Accurate　acquisition　of　air　quality　is　important　for　improving　human　well-being.　However,　directly　monitoring　air　quality　at　all　locations　is　costly.　The　challenge　is　how　we　can　select　a　small　number　of　locations　to　monitor　the　air　quality　such　that　the　estimation　error　of　air　quality　at　other　locations　can　be　minimized.　In　this　paper,　a　general　location　selection　strategy　is　proposed　based　on　active　learning,　which　involves　iterations　of　a　selector　and　an　estimator.　We　implement　four　instances　of　this　general　strategy　to　embody　it:　KAL　(Active　Learning　based　on　Kriging),　TAL　(Active　Learning　based　on　Regression　Tree),　KMAL　(Active　Learning　based　on　Kriging　and　MPGR)　and　TMAL　(Active　Learning　based　on　Regression　Tree　and　MPGR).　The　estimator　of　KAL　or　TAL　can　estimate　the　air　quality　at　remaining　locations　from　air　quality　samples　at　monitoring　locations,　leveraging　spatial　or　cross-domain　correlation　of　air　quality.　The　selecting　indicators　of　their　selectors　are　designed　to　measure　the　uncertainty　of　unlabeled　samples　according　to　their　estimators.　KMAL　and　TMAL　are　upgrades　of　the　former　two,　respectively,　by　introducing　MPGR　(Manifold　Preserving　Graph　Reduction)　to　also　take　the　representativeness　of　unlabeled　samples　into　account.　The　experimental　results　show　that　the　proposed　strategy　can　achieve　a　low　estimation　error　with　few　monitoring　locations.　Particularly,　given　the　same　budget　(i.e.,　the　number　of　monitoring　locations),　the　estimation　error　is　reduced　from　about　20　percent　of　baselines　to　15　percent　by　KAL　and　to　5　percent　by　KMAL;　and　TAML　likewise.