基于贝叶斯时空分位数回归的臭氧浓度分析
Analyzing Ozone Concentration by Bayesian Spatio-temporal Quantile Regression
摘要：
地面臭氧是六种常见的空气污染物之一，美国环保署已经制定了国家空气质量标准。为了捕捉美国1小时和8小时平均臭氧浓度的时空变化趋势，我们发展了一种时空同步分位数回归方法。与现有的程序不同，在所提出的方法中，平滑的网站被纳入建模假设，从而允许跨位置的信息，当在每个位置的样本的数量是低的，一个重要的步骤借款。分位数函数被假定为在时间上是线性的，在空间上是平滑的，并且在任何给定的位置由两个单调递增函数$ξ_1$和$ξ_2$的凸组合给出，而不依赖于时间。一个B-样条基扩展与增加的系数在空间上平滑变化的是用来把先验和贝叶斯分析进行。利用该方法对美国和加利福尼亚州2006-2015年臭氧日平均1小时最大值和8小时最大值浓度水平数据进行了分析。值得注意的是，在过去十年中，美国大部分地区的1小时最高和8小时最高臭氧浓度水平总体呈下降趋势。在加利福尼亚州，1小时最高臭氧水平的整体趋势是下降的，而8小时最高臭氧水平的整体趋势则没有观察到。
英文摘要：
Ground level Ozone is one of the six common air-pollutants on which the EPA has set national air quality standards. In order to capture the spatio-temporal trend of 1-hour and 8-hour average ozone concentration in the US, we develop a method for spatio-temporal simultaneous quantile regression. Unlike existing procedures, in the proposed method, smoothing across the sites is incorporated within modeling assumptions thus allowing borrowing of information across locations, an essential step when the number of samples in each location is low. The quantile function has been assumed to be linear in time and smooth over space and at any given site is given by a convex combination of two monotone increasing functions $ξ_1$ and $ξ_2$ not depending on time. A B-spline basis expansion with increasing coefficients varying smoothly over the space is used to put a prior and a Bayesian analysis is performed. We analyze the average daily 1-hour maximum and 8-hour maximum ozone concentration level data of US and California during 2006-2015 using the proposed method. It is noted that in the last ten years, there is an overall decreasing trend in both 1-hour maximum and 8-hour maximum ozone concentration level over the most parts of the US. In California, an overall a decreasing trend of 1-hour maximum ozone level is observed while no particular overall trend has been observed in the case of 8-hour maximum ozone level.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
