Arnaud Can

Correlation analysis of noise and ultrafine particle counts in a street canyon

Ultrafine particles (UFP, diameter<100 nm) are very likely to negatively affect human health, as underlined by some epidemiological studies. Unfortunately, further investigation and monitoring are hindered by the high cost involved in measuring these UFP. Therefore we investigated the possibility to correlate UFP counts with data coming from low-cost sensors, most notably noise sensors. Analyses are based on an experiment where UFP counts, noise levels, traffic counts, nitrogen oxide (NO, NO(2) and their combination NO(x)) concentrations, and meteorological data were collected simultaneously in a street canyon with a traffic intensity of 3200 vehicles/day, over a 3-week period during summer. Previous reports that NO(x) concentrations could be used as a proxy to UFP monitoring were verified in our setup. Traffic intensity or noise level data were found to correlate with UFP to a lesser degree than NO(x) did. This can be explained by the important influence of meteorological conditions (mainly wind and humidity), influencing UFP dynamics. Although correlations remain moderate, sound levels are more correlated to UFP in the 20-30 nm range. The particles in this size range have indeed rather short atmospheric residence times, and are thus more closely short-term traffic-related. Finally, the UFP estimates were significantly improved by grouping data with similar relative humidity and wind conditions. By doing this, we were able to devise noise indicators that correlate moderately with total particle counts, reaching a Spearman correlation of R=0.62. Prediction with noise indicators is even comparable to the more-expensive-to-measure NO(x) for the smallest UFP, showing the potential of using microphones to estimate UFP counts.

Effects of traffic signal coordination on noise and air pollutant emissions

Traffic management solutions are increasingly called for to address problems of transport and mobility. In particular, coordinated traffic lights that create green waves along major arterials are an increasingly used strategy to reduce travel times. Although it is usually assumed that an improved traffic flow will result in lower vehicle emissions, little scientific research has been spent on the effects of synchronized traffic lights on emissions. Moreover, because changes in traffic flow do not necessarily influence travel times, noise and air quality in the same way, there is a clear need for a combined approach. This paper reports on a computational study in which a microscopic traffic simulation model (Paramics) is combined with submodels for the emission of noise (Imagine) and air pollutants (VERSIT+). Through the simulation of a range of scenarios, the model is used to investigate the influence of traffic intensity, signal coordination schemes and signal parameters on the noise, carbon dioxide, nitrogen oxides and particulate matter emissions along an arterial road equiped with a series of traffic lights. It was found that the introduction of a green wave could potentially lower the emissions of the considered air pollutants by 10%-40% in the most favorable conditions, depending on traffic flow and signal timing settings. Sound pressure levels were found to decrease by up to 1?dB(A) near the traffic signals, but to increase by up to 1.5?dB(A) in between intersections. Traffic intensity and green split were found to have the largest influence on emissions, while the cycle time did not have a significant influence on emissions.

Sampling approaches to predict urban street noise levels using fixed and temporary microphones

Requirements for static (prediction of L(den) and diurnal averaged noise pattern) and dynamic (prediction of 15 min and 60 min evolution of L(Aeq) and statistical levels L(A90,)L(A50) and L(A10)) noise level monitoring are investigated in this paper. Noise levels are measured for 72 consecutive days at 5 neighboring streets in an inner-city noise measurement network in Gent, Flanders, Belgium. We present a method to make predictions based on a fixed monitoring station, combined with short-term sampling at temporary stations. It is shown that relying on a fixed station improves the estimation of L(den) at other locations, and allows for the reduction of the number of samples needed and their duration; L(den) is estimated with an error that does not exceed 1.5 dB(A) to 3.4 dB(A) according to the location, for 90% of the 3 × 15 min samples. Also the diurnal averaged noise pattern can be estimated with a good accuracy in this way. It was shown that there is an optimal location for the fixed station which can be found by short-term measurements only. Short-term level predictions were shown to be more difficult; 7 day samples were needed to build models able to estimate the evolution of L(Aeq,60min) with a RMSE ranging between 1.4 dB(A) and 3.7 dB(A). These higher values can be explained by the very pronounced short-term variations appearing in typical streets, which are not correlated between locations. On the other hand, moderately accurate predictions can be achieved, even based on short-term sampling (a 3 × 15 minute sampling duration seems to be sufficient for many of the accuracy goals set related to static and dynamic monitoring). Finally, the method proposed also allows for the prediction of the evolution of statistical indicators.

