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      NoiseModelling: An Open Source GIS Based Tool to Produce Environmental Noise Maps

      , , , ,
      ISPRS International Journal of Geo-Information
      MDPI AG

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          Abstract

          The urbanisation phenomenon and related cities expansion and transport networks entail preventing the increase of population exposed to environmental pollution. Regarding noise exposure, the Environmental Noise Directive demands on main metropolis to produce noise maps. While based on standard methods, these latter are usually generated by proprietary software and require numerous input data concerning, for example, the buildings, land use, transportation network and traffic. The present work describes an open source implementation of a noise mapping tool fully implemented in a Geographic Information System compliant with the Open Geospatial Consortium standards. This integration makes easier at once the formatting and harvesting of noise model input data, cartographic rendering and output data linkage with population data. An application is given for a French city, which consists in estimating the impact of road traffic-related scenarios in terms of population exposure to noise levels in relation to both a threshold value and level classes.

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          Most cited references37

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          Assuring the quality of volunteered geographic information

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            Quality Assessment of the French OpenStreetMap Dataset

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              WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Annoyance

              Background: This paper describes a systematic review and meta-analyses on effects of environmental noise on annoyance. The noise sources include aircraft, road, and rail transportation noise as well as wind turbines and noise source combinations. Objectives: Update knowledge about effects of environmental noise on people living in the vicinity of noise sources. Methods: Eligible were published studies (2000–2014) providing comparable acoustical and social survey data including exposure-response functions between standard indicators of noise exposure and standard annoyance responses. The systematic literature search in 20 data bases resulted in 62 studies, of which 57 were used for quantitative meta-analyses. By means of questionnaires sent to the study authors, additional study data were obtained. Risk of bias was assessed by means of study characteristics for individual studies and by funnel plots to assess the risk of publication bias. Main Results: Tentative exposure-response relations for percent highly annoyed residents (%HA) in relation to noise levels for aircraft, road, rail, wind turbine and noise source combinations are presented as well as meta-analyses of correlations between noise levels and annoyance raw scores, and the OR for increase of %HA with increasing noise levels. Quality of evidence was assessed using the GRADE terminology. The evidence of exposure-response relations between noise levels and %HA is moderate (aircraft and railway) or low (road traffic and wind turbines). The evidence of correlations between noise levels and annoyance raw scores is high (aircraft and railway) or moderate (road traffic and wind turbines). The evidence of ORs representing the %HA increase by a certain noise level increase is moderate (aircraft noise), moderate/high (road and railway traffic), and low (wind turbines). Strengths and Limitations: The strength of the evidence is seen in the large total sample size encompassing the included studies (e.g., 18,947 participants in aircraft noise studies). Main limitations are due to the variance in the definition of noise levels and %HA. Interpretation: The increase of %HA in newer studies of aircraft, road and railway noise at comparable L den levels of earlier studies point to the necessity of adjusting noise limit recommendations. Funding: The review was funded by WHO Europe.
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                Author and article information

                Journal
                ISPRS International Journal of Geo-Information
                IJGI
                MDPI AG
                2220-9964
                March 2019
                March 04 2019
                : 8
                : 3
                : 130
                Article
                10.3390/ijgi8030130
                ada1a416-15a5-4424-bde1-43edaaf14da4
                © 2019

                https://creativecommons.org/licenses/by/4.0/

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