Suppr超能文献

噪音太大?:利用手机进行交通噪音暴露的社区地图绘制。

2Loud?: Community mapping of exposure to traffic noise with mobile phones.

作者信息

Leao Simone, Ong Kok-Leong, Krezel Adam

机构信息

Deakin University, Geelong Waterfront Campus, Locked Bag 20,001, Geelong, Victoria, 3220, Australia,

出版信息

Environ Monit Assess. 2014 Oct;186(10):6193-206. doi: 10.1007/s10661-014-3848-9. Epub 2014 Jun 12.

Abstract

Despite ample medical evidence of the adverse impacts of traffic noise on health, most policies for traffic noise management are arbitrary or incomplete, resulting in serious social and economic impacts. Surprisingly, there is limited information about citizen's exposure to traffic noise worldwide. This paper presents the 2Loud? mobile phone application, developed and tested as a methodology to monitor, assess and map the level of exposure to traffic noise of citizens with focus on the night period and indoor locations, since sleep disturbance is one of the major triggers for ill health related to traffic noise. Based on a community participation experiment using the 2Loud? mobile phone application in a region close to freeways in Australia, the results of this research indicates a good level of accuracy for the noise monitoring by mobile phones and also demonstrates significant levels of indoor night exposure to traffic noise in the study area. The proposed methodology, through the data produced and the participatory process involved, can potentially assist in planning and management towards healthier urban environments.

摘要

尽管有充分的医学证据表明交通噪音对健康有不利影响,但大多数交通噪音管理政策都是随意的或不完整的,从而产生了严重的社会和经济影响。令人惊讶的是,全球范围内关于公民接触交通噪音的信息有限。本文介绍了一款名为2Loud?的手机应用程序,该程序作为一种方法进行了开发和测试,用于监测、评估和绘制公民接触交通噪音的水平,重点关注夜间时段和室内场所,因为睡眠干扰是与交通噪音相关的健康问题的主要触发因素之一。基于在澳大利亚高速公路附近一个地区使用2Loud?手机应用程序进行的社区参与实验,本研究结果表明手机进行噪音监测的准确性较高,同时也表明研究区域内室内夜间交通噪音暴露水平较高。通过所产生的数据和所涉及的参与过程,所提出的方法有可能有助于规划和管理更健康的城市环境。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验