In accordance with the definition in Klise et al. , an emission scenario is determined by a combination of wind conditions and emission characteristics during a period of time. To generate emission scenarios, two probability distribution functions are constructed: a joint distribution f e of emission source attributes including locations x 0 , durations τ , and rate q, and a joint distribution f w of wind speed ws(t) and wind direction wd(t), t ∈ [0, τ ]. The wind distribution can be inferred from historical wind data recorded on or in the vicinity of the site. The emission distribution can be obtained from operational logs and/or expertise from operators. Practically, we can decompose the joint distribution f e using conditional distributions, i.e., f e (x 0 , τ, q) = f (τ, q|x 0 )×f (x 0 ). That is, each potential source, based on its functionality, may have its own emission pattern. The two distributions f w and f e are deemed independent, which we believe to be a reasonable assumption since the wind conditions should not affect the source attributes of an emission event and vice versa. Nevertheless, it is acknowledged that correlations may arise via confounding temporal factors. For instance, a certain type of emission may tend to occur at a specific hour of the day or in a specific season when the wind might have a specific pattern. In cases where prior knowledge pertaining to such relationships is available, it can be readily incorporated into the distributions facilitating a more nuanced modeling approach. Then, emission scenarios, which are defined by a tuple of (x 0 , q, τ, ws(t), wd(t), t ∈ [0, τ ]), are generated by randomly sampling from f e and f w .