Inversion　based　on　observational　data　and　the　source-receptor　relationship　(SRR)　simulated　by　an　air　quality　model　is　an　effective　means　to　estimate　NOx　emissions.　However,　the　SRR　bias　induced　by　the　inherent　uncertainty　of　the　simulated　model　leads　to　potential　errors　in　the　inversed　emissions,　but　this　is　seldom　considered　in　NOx　inversion.　In　this　study,　we　constructed　an　inversion　model　based　on　the　SRR　correction　(IMSC)　by　introducing　a　correction　matrix,　combined　with　joint　regularization　scheme　and　genetic　algorithm.　We　innovated　the　dynamic　acquisition　method　of　center-restricted　parameters　for　the　correction　matrix,　combined　this　with　other　parts　of　the　IMSC,　attaining　the　multi-month　and　multi-region　pollutant　emission　inversion.　Hypothetical　examples　demonstrated　that　the　IMSC　effectively　corrected　the　SRR　and　accurately　estimated　the　NOx　emissions.　The　IMSC　was　used　to　estimate　Linyi　county-level　NOx　emissions　for　January,　April,　July　and　October　(typical　months)　during　2020　and　2021.　An　inversion　model　without　SRR　correction　(IMWSC)　was　developed　for　comparison　with　the　IMSC.　Results　showed　that　the　IMSC　more　accurately,　robustly,　and　reasonably　estimated　NOx　emissions.　Compared　to　the　IMWSC,　the　IMSC　improved　the　mean　correlation　between　NOx　emissions　and　NO2　observational　concentrations　by　25.0%,　enhancing　the　correlation　between　NOx　emissions　and　NO2　column　concentrations　by　111.3%.　The　similar　NOx　emission　change　ratios　(σaverage　=　5.1%)　between　the　typical　months　in　2021　and　2020　among　the　different　counties　indicated　a　more　robust　performance　of　the　IMSC　than　the　IMWSC　(σaverage　=　55.2%).　In　addition,　the　NOx　emissions　inversed　by　the　IMSC　also　showed　better　consistency　with　social　activity　levels　(i.e.,　electricity　consumption).　The　typical　monthly　Linyi＇s　county-level　NOx　emission　characterization　was　also　studied.　NOx　emissions　were　lower　in　April,　July,　and　October　2021　than　the　same　period　in　2020　due　to　COVID-19　and　pollution　controls.　This　study　provides　strategies　for　swiftly　and　accurately　estimating　pollutant　emissions.　©　2024　Elsevier　Ltd