Ongoing studies

  • Top-down estimation of global aerosol emissions (Chen et al. 2019; Elguindi et al. 2020)
    Studies by Elguindi et al. (2020) compared state-of-the-art top-down emission inventories of the BC and OC anthropogenic emissions with known regional bottom‐up inventories from five Shared Socioeconomic Pathways (SSPs) in three regions (China, India+ and West Africa). The global top-down carbonaceous (BC and OC) emissions obtained by Chen et al. (2019) in the frame of CaPPA for the period 2006-2011 were included in the inter-comparison study. These top-down emissions were developed by constraining GEOS-Chem model using PARASOL/GRASP AOD and AAOD retrievals. Elguindi et al. (2020) concluded that top-down emissions have a superior potential to capture the emissions trends than the bottom-up inventories especially over regions such as China and India, which have recently undergone dynamical economic growth and changes in air quality regulations. At the same time, the top-down emissions (Fig. 1) show a stronger inter-annual variability than the bottom-up inventories. Thus, although in most cases the general trends agree reasonably well, the uncertainty in the bottom-up BC and OC emissions constrained by nth measurements of AOD and AAOD need quantitative refinement in the future.
Figure 1: BC and OC emissions from bottom-up and top-down inventories and the SSPs, which is developed for the sixth Climate Model Inter-comparison Project (CMIP6) (adapted from Elguindi et al 2020)
  • Large-scale particulate air pollution and chemical fingerprint of volcanic sulfate aerosols from the 2014–2015 Holuhraun flood lava eruption of Bárðarbunga Icelandic volcano (Boichu et al., ACP 2019 ; highlight paper)

Whereas their key role in air quality and climate, the lifecycle of volcanic sulfate aerosols is still poorly known, especially in the troposphere. Through an inter-WP (WP4/WP3) study, Boichu et al. (ACP 2019) partly fill this gap by assessing the rate of oxidation of sulfur dioxide (SO2) precursor gas to sulfate aerosols (SO4) in volcanic plumes from the 2014–2015 Holuhraun eruption of Bárdarbunga Icelandic volcano (Fig 2c). This research also demonstrates a large-scale European pollution, in both SO2 and SO4, associated with this eruption, from Scandinavia to France. While widespread SO2 ground-level anomalies almost entirely result from the Holuhraun eruption (Fig. 2a), volcanic emissions contribute as much as anthropogenic emissions, from Eastern Europe and Great Britain, to weeks-long persistent large-scale pollution in sulfate (Fig. 2b). Eventually, supported by thermodynamic model simulations, volcanic sulfate aerosols are shown to exhibit a distinct chemical fingerprint relative to background and industrial aerosols, with a remarkable decrease in the production of particulate ammonium nitrate (NH4NO3) under high concentrations of volcanic sulfates. Most current studies generally focus on SO2, an unambiguous and more readily measured marker of the volcanic plume. However, the long persistence of the chemical fingerprint of volcanic sulfate aerosols at continental scale, as shown for the Holuhraun eruption here, casts light on the impact of tropospheric eruptions and passive degassing activities on air quality, health, atmospheric chemistry and climate.
To achieve these results, Boichu et al. (2019) exploited a large panel of SO2 observations from satellite (OMPS and IASI), AERONET (AErosol RObotic NETwork) sunphotometric observations and ground-level in situ measurements from 27 air quality monitoring stations of the EMEP (European Monitoring and Evaluation Programme) network together with high temporal resolution mass spectrometry measurements of two Aerosol Chemical Speciation Monitors (ACSM) in France.

igure 2: (a,b) Multi-site concentration-weighted trajectory analysis for SO2 and SO4 mass concentrations measured in September–October 2014 at a set of eight selected EMEP stations in Europe : retrieved source mass concentrations of (a) SO2 and (b) non-marine SO4. Contribution to the widespread atmospheric pollution of Icelandic volcanism (A and C green areas) and anthropogenic (B and D pink areas) sources. SO2 emission sources for 2013 derived from OMI satellite sensor observations (from Fioletov et al., 2016) are indicated for comparison by dark green circles. (c) Determination of the rate of oxidation of SO2 to SO4 in the Holuhraun plume. Adapted from Boichu et al. ACP 2019.
  • Strength of Sentinel-5P/TROPOMI observations on the retrieval of volcanic SO2 emissions at high temporal resolution from space and on plume dispersal forecasts (Behera et al. 2020)
    A fast inverse modeling approach (Boichu et al. 2015, Behera et al. 2020), exploiting satellite snapshots of the volcanic SO2 cloud from hyperspectral sensors with CHIMERE chemistry-transport model, allows to retrieve volcanic SO2 emissions, in terms of both flux and altitude of injection, at an hourly resolution (Top of Fig. 3) On the case-study of 2018 Ambrym eruption (Vanuatu), Behera et al. (2020) have shown the improvement achieved by assimilating Sentinel 5P/TROPOMI observations, in comparison with lower spatial resolution observations from Suomi-NPP/OMPS data, to better simulate and forecast the location and load of the forefront volcanic plume as well as dense parcels of volcanic SO2 that should be avoided for aviation safety (Fig. 3).
Figure 3: Prévisions numériques (à +24h (i) et +48 h (ii)) de la dispersion du panache volcanique riche en SO2 issu de l’éruption du volcan Ambrym (b, c) initialisées à t0 (16 Dec 2018) à l’aide des émissions de SO2 à échelle horaire (a) restituées grâce à l’assimilation par modélisation inverse des observations OMPS (en bleu) ou TROPOMI (en rouge). Adapté de Behera et al. 2020.
  • Intercomparison of source apportionment of aerosols (Belis et al. (2020)
    Within the framework of the Forum for air quality modelling in Europe (FAIRMODE) Working Group 3, 40 different research groups in Europe have evaluated two types of source apportionment models on a similar real dataset, leading to 49 independent source apportionment results. Receptor models (RMs) mostly used Positive Matrix Factorization (PMF) to identify and quantify the sources of PM10 aerosols, while Chemistry-Transport Models (CTMs) based their estimations on either the brute force (or emission reduction impact) or the tagged species methods. RMs presented comparable results in the majority of cases, likely due to the use of the same model and despite a large dispersion observed in some sources (e.g. industry). Differences were measurable between the two CTM approaches, in particular for secondary inorganic aerosol deriving from precursors emitted from different sources (energy, traffic, agriculture).
Figure 2. z-scores performance indicator values arranged by participant (a) and by source category (b). The areas of acceptance and warning are indicated with green and orange background, respectively. Only candidates with warning or bad scores are annotated in the plots (letter: result; sequential number: candidate). In (a) the used RM is indicated under each participant (red for models different than PMF5). The source category codes are given in Belis et al., Table 2. The scores of result Q are not shown because out-of-scale

Material and methods

Scientific objectives and publications

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