U.S. Environmental Protection Agency, Office of Research and Development, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.
J Air Waste Manag Assoc. 2013 Dec;63(12):1369-85. doi: 10.1080/10962247.2012.758061.
Recent interest in near-road exposure to air pollutants and related health and environmental justice issues has highlighted the importance of improving the accuracy of intraurban ambient concentration estimates. Unfortunately, except in rare cases, no single source of information can accurately estimate the concentration at the desired spatial and temporal resolution over the full time period of epidemiological interest. However, it is possible to blend information from several sources so as to exploit the strengths and offset the weaknesses of each. Specifically, we are interested in combining data from ambient monitors with output from deterministic air pollution computer models. Monitor networks are sparse in both space and time, are costly to maintain, and are usually designed expressly to avoid detecting local-scale features. We use two types of computer models to compensate for these drawbacks. The first, a grid-based regional photochemical model, Community Multiscale Air Quality (CMAQ), covers large areas at high time resolution but cannot resolve features smaller than a grid cell, usually 4, 12, or 36 km across. The second, a plume dispersion model, AMS/EPA Regulatory Model (AERMOD), can resolve these features but cannot track long-distance transport or chemical reactions. We present a new Bayesian method that combines these three sources of information to resolve the intraurban pollution field. This method represents the true latent field using a two-dimensional wavelet basis, which allows direct, efficient incorporation of data at multiple levels of resolution. It furthermore allows a priori selection of the relative importance of each data source. We test its predictive accuracy and precision in a realistic urban-scale simulation. Finally, in the context of two air pollution health studies in Atlanta, Georgia, we use our model to estimate the daily mean concentrations of oxides of nitrogen (NO(x)), particulate matter with an aerodynamic diameter < or = 2.5 microm (PM2.5), and carbon monoxide (CO) at a mixture of census block group and zip code centroids for the years 2001-2002.
近期，人们对近路空气污染物暴露及其相关健康和环境公平问题的兴趣日益浓厚，这凸显了提高城市环境浓度估算准确性的重要性。遗憾的是，除了在极少数情况下，没有单一的信息来源能够在整个流行病学关注时间段内以所需的时空分辨率准确地估算浓度。然而，可以融合来自多个来源的信息，以充分利用每种信息的优势并弥补其劣势。具体来说，我们有兴趣将来自环境监测器的数据与确定性空气污染计算机模型的输出相结合。监测网络在空间和时间上都很稀疏，维护成本高，并且通常是专门设计的，以避免检测局部尺度的特征。我们使用两种类型的计算机模型来弥补这些缺陷。第一种是基于网格的区域光化学模型，即社区多尺度空气质量模型（CMAQ），它以高时间分辨率覆盖大面积区域，但无法解析小于网格单元的特征，通常为 4、12 或 36 公里。第二种是羽流扩散模型，即美国环保署空气质量模型（AERMOD），可以解析这些特征，但无法追踪长距离运输或化学反应。我们提出了一种新的贝叶斯方法，该方法结合了这三种信息来源来解析城市内部的污染场。该方法使用二维小波基表示真实的潜在场，从而可以直接、高效地将多分辨率的数据纳入其中。此外，它还允许先验选择每个数据源的相对重要性。我们在现实的城市尺度模拟中测试了其预测精度和精度。最后，在佐治亚州亚特兰大的两项空气污染健康研究中，我们使用模型来估算 2001 年至 2002 年期间，氮氧化物（NO(x)）、空气动力学直径≤2.5 微米（PM2.5）和一氧化碳（CO）的日平均浓度，混合使用普查区组和邮政编码中心。
