Wang Meng, Gehring Ulrike, Hoek Gerard, Keuken Menno, Jonkers Sander, Beelen Rob, Eeftens Marloes, Postma Dirkje S, Brunekreef Bert
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
Environ Health Perspect. 2015 Aug;123(8):847-51. doi: 10.1289/ehp.1408541. Epub 2015 Apr 3.
There is limited knowledge about the extent to which estimates of air pollution effects on health are affected by the choice for a specific exposure model.
We aimed to evaluate the correlation between long-term air pollution exposure estimates using two commonly used exposure modeling techniques [dispersion and land use regression (LUR) models] and, in addition, to compare the estimates of the association between long-term exposure to air pollution and lung function in children using these exposure modeling techniques.
We used data of 1,058 participants of a Dutch birth cohort study with measured forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF) measurements at 8 years of age. For each child, annual average outdoor air pollution exposure [nitrogen dioxide (NO2), mass concentration of particulate matter with diameters ≤ 2.5 and ≤ 10 μm (PM2.5, PM10), and PM2.5 soot] was estimated for the current addresses of the participants by a dispersion and a LUR model. Associations between exposures to air pollution and lung function parameters were estimated using linear regression analysis with confounder adjustment.
Correlations between LUR- and dispersion-modeled pollution concentrations were high for NO2, PM2.5, and PM2.5 soot (R = 0.86-0.90) but low for PM10 (R = 0.57). Associations with lung function were similar for air pollutant exposures estimated using LUR and dispersion modeling, except for associations of PM2.5 with FEV1 and FVC, which were stronger but less precise for exposures based on LUR compared with dispersion model.
Predictions from LUR and dispersion models correlated very well for PM2.5, NO2, and PM2.5 soot but not for PM10. Health effect estimates did not depend on the type of model used to estimate exposure in a population of Dutch children.
关于空气污染对健康影响的估计在多大程度上受特定暴露模型选择的影响,相关知识有限。
我们旨在评估使用两种常用暴露建模技术(扩散模型和土地利用回归(LUR)模型)得出的长期空气污染暴露估计值之间的相关性,此外,还比较使用这些暴露建模技术得出的儿童长期空气污染暴露与肺功能之间关联的估计值。
我们使用了荷兰一项出生队列研究中1058名参与者的数据,这些参与者在8岁时测量了1秒用力呼气量(FEV1)、用力肺活量(FVC)和呼气峰值流速(PEF)。对于每个孩子,通过扩散模型和LUR模型根据参与者当前住址估计其年度平均室外空气污染暴露情况[二氧化氮(NO2)、直径≤2.5和≤10μm的颗粒物质量浓度(PM2.5、PM10)以及PM2.5烟灰]。使用线性回归分析并进行混杂因素调整来估计空气污染暴露与肺功能参数之间的关联。
LUR模型和扩散模型模拟的污染浓度之间,NO2、PM2.5和PM2.5烟灰的相关性较高(R = 0.86 - 0.90),但PM10的相关性较低(R = 0.57)。使用LUR模型和扩散模型估计的空气污染物暴露与肺功能的关联相似,但PM2.5与FEV1和FVC的关联除外,基于LUR模型的暴露与扩散模型相比更强但精度更低。
LUR模型和扩散模型对PM2.5、NO2和PM2.5烟灰的预测相关性很好,但对PM10则不然。在荷兰儿童群体中,健康影响估计值并不取决于用于估计暴露的模型类型。