Livia Trizio

Exhaled volatile organic compounds identify patients with colorectal cancer

An effective screening tool for colorectal cancer is still lacking. Analysis of the volatile organic compounds (VOCs) linked to cancer is a new frontier in cancer screening, as tumour growth involves several metabolic changes leading to the production of specific compounds that can be detected in exhaled breath. This study investigated whether patients with colorectal cancer have a specific VOC pattern compared with the healthy population.

Assessment of the impact of the vehicular traffic on BTEX concentration in ring roads in urban areas of Bari (Italy).

A BTEX monitoring campaign, consisting of two weekly periods, was carried out in Bari, south-eastern Italy, in order to evaluate the impact of the vehicular traffic on the air quality at the main access roads of the city. Twenty-one sampling sites were selected: the pollution produced by the traffic in the vicinity of all exits from the ring road and some access roads to the city, those with higher traffic density, were monitored. Contemporarily the main meteorological parameters (ambient temperature, wind, atmospheric pressure and natural radioactivity) were investigated. It was found that in the same traffic conditions, barriers, buildings and local meteorological conditions can have important effects on the atmospheric dispersion of pollutants. This situation is more critical in downtown where narrow roads and high buildings avoid an efficient dispersion producing higher levels of BTEX. High spatial resolution monitoring allowed both detecting the most critical areas of the city with high precision and obtaining information on the mean level of pollution, meaning air quality standard of the city. The same concentration pattern and the correlation among BTEX levels in all sites confirmed the presence of a single source, the vehicular traffic, having a strong impact on air quality.

Chemical characterization of exhaled breath to differentiate between patients with malignant plueral mesothelioma from subjects with similar professional asbestos exposure

Malignant pleural mesothelioma (MPM) is an aggressive tumour whose main aetiology is the long-term exposure to asbestos fibres. The diagnostic procedure of MPM is difficult and often requires invasive approaches; therefore, it is clinically important to find accurate markers for MPM by new noninvasive methods that may facilitate the diagnostic process and identify patients at an earlier stage. In the present study, the exhaled breath of 13 patients with histology-established diagnosis of MPM, 13 subjects with long-term certified professional exposure to asbestos (EXP) and 13 healthy subjects without exposure to asbestos (healthy controls, HC) were analysed. An analytical procedure to determine volatile organic compounds by sampling of air on a bed of solid sorbent and thermal desorption GC-MS analysis was developed in order to identify the compounds capable of discriminating among the three groups. The application of univariate (ANOVA) and multivariate statistical treatments (PCA, DFA and CP-ANN) showed that cyclopentane and cyclohexane were the dominant variables able to discriminate among the three groups. In particular, it was found that cyclohexane is the only compound able to differentiate the MPM group from the other two; therefore, it can be a possible marker of MPM. Cyclopentane is the dominant compound in the discrimination between EXP and the other groups (MPM and HC); then, it can be considered a good indicator for long-term asbestos exposure. This result suggests the need to perform frequent and thorough investigations on people exposed to asbestos in order to constantly monitor their state of health or possibly to study the evolution of disease over time.

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANNs forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies.

Indoor air quality (IAQ) assessment in a multistorey shopping mall by high-spatial-resolution monitoring of volatile organic compounds (VOC)

In order to assess indoor air quality (IAQ), two 1-week monitoring campaigns of volatile organic compounds (VOC) were performed in different areas of a multistorey shopping mall. High-spatial-resolution monitoring was conducted at 32 indoor sites located in two storehouses and in different departments of a supermarket. At the same time, VOC concentrations were monitored in the mall and parking lot area as well as outdoors. VOC were sampled at 48-h periods using diffusive samplers suitable for thermal desorption. The samples were then analyzed with gas chromatography–mass spectrometry (GC–MS). The data analysis and chromatic maps indicated that the two storehouses had the highest VOC concentrations consisting principally of terpenes. These higher TVOC concentrations could be a result of the low efficiency of the air exchange and intake systems, as well as the large quantity of articles stored in these small spaces. Instead, inside the supermarket, the food department was the most critical area for VOC concentrations. To identify potential emission sources in this department, a continuous VOC analyzer was used. The findings indicated that the highest total VOC concentrations were present during cleaning activities and that these activities were carried out frequently in the food department. The study highlights the importance of conducting both high-spatial-resolution monitoring and high-temporal-resolution monitoring. The former was able to identify critical issues in environments with a complex emission scenario while the latter was useful in interpreting the dynamics of each emission source.

