scholarly journals Accuracy of the Dynamic Acoustic Map in a Large City Generated by Fixed Monitoring Units

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 412 ◽  
Author(s):  
Roberto Benocci ◽  
Chiara Confalonieri ◽  
Hector Eduardo Roman ◽  
Fabio Angelini ◽  
Giovanni Zambon
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Low Cost ◽  
Traffic Noise ◽  
Large City ◽  
Field Measurements ◽  
Life Project ◽  
Noise Data ◽  
Comparable Accuracy ◽  
Monitoring Stations

DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in two pilot areas located in the agglomeration of Milan (Italy) and along the Motorway A90 (Rome-Italy). The paper reports the final assessment of the system installed in the pilot area of Milan. Traffic noise data collected by the monitoring stations, each one representative of a number of roads (groups) sharing similar characteristics (e.g., daily traffic flow), are used to build-up a “real-time” noise map. In particular, we focused on the results of the testing campaign (21 sites distributed over the pilot area and 24 h duration of each recording). It allowed evaluating the accuracy and reliability of the system by comparing the predicted noise level of DYNAMAP with field measurements in randomly selected sites. To this end, a statistical analysis has been implemented to determine the error associated with such prediction, and to optimize the system by developing a correction procedure aimed at keeping the error below some acceptable threshold. The steps and the results of this procedure are given in detail. It is shown that it is possible to describe a complex road network on the basis of a statistical approach, complemented by empirical data, within a threshold of 3 dB provided that the traffic flow model achieves a comparable accuracy within each single groups of roads in the network.

scholarly journals Study of Temporal Correlations in the Urban Noise Monitoring Network of Milan, Italy

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Low Cost ◽  
Traffic Noise ◽  
Flow Characteristics ◽  
Correlation Coefficients ◽  
Monitoring Network ◽  
Life Project ◽  
Temporal Correlations ◽  
Noise Data ◽  
Extended Area ◽  
Arbitrary Location

The European Life project, called DYNAMAP, has been devoted to provide a realimage of the noise generated by vehicular trafficin urban and suburban areas, developing a dynamic acoustic map based on a limited numberof low-cost permanent noise monitoring stations.In the urban area of Milan, the system has beenimplemented over the pilot area named Area 9.Traffic noise data, collected by the monitoringstations, each one representative of a numberof roads with similar characteristics (e.g. dailytraffic flow), are used to build-up a “real time”noise map. DYNAMAP has a statistical structure and this implies that information capturedby each sensor must be representative of an extended area, thus uncorrelated from other stations. The study of the correlations among thesensors represents a key-point in designing themonitoring network. Another important aspectregards the “contemporaneity” of noise fluctuations predicted by DYNAMAP with those effectively measured at an arbitrary location. Integration times heavily affect the result, with correlation coefficients up to 0.8-0.9 for updating timesof 1h. Higher correlations are observed when averaging over groups of roads with similar traffic flow characteristics

scholarly journals Classification of Urban Road Traffic Noise based on Sound Energy and Eventfulness Indicators

2020 ◽  
Vol 10 (7) ◽  
pp. 2451 ◽  
Author(s):  
Giovanni Brambilla ◽  
Roberto Benocci ◽  
Chiara Confalonieri ◽  
Hector Eduardo Roman ◽  
Giovanni Zambon
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Clustering Methods ◽  
Cluster Membership ◽  
Urban Road ◽  
Local Roads ◽  
Noise Data

Noise energetic indicators, like Lden, show good correlations with long term annoyance, but should be supplemented by other parameters describing the sound fluctuations, which are very common in urban areas and negatively impact noise annoyance. Thus, in this paper, the hourly values of continuous equivalent level LAeqh and the intermittency ratio (IR) were both considered to describe the urban road traffic noise, monitored in 90 sites in the city of Milan and covering different types of road, from motorways to local roads. The noise data have been processed by clustering methods to detect similarities and to figure out a criterion to classify the urban sites taking into account both equivalent noise levels and road traffic noise events. Two clusters were obtained and, considering the cluster membership of each site, the decimal logarithm of the day-time (06:00–22:00) traffic flow was used to associate each new road with the clusters. In particular, roads with average day-time hourly traffic flow ≥1900 vehicles/hour were associated with the cluster with high traffic flow. The described methodology could be fruitfully applied on road traffic noise data in other cities.

scholarly journals Vehicular Traffic Noise Modelling of Urban Area- A Contouring & Artificial Neural Network-Based Approach

