 250hPa. In the Southern Hemisphere, COVID-19 related ozone reductions by 4%–6% were masked by comparable ozone increases due to other changes in 2020.

We use the global Community Earth System Model to investigate the response of secondary pollutants (ozone O3, secondary organic aerosols SOA) in different parts of the world in response to modified emissions of primary pollutants during the COVID‐19 pandemic. We quantify the respective effects of the reductions in NOx and in volatile organic carbon (VOC) emissions, which, in most cases, affect oxidants in opposite ways. Using model simulations, we show that the level of NOx has been reduced by typically 40% in China during February 2020 and by similar amounts in many areas of Europe and North America in mid‐March to mid‐April 2020, in good agreement with space and surface observations. We show that, relative to a situation in which the emission reductions are ignored and despite the calculated increase in hydroxyl and peroxy radicals, the ozone concentration increased only in a few NOx‐saturated regions (northern China, northern Europe, and the US) during the winter months of the pandemic when the titration of this molecule by NOx was reduced. In other regions, where ozone is NOx‐controlled, the concentration of ozone decreased. SOA concentrations decrease in response to the concurrent reduction in the NOx and VOC emissions. The model also shows that atmospheric meteorological anomalies produced substantial variations in the concentrations of chemical species during the pandemic. In Europe, for example, a large fraction of the ozone increase in February 2020 was associated with meteorological anomalies, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants., Plain Language Summary: With the reduction in economic activities following the COVID‐19 pandemic outbreak in early 2020, most emissions of air pollutants (i.e., nitrogen oxides [NOx], carbon monoxide [CO], sulfur dioxide [SO2], volatile organic carbon [VOC], black carbon [BC], organic carbon [OC]) have decreased substantially during several months in different regions of the world. This unintended global experiment offered a glimpse into a potential future in which air quality would be improved. Here, a global atmospheric model is used to assess the changes in the chemical composition of the atmosphere during the pandemic period and in the related chemical processes that lead to the formation of ozone (O3) and secondary organic aerosols (SOA). The study illustrates the nonlinearity of the air quality response to reduced NOx and VOC emissions, which depends on the chemical environment including the background level of nitrogen oxides. Meteorological variability can lead to anomalies in the concentration of chemical species with magnitudes that are as large or even larger than the perturbations due to COVID‐induced changes in the emissions., Key Points: During the COVID‐19 lockdown, the atmospheric concentration of primary pollutants (NOx, VOCs, CO, SO2) was considerably reduced The concentration of secondary pollutants increased in NOx‐saturated areas and decreased in NOx‐limited areas The response of the chemical system depends on the relative changes in NOx and VOC emissions, and is affected by weather variability, AQ‐WATCH European project, HORIZON 2020 Research and Innovation Action, Hong Kong Research Grants Council

During the COVID-19 lockdown in China low air pollution levels were reported as a consequence of the reduced economic and social activities. Quantification of the pollution reduction is not straigh...

