Found 2647 publications. Showing page 33 of 265:
Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau
The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the source of many major rivers in Asia. An accurate long-range (i.e. seasonal) meteorological forecast is of great importance for this region. The fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5) provides global long-range meteorological forecasts including over the TP. However, SEAS5 uses land initial conditions produced by assimilating Interactive Multisensor Snow and Ice Mapping System (IMS) snow data only below 1500 m altitude, which may affect the forecast skill of SEAS5 over mountainous regions like the TP. To investigate the impacts of snow assimilation on the forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer 2018. Significant changes occur in the springtime. Without snow assimilation, the reforecasts overestimate snow cover and snow depth while underestimating daily temperature over the TP. Compared to satellite-based estimates, precipitation reforecasts perform better in the west TP (WTP) than in the east TP (ETP). With snow assimilation, the reforecasts of snow cover, snow depth and temperature are consistently improved in the TP in the spring. However, the positive bias between the precipitation reforecasts and satellite observations worsens in the ETP. Compared to the experiment with no snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the ETP and around the longitude 95∘ E. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the effects of a more realistic, decreased snowpack, providing favourable conditions for generating more precipitation. Overall, snow assimilation can improve seasonal forecasts through the interaction between land and atmosphere.
European Geosciences Union (EGU)
2022
Using the example of sulfur hexafluoride (SF6), we investigate the use of Lagrangian particle dispersion models (LPDMs) for inverse modeling of greenhouse gas (GHG) emissions and explore the limitations of this approach. We put the main focus on the impacts of baseline methods and the LPDM backward simulation period on the a posteriori emissions determined by the inversion. We consider baseline methods that are based on a statistical selection of observations at individual measurement sites and a global-distribution-based (GDB) approach, where global mixing ratio fields are coupled to the LPDM back-trajectories at their termination points. We show that purely statistical baseline methods can cause large systematic errors, which lead to inversion results that are sensitive to the LPDM backward simulation period and can generate unrealistic global total a posteriori emissions. The GDB method produces a posteriori emissions that are far less sensitive to the backward simulation period and that show a better agreement with recognized global total emissions. Our results show that longer backward simulation periods, beyond the often used 5 to 10 d, reduce the mean squared error and increase the correlation between a priori modeled and observed mixing ratios. Also, the inversion becomes less sensitive to biases in the a priori emissions and the global mixing ratio fields for longer backward simulation periods. Further, longer periods might help to better constrain emissions in regions poorly covered by the global SF6 monitoring network. We find that the inclusion of existing flask measurements in the inversion helps to further close these gaps and suggest that a few additional and well-placed flask sampling sites would have great value for improving global a posteriori emission fields.
2022
High-Resolution Emissions from Wood Burning in Norway—The Effect of Cabin Emissions
Emissions from wood burning for heating in secondary homes or cabins is an important part in the development of high-resolution emissions in specific areas. Norway is used as case study as 20% of the national wood consumption for heating occurs in cabins. Our study first shows a method to estimate emissions from cabins based on traffic data to derive cabin occupancy, which combined with heating need allows for the spatial and temporal distribution of emissions. The combination of residential (RWC) and cabin wood combustion (CWC) emissions shows large spatial and temporal differences, and a temporally “cabin population” can in areas be orders of magnitude larger than the registered population. While RWC emissions have been steadily reduced, CWC have kept relatively constant or even increased, which results in an increase in the cabin share to total heating emissions up to 25–35%. When comparing with regional emission inventories, our study shows that the gradient between rural and urban areas is not well-represented in regional inventories, which resembles a population-based distribution and does not allocate emissions in cabin municipalities. CWC emissions may become an increasing environmental concern as higher densification trends in mountain areas are observed.
MDPI
2022
Total oxidizable precursors assay for PFAS in human serum
Per- and polyfluoroalkyl substances (PFAS) are a class of chemicals including over 4700 substances. As a limited number of PFAS is routinely analyzed in human serum, complementary analytical methods are required to characterize the overlooked fraction. A promising tool is the total oxidizable precursors (TOP) assay to look for precursors by oxidation to perfluoroalkyl acids (PFAA). The TOP assay was originally developed for large volumes of water and had to be adapted for 250 μL of human serum. Optimization of the method was performed on serum samples spiked with model precursors. Oxidative conditions similar to previous TOP assay methods were not sufficient for complete oxidation of model precursors. Prolonged heating time (24 h) and higher oxidant amount (95 mg of Na2S2O8 per 225 μL of serum) were needed for complete conversion of the model precursors and accomplishing PFAA yields of 35–100 %. As some precursors are not fully converted to PFAA, the TOP assay can only provide semi-quantitative estimates of oxidizable precursors in human serum. However, the TOP assay can be used to give indications about the identity of unknown precursors by evaluating the oxidation products, including perfluoroalkyl sulfonic acids (PFSA) and perfluoroalkyl ether carboxylic acids (PFECA). The optimized TOP assay for human serum opens the possibility for high-throughput screening of human serum for undetected PFAA precursors.
