Found 9758 publications. Showing page 340 of 391:
While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios.
In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, O3, BC, and SO), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs.
Of the SLCFs in our study, model biases were smallest for CH4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.
2022
Three-dimensional (3D) cloud structures may impact atmospheric trace gas products from ultraviolet–visible (UV–Vis) sounders. We used synthetic and observational data to identify and quantify possible cloud-related bias in NO2 tropospheric vertical column density (TVCD). The synthetic data were based on high-resolution large eddy simulations which were input to a 3D radiative transfer model. The simulated visible spectra for low-earth-orbiting and geostationary geometries were analysed with standard retrieval methods and cloud correction schemes that are employed in operational NO2 satellite products. For the observational data, the NO2 products from the TROPOspheric Monitoring Instrument (TROPOMI) were used, while the Visible Infrared Imaging Radiometer Suite (VIIRS) provided high-spatial-resolution cloud and radiance data. NO2 profile shape, cloud shadow fraction, cloud top height, cloud optical depth, and solar zenith and viewing angles were identified as the metrics being the most important in identifying 3D cloud impacts on NO2 TVCD retrievals. For a solar zenith angle less than about 40∘ the synthetic data show that the NO2 TVCD bias is typically below 10 %, while for larger solar zenith angles the NO2 TVCD is low-biased by tens of percent. The horizontal variability of NO2 and differences in TROPOMI and VIIRS overpass times make it challenging to identify a similar bias in the observational data. However, for optically thick clouds above 3000 m, a low bias appears to be present in the observational data.
2022
Do cytotoxicity and cell death cause false positive results in the in vitro comet assay?
The comet assay is used to measure DNA damage induced by chemical and physical agents. High concentrations of test agents may cause cytotoxicity or cell death, which may give rise to false positive results in the comet assay. Systematic studies on genotoxins and cytotoxins (i.e. non-genotoxic poisons) have attempted to establish a threshold of cytotoxicity or cell death by which DNA damage results measured by the comet assay could be regarded as a false positive result. Thresholds of cytotoxicity/cell death range from 20% to 50% in various publications. Curiously, a survey of the latest literature on comet assay results from cell culture studies suggests that one-third of publications did not assess cytotoxicity or cell death. We recommend that it should be mandatory to include results from at least one type of assay on cytotoxicity, cell death or cell proliferation in publications on comet assay results. A combination of cytotoxicity (or cell death) and proliferation (or colony forming efficiency assay) is preferable in actively proliferating cells because it covers more mechanisms of action. Applying a general threshold of cytotoxicity/cell death to all types of agents may not be applicable; however, 25% compared to the concurrent negative control seems to be a good starting value to avoid false positive comet assay results. Further research is needed to establish a threshold value to distinguish between true and potentially false positive genotoxic effects detected by the comet assay.
2022
2022
In vivo Mammalian Alkaline Comet Assay: Method Adapted for Genotoxicity Assessment of Nanomaterials
The in vivo Comet assay measures the generation of DNA strand breaks under conditions in which the DNA will unwind and migrate to the anode in an electrophoresis assay, producing comet-like figures. Measurements are on single cells, which allows the sampling of a diversity of cells and tissues for DNA damaging effects. The Comet assay is the most common in vivo method for genotoxicity assessment of nanomaterials (NM). The Method outlined here includes a recommended step-by-step approach, consistent with OECD 489, taking into consideration the issues impacting assessment of NM, including choice of cells or systems, handling of NM test articles, dose determination, assay methods and data assessment. This method is designed to be used along with the accompanying “Common Considerations” paper, which discusses issues common to any genotoxicity assay using NM as a test article.
Frontiers Media S.A.
2022
Monitoring of greenhouse gases and aerosols at Svalbard and Birkenes in 2021. Annual report.
This annual report for 2021 summarizes the activities and results of the greenhouse gas monitoring at the Zeppelin Observatory, situated on Svalbard, during the period 2001-2021, and the greenhouse gas monitoring and aerosol observations from Birkenes for 2009-2021.
NILU
2022
2022
Multisensory Representation of Air Pollution in Virtual Reality: Lessons from Visual Representation
The world is facing the problem of anthropogenic climate
change and air pollution. Despite many years of development, already
established methods of influencing behaviour remain ineffective. The
effect of such interventions is very often a declaration of behaviour change
that is not followed by actual action. Moreover, despite intensive informa-
tion campaigns, many people still do not have adequate knowledge on the
subject, are not aware of the problem or, worse, deny its existence. Pre-
vious attempts to introduce real change were based on providing infor-
mation, persuasion or visualisation. We propose the use of multi-sensory
virtual reality to investigate the problem more thoroughly and then design
appropriate solutions. In this paper, we introduce a new immersive virtual
environment that combines free exploration with a high level of experi-
mental control, physiological and behavioural measures. It was created on
the basis of transdisciplinary scientific cooperation, participatory design
and research. We used the unique features of virtual environments to
reverse and expand the idea of pollution pods by Pinsky. Instead of closing
participants in small domes filled with chemical substances imitating pol-
lution, we made it possible for them to freely explore an open environment
- admiring the panorama of a small town from the observation deck located
on a nearby hill. Virtual reality technology enables the manipulation of
representations of air pollution, the sensory modalities with which they are
transmitted (visual, auditory, tactile and smell stimuli) and their intensity.
