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Characterizing and predicting ultrafine particle counts in Canadian homes, schools, and transportation environments: An exposure modeling study with implications in environmental epidemiology.

机译:表征和预测加拿大家庭,学校和交通环境中的超细颗粒数量:一项暴露建模研究,对环境流行病学有影响。

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摘要

Airborne particulate matter has a negative effect on respiratory health in both children and adults, and the ultrafine fraction of particulate air pollution is of particular interest owing to its increased ability to cause oxidative stress and inflammation in the lungs. In this investigation, our objective was to characterize ultrafine particle (UFP) counts in homes, schools, and transportation environments and to develop models to predict such exposures. A number of important determinants of UFP exposures were identified including ambient temperature and wind speed for transportation environments, outdoor UFPs for classrooms, and electric oven use, cigarette smoking, indoor relative humidity, and volume for homes. In general, our findings suggest that classrooms and transportation environments may be more suitable for UFP exposure modeling than homes. However, large diesel vehicles and in-school UFP sources had a negative influence on model performance, and future studies should include factors such as traffic counts/characteristics, vehicle ventilation settings, and in-school UFP sources to improve the predictive performance of the models presented. Nevertheless, our findings are encouraging in that we demonstrate for the first time the possibility of obtaining UFP exposure estimates for homes, schools, and transportation environments using models based on ambient weather data and other readily available determinant information. As such, similar models may be useful in population-based studies interested in the potential health effects of UFP exposures.
机译:空气中的颗粒物对儿童和成人的呼吸系统健康均具有负面影响,由于其引起肺部氧化应激和发炎的能力增强,因此特别令人关注的是空气中的超细颗粒物。在这项调查中,我们的目标是表征家庭,学校和交通环境中的超细颗粒(UFP)数量,并开发模型来预测此类暴露。确定了UFP暴露的许多重要决定因素,包括运输环境的环境温度和风速,教室的室外UFP,电烤箱的使用,吸烟,室内相对湿度和房屋的体积。总的来说,我们的发现表明,教室和交通环境可能比房屋更适合于UFP暴露建模。但是,大型柴油车辆和学校UFP来源对模型性能有负面影响,未来的研究应包括交通流量/特征,车辆通风设置和学校UFP来源等因素,以改善模型的预测性能提出了。但是,我们的发现令人鼓舞,因为我们首次证明了使用基于环境天气数据和其他易于确定的信息的模型来获得针对家庭,学校和交通环境的UFP暴露估计的可能性。因此,类似的模型可能在对UFP暴露的潜在健康影响感兴趣的基于人群的研究中有用。

著录项

  • 作者

    Weichenthal, Scott Andrew.;

  • 作者单位

    McGill University (Canada).;

  • 授予单位 McGill University (Canada).;
  • 学科 Health Sciences Occupational Health and Safety.;Environmental Sciences.;Health Sciences Epidemiology.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 284 p.
  • 总页数 284
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 职业性疾病预防;环境科学基础理论;
  • 关键词

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