Publications

Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling

Tichý, Ondřej; Eckhardt, Sabine; Balkanski, Yves; Hauglustaine, Didier; Evangeliou, Nikolaos

Ammonia (NH3), a significant precursor of particulate matter, affects not only biodiversity, ecosystems, and soil acidification but also climate and human health. In addition, its concentrations are constantly rising due to increasing feeding needs and the large use of fertilization and animal farming. Despite the significance of ammonia, its emissions are associated with large uncertainties, while its atmospheric abundance is difficult to measure. Nowadays, satellite products can effectively measure ammonia with low uncertainty and a global coverage. Here, we use satellite observations of column ammonia in combination with an inversion algorithm to derive ammonia emissions with a high resolution over Europe for the period 2013–2020. Ammonia emissions peak in northern Europe due to agricultural application and livestock management, in western Europe (industrial activity), and over Spain (pig farming). Emissions have decreased by −26 % since 2013 (from 5431 Gg in 2013 to 3994 Gg in 2020), showing that the abatement strategies adopted by the European Union have been very efficient. The slight increase (+4.4 %) in 2015 is also reproduced here and is attributed to some European countries exceeding annual emission targets. Ammonia emissions are low in winter (286 Gg) and peak in summer (563 Gg) and are dominated by the temperature-dependent volatilization of ammonia from the soil. The largest emission decreases were observed in central and eastern Europe (−38 %) and in western Europe (−37 %), while smaller decreases were recorded in northern (−17 %) and southern Europe (−7.6 %). When complemented with ground observations, modelled concentrations using the posterior emissions showed improved statistics, also following the observed seasonal trends. The posterior emissions presented here also agree well with respective estimates reported in the literature and inferred from bottom-up and top-down methodologies. These results indicate that satellite measurements combined with inverse algorithms constitute a robust tool for emission estimates and can infer the evolution of ammonia emissions over large timescales.

Isoscapes Norway

Johansen, Ingar; Polteau, Stephane; Vogt, Rolf David; Uggerud, Hilde Thelle; Clayer, Francois

The AirGAM 2022r1 air quality trend and prediction model

Walker, Sam-Erik; Solberg, Sverre; Schneider, Philipp; Guerreiro, Cristina

This paper presents the AirGAM 2022r1 model – an air quality trend and prediction model developed at the Norwegian Institute for Air Research (NILU) in cooperation with the European Environment Agency (EEA) over 2017–2021. AirGAM is based on nonlinear regression GAMs – generalised additive models – capable of estimating trends in daily measured pollutant concentrations at air quality monitoring stations, discounting for the effects of trends and time variations in corresponding meteorological data. The model has been developed primarily for the compounds NO2, O3, PM10, and PM2.5. Meteorological input data consist of temperature, wind speed and direction, planetary boundary layer height, relative and absolute humidity, cloud cover, and precipitation over the period considered. The exact set of meteorological variables used in the model depends on the compound selected for analysis. In addition to meteorological variables introduced in the model as covariates, i.e. explanatory variables for the concentration levels, the model also incorporates time variables such as the day of the week, day of the year, and overall time, which is related to the model's trend term. The trend analysis is performed at each station separately. Thus, the model only considers the temporal features of concentrations and meteorology at a station, rather than any spatial correlations or dependencies between stations. AirGAM is implemented using the R language for statistical computing and, in particular, the GAM package mgcv. In the model, meteorological and time covariates are represented and estimated as smooth nonlinear functions of the corresponding variables. Thus, the trend term is defined and estimated as a smooth nonlinear function of time over the period selected for analysis. Once fitted to training data, the model may be used as a prediction tool capable of predicting air pollutant concentrations for new sets of meteorological and time data which are not in the training set – e.g. for cross-validation or forecasting purposes. The model does not explicitly use emissions or background concentrations – these are sought to be implicitly represented through the estimated nonlinear relations between meteorology, time, and concentrations. In addition to meteorology-adjusted trends, the program also produces unadjusted trends – i.e. trends based on the same regression set-up but only including the time covariates. Both types of trends can be output in the same run, making it possible to compare them. Ideally, the meteorology-adjusted trend will show the trend in concentration mainly due to changes in emissions or physicochemical processes not induced by changes in meteorology. AirGAM has been developed and tested primarily in trend studies based on measurement data hosted by the EEA, including the AirBase data (before 2013) and the Air Quality e-Reporting (AQER) data from 2013 and onwards. Still, the model is general and could be applied in other regions with other input data. The EEA data provide daily or hourly surface measurements at individual monitoring stations in Europe. For input meteorological data, we extract time series from the gridded meteorological re-analysis (ERA5) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for each monitoring station. The paper presents results with the model for all AirBase/AQER stations in Europe from the latest EEA trend study for 2005–2019.

