Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data
Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data
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DOI：
10.1016/j.atmosenv.2011.04.073
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发表时间：
2011-11-01
影响因子：
5
通讯作者：
Kaufman, Joel D.
中科院分区：
文献类型：
--
作者：
Sampson, Paul D.;Szpiro, Adam A.;Kaufman, Joel D.
空气污染对健康影响的统计分析越来越多地使用基于地理信息系统的协变量，在“土地利用”回归模型中预测环境空气质量。最近，这些空间回归模型占空间相关结构相结合的监测数据与土地利用协变量。我们提出了一个灵活的时空建模框架和务实的，多步的估计过程，基本上可以容纳任意模式的缺失数据相对于一个理想的完整的空间由时间矩阵的观测网络上的监测站点。该方法采用了一个模型，根据偏最小二乘回归的一个大的地理协变量和非平稳建模的时空残差从这些回归平滑的时间趋势的系数在空间上变化。这项工作的目的是为多民族动脉粥样硬化和空气污染研究（梅萨空气）提供PM2.5浓度的空间点预测，该研究使用来自AQS监管监测网络的不定期监测数据，并进行补充性短时间尺度监测活动，以更好地预测城市内空气质量的变化。我们展示了这种方法的解释和准确性，从2000年到2006年在美国六个大都市地区的建模数据，并建立了基于似然估计的基础。(C)2011爱思唯尔有限公司保留所有权利。
Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation. (C) 2011 Elsevier Ltd. All rights reserved.