• Open Access close
• Research software close
• Other research products close
• IT close
• ENEA Open Archive close
• Knowmad Institut close

In questo tempo di crisi, scopriamo la necessità di valori antropologici per guidare la navigazione della nostra specie su questo pianeta. Dal 2000 EcoOne, sezione ecologica dell’Associazione Internazionale New Humanity-NGO (status consultivo generale dell’ECOSOC delle Nazioni Unite, partner ufficiale dell’UNESCO, accreditata presso l’UNEP) ha organizzato numerose conferenze internazionali su temi ambientali. L’incontro del 2020 “Nuove vie verso l’ecologia integrale” ha discusso le sfide economiche, ambientali e sociali della transizione ecologica: alcuni paper selezionati della conferenza sono pubblicati in questo rapporto. In this time of crisis, we discover the need for anthropological values to guide the navigation of our species on this planet. Since 2000 EcoOne, the ecological section of the International New Humanity Association-NGO (general consultative status of the United Nations ECOSOC, official partner of UNESCO, accredited to UNEP) has organized numerous international conferences on environmental issues. The 2020 meeting “New Pathways to Integral Ecology” discussed the economic, environmental and social challenges of the ecological transition: some selected papers from the conference are published in this report.

Questo documento presenta uno studio preliminare riguardo la valutazione delle performance di una rete neurale artificiale (ANN) in uno scenario di classificazione spettrale. Differenti architetture di reti neurali sono state testate al fine di risolvere problemi di identificazione spettrale, iniziando da un semplice insieme di input, per poi raffinare il problema un passo alla volta. Dato che l’obiettivo in futuro è quello di definire un sistema robusto ed efficiente per la classificazione di spettri Raman in condizioni non di laboratorio, l’insieme di spettri di input è stato costruito sul modello di spettri Raman, con segnali rumorosi contenenti picchi gaussiani stretti. Il comportamento delle reti neurali è stato valutato variando le principali proprietà di input e analizzando gli indicatori chiave di performance delle reti neurali. Tutti i test contenuti in questo documento sono stati sviluppati e generati con il software MATLAB®. Dopo una descrizione generale delle reti neurali e delle loro proprietà, il documento analizza l’identificazione del problema, la definizione di rumore e alcuni concetti nel campo dell’inferenza statistica. Quindi, vengono illustrati una serie di test con i relativi grafici di performance, in cui sono stati usati sia spettri artificiali che spettri reali generati in laboratorio con una lampada a vapori di mercurio. Alla fine del documento sono state prodotte una serie di conclusioni, con ulteriori analisi e raffinamenti del problema da eseguire in futuro. This report is a preliminary study about the evaluation of the Artificial Neural Network (ANN) performances in a spectral classification scenario. Several different ANN architectures has been tested in order to solve signal identification tasks, starting from a simple input set and refining the task step to step. Since the aim is to define in the future a robust method to classify Raman spectra in non-laboratory conditions, the input set has been built as a Raman-like spectra set, with noisy signals containing narrow Gaussian peaks. The behavior of the neural networks has been evaluated varying the main input properties and analyzing the standard ANN key performance indicators. All the tests in this paper have been developed and generated with MATLAB®. After a description of the artificial neural networks and their features, the report analyzes the task identification, the noise definition and some concepts in the statistic theory field. Then, a series of tests with their performance plots are illustrated, both with artificial spectra and real laboratory spectra generated with a mercury-vapor lamp. In the end, the report produces a list of conclusions with some further analysis and task refinements to do.

Similarities and differences in the chemical composition of the submicron atmospheric aerosol are analyzed from a unique set of measurements performed at 21 sites across Europe for at least one year. These sites are located between 35 and 62°N and 10° W – 26°E, and represent various types of settings (remote, coastal, rural, industrial, urban). Measurements were all carried out on-line with a 30-minute time resolution using mass spectroscopy based instruments known as Aerosol Chemical Speciation Monitors (ACSM) and Aerosol Mass Spectrometers (AMS) and following common measurement guidelines. Data regarding organics, sulfate, nitrate and ammonium concentrations, as well as the sum of them called non-refractory submicron aerosol mass concentration ([NR-PM1]) are discussed. NR-PM1 concentrations generally increase from remote to urban sites. They are mostly larger in the mid-latitude band than in southern and northern Europe. On average, organics account for the major part (36-64%) of NR-PM1 followed by sulfate (12-44%) and nitrate (6-35%). The annual mean chemical composition of NR-PM1 at rural (or regional background) sites and urban background sites are very similar. Considering rural and regional background sites only, nitrate contribution is higher and sulfate contribution is lower in mid-latitude Europe compared to northern and southern Europe. Large seasonal variations in concentrations (µg/m³) of one or more components of NR-PM1 can be observed at all sites, as well as in the chemical composition of NR-PM1 (%) at most sites. Significant daily cycles in the contribution to [NR-PM1] of organics, sulfate, and nitrate can be observed at a majority of sites both in winter and summer. Early morning minima in organics in concomitance with maxima in nitrate are common features at regional and urban background sites. Daily variations are much smaller at a number of coastal and rural sites. Looking at NR-PM1 chemical composition as a function of NR-PM1 mass concentration reveals that although organics account for the major fraction of NR-PM1 at all concentration levels at most sites, nitrate contribution generally increases with NR-PM1 mass concentration and predominates when NR-PM1 mass concentrations exceed 40 µg/m³ at half of the sites. JRC.C.5 - Air and Climate

Sommario 1. Introduzione 3. Progetti 3.1.1 Fonte di finanziamento 3.1.2 Ambiti di ricerca 3.1.3 Repository 3.1.4 Personale 3. Competenze 3.1.1 Ambiti 3.1.2 ALTRO 3.1.3 COMPETENZE VERTICALI 3.1.4 SUGGERIMENTI 5. CONCLUSIONI ALLEGATI A. PROGETTI PER AMBITI DI RIFERIMENTO B. RICOGNIZIONE CAWI (COMPUTER ASSISTED WEB INTERVIEW) C. SCHEDE INVIATE PER LA COMPILAZIONE ON-LINE