Nonstationary Spatial Modeling for Multiple Point Sources, with Applications to Enviromnental Data

多点源的非平稳空间建模及其在环境数据中的应用

基本信息

  • 批准号:
    0084378
  • 负责人:
  • 金额:
    $ 20万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2000
  • 资助国家:
    美国
  • 起止时间:
    2000-10-01 至 2004-09-30
  • 项目状态:
    已结题

项目摘要

This research develops, assesses, and provides convenient tools for implementing parametric modeling of processes that are driven by point sources. The project uses a hierarchical Bayesian approach to an extension of a process decomposition model that was recently introduced in the context of modeling the effect of point sources. The process decomposition model decomposes the observed process into a trend surface, a baseline error process, and additional error processes (one for each point source) that may be viewed as shocks to the baseline. This approach allows flexibility and autonomy to modeling the individual sources. The Bayesian approach properly accounts for uncertainty in parameter estimates when evaluating prediction uncertainty, easily incorporates prior information, and is more flexible for assigning ranks according to the impact of several point sources. A Markov random field or conditional autoregressive model based on a distance-to-source neighborhood structure is used for the error processes. A multivariate distribution of weights describes the relative ranks of the sources. Because all models are parametric, significance testing is easily accomplished. The project accomplishes the following technical goals: A. test the impact of several point sources, where exact locations of the point sources are known; B. rank several point sources according to impact; C. provide models that are useful for determining appropriate corrective action for altering an observed process or for optimizing a designed process; D. provide predictions, and appropriate measures of prediction uncertainties, at unsampled sites by accounting for the effects of point sources. The educational component of the project involves teaching high school students the rudiments of the methodology developed and its relevance to major issues such as environmental equity.The Clean Air Act of 1970 and its 1977 and 1990 amendments defined a pollution source as "any place or object from which pollutants are released. A source can be a power plant, factory, dry cleaning business, gas station or farm. Cars, trucks and other motor vehicles are sources, and consumer products and machines used in industry can be sources too." The effects of these sources on human health are well-documented, as is the importance of modeling these effects. This project responds to this need by developing, assessing, and providing convenient tools for modeling processes that are driven by point sources. The resulting methodology is applied to address a public health and welfare concern of the Environmental Protection Agency, namely to determine which nitrogen oxide (NOx) and sulfur dioxide (SO2) emission sites are most responsible for site-specific ambient concentrations far from the emission sites. These site-specific ranks will help determine which emission sites require stricter regulations for controlling their impact at different spatial locations. More generally, project results may impact the regulation of point sources, the assignment of equitable consequences to several point sources within the vicinity of an ecological or environmental disaster, and help in determining appropriate corrective actions. Furthermore, the creation of teaching modules aimed at high school students will improve the readiness of these students for studies in the mathematical, physical, and biological sciences. This project is jointly supported by the Statistics Program in the Division of Mathematical Sciences and the Office of Multidisciplinary Activities in MPS.
本研究开发,评估,并提供了方便的工具,用于实现参数化建模的过程中,点源驱动。该项目使用了一个层次贝叶斯方法的过程分解模型,最近推出的点源的影响建模的上下文中的扩展。 过程分解模型将观察到的过程分解为趋势面、基线误差过程和附加误差过程(每个点源一个),这些附加误差过程可以被视为对基线的冲击。这种方法允许灵活性和自主性来建模各个源。贝叶斯方法在评估预测不确定性时适当地考虑了参数估计的不确定性,易于合并先验信息,并且根据几个点源的影响更灵活地分配等级。一个马尔可夫随机场或条件自回归模型的基础上的距离源邻域结构的误差过程中使用。权重的多元分布描述了源的相对等级。 因为所有模型都是参数化的,所以显著性检验很容易完成。该项目主要实现了以下技术目标:A.测试若干点源的影响,其中所述点源的确切位置是已知的; B.根据影响对几个点源进行排序; C.提供模型,所述模型对于确定用于改变所观察的过程或用于优化所设计的过程的适当的校正动作是有用的; D.通过考虑点源的影响,在未采样点提供预测和预测不确定性的适当测量。 该项目的教育部分涉及向高中学生讲授所开发方法的基础知识及其与环境公平等重大问题的相关性。1970年的《清洁空气法》及其1977年和1990年的修正案将污染源定义为“任何释放污染物的地方或物体。一个来源可以是发电厂,工厂,干洗业务,加油站或农场。汽车、卡车和其他机动车辆是污染源,工业中使用的消费品和机器也可以是污染源。“这些来源对人类健康的影响是有据可查的,模拟这些影响的重要性也是如此。本项目通过开发、评估和提供方便的工具来对点源驱动的过程进行建模,从而满足了这一需求。由此产生的方法是适用于解决公众健康和福利的环境保护局的关注,即确定氮氧化物(NOx)和二氧化硫(SO2)的排放网站是最负责的网站特定的环境浓度远离排放网站。这些特定地点的等级将有助于确定哪些排放地点需要更严格的法规来控制其在不同空间位置的影响。更一般地说,项目结果可能会影响点源的管理,对生态或环境灾害附近的几个点源分配公平的后果,并有助于确定适当的纠正行动。此外,针对高中生创建的教学模块将提高这些学生学习数学、物理和生物科学的准备程度。该项目由数学科学部统计计划和MPS多学科活动办公室联合支持。

项目成果

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Jacqueline Hughes-Oliver其他文献

Jacqueline Hughes-Oliver的其他文献

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{{ truncateString('Jacqueline Hughes-Oliver', 18)}}的其他基金

Statistics in Drug Discovery
药物发现统计
  • 批准号:
    0072809
  • 财政年份:
    2000
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Mathematical Sciences: "Nonlinear Modeling of Spatially Correlated Data: Preliminary Investigations"
数学科学:“空间相关数据的非线性建模:初步研究”
  • 批准号:
    9631877
  • 财政年份:
    1996
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant

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