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Increased ozone pollution alongside reduced nitrogen dioxide concentrations during Vienna's first COVID-19 lockdown: Significance for air quality management

机译:在维也纳的第一个Covid-19锁定期间,增加臭氧污染,减少二氧化氮浓度:空气质量管理的重要性

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Background: Lockdowns amid the COVID-19 pandemic have offered a real-world opportunity to better understand air quality responses to previously unseen anthropogenic emission reductions.Methods and main objective: This work examines the impact of Vienna's first lockdown on ground-level concentrations of nitrogen dioxide (NO2), ozone (O-3) and total oxidant (O-x). The analysis runs over January to September 2020 and considers business as usual scenarios created with machine learning models to provide a baseline for robustly diagnosing lockdown-related air quality changes. Models were also developed to normalise the air pollutant time series, enabling facilitated intervention assessment.Core findings: NO2 concentrations were on average -20.1% [13.7-30.4%] lower during the lockdown. However, this benefit was offset by amplified O-3 pollution of +8.5% [3.7-11.0%] in the same period. The consistency in the direction of change indicates that the NO2 reductions and O-3 increases were ubiquitous over Vienna. O x concentrations increased slightly by +4.3% [1.8-6.4%], suggesting that a significant part of the drops in NO2 was compensated by gains in O-3. Accordingly, 82% of lockdown days with lowered NO2 were accompanied by 81% of days with amplified O-3. The recovery shapes of the pollutant concentrations were depicted and discussed. The business as usual-related outcomes were broadly consistent with the patterns outlined by the normalised time series. These findings allowed to argue further that the detected changes in air quality were of anthropogenic and not of meteorological reason. Pollutant changes on the machine learning baseline revealed that the impact of the lockdown on urban air quality were lower than the raw measurements show. Besides, measured traffic drops in major Austrian roads were more significant for light-duty than for heavy-duty vehicles. It was also noted that the use of mobility reports based on cell phone movement as activity data can overestimate the reduction of emissions for the road transport sector, particularly for heavy-duty vehicles. As heavy-duty vehicles can make up a large fraction of the fleet emissions of nitrogen oxides, the change in the volume of these vehicles on the roads may be the main driver to explain the change in NO2 concentrations.Interpretation and implications: A probable future with emissions of volatile organic compounds (VOCs) dropping slower than emissions of nitrogen oxides could risk worsened urban O-3 pollution under a VOC-limited photochemical regime. More holistic policies will be needed to achieve improved air quality levels across different regions and criteria pollutants. (C) 2021 The Author. Published by Elsevier Ltd.
机译:背景:Covid-19 Pandemase的锁定提供了一个真实的机会,以更好地了解以前看不见的人为排放减少的空气质量反应。方法和主要目标:这项工作审查了维也纳第一次锁定对地面血液浓度的影响二氧化氮(NO2),臭氧(O-3)和总氧化剂(牛)。该分析在1月至9月20日期间运行,并将业务视为使用机器学习模型创建的通常情况,以提供稳健诊断锁定相关空气质量变化的基线。还开发了模型以使空气污染时间序列正常化,使促进的干预评估能够进行促进的干预评估。在锁定期间,NO 2浓度平均为-20.1%[13.7-30.4%]。但是,这种益处在同一时期的扩增O-3污染+ 8.5%[3.7-11.0%]抵消。变化方向的一致性表明NO 2减少和O-3增加普遍存在维也纳。 o X浓度略微增加+ 4.3%[1.8-6.4%],表明NO2中滴剂的大部分液滴由O-3的收益补偿。因此,82%的No2的锁定天数伴有81%的天数,具有扩增的O-3。描绘并讨论了污染物浓度的恢复形状。作为常用相关的业务与规范化时间序列概述的模式广泛一致。这些发现允许进一步争辩说,检测到的空气质量的变化是人为的,而不是气象原因。机器学习基线的污染物变化显示,锁定对城市空气质量的影响低于原始测量展。此外,奥地利主要道路上的测量交通跌落比重型车辆更重要。还有人指出,根据电池移动作为活动数据的移动性报告的使用可以高估到公路运输领域的排放的减少,特别是对于重型车辆。由于重型车辆可以弥补氮氧化物的舰队排放量的大部分,因此这些车辆在道路上的变​​化可能是解释NO2浓度的变化的主要驱动因素。解释和影响:可能的未来随着挥发性有机化合物(VOC)的排放比氮氧化物的排放慢,可能会使城市O-3在VOC有限的光化学制度下恶化。需要更多整体政策来实现不同地区和标准污染物的改善的空气质量水平。 (c)2021提交人。 elsevier有限公司出版

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