Found 10008 publications. Showing page 60 of 401:
2022
2022
Is Glacial Meltwater a Secondary Source of Legacy Contaminants to Arctic Coastal Food Webs?
Climate change-driven increases in air and sea temperatures are rapidly thawing the Arctic cryosphere with potential for remobilization and accumulation of legacy persistent organic pollutants (POPs) in adjacent coastal food webs. Here, we present concentrations of selected POPs in zooplankton (spatially and seasonally), as well as zoobenthos and sculpin (spatially) from Isfjorden, Svalbard. Herbivorous zooplankton contaminant concentrations were highest in May [e.g., ∑polychlorinated biphenyls (8PCB); 4.43, 95% CI: 2.72–6.3 ng/g lipid weight], coinciding with the final stages of the spring phytoplankton bloom, and lowest in August (∑8PCB; 1.6, 95% CI: 1.29–1.92 ng/g lipid weight) when zooplankton lipid content was highest, and the fjord was heavily impacted by sediment-laden terrestrial inputs. Slightly increasing concentrations of α-hexachlorocyclohexane (α-HCH) in zooplankton from June (1.18, 95% CI: 1.06–1.29 ng/g lipid weight) to August (1.57, 95% CI: 1.44–1.71 ng/g lipid weight), alongside a higher percentage of α-HCH enantiomeric fractions closer to racemic ranges, indicate that glacial meltwater is a secondary source of α-HCH to fjord zooplankton in late summer. Except for α-HCH, terrestrial inputs were generally associated with reduced POP concentrations in zooplankton, suggesting that increased glacial melt is not likely to significantly increase exposure of legacy POPs in coastal fauna.
2022
2022
2022
Copper oxide nanoparticles (CuO NPs) are increasingly used in various industry sectors. Moreover, medical application of CuO NPs as antimicrobials also contributes to human exposure. Their toxicity, including toxicity to the immune system and blood, raises concerns, while information on their immunotoxicity is still very limited. The aim of our work was to evaluate the effects of CuO NPs (number concentration 1.40×106 particles/cm3, geometric mean diameter 20.4 nm) on immune/inflammatory response and antioxidant defense in mice exposed to 32.5 µg CuO/m3 continuously for 6 weeks. After six weeks of CuO NP inhalation, the content of copper in lungs and liver was significantly increased, while in kidneys, spleen, brain, and blood it was similar in exposed and control mice. Inhalation of CuO NPs caused a significant increase in proliferative response of T-lymphocytes after mitogenic stimulation and basal proliferative activity of splenocytes. CuO NPs significantly induced the production of IL-12p70, Th1-cytokine IFN-γ and Th2-cytokines IL-4, IL-5. Levels of TNF-α and IL-6 remained unchanged. Immune assays showed significantly suppressed phagocytic activity of granulocytes and slightly decreased respiratory burst. No significant differences in phagocytosis of monocytes were recorded. The percentage of CD3+, CD3+CD4+, CD3+CD8+, and CD3-CD19+ cell subsets in spleen, thymus, and lymph nodes did not differ between exposed and control animals. No changes in hematological parameters were found between the CuO NP exposed and control groups. The overall antioxidant protection status of the organism was expressed by evaluation of GSH and GSSG concentrations in blood samples. The experimental group exposed to CuO NPs showed a significant decrease in GSH concentration in comparison to the control group. In summary, our results indicate that sub-chronic inhalation of CuO NPs can cause undesired modulation of the immune response. Stimulation of adaptive immunity was indicated by activation of proliferation and secretion functions of lymphocytes. CuO NPs elicited pro-activation state of Th1 and Th2 lymphocytes in exposed mice. Innate immunity was affected by impaired phagocytic activity of granulocytes. Reduced glutathione was significantly decreased in mice exposed to CuO NPs.
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The chemical pollution crisis severely threatens human and environmental health globally. To tackle this challenge the establishment of an overarching international science–policy body has recently been suggested. We strongly support this initiative based on the awareness that humanity has already likely left the safe operating space within planetary boundaries for novel entities including chemical pollution. Immediate action is essential and needs to be informed by sound scientific knowledge and data compiled and critically evaluated by an overarching science–policy interface body. Major challenges for such a body are (i) to foster global knowledge production on exposure, impacts and governance going beyond data-rich regions (e.g., Europe and North America), (ii) to cover the entirety of hazardous chemicals, mixtures and wastes, (iii) to follow a one-health perspective considering the risks posed by chemicals and waste on ecosystem and human health, and (iv) to strive for solution-oriented assessments based on systems thinking. Based on multiple evidence on urgent action on a global scale, we call scientists and practitioners to mobilize their scientific networks and to intensify science–policy interaction with national governments to support the negotiations on the establishment of an intergovernmental body based on scientific knowledge explaining the anticipated benefit for human and environmental health.
