Found 9990 publications. Showing page 33 of 400:
This paper examines the creation of fine resolution maps at 100 m x 100 m resolution using statistical downscaling for the area of Prague, as a case study. This Czech city was selected due to the fine resolution proxy data available for this city. The reference downscaling methodology used is the linear regression and the interpolation of its residuals by the area-to-point kriging. Next to this, several other methods of statistical downscaling have been also executed. The results of different downscaling methods have been compared mutually and against the data from the monitoring stations of Prague, separately for urban background and traffic areas.
The downscaled maps in 100 m x 100 m resolution have been constructed for the area of Prague for three pollutants, namely for NO2, PM10 and PM2.5. Several methods of the statistical downscaling have been compared mutually and against the data from the monitoring stations. In general, the best results are given by the linear regression and the interpolation of its residuals, either by the area-to-point kriging or the bilinear interpolation. In the maps, one can see overall realistic spatial patterns, the main roads in Prague are visible through higher air pollution levels. This is distinct especially for NO2, while for PM10 and PM2.5 the differences between road increments and urban background are smaller as would be expected. The results of the case study for Prague have proven the usefulness of the statistical downscaling for the air quality mapping, especially for NO2. In addition, the population exposure estimates based on the downscaled mapping results have been also calculated.
ETC/HE
2023
2023
NORMAN guidance on suspect and non-target screening in environmental monitoring
Increasing production and use of chemicals and awareness of their impact on ecosystems and humans has led to large interest for broadening the knowledge on the chemical status of the environment and human health by suspect and non-target screening (NTS). To facilitate effective implementation of NTS in scientific, commercial and governmental laboratories, as well as acceptance by managers, regulators and risk assessors, more harmonisation in NTS is required. To address this, NORMAN Association members involved in NTS activities have prepared this guidance document, based on the current state of knowledge. The document is intended to provide guidance on performing high quality NTS studies and data interpretation while increasing awareness of the promise but also pitfalls and challenges associated with these techniques. Guidance is provided for all steps; from sampling and sample preparation to analysis by chromatography (liquid and gas—LC and GC) coupled via various ionisation techniques to high-resolution tandem mass spectrometry (HRMS/MS), through to data evaluation and reporting in the context of NTS. Although most experience within the NORMAN network still involves water analysis of polar compounds using LC–HRMS/MS, other matrices (sediment, soil, biota, dust, air) and instrumentation (GC, ion mobility) are covered, reflecting the rapid development and extension of the field. Due to the ongoing developments, the different questions addressed with NTS and manifold techniques in use, NORMAN members feel that no standard operation process can be provided at this stage. However, appropriate analytical methods, data processing techniques and databases commonly compiled in NTS workflows are introduced, their limitations are discussed and recommendations for different cases are provided. Proper quality assurance, quantification without reference standards and reporting results with clear confidence of identification assignment complete the guidance together with a glossary of definitions. The NORMAN community greatly supports the sharing of experiences and data via open science and hopes that this guideline supports this effort.
2023
There is considerable interest in identifying chemicals which have the potential to undergo long-range environmental transport (LRTP), accumulate in remote regions, and represent a possible risk to environmental and human health. In this report, we have screened a list of 1,000 organic chemicals, as well as selected brominated dioxins and furans (PBDD/Fs), for their potential to be dispersed, transferred to, and accumulated in remote regions. This screening was carried out applying a new set of LRTP metrics, collectively referred to as the emissions fractions approach (EFA), as implemented in a modified version of the OECD POV and LRTP (long-range transport potential) Screening Tool (The Tool).
NILU
2023
Few studies report the occurrence of microplastics (MP), including tire wear particles (TWP) in the marine atmosphere, and little data is available regarding their size or sources. Here we present active air sampling devices (low- and high-volume samplers) for the evaluation of composition and MP mass loads in the marine atmosphere. Air was sampled during a research cruise along the Norwegian coast up to Bear Island. Samples were analyzed with pyrolysis-gas chromatography-mass spectrometry, generating a mass-based data set for MP in the marine atmosphere. Here we show the ubiquity of MP, even in remote Arctic areas with concentrations up to 37.5 ng m−3. Cluster of polyethylene terephthalate (max. 1.5 ng m−3) were universally present. TWP (max. 35 ng m−3) and cluster of polystyrene, polypropylene, and polyurethane (max. 1.1 ng m−3) were also detected. Atmospheric transport and dispersion models, suggested the introduction of MP into the marine atmosphere equally from sea- and land-based emissions, transforming the ocean from a sink into a source for MP.