In response to changes in environmental factors (e.g., temperature, radiation, soil moisture), plants emit biogenic volatile organic compounds (BVOCs). Once released in the atmosphere, BVOCs influence levels of greenhouse gases and air pollutants (e.g., methane, ozone and aerosols), thus affecting both climate and air quality. In turn, climate change may alter BVOC emissions by modifying the driving environmental conditions and by increasing the occurrence and intensity of severe stresses that alter plant functioning. To understand and better constrain the evolution of BVOC emissions under future climates, it is important to reduce the uncertainties in global and regional estimates of BVOC emissions under present climate. Part of the uncertainty in the estimates of BVOC emissions is related to the impact that water stress might have on BVOC emissions. Field campaign, in-situ and laboratory experiments investigated the effect of different regimes of water stress (short- vs. long-term) on BVOC emissions. However, these studies provide geographically scattered and uneven results. To explore the relationship between BVOC emissions and water stress globally, we use remotely sensed soil moisture and formaldehyde, a proxy of BVOC emissions. As BVOCs include a multitude of gas tracers with lifetime ranging from few hours to days, a fully characterisation of these components is virtually impossible. Nevertheless, in the continental boundary layer, formaldehyde is an intermediate by-product of the oxidation of BVOCs, it thus provides a proxy for probing local BVOC emissions, and in particular isoprene, which accounts for about 50% of the total BVOC emissions.In the present study, retrievals of formaldehyde from the Ozone Monitoring Instrument (OMI) are combined with observations of soil moisture, biomass, aerosols, evapotranspiration, drought index, temperature and precipitation. Firstly, we look into the linear annual trend of the selected fields. Secondly, assuming formaldehyde as the dependent variable, we apply a linear mixed model analysis that extends the application of a simple linear regression model by accounting for both fixed (i.e., explained by the independent variables) and random (i.e., due to dependence in the data) effects. The analysis of the linear trend of formaldehyde concentrations shows a positive trend over the Amazon and Central Africa and a negative trend over South Africa and Australia. Over the Amazon, formaldehyde is negatively correlated with the Standardised Precipitation-Evapotranspiration Index (SPEI), a drought index that accounts for both changes in temperature and precipitation, with positive and negative values identifying wet and dry events, respectively. The outcomes of this analysis might provide new insights in the relationship between BVOC emissions and water stress and might help in improving parameterizations that link soil moisture to BVOC emissions in numerical models.

Isoprene regulates large-scale biogeochemical cycles by influencing atmospheric chemical and physical processes, and its dominant sources to the global atmosphere are the tropical forests. Although global and regional model estimates of isoprene emission have been optimized in the last decades, modeled emissions from tropical vegetation still carry high uncertainty due to a poor understanding of the biological and environmental controls on emissions. It is already known that isoprene emission quantities may vary significantly with plant traits, such as leaf phenology, and with the environment; however, current models still lack of good representation for tropical plant species due to the very few observations available. In order to create a predictive framework for the isoprene emission capacity of tropical forests, it is necessary an improved mechanistic understanding on how the magnitude of emissions varies with plant traits and the environment in such ecosystems. In this light, we aimed to quantify the isoprene emission capacity of different tree species across leaf ages, and combine these leaf measurements with long-term canopy measurements of isoprene and its biological and environmental drivers; then, use these results to better parameterize isoprene emissions estimated by MEGAN. We measured at the Amazon Tall Tower Observatory (ATTO) site, central Amazonia: (1) isoprene emission capacity at different leaf ages of 21 trees species; (2) isoprene canopy mixing ratios during six campaigns from 2013 to 2015; (3) isoprene tower flux during the dry season of 2015 (El-Niño year); (3) environmental factors – air temperature and photosynthetic active radiation (PAR) - from 2013 to 2018; and (4) biological factors – leaf demography and phenology (tower based measurements) from 2013 to 2018. We then parameterized the leaf age algorithm of MEGAN with the measurements of isoprene emission capacity at different leaf ages and the tower-based measurements of leaf demography and phenology. Modeling estimates were later compared with measurements (canopy level) and five years of satellite-derived isoprene emission (OMI) from the ATTO domain (2013-2017). Leaf level of isoprene emission capacity showed lower values for old leaves (> 6 months) and young leaves (< 2 months), compared to mature leaves (2-6 months); and our model results suggested that this affects seasonal ecosystem isoprene emission capacity, since the demography of the different leaf age classes varied a long of the year. We will present more results on how changes in leaf demography and phenology and in temperature and PAR affect seasonal ecosystem isoprene emission, and how modeling can be improved with the optimization of the leaf age algorithm. In addition, we will present a comparison of ecosystem isoprene emission of normal years (2013, 2014, 2017 years) and anomalous years (2015 - El-Niño; and 2016 - post El-Niño), and discuss how a strong El-Niño year can influence plant functional strategies that can be carried over to the consecutive year and potentially affect isoprene emission.

