Dynamic Noise Mapping in the Suburban Area of Rome (Italy)

Benocci, R.; Bellucci, Patrizia; Peruzzi, Laura; Bisceglie, A; Angelini, Fábio Janson; Confalonieri, Chiara; Zambon, G

doi:10.3390/environments6070079

Cited by 31 publications

(27 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the one hand, the nodes distribution across the urban area of Milan is based on the clustering of traffic noise profiles in order to place the best sensor locations for different road categories [33]. On the other hand, in the Rome suburban area, the sensor nodes have been spread along the A90 highway, considering several scenarios of different complexity (single road, crossings, nearby railways and multiple connections) [34,35]. Figure 2 depicts two examples of the sensor placements in both urban and suburban areas, and Appendix A details the sensors' Ids as well as the description of their locations within Tables A1 and A2 for the urban and suburban environments, respectively.…”

Section: Wasn-based Environmental Databasesmentioning

confidence: 99%

“…Moreover, the DYNAMAP project aims at developing a WASN-based dynamic noise mapping system to monitor the acoustic impact of road infrastructures through the creation of noise maps in real time [30]. The project includes two pilot areas: one in the District 9 of Milan as urban area [33], and another in the A90 highway surrounding Rome as a suburban area [34,35]. As the system focuses on measuring RTN levels solely, the ANEs present in the acoustic environments should be automatically removed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

Álías

Orga

Alsina-Pagès

et al. 2020

Sensors

View full text Add to dashboard Cite

Environmental noise can be defined as the accumulation of noise pollution caused by sounds generated by outdoor human activities, Road Traffic Noise (RTN) being the main source in urban and suburban areas. To address the negative effects of environmental noise on public health, the European Environmental Noise Directive requires EU member states to tailor noise maps and define the corresponding action plans every five years for major agglomerations and key infrastructures. Noise maps have been hitherto created from expert-based measurements, after cleaning the recorded acoustic data of undesired acoustic events, or Anomalous Noise Events (ANEs). In recent years, Wireless Acoustic Sensor Networks (WASNs) have become an alternative. However, most of the proposals focus on measuring global noise levels without taking into account the presence of ANEs. The LIFE DYNAMAP project has developed a WASN-based dynamic noise mapping system to analyze the acoustic impact of road infrastructures in real time based solely on RTN levels. After studying the bias caused by individual ANEs on the computation of the A-weighted equivalent noise levels through an expert-based dataset obtained before installing the sensor networks, this work evaluates the aggregate impact of the ANEs on the RTN measurements in a real-operation environment. To that effect, 304 h and 20 min of labeled acoustic data collected through the two WASNs deployed in both pilot areas have been analyzed, computing the individual and aggregate impacts of ANEs for each sensor location and impact range (low, medium and high) for a 5 min integration time. The study shows the regular occurrence of ANEs when monitoring RTN levels in both acoustic environments, which are especially common in the urban area. Moreover, the results reveal that the aggregate contribution of low- and medium-impact ANEs can become as critical as the presence of high-impact individual ANEs, thus highlighting the importance of their automatic removal to obtain reliable WASN-based RTN maps in real-operation environments.

show abstract

Section: Wasn-based Environmental Databasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

Álías

Orga

Alsina-Pagès

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…CNOSSOS has also been implemented in noise simulation software, such as Geomilieu [25], MithraSIG [26], IMMI [27], Predictor-LimA [28], CadnaA [29], etc. In addition, new noise mapping systems, such as DYNAMAP [30], can be easily interfaced with CNOSSOS.…”

Section: Cnossos-eu: Where Does It Stand?mentioning

confidence: 99%

A Harmonized Data Model for Noise Simulation in the EU

Kumar

Ledoux

Schmidt³

et al. 2020

IJGI

View full text Add to dashboard Cite

This paper presents our implementation of a harmonized data model for noise simulations in the European Union (EU). Different noise assessment methods are used by different EU member states (MS) for estimating noise at local, regional, and national scales. These methods, along with the input data extracted from the national registers and databases, as well as other open and/or commercially available data, differ in several aspects and it is difficult to obtain comparable results across the EU. To address this issue, a common framework for noise assessment methods (CNOSSOS-EU) was developed by the European Commission’s (EC) Joint Research Centre (JRC). However, apart from the software implementations for CNOSSOS, very little has been done for the practical guidelines outlining the specifications for the required input data, metadata, and the schema design to test the real-world situations with CNOSSOS. We describe our approach for modeling input and output data for noise simulations and also generate a real world dataset of an area in the Netherlands based on our data model for simulating urban noise using CNOSSOS.

show abstract

“…The paper of this purpose was to analyze the noise equivalent level (Leq (dBA)) [10][11][12][13][14] in a work environment that contained the most seriously affected zones. It was evident from long-term monitoring that the highest dBA levels occurred on a certain day every week, that the dBA of certain frequency bands was always higher, and that the dBA levels differed between morning and night.…”

Section: Introductionmentioning

confidence: 99%

Noise Prediction Using Machine Learning with Measurements Analysis

Wen

Huang

2020

Applied Sciences

View full text Add to dashboard Cite

The noise prediction using machine learning is a special study that has recently received increased attention. This is particularly true in workplaces with noise pollution, which increases noise exposure for general laborers. This study attempts to analyze the noise equivalent level (Leq) at the National Synchrotron Radiation Research Center (NSRRC) facility and establish a machine learning model for noise prediction. This study utilized the gradient boosting model (GBM) as the learning model in which past noise measurement records and many other features are integrated as the proposed model makes a prediction. This study analyzed the time duration and frequency of the collected Leq and also investigated the impact of training data selection. The results presented in this paper indicate that the proposed prediction model works well in almost noise sensors and frequencies. Moreover, the model performed especially well in sensor 8 (125 Hz), which was determined to be a serious noise zone in the past noise measurements. The results also show that the root-mean-square-error (RMSE) of the predicted harmful noise was less than 1 dBA and the coefficient of determination (R2) value was greater than 0.7. That is, the working field showed a favorable noise prediction performance using the proposed method. This positive result shows the ability of the proposed approach in noise prediction, thus providing a notification to the laborer to prevent long-term exposure. In addition, the proposed model accurately predicts noise future pollution, which is essential for laborers in high-noise environments. This would keep employees healthy in avoiding noise harmful positions to prevent people from working in that environment.

show abstract

Dynamic Noise Mapping in the Suburban Area of Rome (Italy)

Cited by 31 publications

References 17 publications

Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

A Harmonized Data Model for Noise Simulation in the EU

Noise Prediction Using Machine Learning with Measurements Analysis

Contact Info

Product

Resources

About