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The Covid-19 crisis forced governments around the world to rapidly enact several restrictions to face the associated health emergency. The Portuguese government was no exception and, following the example of other countries, established various limitations to flat the contagions curve. This led to inevitable repercussions on mobility and environmental indicators including noise. This research aims to assess the impact of the lockdown due to Covid-19 disease on the noise levels recorded in the city of Porto, Portugal. Data from four noise sensors located in strategic spots of the city were used to calibrate and validate Time Series Models, allowing to impute the missing values in the datasets and rebuild them. The trend and the cyclic information were extracted from the reconstructed datasets using decomposition techniques. Finally, a Spearman correlation analysis between noise levels values and traffic volumes (extracted from five inductive loop detectors, located nearby the noise sensors) was performed. Results show that the noise levels series present a daily seasonal pattern and the trends values decreased from 6.7 to 7.5 dBA during the first lockdown period (March-May 2020). Moreover, the noise levels tend to gradually rise after the removal of restrictions. Finally, there is a monotonic relationship between noise levels and traffic volumes values, as confirmed by the positive moderate-to-high correlation coefficients found, and the sharp drop of the former during March-May 2020 can be attributed to the strong reduction of road traffic flows in the city. The authors acknowledge the municipality of Porto for having provided access to the data used in this work. published

Although variability of noise and pollutant emissions are usually associated with vehicular speed and acceleration, driving style, mainly related to gear-shift and internal engine variables such as revolutions per minute (RPM) or engine load (EL), can also play a key role. Moreover, the contribution of each variable for fuel consumption, noise and pollutant emissions can vary for different vehicle-motorization types. However, the effect of such internal variables on noise and pollutant emissions is not fully exploited in the literature. Thus, this work aims to assess the impact of the gear selection, RPM, and EL on fuel consumption, and carbon dioxide (CO2), nitrogen oxides (NOx), and noise in terms of sound power level ( Lw) emissions for a diesel passenger vehicle. This is focused on a speed and gear-based controlled on-road environment. Internal observable (fuel consumption, RPM, and EL) and kinematic (speed and acceleration) variables were recorded on a second-by-second time basis using an On-Board Diagnostic System, and noise data were recorded with a Sound Level Meter. Pollutant emissions were estimated using the Vehicle Specific Power (VSP) methodology with a 1Hz frequency. In this study, Clustering and Disjoint Principal Component Analysis is applied to find patterns hidden in data. An Ordered Logit model to predict the gear based on exploring kinetic and internal engine variables, that are influenced by driver’s driving style, is developed. Preliminary results highlight the potential of the developed model and show the potential influence of gear selection for minimizing fuel consumption, noise and pollutant emissions. These findings establish a foundation for developing a sustainability gear-shift indicator that not only is focused on minimizing fuel consumption, but also noise and pollutant emissions. These are relevant for a driver to understand vehicle performance and alleviate the relative impacts of the driving style if integrated into vehicle engine control units. Published

Motorsport races significantly affect, on a local scale, noise pollution even if they do not represent the majority of its contribution, which is a prerogative of road transportation, railways, airports, and industries. Nevertheless, such noise emissions surely affect the well-being of inhabitants in the surrounding area of the circuit. In fact, during a motor race event, vehicles produce high noise emissions while on tests, qualifying, and race sessions. Since noise indicators commonly used in national regulations are computed over fixed times, it is challenging to properly assess the total noise emission and immission at the receivers during such events. Moreover, in literature, only a few works can be found assessing this specific issue, and consequently, there’s also a lack of appropriate methods to properly measure the global noise emission of each event. In this contribution, the authors report the characterization of noise emission during motor race events by using two new acoustic indicators, namely LEL (Lap Equivalent Level) and REL (Race Equivalent Level) starting from noise data collected on different points along a racing circuit. Measurements show that the REL tends to stabilize its value during a race, suggesting that its modelling can be achieved only based on the average LEL and the number of vehicles participating in a race. These indicators will allow predicting the total noise emission at a certain receiver of a motor race event by knowing the number and type of cars involved, without using the duration of the race itself.