This study compares recent CO, NO, NMVOC, SO, BC and OC anthropogenic emissions from several state-of-the-art top-down estimates to global and regional bottom-up inventories and projections from fi...

We present an investigation of biomass burning (BB) plumes originating from Africa and Madagascar based on measurements of a suite of volatile organic compounds (VOCs), carbon monoxide (CO), ozone (O3) and nitrogen dioxide (NO2) obtained during the dry season of 2018 and 2019 at the high-altitude Maïdo observatory (21.1∘ S, 55.4∘ E, 2160 ma.s.l.), located on the remote island of La Réunion in the south-west Indian Ocean (SWIO). Biomass burning plume episodes were identified from increased acetonitrile (CH3CN) mixing ratios. Enhancement ratios (EnRs) – relative to CO – were calculated from in situ measurements for CH3CN, acetone (CH3COCH3), formic acid (HCOOH), acetic acid (CH3COOH), benzene (C6H6), methanol (CH3OH) and O3. We compared the EnRs to emission ratios (ERs) – relative to CO – reported in the literature in order to estimate loss or production of these compounds during transport. For CH3CN and CH3COOH, the calculated EnRs are similar to the ERs. For C6H6 and CH3OH, the EnR is lower than the ER, indicating a net sink of these compounds which was found to be in line with the expected atmospheric lifetime. For CH3COCH3 and HCOOH, the calculated EnRs are larger than the ERs. The discrepancy reaches an order of magnitude for HCOOH (18–34 pptv ppbv−1 compared to 1.8–4.5 pptv ppbv−1). This points to significant secondary production of HCOOH during transport. The Copernicus Atmospheric Monitoring Service (CAMS) global model simulations reproduce the temporal variation of CO mixing ratios well at the observatory but underestimate O3 and NO2 mixing ratios in the plumes by on average 16 ppbv and 60 pptv respectively. This discrepancy between modelled and measured O3 mixing ratios was attributed to (i) large uncertainties in VOC and NOx (NO+NO2) emissions due to BB in CAMS and (ii) misrepresentation of NOx recycling in the model during transport. Finally, transport of pyrogenically emitted CO is calculated with FLEXPART in order to (i) determine the mean plume age during the intrusions at the observatory and (ii) estimate the impact of BB on the pristine marine boundary layer (MBL). By multiplying the excess CO in the MBL with inferred EnRs at the observatory, we calculated the expected impact of BB on CH3CN, CH3COCH3, CH3OH and C6H6 concentrations in the MBL. These excesses constitute increases of ∼20 %–150 % compared to background measurements in the SWIO MBL reported in the literature.

Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50 % can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.For the present work, the HCHO retrieval settings have been optimized based on experience gained from past studies and have been applied consistently at the 21 participating stations. Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12 % and 27 % and between 1 and 11×1014 molec cm−2, respectively. The median values among all stations are 13 % and 2.9×1014 molec cm−2 for the total systematic and random uncertainties.This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles. The network covers very different concentration levels of formaldehyde, from very clean levels at the limit of detection (few 1013 molec cm−2) to highly polluted levels (7×1016 molec cm−2). Because the measurements can be made at any time during daylight, the diurnal cycle can be observed and is found to be significant at many stations. These HCHO time series, some of them starting in the 1990s, are crucial for past and present satellite validation and will be extended in the coming years for the next generation of satellite missions.

