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Air pollution is a dramatic issue that grips the majority of densely populated cities in the world. It is nowadays quite evident that there is a relationship between air quality and some types of cancers (i.e. lung and bladder cancer), as reported by the International Agency for Research on Cancer (IARC 2012). Although the time spent commuting usually represents a small portion of a person's daily time (3-6%), it is responsible around for 21% of daily personal exposure and roughly 30% of the total inhaled dose (Dons et al. 2012). To gain information on this topic, we conducted an air quality measurement campaign (six weeks between November and December 2021) on three different routes within the metropolitan city of Lyon (France). These routes were chosen to be representative of different urban areas (e.g. city centre, periphery, vegetated areas). The measurements were taken two times for day (i.e. in the morning and the evening, in order to simulate the commuters round trip) using four different modes (walk, bike, car and public transport). Two different portable air quality sensors were used to measure the pollutants: the MONICA sensors (developed by ENEA, De Vito et al. 2021) that measure PM1, PM2.5, PM10, NO2, CO and O3 and the AirBeam 2 sensors (provided by ATMO AURA) that measure only the particular matters. The objective of this study is twofold: from one side to assess the exposure choosing different modes of commuting and, from the other side, to evaluate how the influence of the meteorological-climatic variables (e.i. temperature, relative humidity, precipitation, wind direction, wind speed, cloud cover, solar radiation and atmospheric boundary layer stability/instability) affect the air quality. Preliminary results show that private car users are generally affected by lower levels of air pollution with respect to the other modes (as expected, Okokon et al. 2017), but this is strongly influenced by the type of ventilation used (internal or external air recirculation, open and closed windows). ReferenceDe Vito, S., Esposito, E., Massera, E., Formisano, F., Fattoruso, G., Ferlito, S., ... & Di Francia, G. (2021). Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation. Sensors, 21(15), 5219.Dons, E., Panis, L. I., Van Poppel, M., Theunis, J., & Wets, G. (2012). Personal exposure to black carbon in transport microenvironments. Atmospheric Environment, 55, 392-398.IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. (2012). Chemical agents and related occupations. IARC monographs on the evaluation of carcinogenic risks to humans, 100, 9–562.Okokon, E. O., Yli-Tuomi, T., Turunen, A. W., Taimisto, P., Pennanen, A., Vouitsis, I., ... & Lanki, T. (2017). Particulates and noise exposure during bicycle, bus and car commuting: A study in three European cities. Environmental Research, 154, 181-189.

Recent advances in IoT and chemical sensors calibration technologies have led to the proposal of Hierarchical air quality monitoring networks. They are indeed complex systems relying on sensing nodes which differs from size, cost, accuracy, technology, maintenance needs while having the potential to empower smart cities and communiities with increased knowledge on the highly spatiotemporal variance Air Quality phenomenon (see [1]). The AirHeritage project, funded by Urban Innovative Action program have developed and implemented a hierarchical monitoring system which allows for offering real time assessments and model based forecasting services including 7 fixed low cost sensors station, one (mobile and temporary located) regulatory grade analyzer and a citizen science based ultra high resolution AQ mapping tool based on field calibrated mobile analyzers. This work will analyze the preliminary results of the project by focusing on the machine learning driven sensors calibration methodology and citizen science based air quality mapping campaigns. Thirty chemical and particulate matter multisensory devices have been deployed in Portici, a 4Km2 city located 7 km south of Naples which is affected by significant car traffic. The devices have been entrusted to local citizens association for implementing 1 preliminary validation campaign (see [2]) and 3 opportunistic 2-months duration monitoring campaigns. Each 6 months, the devices undergoes a minimum 3 weeks colocation period with a regulatory grade analyzer allowing for training and validation dataset building. Multilinear regression sw components are trained to reach ppb level accuracy (MAE [1] Nuria Castell et Al., Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environment International, Volume 99, 2017, Pages 293-302 ISSN 0160-4120, https://doi.org/10.1016/j.envint.2016.12.007.[2] De Vito, S, et al., Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation. Sensors 2021, 21, 5219. https://doi.org/10.3390/s21155219[3] https://airindex.eea.europa.eu/Map/AQI/Viewer/

A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.

Carbon Fiber reinforced polymer (CFRP) bonding is a paramount asset for enabling the transition towards a green aircraft based transport industry. Guaranteeing the quality of the bonding is crucial for ensuring its strength and its robustness to mechanical stresses. Here, we present the results of an Ion Mobility Spectrometer based no-contact methodology to detect the presence of two contaminants that are usually present on surfaces before bonding. False negative rate was found to be less than 5% at an accuracy level of 92% for detection which look promising for further development aiming at field deployment.

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Tungsten disulfide (WS2) nanotubes (NTs) are examined here as a filler for polylactide (PLA) for their ability to accelerate PLA crystallization and for their promising biocompatibility in relevant to biomedical applications of PLA-WS2 nanocomposites. In this work, we have studied the structural and thermal properties of PLA-WS2 nanocomposite films varying the concentration of WS2 NTs from 0 (neat PLA) to 0.6 wt%. The films were uniaxially drawn at 90 °C and annealed at the same temperature for 3 and 10 min. Using wide angle x-ray scattering, Raman spectroscopy and differential scanning calorimetry, we probed the effects of WS2 NT addition on the structure of the PLA films at various stages of processing (unstretched, stretching, annealing). We found that 0.6 wt% of WS2 induces the same level of crystallinity in as stretched PLA-WS2 as annealing in neat PLA for 10 min. These data provide useful insights into the role of WS2 NTs on the structural evolution of PLA-WS2 composites under uniaxial deformation, and extend their applicability to situations where fine tuning of PLA crystallinity is desirable.