The main objective of this vision paper is to present the project “DICA-VE: Driving Information in a Connected and Autonomous Vehicle Environment: Impacts on Safety and Emissions”, which aims to develop an integrated methodology to assess driving behavior volatility and develop warnings to reduce road conflicts and pollutants/noise emissions in a vehicle environment. A particular attention will be given to the interaction of motor vehicles with vulnerable road users (pedestrians and cyclists). The essence of assessing driving volatility aims the capture of the existence of strong accelerations and aggressive maneuvers. A fundamental understanding of instantaneous driving decisions (through a deep characterization of individual driver decision mechanisms, distinguishing normal from anomalous) is needed to develop a framework for optimizing these impacts. Thus, the research questions are: 1) Which strategies are adopted by each driver when he/she performs short-term driving decisions and how can these intentions be mapped, in a certain road network?; 2) How is driver’s volatility affected by the proximity of other road users, namely pedestrians or cyclists?; 3) How can driving volatility information be integrated into a platform to alert road users about potential dangers in the road infrastructure and prevent the occurrence of crash situations?; 4) How can anomalous driving variability be reduced in autonomous cars, in order to prevent road crashes and have a performance with a minimum degree of emissions? This paper brings a literature review on this topic and an evaluation of methods that can be used to assess driving behavior patterns and their influence on road safety, pollutant and noise emissions.

Short-term effects of air pollution on the number of hospital admissions in eight municipalities of the Metropolitan Area of Monterrey, Mexico, were assessed from 2016 to 2019 using a time-series approach. Air quality data were obtained from the Atmospheric Monitoring System of Nuevo Leon State (SIMA) which belongs to SINAICA (National System of Air Quality Information), providing validated data for this study. Epidemiological data were provided by SINAIS (National System of Health Information), considering admission by all causes and specific causes, gender and different age groups. Guadalupe had the highest mean concentrations for SO2, CO and O3, whereas Santa Catarina showed the highest NO2 concentrations. Escobedo and Garcia registered the highest levels for PM10. Only PM10 and O3 exceeded the permissible maximum values established in Mexican official standards. A basal Poisson model was constructed to assess the association between daily morbidity and air pollutants, from this, a second scenario in which daily mean concentrations of air pollutant criteria increase by 10% was considered. Most of pollutants and municipalities studied showed a great number of associations between an increase of 10% in their current concentrations and morbidity, especially for the age group between 5 and 59 years during cold months, excepting ozone which showed a strongest correlation during summer. Results were comparable to those reported by other authors around the world, however, in spite of relative risk index (RRI) values being low, they are of public concern. This study demonstrated that considering the nature of their activities, economically active population and students, they could be more vulnerable to air pollution effects. Results found in this study can be used by decision makers to develop public policies focused on protecting this specific group of the population in metropolitan areas in Mexico.

This work proposes a methodology suitable for analysing the sound power levels (Lw), and carbon dioxide (CO2) and nitrogen oxides (NOx) emissions along a travel, and consequentially assessing the related critical hotspots. The estimation of noise and pollutant emissions from six vehicles driven along three different routes (one National Road and two highways) was conducted, in combined way, through seven Noise Emissions Models (NEMs) and Vehicle Specific Power (VSP) methodology, respectively. The inputs required by the models (namely, vehicle speed and acceleration and road grade) were extrapolated from On-Board Diagnostic (OBD) system and Global Positioning System (GPS) data recorded during monitoring campaigns. The specificities of each model were analysed, and the role played by the kinematic variables in noise and exhaust emissions assessment was highlighted. Results show that all the tested NEMs estimated higher noise levels on the highways, while VSP predicted higher emissions on the National Road. This happens because speed is the main input variable in NEMs, while acceleration has an impact on noise estimation in the low-speed range (below 50 km/h). For pollutant emissions evaluation, acceleration plays a fundamental role also at high-speed range, where a transition from a cruising condition to an acceleration phase leads to significant variations in terms of VSP values. Lw values, estimated with NEMs that use acceleration correction terms, present positive moderate-to-high correlation with VSP ones. Moreover, the models that neglect acceleration in noise estimation fail to recognize traffic control treatments as critical hotspots. A. Pascale acknowledges the support of FCT for the Scholarship 2020.05106.BD. published