2021 ◽  
Author(s):  
Abhijit Debnath ◽  
Prasoon Kumar Singh ◽  
Sushmita Banerjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Noise Level ◽  
Urban Areas ◽  
Traffic Noise ◽  
Traffic Volume ◽  
Noise Levels ◽  
Noise Characteristics ◽  
Artificial Neural

Abstract Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causing health risks among the urban populations. In this study we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and finds the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, Speed, traffic flow, road gradient, pavement, road side carriageway distance factors taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59% 2-wheelers and different vehicle specifications with varying speeds also effects driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicates high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 & .029 in training and 0.458 & 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ±0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

Quantification of sewer leakage by a continuous tracer method

2011 ◽  
Vol 64 (1) ◽  
pp. 132-138 ◽  
Author(s):  
V. Prigiobbe ◽  
M. Giulianelli
Keyword(s):  
Urban Areas ◽  
Structural Damage ◽  
Laboratory Tests ◽  
Low Cost ◽  
Field Measurements ◽  
Tracer Method ◽  
Catchment Scale ◽  
Chemical Tracers ◽  
Challenging Tasks ◽  
Dry Weather Flow

Water authorities interested in the evaluation of the structural state of a sewer must quantify leakage to plan strategic intervention. However, the quantification of the exfiltration and the localisation of structural damage are challenging tasks that usually require expensive and time-consuming inspections. Herein, we report one of the first applications of the QUEST-C method to quantify the exfiltration in a continuously operating sewer by dosing two chemical tracers, sodium bromide (NaBr) and lithium chloride (LiCl). The method was applied at the catchment scale in a 14-year-old sewer in Rome, Italy. Preliminary laboratory tests, field measurements, and numerical simulations showed that reliable results require the QUEST-C method to be applied to sewers without lateral inflows, during periods of quasi-steady flow, and that the travel time of the NaBr tracer is minimised. Three sewer reaches were tested and the estimated exfiltration, as a fraction of the dry weather flow (DWF), increased from 0.128 in the agricultural area to 0.208 in the urban area. Although our estimates are at the lower end of the range given in the literature (0.01–0.56 DWF), the exfiltration was not negligible, and interventions should focus on the sewers in urban areas. This illustrates the capability of the QUEST-C method to guide strategic intervention at low cost and without an interruption of sewer operation. However, careful interpretation of the results is recommended for sewers with many lateral inflows, where leakage may be overestimated.

Das Ohr hat kein Lid1) – Der Einfluss nächtlichen innerstädtischen Verkehrslärms auf die kognitive Entwicklung von Kindern/The ear has no lid – The effect of inner-city traffic noise at night on cognitive development of children

2019 ◽  
Vol 14 (02) ◽  
pp. 42-49
Author(s):  
A. M. Makles ◽  
K. Schneider
Keyword(s):  
Cognitive Development ◽  
Inner City ◽  
School Readiness ◽  
Urban Areas ◽  
Noise Exposure ◽  
Traffic Noise ◽  
Large City ◽  
Cost Benefit ◽  
Kognitive Entwicklung ◽  
German Cities

Lärm beeinträchtigt sowohl die Gesundheit als auch die kognitive Entwicklung von Kindern. Dies gilt nicht nur für extreme Lärmpegel, wie beispielsweise die des Flugverkehrs, sondern auch für den täglichen Verkehrslärm in Ballungsgebieten. Die Analyse von Daten mehrerer Einschulungskohorten einer Großstadt zeigt, dass Kinder, die Lärm durch den Straßen- und Schienenverkehr ausgesetzt sind, in ihrer Schulreife zurückfallen. Im Durchschnitt zeigt sich, dass eine Reduktion des Lärms um 2 dB(A) denselben Effekt auf die Schulreife hätte wie ein weiterer Monat im Kindergarten. Anders als in anderen Studien verwenden wir ausschließlich administrative Daten und betrachten die alltägliche Lärmbelastung in deutschen Städten. Die Analysen sind auf Grund der verwendeten Daten und Methoden leicht auf andere Regionen übertragbar. Erste Kosten-Nutzen-Analysen zeigen zudem, dass die Nutzenschwelle von Lärmschutzmaßnahmen gerade in dicht besiedelten innerstädtischen Bereichen schnell erreicht ist.   Summary Noise exposure affects both the health and cognitive development of children. This applies not only to extreme noise pollution, for example from air traffic, but also to daily traffic noise in urban areas. The analysis of data from several preschool cohorts in a large city in Germany shows that children who are exposed to noise from road and rail traffic fall behind in their school readiness. On average, a reduction in noise of 2 dB(A) would have the same effect on school readiness as a further month in kindergarten. Unlike other studies, we use administrative data and look at everyday noise exposure in German cities. The analyses are easily transferable to other regions due to the data and methods used. Tentative cost-benefit analyses also show that the break-even point of noise abatement measures is quickly reached, especially in densely populated inner-city areas.

