Found 10000 publications. Showing page 341 of 400:
2021
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Copernicus Atmosphere Monitoring Service
2022
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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
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
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
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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From 2015 to 2021, we optimized mass cultivation of diatoms in our own developed vertical column airlift photobioreactors using natural and artificial light (LEDs). The project took place at the ferrosilicon producer Finnfjord AS in North Norway as a joint venture with UiT—The Arctic University of Norway. Small (0.1–6–14 m3) reactors were used for initial experiments and to produce inoculum cultures while upscaling experiments took place in a 300 m3 reactor. We here argue that species cultivated in reactors should be large since biovolume specific self-shadowing of light can be lower for large vs. small cells. The highest production, 1.28 cm3 L−1 biovolume (0.09–0.31 g DW day−1), was obtained with continuous culture at ca. 19% light utilization efficiency and 34% CO2 uptake. We cultivated 4–6 months without microbial contamination or biofouling, and this we argue was due to a natural antifouling (anti-biofilm) agent in the algae. In terms of protein quality all essential amino acids were present, and the composition and digestibility of the fatty acids were as required for feed ingredients. Lipid content was ca. 20% of ash-free DW with high EPA levels, and omega-3 and amino acid content increased when factory fume was added. The content of heavy metals in algae cultivated with fume was well within the accepted safety limits. Organic pollutants (e.g., dioxins and PCBs) were below the limits required by the European Union food safety regulations, and bioprospecting revealed several promising findings.
2022
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
We combine observations from Western USA and inverse modelling to constrain global atmospheric emissions of microplastics (MPs) and microfibers (MFs). The latter are used further to model their global atmospheric dynamics. Global annual MP emissions were calculated as 9.6 ± 3.6 Tg and MF emissions as 6.5 ± 2.9 Tg. Global average monthly MP concentrations were 47 ng m-3 and 33 ng m-3 for MFs, at maximum. The largest deposition of agricultural MPs occurred close to the world’s largest agricultural regions. Road MPs mostly deposited in the East Coast of USA, Central Europe, and Southeastern Asia; MPs resuspended with mineral dust near Sahara and Middle East. Only 1.8% of the emitted mass of oceanic MPs was transferred to land, and 1.4% of land MPs to ocean; the rest were deposited in the same environment. Previous studies reported that 0.74–1.9 Tg y-1 of land-based atmospheric MPs/MFs (
2022
Atmospheric composition in the European Arctic and 30 years of the Zeppelin Observatory, Ny-Ålesund
The Zeppelin Observatory (78.90∘ N, 11.88∘ E) is located on Zeppelin Mountain at 472 m a.s.l. on Spitsbergen, the largest island of the Svalbard archipelago. Established in 1989, the observatory is part of Ny-Ålesund Research Station and an important atmospheric measurement site, one of only a few in the high Arctic, and a part of several European and global monitoring programmes and research infrastructures, notably the European Monitoring and Evaluation Programme (EMEP); the Arctic Monitoring and Assessment Programme (AMAP); the Global Atmosphere Watch (GAW); the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS); the Advanced Global Atmospheric Gases Experiment (AGAGE) network; and the Integrated Carbon Observation System (ICOS). The observatory is jointly operated by the Norwegian Polar Institute (NPI), Stockholm University, and the Norwegian Institute for Air Research (NILU). Here we detail the establishment of the Zeppelin Observatory including historical measurements of atmospheric composition in the European Arctic leading to its construction. We present a history of the measurements at the observatory and review the current state of the European Arctic atmosphere, including results from trends in greenhouse gases, chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), other traces gases, persistent organic pollutants (POPs) and heavy metals, aerosols and Arctic haze, and atmospheric transport phenomena, and provide an outline of future research directions.