2022
Evaluation and Global-Scale Observation of Nitrous Oxide from IASI on Metop-A
Nitrous oxide (N2O) is a greenhouse gas difficult to estimate by satellite because of its weak spectral signature in the infra-red band and its low variability in the troposphere. Nevertheless, this study presents the evaluation of new tropospheric N2O observations from the Infrared Atmospheric Sounder Interferometer (IASI) on Metop-A using the Toulouse N2O Retrieval Version 2.0 tool. This tool is based on the Radiative Transfer for Tiros Operational Vertical sounder (RTTOV) model version 12.3 coupled to the Levenberg-Marquardt optimal estimation method enabling the simultaneous retrieval of methane, water vapour, temperature profiles together with surface temperature and emissivity within the 1240–1350 cm−1 window. In this study, we focused on the upper troposphere (300 hPa) where the sensitivity of IASI is significant. The IASI N2O data has been evaluated using aircraft N2O observations from the High-performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations (HIPPO) campaigns in 2009, 2010, and 2011 and from the National Oceanic and Atmospheric Administration’s (NOAA) Global Greenhouse Gas Reference Network (GGGRN) in 2011. In addition, we evaluated the IASI N2O using ground-based N2O measurements from 9 stations belonging to the Network for the Detection of Atmospheric Composition Change (NDACC). We found a total random error of ∼2 ppbv (0.6%) for one single retrieval at 300 hPa. Under favorable conditions, this error is also found in the vertical level pressure range 300–500 hPa. It decreases rapidly to ∼0.4 ppbv (0.1%) when we average on a 1° × 1° box. In addition, independent observations allows the estimation of bias with the IASI TN2OR v2.0 N2O. The bias between IASI and aircraft N2O data at 300 hPa is ∼1.0 ppbv (∼0.3%). We found an estimated random error of ∼2.3 ppbv (∼0.75%). This study also shows relatively high correlations between IASI data and aircraft in situ profiles but more varying correlations over the year 2011 depending on the location between IASI and NDACC remote sensing data. Finally, we present daily, monthly, and seasonal IASI N2O horizontal distributions in the upper troposphere as well as cross sections for different seasons that exhibit maxima in the Tropical band especially over Africa and South America.
2022
Decades of atmospheric and oceanic long-range transport from lower latitudes have resulted in deposition and storage of persistent organic pollutants (POPs) in Arctic regions. With increased temperatures, melting glaciers and thawing permafrost may serve as a secondary source of these stored POPs to freshwater and marine ecosystems. Here, we present concentrations and composition of legacy POPs in glacier- and permafrost-influenced rivers and coastal waters in the high Arctic Svalbard fjord Kongsfjorden. Targeted contaminants include polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB), dichlorodiphenyltrichloroethanes (DDTs), hexachlorocyclohexanes (HCHs) and chlordane pesticides. Dissolved (defined as fraction filtered through 0.7 μm GF/F filter) and particulate samples were collected from rivers and near-shore fjord stations along a gradient from the heavily glaciated inner fjord to the tundra-dominated catchments at the outer fjord. There were no differences in contaminant concentration or pattern between glacier and tundra-dominated catchments, and the general contaminant pattern reflected snow melt with some evidence of pesticides released with glacial meltwater. Rivers were a small source of chlordane pesticides, DDTs and particulate HCB to the marine system and the particle-rich glacial meltwater contained higher concentrations of particle associated contaminants compared to the fjord. This study provides rare insight into the role of small Arctic rivers in transporting legacy contaminants from thawing catchments to coastal areas. Results indicate that the spring thaw is a source of contaminants to Kongsfjorden, and that expected increases in runoff on Svalbard and elsewhere in the Arctic could have implications for the contamination of Arctic coastal food-webs.
2022
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Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data, and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering.
In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC (Monte Carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres). The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMFs) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos.
Two cloud setups are considered: the first includes simple box clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical of central Europe such as shallow cumulus, convective cloud cells, cirrus and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically.
Along with the dataset, the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analysed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %.
The dataset is available for the scientific community to assess the behaviour of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.
2022
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2022
FAIRMODE Guidance Document on Modelling Quality Objectives and Benchmarking. Version 3.3.
