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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
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
2022
The Forum for Air Quality Modelling (FAIRMODE) is a European network to exchange experiences and competences on the use of air quality models in the context of the Ambient Air Quality Directives. Its purpose is to identify and promote the use of good practices for air quality modelling and to propose harmonized ways to assess the quality of model-based air quality applications by EU Member States. The recommendations in this document are part of FAIRMODE’s contribution to the on-going revision of the EU Ambient Air Quality Directives (Directives 2008/50/EC and 2004/107/EC, hereafter AAQDs) initiated by the European Commission and are an update of the previous recommendations to the Fitness check of those Directives (Thunis et al. 2019). This document builds on the existing recommendations from FAIRMODE provided in 2019 regarding modelling applications. The current document has been revised in view of the latest consensus on the maturity of modelling applications and their uses for air quality management purposes. It provides strategic and technical recommendations where there is significant level consensus within the FAIRMODE expert community. It identifies how and where these recommendations may be included in the context of the revision of the AAQDs. These recommendations would require additional work of Member States were they to be implemented and would have implications for the work of the FAIRMODE network concerning the development of relevant guidance documents to support the recommendations.
Publications Office of the European Union
2022
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2022
Status report of air quality in Europe for year 2020, using validated data
This report presents summarised information on the status of air quality in Europe in 2020, based on validated air quality monitoring data officially reported by the member and cooperating countries of the EEA. It aims at informing on the status of ambient air quality in Europe in 2019 and on the progress towards meeting the European air quality standards for the protection of health, as well as the new WHO air quality guidelines. The report also compares the air quality status in 2020 with the previous three years. The pollutants covered in this report are particulate matter (PM10 and PM2.5), O3, NO2, benzo(a)pyrene (BaP), SO2, CO, benzene and toxic metals (As, Cd, Ni, Pb). Measured concentrations above the European air quality standards for PM10, PM2.5, O3, NO2 were reported by 20, 6, 21, and 8 European countries for 2020, respectively. Exceedances of the air quality standards for BaP, SO2, CO, and benzene were measured in, respectively, 11, 19, 2, and 0 European countries in 2020. Exceedances of European standards for toxic metals were reported by 7 stations for As, 0 for PB, 1 for Cd and 2 for Ni.
ETC/HE
2022
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2022
The National Mercury (Hg) Assessment in Norway evaluates the connections among: (a) national, regional and global Hg policies and regulations, (b) emissions, releases, uses and exposure pathways of Hg, and (c) concentrations of Hg in the environment, biota, and humans, measured during 2000-2020. Our findings suggest that the key changes of Hg in humans and the environment are highly dependent on the quality of the datasets, yet connections both to national and regional sources, as well as climate related drivers could be made for some data sets.
Norwegian Environment Agency
2022