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

Acoustic emissions have been widely used as a means to investigate the damage state of concrete structures. While successful applications have regarded the localisation of cracks, quantification of the damage and safety margin estimation have been elusive because the main approaches are mostly based on empirical observations. In this work, a methodology for the numerical simulation of acoustic emission events in reinforced concrete structures is proposed with the aim of filling this gap. It relies on a numerical model for reinforced concrete structures at the macro-scale which simulates the mechanical cyclic behaviour of the structure. Analysis of the stress and strain states in the numerical model provides the basis for the simulation of the occurrence and quantification of the events. A simple attenuation law is then used to estimate the acoustic event intensity recorded by the sensors. Application to a four-point bending test on a reinforced concrete beam confirms the capability of the model to reproduce the data recorded during the test, including the Felicity effect and the cumulative intensity curve. This could potentially open the path to a more quantitative use of acoustic emission data for structural assessment of reinforced concrete structures, directly linking mechanical models and acoustic observations.

This paper presents a methodology for building a link-based eco-indicator which includes different impacts of road traffic. The proposed traffic management tool can be updated in real-time through multiple information sources and translated into a cost factor to be straightforwardly applied into eco-routing algorithms and/or intelligent road pricing systems. The link-based eco-indicator has been tested in several urban and rural links of heterogeneous characteristics during peak and off-peak hours. It has been shown that both dynamic adjustment factors related to online background concentrations and/or citizen’s activity may lead to different sustainable traffic management strategies. When evaluating and comparing costs of a single link-based eco-based indicator with National Average (without taking in account people exposure) and a Vulnerability Factor (VF), the total costs using VF increase 158%. In the origin-destination routing problem performed using the distance and travel time as criteria, the costs (for off-peak hours) using VF factor are 80% and 15% higher, respectively. The results achieved in this paper highlight the importance of taking into account vulnerability of people exposure when implementing eco traffic management measures.

In order to manage noise pollution and reduce its environmental impact and health outcomes, several regulations have been issued in the last few decades, defining acoustic indicators and their thresholds. However, the acoustic environment can be considered a resource, focusing on people&rsquo, s subjective perception of sounds in accordance with the soundscape approach. The integration of the tools, already applied by the legislation, and the soundscape technique produces a more thorough and comprehensive evaluation of the environmental noise that is necessary for its management. Starting from the best practice of the soundscape in urban planning, this paper presents an application of this approach at the Fisciano campus of the University of Salerno (Italy). The overarching goal is the comparison between the physical parameters, obtained by measuring the sound pressure level, and the psychoacoustic ones, derived by questionnaires given to a group of local experts during a soundwalk. The results will show, for example, some areas characterized by high sound pressure levels and a good perception of the soundscape. As a consequence, the application would seem to have discrepancies between the results of the two methods, but a deeper analysis can reveal further information to the traditional measurements that allow a more accurate knowledge of the acoustic environment.

To reduce environmental noise pollution and to safeguard people’s well-being, it is urgently necessary to move towards sustainable urban development and reconcile demographic and economic growth with the protection and restoration of the environment and the improvement of the quality of human lives. This challenge should be a concern to policymakers, who must issue regulations and define the appropriate actions for noise monitoring and management, and citizens, who must be sensitive to the problem and act accordingly. Starting from an analysis of several crowdsourcing noise data collection tools, this paper focuses on the definition of a methodology for data analysis and mapping. The sound sensing system, indeed, enables mobile devices, such as smartphones and tablets, to become a low-cost data collection for monitoring environmental noise. For this study, the “NoiseCapture” application developed in France by CNRS and IFSTTAR has been utilized. The measurements acquired in 2018 and 2019 at the Fisciano Campus at the University of Salerno were integrated with the kernel density estimation. This is a spatial analysis technique that allows for the elaboration of sound level density maps, defined spatially and temporally. These maps, overlaid on a campus facilities map, can become tools to support the appropriate mitigation actions.

