The Sensitivity of Concentration-Response Functions to the Explicit Modeling of Space-Time Dependence

浓度响应函数对时空依赖性显式建模的敏感性

• 批准号: 0084213
• 负责人: James Murdoch
• 金额: $ 14.25万
• 依托单位: University of Texas at Dallas
• 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: 2000-09-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0084213&HistoricalAwards=false
• 关键词: Sensitivity; Concentration; Response; Functions; Explicit

This research will assess the gains from explicitly incorporating space-time dependence in traditional air pollution concentration-response functions. It has a methodological and an application component. The methodological component involves evaluating the benefits and costs of various models that are based on sometimes conflicting statistical paradigms; e.g., Bayesian vs. frequentist time series extensions, geostatistical vs. lattice spatial models, and analytical vs. computational models. The application concerns the measurement of the effects of air pollution on human health, as measured by hospital admissions, in California's South Coast Air Basin. Previous applications have ignored the spatial variation in the data, losing potentially valuable information. The research requires (1) space-time interpolation to obtain reliable surfaces of pollutant concentrations; (2) exploratory spatial data analysis (ESDA) and visualization of model fit; and (3) estimation of space-time linear statistical models. The research agenda is designed to uncover and highlight the most promising areas where space-time analyses can contribute to policy analysis.An important aspect of research underlying U.S. environmental policy is concerned with the precise measurement of the relation between the ambient concentrations of criteria pollutants and socio-economic and health outcomes. The anticipated increase in precision and accuracy from space-time analysis facilitates an assessment of several key policy questions, including: what are the best empirical measures for air pollution concentrations; what is the role of various pollutants in explaining responses; are there no observable effects thresholds in concentration-response functions; and what is the range of uncertainty in the predictions from the various models. To the extent that, as a result of this methodological and empirical investigation, society gains insight into the accuracy and uncertainty from estimated models, this research will contribute to the refinement of the analysis of the costs and benefits of US environmental policy.

本研究将评估将时空依赖性明确纳入传统空气污染浓度-响应函数的收益。它有方法论和应用程序组件。方法部分涉及评估基于有时相互冲突的统计范式的各种模型的收益和成本；例如，贝叶斯vs.频率时间序列扩展，地统计vs.点阵空间模型，分析vs.计算模型。该申请涉及测量加州南海岸空气盆地的空气污染对人类健康的影响，以医院入院率来衡量。以前的应用程序忽略了数据的空间变化，失去了潜在的有价值的信息。研究要求：(1)时空插值获得可靠的污染物浓度曲面；(2)探索性空间数据分析（ESDA）与模型拟合可视化；(3)时空线性统计模型估计。研究议程旨在揭示和突出最有前途的领域，在这些领域中，时空分析可以为政策分析做出贡献。美国环境政策研究的一个重要方面是精确测量标准污染物的环境浓度与社会经济和健康结果之间的关系。预计时空分析的精度和准确性将有所提高，这有助于对几个关键政策问题进行评估，包括：空气污染浓度的最佳经验措施是什么；各种污染物在解释反应中的作用是什么？浓度-响应函数中是否没有可观察到的效应阈值？各种模型预测的不确定性范围是什么？在某种程度上，由于这种方法和实证调查的结果，社会对估计模型的准确性和不确定性有了深入的了解，这项研究将有助于改进对美国环境政策成本和收益的分析。