Isoprene fluxes vary seasonally with changes in environmental factors (e.g., solar radiation and temperature) and biological factors (e.g., leaf phenology). However, our understanding of the seasonal patterns of isoprene fluxes and the associated mechanistic controls is still limited, especially in Amazonian evergreen forests. In this paper, we aim to connect intensive, field-based measurements of canopy isoprene flux over a central Amazonian evergreen forest site with meteorological observations and with tower-mounted camera leaf phenology to improve our understanding of patterns and causes of isoprene flux seasonality. Our results demonstrate that the highest isoprene emissions are observed during the dry and dry-to-wet transition seasons, whereas the lowest emissions were found during the wet-to-dry transition season. Our results also indicate that light and temperature cannot totally explain isoprene flux seasonality. Instead, the camera-derived leaf area index (LAI) of recently mature leaf age class (e.g., leaf ages of 3–5 months) exhibits the highest correlation with observed isoprene flux seasonality (R2=0.59, p). Attempting to better represent leaf phenology in the Model of Emissions of Gases and Aerosols from Nature (MEGAN 2.1), we improved the leaf age algorithm by utilizing results from the camera-derived leaf phenology that provided LAI categorized into three different leaf ages. The model results show that the observations of age-dependent isoprene emission capacity, in conjunction with camera-derived leaf age demography, significantly improved simulations in terms of seasonal variations in isoprene fluxes (R2=0.52, p). This study highlights the importance of accounting for differences in isoprene emission capacity across canopy leaf age classes and identifying forest adaptive mechanisms that underlie seasonal variation in isoprene emissions in Amazonia.

Isoprene is a highly reactive volatile organic compound emitted by vegetation, known to be a precursor of secondary organic aerosols and to enhance tropospheric ozone formation under polluted conditions. Isoprene emissions respond strongly to changes in meteorological parameters such as temperature and solar radiation. In addition, the increasing CO2 concentration has a dual effect, as it causes both a direct emission inhibition as well as an increase in biomass through fertilization. In this study we used the MEGAN (Model of Emissions of Gases and Aerosols from Nature) emission model coupled with the MOHYCAN (Model of HYdrocarbon emissions by the CANopy) canopy model to calculate the isoprene fluxes emitted by vegetation in the recent past (1979–2014) and in the future (2070–2099) over Europe at a resolution of 0.1∘×0.1∘. As a result of the changing climate, modeled isoprene fluxes increased by 1.1 % yr−1 on average in Europe over 1979–2014, with the strongest trends found over eastern Europe and European Russia, whereas accounting for the CO2 inhibition effect led to reduced emission trends (0.76 % yr−1). Comparisons with field campaign measurements at seven European sites suggest that the MEGAN–MOHYCAN model provides a reliable representation of the temporal variability of the isoprene fluxes over timescales between 1 h and several months. For the 1979–2014 period the model was driven by the ECMWF ERA-Interim reanalysis fields, whereas for the comparison of current with projected future emissions, we used meteorology simulated with the ALARO regional climate model. Depending on the representative concentration pathway (RCP) scenarios for greenhouse gas concentration trajectories driving the climate projections, isoprene emissions were found to increase by +7 % (RCP2.6), +33 % (RCP4.5), and +83 % (RCP8.5), compared to the control simulation, and even stronger increases were found when considering the potential impact of CO2 fertilization: +15 % (RCP2.6), +52 % (RCP4.5), and +141 % (RCP8.5). However, the inhibitory CO2 effect goes a long way towards canceling these increases. Based on two distinct parameterizations, representing strong or moderate inhibition, the projected emissions accounting for all effects were estimated to be 0–17 % (strong inhibition) and 11–65 % (moderate inhibition) higher than in the control simulation. The difference obtained using the two CO2 parameterizations underscores the large uncertainty associated to this effect.

Biogenic non-methane volatile organic compounds (NMVOCs) emitted from vegetation are a primary source for the chemical production of carbon monoxide (CO) in the atmosphere and these biogenic emissions account for about 18 % of the global CO burden. Partitioning CO fluxes to different source types in top-down inversion methods is challenging and typically a simple scaling of the posterior flux to prior flux values for fossil fuel, biogenic and biomass burning sources is used. Here we show top-down estimates of biogenic CO fluxes using a Bayesian inference approach, which explicitly accounts for both posterior and a priori CO flux uncertainties. This approach re-partitions CO fluxes following inversion of Measurements Of Pollution In The Troposphere (MOPITT) CO observations with the GEOS-Chem model, a global chemical transport model driven by assimilated meteorology from the NASA Goddard Earth Observing System (GEOS). We compare these results to the prior information for CO used to represent biogenic NMVOCs from GEOS-Chem, which uses the Model of Emissions of Gases and Aerosols from Nature (MEGAN) for biogenic emissions. We evaluate the a posteriori biogenic CO fluxes against top-down estimates of isoprene fluxes using Ozone Monitoring Instrument (OMI) formaldehyde observations. We find similar seasonality and spatial consistency in the posterior CO and top-down isoprene estimates globally. For the African savanna region, both top-down CO and isoprene seasonality vary significantly from the MEGAN apriori inventory. This method for estimating biogenic sources of CO will provide an independent constraint on modelled biogenic emissions and has the potential for diagnosing decadal-scale changes in emissions due to land-use change and climate variability.

