An investigation of the impacts of a successful COVID-19 response and meteorology on air quality in New Zealand

被引:13
|
作者
Talbot, Nick [1 ,3 ]
Takada, Akika [2 ]
Bingham, Andrew H. [2 ]
Elder, Dan [2 ]
Yee, Samantha Lay [1 ]
Golubiewski, Nancy E. [1 ]
机构
[1] Minist Environm, Auckland, New Zealand
[2] Minist Environm, Wellington, New Zealand
[3] Univ Auckland, Sch Environm, Auckland, New Zealand
关键词
COVID-19; On-road vehicles; Machine learning; Atmospheric pollutants; ABSOLUTE ERROR MAE; RMSE;
D O I
10.1016/j.atmosenv.2021.118322
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 (NO2) were reduced 48-54% compared to Businessas-usual (BAU) values. NO2 concentrations rapidly returned to near mean levels as the alert levels decreased and restrictions eased. PM10 and PM2.5 responded differently to NO2 during the different alert levels. This is due to particulates having multiple sources, many of natural origin and therefore less influenced by human activity. PM10 and PM2.5 concentrations were reduced during alert level 4 but to a lesser extent than NO2 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 NO2 as well as PM2.5 and PM10 away from coastal regions.
引用
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页数:14
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