The operational management of tanks for urban water distribution networks is usually a critical element due to the dynamic nature of the water demand and the age of the distribution networks themselves. Today, in a context of water resource scarcity, optimal management is a key point for the sustainable management of urban systems. For this purpose, it is useful to implement predictive tools, able to provide short-term forecasts to inform urban water managers on the most suitable procedure to be applied in the case of routine or critical events. A possible approach is to use autoregressive integrated moving average (ARIMA) models, which combine the autoregression and the moving average approaches, with the possibility to work on a differenced series of the data. They can further embed a seasonal- component (Seasonal ARIMA models), to account for possible periodic patterns in the observed data. In this study, the data of water levels measured from May 2018 to 10 January 2019 in a water storage tank in the area of Benevento, Campania region (Italy), were considered as a case study. The standard ARIMA techniques were applied to find the best model for this dataset, according to “Deviance Information Criterion” (DIC) and “Bayesian Information Criterion” (BIC) optimization. The results are discussed, shedding light on the behaviour of the time series with reference to the management of the infrastructure and the dataset. The residual analysis, carried out to check if the autocorrelation was still present and if the residuals were normally distributed, revealed a narrow distribution. Small values were found throughout the dataset, except for a few periods, corresponding to the imputed data. This application represents a preliminary step of more detailed research that will be carried out to detect the best model for forecasting tank levels for the case study to help to manage the urban water supply.

Levels of BTEX (benzene, toluene, ethylbenzene, and p-xylene) were determined in ambient air of an urban site located at the center of Mexico (Leon City in Guanajuato State) during two climatic seasons of 2018 (summer and autumn). Ethylbenzene (11.86 &micro, g m&minus, 3) and toluene (11.85 &micro, 3) showed the highest median concentrations during the study period. BTEX concentrations did not show a diurnal pattern but a seasonal trend was observed for benzene and toluene at a significant level of &alpha, = 0.05. Bi-variate and multivariate analysis showed significant positive correlations (at &alpha, = 0.05) among BTEX (excepting benzene), indicating common sources for toluene, ethylbenzene, and p-xylene and a different origin for benzene. A meteorological study was also conducted in order to determine the origin of air masses that could influence the BTEX concentrations in the study site. Finally, it was found that all BTEX species presented hazard quotient values (HQs) &lt, 1, indicating that there is no risk of non-cancer during the studied period. Lifetime cancer risk due to benzene exposure for the adult and child populations studied were estimated to be 7 in 1,000,000 and 1 in 100,000, respectively.

In recent decades, the awareness that noise pollution caused by traffic, industry and recreational activities constitutes one of the main environmental problems is growing. In order to control the environmental noise, many regulations propose the creation of noise maps according to standard procedures that involves the data acquisition, analysis and elaboration. In this paper, crowdsourcing noise data collection is described. It involves volunteers that can record sound pressure levels thanks to specific applications on their mobile devices, such as the “NoiseCapture” app developed in France by CNRS and IFSTTAR, and upload the measurement, with their GPS location data, on a continuously updated map. This approach mainly contributes to citizens’ greater awareness about noise pollution in an urban area. Beside this mode of data acquisition, the paper focuses on the analysis of the acquired data with the kernel density estimation technique, implemented in a GIS environment. The results allows the elaboration of sound density maps, defined from the spatial and temporal point of view, that can support the appropriate mitigation actions.

