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Background Human mobility is fundamental to understanding global issues in the health and social sciences such as disease spread and displacements from disasters and conflicts. Detailed mobility data across spatial and temporal scales are difficult to collect, however, with movements varying from short, repeated movements to work or school, to rare migratory movements across national borders. While typical sources of mobility data such as travel history surveys and GPS tracker data can inform different typologies of movement, almost no source of readily obtainable data can address all types of movement at once. Methods Here, we collect Google Location History (GLH) data and examine it as a novel source of information that could link fine scale mobility with rare, long distance and international trips, as it uniquely spans large temporal scales with high spatial granularity. These data are passively collected by Android smartphones, which reach increasingly broad audiences, becoming the most common operating system for accessing the Internet worldwide in 2017. We validate GLH data against GPS tracker data collected from Android users in the United Kingdom to assess the feasibility of using GLH data to inform human movement. Results We find that GLH data span very long temporal periods (over a year on average in our sample), are spatially equivalent to GPS tracker data within 100 m, and capture more international movement than survey data. We also find GLH data avoid compliance concerns seen with GPS trackers and bias in self-reported travel, as GLH is passively collected. We discuss some settings where GLH data could provide novel insights, including infrastructure planning, infectious disease control, and response to catastrophic events, and discuss advantages and disadvantages of using GLH data to inform human mobility patterns. Conclusions GLH data are a greatly underutilized and novel dataset for understanding human movement. While biases exist in populations with GLH data, Android phones are becoming the first and only device purchased to access the Internet and various web services in many middle and lower income settings, making these data increasingly appropriate for a wide range of scientific questions.

(1) Background: Small, lightweight, low-cost optical particulate matter (PM) monitors are becoming popular in the field of occupational exposure monitoring, because these devices allow for real-time static measurements to be collected at multiple locations throughout a work site as well as being used as wearables providing personal exposure estimates. Prior to deployment, devices should be evaluated to optimize and quantify measurement accuracy. However, this can turn out to be difficult, as no standardized methods are yet available and different deployments may require different evaluation procedures. To gain insight in the relevance of different variables that may affect the monitor readings, six PM monitors were selected based on current availability and evaluated in the laboratory; (2) Methods: Existing strategies that were judged appropriate for the evaluation of PM monitors were reviewed and seven evaluation variables were selected, namely the type of dust, within- and between-device variations, nature of the power supply, temperature, relative humidity, and exposure pattern (peak and constant). Each variable was tested and analyzed individually and, if found to affect the readings significantly, included in a final correction model specific to each monitor. Finally, the accuracy for each monitor after correction was calculated; (3) Results: The reference materials and exposure patterns were found to be main factors needing correction for most monitors. One PM monitor was found to be sufficiently accurate at concentrations up to 2000 µg/m3 PM2.5, with other monitors appropriate at lower concentrations. The average accuracy increased by up to three-fold compared to when the correction model did not include evaluation variables; (4) Conclusions: Laboratory evaluation and readings correction can greatly increase the accuracy of PM monitors and set boundaries for appropriate use. However, this requires identifying the relevant evaluation variables, which are heavily reliant on how the monitors are used in the workplace. This, together with the lack of current consensus on standardized procedures, shows the need for harmonized PM monitor evaluation methods for occupational exposure monitoring.

REACH (Registration, Evaluation and Authorization of CHemicals) requires improved exposure models that can be incorporated into screening tools and refined assessment tools. These are referred to as tier 1 and 2 models, respectively. There are a number of candidate in tier 1 models that could be used with REACH. Tier 2 models, producing robust and realistic exposure assessments, are currently not available. A research programme is proposed in this paper that will result in a new, advanced exposure assessment tool for REACH. In addition, issues related to variability and uncertainty are discussed briefly, and some examples of tier 1 screening tools are presented. The proposed framework for the tier 2 tool is based on a Bayesian approach, and makes full use of mechanistically modelled estimates and any relevant measurements of exposure. The new approach will preclude the necessity to conduct of case-by-case exposure measurements for each chemical and scenario, since the system will allow for the use of analogous exposure data from relatively comparable scenarios. The development of the new approach requires substantial effort in the area of mechanistic modelling, database development and Bayesian statistical techniques. In this paper, the data gaps and areas for future research are identified to help realise and further improve this type of approach within REACH. A structured data collection and storage system is a central element of the research programme and the availability of this type of tool may also facilitate the sharing of exposure data down and up the supply chain. In addition, new data that are stored according to the proposed structure could enable the validation of any exposure model and thus this programme enhances the exposure assessment field as a whole.

