Abstract. The COVID-19 (COronaVIrus Disease 2019) pandemic provided the unique opportunity to evaluate the role of a sudden and deep decline in air pollutant emissions in the ambient air of numerous cities worldwide. Argentina, in general, and the Metropolitan Area of Buenos Aires (MABA), in particular, were under strict control measures from March to May 2020. Private vehicle restrictions were intense, and primary pollutant concentrations decreased substantially. To quantify the changes in CO, NO, NO₂, PM₁₀, SO₂ and O₃ concentrations under the stay-at-home orders imposed against COVID-19, we compared the observations during the different lockdown phases with both observations during the same period in 2019 and concentrations that would have occurred under a business-as-usual (BAU) scenario under no restrictions. We employed a Random Forest (RF) algorithm to estimate the BAU concentration levels. This approach exhibited a high predictive performance based on only a handful of available indicators (meteorological variables, air quality concentrations and emission temporal variations) at a low computational cost. Results during testing showed that the model captured the observed daily variations and the diurnal cycles of these pollutants with a normalized mean bias (NMB) of less than 11 % and Pearson correlation coefficients of the diurnal variations of between 0.65 and 0.89 for all the pollutants considered. Based on the Random Forest results, we estimated that the lockdown implied concentration decreases of up to 47 % (CO), 60 % (NO_x) and 36 % (PM₁₀) during the strictest mobility restrictions. Higher O₃ concentrations (up to 87 %) were also observed, which is consistent with the response in a VOC-limited chemical regime to the decline in NO_x emissions. Relative changes with respect to the 2019 observations were consistent with those estimated with the Random Forest model, but indicated that larger decreases in primary pollutants and lower increases in O₃ would have occurred. This points out to the need of accounting not only for the differences in emissions, but also in meteorological variables to evaluate the lockdown effects on air quality. The findings of this study may be valuable for formulating emission control strategies that do not disregard their implication on secondary pollutants. The data set used in this study and an introductory machine learning code are openly available at https://data.mendeley.com/datasets/h9y4hb8sf8/1 (Diaz Resquin et al., 2021).

中文翻译：

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一种解决阿根廷布宜诺斯艾利斯 COVID-19 封锁期间空气质量变化的机器学习方法

摘要。COVID-19（2019 年冠状病毒病）大流行为评估全球众多城市环境空气中空气污染物排放量突然和深度下降的作用提供了独特的机会。2020 年 3 月至 5 月，阿根廷总体上，特别是布宜诺斯艾利斯都市区（MABA）实行严格管控，私家车限行力度大，主要污染物浓度大幅下降。量化 CO、NO、NO ₂、PM ₁₀、SO ₂和 O _{3 的变化}在针对 COVID-19 实施的居家令下的浓度，我们将不同锁定阶段的观察结果与 2019 年同期的观察结果和在一切照旧 (BAU) 情景下发生的浓度进行了比较在没有任何限制的情况下。我们采用随机森林 (RF) 算法来估计 BAU 浓度水平。该方法仅基于少数可用指标（气象变量、空气质量浓度和排放时间变化）以较低的计算成本表现出较高的预测性能。测试期间的结果表明，该模型以归一化平均偏差（NMB) 小于 11 %，并且对于所有考虑的污染物，日变化的 Pearson 相关系数在 0.65 和 0.89 之间。根据随机森林的结果，我们估计在最严格的流动性限制期间，锁定隐含浓度降低了 47% (CO)、60% (NO _x ) 和 36% (PM ₁₀ )。还观察到更高的 O ₃浓度（高达 87%），这与 VOC 限制的化学体系对 NO _x排放量下降的响应一致。2019 年观测的相对变化与随机森林模型估计的结果一致，但表明主要污染物减少幅度较大，O ₃增加幅度较小会发生。这表明不仅需要考虑排放差异，还需要考虑气象变量，以评估封锁对空气质量的影响。本研究的结果对于制定不忽视其对二次污染物影响的排放控制策略可能是有价值的。本研究中使用的数据集和介绍性机器学习代码可在 https://data.mendeley.com/datasets/h9y4hb8sf8/1（Diaz Resquin 等人，2021 年）上公开获得。