Source Characterization with a Genetic Algorithm–Coupled Dispersion–Backward Model Incorporating SCIPUFF

TLDR

The study extends a GA‑coupled forward–backward dispersion framework by integrating the SCIPUFF model to improve source characterization. The authors validate the GA‑SCIPUFF system with synthetic and Dipole Pride 26 field data, performing sensitivity analyses to develop a multistage procedure that enhances source estimation. The GA‑SCIPUFF model accurately identifies emission source locations, times, and magnitudes even with moderate noise, demonstrating robustness and applicability to real‑world source characterization.

Abstract

Abstract This paper extends the approach of coupling a forward-looking dispersion model with a backward model using a genetic algorithm (GA) by incorporating a more sophisticated dispersion model [the Second-Order Closure Integrated Puff (SCIPUFF) model] into a GA-coupled system. This coupled system is validated with synthetic and field experiment data to demonstrate the potential applicability of the coupled model to emission source characterization. The coupled model incorporating SCIPUFF is first validated with synthetic data produced by SCIPUFF to isolate issues related directly to SCIPUFF’s use in the coupled model. The coupled model is successful in characterizing sources even with a moderate amount of white noise introduced into the data. The similarity to corresponding results from previous studies using a more basic model suggests that the GA’s performance is not sensitive to the dispersion model used. The coupled model is then tested using data from the Dipole Pride 26 field tests to determine its ability to characterize actual pollutant measurements despite the stochastic scatter inherent in turbulent dispersion. Sensitivity studies are run on various input parameters to gain insight used to produce a multistage process capable of a higher-quality source characterization than that produced by a single pass. Overall, the coupled model performed well in identifying approximate locations, times, and amounts of pollutant emissions. These model runs demonstrate the coupled model’s potential application to source characterization for real-world problems.