Rybarczyk Yves, Zalakeviciute Rasa
The Department of Data and Information Sciences Dalarna University Falun Sweden.
SI2 Lab Universidad de Las Americas Quito Ecuador.
Geophys Res Lett. 2021 Feb 28;48(4):e2020GL091202. doi: 10.1029/2020GL091202. Epub 2021 Feb 16.
The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross-validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by -53 ± 2%, -45 ± 11%, -30 ± 13%, and -15 ± 9% for NO, SO, CO, and PM, respectively. The traffic-busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.
关于2019冠状病毒病封锁措施对空气质量影响的全球研究倡议在污染减少方面达成了共识，但污染减少量化的最可靠方法仍存在争议。本文基于梯度提升机算法构建机器学习模型，以评估疫情对厄瓜多尔基多空气质量的影响。首先，通过对封锁前四年的数据进行交叉验证来评估预测精度，结果显示该模型在估计实际污染水平方面具有较高的准确性。然后，对污染变化进行量化。在全面封锁期间，一氧化氮（NO）、二氧化硫（SO）、一氧化碳（CO）和颗粒物（PM）的空气污染分别下降了-53±2%、-45±11%、-30±13%和-15±9%。交通繁忙的地区是该市受影响最大的区域。在过渡到部分解封后，污染物浓度几乎恢复到疫情前的水平。对预测可信度的评估支持了污染下降的量化结果。
