While athletes have high exposures to air pollutants due to their increased breathing rates, sport governing bodies have little guidance to support events scheduling or protect stadium users. A key limitation for this is the lack of hyper-local, high time-resolved air quality data representative of exposures in stadia. This work aimed to evaluate whether air quality sensors can describe ambient air quality in Athletics stadia. Sensing nodes were deployed in 6 stadia in major cities around the globe, monitoring NO2, O3, NO, PM10, PM2.5, PM1, CO, ambient temperature, and relative humidity. Results demonstrated that the interpretation of hourly pollutant patterns, in combination with self-organising maps (SOMs), enabled the interpretation of probable emission sources (e.g., vehicular traffic) and of atmospheric processes (e.g., local vs. regional O formation). The ratios between PM size fractions provided insights into potential emission sources (e.g., local dust re-suspension) which may help design mitigation strategies. The high resolution of the data facilitated identifying optimal periods of the day and year for scheduling athletic trainings and/or competitions. Provided that the necessary data quality checks are applied, sensors can support stadium operators in providing athlete communities with recommendations to minimise exposure and provide guidance for event scheduling.

This paper demonstrates how a short-term prediction of the effective reproduction number (Rt) of COVID-19 in regions of Greece is achieved based on online mobility data. Various machine learning methods are applied to predict Rt and attribute importance analysis is performed to reveal the most important variables that affect the accurate prediction of Rt. Work and Park categories are identified as the most important mobility features when compared to the other attributes, with values of 0.25 and 0.24, respectively. Our results are based on an ensemble of diverse Rt methodologies to provide non-precautious and non-indulgent predictions. Random Forest algorithm achieved the highest R2 (0.8 approximately), Pearson’s and Spearman’s correlation values close to 0.9, outperforming in all metrics the other models. The model demonstrates robust results and the methodology overall represents a promising approach towards COVID-19 outbreak prediction. This paper can help health-related authorities when deciding on non-nosocomial interventions to prevent the spread of COVID-19.

In this paper, we demonstrate the realization process of a pragmatic approach on developing a template for capturing field monitoring data in nanomanufacturing processes. The template serves the fundamental principles which make data scientifically Findable, Accessible, Interoperable and Reusable (FAIR principles), as well as encouraging individuals to reuse it. In our case, the data shepherds’ (the guider of data) template creation workflow consists of the following steps: (1) Identify relevant stakeholders, (2) Distribute questionnaires to capture a general description of the data to be generated, (3) Understand the needs and requirements of each stakeholder, (4) Interactive simple communication with the stakeholders for variables/descriptors selection, and (5) Design of the template and annotation of descriptors. We provide an annotated template for capturing exposure field campaign monitoring data, and increase their interoperability, while comparing it with existing templates. This paper enables the data creators of exposure field campaign data to store data in a FAIR way and helps the scientific community, such as data shepherds, by avoiding extensive steps for template creation and by utilizing the pragmatic structure and/or the template proposed herein, in the case of a nanotechnology project (Anticipating Safety Issues at the Design of Nano Product Development, ASINA).

In this paper we describe the pragmatic approach of initiating, designing and implementing the Data Management Plan (DMP) and the data FAIRification process in the multidisciplinary Horizon 2020 nanotechnology project, Anticipating Safety Issues at the Design Stage of NAno Product Development (ASINA). We briefly describe the general DMP requirements, emphasizing that the initial steps in the direction towards data FAIRification must be conceptualized and visualized in a systematic way. We demonstrate the use of a generic questionnaire to capture primary data and metadata description from our consortium (data creators/experimentalists and data analysts/modelers). We then display the interactive process with external FAIR data initiatives (data curators/quality assessors), regarding guidance for data and metadata capturing and future integration into repositories. After the preliminary data capturing and FAIRification template is formed, the inner-communication process begins between the partners, which leads to developing case-specific templates. This paper assists future data creators, data analysts, stewards and shepherds engaged in the multi-faceted data shepherding process, in any project, by providing a roadmap, demonstrated in the case of ASINA.

Abstract Apart from educating students and conducting scientific research, technology transfer (TT) is the third “mission pillar” of modern universities. TT from universities to business as a source of innovations and inventions is, on the one hand, an important factor in socio-economic development, on the other hand, it generates income for universities and, by creating links with market practice, also affects their scientific development and quality of teaching. However, due to its complexity and due to the pluralism and heterogeneity of the approaches involved in TT processes at a country (or even regional) level, participation is a difficult challenge for the academic community. In the article the main factors influencing TT between universities and business are identified and a relevant process emphasizing into the requirements of universities being freshmen in this field is presented. In the frame of the current work a two-fold approach was applied: (a) a literature collection and review were undertaken aiming at identifying TT-related methods, tools, procedures and best practices across universities at an international level and (b) an detailed analysis of the TT-related procedures at the four European universities participating in the study.

