Antonello Pasini

Monitoring of ambient BTX at Monterotondo (Rome) and indoor–outdoor evaluation in school and domestic sites

Results from a five month monitoring campaign of BTX (benzene, toluene and the sum of ethyl benzene, o-xylene, m-xylene and p-xylene), performed at Monterotondo, a little town 23 km NE of Rome, and correlation of the measured concentrations with meteorological and natural radioactivity data are presented and discussed. This campaign allowed us to extrapolate a pattern of the annual distribution of benzene in the town. A map, showing the average distribution of pollutants, is presented, as a useful tool to manage an appropriate policy of air pollution. A contemporary indoor-outdoor investigation has been performed at five homes and two classrooms of Monterotondo and at thirteen homes and thirteen classrooms in the outskirts of Rome. Non-smoking people and low polluted sites were chosen for this campaign, in order to highlight if commonly used domestic materials could act as internal sources. Data, obtained by employing long-term diffusive samplers, are compared with a parallel experiment showing the day-night indoor-outdoor trend. The obtained results, confirmed through statistical analysis of data, let us conclude that there is evidence of internal sources at homes whilst, in the schools, depletion phenomena prevail, probably due to the walls adsorption.

Artificial neural networks for small dataset analysis

Artificial neural networks (ANNs) are usually considered as tools which can help to analyze cause-effect relationships in complex systems within a big-data framework. On the other hand, health sciences undergo complexity more than any other scientific discipline, and in this field large datasets are seldom available. In this situation, I show how a particular neural network tool, which is able to handle small datasets of experimental or observational data, can help in identifying the main causal factors leading to changes in some variable which summarizes the behaviour of a complex system, for instance the onset of a disease. A detailed description of the neural network tool is given and its application to a specific case study is shown. Recommendations for a correct use of this tool are also supplied.

Analysis of spontaneous pneumothorax in the city of Cuneo: environmental correlations with meteorological and air pollutant variables

Background and PurposePrimary spontaneous pneumothorax (PSP) tends to cluster. Previous studies have found a correlation between PSP and atmospheric pressure variations or thunderstorms. We conducted this study to analyze the PSP correlations with meteorological variables and the concentrations of air pollutants in the city of Cuneo in Italy (IT).MethodsWe evaluated prospectively 451 consecutive PSP patients treated between 2004 and 2010. For each day within the period analyzed, the meteorological parameters and pollutants data were recorded. Statistical analyses on PSP were done for distribution characteristics, spectral autocorrelation, and spectral analysis. Multivariate regression analyses were performed using artificial neural networks.ResultsAnalysis of annual, seasonal, and monthly distributions showed no significant correlation between PSP and the time series. The spectral analysis showed that PSP events were not random. Correlations between meteorological and environmental variables confirmed that PSP was significantly more likely to occur on warm windy days with high atmospheric pressure and high mean nitrogen dioxide concentration.ConclusionsMeteorological parameters and atmospheric pollutants might explain the cluster onset of PSP.

Attribution of recent temperature behaviour reassessed by a neural-network method

Attribution studies on recent global warming by Global Climate Model (GCM) ensembles converge in showing the fundamental role of anthropogenic forcings as primary drivers of temperature in the last half century. However, despite their differences, all these models pertain to the same dynamical approach and come from a common ancestor, so that their very similar results in attribution studies are not surprising and cannot be considered as a clear proof of robustness of the results themselves. Thus, here we adopt a completely different, non-dynamical, data-driven and fully nonlinear approach to the attribution problem. By means of neural network (NN) modelling, and analysing the last 160 years, we perform attribution experiments and find that the strong increase in global temperature of the last half century may be attributed basically to anthropogenic forcings (with details on their specific contributions), while the Sun considerably influences the period 1910–1975. Furthermore, the role of sulphate aerosols and Atlantic Multidecadal Oscillation for better catching interannual to decadal temperature variability is clarified. Sensitivity analyses to forcing changes are also performed. The NN outcomes both corroborate our previous knowledge from GCMs and give new insight into the relative contributions of external forcings and internal variability to climate.

An overview of the use of artificial neural networks in lung cancer research

The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification. ANN inference has applications in tasks that require attention focusing. ANNs also have a niche to carve in clinical decision support, but their success depends crucially on better integration with clinical protocols, together with an awareness of the need to combine different paradigms to produce the simplest and most transparent overall reasoning structure, and the will to evaluate this in a real clinical environment. We have performed an assessment of the evidence for improvements in the use of ANN in lung cancer research. Our analysis showed that often the use of ANN in the medical literature had not been performed in an accurate manner. A strict cooperation between physician and biostatisticians could be helpful in determine and resolve these errors.

Quantitative Interpretation of Air Radon Progeny Fluctuations in Terms of Stability Conditions in the Atmospheric Boundary Layer

Determining the mixing height using a tracer can improve the information obtained using traditional techniques. Here we provide an improved box model based on radon progeny measurements, which considers the vertical entrainment of residual layers and the variability in the soil radon exhalation rate. The potential issues in using progeny instead of radon have been solved from both a theoretical and experimental perspective; furthermore, the instrumental efficiency and the counting scheme have been included in the model. The applicability range of the box model has been defined by comparing radon-derived estimates with sodar and lidar data. Three intervals have been analyzed (“near-stable”, “transition” and “turbulent”), and different processes have been characterized. We describe a preliminary application case performed in Rome, Italy, while case studies will be required to determine the range limits that can be applied in any circumstances.

Modeling Radon Behavior for Characterizing and Forecasting Geophysical Variables at the Atmosphere–Soil Interface

As well known, noble gases are often used as stable tracers in several geophysical environments, due to their basic property of being chemical noninteracting. Among these noble gases, the attention of researchers in the last decades has been focused on radon.

Arctic amplification: evidence from a cluster analysis of temperature time series for eight latitude bands

The warming trend in the Arctic is almost twice as large as the global average in recent decades. This is known as Arctic amplification. In this paper, we perform a cluster analysis of temperature time series for eight latitude bands. Our empirical findings confirm the Arctic amplification and further shed light on this phenomenon. In particular, our investigation allows us to go beyond the simple descriptive analysis of the data. The adopted distance measures the differences between the data generating processes behind the series. Differences in the dynamic structures of the considered series are then taken into consideration allowing for a more comprehensive understanding of the phenomenon.

Climate actions in a changing world

Our Anthropocene era is characterized by an increasingly complex and inter-connected world, where problems such as climate change, food security, conflicts, terrorism and migrations are strongly linked and must be faced with common strategies. In this framework, climate actions represent a critical component of effective integrated responses.