Elsevier
2022
With the current possible presence of thousands of PFAS compounds in industrial emissions, there is an increasing need to assess the impacts of PFAS regulation of conventional PFAS on one hand and the exposure to emerging and yet unknown PFAS on the other. Today’s analytical methodologies using targeted approaches are not sufficient to determine the complete suite of PFAS present. To evaluate the presence of unknown PFAS, we investigated in this study the occurrence of an extended range of target PFAS in various species from the marine and terrestrial Norwegian environment, in relation to the extractable organic fluorine (EOF), which yields the total amount of organic fluorine. The results showed a varying presence of extractable fluorinated organics, with glaucous gull eggs, otter liver and polar bear plasma showing the highest EOF and a high abundance of PFAS as well. The targeted PFAS measurements explained 1% of the organic fluorine for moose liver as the lowest and 94% for otter liver as the highest. PFCAs like trifluoro acetic acid (TFA, reported semi-quantitatively), played a major role in explaining the organic fluorine present. Emerging PFAS as the perfluoroethylcyclohexane sulfonate (PFECHS), was found in polar bear plasma in quantifiable amounts for the first time, confirming earlier detection in arctic species far removed from emission sources. To enable a complete organic fluorine mass balance in wildlife, new approaches are needed, to uncover the presence of new emerging PFAS as cyclic- or ether PFAS together with chlorinated PFAS as well as fluorinated organic pesticides and pharmaceuticals.
Elsevier
2022
A freely available “in vitro dosimetry” web application is presented enabling users to predict the concentration of nanomaterials reaching the cell surface, and therefore available for attachment and internalization, from initial dispersion concentrations. The web application is based on the distorted grid (DG) model for the dispersion of engineered nanoparticles (NPs) in culture medium used for in vitro cellular experiments, in accordance with previously published protocols for cellular dosimetry determination. A series of in vitro experiments for six different NPs, with Ag and Au cores, are performed to demonstrate the convenience of the web application for calculation of exposure concentrations of NPs. Our results show that the exposure concentrations at the cell surface can be more than 30 times higher compared to the nominal or dispersed concentrations, depending on the NPs’ properties and their behavior in the cell culture medium. Therefore, the importance of calculating the exposure concentration at the bottom of the cell culture wells used for in vitro arrays, i.e., the particle concentration at the cell surface, is clearly presented, and the tool introduced here allows users easy access to such calculations. Widespread application of this web tool will increase the reliability of subsequent toxicity data, allowing improved correlation of the real exposure concentration with the observed toxicity, enabling the hazard potentials of different NPs to be compared on a more robust basis.
MDPI
2022
An NO2 sensor based on WO3 thin films for automotive applications in the microwave frequency range
Elsevier
2022
Plastic burdens in northern fulmars from Svalbard: looking back 25 years
The northern fulmar Fulmarus glacialis ingests a larger number of (micro)plastics than many other seabirds due to its feeding habits and gut morphology. Since 2002, they are bioindicators of marine plastics in the North Sea region, and data are needed to extend the programme to other parts of their distribution areas, such as the Arctic. In this study, we provide data on ingested plastics by fulmars collected in 1997 in Kongsfjorden, Svalbard. An extraction protocol with KOH was used and for half of the birds, the gizzard and the proventricular contents were analysed separately. Ninety-one percent of the birds had ingested at least one piece of plastic with an average of 10.3 (±11.9 SD) pieces. The gizzards contained significantly more plastics than the proventriculus. Hard fragments and polyethylene were the most common characteristics. Twelve percent of the birds exceeded the EcoQO value of 0.1 g.
Elsevier
2022
Comparison of particle number size distribution trends in ground measurements and climate models
Despite a large number of studies, out of all drivers of radiative forcing, the effect of aerosols has the largest uncertainty in global climate model radiative forcing estimates. There have been studies of aerosol optical properties in climate models, but the effects of particle number size distribution need a more thorough inspection. We investigated the trends and seasonality of particle number concentrations in nucleation, Aitken, and accumulation modes at 21 measurement sites in Europe and the Arctic. For 13 of those sites, with longer measurement time series, we compared the field observations with the results from five climate models, namely EC-Earth3, ECHAM-M7, ECHAM-SALSA, NorESM1.2, and UKESM1. This is the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five earth system models (ESMs). We found that the trends of particle number concentrations were mostly consistent and decreasing in both measurements and models. However, for many sites, climate models showed weaker decreasing trends than the measurements. Seasonal variability in measured number concentrations, quantified by the ratio between maximum and minimum monthly number concentration, was typically stronger at northern measurement sites compared to other locations. Models had large differences in their seasonal representation, and they can be roughly divided into two categories: for EC-Earth and NorESM, the seasonal cycle was relatively similar for all sites, and for other models the pattern of seasonality varied between northern and southern sites. In addition, the variability in concentrations across sites varied between models, some having relatively similar concentrations for all sites, whereas others showed clear differences in concentrations between remote and urban sites. To conclude, although all of the model simulations had identical input data to describe anthropogenic mass emissions, trends in differently sized particles vary among the models due to assumptions in emission sizes and differences in how models treat size-dependent aerosol processes. The inter-model variability was largest in the accumulation mode, i.e. sizes which have implications for aerosol–cloud interactions. Our analysis also indicates that between models there is a large variation in efficiency of long-range transportation of aerosols to remote locations. The differences in model results are most likely due to the more complex effect of different processes instead of one specific feature (e.g. the representation of aerosol or emission size distributions). Hence, a more detailed characterization of microphysical processes and deposition processes affecting the long-range transport is needed to understand the model variability.
2022
An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry
Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.
Oxford University Press
2022