Purified terephthalic acid (PTA) is an extremely important bulk organic raw material; it plays a central connecting role in the PX–PTA–polyester industry chain, while its significant carbon intensity remains poorly quantified. Through process-level life cycle assessment (LCA) based on in situ industrial data, this study establishes a comprehensive material-energy inventory for PTA production. The results show that the total greenhouse gas (GHG) emissions of the entire PTA process reached 1600.9 kg of CO₂ eq·t⁻¹, exceeding those of common primary chemicals, like aromatics, butadiene and styrene. The end process of the PTA unit (PU) dominates GHG emissions, reaching 365.6 kg CO₂ eq·t⁻¹, accounting for 22.3%, driven by extra xylene input, various catalyst consumption, auxiliary chemicals, and energy intensity. After allocating steam-related emissions from coal-fired power stations, the GHG emissions of the PU rise to 400.9 kg CO₂ eq·t⁻¹. Sensitivity analysis demonstrates that replacing conventional hydrogen with green hydrogen slashes hydrogen-related global warming potential (GWP) contribution by 61.5%. In addition, a 10% increase in electricity, coal, or steam elevates system GWP by 0.80%, 0.036% and 2.48%, respectively. The findings demonstrate that energy structure optimization and green hydrogen integration represent decisive levers for PTA decarbonization, providing data-driven insights for industrial transition under a carbon reduction policy framework.

Fine-scale ozone pollution estimation serves as a crucial prerequisite for promoting sustainable air quality management and facilitating the green industrial transition, especially in petrochemical parks where high concentrations of precursor pollutants are emitted. In this context, focusing on a typical petrochemical industrial park, this study adopts and compares both unsupervised and supervised machine learning methods to evaluate their O₃ concentration estimation performance. Six unsupervised algorithms were evaluated using the Silhouette Score (SS) and Calinski–Harabasz Index (CH). The proposed hybrid SK model (SOM + k-means) achieved the best clustering performance (SS = 0.6615, CH = 13,946.60), identifying nine meteorological patterns, two of which were strongly associated with elevated ozone. Among the supervised models, after hyperparameter tuning with 5-fold cross-validation, XGBoost outperformed support vector regression, random forest, and Convolutional Neural Networks on the held-out test set, achieving R² = 0.87, RMSE = 13.67 μg/m³, and MAE = 10.02 μg/m³. SHAP analysis indicated that northeasterly winds, moderate wind speed (≤4 m/s), low humidity, and temperatures between 10 and 20 °C were positively associated with high ozone levels, whereas southwesterly winds and high humidity showed negative associations. These findings highlight the complex interactions between meteorological factors and ozone dynamics. The proposed meteorology-driven estimation framework provides candidate indicators for ozone pollution risk screening within petrochemical industrial zones, offering preliminary meteorological references for subsequent pollution mechanism research and follow-up validated operational modeling, and contributing to regional environmental sustainability.

Purified terephthalic acid (PTA) is a key intermediate in the global polyester industrial chain, and its carbon intensity directly governs the sustainability of downstream products. The production of PTA involves high-temperature oxidation and catalytic refining processes, generating large volumes of organic wastewater whose on-site anaerobic treatment converts dissolved organic carbon into methane. This study presents a gate-to-gate process emission assessment methodology for PTA production, coupling energy-based emission factors, process carbon balance, and wastewater methane accounting within the system boundary, thereby extending conventional process-accounting frameworks. Based on a gate-to-gate life-cycle assessment of four representative PTA production systems across three industrial settings, we quantified emissions from energy consumption, process-related carbon losses, and wastewater treatment, with particular emphasis on the methane generated in the anaerobic treatment stage. Results demonstrate that steam constitutes the dominant carbon-emission contributor, accounting for 62–70% of total emissions, indicating that heat structure and recovery optimization are the highest-priority targets for emission reduction. According to carbon-flow analysis, although PX accounts for 97–98% of total carbon input, PTA products retain 95–97% of the carbon. Critically, wastewater treatment emerges as a significant yet overlooked emission pathway of methane generated during anaerobic digestion owing to its high global warming potential (GWP), so omitting methane emissions from wastewater treatment understates the total emission intensity by 7.5% to 10.8%, whereas the wastewater-treatment stage as a whole contributes 10.84% to 16.13% of total emissions. In addition, the results provide local carbon-emission factors for similar technologies, with advanced technology systems achieving 0.214 t CO₂/t PTA, while less integrated systems reach 0.395 t CO₂/t PTA. Low-carbon steam supply, improved heat integration, oxidation-loop control, solvent-cycle stability, and methane capture or oxidation in wastewater treatment are therefore important measures for reducing emissions, supporting the Sustainable Development Goals of responsible consumption and production and climate action. This integrated approach provides actionable insights for guiding low-carbon process optimization and more accurate carbon-accounting practices in the PTA industry.