Li Zhiyuan
School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, China.
Toxics. 2024 Aug 10;12(8):580. doi: 10.3390/toxics12080580.
So far, a large number of studies have quantified the effect of COVID-19 lockdown measures on air quality in different countries worldwide. However, few studies have compared the influence of different approaches on the estimation results. The present study aimed to utilize a random forest machine learning approach as well as a difference-to-difference approach to explore the effect of lockdown policy on nitrogen dioxide (NO) concentration during COVID-19 outbreak period in mainland China. Datasets from 2017 to 2019 were adopted to establish the random forest models, which were then applied to predict the NO concentrations in 2020, representing a scenario without the lockdown effect. The results showed that random forest models achieved remarkable predictive accuracy for predicting NO concentrations, with index of agreement values ranging between 0.34 and 0.76. Compared with the modelled NO concentrations, on average, the observed NO concentrations decreased by approximately 16 µg/m in the lockdown period in 2020. The difference-to-difference approach tended to underestimate the influence of COVID-19 lockdown measures. Due to the improvement of NO pollution, around 3722 non-accidental premature deaths were avoided in the studied population. The presented machine learning modelling framework has a great potential to be transferred to other short-term events with abrupt pollutant emission changes.
到目前为止，大量研究已经量化了新冠疫情封锁措施对全球不同国家空气质量的影响。然而，很少有研究比较不同方法对估计结果的影响。本研究旨在利用随机森林机器学习方法以及双重差分法，探讨中国大陆新冠疫情爆发期间封锁政策对二氧化氮（NO）浓度的影响。采用2017年至2019年的数据集建立随机森林模型，然后将其应用于预测2020年的NO浓度，代表无封锁影响的情景。结果表明，随机森林模型在预测NO浓度方面具有显著的预测准确性，一致性指数值在0.34至0.76之间。与模拟的NO浓度相比，2020年封锁期间观测到的NO浓度平均下降了约16微克/立方米。双重差分法往往低估了新冠疫情封锁措施的影响。由于NO污染的改善，研究人群中避免了约3722例非意外过早死亡。所提出的机器学习建模框架具有很大的潜力，可以应用于其他具有污染物排放突然变化的短期事件。
