A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.
A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.
复制标题
DOI：
10.1007/s10651-013-0261-4
复制
发表时间：
2014-09
影响因子：
3.8
通讯作者：
Sheppard, Lianne
中科院分区：
文献类型：
--
作者：
Lindstrom, Johan;Szpiro, Adam A.;Sampson, Paul D.;Oron, Assaf P.;Richards, Mark;Larson, Tim V.;Sheppard, Lianne
发展能够在小空间尺度上对环境空气污染进行准确的时空预测的模式，对于评估空气污染对健康的潜在影响非常重要。在这里，我们提出了一个时空框架，通过结合几个不同监测网的数据和确定性空气污染模型(S)和地理信息系统协变量预测环境空气污染。本文提出的模型已在CRAN上提供的时空R包中实现。EPA资助的动脉粥样硬化和空气污染多种族研究(MESA Air)使用该模型来估计环境空气污染；MESA Air使用估计值来调查长期暴露于空气污染和心血管疾病之间的关系。在本文中，我们使用该模型来预测十年期间洛杉矶地区NOx的长期平均浓度。预测基于EPA空气质量系统、台地空气特定监测的测量，以及交通相关空气污染源扩散模型(Caline3QHCR)的输出。预测长期平均浓度的准确性是使用复杂的交叉验证设置进行评估的，该设置解释了数据中的稀疏时空采样模式，并针对时间影响进行了调整。模型的预测能力较好，在受试点的交叉验证R2约为0.7。用Caline3QHCR扩散模型的输出替换交通密度的四个地理协变量指标，可以得到与更简明、更具解释性的模型非常相似的预测精度。将与交通相关的地理协变量添加到包含Caline3QHCR的模型中并没有进一步提高预测精度。
The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NOx in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R2 of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.
登录
查看更多内容
影响因子：
1.7
作者：
Fanshawe, T. R.;Diggle, P. J.;Pless-Mulloli, T.
通讯作者：
Pless-Mulloli, T.
影响因子：
5.4
作者：
Brauer, M;Hoek, G;Brunekreef, B
通讯作者：
Brunekreef, B
DOI：
10.1111/j.1467-9868.2008.00663.x
发表时间：
2008-09-01
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
Banerjee S;Gelfand AE;Finley AO;Sang H
通讯作者：
Sang H
DOI：
10.1038/sj.jea.7500388
发表时间：
2005-03-01
期刊：
JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY
影响因子：
--
作者：
Jerrett, M;Arain, A;Giovis, C
通讯作者：
Giovis, C
影响因子：
1.7
作者：
Calder, Catherine A.
通讯作者：
Calder, Catherine A.