Daily Ambient NO2 Concentration Predictions Using Satellite Ozone Monitoring Instrument NO2 Data and Land Use Regression
Daily Ambient NO2 Concentration Predictions Using Satellite Ozone Monitoring Instrument NO2 Data and Land Use Regression
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DOI：
10.1021/es404845f
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发表时间：
2014-02-18
影响因子：
11.4
通讯作者：
Koutrakis, Petros
中科院分区：
文献类型：
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作者：
Lee, Hyung Joo;Koutrakis, Petros
尽管地面测量有助于揭示流行病学研究中环境空气污染与健康影响之间的关联，但由于地面监测器空间分布稀疏，可能会导致暴露测量误差。在本研究中，我们利用卫星遥感数据结合土地利用回归估算了 2005 年至 2010 年期间美国新英格兰地区每日地面 NO2 浓度。为了估计地面 NO2 浓度，我们利用卫星臭氧监测仪器 (OMI) 对流层 NO2 柱密度的空间和时间变化构建了一个混合效应模型。使用精细的土地利用参数，我们得出了点位置的二氧化氮浓度，这可以进一步用于流行病学研究中特定主题的暴露估计。混合效应模型对每日 NO2 浓度显示出相当高的预测能力（交叉验证 R-2 = 0.79）。我们观察到该模型在每个季节、年份和州的表现相似。模型估计的空间格局反映了研究区域的排放源区域（例如人口稠密/交通繁忙的区域），并揭示了NO2的季节特征。这项研究表明，卫星遥感和土地利用回归的结合可用于空间和时间解析的二氧化氮暴露评估。
Although ground measurements have contributed to revealing the association between ambient air pollution and health effects in epidemiological studies, exposure measurement errors are likely to be caused because of the sparse spatial distribution of ground monitors. In this study, we estimate daily ground NO2 concentrations in the New England region, U.S., for the period 2005-2010 using satellite remote sensing data in combination with land use regression. To estimate ground-level NO2 concentrations, we constructed a mixed effects model by taking advantage of spatial and temporal variability in satellite Ozone Monitoring Instrument (OMI) tropospheric column NO2 densities. Using fine-scale land use parameters, we derived NO2 concentrations at point locations, which can be further used for subject-specific exposure estimates in epidemiological studies. A mixed effects model showed a reasonably high predictive power for daily NO2 concentrations (cross-validation R-2 = 0.79). We observed that the model performed similarly in each season, year, and state. The spatial patterns of model estimates reflected emission source areas (such as high populated/traffic areas) in the study region and revealed the seasonal characteristics of NO2. This study suggests that a combination of satellite remote sensing and land use regression can be useful for both spatially and temporally resolved exposure assessments of NO2.