The main purpose of this work is to develop a single vehicle noise emission model that uses speed as input variable and returns as output a parameter directly referable to the noise source, namely the source sound power level (Lw). The model was tested on three light-duty vehicles with different motorizations: diesel, gasoline and gasoline-electric hybrid. Field measurements were conducted on a straight road and for different speed values (10-90 km/h). The influence of the engaged gear on the noise at different constant speed values was also explored for gasoline and diesel vehicles using one-way analysis of variance (ANOVA). Results revealed that the source power level emitted by different typologies of cars against speed followed significantly different trends, more evident at speeds lower than 40 km/h. In such cases, the contribution of the engine on the noise is prevalent and ANOVA test confirmed that the gear choice influenced the noise at low speeds. At higher speed values such difference disappears. published

This paper explores the potential of using crowdsourcing tools, namely Google “Popular times” (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emissions and noise of different areas in different periods. Different data sets were collected: i) crowdsourcing information from Google Maps; ii) traffic dynamics with the use of a probe car equipped with a Global Navigation Satellite System data logger; and iii) traffic volumes. The emissions estimation was based on the Vehicle Specific Power methodology, while noise estimations were conducted with the use of “The Common Noise Assessment Methods in Europe” (CNOSSOS-EU) model. This study shows encouraging results, as it was possible to establish clear relationships between GPT and traffic and environmental performance.

Among the most common techniques and methodologies for the analysis of the exposure to polluting agents, the definition of environmental indexes represents a very useful tool to easily describe the quality of the environment. Based on experimental measurements, an approach using indicators quantitatively and qualitatively describes the ‘health status’ of the environments. For this reason, it is considered one of the most clear and efficient tools to support decisions and actions for the competent control authorities. In this paper the development of a general Physical Agents environmental Quality Index (PAQI) describing the potential impact related to the presence of all the different physical pollutants is discussed. The result is represented by a quantity that measures a weighted combination of sub-indices relating to each pollutant and described by means of an appropriate normalized mathematical function. The main idea is to provide an indicator able to describe, in a clear and concise way, the grade of physical pollution of the environment, suggesting in which areas it would be necessary to intervene or not, in order to improve the general environmental conditions.

With reference to the European goals concerning the noise pollution control, the present research aims at verifying the existence and, eventually, determining the influence level of the acoustic component on the selling prices in the residential segment. The analysis has been contextualized to the city of Bari (Southern Italy) and the study sample concerns one-hundred residential units recently sold, for which the main influencing factors in the selling price formation processes have been identified. The implemented methodological tool is a data-driven technique, that employs a genetic algorithm to identify the functional relationships among the considered variables. The application to the case study has allowed the identification of a model characterized by good statistical accuracy and an algebraic form of simple interpretation, which denotes an appreciable contribution of the noise pollution factor on the housing prices.

The degradation of the sound environment contributes significantly to the external costs of mobility and is an obstacle to the development of cities. Action plans aiming at fighting traffic noise often take a long time to reach mature implementation. Therefore, it is advantageous to envisage how societal and urban changes and associated changes in mobility practices will modify the urban sound environment of tomorrow. In this article, an interdisciplinary team of seven researchers, whose work focuses on different fields of acoustics and mobility, reviews the potential impact of ongoing mobility and society changes on sound environments. First, the team identified the trends dealing with urban renewal, societal changes and new mobility drivers that have the greatest influence on sound environments, and analyzed in detail. From this analysis, insights emerged for urban noise impact and innovative noise mitigation solutions in the light of improved assessment of the links between mobility and urban sound environments.

Road traffic has a significant influence on the environmental noise pollution, producing harmful effects on human health and collective well-being. Since noise measurements cannot be performed everywhere, or even in a large number of sites, because of high costs and time consumption, traffic noise predictions are necessary for noise abatement and control. Consequently, many scientific models have been developed in recent years focusing on this aspect with the definition of source emission and sound propagation empirical formulations exclusively. This paper focuses on some Emission Models, describing the comparison of the results, in terms of sound power level emitted by a single vehicle, obtained with their application to different simulations of driving conditions and to two real study cases. After a preliminary comparison between the models, the evaluation of two indicators, namely the average and total source power level, will be discussed in relation to different conditions of the vehicle kinematics. The application of these two indicators to real study cases will demonstrate their validity. It will be shown that this procedure can become a tool for supporting people route choices according to a less impact of environmental noise. This will open the way to new scenarios of eco-routing, by means of implementation of models prediction in dedicated software platforms and/or in car navigation systems.

