With the rapid progression of global industrialization and urbanization, emerging contaminants (ECs) have become pervasive in environmental media, posing considerable risks to ecosystems and human health. While multidisciplinary evidence continues to accumulate regarding their environmental persistence and bioaccumulative hazards, critical knowledge gaps persist in understanding their spatiotemporal distribution, cross-media migration mechanisms, and cascading ecotoxicological consequences. This review systematically investigates the global distribution patterns of ECs in aquatic environments over the past five years and evaluates their potential ecological risks. Furthermore, it examines the performance of various treatment technologies, focusing on economic cost, efficiency, and environmental sustainability. Methodologically aligned with PRISMA 2020 guidelines, this study implements dual independent screening protocols, stringent inclusion–exclusion criteria (n = 327 studies). Key findings reveal the following: (1) Occurrences of ECs show geographical clustering in highly industrialized river basins, particularly in Asia (37.05%), Europe (24.31%), and North America (14.01%), where agricultural pharmaceuticals and fluorinated compounds contribute disproportionately to environmental loading. (2) Complex transboundary pollutant transport through atmospheric deposition and oceanic currents, coupled with compound-specific partitioning behaviors across water–sediment–air interfaces. (3) Emerging hybrid treatment systems (e.g., catalytic membrane bioreactors, plasma-assisted advanced oxidation) achieve > 90% removal for recalcitrant ECs, though requiring 15–40% cost reductions for scalable implementation. This work provides actionable insights for developing adaptive regulatory frameworks and advancing green chemistry principles in environmental engineering practice.

With the rapid expansion of the new energy vehicle (NEV) market, a shift from function-oriented toward emotion-oriented interior design has emerged as a key factor in enhancing user experience and brand differentiation. Current research on emotion optimization in new energy vehicle interiors lacks efficient nonlinear modeling methods. To address this gap, a hybrid Transformer-LSTM-SVR model is proposed, which significantly enhances emotion prediction accuracy by integrating attention mechanisms and temporal modeling. This model addresses the complex nonlinear relationship between users’ emotional satisfaction and interior design attributes. Furthermore, interpretability analysis reveals key design feature differences among user groups. The proposed method combines a Transformer module, which captures higher-order interactions among multidimensional design parameters (e.g., sentiment evaluation coefficients and task completion time), and a Long Short-Term Memory (LSTM) network, configured to enhance time-series feature capture through adjustments to hidden unit count and sequence length. Potential representations from both modules are combined into high-dimensional vectors via a feature fusion mechanism and subsequently fed into a Support Vector Regression (SVR) module for fitting nonlinear relationships. This hierarchical architecture effectively mitigates the limitations of traditional SVR in modeling dynamic time series and nonlinear relationships, while enhancing model robustness through the synergy between global context-awareness and time-series dependency. Performance comparisons against benchmark models—including traditional SVR, particle swarm optimization (PSO)-tuned SVR, PSO-tuned random forest (RF), Backpropagation Neural Network (BPNN), and Gradient Boosting Regression (GBR)—demonstrate that the proposed model significantly outperforms these baselines, improving prediction accuracy by 12.7% to 23.4%. Compared to traditional KE methods, the synergistic integration of the LSTM’s temporal attention mechanism and the Transformer’s global context modeling improves system robustness against noisy user feedback data by 18.9%. Results indicate that the model significantly enhances the prediction accuracy of users’ emotional needs, offering a viable approach for emotion-oriented and sustainable interior design in new energy vehicles. This study provides practical tools for designers, thereby enhancing the market competitiveness of new energy vehicles and promoting sustainable development.

Mapping human-machine requirements in smart product design remains challenging. An integrated framework combining semiotic architecture product design (SAPAD), dual machine learning (DML), hesitant fuzzy quality function deployment (HFQFD), and multi-objective optimization is proposed. Home companion robots are used as a case study. User behavior is deconstructed via SAPAD to generate demand hypotheses. Causal inference is performed by DML to identify core demands impacting user experience, with priorities defined by integrating expert-assigned and causal weights. Evaluation uncertainty is addressed by HFQFD, through which validated demands are converted into design parameters. A hybrid approach integrating Cuckoo Catfish Optimizer (CCO), least squares support vector machine (LSSVM), and non-dominated sorting genetic algorithm II (NSGA-II) is developed for multi-objective optimization. Shapley Additive exPlanations (SHAP) analysis is employed to quantify each parameter’s marginal contribution to conflicting requirements. A closed-loop process from behavioral observation to parameter optimization delivers transparent decision support for complex human-machine system design.