scholarly journals Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Traffic Noise

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammad Maghrour Zefreh ◽  
Adam Torok
Keyword(s):  
Traffic Flow ◽  
Noise Level ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Urban Traffic ◽  
Road Traffic Noise ◽  
Traffic Lights ◽  
Urban Road ◽  
Traffic Conditions

Road traffic noise is one of the most relevant sources in the environmental noise pollution of the urban areas where dynamics of the traffic flow are much more complicated than uninterrupted traffic flows. It is evident that different traffic conditions would play the role in the urban traffic flow considering the dynamic nature of the traffic flow on one hand and presence of traffic lights, roundabouts, etc. on the other hand. The main aim of the current paper is to investigate the effect of different traffic conditions on urban road traffic noise. To do so, different traffic conditions have been theoretically generated by the Monte Carlo Simulation technique following the distribution of traffic speed in the urban roads. The “ASJ RTN-Model” has been considered as a base road traffic noise prediction model which would deal with different traffic conditions including steady and nonsteady traffic flow that would cover the urban traffic flow conditions properly. Having generated the vehicles speeds in different traffic conditions, the emitted noise (LWA) and subsequently the noise level at receiver (LA) were estimated by “ASJ RTN-Model.” Having estimated LWA and LA for each and every vehicle in each traffic condition and taking the concept of transient noise into account, the single event sound exposure levels (SEL) in different traffic conditions are calculated and compared to each other. The results showed that decelerated traffic flow had the lowest contribution, compared to congestion, accelerated flow, free flow, oversaturated congestion, and undersaturated flow by 16%, 14%, 12%, 12%, and 10%, respectively. Moreover, the distribution of emitted noise and noise level at receiver were compared in different traffic conditions. The results showed that traffic congestion had considerably the maximum peak compared to other traffic conditions which would highlight the importance of the range of generated noise in different traffic conditions.

scholarly journals Application of the Intermittency Ratio Metric for the Classification of Urban Sites Based on Road Traffic Noise Events

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5136 ◽  
Author(s):  
Giovanni Brambilla ◽  
Chiara Confalonieri ◽  
Roberto Benocci
Keyword(s):  
Traffic Flow ◽  
Noise Exposure ◽  
Road Traffic ◽  
Energy Content ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Clustering Methods ◽  
Hourly Data ◽  
Noise Data ◽  
Urban Sites

Human hearing adapts to steady signals, but remains very sensitive to fluctuations as well as to prominent, salient noise events. The higher these fluctuations are, the more annoying a sound is possibly perceived. To quantify these fluctuations, descriptors have been proposed in the literature and, among these, the intermittency ratio (IR) has been formulated to quantify the eventfulness of an exposure from transportation noise. This paper deals with the application of IR to urban road traffic noise data, collected in terms of 1 s A-weighted sound pressure level (SPL), without being attended, monitored continuously for 24 h in 90 sites in the city of Milan. IR was computed on each hourly data of the 251 time series available (lasting 24 h each), including different types of roads, from motorways to local roads with low traffic flow. The obtained hourly IR values have been processed by clustering methods to extract the most significant temporal pattern features of IR in order to figure out a criterion to classify the urban sites taking into account road traffic noise events, which potentially increase annoyance. Two clusters have been obtained and a “non-acoustic” parameter x, determined by combination of the traffic flow rate in three hourly intervals, has allowed to associate each site with the cluster membership. The described methodology could be fruitfully applied on road traffic noise data in other cities. Moreover, to have a more detailed characterization of noise exposure, IR, describing SPL short-term temporal variations, has proved to be a useful supplementary metric accompanying LAeq, which is limited to measure the energy content of the noise exposure.

scholarly journals Noise Indicators for Size Distributions of Airborne Particles and Traffic Activities in Urban Areas

2018 ◽  
Vol 10 (12) ◽  
pp. 4599 ◽  
Author(s):  
Jin Jeon ◽  
Joo Hong ◽  
Sung Kim ◽  
Ki-Hyun Kim
Keyword(s):  
Urban Areas ◽  
Particle Number ◽  
Spectral Characteristics ◽  
Field Measurements ◽  
Noise Pollution ◽  
Submicron Particles ◽  
Noise Levels ◽  
Traffic Conditions ◽  
Noise Data ◽  
Noise Frequency

