Lagrangian particle models are useful for modelling pollutants in the atmosphere. They simulate the spread of pollutants by modelling trajectories of individual particles. However, to be useful, these models require a density estimate. The standard method to use has been boxcounting, but kernel density estimator (KDE) is an alternative. How KDE is used varies as there is no standard implementation. Primarily, it is the choice of kernel and bandwidth estimator that determines the model. In this report I have implemented a KDE for FOI’s Lagrangian particle model LPELLO. The kernel I have used is a combination between a uniform and Gaussian kernel. Four different bandwidth estimators has been tested, where two are global and two are variable. The first variable bandwidth estimator is based on the age of released particles, and the second is based on the turbulence history of the particles. The methods have then been tested against boxcounting, which by using an exceedingly large number of particles can be seen as the true concentration. The tests indicate that KDE method generally performs better than boxcounting at low particle numbers. The variable bandwidth estimators also performed better than both global bandwidth estimators. To achive a firmer conclusion, more testing is needed. The results indicate that KDE in general, and variable bandwidth estimators in specific, are useful tools for concentration estimate.