Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia
Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia
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DOI：
10.1016/j.envres.2019.03.068
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发表时间：
2019-07-01
影响因子：
8.3
通讯作者：
Marks, Guy B.
中科院分区：
文献类型：
--
作者：
Cowie, Christine T.;Garden, Frances;Marks, Guy B.
背景资料：流行病学研究中估算空气污染物暴露的方法正变得越来越复杂，以尽量减少暴露误差及其相关偏倚。虽然土地利用回归（LUR）建模现在是一种既定的方法，但LUR与其他最近更复杂的估计方法之间的比较很少。我们的目的是开发一个LUR模型来估计城市内暴露于二氧化氮（NO2）的悉尼队列，并比较这些估计从一个国家的基于卫星的LUR模型（Sat-LUR）和区域贝叶斯最大熵（BME）model.Methods：基于卫星的LUR和BME估计使用现有的模型。我们使用了与欧洲空气污染影响队列研究（ESCAPE）方法相一致的方法来开发NO2和NOx的LUR模型。我们在2013/2014年期间在悉尼西部部署了46个Ogawa被动采样器，并采集了研究区域的土地利用、人口密度和交通量数据。2013年的年平均NO2浓度估计为947地址在研究区域使用三个模型：标准LUR，Sat-LUR和BME模型。使用类间相关系数（ICC），Bland-Altman方法和相关性分析（CC），从三个模型的估计值之间的协议进行了评估。结果：NO2 LUR模型预测84%的空间变异，年平均NO2（RMSE：1.2 ppb;交叉验证R-2：0.82）与主要道路，人口和居住密度，交通繁忙和商业用地的预测。开发了一个单独的模型，该模型捕获了NOx中92%的变异性（RMSE 2.3 ppb;交叉验证R-2：0.90）。LUR、Sat-LUR和BME模式的NO2年平均浓度分别为7.31 ppb（SD：1.91）、7.01 ppb（SD：1.92）和7.90 ppb（SD：1.85）。比较标准LUR与Sat-LUR NO2队列估计值，LUR的平均估计值比Sat-LUR估计值高4%，ICC为0.73。LUR与Sat-LUR值的Pearson相关系数（CC）为r = 0.73（对数转换数据）和r = 0.69（未转换数据）。从LUR模型与BME混合模型的NO2队列估计值的比较表明，LUR平均估计值比BME估计值低8%。LUR与BME估计值的ICC为0.73。记录的LUR与BME估计的CC为r = 0.73，未记录的估计为r = 0.69。结论：我们的LUR模型解释了悉尼西部年平均NO2和NOx的高度空间变异性。结果表明，非常好的协议之间的城市内LUR，国家规模的卫星LUR，和区域BME模型估计NO2的一个队列的儿童居住在悉尼，尽管不同的数据输入和模型的空间尺度的差异，提供信心，在流行病学研究中使用。
Background: Methods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO2) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model.Methods: Satellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO2 and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO2 concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC).Results: The NO2 LUR model predicted 84% of spatial variability in annual mean NO2 (RMSE: 1.2 ppb; cross-validated R-2: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R-2: 0.90). The annual average NO2 concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO2 cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO2 cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69.Conclusions: Our LUR models explained a high degree of spatial variability in annual mean NO2 and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO2 for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies.