For a more comprehensive comparison, we now consider all emission events that occurred during the METEC ADED 2022 experiment. We assess the accuracy of both models by comparing their output to the CMS observations using the Pearson correlation coefficient, which measures similarity in patterns without accounting for amplitude, and the root mean squared error (RMSE), which measures the averaged absolute difference in amplitude between the simulation and the observation. Specifically, for each emission event, we compute both metrics for each sensor and then average the metric values across all 8 sensors. Fig. and 2c summarize model accuracy throughout the experiment, with each point representing a separate emission event. In Fig. , we see that the Gaussian puff simulations are more correlated to the observations compared to the Gaussian plume simulations in general. Specifically, 80.06% of the emission events demonstrated superior correlation with observations when modeled using Gaussian puff simulations as opposed to plume simulations. In instances where Gaussian plume simulations performed better, the improvement was marginal, with an average increase in the correlation coefficient of only 0.05 (refer to the gray dotted line in Fig. . Conversely, in scenarios where Gaussian puff simulations performed better, they demonstrated a significantly larger average increase in the correlation coefficient, approximately 0.13 (refer to the gray dashed line in Fig. . Note that for many emission events either the observations or predictions for a given sensor are all zero, which yields an undefined correlation coefficient. To account for this, we add the same Gaussian noise to both the observations and predictions. The noise is centered at 0 ppm with a standard deviation of 0.01 ppm, which is small enough to have essentially no impact on the correlation coefficient for emission events that have nonzero observations and predictions, and results in a correlation coefficient of 1 when the observations and predictions are both zero. In Fig. , we can see that, in general, the Gaussian puff simulations are closer to the observations in terms of RMSE, namely 68.04% of the emission events exhibited lower RMSE in puff simulations compared to plume simulations. It should be noted that RMSE is used as a metric to assess the average error between two time series, effectively smoothing out detailed differences. Concurrently, the Gaussian plume model is considered a smooth approximation to the puff model. As a result, RMSE alone may underestimate the advantages of the puff model over the plume model.