Soil heavy metal pollution caused by human activities has become one of the most critical environmental issues with a global concern. Phytoremediation is widely used due to its low cost and environmental friendliness. However, the impact of heavy metals on plant growth remains unclear. This study investigated the effects on the growth and photosynthetic activity of Picris divaricata Vant. under different cadmium concentrations using a hydroponics cultivation system. The results showed that the growth and photosynthetic processes of P. divaricata exhibited a phenomenon of promotion in low Cd concentrations and inhibition in high Cd concentrations. Under a low to medium Cd concentration (≤25 μM), there was no Cd toxicity in terms of plant growth, but high concentrations of Cd inhibited plant growth. The Fe content of leaves gradually increased as the Cd concentration increased; it reached 201.8 mg kg⁻¹ in 75 μM Cd. However, there was no significant difference in Mn between the 75 μM Cd treatment and the control (p > 0.05). The contents of carotenoid ranged between 3.06 and 3.26 mg/g across the different Cd treatments, showing no significant differences. The treatment with 5–75 μM Cd did not directly affect the photosynthesis of P. divaricata. Higher Cd concentrations reduced the stomatal density on the of P. divaricata leaves, resulting in stomatal and mesophyll conductance limitations, indirectly affecting P. divaricata photosynthesis. These research results provide a reference for evaluating and selecting heavy metal tolerant plants and provide environmentally friendly approaches to remediate heavy metal pollution.

Selecting representative groundwater monitoring wells in polluted areas is crucial to comprehensively assess groundwater pollution, thereby ensuring effective groundwater remediation. However, numerous factors can affect the effectiveness of groundwater monitoring well network optimizations. A local sensitivity analysis method was used in this study to analyze the hydrogeological parameters of a simulation groundwater solute transport model. The results showed a strong effect of longitudinal dispersion and transverse dispersion on the output results of the simulation model, and a good fit between the backpropagation neural network (BPNN)-based alternative model’s results and those obtained using the solute transport simulation model, accurately reflecting the input and output relationship of the simulation model. The optimized groundwater monitoring layout scheme consisted of four groundwater monitoring wells, namely no. 7, no. 16, no. 23, and no. 24. These wells resulted in a groundwater fluoride pollution rate of 98.44%, which was substantially higher than that obtained using the random layout scheme. In addition, statistical analysis of the fluoride groundwater pollution results obtained using the Monte Carlo random simulation highlighted continuous and high groundwater fluoride levels in the second and third pollution sources and their downstream groundwater. Therefore, more attention should be devoted to these sources to ensure the effective remediation of groundwater pollution in the study area.