Since the very beginning developments in data analysis and time series methods have been closely associated with phenomena in the natural environment, from the power spectra of Schuster motivated by earthquakes and sunspots, Walker's analysis of the Southern Oscillation in attempting to predict the indian monsoon, Tukey's cross-spectrum for the analysis of seismic waves, Gumbel's extreme value analysis inspired by meteorological and hydrological phenomena or Hurst's long memory concept from observations of Nile's water levels. In the modern era of disposable, low-power devices and cheap storage, the natural environment is monitored at an incredible pace, yielding copious time series of high-resolution (sub-hourly) observations which are widely available. Despite the many computationally-intensive approaches developed and currently available to handle streams of data, their success in producing new and environmentally-relevant information is surprisingly low. An obvious challenge is the integration of problem-related knowledge and context in the data analysis process, and going from data summaries/visualisations/alarms to an exploratory analysis aiming to discover new and physically-relevant information from the environmental data. This talk addresses the practical challenges and opportunities in the analysis of high-resolution environmental time series, as illustrated by time series of environmental radioactivity and by measurements from the ongoing gamma radiation monitoring campaign in the Azores.