Participants’ reactions from the initial tests of the application showed that
it is a promising solution. We present the possibilities of applying the new
solution in psychological research and its further design and development
opportunities in collaboration with communities and other stakeholders
in the spirit of citizen science.
2022
NILU and Hydro Aluminium performed a test campaign for measurements of CF4 and C2F6 for stack emissions at Husnes Aluminium Smelter. Time-integrated samples were taken with evacuated canisters combined with low-flow restrictors for continuous sampling periods as long as 6 weeks. The samples were analyzed at NILU with a Medusa preconcentration method combined with GC-MS SIM. As a main conclusion, time integrated sampling together with Medusa GC-MS methodology is a very precise alternative to the traditional attempts to quantify PFC-emission.
NILU
2022
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2022
The way Norway is spearheading electrification in the transport sector is of global interest. In this study, we used the Norwegian Emissions from Road Vehicle Exhaust (NERVE) model, a bottom-up high-resolution traffic emission model, to calculate all emissions in Norway (2009–2020) and evaluate potential co-benefit and trade-offs of policies to target climate change mitigation, air quality and socioeconomic factors. Results for municipal data with regard to traffic growth, road network influences, vehicle composition, emissions and energy consumption are presented. Light vehicle CO2 emissions per kilometer have been reduced by 22% since 2009, mainly driven by an increasing bio-fuel mixing and battery electric vehicles (BEV) share. BEVs are mostly located in and around the main cities, areas with young vehicle fleets, and strong local incentives. Beneficiaries of BEVs incentives have been a subset of the population with strong economic indicators. The incentivized growth in the share of diesel-fuelled passenger vehicles has been turned, and together with Euro6 emission standards, light vehicle NOx emissions have been halved since peaking in 2014. BEVs represent an investment in emission reductions in years to come, and current sales set Norway up for an accelerated decline in all exhaust emissions despite the continual growth in traffic.
MDPI
2022
Mercury isotope evidence for Arctic summertime re-emission of mercury from the cryosphere
During Arctic springtime, halogen radicals oxidize atmospheric elemental mercury (Hg0), which deposits to the cryosphere. This is followed by a summertime atmospheric Hg0 peak that is thought to result mostly from terrestrial Hg inputs to the Arctic Ocean, followed by photoreduction and emission to air. The large terrestrial Hg contribution to the Arctic Ocean and global atmosphere has raised concern over the potential release of permafrost Hg, via rivers and coastal erosion, with Arctic warming. Here we investigate Hg isotope variability of Arctic atmospheric, marine, and terrestrial Hg. We observe highly characteristic Hg isotope signatures during the summertime peak that reflect re-emission of Hg deposited to the cryosphere during spring. Air mass back trajectories support a cryospheric Hg emission source but no major terrestrial source. This implies that terrestrial Hg inputs to the Arctic Ocean remain in the marine ecosystem, without substantial loss to the global atmosphere, but with possible effects on food webs.
2022
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2022
Equal abundance of summertime natural and wintertime anthropogenic Arctic organic aerosols
Aerosols play an important yet uncertain role in modulating the radiation balance of the sensitive Arctic atmosphere. Organic aerosol is one of the most abundant, yet least understood, fractions of the Arctic aerosol mass. Here we use data from eight observatories that represent the entire Arctic to reveal the annual cycles in anthropogenic and biogenic sources of organic aerosol. We show that during winter, the organic aerosol in the Arctic is dominated by anthropogenic emissions, mainly from Eurasia, which consist of both direct combustion emissions and long-range transported, aged pollution. In summer, the decreasing anthropogenic pollution is replaced by natural emissions. These include marine secondary, biogenic secondary and primary biological emissions, which have the potential to be important to Arctic climate by modifying the cloud condensation nuclei properties and acting as ice-nucleating particles. Their source strength or atmospheric processing is sensitive to nutrient availability, solar radiation, temperature and snow cover. Our results provide a comprehensive understanding of the current pan-Arctic organic aerosol, which can be used to support modelling efforts that aim to quantify the climate impacts of emissions in this sensitive region.
2022
2022
The main goal of this feasibility study was to evaluate the potential of adding value to the Sentinel 5P TROPOMI methane product over Norway and the Arctic through the synergistic use of relevant observations from other Sentinel satellites and machine learning. We assessed the data availability of ESA operational and research-based WFMD XCH4 products over the Northern hemisphere, the Nordic countries and the Arctic/Northern latitudes. ESA’s XCH4 data have poor coverage over Norway. Seeing the two datasets as complementary, seems to be the most reasonable approach for utilization them. Furthermore, we investigated potential synergies between satellite products from different platforms. A random forest (RF) machine learning algorithm was implemented. It shows the importance of daytime land surface temperature (LST) as predictor variable for CH4. Our results indicate that the RF-model has a very good capability of filling small gaps in the data.
NILU
2022