The poor air quality in the London metropolis has sparked our interest in studying the time series dynamics of air pollutants in the city. The dataset consists of roadside and background air quality for seven standard pollutants: nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), ozone (O-3), particulate matter (PM(10)and PM2.5) and sulphur dioxide (SO2), using fractional integration to investigate issues such as persistence, seasonality and time trends in the data. Though we notice a large degree of heterogeneity across pollutants and a persistent behaviour based on a long memory pattern is observed practically in all cases. Seasonality and decreasing linear trends are also found in some cases. The findings in the paper may serve as a guide to air pollution management and European Union (EU) policymakers.