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

We modeled the carbon (C) cycle in Mexico with a process-based approach. We used different available products (satellite data, field measurements, models and flux towers) to estimate C stocks and fluxes in the country at three different time frames: present (defined as the period 2000–2005), the past century (1901–2000) and the remainder of this century (2010–2100). Our estimate of the gross primary productivity (GPP) for the country was 2137 ± 1023 TgC yr−1 and a total C stock of 34 506 ± 7483 TgC, with 20 347 ± 4622 TgC in vegetation and 14 159 ± 3861 in the soil.Contrary to other current estimates for recent decades, our results showed that Mexico was a C sink over the period 1990–2009 (+31 TgC yr−1) and that C accumulation over the last century amounted to 1210 ± 1040 TgC. We attributed this sink to the CO2 fertilization effect on GPP, which led to an increase of 3408 ± 1060 TgC, while both climate and land use reduced the country C stocks by −458 ± 1001 and −1740 ± 878 TgC, respectively. Under different future scenarios, the C sink will likely continue over the 21st century, with decreasing C uptake as the climate forcing becomes more extreme. Our work provides valuable insights on relevant driving processes of the C cycle such as the role of drought in drylands (e.g., grasslands and shrublands) and the impact of climate change on the mean residence time of soil C in tropical ecosystems.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil-fuel combustion and cement production (EFF) are based on energy statistics, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated for the first time in this budget with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2 and land cover change (some including nitrogen–carbon interactions). All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2003–2012), EFF was 8.6 ± 0.4 GtC yr−1, ELUC 0.9 ± 0.5 GtC yr−1, GATM 4.3 ± 0.1 GtC yr−1, SOCEAN 2.5 ± 0.5 GtC yr−1, and SLAND 2.8 ± 0.8 GtC yr−1. For year 2012 alone, EFF grew to 9.7 ± 0.5 GtC yr−1, 2.2% above 2011, reflecting a continued growing trend in these emissions, GATM was 5.1 ± 0.2 GtC yr−1, SOCEAN was 2.9 ± 0.5 GtC yr−1, and assuming an ELUC of 1.0 ± 0.5 GtC yr−1 (based on the 2001–2010 average), SLAND was 2.7 ± 0.9 GtC yr−1. GATM was high in 2012 compared to the 2003–2012 average, almost entirely reflecting the high EFF. The global atmospheric CO2 concentration reached 392.52 ± 0.10 ppm averaged over 2012. We estimate that EFF will increase by 2.1% (1.1–3.1%) to 9.9 ± 0.5 GtC in 2013, 61% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the economy. With this projection, cumulative emissions of CO2 will reach about 535 ± 55 GtC for 1870–2013, about 70% from EFF (390 ± 20 GtC) and 30% from ELUC (145 ± 50 GtC). This paper also documents any changes in the methods and data sets used in this new carbon budget from previous budgets (Le Quéré et al., 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2013_V2.3).

The maximum cumulative ratio (MCR) method allows the categorisation of mixtures according to whether the mixture is of concern for toxicity and if so whether this is driven by one substance or multiple substances. The aim of the present study was to explore, by application of the MCR approach, whether health risks due to indoor air pollution are dominated by one substance or are due to concurrent exposure to various substances. Analysis was undertaken on monitoring data of four European indoor studies (giving five datasets), involving 1800 records of indoor air or personal exposure. Application of the MCR methodology requires knowledge of the concentrations of chemicals in a mixture together with health-based reference values for those chemicals. For this evaluation, single substance health-based reference values (RVs) were selected through a structured review process. The MCR analysis found high variability in the proportion of samples of concern for mixture toxicity. The fraction of samples in these groups of concern varied from 2% (Flemish schools) to 77% (EXPOLIS, Basel, indoor), the variation being due not only to the variation in indoor air contaminant levels across the studies but also to other factors such as differences in number and type of substances monitored, analytical performance, and choice of RVs. However, in 4 out of the 5 datasets, a considerable proportion of cases were found where a chemical-by-chemical approach failed to identify the need for the investigation of combined risk assessment. Although the MCR methodology applied in the current study provides no consideration of commonality of endpoints, it provides a tool for discrimination between those mixtures requiring further combined risk assessment and those for which a single-substance assessment is sufficient.

