Pump-and-treat (P&T) techniques are among the most commonly applied methods for groundwater contamination remediation. This study develops a groundwater flow model and a solute transport model using MODFLOW and RT3D to design and optimize P&T strategies for a polluted chemical park in northern China. Five scenarios were analyzed to evaluate the effects of well placement and pumping rates on remediation performance. Simulation results indicate that the existing number of extraction wells is inadequate to meet remediation targets. While increasing the pumping rate of individual wells accelerates remediation, it also increases the total extracted water volume. Similarly, higher extraction rates in heavily contaminated areas shorten remediation times but lead to greater water extraction. The findings demonstrate that numerical modeling can effectively optimize P&T designs, enabling the minimization of water extraction volumes while achieving remediation objectives within the required timeframe.

Groundwater vulnerability assessments serve as essential tools for sustainable groundwater management, particularly in regions with intensive anthropogenic activities. However, improving the objectivity and predictive reliability of vulnerability assessment frameworks remains a critical scientific challenge in groundwater science, especially for coastal aquifer systems characterized by strong heterogeneity and complex hydrogeological processes. The traditional DRASTIC model is a widely recognized method but suffers from subjectivity in assigning parameter ratings and weights, often leading to arbitrary and potentially inaccurate vulnerability maps. This limitation also restricts its applicability in areas with complex hydrogeological conditions. To enhance the accuracy and adaptability of the traditional DRASTIC model, a hybrid PSO-BP-DRASTIC framework was developed and applied it to a coastal city in China. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the particle swarm optimization (PSO) algorithm to refine the initial weights and thresholds of the BP-NN. By introducing a data-driven and globally optimized weighting mechanism, the proposed framework effectively overcomes the inherent subjectivity of conventional empirical weighting schemes. Using ten-fold cross-validation and observed nitrate concentration data, the traditional DRASTIC, BP-DRASTIC, and PSO-BP-DRASTIC models were systematically validated and compared. The results demonstrate that (1) the PSO-BP-DRASTIC model achieved the highest classification accuracy on the test set, the highest stability across ten-fold cross-validation, and the strongest correlation with the nitrate concentrations; (2) the importance analysis identified the aquifer thickness and depth to the groundwater table as the most influential factors affecting groundwater vulnerability in Yantai; and (3) the spatial assessments revealed that high-vulnerability zones (7.85% of the total area) are primarily located in regions with intensive agricultural activities and high aquifer permeability. The hybrid PSO-BP-DRASTIC model effectively mitigates the subjectivity of the traditional DRASTIC method and the local optimum issues inherent in BP-NNs, significantly improving the assessment accuracy, stability, and objectivity. From a scientific perspective, this study demonstrates the feasibility of integrating swarm intelligence and neural learning into groundwater vulnerability assessment, providing a transferable and high-precision methodological paradigm for data-driven hydrogeological risk evaluation. This novel hybrid model provides a reliable scientific basis for the reasonable assessment of groundwater vulnerability. Moreover, these findings highlight the importance of integrating a hybrid optimization strategy into the traditional DRASTIC model to enhance its feasibility in coastal cities and other regions with complex hydrogeological conditions.