High-resolution space-time ozone modeling for assessing trends
High-resolution space-time ozone modeling for assessing trends
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DOI：
10.1198/016214507000000031
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发表时间：
2007-12-01
影响因子：
3.7
通讯作者：
Holland, David M.
中科院分区：
文献类型：
--
作者：
Sahu, Sujit K.;Gelfand, Alan E.;Holland, David M.
本文提出了一个空间-时间模型，每日8小时最高臭氧水平提供输入的监管活动：检测，评估和分析臭氧摘要的空间格局和时间趋势。该模型适用于分析的数据，从俄亥俄州，包含城市，郊区和农村臭氧监测站点的混合。拟议的时空模型是自回归模型，并纳入了在一系列臭氧监测站点以及尚未观测到臭氧水平的几个气象站观测到的最重要的气象变量。这种错位是通过空间建模来处理的。在这样做时，我们采用了一种计算方便的方法，根据气象变量的连续每日增量。由此产生的层次模型内指定的贝叶斯框架，并使用马尔可夫链蒙特卡罗技术拟合。充分推断模型未知数，以及在时间和空间的预测，年度总结的评价，并评估趋势。
This article proposes a space-time model for daily 8-hour maximum ozone levels to provide input for regulatory activities: detection, evaluation, and analysis of spatial patterns and temporal trend in ozone summaries. The model is applied to the analysis of data from the state of Ohio that contains a mix of urban, suburban, and rural ozone monitoring sites. The proposed space-time model is autoregressive and incorporates the most important meteorological variables observed at a collection of ozone monitoring sites as well as at several weather stations where ozone levels have not been observed. This misalignment is handled through spatial modeling. In so doing we adopt a computationally convenient approach based on the successive daily increments in meteorological variables. The resulting hierarchical model is specified within a Bayesian framework and is fitted using Markov chain Monte Carlo techniques. Full inference with regard to model unknowns as well as for predictions in time and space, evaluation of annual summaries, and assessment of trends are presented.