1,721,519 research outputs found
How much is particulate matter near the ground influenced by upper-level processes within and above the PBL? A summertime case study in Milan (Italy) evidences the distinctive role of nitrate
No full textChemical and dynamical processes lead to the formation of aerosol layers in the upper planetary boundary layer (PBL) and above it. Through vertical mixing and entrainment into the PBL these layers may contribute to the ground-level particulate matter (PM); however, to date a quantitative assessment of such a contribution has not been carried out. This study investigates this aspect by combining chemical and physical aerosol measurements with WRF/Chem (Weather Research and Forecasting with Chemistry) model simulations. The observations were collected in the Milan urban area (northern Italy) during the summer of 2007. The period coincided with the passage of a meteorological perturbation that cleansed the lower atmosphere, followed by a high-pressure period favouring pollutant accumulation. Lidar observations revealed the formation of elevated aerosol layers and evidence of their entrainment into the PBL. We analysed the budget of ground-level PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) with the help of the online meteorology-chemistry WRF/Chem model, focusing in particular on the contribution of upper-level processes. Our findings show that an important player in determining the upper-PBL aerosol layer is particulate nitrate, which may reach higher values in the upper PBL (up to 30% of the aerosol mass) than in the lower PBL. The nitrate formation process is predicted to be largely driven by the relative-humidity vertical profile, which may trigger efficient aqueous nitrate formation when exceeding the ammonium nitrate deliquescence point. Secondary PM2.5 produced in the upper half of the PBL may contribute up to 7-8 μg m-3 (or 25%) to ground-level concentrations on an hourly basis. The residual aerosol layer above the PBL is also found to potentially play a large role, which may occasionally contribute up to 10-12 μg m-3 (or 40%) to hourly ground-level PM2.5 concentrations during the morning hours. Although the results presented here refer to one relatively short period in one location, this study highlights the importance of considering the interplay between chemical and dynamical processes occurring within and above the PBL when interpreting ground-level aerosol observations. © Author(s) 2015
Some remarks about lidar data preprocessing and different implementations of the gradient method for determining the aerosol layers
No full textThe determination of atmospheric aerosol layers from lidar returns is possible through automated algorithms. This product is useful, for example, in monitoring the Boundary Layer Height (BLH) as well as volcanic plumes. Aerosol layers are usually detected using the gradient method, i.e. by finding the inflection points of the range-corrected backscattered signal. These points can be either calculated as the minima of the numerical derivative of the signal, or by zero-order Digital Wavelet Transforms. Since for low signal-to-noise ratios the numerical derivative is very prone to noise-induced fluctuations, a moving average is often performed. We demonstrate why this procedure should be avoided in the gradient calculation. Finally, an alternative approach to the Digital Wavelet Transform is proposed, giving the same results but lowering the computational times by about one order of magnitude. © 2014 by the Istituto Nazionale di Geofisica e Vulcanologia. All rights reserved
A COTS-based low-cost bistatic lidar for qualitative and semi-quantitative aerosol measurements
Full text linkUno strumento laser a basso costo per la rilevazione e la quantificazione delle goccioline liquide è stato messo a punto e collaudato con componenti commerciali e di basso costo. Il sistema è stato testato per valutare l'efficienza di filtraggio di alcune maschere facciali durante la pandemia da covid19. Un reflex a lente singola full-frame, dotato di una macro F/2.8 da 100 mm, è stata installata in una geometria a scattering in avanti per catturare la luce rossa ad angolo ridotto proveniente da un diodo laser allo stato solido (che emette a 650 nm) disperso da goccioline emesse da uno spruzzatore di vernice elettrico e attraversando una maschera. Il canale rosso delle immagini è stato elaborato dal formato raw Nikon (14 bit, compresso senza perdita) per garantire la linearità della risposta e ottenere la sezione trasversale di dispersione integrata del cloud. Lo strumento, installato a casa durante il lockdown, è stato testato per stimare la capacità di filtrazione delle maschere facciali, misurando la quantità normalizzata di particelle che passano attraverso diversi tipi di maschere di filtraggio. I risultati hanno dimostrato che il sistema è in grado di rilevare piccole quantità di goccioline e anche di fornire immagini delle sezioni del pennacchio degli aerosol.A low-cost, laser-based, instrument for liquid droplet detection and quantification has been set up and tested to evaluate the filtering efficiencies of some face masks. A full-frame Single Lens Reflex, equipped with a 100 mm F/2.8 macro, was installed in a forward scattering geometry to capture the small angle red light from a solid state laser diode (emitting at 650 nm) scattered from droplets emitted by an electric paint sprayer and crossing a face mask. The red channel of the pictures has been processed from the Nikon raw format (14-bit, lossless compressed) to ensure response linearity, and obtain the integrated scattering cross section of the cloud. The instrument, set up at home during the lockdown, was tested to estimate the filtering capability of facial masks, measuring the normalized amount of particles passing through different kinds of filtering masks. The results showed that the system is able to detect low quantities of droplets and also to provide imaging of the aerosol plume sections
