C Vaquero

Limitations and information needs for engineered nanomaterial-specific exposure estimation and scenarios: recommendations for improved reporting practices

The aim of this paper is to describe the process and challenges in building exposure scenarios for engineered nanomaterials (ENM), using an exposure scenario format similar to that used for the European Chemicals regulation (REACH). Over 60 exposure scenarios were developed based on information from publicly available sources (literature, books, and reports), publicly available exposure estimation models, occupational sampling campaign data from partnering institutions, and industrial partners regarding their own facilities. The primary focus was on carbon-based nanomaterials, nano-silver (nano-Ag) and nano-titanium dioxide (nano-TiO2), and included occupational and consumer uses of these materials with consideration of the associated environmental release. The process of building exposure scenarios illustrated the availability and limitations of existing information and exposure assessment tools for characterizing exposure to ENM, particularly as it relates to risk assessment. This article describes the gaps in the information reviewed, recommends future areas of ENM exposure research, and proposes types of information that should, at a minimum, be included when reporting the results of such research, so that the information is useful in a wider context.

Nanofiber-based filters as novel barrier systems for nanomaterial exposure scenarios

In this article our latest advances in the development of efficient barrier systems against micro- and nano-particulate materials are presented. Nanofibrous polyamide 6 (PA6) mats were fabricated by electrospinning onto a nonwoven viscose substrate. The influence of electrospinning parameters including solution concentration, viscosity, and conductivity, were studied for the production of nonwovens with different fiber size distribution ranging from 74 to 261 nm in diameters. Moreover, the formation of nanowebs (30-40 nm) which are widely distributed among fibers was observed. By varying several process parameters, membranes with different thickness of the nanofiber layer and therefore air permeability and nanoparticle filtration efficiency were obtained.

A Computer-Aided Decision Support System for Shoulder Pain Pathology

A musculoskeletal disorder is a condition of the musculoskeletal system which consists in that part of it is injured continuously over time. Shoulder disorders are one of the most common musculoskeletal cases attended in primary health care services. Shoulder disorders cause pain and limit the ability to perform many routine activities and affect about 15-25 % of the general population. Several clinical tests have been described to aid diagnosis of shoulder disorders. However, the current literature acknowledges a lack of concordance in clinical assessment, even among musculoskeletal specialists. In this work a Computer-Aided Decision Support (CADS) system for Shoulder Pain Pathology has been developed. The paper presents the medical method and the development of the database and the (CADS) system based on several classical classification paradigms improve by covariance estimation methods. Finally the system was evaluated by a medical specialist.

Tutor Project: An Intelligent Tutoring System to Improve Cognitive Disabled People Integration

Nowadays, the integration of people with cognitive disabilities, especially in the work environment in a growing competitive market, is a difficult task. The TUTOR project is addressed to the development and testing of an Intelligent Tutoring System (ITS). The ITS runs on a handheld device and its aim is to increase the autonomy of people with cognitive disabilities in labour and daily life activities, therefore, improving the social and labour integration of this collective and its quality of live. This paper describes the objectives of this project, the methodology followed, some preliminary achieved results and the future planned activities of this research group.

Occupational exposure to nano-TiO2 in the life cycle steps of new depollutant mortars used in construction

The present work is focused on the measurement of workers exposure to nano-TiO2 in the life cycle steps of depollutant mortars. It has been done in the framework of the SCAFFOLD project, which aims at the management of potential risks arising from the use of manufactured nanomaterials in construction. Main findings can be summarized as follows: (1) The occupational exposure to nano-TiO2 is below 0.3 mg/m3 for all measured scenarios. The highest concentrations were measured during the cleaning task (in the nano-TiO2 manufacturing process) and during the application (spraying) of depollutant coatings on a wall.It was found a high release of particles above the background in several tasks as expected due to the nature of the activities performed. The maximum concentration was measured during drilling and during adding powder materials (mean total particle concentration up to 5.591E+04 particles/cm3 and 5.69E+04 particles/cm3). However, considering data on total particle concentration released, no striking differences have been observed when tasks have been performed using conventional materials in the sector (control) and when using materials doped with nano-objects.

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzersin industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed.

Polyfire project- an example of an industrial research project promoting safe industrial production of fire-resistant nanocomposites

New developments based on nanotechnology have to guarantee safe products and processes to be accepted by society. The Polyfire project will develop and scale-up techniques for processing halogen-free, fire-retardant nanocomposite materials and coatings based on unsaturated polyester resins and organoclays. The project includes a work package that will assess the Health and Environmental impacts derived from the manipulation of nanoparticles. This work package includes the following tasks: (1) Identification of Health and Environment Impacts derived from the processes, (2) Experimentation to study specific Nanoparticle Emissions, (3) Development of a Risk Management Methodology for the process, and (4) A Comparison of the Health and Environmental Impact of New and Existing Materials. To date, potential exposure scenarios to nanomaterials have been identified through the development of a Preliminary Hazard Analysis (PHA) of the new production processes. In the next step, these scenarios will be studied and simulated to evaluate potential emissions of nanomaterials. Polyfire is a collaborative European project, funded by the European Commission 7th Framework Programme (Grant Agreement No 229220). It features 11 partners from 5 countries (5 SMEs, 3 research institutes, 2 large companies, 1 association) and runs for three years (1st September 2009 – 31st August 2012). This project is an example of an industrial research development which aims to introduce to the market new products promoting the safe use of nanomaterials.

Towards an optimal adaptation of exposure to NOAA assessment methodology in Multi-Source Industrial Scenarios (MSIS): the challenges and the decision-making process

This paper exploits the results of the research developed by projects SCAFFOLD and EHS Advance. Project SCAFFOLD received funding from the European Unions FP7 research and innovation programme, under grant agreement No. 280535. Project EHS Advance received funding from the Basque Government research programme (Etortek). The authors would also like to express their grateful to companies BOSTLAN (Bizkaia, Spain) and TECNAN (Navarra, Spain) for the support provided to carry out the exposure measurements in their respective facilities.

On the design of a CADS for shoulder pain pathology

A musculoskeletal disorder is a condition of the musculoskeletal system, which consists in part of it being injured continuously over time Shoulder disorders are one of the most common musculoskeletal cases attended in primary health care services Shoulder disorders cause pain and limit the ability to perform many routine activities, affecting about 15-25 % of the general population Several clinical tests have been described to aid diagnosis of shoulder disorders However, the current literature acknowledges a lack of concordance in clinical assessment, even among musculoskeletal specialists We are working on the design of a Computer-Aided Decision Support (CADS) system for Shoulder Pain Pathology The paper presents the results of our efforts to build a CADS system testing several classical classification paradigms, feature reduction methods (PCA) and K-means unsupervised clustering The small database size imposes the use of robust covariance matrix estimation methods to improve the system performance Finally, the system was evaluated by a medical specialist.