Fant 2611 publikasjoner. Viser side 27 av 262:
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface mode
Ammonia (NH3) is an important atmospheric constituent. It plays a role in air quality and climate through the formation of ammonium sulfate and ammonium nitrate particles. It has also an impact on ecosystems through deposition processes. About 85 % of NH3 global anthropogenic emissions are related to food and feed production and, in particular, to the use of mineral fertilizers and manure management. Most global chemistry transport models (CTMs) rely on bottom-up emission inventories, which are subject to significant uncertainties. In this study, we estimate emissions from livestock by developing a new module to calculate ammonia emissions from the whole agricultural sector (from housing and storage to grazing and fertilizer application) within the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) global land surface model. We detail the approach used for quantifying livestock feed management, manure application, and indoor and soil emissions and subsequently evaluate the model performance. Our results reflect China, India, Africa, Latin America, the USA, and Europe as the main contributors to global NH3 emissions, accounting for 80 % of the total budget. The global calculated emissions reach 44 Tg N yr−1 over the 2005–2015 period, which is within the range estimated by previous work. Key parameters (e.g., the pH of the manure, timing of N application, and atmospheric NH3 surface concentration) that drive the soil emissions have also been tested in order to assess the sensitivity of our model. Manure pH is the parameter to which modeled emissions are the most sensitive, with a 10 % change in emissions per percent change in pH. Even though we found an underestimation in our emissions over Europe (−26 %) and an overestimation in the USA (+56 %) compared with previous work, other hot spot regions are consistent. The calculated emission seasonality is in very good agreement with satellite-based emissions. These encouraging results prove the potential of coupling ORCHIDEE land-based emissions to CTMs, which are currently forced by bottom-up anthropogenic-centered inventories such as the CEDS (Community Emissions Data System).
2023
On coarse patterns in the atmospheric concentration of ice nucleating particles
The atmospheric concentration of ice nucleating particles active at around −10 °C (INP−10) is very low. Nevertheless, these particles play a role in the development of cloud systems, so their spatial and temporal patterns merit attention. We collated available datasets on INP−10 to identify such patterns. Among the five low altitude observatories in northern Eurasia, median values throughout May to October were lowest in Scandinavia (4 and 6 m−3), somewhat higher in central Europe (11 m−3), substantially higher in the West Siberian Plain (69 m−3) and highest in the Central Yakutian Lowland (204 m−3), suggesting that the abundance of INP−10 in northern Eurasia may increase with continentality and from West to East. The range of values at the same observatories was narrower throughout November to April (2 to 27 m−3). On average, by an order of magnitude smaller values were reported for the four Arctic observatories. Consequently, increasing poleward transport of air masses from the midlatitudes likely raises the concentration of INP−10 in the Arctic, particularly when air masses had surface contact in eastern parts of northern Eurasia.
Elsevier
2023
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.
2023
Sheath formation time for spherical Langmuir probes
The formation time of the surrounding sheath of Langmuir probes in an ionospheric plasma has been studied to better understand the constraints this puts on the sampling frequency of a probe. A fully kinetic three-dimensional particle-in-cell model is used to simulate the temporal effects in the electron saturation region as the sheath forms. The stability of the probe current and the stability of the ion and electron density in the vicinity of the probe have been used to evaluate when the sheath was formed. Simulated results were compared with theoretical models and are in good agreement with the theoretical results. This shows that theoretical models can be used as guidance to estimate the formation time and to determine the sampling rate for a swept bias Langmuir system. Our results also show that the formation time is less affected by the plasma temperature and bias voltage as we move into the thick sheath regime, and will instead be determined by the plasma density. The presented results also show that applying a step function to the probe could be used to characterise ions species composition, or to estimate the ion density.
Cambridge University Press
2023
An Unprecedented Arctic Ozone Depletion Event During Spring 2020 and Its Impacts Across Europe
The response of the ozone column across Europe to the extreme 2020 Arctic ozone depletion was examined by analyzing ground-based observations at 38 European stations. The ozone decrease at the northernmost site, Ny-Ålesund (79°N) was about 43% with respect to a climatology of more than 30 years. The magnitude of the decrease declined by about 0.7% deg−1 moving south to reach nearly 15% at 40°N. In addition, it was found that the variations of the ozone column at each of the selected stations in March-May were similar to those observed at Ny-Ålesund but with a delay increasing to about 20 days at mid-latitudes with a gradient of approximately 0.5 days deg−1. The distributions of reconstructed ozone column anomalies over a sector covering a large European area show decreasing ozone that started from the north at the beginning of April 2020 and spread south. Such behavior was shown to be similar to that observed after the Arctic ozone depletion in 2011. Stratospheric dynamical patterns in March–May 2011 and during 2020 suggested that the migration of ozone-poor air masses from polar areas to the south after the vortex breakup caused the observed ozone responses. A brief survey of the ozone mass mixing ratios at three stratospheric levels showed the exceptional strength of the 2020 episode. Despite the stronger and longer-lasting Arctic ozone loss in 2020, the analysis in this work indicates a similar ozone response at latitudes below 50°N to both 2011 and 2020 phenomena.