Noise measurements as proxies for traffic parameters in monitoring networks.

The present research describes how microphones could be used as proxies for traffic parameter measurements for the estimation of airborne pollutant emissions. We consider two distinct measurement campaigns of 7 and 12 days, at two different locations along the urban ring road in Antwerp, Belgium, where sound pressure levels and traffic parameters were measured simultaneously. Noise indicators are calculated and used to construct models to estimate traffic parameters. It is found that relying on different statistical levels and selecting specific sound frequencies permits an accurate estimation of traffic intensities and mean vehicle speeds, both for light and heavy vehicles. Estimations of R(2) values ranging between 0.81 and 0.92 are obtained, depending on the location and traffic parameters. Furthermore, the usefulness of these estimated traffic parameters in a monitoring strategy is assessed. Carbon monoxide, hydrocarbon and nitrogen oxide emissions are calculated with the airborne pollutant emission model Artemis. The Artemis outputs fed with directly measured and estimated traffic parameters (based on noise measurements) are very similar. Finally, a method is proposed to enable using a model calibrated at one location at another location without the need for new calibration, making it straightforward to include new measurement locations in a monitoring network.

Noise mapping based on participative measurements

Abstract The high temporal and spatial granularities recommended by the European regulation for the purpose of environmental noise mapping leads to consider new alternatives to simulations for reaching such information. While more and more European cities deploy urban environmental observatories, the ceaseless rising number of citizens equipped with both a geographical positioning system and environmental sensors through their smartphones legitimates the design of outsourced systems that promote citizen participatory sensing. In this context, the OnoM@p system aims at offering a framework for capitalizing on crowd noise data recorded by inexperienced individuals by means of an especially designed mobile phone application. The system fully rests upon open source tools and interoperability standards defined by the Open Geospatial Consortium. Moreover, the implementation of the Spatial Data Infrastructure principle enables to break up as services the various business modules for acquiring, analysing and mapping sound levels. The proposed architecture rests on outsourced processes able to filter outlier sensors and untrustworthy data, to cross- reference geolocalised noise measurements with both geographical and statistical data in order to provide higherlevel indicators, and to map the collected and processed data based on web services.

Towards Traffic Situation Noise Emission Models

This article proposes a methodology to account for vehicle kinematics in a fast and efficient way when using single vehicle noise emission models such as the Harmonoise/Imagine, Nord2000 or NMPB. A model is built, which mimics the traffic situation emission models developed in the field of airborne pollutants research. The model aggregates the sound power emitted over driving cycles which are statistically representative of real-world driving conditions. Four different driving conditions are included in the cycles, ranging from free-flowing to stop-and-go traffic conditions. The sound power levels estimated with this new approach are significantly different from the ones estimated with the mean speed approach recommended by the noise mapping guidelines, especially when traffic is congested, suggesting that the method could prove relevant for improving noise map accuracy, in particular in urban context.

Selecting Noise Source and Traffic Representations that Capture Road Traffic Noise Dynamics Near Traffic Signals

Considering trafic dynamics greatly improves noise estimation in urban area. This can be achieved by coupling a dynamic trafic model with both noise emission laws and sound propagation calculation. Determining the relevant noise source and trafic representations to estimate classical noise descriptors (L-Aeq and statistical descriptors) near trafic signals has been recently studied. This research topic is extended in this paper to more specific descriptors which are able to capture noise dynamics at the trafic signal scale, for usual urban trafic situations (upstream, in front of, and downstream a trafic signal) and different distances from the road (5.5, 10 and 15 m). It appears that 14m-line sources ensure an estimation of all descriptors with errors below 2 dB(A) if trafic dynamics is precisely described. Macroscopic and microscopic car-following models are both relevant to highlight noise dynamics triggered by the trafic signal, but some differences between those traffic representations are observed.