This article illustrates the methods and findings of as- sessing the health risks of exposure to indoor particulate matter in the frame of the European INDEX-PM project (12). The evaluation was performed by taking into ac- count all sources and types of indoor particulate matter, and distinguishing it to particulate matter (PM) from outdoor origin, from indoor combustion, environmental tobacco smoke, indoor air particles of biological origin, indoor resuspended mineral dust particles, semi-volatile organic compounds, as well as secondary particles gener- ated by indoor air chemistry. It comprises an outline of a state-of-the-art review of existing exposure and dose- response data of indoor PM and focuses at deriving general and by-source risk characterisation and risk management options for reducing potential health effects associated with exposure to indoor PM.

Among aerosol characteristics, their chemical composition is by far one of the most important and at the same time quite difficult to follow, as it gathers several inorganic and lots of organic species. We present here the aerosol module inserted in the MARS-MUSE 3D dispersion model and some preliminary results for urban areas. In this module, the aerosol phase is described as a multimodal (fine, accumulation and coarse) internally mixed distribution. For inorganics, an equilibrium model has been built especially for dry, coastal and urban regions, which contains common inorganic species and also crustal species. For secondary organics, the SORGAM module has been incorporated into the model. The simulations conducted revealed the capability of the model to describe PM pollution in Southern California. Most deviation from the observed data are probably not exclusively related to the module, but to inaccuracies in emission input data and simulation of meteorological parameters.

Understanding where and how chemicals are used throughout their life cycle is becoming increasingly important. In 2003, within the context of REACH and GPSD legislation, the European Commission started developing a European and global infrastructure of exposure methods and tools. The infrastructure aims (1) to link modeling tools and exposure-related data and scenarios in a single framework so that harmonized exposure assessment procedures can be developed for consumer products in the EU and (2) to make this framework flexible enough to allow global application. A number of issues are raised by a global infrastructure of consumer exposure modeling that answers to multi-legislative mandates. These include transparency, consistency, usability, and defensibility of the models, including the relevant degree of complexity for priority setting versus assessment. As part of the initiative to set up a harmonized global infrastructure on consumer exposure assessment, these issues were presented, discussed, and further developed in a series of European Commission-sponsored workshops organized in October 2004 and June 2005 as part of the "Harmonization of Consumer Exposure Models on a Global Scale" project. The project focused on development, harmonization, and validation of consumer exposure modeling approaches. The workshops included experts from the EU, USA, Japan, and Canada. The conclusions and recommendations made on the basis of this work are described. To help achieve harmonization of approaches, the European Commission's Joint Research Centre is proposing a framework (1) to compare information on elements of chemical risk assessment to understand exposure regulations in different countries, (2) to save time and expense by sharing information and models, and (3) to promote credible science through better communication among organizations and by peer review of assessments and assessment procedures.

TS1-16 Abstract: Exposure information on chemicals related to consumer products and articles is difficult to find or missing altogether, and this is often a major bottleneck in developing safety and risk assessments. The need to build knowledge and develop European and global infrastructure, methods, and tools for understanding the reasonably foreseeable uses and misuses and accidental consumer exposure is becoming increasingly important. Experiences arising from the implementation of the General Product Safety Directive (GPSD, 2001/95/EC) and the first adoption of the proposal for a EU regulatory framework for the Registration, Evaluation and Authorization of Chemicals (REACH) on November 17, 2005, by the European Parliament, emphasize this need. This led the European Commission's DG SANCO (Health and Consumer Protection Directorate General) and the Joint Research Centre (JRC) to launch the “European Information System on risks from chemicals released from consumer products and articles” (EIS-ChemRisks) in 2003 with the aim to provide tools and reference data that enable harmonized exposure assessment procedures for consumer products and articles in the EU. A key deliverable of EIS-ChemRisks is the “EU Exposure Assessment Toolbox.” It was developed over the period 2003–2006 and offers an infrastructure to harmonize exposure assessment practices across different policy areas. A key deliverable of EIS-ChemRisks is the “EU Exposure Assessment Toolbox” that represents an infrastructure to systematically support the exchange of information on exposure issues. The EU Toolbox is designed with an open modular architecture and includes standard data taxonomies such as product design and labeling, reference exposure (factor) data, and standard exposure scenario-based assessment procedures to be shared by different policy areas. The current work program for 2006–2007 is: To identify and introduce into the toolbox relevant EU and worldwide information on consumer exposure to chemicals from publicly available risk assessments in cooperation with key authorities, To provide broad stakeholder access in 2006, To configure the informatic infrastructure of the toolbox on the specific needs of the GPSD and REACH (resulting from the REACH Implementation Projects (RIPs), and To develop sectorial exercises in selected areas such as textiles, toys, and household cleaning products in cooperation with the key stakeholders. This paper presents the current development status and provide an overview of results emerging from ongoing projects.