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dc.contributor.authorWei, Weigang
dc.contributor.authorVan Renterghem, Timothy
dc.contributor.authorDe Coensel, Bert
dc.contributor.authorBotteldooren, Dick
dc.date.accessioned2021-01-14T06:27:39Z
dc.date.available2021-01-14T06:27:39Z
dc.date.issued2016
dc.identifier.issn0003-682Xen_US
dc.identifier.doi10.1016/j.apacoust.2015.08.005en_US
dc.identifier.urihttp://hdl.handle.net/10072/401084
dc.description.abstractSince the introduction of the Environmental Noise Directive, strategic noise mapping has been used as a tool for noise policy in many European countries. Although these strategic noise maps have their merits, they also have some shortcomings: accuracy in predicted noise levels in shielded or quiet areas is not very high, the maps fail to capture sounds that are less easy to predict, and above all the dynamics of the sound environment are not included. However, these dynamics might be important to evaluate sleep disturbance and noise annoyance. In this paper, a model to dynamically (every 15 min) update a noise map based on measurements is proposed. This model relies on reasonable good source and propagation models and a not-very-dense measurement network. The least mean squares method (LMS) is used for tuning model parameters. To avoid an under-determined system, the number of degrees of freedom is reduced by grouping the sources and propagation paths into different categories. Source strengths and propagation path attenuations in the same category are corrected by offsetting the same small values from their base levels. The map-based interpolation is performed jointly on <sup>LAeq</sup>,<sup>L10</sup> and <sup>L90</sup>, and takes into account 1/3-octave band spectra. The efficiency of the proposed method was validated in a case study in the Katendrecht district of Rotterdam, the Netherlands. The results showed that more than 75% of the <sup>LAeq</sup> predictions are closer to the measurement than the ab initio calculations based on traffic data. Values for <sup>L10</sup> and <sup>L90</sup> are closer to measurements for 55% and 90% of the observations, respectively.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherElsevier
dc.relation.ispartofpagefrom127en_US
dc.relation.ispartofpageto140en_US
dc.relation.ispartofjournalApplied Acousticsen_US
dc.relation.ispartofvolume101en_US
dc.subject.fieldofresearchClassical Physicsen_US
dc.subject.fieldofresearchMechanical Engineeringen_US
dc.subject.fieldofresearchArchitectureen_US
dc.subject.fieldofresearchcode0203en_US
dc.subject.fieldofresearchcode0913en_US
dc.subject.fieldofresearchcode1201en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsAcousticsen_US
dc.subject.keywordsDynamic noise mapen_US
dc.subject.keywordsEnvironmental noise monitoringen_US
dc.titleDynamic noise mapping: A map-based interpolation between noise measurements with high temporal resolutionen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationWei, W; Van Renterghem, T; De Coensel, B; Botteldooren, D, Dynamic noise mapping: A map-based interpolation between noise measurements with high temporal resolution, Applied Acoustics, 2016, 101, pp. 127-140en_US
dc.date.updated2021-01-14T06:25:25Z
dc.description.versionAccepted Manuscript (AM)en_US
gro.rights.copyright© 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.en_US
gro.hasfulltextFull Text
gro.griffith.authorDe Coensel, Bert


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