IntroductionLow cost sensors have potential for occupational exposure assessment by providing information on exposure profiles rather than time weighted averages (TWA). High resolution exposure data may advance our knowledge on how exposure patterns may affect (acute) health. We aimed to develop and deploy a multi-sensor box for assessing working life exposures (exposure at and outside work) during a working week in a case study on respiratory health as part of the EU Exposome Project for Health and Occupational Research (EPHOR) project.Material and MethodsA multi-exposure sensor box (particulate matter (PM), noise, light, UV and temperature) has been developed and is currently being deployed with the aim to assess exposures during a working week in relation to acute respiratory health among 300 mild asthma patients. The sensors were evaluated against conventional equipment separately. Several PM sensors were co-located in different occupational settings with gravimetric samplers and the Aerodynamic Particle Sizer (APS). Sensors for noise, light, UV and temperature were tested against conventional instruments in various environmental settings.Results and ConclusionsLow-cost PM sensors and the APS correlated reasonably well in different occupational settings (high-resolution data) (R2=0.4–0.6). Comparing the low-cost PM2.5 mass concentration from the sensors with the respirable gravimetric results (TWA) showed a moderate correlation (R2~0.5). A semi-quantitative comparison of TWA exposures with PM mass concentrations showed higher correlations (R2>0.75). A method for calibrating the PM sensor results to reflect different workplace and nonworkplace aerosols is being developed. The noise, light, UV and temperature sensors demonstrated R2 values of 0.9 and above with reference monitors in laboratory or field comparisons. Calibration equations have been developed based on these relationships. Along with the evaluation results of the different sensors, the preliminary results of the multi sensor box among ~25 case study subjects will be presented.

Printed circuit board assembly (PCBA) is a cost-effective hardware device used in mechanical, process, electrical, electronic, military, and medical equipment providing automated and digital functionalities for users. Keeping high quality standards in the PCBA production process is a major challenge for the electronics production industry. Defective PCBAs are submitted to analysis, debug, and repair processes. This paper presents an augmented reality (AR) fault diagnosis support system for assembled electronic systems—the Cadence inspectAR Augmented Reality Electronics Platform. The system’s functional concept and components are described. The steps of the diagnostic process are presented and discussed. The diagnostic capabilities of the system are illustrated with an example of the system’s use in industrial practice. The planned steps in the development of the elaborated system are indicated.

RationaleInsight into the effectiveness of intervention strategies will help realise a decrease in the occupational disease burden from (allergic) respiratory diseases in the bakery population.ObjectivesTo use a simulation model to assess the impact of different intervention strategies on the disease burden of the bakery population over time.MethodsA recently developed dynamic population based model was used to prospectively evaluate the impact on disease burden resulting from different intervention strategies. We distinguished interventions based on exposure reductions for flour dust and fungal α-amylase, health surveillance combined with reduction in exposure, and pre-employment screening.Main ResultsThe impact of most interventions on disease burden was limited, generally less than 50% for lower respiratory symptoms and disabling occupational asthma. Only the rigorous health surveillance strategy, identifying workers who are sensitised or report upper respiratory symptoms and decreasing their individual exposures by 90% shortly after diagnosis, resulted in a decrease of almost 60% in disease burden after 20 years.ConclusionsThis study demonstrates that different intervention strategies have substantially different impacts on the burden of disease. The time window during which changes occur differs considerably between strategies. This information can assist policy makers in their choice of intervention and gives guidance for achievable reductions in disease burden.

ObjectivesStudies have shown that exposure to respirable crystalline silica (RCS) can lead to an increased risk of chronic obstructive pulmonary disease (COPD). Approximately five million EU workers are thought to be currently exposed to RCS in the EU, four million in construction work. The aim of this study was to evaluate the potential impact of intervention scenarios, designed to reduce RCS exposure within the EU, on the future burden of COPD in exposed workers.MethodsA microsimulation model was developed to simulate workers, incorporating the effects of RCS exposure and smoking on the development of COPD. A baseline scenario was carried out, simulating workers with the highest median RCS exposures based on previous measures in these sectors; including construction (0.09 mg/m3; 4.1 million workers) and manufacturing of mineral products (0.045 mg/m3; 535,000 workers), and assuming future exposures remain at current levels. Various intervention scenarios were then simulated; one modelled a 6% decline per year in median exposures from current levels; another modelled an increase in compliance with a 0.1 mg/m3 exposure limit (the proposed Binding Occupational Limit Value across the EU).ResultsUnder the intervention scenario where median exposures decline by 6% annually, approximately 630,000 new cases of COPD are expected to be prevented (of a total of 2.8 million under the baseline scenario) over the next 25 years. An alternative scenario where 90% compliance with a 0.1 mg/m3 limit is achieved results in approximately 980,000 cases prevented. In both scenarios, the greatest impact would be in countries with the largest construction sectors.ConclusionFor reductions in RCS exposure by increasing compliance or reducing median levels, the model predicts a substantial reduction in future burden of COPD, mainly within construction; however there remains uncertainty in the characterisation of exposures and the effects of exposure on lung function.