A new chemical mechanism for the oxidation of biogenic volatile organic compounds (BVOCs) is presented and implemented in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.1). With a total of 105 organic species and over 265 gas-phase reactions, 69 photodissociations, and 7 heterogeneous reactions, the mechanism treats the chemical degradation of isoprene – its main focus – as well as acetaldehyde, acetone, methylbutenol, and the family of monoterpenes. Regarding isoprene, the mechanism incorporates a state-of-the-art representation of its oxidation scheme accounting for all major advances put forward in recent theoretical and laboratory studies. The recycling of OH radicals in isoprene oxidation through the isomerization of Z-δ-hydroxyperoxy radicals is found to enhance OH concentrations by up to 40 % over western Amazonia in the boundary layer and by 10 %–15 % over the southeastern US and Siberia in July. The model and its chemical mechanism are evaluated against the suite of chemical measurements from the SEAC4RS (Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) airborne campaign, demonstrating a good overall agreement for major isoprene oxidation products, although the aerosol hydrolysis of tertiary and non-tertiary nitrates remain poorly constrained. The comparisons for methylnitrate indicate a very low nitrate yield (<3×10-4) in the CH3O2+NO reaction. The oxidation of isoprene, acetone, and acetaldehyde by OH is shown to be a substantial source of enols and keto-enols, primarily through the photolysis of multifunctional carbonyls generated in their oxidation schemes. Oxidation of those enols by OH radicals constitutes a sizable source of carboxylic acids estimated at 9 Tg (HC(O)OH) yr−1 and 11 Tg(CH3C(O)OH) yr−1 or ∼20 % of their global identified source. The ozonolysis of alkenes is found to be a smaller source of HC(O)OH (6 Tg HC(O)OH yr−1) than previously estimated, due to several factors including the strong deposition sink of hydroxymethyl hydroperoxide (HMHP).

As one of the most abundant oxygenated volatile organic compounds in the atmosphere, acetone (CH 3 C[O]CH 3 ) influences atmospheric oxidants levels and ozone formation. Here we report the first unambiguous identification of acetone from the nadir-viewing satellite sounder Infrared Atmospheric Sounding Interferometer (IASI). Via a neural network-based retrieval approach that was previously applied to the retrieval of other weak absorbers, we obtain daily global acetone retrievals. A first intercomparison with independent measurements is conducted. As the retrieval method is computationally fast, it allowed the full reprocessing of the 2007–2018 IASI time series. Analysis of the retrieved global product and its seasonality suggests that emissions of acetone and precursors from the terrestrial biosphere at Northern Hemisphere middle and high latitudes are the main contributors to the atmospheric acetone abundance, more than year-round oxidation of anthropogenic isoalkanes. Remarkably, biomass burning does not appear to be a strong global source of acetone., SCOPUS: ar.j, info:eu-repo/semantics/published

