Sensitivity analysis of an operational advanced Gaussian model to different turbulent regimes

A non-reactive air pollution model evaluating ground level concentration is presented. It relies on a new Gaussian formulation (LUPINI R. and TIRABASSI T., J. Appl. Meteor., 20 (1981) 565-570; TIRABASSI T. and RIZZA U., Atmos. Environ., 28 (1994) 611-615) for transport and vertical diffusion in the Atmospheric Boundary Layer (ABL). In this formulation, the source height is replaced by a virtual height expressed by simple functions of meteorological variables. The model accepts a general profile of wind u(z) and eddy diffusivity coefficient Kz . The lateral dispersion coefficient is based on Taylor’s theory (TAYLOR G. I., Proc. London Math. Soc., 20 (1921) 196-204). The turbulence in the ABL is subdivided into various regimes, each characterized by different parameters for length and velocity scales. The model performances under unstable conditions have been tested utilizing two different data sets

Modelling local winds over the Salento peninsula

A three-day mesoscale numerical simulation has been performed over the narrow Salento peninsula (south-eastern Italy) during summer conditions characterised by weak synoptic forcing. These atmospheric conditions favour the development of complex sea-breeze systems and convergence zones on the peninsula. The aim of this work is to investigate the ability of an atmospheric mesoscale model to reproduce the surface fields of meteorological variables in the presence of local-scale forcing and breeze circulations, which are fundamental in applications such as air pollution modelling and nowcasting. The modelled fields have been compared with available surface measurements and sodar data. Results indicate that the model can simulate the general mean wind field in a realistic way. The diurnal evolution of the wind is well reproduced and the maximum deviations mostly occur during the night, being associated with calm conditions. Statistical analysis indicates that the typical mean bias is found to be about 1 m s−1 for hourly averaged wind speed, less than 20° for wind direction and about 1°C for temperature. The root mean square error (rmse) varies from 1 to 3 m s−1 for wind speed, from 50° to 70° for wind direction, and is about 2.4°C for temperature. All the values of the numerical indexes are within ranges which are characteristic of those found for other state-of-the-art models applied to similar cases studies. Despite a good overall agreement between predictions and observations, some discrepancies were found in the individual profiles due both to the limited spatial representation of the local details and to the complex wind field which makes the space–time matching between the model and the observations quite critical. The structures of the thermal mixed layer and the breeze convergence zone are similar to numerical studies relative to more idealised conditions. Copyright © 2004 Royal Meteorological Societ

Development of a grid-dispersion model in a large-eddy-simulation–generated planetary boundary layer

Numerical simulations of dispersion experiments within the planetary boundary layer are actually feasible making use of Large Eddy Simulations (LES). In Eulerian framework, a conservation equation for a passive scalar may be superimposed on LES wind/turbulence fields to get a realistic description of timevarying concentration field. Aim of this work is to present a numerical technique to solve the Eulerian conservation equation. The technique is based on Fractional Step/Locally One-Dimensional (LOD) methods. Advection terms are calculated with a semi-Lagrangian cubic-spline technique, while diffusive terms are calculated with Crank-Nicholson implicit scheme. To test the grid model, the dispersion of contaminants emitted from an elevated continuous point source in a convective boundary layer is simulated. Results show that the calculated concentration distributions agree quite well with numerical and experimental data found in the literature

An architecture for ultra-low-voltage ultra-low-power compressed sensing-based acquisition systems

Compressed Sensing (CS) has been addressed as a paradigm capable of lowering energy requirements in acquisition systems. Furthermore, the capability of simultaneously acquiring and compressing an input signal makes this paradigm perfectly suitable for low-power devices. However, the need for analog hardware blocks makes the adoption of most of standard solutions proposed so far in the literature problematic when an aggressive voltage and energy scaling is considered, as in the case of ultra-low-power IoT devices that need to be battery-powered or energy harvesting-powered. Here, we investigate a recently proposed architecture that, due to the lack of any analog block (except for the comparator required in the following A/D stage) is compatible with the aggressive voltage scaling required by IoT devices. Feasibility and expected performance of this architecture are investigated according to the most recent state-of-the-art literature

A model for the estimation of standard deviation of air pollution concentration in different stability conditions

We propose to estimate the standard deviations of the air pollution concentration in the horizontal and vertical direction, σy and σz, based on Pasquill’s well-known equation, in terms of the wind variance and the Lagrangian integral time scales, on the basis of an atmospheric turbulence spectra model. The main advantage of the spectral model is its treatment of turbulent kinetic energy spectra as the sum of buoyancy and a shear produced part, modelling each one separately. The formulation represents both shear and buoyant turbulent mechanisms characterizing the various regimes of the Planetary Boundary Layer, and gives continuous values at any elevation and all stability conditions from unstable to stable. As a consequence, both the wind variance and the Lagrangian integral time scales in the dispersion parameters are more general than those found in literature, because they are not derived from diffusion experiments as most parameterizations. Furthermore, they provide a formulation continuous for the whole boundary layer resulting more physically consistent. The σy, σz parameters, included in a Gaussian model have been tested and compared with a dispersion scheme reported in the literature, using experimental data in different emission conditions (low and tall stacks) and in several meteorological conditions ranging from stable to convective. Results show that the dispersion model with the sigmas parameterisation included, produces a good fitting of the measured ground-level concentration data in all the experimental conditions considered, performing slightly better than other state-of-art models

Wolbachia, doxycycline and macrocyclic lactones: New prospects in the treatment of canine heartworm disease