Preface of the 5th International Conference on Applied Physics, Simulation and Computing (APSAC 2021), Salerno, Italy, September 3-5, 2021 The 5th International Conference on Applied Physics, Simulation and Computing (APSAC 2021) was held online from September 3rd to September 5th, 2021 for safety reasons that are related to the sanitary emergency conditions that every country has taken in order to fight against coronavirus and for the preservation of the public health. Virtual conference rooms were arranged, on the Microsoft Teams platform, in which participants could attend live or recorded presentations, interacting with questions and answers. In line with the tradition of this conference, several professors, researchers, scholars and students shared their latest research results on the topics of the conference. The direction of the event was at the University of Salerno. The Applied Physics Laboratory of the Department of Civil Engineering, University of Salerno, was the headquarter of the local organizing committee, that handled the schedule of the presentations and chaired the sessions. The conference program included regular and special sessions divided by topics in order to increase the quality of the discussions. In addition, the conference hosted two reputable events, the 2nd International Workshop on “MOdelling, SImulation and DAta Analysis in Engineering and Physics Applications” (MOSIDA21) and the 2nd edition of the International Symposium on “Sustainable Construction” (SustaCon21). There were more than 100 participants, considering lecturers, presenters, co-authors and attendants, coming from many academic and scientific institutions from all over the world. The range of topics increased, bringing this year novelties and new researches in the focus of the events. List of Organizing Committee, International Program Committee are available in this pdf.

Increasing concern about global warming and air quality has meant an increasing use of energetic and environmental indicators in roundabout design. This research compares different suburban roundabouts in terms of traffic performance, pollutant and noise emissions through an integrated empirical assessment. Field measurements were carried out with a light duty vehicle in single-lane (SL), compact two-lane (CTL) and multi-lane (ML) roundabouts using Portable Emission Measurements Systems, On-Board Diagnostic scan tool and Sound Level Meter, to measure real-world exhaust emissions, engine activity and acoustic data, respectively. Afterwards, predictive discrete choice models that correlate the probability of occurrence of speed profiles (no stop, stop once and multiple stops) with roundabout operational parameters were developed. Although SL yielded the lowest carbon dioxide (CO2) per vehicle, a high equivalent continuous A-weighted sound level (LAeq) was returned because vehicles drove at moderate speeds in the approach and low conflicting traffic was identified when compared to the other layouts. CTL was the worst option in terms of both CO2 and nitrogen oxides (NOx). The proposed methodology can be used to quantify the performance of roundabout layouts in suburban areas by simply identifying their traffic volumes, noise level, pollutant emission and representative speed profiles. This can help researchers, traffic planners or practitioners to reduce congestion and emissions, and enhance road traffic management near urban areas.

In the acoustics of human voice, an important role is reserved for the study of larynx movements. One of the most important aspects of the physical behavior of the larynx is the proper description and simulation of swallowing and singing register changes, which require complex laryngeal manoeuvres. In order to describe (and solve, in some cases) these actions, it is fundamental to analyze the accurate synchronization of vocal fold adduction/abduction and the change of the larynx position. In the case of dysfunction, which often occurs for professional singers, this synchronization can be disturbed. The simultaneous assessment of glottal dynamics (typically electroglottograph, EGG signal) and larynx position might be useful for the diagnosis of disordered voice and swallowing. Currently, it is very difficult to instantaneously gather this information because of technology problems. In this work, we implemented a time-multiplex measurement approach of space-resolved transfer impedances through the larynx (Multi-Channel electroglottograph MC-EGG). For this purpose, we developed specific software (Labview code) for the visualization of the main waveforms in the study of the EGG signals. Moreover, the data acquired by the Labview code have been used to create a theoretical algorithm for deriving the position of the larynx inside the neck. Finally, we verified the results of the algorithm for the 3D larynx movement by comparing the data acquired with the values described in the literature. The paths of the larynx and the displacement on the sagittal and transverse plans matched the ones known for the emission of low/high notes and for swallowing. Besides, we have introduced the possibility to study the movement on the coronal (x) plan (so far, unexplored), which might be a starting point for further analysis.

