Dispersion Models

Predict the evolution of pollution during emergencies.

Modelling provides crucial information to help decision-making

Water pollution-dispersal models assess the impact of pollutant discharges on rivers, coast and groundwater.

Predicting the spread and development of a pollutant is vital to environmental protection. In emergencies, such as accidental discharges in a stream, the anticipation of the pollutant evolution is crucial to effective and rapid decision-making to protect the affected area. Accidental or intentional contaminant spillages are a constant concern to anyone using water from streams, rivers or coastal bathing waters.

ADASA is working on dispersion models for Barcelona and Berlin as part of the iBathwater project, a new integrated management system for the urban sewerage network. It aims to reduce the discharge of untreated water on the environment during and after intense rainfall.

iBathwater Interface Image

A river pollution dispersion model traces hazardous substances throughout the river course, from the sewerage system’s discharge points to bathing areas. A coastal dispersal model combines wind, tidal, wave and weather information and calculates its effect on a sewerage system’s discharges during rainfall. Combined, this simulates transport and dispersal pollution indicator parameters and their potential effects on bathing areas.

Mathematical simulations predict how pollutants disperse, including algorithms that solve the mathematical equations that govern pollutant dispersions, and model different phenomena, such as: dispersion, diffusion, advection, sedimentation, adsorption and more.

Advantages

  • Overarching view of problems with general analysis results displayed geographically.
  • Analyse affected sectors, pollutants involved, and which are exempt.
  • Locate concentration peaks and analyse the effect of pollutant’s chemical interactions.
  • Synergy: Compare real measurements with model results to create a framework that fits the desired purpose.
  • Perform scenario studies to calculate the most efficient emission reduction measures.

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