Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states.
Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states.
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DOI：
10.1021/es302673e
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发表时间：
2012-11-06
影响因子：
11.4
通讯作者：
Schwartz J
中科院分区：
文献类型：
--
作者：
Kloog I;Nordio F;Coull BA;Schwartz J
卫星气溶胶光学厚度测量有可能提供对长期和短期暴露的时空分辨预测，但以前的研究一般显示出中等的预测能力，缺乏对大范围的详细的高时空分辨率预测。我们的目的是通过在另一个具有不同地理和计量特征的地区验证我们的模型来扩展我们之前的工作，并结合小尺度土地利用回归和非随机遗漏来更好地预测有或没有卫星AOD测量的天数的PM2.5浓度。我们首先校准大西洋中部地区2000-2008年的AOD数据。我们使用了混合模型，将PM2.5测量值与特定日期的随机截获数据进行回归，并使用固定和随机的气溶胶光学厚度和温度斜率。我们使用逆概率加权来考虑AOD的非随机失配，用天数内的嵌套区域来捕捉日常校准中的空间变化，并引入了一种惩罚方法，该方法不需要在不同的位置选择不同的预测因子，而是对大量的时空预测因子进行降维。然后，我们利用网格单元特定的AOD值与PM2.5监测数据之间的关联，以及相邻网格单元中的AOD值之间的关联来开发当AOD缺失时的网格单元预测。最后，为了得到本地预测(分辨率为50m)，我们将每个监视器的预测的残差与每个监视器特定的本地土地利用变量进行回归。“样本外”十倍交叉验证被用来量化我们在每一步预测的准确性。在没有AOD值的所有日子里，模型的表现都很好(平均“样本外”R2=0.81，年际变化0.79-0.84)。在去除PM2.5监测数据中的异常值后，交叉验证过程的结果甚至更好(总体平均“超出样本”R2为0.85)。此外，交叉验证结果显示预测浓度没有偏差(观察值与预测值的斜率=0.97-1.01)。我们的模型允许人们可靠地评估短期和长期人类暴露，以便分别调查环境颗粒物的急性和影响。
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 shown moderate predictive power and lacked detailed high spatio-temporal resolution predictions across large domains. We aimed at extending our previous work by validating our model in another region with different geographical and metrological characteristics, and incorporating fine scale land use regression and nonrandom missingness to better predict PM2.5 concentrations for days with or without satellite AOD measures. We start by calibrating AOD data for 2000–2008 across the Mid-Atlantic. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We used inverse probability weighting to account for nonrandom missingness of AOD, nested regions within days to capture spatial variation in the daily calibration, and introduced a penalization method that reduces the dimensionality of the large number of spatial and temporal predictors without selecting different predictors in different locations. We then take advantage of the association between grid-cell specific AOD values and PM2.5 monitoring data, together with associations between AOD values in neighboring grid cells to develop grid cell predictions when AOD is missing. Finally to get local predictions (at the resolution of 50m), we regressed the residuals from the predictions for each monitor from these previous steps against the local land use variables specific for each monitor. “Out-of-sample” ten-fold-cross validation was used to quantify the accuracy of our predictions at each step. For all days without AOD values, model performance was excellent (mean “out-of-sample” R2=0.81, year-to-year variation 0.79–0.84). Upon removal of outliers in the PM2.5 monitoring data, the results of the cross validation procedure was even better (overall mean ”out of sample” R2 of 0.85). Further, cross validation results revealed no bias in the predicted concentrations (Slope of observed vs. predicted = 0.97–1.01). Our model allows one to reliably assess short-term and long-term human exposures in order to investigate both the acute and effects of ambient particles, respectively.