Abrupt Change in the Lower Thermospheric Mean Meridional Circulation during Sudden Stratospheric Warmings and its Impact on Trace Species

Orsolini, Yvan Joseph Georges Emile G.; Zhang, Jiarong; Limpasuvan, Varavut

Deposition of sulfur and nitrogen in Norway 2017-2021

Blake, Lewis R.; Aas, Wenche; Denby, Bruce; Hjellbrekke, Anne-Gunn; Mu, Qing; Simpson, David; Ytre-Eide, Martin; Fagerli, Hilde

Norwegian Meteorological Institute

Added value of the emissions fractions approach when assessing a chemical's potential for adverse effects as a result of long-range transport

Breivik, Knut; McLachlan, Michael S.; Wania, Frank

It is of considerable interest to identify chemicals which may represent a hazard and risk to environmental and human health in remote areas. The OECD POV and LRTP Screening Tool (“The Tool”) for assessing chemicals for persistence (P) and long-range transport potential (LRTP) has been extensively used for combined P and LRTP assessments in various regulatory contexts, including the Stockholm Convention (SC) on Persistent Organic Pollutants (POPs). The approach in The Tool plots either the Characteristic Travel Distance (CTD, in km), a transport-oriented metric, or the Transfer Efficiency (TE, in %), which calculates the transfer from the atmosphere to surface compartments in a remote region, against overall persistence (POV). For a chemical to elicit adverse effects in remote areas, it not only needs to be transported and transferred to remote environmental surface media, it also needs to accumulate in these media. The current version of The Tool does not have a metric to quantify this process. We screened a list of >12 000 high production volume chemicals (HPVs) for the potential to be dispersed, transferred, and accumulate in surface media in remote regions using the three corresponding LRTP metrics of the emission fractions approach (EFA; ϕ1, ϕ2, ϕ3), as implemented in a modified version of The Tool. Comparing the outcome of an assessment based on CTD/TE and POV with the EFA, we find that the latter classifies a larger number of HPVs as having the potential for accumulation in remote regions than is classified as POP-like by the existing approach. In particular, the EFA identifies chemicals capable of accumulating in remote regions without fulfilling the criterion for POV. The remote accumulation fraction of the EFA is the LRTP assessment metric most suited for the risk assessment stage in Annex E of the SC. Using simpler metrics (such as half-life criteria, POV, and LRTP–POV combinations) in a hazard-based assessment according to Annex D is problematic as it may prematurely screen out many of the chemicals with potential for adverse effects as a result of long-range transport.

Royal Society of Chemistry (RSC)

Atmospheric Microplastic in the Arctic and the Norwegian mainland

Herzke, Dorte; Evangeliou, Nikolaos; Bjørnsen, Astrid E.; Eckhardt, Sabine

Sources and long-range transport of atmospheric microplastics in the Northern Atlantic Ocean

Gossmann, Isabel; Herzke, Dorte; Held, Andreas; Schulz, Janina; Nikiforov, Vladimir; Georgi, Christoph; Evangeliou, Nikolaos; Eckhardt, Sabine; Gerdts, Gunnar; Wurl, Oliver; Scholz-Böttcher, Barbara

Local pollution in Svalbard - Whereabouts of anthropogenic particles in an Arctic fjord system

Philipp, Carolin; Collard, France; Husum, Katrine; Halsband, Claudia; Herzke, Dorte; Corami, Fabiana; Gabrielsen, Geir Wing; Hallanger, Ingeborg G.

