Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children
Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children
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DOI：
10.1038/jes.2013.1
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发表时间：
2013-05-01
影响因子：
4.5
通讯作者：
Wheeler, Amanda
中科院分区：
文献类型：
--
作者：
Johnson, Markey;MacNeill, Morgan;Wheeler, Amanda
在流行病学研究中，监管监测数据和土地利用回归（LUR）模型已被广泛用于估计个人对环境空气污染的暴露。然而，LUR模型缺乏精细尺度的时间分辨率来预测急性暴露和监管监测提供每日浓度，但未能捕捉城市地区内的空间变异性。本研究耦合LUR模型与连续监管监测，以预测每日环境中的二氧化氮（NO2）和颗粒物（PM2.5）在温莎，安大略的50个家庭。我们比较了预测与测量的每日室外浓度为5天在冬季和5天在夏季在每个家庭。我们还研究了使用模型与测量的每日污染物浓度来预测生活在这些家庭中的哮喘儿童的每日肺功能的影响。混合效应分析表明，时间上完善的LUR模型解释了更大比例的空间和时间的变化，在日常家庭水平的室外NO2测量与日常浓度的基础上监管监测。与监管监测数据相比，时间细化的LUR模型捕获了40%（夏季）和10%（冬季）的空间方差。环境PM2.5的空间变化不大，因此，每天的PM2.5模型是类似的监管监测数据的方差解释的比例。此外，基于模拟污染物浓度的1秒用力呼气量（FEV 1）和呼气峰流量（PEF）的影响估计值与基于家庭水平测量的NO2和PM2.5的影响一致。这些结果表明，LUR模型可以与连续监管监测数据相结合，以预测日常家庭水平暴露于环境空气污染。与监管监测数据相比，时间上改进的LUR模型在估计每日家庭水平NO2方面提供了适度的改进，这表明这种方法可能会改善空间异质性污染物的暴露估计。这些发现对流行病学研究具有重要意义，特别是对短期暴露和健康影响的研究。Journal of Exposure Science and Environmental Epidemiology（2013）23，259-267; doi：10.1038/jes.2013.1; 2013年3月27日在线发表
Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO2) and particulate matter (PM2.5) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO2 measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM2.5 showed little spatial variation; therefore, daily PM2.5 models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO2 and PM2.5. These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO2 compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies in particular, for research focused on short-term exposure and health effects. Journal of Exposure Science and Environmental Epidemiology (2013) 23, 259-267; doi:10.1038/jes.2013.1;published online 27 March 2013