Evaluation of road traffic noise exposure based on high-resolution population distribution and grid-level noise data

Abstract The primary objective of this study is to propose a method for assessing population exposure to road traffic noise based on high-resolution population and grid-level noise data. First, after meshing the study region and setting the receivers at the nodes, the noise values of all receivers are calculated by regarding roads as linear sound sources, and these values are rendered as the traffic noise distribution. Next, a population distribution model is trained with point of interest (POI) sample data, population sample data, and the random forests algorithm. Then, the statistics of the POIs in the grids are input to the trained model as the noise distribution; the grid-level population data are obtained and used to depict the population distribution. Then, to facilitate the assessment, the grid-level traffic noise distribution data and population distribution data are combined according to the ID or location of the nodes of grids. Finally, evaluation indexes considering the traffic noise and population distribution are used to assess the population exposure to traffic noise. The proposed method is applied to three types of regions in Guangzhou: a residential area, a commercial area and an industrial area. The noise pollution levels of all study regions are ‘Mild’, but the pollution of road traffic noise is most severe in the residential area with 12.00% of the population and 7.60% of the area affected due to the denser road network and stricter standards. These results indicate that people in residential areas are more sensitive to road traffic noise.

[1]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[2]  Ming Cai,et al.  Road traffic noise mapping in Guangzhou using GIS and GPS , 2015 .

[3]  B. D. Tripathi,et al.  Evaluation of traffic noise pollution and attitudes of exposed individuals in working place , 2008 .

[4]  A. Muzet.  Environmental noise, sleep and health. , 2007, Sleep medicine reviews.

[5]  ADVANCING ENVIRONMENTAL NOISE POLLUTION ANALYSIS IN URBAN AREAS BY CONSIDERING THE VARIATION OF POPULATION EXPOSURE IN SPACE AND TIME , 2013 .

[6]  Ragnar Rylander,et al.  Effects of nighttime low frequency noise on the cortisol response to awakening and subjective sleep quality. , 2003, Life sciences.

[7]  Luca Fredianelli,et al.  A procedure for the assessment of wind turbine noise , 2016 .

[8]  D A Griffith,et al.  Modelling urban population density in a multi-centered city. , 1981, Journal of urban economics.

[9]  Julien Morel,et al.  Noise annoyance assessment of various urban road vehicle pass-by noises in isolation and combined with industrial noise: A laboratory study , 2016 .

[10]  Haibo Wang,et al.  Evaluation of an urban traffic Noise-Exposed population based on points of interest and noise maps: The case of Guangzhou. , 2018, Environmental pollution.

[11]  A. Schäfer,et al.  Removal of the natural hormone estrone from aqueous solutions using nanofiltration and reverse osmosis. , 2003, Environmental science & technology.

[12]  M Bell,et al.  International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies. , 2015, Environmental pollution.

[13]  Mario Soliño,et al.  Transportation planning and quiet natural areas preservation: Aircraft overflights noise assessment in a National Park , 2015 .

[14]  Paulo Henrique Trombetta Zannin,et al.  Assessment of railway noise in an urban setting , 2016 .

[15]  A. Hansell,et al.  Exposure modifiers of the relationships of transportation noise with high blood pressure and noise annoyance. , 2012, The Journal of the Acoustical Society of America.

[16]  Antonio J. Torija,et al.  Selection of suitable alternatives to reduce the environmental impact of road traffic noise using a fuzzy multi-criteria decision model , 2016 .

[17]  Seo Il Chang,et al.  Transportation noise and exposed population of an urban area in the Republic of Korea. , 2011, Environment international.

[18]  F. Zacarías,et al.  Noise Exposure in Preterm Infants Treated with Respiratory Support Using Neonatal Helmets , 2013 .

[19]  Kin-che Lam,et al.  Exposure of urban populations to road traffic noise in Hong Kong , 2012 .

[20]  L. Brown,et al.  Burden of disease from environmental noise: Quantification of healthy life years lost in Europe , 2011 .

[21]  K. Small,et al.  Population and employment densities: structure and change. , 1993, Journal of urban economics.

[22]  Wolfgang Babisch,et al.  Traffic Noise and Risk of Myocardial Infarction , 2005, Epidemiology.

[23]  Fabienne Anfosso-Lédée,et al.  Advances in the development of common noise assessment methods in Europe: The CNOSSOS-EU framework for strategic environmental noise mapping. , 2014, The Science of the total environment.

[24]  Matthias Ketzel,et al.  Residential exposure to traffic noise and risk of incident atrial fibrillation: A cohort study. , 2016, Environment international.

[25]  H W Richardson,et al.  The Distribution of Population and Employment in a Polycentric City: The Case of Los Angeles , 1986, Environment & planning A.

[26]  Christian Schindler,et al.  Transportation Noise and Blood Pressure in a Population-Based Sample of Adults , 2011, Environmental health perspectives.

[27]  K. Thomas,et al.  Use of Mobile Device Data To Better Estimate Dynamic Population Size for Wastewater-Based Epidemiology. , 2017, Environmental science & technology.

[28]  Haibo Wang,et al.  A traffic-noise-map update method based on monitoring data. , 2017, The Journal of the Acoustical Society of America.

[29]  E. Murphy,et al.  Population exposure to road traffic noise: Experimental results from varying exposure estimation approaches , 2018 .

[30]  Gaetano Licitra,et al.  Annoyance evaluation due to overall railway noise and vibration in Pisa urban areas. , 2016, The Science of the total environment.

[31]  H. Miedema,et al.  Annoyance from transportation noise: relationships with exposure metrics DNL and DENL and their confidence intervals. , 2001, Environmental health perspectives.

[32]  Ming Ouyang,et al.  Compute spearman correlation coefficient with Matlab/CUDA , 2012, 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[33]  Alexander Zipf,et al.  Fine-resolution population mapping using OpenStreetMap points-of-interest , 2014, Int. J. Geogr. Inf. Sci..

[34]  Eoin A. King,et al.  Scenario analysis and noise action planning : modelling the impact of mitigation measures on population exposure , 2011 .

[35]  Kazumasa Hanaoka,et al.  New insights on relationships between street crimes and ambient population: Use of hourly population data estimated from mobile phone users’ locations , 2018 .

[36]  Gösta Leon Bluhm,et al.  Road traffic noise and hypertension , 2006, Occupational and Environmental Medicine.

[38]  Feng Li,et al.  Dynamic simulation and characteristics analysis of traffic noise at roundabout and signalized intersections , 2017 .

[39]  K. Overvad,et al.  Residential Exposure to Traffic Noise and Health-Related Quality of Life—A Population-Based Study , 2015, PloS one.

[40]  Yatao Zhang,et al.  Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data , 2017, Int. J. Geogr. Inf. Sci..

[41]  Ricardo Hernandez,et al.  Analysis of the Impact of Bus Signal Priority on Urban Noise , 2017 .

[42]  Katya Feder,et al.  Exposure to wind turbine noise: Perceptual responses and reported health effects. , 2016, The Journal of the Acoustical Society of America.

[43]  Markus Meis,et al.  Ambient Noise and Cognitive Processes among Primary Schoolchildren , 2003 .

[44]  Gaetano Licitra,et al.  ADS-B System as a Useful Tool for Testing and Redrawing Noise Management Strategies at Pisa Airport , 2017 .

[45]  A. Tjønneland,et al.  Residential exposure to traffic noise and leisure-time sports - A population-based study. , 2017, International journal of hygiene and environmental health.