Describing and classifying urban sound environments with a relevant set of physical indicators

Categorization is a powerful method for describing urban sound environments. However, it has only been applied, until now, to discrete noise data collection, whereas sound environments vary continuously both in space and time. Therefore, a procedure is developed in this paper for describing the variations of urban sound environments. The procedure consists of mobile measurements, followed by a statistical clustering analysis that selects relevant noise indicators and classifies sound environments. Analysis are based on a 3 days + 1 night survey where geo-referenced noise measurements were collected over 19 1-h soundwalk periods in a district of Marseille, France. The clustering analysis showed that a limited subset of indicators is sufficient to discriminate sound environments. The three indicators that emerged from the clustering, namely, the Leq, A, the standard deviation σL eq, A, and the sound gravity spectrum SGC[50 Hz-10 kHz], are consistent with previous studies on sound environment classification. Moreover, the procedure proposed enables the description of the sound environment, which is classified into homogenous sound environment classes by means of the selected indicators. Thus, the procedure can be adapted to any urban environment, and can, for instance, favorably enhance perceptive studies by delimiting precisely the spatial extent of each typical sound environment.

Characterization of urban sound environments using a comprehensive approach combining open data, measurements, and modeling

Urban noise reduction is a societal priority. In this context, the European Directive 2002/49/EC aims at producing strategic noise maps for large cities. However, nowadays the relevance of such maps is questionable, due to considerable uncertainties, which are rarely quantified. Conversely, the development of noise observatories can provide useful information for a more realistic description of the sound environment, but at the expense of insufficient spatial resolution and high costs. Thus, the CENSE project aims at proposing a new methodology for the production of more realistic noise maps, based on an assimilation of simulated and measured data, collected through a dense network of low-cost sensors that rely on new technologies. In addition, the proposed approach tries to take into account the various sources of uncertainty, either from measurements and modeling. Beyond the production of physical indicators, the project also includes advanced sound environments characterization, through sound recognition and perceptual assessments. CENSE is resolutely a multidisciplinary project, bringing together experts from environmental acoustics, data assimilation, statistics, GIS, sensor networks, signal processing, and noise perception. As the project is in launch state, the present communication will focus on a global overview, emphasizing the innovative and key points of the project.Urban noise reduction is a societal priority. In this context, the European Directive 2002/49/EC aims at producing strategic noise maps for large cities. However, nowadays the relevance of such maps is questionable, due to considerable uncertainties, which are rarely quantified. Conversely, the development of noise observatories can provide useful information for a more realistic description of the sound environment, but at the expense of insufficient spatial resolution and high costs. Thus, the CENSE project aims at proposing a new methodology for the production of more realistic noise maps, based on an assimilation of simulated and measured data, collected through a dense network of low-cost sensors that rely on new technologies. In addition, the proposed approach tries to take into account the various sources of uncertainty, either from measurements and modeling. Beyond the production of physical indicators, the project also includes advanced sound environments characterization, through sound recogniti...

Creation of a corpus of realistic urban sound scenes with controlled acoustic properties

Sound source detection and recognition using acoustic sensors are increasingly used to monitor and analyze the urban environment as they enhance soundscape characterization and facilitate the comparison between simulated and measured noise maps using methods such as Artificial Neural Networks or Non-negative Matrix Factorization. However, the community lacks corpuses of sound scenes whose acoustic properties of each source present within the scene are precisely known. In this study, a set of 40 sound scenes typical of urban sound mixtures is created in three steps: (i) real sound scenes are listened and annotated in terms of events type, (ii) artificial sound scenes are created based on the concatenation of recorded individual sounds, whose intensity and duration are controlled to build scenes that are as close as possible to the real ones, (iii) a test is carried out to validate the level of their perceptual realism of those crafted scenes. Such corpus could be then used by communities interested in the ...