## 1 Introduction
FloPy provides additional convenient ways to explore data, analyze models, and present results related to numerical groundwater flow simulation [17]. [PERSON] [18], one of the developers of FloPy, first presented a basic case study using FloPy in 2013. In 2016, [PERSON] et al. [16] introduced additional methods for creating and post-processing MODFLOW models through other cases. In 2018, [PERSON] [19] developed a new FloPy-based groundwater simulation platform that was combined with GIS technology: FREEWAT. In 2021, [PERSON] [20] developed a FloPy-based 3D regional variable density groundwater model to simulate pumping upper subsurface brackish water in the Horster-meer Polder to prevent contamination of deep freshwater. [PERSON] [21] combined the NSGA-III algorithm with MODFLOW through FloPy to find an optimal solution for an ideal example of groundwater multi-objective management. He concluded that FloPy has outstanding advantages in data processing, model construction, and disciplinary crossover. Even though the research on Python-based numerical groundwater simulation is still in its infancy, [PERSON] et al. [22] believes that Python has become the preferred programming language for researchers in the future-oriented field of combining big data, artificial intelligence, and groundwater. Groundwater quality has been deteriorating under the influence of both natural and human factors, especially sewage from industrial zones, which, once leaked and seeped into the groundwater, will seriously threaten the safety of human drinking water. Therefore, it is necessary to strengthen the monitoring of groundwater quality in chemical industrial park, so that groundwater quality anomalies can be detected in a timely manner, and the source of groundwater contamination can be traced back so that treatment measures can be taken. Groundwater contamination traceability is very important to the field of groundwater research [23]. The commonly used contaminant traceability techniques include numerical simulation, water chemistry characterization, and isotope techniques [24; 25]. Numerical simulation has become a key tool for studying groundwater contaminant traceability as it can completely simulate the release and migration of contaminants and predict the future trend of contaminant dispersion [26]. However, the method uses trial-and-error simulations of the location and intensity of the pollutant source continuously until the simulated pollutant dispersal mirrors the actual situation. These solutions are not unique, and obtaining the most reliable pollutant traceability solution remains a challenge. The development of heuristic optimization algorithms, including the improved genetic algorithm, the SCE-UA algorithm, and the simulated annealing algorithm, makes an efficient solution to the traceability problem possible [27; 28; 29]. However, the high time cost and technical difficulty of building simulation-optimization models using programming languages such as Fortran or C complicate the mastering of these methods by general scholars. The use of Python and FloPy greatly reduces the time cost of building simulation-optimization models and lowers the technical threshold for mastering these methods [30]. It is foreseeable that a lot of research and software based on Python and FloPy will emerge in the future in the combined fields of groundwater, artificial intelligence, and big data [31]. A simulation-optimization model was constructed for this paper to solve the traceability problem of an unknown contaminant source in a chemical industrial park in China, using Python's optimization solver library and the FloPy library. At present, the coupling simulation and optimization model of groundwater quantity and water quality can still describe the migration and transformation law of pollutants more accurately, but still require manual adjustment of parameters.