The development of the procedure for air quality model benchmarking in the context of the Air Quality Directive 2008/50/EC (AQD) has been an on-going activity in the context of the FAIRMODE community, chaired by the JRC. A central part of the studies was the definition of proper modelling quality indicators and criteria to be fulfilled in order to allow sufficient level of quality for a given model application under the AQD. The focus initially on applications related to air quality assessment has gradually been expanded to other applications, such as forecasting and planning. The main purpose of this Guidance Document is to explain and summarise the current concepts of the modelling quality objective methodology, elaborated in various papers and documents in the FAIRMODE community, addressing model applications for air quality assessment and forecast. Other goals of the Document are linked to presentation and explanation of templates for harmonised reporting of modelling results. Giving an overview of still open issues in the implementation of the presented methodology, the document aims at triggering further research and discussions. A core set of statistical indicators is defined using pairs of measurement-modelled data. The core set is the basis for the definition of a modelling quality indicator (MQI) and additional modelling performance indicators (MPI), which take into account the measurement uncertainty. The MQI describes the discrepancy between measurements and modelling results (linked to RMSE), normalised by measurement uncertainty and a scaling factor. The modelling quality objective (MQO) requires MQI to be less than or equal to 1. With an arbitrary selection of the scaling factor of 2, the fulfilment of the MQO means that the allowed deviation between modelled and measured concentrations is twice the measurement uncertainty. Expressions for the MQI calculation based on time series and yearly data are introduced. MPI refer to aspects of correlation, bias and standard deviation, applied to both the spatial and temporal dimensions. Similarly to the MQO for the MQI, modelling performance criteria (MPC) are defined for the MPI; they are necessary, but not sufficient criteria to determine whether the MQO is fulfilled. The MQO is required to be fulfilled at 90% of the stations, a criterion which is implicitly taken into account in the derivation of the MQI. The associated modelling uncertainty is formulated, showing that in case of MQO fulfilment the modelling uncertainty must not exceed 1.75 times the measurement one (with the scaling factor fixed to 2). A reporting template is presented and explained for hourly and yearly average data. In both cases there is a diagram and a table with summary statistics. In a separate section open issues are discussed and an overview of related publications and tools is provided. Finally, a chapter on modelling quality objectives for forecast models is introduced. In Annex 1, we discuss the measurement uncertainty which is expressed in terms of concentration and its associated uncertainty. The methodology for estimating the measurement uncertainty is overviewed and the parameters for its calculation for PM, NO2 and O3 are provided. An expression for the associated modelling uncertainty is also given. This aim of this document is to support modelling groups, local, regional and national authorities in their modelling application, in the context of air quality policy.
Publications Office for the European Union
2022
Information on the origin of pollution is an essential element of air quality management that helps identifying measures to control air pollution. In this document, we review the most widely used source-apportionment (SA) methods for air quality management. The focus is on particulate matter but examples are provided for NO2 as well. Using simple theoretical examples, we explain the differences between these methods and the circumstances where they give different results and thus possibly different conclusions for air quality management. These differences are a consequence of the assumptions that underpin each methodology and determine/limit their range of applicability. We show that ignoring these underlying assumptions is a risk for efficient/successful air quality management when the methods are used outside their scope or range of applicability.
Publications Office for the European Union
2022
Best practices for local and regional air quality management. Version 1.
FAIRMODE is the Forum for Air Quality Modeling created for exchanging experience and results from air quality modeling in the context of the Air Quality Directives (AQD) and for promoting the use of modeling for air quality assessment and management. FAIRMODE is organized in different activities and task, called cross-cutting tasks, to which representative of Member States and experts participate. Among the different activities, one is devoted to Air Quality management practices, called cross-cutting task 5 (CT5). This report is indeed based on the last activities of the FAIRMODE Cross Cutting Task 5 (CT5), focusing, in particular, on elaborating recommendations to support local, regional and national authorities in the use of modelling for the development of air quality plans, defining on how to quantify emission changes associated to a set of measures, and quantifying their impacts in terms of concentration (using an ‘impact pathway approach’ from ‘abatement measure’ to ‘emissions’ to ‘concentrations’). This is done on one side taking advantage of the results already produced by previous FAIRMODE working groups and in coordination with existing activities under other FAIRMODE CTs. On the other side, examples of best practice policies are presented, focusing on Low emission zones: with an example on Antwerp and Copenhagen, Measures on non-exhaust traffic to reduce PM, with an application on Stockholm. How to reduce ozone concentrations, with a focus on local to global contributions. How to build an air quality plan in an integrated way, with an application on Italy. How to evaluate the socio-economic impact of measures, focusing on a case study on UK. The results show how different pollutants should be tackled differently, the importance of integration among different sectoral plans (on emissions, greenhouse gases mitigation, …) and also how other dimensions of the problem (i.e. social aspects) should be considered when building air quality plans.
Publications Office for the European Union
2022
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