The COVID-19 pandemic has severely affected various aspects of life, at different levels and in different countries on almost every continent. In response, many countries have closed their borders and imposed lockdown policies, possibly bringing benefits to people's health with significantly less emission from air pollutants. Currently, most studies or reports are based on local observations at the city or country level. There remains a lack of systematic understanding of the impacts of different lockdown policies on the air quality from a global perspective. This study investigates the impacts of COVID-19 pandemic towards global air quality through examining global nitrogen dioxide (NO2) dynamics from satellite observations between 1 January and 30 April 2020. We used the Apriori algorithm, an unsupervised machine learning method, to investigate the association among confirmed cases of COVID-19, NO2 column density, and the lockdown policies in 187 countries. The findings based on weekly data revealed that countries with new cases adopted various lockdown policies to stop or prevent the virus from spreading whereas those without tended to adopt a wait-and-see attitude without enforcing lockdown policies. Interestingly, decreasing NO2 concentration due to lockdown was associated with international travel controls but not with public transport closure. Increasing NO2 concentration was associated with the "business as usual" strategy as evident from North America and Europe during the early days of COVID-19 outbreak (late January to early February 2020), as well as in recent days (in late April) after many countries have started to resume economic activities. This study enriches our understanding of the heterogeneous patterns of global associations among the COVID-19 spreading, lockdown policies and their environmental impacts on NO2 dynamics. Copyright © 2020. Published by Elsevier B.V.