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首页> 外文会议>Joint annual meeting of the International Society of Exposure Science and the International Society for Environmental Epidemiology >Escape to America: Adapting European Study for Cohorts for Air Pollution Effects (ESCAPE) Methods to the Desert Southwestern U.S.
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Escape to America: Adapting European Study for Cohorts for Air Pollution Effects (ESCAPE) Methods to the Desert Southwestern U.S.

机译：逃到美国：将美国针对大气污染影响（ESCAPE）方法的欧洲研究改编为美国西南沙漠

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The influence of air pollution on respiratory illnesses is well-documented, however few long-running respiratory studies have measured air pollution at participant homes or other locations. To retrospectively predict air pollution exposures, various approaches have been taken, including dispersion modeling, geostatistical interpolation, and land use regression (LUR) modeling. LUR modeling as per the European Study for Cohorts for Air Pollution Effects (ESCAPE) has been used in >30 locations in Europe, but never in the US nor validated with values measured >10 years prior. In this study, we tested the ability of LUR models to predict air pollutant measures from nearly 30 years earlier in Tucson, AZ. Using ESCAPE sampling methods, the Tucson Air Pollution Study (TAPS) sampled NO2 in 40 homes and PM2.5 and PM10 in half of those homes for 3 two-week periods from 2015-2016 in Tucson, AZ. Using comparable sampling methods, the Pima County Workers Study (PCWS) measured N02 in 39 homes and PM2.5 and PM10 in 17 homes for 2 one-week periods in 1987 in Tucson, AZ. LUR models were developed for each pollutant in each dataset and internally validated with ESCAPE methods. Then, TAPS-based LUR models predicted PCWS pollutant levels. During internal validation, TAPS-based LUR models performed better than PCWS-based models: for TAPS, adjusted R2 values for NO2, PM2.5, and PM10 were 0.75, 0.55, and 0.68, respectively, versus, 0.47, 0.25, and 0.21 for PCWS. In retrospective prediction, adjusted R2 values (root-mean-square error) for TAPS-based LUR models for NO2, PM2.5, and PM10 were 0.37 (10.7 ppb), 0.70 (27.6 pg/m3), and 0.30 (17.3 pg/m3), respectively. While predictions for PM2.5 and PM10 levels had limited success, the N02 predictions were within the measured range but generally under-predicted (predicted vs. measured ranges: 2.83 - 8.41 vs. 5.17 - 24.8 ppb). Our project shows promise for using LUR to retrospectively model air pollution levels measured nearly 30 years earlier.
机译：空气污染对呼吸系统疾病的影响已有充分文献记载，但是长期的呼吸研究很少测量参与者房屋或其他地方的空气污染。为了回顾性地预测空气污染暴露，已采取了多种方法，包括扩散模型，地统计插值和土地利用回归（LUR）模型。根据欧洲空气污染影响队列研究（ESCAPE）的LUR建模已在欧洲的30多个地点使用，但从未在美国使用，也未使用10年前测得的值进行验证。在这项研究中，我们测试了近30年前在亚利桑那州图森市的LUR模型预测空气污染物测量值的能力。从2015年至2016年，图森空气污染研究（TAPS）使用ESCAPE采样方法对40户房屋中的NO2以及其中一半房屋中的PM2.5和PM10进行了采样，时间为3个为期两周的时间，分别位于亚利桑那州图森。皮马县工人研究（PCWS）使用可比较的抽样方法，于1987年在亚利桑那州图森进行了2个为期一周的测量，分别测量了39个家庭的N02和17个家庭的PM2.5和PM10。针对每个数据集中的每种污染物开发了LUR模型，并使用ESCAPE方法进行了内部验证。然后，基于TAPS的LUR模型预测PCWS污染物水平。在内部验证期间，基于TAPS的LUR模型的性能优于基于PCWS的模型：对于TAPS，NO2，PM2.5和PM10的调整后R2值分别为0.75、0.55和0.68，而0.47、0.25和0.21用于PCWS。在回顾性预测中，基于TAPS的NO2，PM2.5和PM10的LUR模型的调整后R2值（均方根误差）分别为0.37（10.7 ppb），0.70（27.6 pg / m3）和0.30（17.3 pg） / m3）。尽管对PM2.5和PM10水平的预测取得了有限的成功，但N02预测在测量范围内，但通常被低估了（预测与测量范围：2.83-8.41对5.17-24.8 ppb）。我们的项目显示了使用LUR来对近30年前测得的空气污染水平进行建模的前景。

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