In order to solve the problem of demulsification difficulties in Liaohe Oilfield, 24 kinds of demulsifiers were screened by using the interface generation energy (IFE) module in the molecular dynamics simulation software Materials Studio to determine the ability of demulsifier molecules to reduce the total energy of the oil–water interface after entering the oil–water interface. Neural network analysis (NNA) and genetic function approximation (GFA) were used as technical means to predict the demulsification effect of the Liaohe crude oil demulsifier. The simulation results show that the SDJ9927 demulsifier with ethylene oxide (EO) and propylene oxide (PO) values of 21 (EO) and 44 (PO) reduced the total energy and interfacial tension of the oil–water interface to the greatest extent, and the interfacial formation energy reached −640.48 Kcal/mol. NNA predicted that the water removal amount of the SDJ9927 demulsifier was 7.21 mL, with an overall error of less than 1.83. GFA predicted that the water removal amount of the SDJ9927 demulsifier was 7.41mL, with an overall error of less than 0.9. The predicted results are consistent with the experimental screening results. SDJ9927 had the highest water removal rate and the best demulsification effect. NNA and GFA had high correlation coefficients, and their R²s were 0.802 and 0.861, respectively. The higher R² was, the more accurate the prediction accuracy was. Finally, the demulsification mechanism of the interfacial film breaking due to the collision of fluorinated polyether demulsifiers was studied. It was found that the carbon–fluorine chain had high surface activity and high stability, which could protect the carbon–carbon bond in the demulsifier molecules to ensure that there was no re-emulsion due to the stirring external force.

Surface dust samples were collected from bus stops and parking lots in different functional areas of Binzhou City, Shandong Province, China. This study investigated the contamination characteristics, source apportionment, and potential ecological and health risks of potentially toxic elements (PTEs) in these dust samples. Eight target PTEs, including As, Zn, Pb, Cu, Cd, Cr, Ni, and Mn, were quantitatively analyzed. The results revealed distinct concentration differences in these elements between bus stop dust and parking lot dust. Several PTEs exceeded the corresponding local soil background values, predominantly Zn, Pb, Cu, Cd and Cr. Principal component analysis (PCA) indicated that Zn, Pb, Cu, Cr, Ni, and Mn in bus stop dust were mainly sourced from traffic emissions, whereas As and Cd primarily originated from atmospheric deposition. For parking lot dust, Zn, Pb, Cu, Cd, Cr, and Mn were predominantly attributed to traffic sources, while As and Ni were mainly derived from natural background sources. The geo-accumulation index (I_geo) demonstrated that As, Cr, Ni, and Mn had negligible environmental impact, Pb, Cu, and Cd induced slight pollution, and Zn resulted in moderate pollution. Except for Cd, the average individual potential ecological risk index ($E_r^i$) values for all elements were below 40, suggesting a low ecological risk. Cd posed a moderate ecological hazard, whereas the comprehensive ecological risk index ($E_r^i$) values of all analyzed elements were at an extremely low level. The hazard index (HI) values via different exposure pathways and for all PTEs in both bus stops and parking lots were lower than 1, indicating no significant non-carcinogenic health risk. The carcinogenic risk ranking of elements was Cr > Cd > Ni > As, and their carcinogenic risk values (CR) via inhalation exposure were below 1 × 10⁻⁶, indicating no carcinogenic risk. This study provides a scientific basis for the environmental quality control and risk management of surface dust in urban bus stops and parking lots.