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.　(C)　2020　Published　by　Elsevier　B.V.