Assessment of Impacts Produced by Anthropogenic Sources in a Little City near an Important Industrial Area (Modugno, Southern Italy)

An annual monitoring campaign of VOCs, consisting of twelve sampling periods, was carried out from June 2008 to June 2009 in Modugno, a city located in the Apulia region (Southern Italy), in order to assess the urban air quality, identify the main emission sources, and quantify the cancer and no-cancer risk attributable to inhalation exposures. Monitoring, carried out by using the Radiello diffusive samplers, was conducted in eleven sampling sites throughout the city taking into account the traffic density and the architecture of the city. From the study of the data, it was found that, among all considered VOCs, benzene, toluene, ethylbenzene, and xylenes (BTEX) are the pollutants at higher concentration. The analysis of VOC concentrations, the study of the topography of the city, and the use of different diagnostic ratios between the BTEX species showed that the vehicular traffic emissions were the predominant source of VOCs in the urban area of Modugno. Despite that the annual concentration of benzene is lower than the regulatory limit, the estimation of cancer risk showed that the global lifetime cancer risk attributed to the investigated VOC exposure was not negligible and therefore should be taken into account in future regulatory approaches.

Application of Artificial Neural Networks to a Gas Sensor-Array Database for Environmental Monitoring

A sensors array based on two different types of chemical sensors such as tin dioxide commercial sensors and carbon nanotubes innovative sensors developed in the ENEA laboratories to monitor gases (e.g., CO, NO2, SO2, H2S and CO2) of relevance in polluted air has been analyzed. Measurements of chemical sensing of the sensors array have been performed in laboratory to create a database for applying artificial neural networks (ANNs) algorithms to quantify gas concentration of individual air pollutants and binary gas-mixture. A total number of 3,875 data-samples based on 413 distinct gas concentrations measured by 14 gas sensors has been used in the database. The ANN performance has been assessed for each targeted air-pollutant. The lowest normalized mean square error (NMSE) of 6%, 9% and 11% has been achieved for NO2, SO2 and CO2, respectively. In the contrast, NMSE as high as 28% and 39% has been measured for CO and H2S, respectively. The aim of this study is the selection of an optimal set of gas sensors in the array for enhanced environmental measurements of gas concentration in real-scenario.

A Portable Sensor System for Air Pollution Monitoring and Malodours Olfactometric Control

A portable sensor-system based on solid-state gas sensors has been designed and implemented as proof-of-concept for environmental air-monitoring applications and malodours olfactometric control. Commercial gas sensors (metal-oxides, n-type) and nanotechnology sensors (carbon nanotubes, p-type) are arranged in a configuration of array for multisensing and multiparameter devices. Wireless sensors at low-cost are integrated to implement a portable and mobile node, that can be used as early-detection system in a distributed sensor network. Real-time and continuous monitoring of hazardous air-contaminants (e.g., NO2, CO, SO2, BTEX, etc.) has been performed by in-field measurements. Moreover, monitoring of landfill gas generated by fermentation of wastes in a municipal site has been carried out by the portable sensor-system. Also, it was demonstrated that the sensor-system is able to assess the malodours emitted from a municipal waste site and remarkably compared to the olfactometry method based on a trained test panel.

Odour Impact Assessment by a Multiparametric System (electronic Noses/ch4-nmhc Analyser)

Odour impacts are assessed according to two principal approaches: evaluation and estimation of the pollutant relapse on the territory and monitoring through standard methodologies. In particular, odour monitoring is characterized by a great complexity due principally to the strict association of odour pollution to human perception. The standardized methodology for the determination of odour concentration is represented by an instrumental sensory technique, the dynamic olfactometry, that is affected by some limitations. This methodology provides punctual odour concentration data and it does not allow to perform continuous and field measurements, useful for monitoring the industrial processes causing odour emissions. The need of carrying out a continuous monitoring having been encouraged the use of an odour surrogate monitoring, performed by specific or not specific instruments (chemical analysers or electronic noses). The surrogate measurements employment is based on the fact that the ratio of surrogate concentration to odour units must be relatively constant and known. This paper focuses on the development of a multiparametric system for the evaluation of odour impact caused by an industrial source. The system has been tested during olfactometric monitoring campaigns conducted at the industrial site, coupling the results of electronic noses. The purpose of the research work has been to find an indicator for the odour emissions produced by the examined industrial site, and to correlate it with odour concentrations. This study has allowed to demonstrate the real applicability of not specific instruments to odour continuous monitoring, useful to detect a change of state in operating conditions of industrial processes and control it.

A Gas Sensor Array For Environmental Air Monitoring: A Study Case Of Application Of Artificial Neural Networks

An array of commercial gas sensors and nanotechnology sensors has been integrated to quantify gas concentration of air‐pollutants. A variety of chemoresistive gas sensors, commercial (Figaro and Fis) and developed at ENEA laboratories (metal‐modified carbon nanotubes) were tested to implement a database useful for applied artificial neural networks (ANNs). The ANN algorithm used is the common perceptron multi‐layer feed‐forward network based on error back‐propagation. Electronic Noses based on various sensor arrays related to mammalian olfactory systems have been largely reported [1,2]. Here, we reported on the perceptron‐based ANNs applied to a large database of 3875 datapoints for environmental air monitoring. The ANNs performance has been individually assessed for any targeted gas. The response of the classifier has been measured for NO2, CO, CO2, SO2, and H2S gas. The NO2 characteristics exhibit that real concentrations and predicted concentrations are very close with a normalized mean square error (N...