Predictability of hourly nitrogen dioxide concentration

Abstract

Temporal aggregation of air quality time series is typically used to investigate stylized facts of the underlying series such as multiple seasonal cycles. While aggregation reduces complexity, commonly used aggregates can suffer from non-representativeness or non-robustness. For example, definitions of specific events such as extremes are subjective and may be prone to data contaminations. The aim of this paper is to assess the predictability of hourly nitrogen dioxide concentrations and to explore how predictability depends on (i) level of temporal aggregation, (ii) hour of day, and (iii) concentration level. Exploratory tools are applied to identify structural patterns, problems related to commonly used aggregate statistics and suitable statistical modeling philosophies, capable of handling multiple seasonalities and non-stationarities. Hourly times series and subseries of daily measurements for each hour of day are used to investigate the predictability of pollutant levels for each hour of day, with prediction horizons ranging from one hour to one week ahead. Predictability is assessed by time series cross validation of a loss function based on out-of-sample prediction errors. Empirical evidence on hourly nitrogen dioxide measurements suggests that predictability strongly depends on conditions (i)-(iii) for all statistical models: for specific hours of day, models based on daily series outperform models based on hourly series, while in general predictability deteriorates with exposure level.