Characterizing variability and predictability for air pollutants with stochastic models

We investigate the dynamics of particulate matter, nitrogen oxides, and ozone concentrations in Hong Kong. Using fluctuation functions as a measure for their variability, we develop several simple data models and test their predictive power. We discuss two relevant dynamical properties, namely, the scaling of fluctuations, which is associated with long memory, and the deviations from the Gaussian distribution. While the scaling of fluctuations can be shown to be an artifact of a relatively regular seasonal cycle, the process does not follow a normal distribution even when corrected for correlations and non-stationarity due to random (Poissonian) spikes. We compare predictability and other fitted model parameters between stations and pollutants.

Automated summary

The study investigates the dynamics of particulate matter, nitrogen oxides, and ozone concentrations in Hong Kong, using fluctuation functions to measure their variability. Two relevant dynamical properties, including the scaling of fluctuations and deviations from the Gaussian distribution, are discussed. The research shows that while the scaling of fluctuations is associated with a regular seasonal cycle, the distribution of the process does not follow a normal distribution even after corrections for correlations and non-stationarity. Comparisons of predictability and other model parameters are made between different stations and pollutants.