Formic acid (HCOOH) is one of the most abundant volatile organic compounds in the atmosphere. It is a major contributor to rain acidity in remote areas. There are, however, large uncertainties on the sources and sinks of HCOOH and therefore HCOOH is misrepresented by global chemistry-transport models. This work presents global distributions from 2008 to 2014 as derived from the measurements of the Infrared Atmospheric Sounding Interferometer (IASI), based on conversion factors between brightness temperature differences and representative retrieved total columns over seven regions: Northern Africa, southern Africa, Amazonia, Atlantic, Australia, Pacific, and Russia. The dependence of the measured HCOOH signal on the thermal contrast is taken into account in the conversion method. This conversion presents errors lower than 20% for total columns ranging between 0.5 and 1×1016 molec cm-2 but reaches higher values, up to 78 %, for columns that are lower than 0:3×1016 molec cm-2. Signatures from biomass burning events are highlighted, such as in the Southern Hemisphere and in Russia, as well as biogenic emission sources, e.g. over the eastern USA. A comparison between 2008 and 2014 with ground-based Fourier transform infrared spectroscopy (FTIR) measurements obtained at four locations (Maido and Saint-Denis at La Réunion, Jungfraujoch, and Wollongong) is shown. Although IASI columns are found to correlate well with FTIR data, a large bias (> 100 %) is found over the two sites at La Réunion. A better agreement is found at Wollongong with a negligible bias. The comparison also highlights the difficulty of retrieving total columns from IASI measurements over mountainous regions such as Jungfraujoch. A comparison of the retrieved columns with the global chemistry-transport model IMAGESv2 is also presented, showing good representation of the seasonal and interannual cycles over America, Australia, Asia, and Siberia. A global model underestimation of the distribution and a misrepresentation of the seasonal cycle over India are also found. A small positive trend in the IASI columns is observed over Australia, Amazonia, and India over the 2008-2014 period (from 0.7 to 1.5%year-1), while a decrease of ∼0.8% year-1 is measured over Siberia., SCOPUS: ar.j, info:eu-repo/semantics/published

A new module for calculating the dry deposition of trace gases is presented and implemented in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.0). The dry deposition velocities are calculated using Wesely's classical resistance-in-series approach. While relying on analyses of the European Centre for Medium-range Weather Forecasts (ECMWF) for meteorological fields, the aerodynamic resistance calculation module is based on the ECMWF model equations for turbulent transfer within the surface layer. The stomatal resistance for water vapour is calculated using a Jarvis-type parameterization in a multi-layer canopy environment model accounting for the leaf area index (LAI). The gas-phase diffusion coefficients needed to relate the stomatal resistances of different species are calculated from molecular structure. The cuticular, mesophyll and soil resistances depend on the species reactivity and Henry's Law constant (HLC). The HLCs of organic species for which no experimental data is available are estimated using a newly-developed prediction method based on existing methods for vapour pressures (EVAPORATION, Estimation of VApour Pressure of Organics) and infinite dilution activity coefficients (AIOMFAC, Aerosol Inorganic Organic Mixtures Functional groups Activity Coefficients). Acknowledging the dominance of stomatal uptake for ozone dry deposition, the stomatal resistance model parameters for 6 of the 7 major plant functional types (PFT) are adjusted based on extensive model comparisons with field measurements of ozone deposition velocity at 24 sites worldwide. The modelled OVOC deposition velocities for 25 different OVOCs are evaluated against field data from a total of 20 studies. The comparison shows the need for a species-dependent adjustment of the canopy resistances in order to match the observed variability among different species. This is realized by multiplying the HLC of each OVOC by a species-dependent parameter f1 adjusted based on the comparisons. The values of f1 span a wide range, from values of the order of unity or less for formaldehyde and several trifunctional compounds, to > 104 for compounds seen to deposit rapidly despite their low water-solubility, like MVK, MACR, CH3CHO and PAN. Despite the acknowledged caveats of the approach, the resulting modelled deposition velocities are consistent with the existing experimental data. The results of global-scale MAGRITTE model simulations demonstrate the importance of OVOC dry deposition on their global abundance. It is found to remove from the atmosphere the equivalent of 27 % of the global NMVOC emissions on a carbon basis, as well as about 8 % of NOx emissions in the form of organic nitrates and PAN-like compounds.