Air Quality Sensors Systems as Tools to Support Guidance in Athletics Stadia for Elite and Recreational Athletes.

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.

Data Shepherding in Nanotechnology. The Exposure Field Campaign Template.

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.

Data Shepherding in Nanotechnology. The Initiation.

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).

Nanotoxicology data for tools: a literature review.

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.

Practices and Trends of Machine Learning Application in Nanotoxicology.

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.

Application of the maximum cumulative ratio (MCR) as a screening tool for the evaluation of mixtures in residential indoor air.

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.

Issues in consumer exposure modeling: towards harmonization on a global scale.

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.