An optimised organic carbon/elemental carbon (OC/EC) fraction separation method for radiocarbon source apportionment applied to low-loaded Arctic aerosol filters

Rauber, Martin; Salazar, Gary; Yttri, Karl Espen; Szidat, Sönke

Radiocarbon (14C) analysis of carbonaceous aerosols is used for source apportionment, separating the carbon content into fossil vs. non-fossil origin, and is particularly useful when applied to subfractions of total carbon (TC), i.e. elemental carbon (EC), organic carbon (OC), water-soluble OC (WSOC), and water-insoluble OC (WINSOC). However, this requires an unbiased physical separation of these fractions, which is difficult to achieve. Separation of EC from OC using thermal–optical analysis (TOA) can cause EC loss during the OC removal step and form artificial EC from pyrolysis of OC (i.e. so-called charring), both distorting the 14C analysis of EC. Previous work has shown that water extraction reduces charring. Here, we apply a new combination of a WSOC extraction and 14C analysis method with an optimised separation that is coupled with a novel approach of thermal-desorption modelling for compensation of EC losses. As water-soluble components promote the formation of pyrolytic carbon, water extraction was used to minimise the charring artefact of EC and the eluate subjected to chemical wet oxidation to CO2 before direct 14C analysis in a gas-accepting accelerator mass spectrometer (AMS). This approach was applied to 13 aerosol filter samples collected at the Arctic Zeppelin Observatory (Svalbard) in 2017 and 2018, covering all seasons, which bear challenges for a simplified 14C source apportionment due to their low loading and the large portion of pyrolysable species. Our approach provided a mean EC yield of 0.87±0.07 and reduced the charring to 6.5 % of the recovered EC amounts. The mean fraction modern (F14C) over all seasons was 0.85±0.17 for TC; 0.61±0.17 and 0.66±0.16 for EC before and after correction with the thermal-desorption model, respectively; and 0.81±0.20 for WSOC.

Are Ingredients of Personal Care Products Likely to Undergo Long Range Transport to Remote Regions?

D'Amico, Marianna; Frank, Wania; Breivik, Knut; Gambaro, Andrea; Vecchiato, Marco

Rapid identification of in vitro cell toxicity using an electrochemical membrane screening platform

Kohl, Yvonne; William, Nicola; Elje, Elisabeth; Backes, Nadine; Rothbauer, Mario; Srancikova, Annamaria; Rundén-Pran, Elise; El Yamani, Naouale; Korenstein, Rafi; Madi, Lea; Barbul, Alexander; Kozics, Katarina; Sramkova, Monika; Steenson, Karen; Gabelova, Alena; Ertl, Peter; Dusinska, Maria; Nelson, Andrew

This study compares the performance and output of an electrochemical phospholipid membrane platform against respective in vitro cell-based toxicity testing methods using three toxicants of different biological action (chlorpromazine (CPZ), colchicine (COL) and methyl methanesulphonate (MMS)). Human cell lines from seven different tissues (lung, liver, kidney, placenta, intestine, immune system) were used to validate this physicochemical testing system. For the cell-based systems, the effective concentration at 50 % cell death (EC50) values are calculated. For the membrane sensor, a limit of detection (LoD) value was extracted as a quantitative parameter describing the minimum concentration of toxicant which significantly affects the structure of the phospholipid sensor membrane layer. LoD values were found to align well with the EC50 values when acute cell viability was used as an end-point and showed a similar toxicity ranking of the tested toxicants. Using the colony forming efficiency (CFE) or DNA damage as end-point, a different order of toxicity ranking was observed. The results of this study showed that the electrochemical membrane sensor generates a parameter relating to biomembrane damage, which is the predominant factor in decreasing cell viability when in vitro models are acutely exposed to toxicants. These results lead the way to using electrochemical membrane-based sensors for rapid relevant preliminary toxicity screens.

Elsevier