...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Building and Environment >Modeling indoor exposure to PM_(2.5) and black carbon in densely populated urban slums
【24h】

Modeling indoor exposure to PM_(2.5) and black carbon in densely populated urban slums

机译：在密集的城市贫民窟建模室内接触PM_（2.5）和黑碳

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

Mumbai city, India houses similar to 10 million people in slums, which is half of its total population. Located along the major roadways, most of these smaller sized and poorly constructed slum homes are likely exposed to high levels of indoor air pollutants. Since direct measurement of indoor air pollution levels among the large slum population is infeasible, this study aims to develop predictive regression models for indoor PM2.5 and black carbon (BC) in the densely populated slums in Mumbai. Daily indoor PM2.5 and BC was measured inside the homes of a low-traffic slum (in 44 homes) and a high traffic slum (in 40 homes) during a winter season. Multivariable regression models were developed using measured indoor levels, publicly available ambient PM2.5 and information on local traffic characteristics, building characteristics and occupant activities (collected through questionnaire surveys). Models showed moderate to good performance for BC (adjusted R-2 = 0.50-0.64) and PM2.5 (adjusted R-2 = 0.53-0.57). Ambient PM2.5 was the most significant predictor for both pollutants, accounting for the temporal variation. BC was positively associated with the road length density of major roads while PM2.5 was associated with the density of all road types. Models for both pollutants were robust with Leave-One-Out-Cross-Validation (LOOCV) R-2 ranging 0.43-0.61 (BC) and 0.37-0.51 (PM2.5). The study demonstrates that indoor particulate matter exposures can be reasonably predicted using publicly available air pollution data and information on local traffic and housing characteristics, and also underpins the high exposure to traffic pollution in urban slums.
机译：孟买市印度房屋类似于贫民窟的1000万人，这是其总人口的一半。沿着主要道路，大多数这些较小的大小和构造的贫民窟家庭可能暴露于高水平的室内空气污染物。由于大幅贫民窟人口中的室内空气污染水平的直接测量是不可行的，本研究旨在为孟买密集的贫民窟中的室内PM2.5和黑碳（BC）开发预测性回归模型。每日室内PM2.5和BC在低交通贫民窟（44位房屋中）的家庭内测量，在冬季期间高交通贫民窟（在40家）。使用测量的室内水平，公开的环境PM2.5和有关当地交通特征，建筑特征和乘员活动的信息开发了多变量回归模型，并通过调查问卷调查收集）。模型显示适中的BC性能（调整的R-2 = 0.50-0.64）和PM2.5（调整的R-2 = 0.53-0.57）。环境PM2.5是污染物最重要的预测因子，占时间变异。 BC与主要道路的道路长度密度正相关，而PM2.5与所有道路类型的密度相关。污染物的模型具有休养 - 单交叉验证（LOOCV）R-2，范围为0.43-0.61（BC）和0.37-0.51（PM2.5）。该研究表明，可以使用公开的空气污染数据和关于当地交通和住房特征的信息来合理地预测室内颗粒物质暴露，并且还为城市贫民窟的交通污染高度接触。

著录项

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