Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements
Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements
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DOI：
10.1016/j.atmosenv.2011.08.066
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发表时间：
2011-11-01
影响因子：
5
通讯作者：
Schwartz, Joel
中科院分区：
文献类型：
--
作者：
Kloog, Itai;Koutrakis, Petros;Schwartz, Joel
土地利用回归（LUR）模型提供了很好的估计空间分辨的长期曝光，但在捕捉短期曝光差。卫星气溶胶光学厚度（AOD）测量有可能提供长期和短期暴露的时空分辨预测，但以前的研究一般显示相对较低的预测能力。我们的目标是扩展我们以前的工作，每天特定的校准AOD数据使用地面PM2.5的测量，结合常用的LUR变量和气象变量，从而受益于空间分辨率从LUR模型和卫星模型的时空分辨率。稍后，我们使用空间平滑来预测缺少AOD措施的日期/位置的PM2.5浓度。我们使用混合模型与随机斜率天校准AOD数据为2000-2008年在新英格兰监测PM2.5测量。然后，我们使用了一个广义的添加剂混合模型与空间平滑估计PM2.5的位置天对与失踪的AOD，使用区域测量PM2.5，AOD值在邻近的细胞，和土地利用。最后，本地（100米）的土地利用条款被用来模拟网格单元预测和监测值之间的差异，以捕捉非常本地的交通颗粒。样本外十倍交叉验证被用来量化我们的预测的准确性。对于有可用AOD数据的天数，我们发现样本外R-2较高（平均样本外R-2 = 0.830，年间变化0.725-0.904）。对于没有AOD值的天数，我们的模型性能也很好（平均样本外R-2 = 0.810，年与年之间的变化为0.692-0.887）。重要的是，这些R-2是每天的值，而不是每月或每年的值。我们的模型可以评估短期和长期的人类接触，以调查环境颗粒的急性和慢性影响，分别。(C)2011爱思唯尔有限公司版权所有。
Land use regression (LUR) models provide good estimates of spatially resolved long-term exposures, but are poor at capturing short term exposures. Satellite-derived Aerosol Optical Depth (AOD) measurements have the potential to provide spatio-temporally resolved predictions of both long and short term exposures, but previous studies have generally showed relatively low predictive power. Our objective was to extend our previous work on day-specific calibrations of AOD data using ground PM2.5 measurements by incorporating commonly used LUR variables and meteorological variables, thus benefiting from both the spatial resolution from the LUR models and the spatio-temporal resolution from the satellite models. Later we use spatial smoothing to predict PM2.5 concentrations for day/locations with missing AOD measures. We used mixed models with random slopes for day to calibrate AOD data for 2000-2008 across New-England with monitored PM2.5 measurements. We then used a generalized additive mixed model with spatial smoothing to estimate PM2.5 in location day pairs with missing AOD, using regional measured PM2.5, AOD values in neighboring cells, and land use. Finally, local (100 m) land use terms were used to model the difference between grid cell prediction and monitored value to capture very local traffic particles. Out-of-sample ten-fold cross-validation was used to quantify the accuracy of our predictions. For days with available AOD data we found high out-of-sample R-2 (mean out-of-sample R-2 = 0.830, year to year variation 0.725-0.904). For days without AOD values, our model performance was also excellent (mean out-of-sample R-2 = 0.810, year to year variation 0.692-0.887). Importantly, these R-2 are for daily, rather than monthly or yearly, values. Our model allows one to assess short term and long-term human exposures in order to investigate both the acute and chronic effects of ambient particles, respectively. (C) 2011 Elsevier Ltd. All rights reserved.