Partitioning of Black Carbon between ultrafine and fine particle modes in an urban airport vs. urban background environment
No full textIn this work, we characterize the Black Carbon (BC) aerosol in an urban airport vs. urban background environment with the objective to evaluate when and how the ultrafine BC dominates the bulk aerosol. Aerosol optical and microphysical properties were measured in a Mediterranean urban area (Rome) at sites impacted by BC sources including fossil fuels (FF), and biomass burning (BB). Experimental BC data were interpreted through measurement-constrained simulations of BC microphysics and optical properties. A "scheme" to separate the ultrafine BC was experimented on the basis of the relation found between changes in the BC partitioning between Aitken and accumulation mode particles, and relevant changes in particle size distribution and optical properties of the bulk aerosol. This separation scheme, applied to experimental data, proved useful to reveal the impact of airport and road traffic emissions. Findings may have important atmospheric implications. The experimented scheme can help separating different BC sources (FF, BB, "aged" BC) when BC size distributions may be very difficult to obtain (satellite, columnar observations, routine monitoring). Indeed, separating the ultrafine BC from the fine BC may provide significant benefits in addressing BC impact on air quality and climate. © 2014 Elsevier Ltd
Primi test per la rivelazione di plastiche in acqua mediante fluorescenza indotta da Laser (LIF)
Full text linkVista l’importanza dal punto di vista ambientale che riveste la rapida rivelazione delle microplastiche in acqua, sono stati svolti dei primi test di fattibilità per l’utilizzo di un lidar fluorosensore per la rivelazione ed il riconoscimento di plastiche in ambiente marino. In particolare, è stata testata la capacità del sistema LIF (basato sulla fluorescenza indotta da laser) a scansione di linea del Laboratorio FSNTECFIS-DIM di Frascati di rivelare differenti campioni di plastiche, tra quelle che più comunemente si trovano nelle analisi di acque marine, sotto diversi spessori di acqua. I risultati si sono mostrati interessanti ed aprono le porte a studi più approfonditi per lo sviluppo di sistemi LIF specifici per la rivelazione
di microplastiche in ambiente marino senza la necessità di campionamenti.From the environmental point of view the rapid detection of microplastics in water is of primary importance. Here the first feasibility tests carried out for the implementation of a lidar fluorosensor to detect and recognition of plastics in the marine environment are presented. In particular, the ability of the linear scan system LIF (laser induced fluorescence) developed at the FSN-TECFIS-DIM Laboratory of Frascati to detect different plastics samples, among those that are most commonly found in water analysis, through different thicknesses of water have been carried out. The results have provided
interesting results, opening the way to more in-depth studies for the development of specific spectroscopic systems for the detection of microplastics in the marine environment without sampling
Iterative Learning in Functional Space for Non-Square Linear Systems
No full textMany control problems are naturally expressed in continuous time. Yet, in Iterative Learning Control of linear systems, sampling the output signal has proven to be a convenient strategy to simplify the learning process while sacrificing only marginally the overall performance. In this context, the control action is similarly discretized through zero-order hold-thus leading to a discrete-time system. With this paper, we want to investigate an alternative strategy, which is to track sampled outputs without masking the continuous nature of the input. Instead, we look at the whole input evolution as an element of a functional subspace. We show how standard results in linear Iterative Learning Control naturally extend to this context. As a result, we can leverage the infinite-dimensional nature of functional spaces to achieve exact tracking of strongly non-square systems (number of inputs less than outputs). We also show that constraints-like those imposed by intermittent control-can be naturally integrated within this framework
Evaluation of artificial neural networks performances on spectral classification tasks
Full text linkQuesto 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
- …