This chapter introduces various extended non-destructive testing (ENDT) techniques for surface quality assessment, which are first characterized, then enhanced, and finally applied to assess the level of pre-bond contaminations intentionally applied to carbon fiber reinforced plastic (CFRP) adherends following the procedures described in the previous chapter. Based on two user cases comprising different scenarios that are characteristic of either aeronautical production or repair, the detailed tests conducted on two types of sample geometry, namely flat coupons and scarfed pilot samples with a more complex shape, form the basis for applying the advanced ENDT procedures for the monitoring of realistic and real aircraft parts, as will be described in Chap. 10.1007/978-3-319-92810-4_5. Specifically, the reported investigations were performed to assess the surface quality of first ground and then intentionally contaminated CFRP surfaces using the following ENDT tools: the aerosol wetting test (AWT), optically stimulated electron emission (OSEE), two differently implemented approaches based on electronic noses, laser-induced breakdown spectroscopy (LIBS), Fourier-transform infrared (FTIR) spectroscopy, laser-induced fluorescence (LIF), and laser vibrometry.

This chapter highlights two advances towards a higher maturity of versatile extended non-destructive testing (ENDT) procedures. Full-scale demonstration tests are presented in realistic user application cases that involve typical production or repair scenarios. Subsequently, the investigations used to assess the probability of detection (POD) are detailed for the respective ENDT processes and application-relevant scenarios in a realistic environment. Although some results indicated that some additional in-depth investigations would be even more enlightening, these demonstrations still clearly showed that developments and progress described in the previous chapters have enabled some of the technologies to achieve a maturity that is sufficient to proceed towards industrial implementation. Some ENDT techniques revealed the presence of contaminants on real structural parts with unknown contaminant amounts. For the first time, POD results obtained for ENDT investigations are presented. Some ENDT procedures permitted POD results to be obtained for several scenarios, while others showed technologically relevant POD only for certain scenarios. For two ENDT techniques, determining the POD helped to enhance the respective testing and evaluation procedures. In most of the cases, it was possible to estimate a preliminary quantification of POD by giving the POD90/95. For some techniques, this value was below the lowest contamination degree.

COVID19 Pandemic impacts have been associated with Air Quality (AQ) with its mortality index being significantly affected by high pollution levels. Significant mobility limitations have contributed to slow down the pandemic in Italy having as a side effect a definite decrease of pollution levels. Phase 2 while easing those limits still see a significant reduction of commuting and schools related car traffic emissions. High resolution AQ monitoring can now allow to obtain a picture of AQ if smart cities will be capable to reach long sought emissions goals. Furthermore, it could support the identification and forecasting of possible future local SARS-COV-2 outbursts. In this work, we present the outcomes of a high resolution AQ monitoring opportunistic campaign. These have been achieved through the deployment of field data driven calibration trough machine learning and a smartphone centered IoT infrastructure, capable to store, visualize and give exposome feedback to monitoring volunteers.

The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.

Avio-launched micro satellites with re-entry capability are useful to acquire data which can be used in future space operations to safely accommodate payloads into the cargo compartment. A deployable aero-brake and a thermal-shield with a detachable payload capsule are expected to protect the satellite and allow the recovery phase. This paper presents the contribution to the preliminary project of payload sensor network to perform anomaly detection and direct measure of relevant flight quantities. In particular, during its operation the satellite needs to estimate temperature, pressure, velocity variations, atmospheric drag, position and energy consumptions. The possibility of a direct measurement by means of an heterogeneous sensor network would improve these estimations. A survey on hardware technologies available is among the objectives of this work, and it will constitute the basis for further investigations on appropriate data fusion techniques.

Air Quality Multi-sensors Systems (AQMS) are IoT devices based on low cost chemical microsensors array that recently have showed capable to provide relatively accurate air pollutant quantitative estimations. Their availability permits to deploy pervasive Air Quality Monitoring (AQM) networks that will solve the geographical sparseness issue that affect the current network of AQ Regulatory Monitoring Systems (AQRMS). Unfortunately their accuracy have shown limited in long term field deployments due to negative influence of several technological issues including sensors poisoning or ageing, non target gas interference, lack of fabrication repeatability, etc. Seasonal changes in probability distribution of priors, observables and hidden context variables (i.e. non observable interferents) challenge field data driven calibration models which short to mid term performances recently rose to the attention of Urban authorithies and monitoring agencies. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models enabling continuous learning. Relevant parameters influence in different network and note-to-node recalibration scenario is analyzed. Results are hence useful for pervasive deployment aimed to permanent high resolution AQ mapping in urban scenarios as well as for the use of AQMS as AQRMS backup systems providing data when AQRMS data are unavailable due to faults or scheduled mainteinance., Submitted to Pattern Recognition Letters

Air Quality Multisensor Systems (AQMS) have shown to be able to provide, if properly calibrated, high quality data in terms of indicative measurements. In this work, we show how this capability can be used for using them as a backup tool for reference analyzers providing a way to obtain meaningful data in the case reference data become unavailable due to failures. Field calibration robustness is still debated but the stream-like availability of reference data make possible to continuously update the AQMS calibration function to sensors and concept drifts so to provide optimal performances whenever needed.