Abstract Fluctuations of ambient fine particulate matter (PM 2.5 ) concentrations show clear yearly and weekly patterns, which has been revealed by previous studies. However, reliability of those studies may be affected by their small research areas, short observation periods, and/or the lack of using specialized statistical approaches for time series. The current study applies a recently developed time-series analysis procedure, Prophet, to investigate seasonality of daily PM 2.5 concentrations over nine years (2007–2015) measured at 220 monitoring stations across the United States. Prophet is a new tool for producing high quality forecasts from time series data that have characteristics of multiple temporal patterns with either linear or non-linear growth/decline. Through decomposing each PM 2.5 time series into three major components (i.e., trend, seasonality, and holidays), we observed periodically changing patterns of PM 2.5 concentrations weekly and yearly consistent with previous findings. Specifically, relatively high PM 2.5 concentrations tend to appear in the month of January and on Fridays, and PM 2.5 concentrations on Sunday are generally lower than those on most other days of the week. However, we discovered that high PM 2.5 concentrations are also likely to appear in July. Additionally, compared to Fridays in most studies, the highest PM 2.5 concentrations are found to more likely occur on Saturdays, while the lowest concentrations are found on Monday as universally as on Sunday. Beyond understanding the seasonality of PM 2.5 concentrations, this study revealed the potential use of Prophet, originally designed for business time series, for detecting periodicities of environmental phenomena.

中文翻译：

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美国 PM2.5 浓度的星期几和季节性模式：使用 Prophet 程序进行时间序列分析

摘要 环境细颗粒物 (PM 2.5 ) 浓度的波动显示出明显的年度和每周模式，这已被先前的研究揭示。然而，这些研究的可靠性可能会受到其研究领域小、观察期短和/或缺乏对时间序列使用专门统计方法的影响。当前的研究应用最近开发的时间序列分析程序 Prophet 来调查在美国 220 个监测站测量的九年（2007-2015 年）每日 PM 2.5 浓度的季节性。Prophet 是一种新工具，用于从具有线性或非线性增长/下降的多种时间模式特征的时间序列数据中生成高质量预测。通过将每个 PM 2.5 时间序列分解为三个主要部分（即 趋势、季节性和节假日），我们观察到 PM 2.5 浓度每周和每年定期变化的模式与之前的发现一致。具体而言，PM 2.5 浓度相对较高的月份往往出现在 1 月份和周五，周日 PM 2.5 浓度一般低于一周中大多数其他日子。然而，我们发现高浓度 PM 2.5 也可能出现在 7 月。此外，与大多数研究中的周五相比，发现最高 PM 2.5 浓度更有可能出现在周六，而最低浓度出现在周一和周日一样普遍。除了了解 PM 2.5 浓度的季节性外，这项研究还揭示了 Prophet 的潜在用途，最初设计用于商业时间序列，