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Because it is self-calibrated, it is particularly well adapted to the long time trend monitoring of stratospheric species.
City-specific results were robust to different modeling choices regarding the correlation structure, confounders, seasonal and long time trend.
The long time trend of original observations is unobvious as shown in the left side of Figure 6.
We fit generalized additive Poisson regressions for each city using nonparametric smooth functions to control for long time trend, season, and barometric pressure.
In all the above models, a spline function of time is often used to explain the long time trend, and confounding effects like humidity and air pollution are also controlled by splines.
The following variables were included as potential confounders: barometric pressure, wind speed, 24-hour mean NO2, holidays, day of the week and calendar month and time to control for seasonality and long time trend respectively.
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Unfortunately, there is currently a gap in Landsat archive (for our study sites) between 1988 and 1998 which makes calculations of long time trends unreasonable for the time being.
A sensitivity analysis on alternative modeling choices for controlling seasonal and long time trends was carried out.
A smooth function (natural splines with 6 degrees of freedom per year) of time was used to remove the seasonal patterns and long time trends from the data.
Data from the Notifications of Infectious Disease provides the longest time trend for meningococcal cases in England and Wales, though interpreting these data is complicated by the fact that reporting has changed over time (fig 2).
We adjusted for seasonality and long time-trends using indicator variables for days of the week, holiday and month and linear quadratic terms of time, respectively.
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