While the ozone layer in the upper atmosphere is beneficial and protects us from ultraviolet light, in the lower atmosphere it is a pollutant that has been linked to a number of respiratory diseases as well as heart attacks and premature death. The EPA’s national air quality standard for ozone concentration is 75 parts per billion (ppb); in Europe, the standard is 60 ppb; and according to some studies, at-risk individuals may be adversely affected by ozone levels as low as 40 ppb. Ozone concentrations, however, are not constant, and fluctuate quite a bit from day to day, depending on many factors. This paper aim to predict and analyze ozone concentrations based on the effects of ultraviolet light. Spline regression is piecewise polynomials that connect at join points called knots. In spline regression, parameter estimation were fit by OLS (Ordinary Least Square) method. However, the OLS method will lead to overparameterized and in the plot of estimated regression curve will be fluctuative when using too much knots. Therefore, it needs an additional constraint which contain smoothing parameter, so that will result an ideally fit. This parameter estimation method known as PLS (Penalized Least Square) method. Spline regression that using PLS method is called by penalized spline regression. O’Sullivan (1986) introduced a class of penalised splines based on B-spline basis functions. OPS (O’sullivan Penalized Spline) are a direct generalisation of smoothing splines in that the latter arises when the maximal number of B-spline basis functions are included. One of the top performance measures the predicted regression curve that can be used is the MSE (Mean Square Error). The results show that the OPS method has a smaller MSE than the Penalized Spline Based TPB (Truncated Power Basis) method, so the use of the OPS method for predicting ozone against ultraviolet light is suitable for use.

Keywords: B-Spline, O’sullivan Penalized Spline, Truncated Power Basis, Penalized Least Square