Spatial and temporal estimation of air pollutants in New York City: exposure assignment for use in a birth outcomes study.
Spatial and temporal estimation of air pollutants in New York City: exposure assignment for use in a birth outcomes study.
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DOI：
10.1186/1476-069x-12-51
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发表时间：
2013-06-27
期刊：
影响因子：
--
通讯作者：
Matte T
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文献类型：
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作者：
Ross Z;Ito K;Johnson S;Yee M;Pezeshki G;Clougherty JE;Savitz D;Matte T
最近的流行病学研究调查了空气污染与出生结果之间的关系。然而，在这些研究中经常使用的监管空气质量监测器在空间上是稀疏的，无法捕捉到怀孕期间暴露的相关城市内变化。这项研究对2008-2010年纽约市274，996名新生儿在怀孕期间的细颗粒物（PM2.5）和二氧化氮（NO2）进行了两周平均暴露估计。两周的平均暴露量是通过首先开发土地利用回归（LUR）模型的空间变化的年平均PM2.5和NO2数据从150个地点在纽约市社区空气调查和排放源数据附近的监测器。使用来自监管监测器的时间序列，对空间模型的年平均浓度进行了调整，以反映全市范围的时间趋势。使用第1年数据开发模型，并使用第2年数据进行验证。两周的平均暴露量，然后估计三个缓冲区的母亲地址，并平均到最后六周，三个月，整个妊娠期。我们的特点是暴露估计值的时间变化，PM2.5和NO2之间的相关性，以及跨孕期暴露的相关性。年平均浓度的LUR模型解释了大量的空间变化（R2 = 0.79的PM2.5和NO2的0.80）。在验证中，第2年两周平均浓度的预测显示出与实测浓度的高度一致性（PM2.5的R2 = 0.83，NO2的R2 = 0.79）。PM2.5表现出更大的时间变化比NO2。时间与空间变化在估计暴露量中的相对贡献因时间窗而异。这些污染物的不同季节周期（PM2.5为两年一次，NO2为一年一次）导致了三个月内估计暴露量的不同相关模式。三个空间缓冲水平对估计的暴露量没有实质性影响。空间分辨的监测数据，LUR模型和时间调整的组合使用监管监测数据产生的PM2.5和NO2的暴露估计，在验证测试中表现良好。在未来的研究中，需要考虑空气污染的季节性和怀孕期间的暴露间隔之间的相互作用。
Recent epidemiological studies have examined the associations between air pollution and birth outcomes. Regulatory air quality monitors often used in these studies, however, were spatially sparse and unable to capture relevant within-city variation in exposure during pregnancy. This study developed two-week average exposure estimates for fine particles (PM2.5) and nitrogen dioxide (NO2) during pregnancy for 274,996 New York City births in 2008–2010. The two-week average exposures were constructed by first developing land use regression (LUR) models of spatial variation in annual average PM2.5 and NO2 data from 150 locations in the New York City Community Air Survey and emissions source data near monitors. The annual average concentrations from the spatial models were adjusted to account for city-wide temporal trends using time series derived from regulatory monitors. Models were developed using Year 1 data and validated using Year 2 data. Two-week average exposures were then estimated for three buffers of maternal address and were averaged into the last six weeks, the trimesters, and the entire period of gestation. We characterized temporal variation of exposure estimates, correlation between PM2.5 and NO2, and correlation of exposures across trimesters. The LUR models of average annual concentrations explained a substantial amount of the spatial variation (R2 = 0.79 for PM2.5 and 0.80 for NO2). In the validation, predictions of Year 2 two-week average concentrations showed strong agreement with measured concentrations (R2 = 0.83 for PM2.5 and 0.79 for NO2). PM2.5 exhibited greater temporal variation than NO2. The relative contribution of temporal vs. spatial variation in the estimated exposures varied by time window. The differing seasonal cycle of these pollutants (bi-annual for PM2.5 and annual for NO2) resulted in different patterns of correlations in the estimated exposures across trimesters. The three levels of spatial buffer did not make a substantive difference in estimated exposures. The combination of spatially resolved monitoring data, LUR models and temporal adjustment using regulatory monitoring data yielded exposure estimates for PM2.5 and NO2 that performed well in validation tests. The interaction between seasonality of air pollution and exposure intervals during pregnancy needs to be considered in future studies.