Talbot Nick, Takada Akika, Bingham Andrew H, Elder Dan, Lay Yee Samantha, Golubiewski Nancy E
Ministry for the Environment, Auckland, New Zealand.
University of Auckland, School of Environment, Auckland, New Zealand.
Atmos Environ (1994). 2021 Jun 1;254:118322. doi: 10.1016/j.atmosenv.2021.118322. Epub 2021 Mar 11.
The COVID-19 pandemic brought about national restrictions on people's movements, in effect commencing a socially engineered transport emission reduction experiment. In New Zealand during the most restrictive alert level (Level 4), roadside concentrations of nitrogen dioxide (NO) were reduced 48-54% compared to Business-as-usual (BAU) values. NO concentrations rapidly returned to near mean levels as the alert levels decreased and restrictions eased. PM and PM responded differently to NO during the different alert levels. This is due to particulates having multiple sources, many of natural origin and therefore less influenced by human activity. PM and PM concentrations were reduced during alert level 4 but to a lesser extent than NO and with more variability across regions. Particulate concentrations increased notably during alert level 2 when many airsheds reported concentrations above the BAU means. To provide robust BAU reference concentrations, simple 5-year means were calculated along with predictions from machine learning modelling that, in effect, removed the influence of meteorology on observed concentrations. The results of this study show that latter method was found to be more closely aligned to observed values for NO as well as PM and PM away from coastal regions.
新冠疫情导致各国对人员流动实施限制，实际上开启了一项人为控制的交通排放减少实验。在新西兰，处于限制最严格的警戒级别（4级）期间，路边二氧化氮（NO）浓度与照常营业（BAU）时的值相比降低了48%-54%。随着警戒级别降低和限制放宽，NO浓度迅速回升至接近平均水平。在不同警戒级别期间，PM和PM对NO的反应有所不同。这是因为颗粒物有多种来源，许多是自然来源，因此受人类活动影响较小。在4级警戒级别期间，PM和PM浓度有所降低，但降幅小于NO，且各地区的变化更大。在2级警戒级别期间，许多空气流域报告的浓度高于BAU平均值，颗粒物浓度显著增加。为了提供可靠的BAU参考浓度，计算了简单的5年平均值以及机器学习模型的预测值，实际上消除了气象因素对观测浓度的影响。研究结果表明，后一种方法被发现与远离沿海地区的NO以及PM和PM的观测值更为接近。
