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Statistical Projections of Future Ozone Levels and Their Health Impacts in 5 US Cities

机译：美国5个城市未来臭氧水平及其对健康的影响的统计预测

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Various meteorological conditions are drivers of ambient air quality. Hence, there is increasing interest in quantifying the impacts of climate change on future air pollution levels and their associated health effects. We describe a statistical modeling framework for projecting future ambient ozone levels. Previous studies have typically utilized outputs from numerical models for projecting future ozone levels; however, these models are computationally expensive and provide only deterministic projections. In contrast, a statistical approach, driven by meteorology and precursor levels, can flexibly incorporate various sources of uncertainties in the future projections, which may be useful to inform public health risk assessment. We first develop statistical models for predicting daily maximum 8-hour average ozone levels in Atlanta, Baltimore, Chicago, Houston, and Los Angeles based on observed daily levels of volatile organic compounds and nitrogen oxides. The models account for non-linear associations between precursor levels and meteorology, and achieve an average out-of-sample prediction R2 of 0.60. We then perform future ozone projections using bias-corrected climate model simulations of meteorology and changes in precursor levels. We describe a multivariate bias-correction method to account for the complex dependent structure in meteorological variables that are often not present in climate model outputs. Projections of health impacts, as measured by annual excess mortality and hospital admissions, are also conducted. Finally, we quantify the relative uncertainties in the health impact projections that are associated with heterogeneity in ozone health effects, ozone projection uncertainty, error in climate model bias-correction, and impacts of emission scenario on ozone precursor levels.
机译：各种气象条件是环境空气质量的驱动因素。因此，人们越来越有兴趣量化气候变化对未来空气污染水平及其相关健康影响的影响。我们描述了用于预测未来环境臭氧水平的统计建模框架。以前的研究通常利用数值模型的输出来预测未来的臭氧水平。但是，这些模型的计算量很大，并且仅提供确定性的预测。相反，在气象学和前体水平的驱动下，一种统计方法可以在未来的预测中灵活地纳入各种不确定性来源，这可能有助于公众健康风险评估。我们首先根据观察到的挥发性有机化合物和氮氧化物的每日水平，开发出统计模型来预测亚特兰大，巴尔的摩，芝加哥，休斯敦和洛杉矶的每日最大8小时平均臭氧水平。这些模型考虑了前兆水平和气象之间的非线性关联，并实现了0.60的平均样本外预测R2。然后，我们使用气象学和前兆水平变化的偏差校正气候模型模拟来执行未来的臭氧预测。我们描述了一种多元偏差校正方法来解决气象模型输出中通常不存在的气象变量中的复杂依存结构。还进行了对健康影响的预测，以每年的超额死亡率和住院人数来衡量。最后，我们量化了健康影响预测中的相对不确定性，这些相关性与臭氧健康影响的异质性，臭氧预测不确定性，气候模型偏差校正中的误差以及排放情景对臭氧前体水平的影响有关。

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