Wells Benjamin, Dolwick Pat, Eder Brian, Evangelista Mark, Foley Kristen, Mannshardt Elizabeth, Misenis Chris, Weishampel Anthony
United States Environmental Protection Agency, Research Triangle Park, NC.
North Carolina State University, Raleigh, NC.
Atmos Environ (1994). 2021 Mar 1;248. doi: 10.1016/j.atmosenv.2021.118234.
Daily maximum 8-hour average (MDA8) ozone (O) concentrations are well-known to be influenced by local meteorological conditions, which vary across both daily and seasonal temporal scales. Previous studies have adjusted long-term trends in O concentrations for meteorological effects using various statistical and mathematical methods in order to get a better estimate of the long-term changes in O concentrations due to changes in precursor emissions such as nitrogen oxides (NO) and volatile organic compounds (VOCs). In this work, the authors present improvements to the current method used by the United States Environmental Protection Agency (US EPA) to adjust O trends for meteorological influences by making refinements to the input data sources and by allowing the underlying statistical model to vary locally using a variable selection procedure. The current method is also expanded by using a quantile regression model to adjust trends in the 90 and 98 percentiles of the distribution of MDA8 O concentrations, allowing for a better understanding of the effects of local meteorology on peak O levels in addition to seasonal average concentrations. The revised method is used to adjust trends in the May to September mean, 90 percentile, and 98 percentile MDA8 O concentrations at over 700 monitoring sites in the U.S. for years 2000 to 2016. The utilization of variable selection and quantile regression allow for a more in-depth understanding of how weather conditions affect O levels in the U.S. This represents a fundamental advancement in our ability to understand how interannual variability in weather conditions in the U.S. may impact attainment of the O National Ambient Air Quality Standards (NAAQS).
众所周知，日最大8小时平均（MDA8）臭氧（O₃）浓度受当地气象条件影响，这些条件在每日和季节时间尺度上都会变化。以往的研究使用各种统计和数学方法对气象效应进行调整，以更好地估计由于氮氧化物（NOₓ）和挥发性有机化合物（VOCs）等前体排放变化导致的O₃浓度长期变化。在这项工作中，作者对美国环境保护局（US EPA）目前用于调整O₃趋势以消除气象影响的方法进行了改进，具体做法是优化输入数据源，并通过变量选择程序允许基础统计模型在局部范围内变化。通过使用分位数回归模型来调整MDA8 O₃浓度分布的第90和第98百分位数的趋势，对当前方法进行了扩展，这样除了季节平均浓度外，还能更好地了解当地气象对O₃峰值水平的影响。修订后的方法用于调整2000年至2016年美国700多个监测站点5月至9月平均、第90百分位数和第98百分位数的MDA8 O₃浓度趋势。变量选择和分位数回归的应用有助于更深入地了解天气条件如何影响美国的O₃水平。这代表了我们在理解美国天气条件的年际变化可能如何影响O₃国家环境空气质量标准（NAAQS）达标情况方面能力的重大进步。
