A　modeling　framework　by　linking　air　quality　simulation　with　system　optimization　was　presented　in　this　paper　to　develop　cost-effective　urban　air　quality　management　strategies　in　Fengnan　district　of　China.　The　relation　between　the　total　allowable　emission　and　wind　speed　as　well　as　the　relation　between　the　total　allowable　emission　and　air-quality-guideline　satisfaction　were　quantified　based　on　the　simulation　results　of　the　Gaussian-box　modeling　system.　The　area-source　emission　reduction　objective　in　each　functional　zone　of　the　study　city　during　the　heating　and　non-heating　seasons　was　calculated　based　on　such　relations.　A　linear　programming　model　was　then　developed　to　optimize　the　emission　abatement　which　was　subject　to　a　number　of　dust　and　SO2　control　measures.　The　economic　objective　of　the　air　quality　management　strategy　was　to　minimize　the　total　emission　control　system　cost　while　the　environmental　objective　can　still　be　satisfied.　The　environmental　objective　was　reflected　by　the　emission　reduction　objective　of　TSP,　PM10　and　SO2　corresponding　to　an　air-quality-guideline　satisfaction　percentage　of　80%.　Consequently,　the　modeling　system　comprehensively　took　into　account　the　information　of　emission　reduction　objectives,　emission　abatement　alternatives,　emission　reduction　cost,　and　related　resources　constraints.　An　optimal　emission　abatement　strategy　and　the　related　cost　were　obtained　for　various　pollution　control　measures.　The　results　would　provide　sound　bases　for　decision　makers　in　terms　of　effective　urban　air　quality　management　and　ensuring　healthy　economic　development　in　the　study　city.