2023
The Adverse Outcome Pathway (AOP) framework plays a crucial role in the paradigm shift of toxicity testing towards the development and use of new approach methodologies. AOPs developed for chemicals are in theory applicable to nanomaterials (NMs). However, only subtle efforts have been made to integrate information on NM-induced toxicity into existing AOPs. In a previous study, we identified AOPs in the AOP-Wiki associated with the molecular initiating events (MIEs) and key events (KEs) reported for NMs in scientific literature. In a next step, we analyzed these AOPs and found that mitochondrial toxicity plays a significant role in several of them at the molecular and cellular levels. In this study, we aimed to generate hypothesis-based AOPs related to NM-induced mitochondrial toxicity. This was achieved by integrating science-based information collected on NM-induced mitochondrial toxicity into all existing AOPs in the AOP-Wiki, which already includes mitochondrial toxicity as a MIE/KE. The results showed that several AOPs in the AOP-Wiki related to the lung, liver, cardiovascular and nervous system, with extensively defined KEs and key event relationships (KERs), could be utilized to develop AOPs that are relevant for NMs. Our results also indicate that the majority of the studies included in our literature review were of poor quality, particularly in reporting NM physico-chemical characteristics, and NM-relevant mitochondrial MIEs were scarcely reported. This study highlights the potential role of NM-induced mitochondrial toxicity in human-relevant adverse outcomes and identifies useful AOPs in the AOP-Wiki for the development AOPs that are relevant for NMs.
2023
2023
Soil uptake of VOCs exceeds production when VOCs are readily available
Volatile organic compounds (VOCs) are reactive gaseous compounds with significant impacts on air quality and the Earth's radiative balance. While natural ecosystems are known to be major sources of VOCs, primarily due to vegetation, soils, an important component of these ecosystems, have received relatively less attention as potential sources and sinks of VOCs. In this study, soil samples were collected from two temperate ecosystems: a beech forest and a heather heath, and then sieved, homogenized, and incubated under various controlled conditions such as different temperatures, oxic vs. anoxic conditions, and different ambient VOC levels. A dynamic flow-through system coupled to a proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) was used to measure production and/or uptake rates of selected VOCs, aiming to explore the processes and their controlling mechanisms. Our results showed that these soils were natural sources of a variety of VOCs, and the strength and profile of these emissions were influenced by soil properties (e.g. moisture, soil organic matter), oxic/anoxic conditions, and temperature. The soils also acted as sinks for most VOCs when VOC substrates at parts per billions levels (ranging between 0.18 and 68.65 ppb) were supplied to the headspace of the enclosed soils, and the size of the sink corresponded to the amount of VOCs available in the ambient air. Temperature-controlled incubations and glass bead simulations indicated that the uptake of VOCs by soils was likely driven by microbial metabolism, with a minor contribution from physical adsorption to soil particles. In conclusion, our study suggests that soil uptake of VOCs can mitigate the impact of other significant VOC sources in the near-surface environment and potentially regulate the net exchange of these trace gases in ecosystems.
2023
2023
In order to measure progress towards the aims outlined by the United Nations (UN) 2030 Agenda, data are needed for the different indicators that are linked to each UN Sustainable Development Goal (SDG). Where statistical or scientific data are not sufficient or available, alternative data sources, such as data from citizen science (CS) activities, could be used.
Statistics Norway, together with the Norwegian Association of Local and Regional Authorities, have developed a taxonomy for classifying indicators that are intended to measure the SDGs. The purpose of this taxonomy is to sort, evaluate, and compare different SDG indicators and to assess their usefulness by identifying their central properties and characteristics. This is done by organizing central characteristics under the three dimensions of Goal, Perspective, and Quality. The taxonomy is designed in a way that can help users to find the right indicators across sectors to measure progress towards the SDGs depending on their own context and strategic priorities. The Norwegian taxonomy also offers new opportunities for the re-use of data collected through CS activities. This paper presents the taxonomy and demonstrates how it can be applied for an indicator based on a CS data set, and we also suggest further use of CS data.
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
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
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.
2023
2023
2023
Radiative transfer modeling is used to investigate the effect of aerosol optical properties and water vapor on cloud-free sky radiances at various atmospheric conditions. Simulations are generated by changing the most critical aerosol optical properties, namely aerosol optical depth, Ångström exponent, the single-scattering albedo, the precipitable water, and the solar zenith angle (SZA) in three different spectral ranges: ultraviolet A, visible, and near-infrared.
2023
2023