Spatio-temporal modeling of chronic PM10 exposure for the nurses' health study
Spatio-temporal modeling of chronic PM10 exposure for the nurses' health study
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DOI：
10.1016/j.atmosenv.2008.01.044
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发表时间：
2008-06-01
影响因子：
5
通讯作者：
Suh, Helen H.
中科院分区：
文献类型：
--
作者：
Yanosk, Jeff D.;Paciorek, Christopher J.;Suh, Helen H.
空气中颗粒物（PM）的慢性流行病学研究通常使用城市或县范围的环境浓度来表征其研究人群的慢性PM暴露，这将研究限制在附近监测数据可用的区域，并且忽略了城市内环境PM浓度的空间梯度。为了提供在空间上更加细化和精确的慢性暴露措施，我们使用基于地理信息系统（GIS）的空间平滑模型来预测美国东北部和中西部每月的室外PM10浓度。该模型包括月平滑空间项和平滑回归项的GIS衍生和气象预报。使用交叉验证和其他预先指定的选择标准，道路等级，城市土地利用，街区组和县人口密度，点源和面源PM10排放量，海拔，风速和降水量的道路距离被认为是PM10浓度的重要决定因素，并包括在最终的模型。最终模型性能很强（交叉验证R-2 = 0.62），偏差很小（-0.4 μ g m（-3）），精度很高（6.4 μ g m（-3））。最终模型（每月空间项）的表现优于季节性空间项的模型（交叉验证R-2 = 0.54）。GLS衍生和气象预测的增加提高了预测性能的空间平滑（交叉验证R-2 = 0.51）或逆距离加权插值（交叉验证R-2 = 0.29）方法单独和增加的空间分辨率的预测。该模型在农村和城市地区，跨季节和整个时间段都表现良好。强大的模型性能证明其适用性作为一种手段，估计个人特定的慢性PM10暴露的大群体。(c)2008爱思唯尔有限公司保留所有权利。
Chronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or county-wide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM10 concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM10 emissions, elevation, wind speed, and precipitation were found to be important determinants of PM10 concentrations and were included in the final model. Final model performance was strong (cross-validation R-2 = 0.62), with little bias (-0.4 mu g m(-3)) and high precision (6.4 mu g m(-3)). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R-2 = 0.54). The addition of GlS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R-2 = 0.51) or inverse distance weighted interpolation (crossvalidation R-2 = 0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual- specific chronic PM10 exposures for large populations. (c) 2008 Elsevier Ltd. All rights reserved.