...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Environmental Science & Technology >'Exposure Track'-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution
【24h】

'Exposure Track'-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution

机译：“接触轨迹”-基于移动设备的出行方式对量化人口对空气污染的影响

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Air pollution is now recognized as the world's single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level have not considered spatially- and temporally varying populations present in study regions. Therefore, in the first study of it is kind, we use measured population activity patterns representing several million people to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yield information about where and when people are present, thus collective activity patterns were determined using counts of connections to the cellular network. Population-weighted exposure to PM_(2.5) in New York City (NYC), herein termed "Active Population Exposure" was evaluated using population activity patterns and spatiotemporal PM_(2.5) concentration levels, and compared to "Home Population Exposure", which assumed a static population distribution as per Census data. Areas of relatively higher population-weighted exposures were concentrated in different districts within NYC in both scenarios. These were more centralized for the "Active Population Exposure" scenario. Population-weighted exposure computed in each district of NYC for the "Active" scenario were found to be statistically significantly (p ＜ 0.05) different to the "Home" scenario for most districts. In investigating the temporal variability of the "Active" population-weighted exposures determined in districts, these were found to be significantly different (p ＜ 0.05) during the daytime and the nighttime. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiological studies linking air quality and human health.
机译：如今，空气污染已被公认为是全球最大的环境和人类健康威胁。确实，大量环境流行病学研究已经量化了人口接触污染对健康的影响。在先前的研究中，人口水平的暴露估计未考虑研究区域中存在的随时间变化的人口。因此，在第一个同类研究中，我们使用代表数百万人的实测人口活动模式，在全市范围内评估人口加权的空气污染暴露量。移动和无线设备产生有关人在何处和何时出现的信息，因此使用到蜂窝网络的连接计数来确定集体活动模式。使用人口活动模式和时空PM_（2.5）浓度水平评估了纽约市（NYC）的人口加权PM_（2.5）暴露，这里称为“活跃人口暴露”，并与“家庭人口暴露”进行比较根据人口普查数据的静态总体分布。在两种情况下，人口加权暴露相对较高的区域都集中在纽约市内的不同区域。对于“活跃人口暴露”场景，这些条件更加集中。发现在纽约市每个地区中，针对“活跃”情景所计算的人口加权暴露与大多数地区的“家庭”情景在统计学上显着不同（p ＜0.05）。在调查各地区确定的“主动”人口加权暴露的时间变异性时，发现白天和黑夜的暴露水平存在显着差异（p ＜0.05）。使用时空人口迁移模式评估人口暴露于空气污染的状况值得在未来将空气质量与人类健康联系起来的环境流行病学研究中加以考虑。

著录项

  • 来源
    《Environmental Science & Technology》 |2016年第17期|9671-9681|共11页
  • 作者

    Marguerite Nyhan; Sebastian Grauwin; Rex Britter; Bruce Misstear; Aonghus McNabola; Francine Laden; Steven R. H. Barrett; Carlo Ratti;

  • 作者单位

    Massachusetts Institute of Technology, Senseable City Laboratory, Cambridge, Massachusetts 02139, United States,Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts 02215, United States,Harvard University, Harvard School of Public Health, Boston, MA, USA 02215;

    Massachusetts Institute of Technology, Senseable City Laboratory, Cambridge, Massachusetts 02139, United States;

    Massachusetts Institute of Technology, Senseable City Laboratory, Cambridge, Massachusetts 02139, United States;

    Department of Civil, Structural ＆ Environmental Engineering, Trinity College Dublin, College Green, Dublin 2, Ireland;

    Department of Civil, Structural ＆ Environmental Engineering, Trinity College Dublin, College Green, Dublin 2, Ireland;

    Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts 02215, United States;

    Massachusetts Institute of Technology, Department of Aeronautics ＆ Astronautics, Cambridge, Massachusetts 02139, United States;

    Massachusetts Institute of Technology, Senseable City Laboratory, Cambridge, Massachusetts 02139, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号