High levels of the so-called community noise may produce hazardous effect on the health of a population exposed to them for large periods of time. Hence, the study of the behaviour of those noise measurements is very important. In this work we analyse that in terms of the probability of exceeding a given threshold level a certain number of times in a time interval of interest. Since the datasets considered contain missing measurements, we use a time series model to estimate the missing values and complete the datasets. Once the data is complete, we use a non-homogeneous Poisson model with multiple change-points to estimate the probability of interest. Estimation of the parameters of the models are made using the usual time series methodology as well as the Bayesian point of view via Markov chain Monte Carlo algorithms. The models are applied to data obtained from two measuring sites in Messina, Italy.

Urban water demand forecasting is a powerful supporting tool for specific water utility decision making problems. Among the others, ARIMA is one of the possible approaches to achieve an efficient short-term forecasting.

Road traffic poses negative externalities on society and represents a key challenge in sustainable transportation. However, the existing literature about the assessment of traffic externalities drawn on a common measure is scarce. This paper develops a sustainability indicator that integrates traffic-related externalities as means of traffic congestion, noise, greenhouse gases (GHG) and nitrogen oxides emissions, health impacts and road crash related costs, and adjusted to local contexts of vulnerability. Traffic, road crashes, acoustic and vehicle dynamic data were collected from one real-world intercity corridor pair comprising three alternative routes. The site-specific operations were characterized using a modeling platform of traffic, emissions, noise and air quality. A specific methodology is applied for each road traffic externality and translated in a single factor - external cost. The results indicated that road crashes presented the largest share in the partly rural/urban route while GHG emissions had the highest contribution in external costs for the highway routes. Also, the distribution of external cost component varied according to the type of road, mostly due to different levels of exposed inhabitants. This paper offers a line of research that produced a method for decision-makers with a reliable and flexible cost analysis aimed at reducing the negative impacts of road traffic. It also encourages the design of eco-traffic management policies considering the perspective of drivers, commuters and population. published

Among the various non-destructive techniques for health monitoring in structures, the Acoustic Emission (AE) is well known in scientific literature. Ultrasonic waves emitted by the creation and propagation of cracks in concrete or Reinforced Concrete specimens are usually collected by means of ultrasonic sensors. The signals must be treated in front-end readout process with preamplifiers and filters, to be able to set a proper trigger level and to cut the background noise (belonging to different frequency ranges). In addition, the post processing of the data is important to “clean up” the dataset, removing fake events, and to extract the proper information, useful for structure damage assessment. In this paper, the authors present the experimental set up and the transducers used to acquire the AE signals recorded during a four-point bending test on a RC beam. The ad hoc realized amplifier and filtering circuit used in the test are also described. Then, an example of an AE signal is also reported, in terms of frequency spectrum analysis and noise filtering.

In this paper a statistical study on the time series of water levels measured, during 2014, in the water tank of Cesine, Avellino (Italy), is presented. In particular, the autoregressive integrated moving average (ARIMA) forecasting methodology is applied to model and forecast the daily water levels. This technique combines the autoregression and the moving average approaches, with the possibility to differentiate the data, to make the series stationary. In order to better describe the trend, over time, of the water levels in the reservoir, three ARIMA models are calibrated, validated and compared: ARIMA (2,0,2), ARIMA (3,1,3), ARIMA (6,1,6). After a preliminary statistical characterization of the series, the models' parameters are calibrated on the data related to the first 11 months of 2014, in order to keep the last month of data for validating the results. For each model, a graphical comparison with the observed data is presented, together with the calculation of the summary statistics of the residuals and of some error metrics. The results are discussed and some further possible applications are highlighted in the conclusions.

