Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application.
Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application.
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臭氧观测和模型预测的贝叶斯最大熵积分：全国应用。
DOI：
10.1021/acs.est.6b00096
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发表时间：
2016
影响因子：
11.4
通讯作者：
Vizuete,William
中科院分区：
文献类型：
--
作者：
Xu,Yadong;Serre,MarcL;Reyes,Jeanette;Vizuete,William
为了改进全国范围内环境浓度的臭氧暴露估计，我们引入了新颖的区域空气质量模型性能 (RAMP) 方法，使用贝叶斯最大熵 (BME) 框架将化学物质迁移模型 (CTM) 预测与可用的臭氧观测结果相结合。该框架对空气污染观测和 CTM 预测之间的非线性和非均方差关系进行了建模，并首次考虑了 CTM 模型性能的可变性。仅使用验证半径之外的非并置数据进行验证分析，并通过仅使用臭氧观测的 OBS 场景获得两个每日指标（每日最大 8 小时平均值 (DM8A) 和每日 24 小时平均 (D24A) 臭氧浓度）的观测值与重新估计值之间的 R2，与 RAMP 和恒定空气质量模型性能 (CAMP) 场景进行对比。我们表明，通过考虑模型性能的空间和时间变异性，我们的新型 RAMP 方法能够从 CTM 预测中提取更多有关 R2 增加百分比的信息，其中 DM8A 的增加百分比超过 12 倍，D24A 臭氧浓度的增加百分比超过 3.5 倍（假设模型性能不会随空间和时间变化）。
To improve ozone exposure estimates for ambient concentrations at a national scale, we introduce our novel Regionalized Air Quality Model Performance (RAMP) approach to integrate chemical transport model (CTM) predictions with the available ozone observations using the Bayesian Maximum Entropy (BME) framework. The framework models the nonlinear and nonhomoscedastic relation between air pollution observations and CTM predictions and for the first time accounts for variability in CTM model performance. A validation analysis using only noncollocated data outside of a validation radiusrvwas performed and theR2between observations and re-estimated values for two daily metrics, the daily maximum 8-h average (DM8A) and the daily 24-h average (D24A) ozone concentrations, were obtained with the OBS scenario using ozone observations only in contrast with the RAMP and a Constant Air Quality Model Performance (CAMP) scenarios. We show that, by accounting for the spatial and temporal variability in model performance, our novel RAMP approach is able to extract more information in terms ofR2increase percentage, with over 12 times for the DM8A and over 3.5 times for the D24A ozone concentrations, from CTM predictions than the CAMP approach assuming that model performance does not change across space and time.