A dynamic process convolution approach to modeling ambient particulate matter concentrations
A dynamic process convolution approach to modeling ambient particulate matter concentrations
复制标题
DOI：
10.1002/env.852
复制
发表时间：
2008-01-01
期刊：
影响因子：
1.7
通讯作者：
Calder, Catherine A.
中科院分区：
文献类型：
--
作者：
Calder, Catherine A.
环境空气中颗粒物（PM）水平的升高已被证明与某些不利的人类健康影响有关。因此，在美国各地建立了跟踪PM水平的监测网络。一些较旧的监测器测量直径小于10 μ m的PM（PM10），而较新的监测器跟踪直径小于2.5 μ m的PM水平（PM2.5）;现在认为PM的这种精细成分更可能与PM相关的负面健康影响有关。本文提出了一个PM2.5和PM10浓度的双变量动态过程卷积模型。我们的目标是提取信息PM2.5从PM10监测读数使用潜变量的方法，并提供更好的空间-时间插值的PM2.5浓度相比，仅使用PM2.5监测信息的插值。我们说明的方法使用PM2.5和PM10读数在整个俄亥俄州在2000年。版权所有（C）2007约翰威利父子有限公司
Elevated levels of particulate matter (PM) in the ambient air have been shown to be associated with certain adverse human health effects. As a result, monitoring networks that track PM levels have been established across the United States. Some of the older monitors measure PM less than 10 mu m in diameter (PM10), while the newer monitors track PM levels less than 2.5 mu m in diameter (PM2.5); it is now believed that this fine component of PM is more likely to be related to the negative health effects associated with PM. We propose a bivariate dynamic process convolution model for PM2.5 and PM10 concentrations. Our aim is to extract information about PM2.5 from PM10 monitor readings using a latent variable approach and to provide better space-time interpolations of PM2.5 concentrations compared to interpolations made using only PM2.5 monitoring information. We illustrate the approach using PM2.5 and PM10 readings taken across the state of Ohio in 2000. Copyright (C) 2007 John Wiley & Sons, Ltd.