The aim of this study was to explore the relationships among the particle number concentration (PNC), noise, and traffic conditions. Field measurements were conducted to measure the temporal variabilities of the noise levels and PNC over 24 h in a location adjacent to three main traffic roads in Seoul, Korea. The PNC was measured in the range of 0.3 to 10 µm. The noise data was measured by utilizing both the overall levels and spectral characteristics. Traffic data including volumes and speeds of vehicles on the roads were also collected. The results showed that the correlations among the three key parameters varied depending on changes in the noise frequency and particle size. The noise levels at 100–200 Hz were positively correlated with traffic volume and submicron particles. In contrast, they exhibited inverse correlations with the traffic speed and the number of larger particles (>2.5 µm). Compared to noise levels at 100–200 Hz, noise levels at 1–2 kHz exhibited reverse relationships between the traffic and PNC. Submicron particles (0.3–1.0 µm) tended to be more strongly associated with noise levels during the daytime, while those greater than 2.5 µm maintained relatively stable correlations with the noise throughout the day. The findings address the importance of temporal and spectral-specific monitoring of air and noise pollutants for a better understanding of the exposure of the community to air and noise pollution.

scholarly journals WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4760
Author(s):  
Francesc Alías ◽  
Joan Claudi Socoró ◽  
Rosa Ma Alsina-Pagès
Keyword(s):  
Urban Areas ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Mapping ◽  
General Terms ◽  
Acoustic Sensor Networks ◽  
Time Period ◽  
Acoustic Environments

In addition to air pollution, environmental noise has become one of the major hazards for citizens, being Road Traffic Noise (RTN) as its main source in urban areas. Recently, low-cost Wireless Acoustic Sensor Networks (WASNs) have become an alternative to traditional strategic noise mapping in cities. In order to monitor RTN solely, WASN-based approaches should automatize the off-line removal of those events unrelated to regular road traffic (e.g., sirens, airplanes, trams, etc.). Within the LIFE DYNAMAP project, 15 urban Anomalous Noise Events (ANEs) were described through an expert-based recording campaign. However, that work only focused on the overall analysis of the events gathered during non-sequential diurnal periods. As a step forward to characterize the temporal and local particularities of urban ANEs in real acoustic environments, this work analyses their distribution between day (06:00–22:00) and night (22:00–06:00) in narrow (1 lane) and wide (more than 1 lane) streets. The study is developed on a manually-labelled 151-h acoustic database obtained from the 24-nodes WASN deployed across DYNAMAP’s Milan pilot area during a weekday and a weekend day. Results confirm the unbalanced nature of the problem (RTN represents 83.5% of the data), while identifying 26 ANE subcategories mainly derived from pedestrians, animals, transports and industry. Their presence depends more significantly on the time period than on the street type, as most events have been observed in the day-time during the weekday, despite being especially present in narrow streets. Moreover, although ANEs show quite similar median durations regardless of time and location in general terms, they usually present higher median signal-to-noise ratios at night, mainly on the weekend, which becomes especially relevant for the WASN-based computation of equivalent RTN levels.

scholarly journals Forecasting Model Validation of Particulate Air Pollution by Low Cost Sensors Data

2019 ◽  
Vol 11 (2) ◽  
pp. 63-68 ◽  
Author(s):  
Nicoletta Lotrecchiano ◽  
Filomena Gioiella ◽  
Aristide Giuliano ◽  
Daniele Sofia
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Emission Control ◽  
Quality Monitoring ◽  
Forecasting Model ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Human Beings ◽  
Monitoring Stations

Environmental pollution in urban areas may be mainly attributed to the rapid industrialization and increased growth of vehicular traffic. As a consequence of air quality deterioration, the health and welfare of human beings are compromised. Air quality monitoring networks usually are used not only to assess the pollutant trend but also in the effective set-up of preventive measures of atmospheric pollution. In this context, monitoring can be a valid action to evaluate different emission control scenarios; however, installing a high space-time resolution monitoring network is still expensive. Merge of observations data from low-cost air quality monitoring networks with forecasting models can contribute to improving significantly emission control scenarios. In this work, a validation algorithm of the forecasting model for the concentration of small particulates (PM10 and PM2.5) is proposed. Results showed a satisfactory agreement between the PM concentration forecast values and the measured data from 3 air quality monitoring stations. Final average RMSE values for all monitoring stations are equal to about 4.5 µg/m3.

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