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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. Highlights • Seasonality of PM 2.5 concentrations over the United States was detected. • A new time-series analysis procedure, Prophet, was used to reveal the seasonality. • Yearly highest PM 2.5 is likely to appear in January and July. • Weekly highest PM 2.5 is most likely to appear on Saturday rather than Friday. • Besides Sunday, weekly lowest PM 2.5 is also likely to occur on Monday. [ABSTRACT FROM AUTHOR]

It is known that individual meteorological factors affect the concentrations of fine particulate matter with aerodynamic diameters ≤2.5 μm (PM 2.5 ), yet the specific meteorological effects found in previous studies are largely inconsistent and even conflicting. This study investigates influences of daily and short term changes in synoptic weather on ground-level PM 2.5 concentrations in a large geographical area (75 cities across the contiguous United States (U.S.)) by using ten-year (2001–2010) spatial synoptic classification (SSC) data. We find that in the spring, summer, and fall the presence of the tropical weather types (i.e., dry-tropical (DT) and moist-tropical (MT)) is likely to associate with significantly higher levels of PM 2.5 as compared to an all-weather-type-day average, and the presence of the polar weather types (i.e., dry-polar (DP) and moist-polar (MP)) is associated with significantly lower PM 2.5 concentrations. The short-term (day to day) changes in synoptic weather types in a region are also likely to lead to significant variance in PM 2.5 concentrations. For example, the largest increase in PM 2.5 concentration occurs with the synoptic weather type changing from DP-to-MT. Conversely, a MT-to-DP weather type change results in the largest decrease in PM 2.5 concentrations. Compared to air temperature, the effects of atmospheric moisture on PM 2.5 concentration tend to be subtle, demonstrating that in conjunction with moderate temperature, neither the dry nor the moist air (except moist-moderate (MM) in summer) are associated with significantly high or low PM 2.5 concentrations. Finally, we find that the effects of the synoptic weather type on PM 2.5 concentrations may vary for different seasons and geographical areas. These findings suggest that interactions between atmospheric factors and seasonal and/or geographical factors have considerable impacts on the PM 2.5 concentrations, and therefore should be considered in addition to the SSC when conducting environment health assessments. [ABSTRACT FROM AUTHOR]