Abstract Melarsomine dihydrochloride (Immiticide®, Merial) is the only approved adulticidal drug for the treatment of canine heartworm disease (HWD). However, in cases where arsenical therapy is not possible or is contraindicated, a monthly heartworm preventive along with doxycycline for a 4-week period, which targets the bacterial endosymbiont Wolbachia, might be considered. There are published reports on the efficacy of ivermectin and doxycycline in both experimentally and naturally infected dogs, but no data on the use of other macrocyclic lactones (MLs) with a similar treatment regime. Preliminary results of studies in dogs show that a topical formulation of moxidectin, the only ML currently registered as a microfilaricide, is also adulticidal when combined with doxycycline. It is not yet known if the efficacy of these combination therapies is due to pharmacokinetic synergism. A recent study showed that serum levels of doxycycline in dogs treated with the combination protocol were not statistically different compared to dogs treated with doxycycline alone. However, lungs from dogs treated with the combination therapy showed a marked reduction in T regulatory cells, indicating that treatment efficacy may be due to a heightened immune response against the parasite. Further studies are necessary to evaluate the long-term clinical outcome of combination protocols and to establish the most efficient treatment for HWD in dogs

A GIS BASED AIR QUALITY SYSTEM FOR THE APULIA REGION, SOUTHERN ITALY

Apulia region in the Southern Italy is frequently characterised by high photochemical pollution levels in the warm period and by high levels of PM10 and NO2 in the winter season. Emissions in the area derive essentially from urban, shipping and industrial activities. The main industrial activities are related to the iron and steel industry (one of the largest in Europe) on the western coast and to two coal power plants on the eastern coastline. A GIS based air quality system has been developed to support local authorities in air quality management for the region. The proposed modeling system is based on RAMS (Pielke et al., 1992) and CALMET (Scire et al., 2000) meteorological models and on CALPUFF (Scire et al., 2000)/ CALGRID (Yamartino et al., 1989) dispersion models. Diffuse emissions for the domain were obtained from the national CORINAIR data base (www.sinanet.apat.gov.it) and were preprocessed by a new-developed tool GEM-PP (Gis EMission Pre-Processor) based on open source GIS. Point sources emissions are obtained by local inventory. Meteorological and dispersion simulations were performed for the year 2005. Predictions have been compared with concentration data from the air quality monitoring network. Results evidence a good correlation between predictions and measurements for O3, NOx, SO2 with most of data in factor of two of the measurements for rural stations and a tendency to underestimate measured data in urban stations. Overall the model tends to underestimate CO measurements. The uncertainty of the predictions are analysed and discussed in terms of the emission calculations, dispersion modelling and monitoring site

Event-based Classification with Recurrent Spiking Neural Networks on Low-end Micro-Controller Units

Due to its intrinsic sparsity both in time and space, event-based data is optimally suited for edge-computing applications that require low power and low latency. Time varying signals encoded with this data representation are best processed with Spiking Neural Networks (SNN). In particular, recurrent SNNs (RSNNs) can solve temporal tasks using a relatively low number of parameters, and therefore support their hardware implementation in resource-constrained computing architectures. These premises propel the need of exploring the properties of these kinds of structures on low-power processing systems to test their limits both in terms of computational accuracy and resource consumption, without having to resort to full-custom implementations. In this work, we implemented an RSNN model on a low-end, resource-constrained ARM-Cortex-M4-based Micro Controller Unit (MCU). We trained it on a down-sampled version of the N-MNIST event-based dataset for digit recognition as an example to assess its performance in the inference phase. With an accuracy of 97.2%, the implementation has an average energy consumption as low as 4.1μJ and a worst-case computational time of 150.4μs per time-step with an operating frequency of 180 MHz, so the deployment of RSNNs on MCU devices is a feasible option for small image vision real-time tasks

Turbulent dispersion from tall stack in the unstable boundary layer: a comparison between Gaussian and K-diffusion modelling for non buoyant emissions

Most air quality dispersionmo dels used for regulatory applications are based onGaussianan d K-diffusionform ulations. The reliability of such models strongly depends on how dispersion parameters and eddy diffusivities are computed on the basis of the update understanding of the Planetary Boundary Layer (PBL) meteorology. In this paper, we compare the performances in simulating pollutants released from continuous point source, by using some Gaussian and K-diffusion models with different assumptions concerning the parameterisation of the dispersionpro cesses. Results show that the Gaussianmo del, inwhic h the dispersion parameters are directly related to spectral peak of turbulence energy, gives the best overall performances. This could be due to a more realistic description of spreading processes occurring into the PBL. This suggests that, in the context of the regulatory applications, this model cangiv e the best combinationb etweengroun d level concentration estimates and computer requirements

NLRP3 Inflammasome From Bench to Bedside: New Perspectives for Triple Negative Breast Cancer

The tumor microenvironment (TME) is crucial in cancer onset, progression and response to treatment. It is characterized by an intricate interaction of immune cells and cytokines involved in tumor development. Among these, inflammasomes are oligomeric molecular platforms and play a key role in inflammatory response and immunity. Inflammasome activation is initiated upon triggering of pattern recognition receptors (Toll-like receptors, NOD-like receptors, and Absent in melanoma like receptors), on the surface of immune cells with the recruitment of caspase-1 by an adaptor apoptosis-associated speck-like protein. This structure leads to the activation of the pro-inflammatory cytokines interleukin (IL)-1β and IL-18 and participates in different biological processes exerting its effects. To date, the Nod–Like Receptor Protein 3 (NLRP3) inflammasome has been well studied and its involvement has been established in different cancer diseases. In this review, we discuss the structure, biology and mechanisms of inflammasomes with a special focus on the specific role of NLRP3 in breast cancer (BC) and in the sub-group of triple negative BC. The NLRP3 inflammasome and its down-stream pathways could be considered novel potential tumor biomarkers and could open new frontiers in BC treatment