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Modern space/time geostatistical approaches to mapping point sources of pollution and infectious disease.

机译:现代时空地统计学方法,用于绘制污染和传染病的点源图。

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摘要

Point sources are defined in the context of environmental science as single identifiable locations that emit pollution into the environment. From a modeling perspective, they are "ground zero"---where a contaminant originates or is at a distance of 0 from the source---and they are desirable from the standpoint of mitigation strategies because the pollutant being produced must only be brought under control at the location of release. A set of studies were conducted to investigate whether modern geostatistical techniques, such as Bayesian Maximum Entropy which has the ability to incorporate non-Gaussian space/time data, can improve the estimates of a variable produced by point sources over that of the traditional kriging method. In Study 1, a single point source at the most famous Ground Zero is the focus in order to estimate atmospheric polycyclic aromatic hydrocarbons (PAHs) produced during the collapse and cleanup of the World Trade Center in New York City by modeling the mass fraction of PAH contained in PM2.5. This PAH to PM2.5 model is then applied to existing PM2.5 monitors in the area to expand the number of estimated PAH measurements. Study 2 examines a situation where many point sources across space contribute to pollution in modeling hydrogen sulfide (H2S) concentrations produced by industrial hog operations in an eastern North Carolina county with a high density of these facilities. Passive samplers that recorded H2S were used to create a land use regression model to estimate individual source contribution to the community and then to produce geostatistical estimates after applying a non-Gaussian measurement error model to the data.;Finally, Study 3 researches an unorthodox "point source" by attempting to identify core areas---locations with elevated rates that perpetuate an infection---of syphilis and gonorrhea in North Carolina. To locate these "sources," which have the ability to move in space and time, a Bayesian-derived non-Gaussian model for the error is used to improve estimation of incidence rates on a fine space/time scale. These estimates are then utilized in an outbreak detection algorithm so that a source of infection can be controlled before it increases and/or moves.
机译:在环境科学的背景下,点源被定义为向环境排放污染的单个可识别位置。从建模的角度来看,它们是“零地面”(即污染物起源或与污染源的距离为零),从缓解策略的角度来看,它们是理想的,因为必须将产生的污染物带入环境中。处于释放位置的控制之下。进行了一系列研究以调查现代地统计学技术(例如具有合并非高斯时空数据能力的贝叶斯最大熵)是否可以改善点源产生的变量的估计,而不是传统的克里格方法。 。在研究1中,重点是通过模拟PAH的质量分数来估算纽约世界贸易中心倒塌和清理期间产生的大气多环芳烃(PAH),这是最著名的地面零点的单点源。包含在PM2.5中。然后将此PAH到PM2.5模型应用于该地区现有的PM2.5监视器,以扩展估计的PAH测量数量。研究2研究了在北卡罗来纳州东部一个县城中,工业猪场生产的硫化氢(H2S)浓度高,这些设施密度高的情况,跨空间的许多点源都造成了污染。记录H2S的被动采样器用于创建土地利用回归模型,以估计单个源对社区的贡献,然后在对数据应用非高斯测量误差模型后产生地统计估计。最后,研究3研究了非正统的“试图找出北卡罗来纳州梅毒和淋病的核心区域-感染率高的地方,从而使感染持续不断-”。为了找到具有时空移动能力的“源”,可以使用贝叶斯派生的非高斯误差模型来改善在精细时空尺度上的发生率估计。然后,将这些估计值用于爆发检测算法中,以便可以在感染源增加和/或移动之前对其进行控制。

著录项

  • 作者单位

    The University of North Carolina at Chapel Hill.;

  • 授予单位 The University of North Carolina at Chapel Hill.;
  • 学科 Environmental science.;Geography.;Epidemiology.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 131 p.
  • 总页数 131
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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