Latest reports of the European Environment Agency and Agência Portuguesa do Ambiente raise a reasonable doubt on the satisfaction of 2030 targets imposed by supranational regulation for sulfur dioxide emissions in Portugal. As such, efforts to predict the evolution and estimate statistically significant effects of covariates related to this air pollutant are recommended. Bayesian, econometrics and machine learning models are applied to predict future values of sulfur dioxide emissions in the vicinity of the most relevant thermoelectric power plant located in Portugal. Based on a multivariate time series analysis containing data that ranges from July 2017 to April 2020, several conclusions are identified. Predicted values of sulfur dioxide emissions of the five models exhibiting the lowest forecast error are strongly correlated, particularly in the interval 0.35$\pm$ $0.10\mathrm{\mu }\mathrm{g}$/m³. The application of multi-step ahead forecasting analysis and nonlinear ensemble algorithms reinforces the main result from the one-step ahead forecasting exercise, where it is demonstrated that machine learning models have a better generalization power compared to classical approaches. Additionally, an identification strategy is proposed to assess the efficacy of a firm-specific measure adopted in 2017 (i.e., qualitative improvement of the desulfurization process to reduce the level of sulfur dioxide emissions). Super learning algorithms confirm that sulfur dioxide emissions in 2017 were approximately 19% greater relative to the period 2018–2020, which allows to conclude that the effort promoted by the firm was effective. From a regulatory point of view, this study confirms that Portugal is likely to satisfy 2030 targets imposed by supranational regulation for sulfur dioxide emissions and provides useful recommendations to ensure the persistence of best air quality sustainability practices.

欧洲环境署和 Agência Portuguesa do Ambiente 的最新报告对葡萄牙对二氧化硫排放的超国家监管规定的 2030 年目标的满足程度提出了合理的怀疑。因此，建议努力预测与这种空气污染物相关的协变量的演变和估计统计上的显着影响。应用贝叶斯、计量经济学和机器学习模型来预测葡萄牙最相关的热电厂附近二氧化硫排放的未来值。基于包含 2017 年 7 月至 2020 年 4 月数据的多元时间序列分析，确定了几个结论。表现出最低预测误差的五个模型的二氧化硫排放预测值具有很强的相关性，$\pm$ $0.10\mathrm{\mu }\mathrm{G}$/米³. 多步超前预测分析和非线性集合算法的应用强化了单步超前预测练习的主要结果，证明机器学习模型与经典方法相比具有更好的泛化能力。此外，还提出了一项识别策略，以评估 2017 年采用的企业特定措施（即脱硫工艺的质量改进以降低二氧化硫排放水平）的有效性。超级学习算法证实，2017 年的二氧化硫排放量比 2018 年至 2020 年期间增加了约 19%，由此可以得出结论，该公司推动的努力是有效的。从监管的角度来看，