2022
Temporal trends of industrial organic contaminants can show how environmental burdens respond to changes in production, regulation, and other anthropogenic and environmental factors. Numerous studies have documented such trends from the Northern Hemisphere, while there is very limited data in the literature from sub-Saharan Africa. We hypothesized that the temporal trends of legacy and contemporary industrial contaminants in sub-Saharan Africa could greatly differ from the regions in which many of these chemicals were initially produced and more extensively used. For this purpose, a dated sediment core covering six decades from a floodplain system in urban Dar es Salaam, Tanzania, was analysed. The samples were analysed for selected legacy persistent organic pollutants (POPs) [polychlorinated biphenyls (PCBs) and polybrominated biphenyl ethers (PBDEs)] and chemicals of emerging concern (CECs) [alternative brominated flame retardants (aBFRs), chlorinated paraffins (CPs), and dechloranes]. All groups of chemicals showed a steep increase in concentrations towards the uppermost sediment layers reflecting the more recent years. Concentrations of the individual compound groups in surface sediment were found in the order CPs >> aBFRs ∼ ∑25PBDEs > dechloranes ∼ ∑32PCBs. Time trends for the individual compounds and compound groups differed, with ∑32PCBs showing presence in sediments since at least the early 1960s, while some CECs first occurred in sediments corresponding to the last decade. Investigations into potential drivers for the observed trends showed that socioeconomic factors related to growth in population, economy, and waste generation have contributed to increasing concentrations of PBDEs, aBFRs, CPs, and Dechlorane Plus. Further monitoring of temporal trends of industrial organic contaminants in urban areas in the Global South is recommended.
2022
Earth system and environmental impact studies need high quality and up-to-date estimates of atmospheric deposition. This study demonstrates the methodological benefits of multimodel ensemble and measurement-model fusion mapping approaches for atmospheric deposition focusing on 2010, a year for which several studies were conducted. Global model-only deposition assessment can be further improved by integrating new model-measurement techniques, including expanded capabilities of satellite observations of atmospheric composition. We identify research and implementation priorities for timely estimates of deposition globally as implemented by the World Meteorological Organization.
2022
Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines
Wind turbines are one of the primary sources of renewable energy, which leads to a sustainable and efficient energy solution. It does not release any carbon emissions to pollute our planet. The wind farms monitoring and power generation prediction is a complex problem due to the unpredictability of wind speed. Consequently, it limits the decision power of the management team to plan the energy consumption in an effective way. Our proposed model solves this challenge by utilizing a 5G-Next Generation-Radio Access Network (5G-NG-RAN) assisted cloud-based digital twins’ framework to virtually monitor wind turbines and form a predictive model to forecast wind speed and predict the generated power. The developed model is based on Microsoft Azure digital twins infrastructure as a 5-dimensional digital twins platform. The predictive modeling is based on a deep learning approach, temporal convolution network (TCN) followed by a non-parametric k-nearest neighbor (kNN) regression. Predictive modeling has two components. First, it processes the univariate time series data of wind to predict its speed. Secondly, it estimates the power generation for each quarter of the year ranges from one week to a whole month (i.e., medium-term prediction) To evaluate the framework the experiments are performed on onshore wind turbines publicly available datasets. The obtained results confirm the applicability of the proposed framework. Furthermore, the comparative analysis with the existing classical prediction models shows that our designed approach obtained better results. The model can assist the management team to monitor the wind farms remotely as well as estimate the power generation in advance.
2022
Electrification of residential heating and investment in building energy efficiency are central pillars of many national strategies to reduce carbon emissions from the built environment sector. Ireland has a strong dependence on oil use for central heating and a substantial share of homes still using solid fuels. The current national strategy calls for the retrofitting of 400,000 home heating systems with heat pumps by 2030, principally replacing oil fired heating systems. Displacing natural gas, oil and solid fuel boilers with heat pumps will have a favourable impact on climate outcomes. However, the impact on air pollutant outcomes is far more favourable when solid fuels are replaced, and the positive impact on ambient air quality is much enhanced where concentrated clusters of solid-fuel use are targeted. This research spatially analyses emissions and air pollutant concentration outcomes for both targeted and non-targeted deployments of heat pumps and shows that a focused deployment of just 3% of the national heat pump target on solid-fuel homes could offer similar progress on climate goals but with a substantial impact in terms of reducing air pollution hot spots. For the Irish residential heating season (October–March), the targeted solid fuel scenario delivers average PM2.5 concentration decreases of 20–34%. This paper shows that these targeted communities are often in areas of relative deprivation, and as such, direct support for fabric retrofitting and heat pump technology installation offers the potential to simultaneously advance climate, air and just transition policy ambitions.
2022