In this paper a time series of hourly equivalent noise levels acquired near the international airport of Nice, France, is analysed. Two different techniques are proposed to model and forecast the time series: deterministic decomposition and seasonal autoregressive moving average. The two models are defined and fitted on the calibration dataset. Subsequently, the developed models are tested comparing their forecasts with 25 noise level data not used in the calibration phase. A detailed error analysis, by means of statistics and metrics, will be presented to test the models performances.

Trace metal concentrations in PM10 were measured in an urban&ndash, industrial site in Le&oacute, n, Mexico in three different seasons. PM10 were collected in quartz fiber filters of 47 mm diameter using low volume equipment operating with a controlled flow of 5 L min&minus, 1 over 24 h. Mass concentrations were gravimetrically determined and it was found that PM10 samples showed values in excess of the Mexican standard and the established values by WHO during cold dry and warm dry seasons. Morphology of selected particles was studied by SEM-EDS analysis, and the elemental composition was determined. Collected samples were analyzed by atomic absorption spectrometry in order to quantify ambient air concentrations of some trace metals (Cu, Co, Zn, Cd, Fe, Mg, and Mn). Median concentrations of trace metals showed the maximum value for iron (3.079 &mu, gm&minus, 3) and the minimum value for Cd (0.050 &mu, 3) over the entire period. From the meteorological analysis, it was found that sources located SW and ESE of the sampling site contributed to the levels of trace metals in PM10 in the studied site. The health risk assessment found that the population of Le&oacute, n is at increased lifetime risk of experiencing cancer because of exposure to these concentrations of PM10 and their trace metal content.

The assessment and control of acoustic noise in every place in which humans live is usually performed measuring the sound pressure levels and comparing these results with the thresholds defined by regulations. The latest approaches include the possibility to consider the subjective perception of sounds, using the so-called “soundscape” approach. In this paper the authors present a practical application of this approach performed in the Campus of Fisciano, University of Salerno (Italy), with the aim to compare the physical parameters, obtained by measuring the sound level, and the psychoacoustics one, acquired by administering questionnaires to a group of students during a soundwalk. Results will show that the higher sound pressure levels will not always correspond to the more annoying places. In particular, the main park of the University campus will present a positive soundscape, even though noise from the nearby highway will be highly present. Similar conditions will occur in vibrant areas of the campus, in which the interviews will highlight a good perception of the soundscape, even with quite high sound pressure levels.

4th International Conference on Applied Physics, Simulation and Computing (APSAC 2020) This volume is from proceedings of the 4th International Conference on Applied Physics, Simulation and Computers. The conference focus was in the areas of applied physics, simulation and computers and was held May 23-25, 2020, in Rome, Italy. The scope of the conference was to provide a forum for physicists and applied mathematicians as well as for engineers to present new analytical methods, new simulation methods and mathematical models in their disciplines. The book is divided into four sections that focus on different topics, with the first section focusing on applied physics, the second focusing on simulation, the third focusing on computing and the forth focusing on acoustics. All chapters are self-contained and peer-reviewed. Due to the COVID-19 virus and travel restrictions, the 4th International Conference on Applied Physics, System Science and Computers was held online using video conferencing tool. This online conference was attended by 42 participants, who were from Austria, United Kingdom, Czech Republic, Italy, Poland, Russia, Spain, Argentina, Latvia, Lithuania, Ukraine and other countries. The organizers coordinated the presentations from Rome, Italy and every speaker had 30 minutes for the online presentation. This online remarkable event allowed the communication of professors, researchers, scholars and students in the field of Applied Physics, Simulation and Computing making the conference a perfect platform to share experience, foster collaborations across industry and academia, and evaluate emerging technologies across the globe. The Editors Prof. Badea Lepadatescu, Transylvania University of Brasov, Brasov, Romania Prof. Claudio Guarnaccia, University of Salerno, Salerno, Italy