American Geophysical Union (AGU)
2023
Plastic pollution has long been identified as one of the biggest challenges of the 21st century. To tackle this problem, governments are setting stringent recycling targets to keep plastics in a closed loop. Yet, knowledge of the stocks and flows of plastic has not been well integrated into policies. This study presents a dynamic probabilistic economy-wide material flow analysis (MFA) of seven plastic polymers (HDPE, LDPE, PP, PS, PVC, EPS, and PET) in Norway from 2000 to 2050. A total of 40 individual product categories aggregated into nine industrial sectors were examined. An estimated 620 ± 23 kt or 114 kg/capita of these seven plastic polymers was put on the Norwegian market in 2020. Packaging products contributed to the largest share of plastic put on the market (∼40%). The accumulated in-use stock in 2020 was about 3400 ± 56 kt with ∼60% remaining in buildings and construction sector. In 2020, about 460 ± 22 kt of plastic waste was generated in Norway, with half originating from packaging. Although ∼50% of all plastic waste is collected separately from the waste stream, only around 25% is sorted for recycling. Overall, ∼50% of plastic waste is incinerated, ∼15% exported, and ∼10% landfilled. Under a business-as-usual scenario, the plastic put on the market, in-use stock, and waste generation will increase by 65%, 140%, and 90%, respectively by 2050. The outcomes of this work can be used as a guideline for other countries to establish the stocks and flows of plastic polymers from various industrial sectors which is needed for the implementation of necessary regulatory actions and circular strategies. The systematic classification of products suitable for recycling or be made of recyclate will facilitate the safe and sustainable recycling of plastic waste into new products, cap production, lower consumption, and prevent waste generation.
Elsevier
2023
Black carbon emitted by incomplete combustion of fossil fuels and biomass has a net warming effect in the atmosphere and reduces the albedo when deposited on ice and snow; accurate knowledge of past emissions is essential to quantify and model associated global climate forcing. Although bottom-up inventories provide historical Black Carbon emission estimates that are widely used in Earth System Models, they are poorly constrained by observations prior to the late 20th century. Here we use an objective inversion technique based on detailed atmospheric transport and deposition modeling to reconstruct 1850 to 2000 emissions from thirteen Northern Hemisphere ice-core records. We find substantial discrepancies between reconstructed Black Carbon emissions and existing bottom-up inventories which do not fully capture the complex spatial-temporal emission patterns. Our findings imply changes to existing historical Black Carbon radiative forcing estimates are necessary, with potential implications for observation-constrained climate sensitivity.
Springer Nature
2023
The AirGAM 2022r1 air quality trend and prediction model
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.
2023
For the next-generation risk assessment (NGRA) of chemicals and nanomaterials, new approach methodologies (NAMs) are needed for hazard assessment in compliance with the 3R’s to reduce, replace and refine animal experiments. This study aimed to establish and characterize an advanced respiratory model consisting of human epithelial bronchial BEAS-2B cells cultivated at the air–liquid interface (ALI), both as monocultures and in cocultures with human endothelial EA.hy926 cells. The performance of the bronchial models was compared to a commonly used alveolar model consisting of A549 in monoculture and in coculture with EA.hy926 cells. The cells were exposed at the ALI to nanosilver (NM-300K) in the VITROCELL® Cloud. After 24 h, cellular viability (alamarBlue assay), inflammatory response (enzyme-linked immunosorbent assay), DNA damage (enzyme-modified comet assay), and chromosomal damage (cytokinesis-block micronucleus assay) were measured. Cytotoxicity and genotoxicity induced by NM-300K were dependent on both the cell types and model, where BEAS-2B in monocultures had the highest sensitivity in terms of cell viability and DNA strand breaks. This study indicates that the four ALI lung models have different sensitivities to NM-300K exposure and brings important knowledge for the further development of advanced 3D respiratory in vitro models for the most reliable human hazard assessment based on NAMs.
MDPI
2023
2023