Lagrangian Particle Dispersion Modeling in Atmospheric Studies

Lagrangian particle dispersion models represent atmospheric transport by tracking individual particles or air parcels moving with the flow. This approach contrasts with Eulerian models, which solve concentration fields on fixed grids. Since their inception in the mid-1990s, such models have evolved into versatile tools applicable at scales ranging from local urban environments to global circulation. They couple wind fields and stochastic turbulence parameterisations with meteorological inputs from numerical weather prediction, enabling simulation of gases, aerosols, radionuclides and intermediate-lifetime reactive species. Key developments include advanced turbulence schemes that account for convective updrafts and downdrafts, density gradients and mixing-length variations, as well as backward-mode operation for source attribution and inverse modelling. Ensemble strategies integrate multiple meteorological datasets to quantify forecast uncertainty, while high-resolution implementations ingest mesoscale outputs to capture orographic effects. Applications span air-quality forecasting, volcanic ash transport, greenhouse-gas footprinting and emergency response after accidental releases. This methodology underpins environmental regulation, climate assessment and public-health protection by tracing pollutant pathways, informing mitigation strategies and enhancing understanding of atmospheric processes worldwide.

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Lagrangian Particle Dispersion Modeling in Atmospheric Studies publication trend

The graph below shows the total number of articles in lagrangian particle dispersion modeling in atmospheric studies across all publications each year (not limited to Nature Index journals).

Technical terms

Lagrangian particle dispersion model: A simulation framework that follows individual particles or air parcels through time, using stochastic methods to represent advection and unresolved turbulent motions in the atmosphere.

Ensemble modelling: A technique involving multiple model runs with varied input data or parameter settings to characterise uncertainty and robustness in dispersion forecasts.

Turbulence parameterisation: Mathematical schemes used to represent the effects of small-scale turbulent eddies on particle transport and mixing, particularly within the convective and stable boundary layers.

Boundary layer: The lowest part of the atmosphere directly influenced by surface friction and heating, where turbulence intensity and mixing processes strongly affect dispersion.

Source–receptor relationship: A diagnostic tool that quantifies the sensitivity of pollutant concentrations at a receptor site to emissions from potential source regions, often derived via backward-mode simulations.

References

1. Spatiotemporal variation of radionuclide dispersion from nuclear power plant accidents using FLEXPART mini-ensemble modeling. Atmospheric Chemistry and Physics (2023).
2. The Danish Lagrangian Model (DALM): Development of a new local-scale high-resolution air pollution model. Environmental Modelling & Software (2024).
3. A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event. Big Earth Data (2024).