Apportion　soil　heavy　metal　sources　across　large-scale　regions　is　a　challenging　task.　The　present　study　developed　a　modified　receptor　model　to　estimate　the　contributions　of　various　sources　to　soil　heavy　metals　and　the　associated　health　risks　at　a　large　scale.　A　positive　matrix　factorization　model　based　on　a　partition　computing　approach　was　employed;　the　entire　study　area　was　divided　into　several　zones　for　the　source　apportionment　and　then　calculated　together,　termed　partition　computing-PMF　(PC-PMF).　The　agricultural　soil　in　Tianjin,　China,　was　chosen　for　the　case　study.　The　PC-PMF　results　showed　that　irrigation,　atmospheric　deposition　and　sludge　application　were　the　main　anthropogenic　sources,　with　contributions　of　26.60　%,　19.56　%　and　2.86　%,　respectively.　We　subsequently　combined　PC-PMF　with　a　human　health　risk　assessment　model　(HHRA)　to　obtain　the　human　health　risk　of　every　source　category.　The　natural　background　was　regarded　as　a　major　factor　influencing　human　health　in　the　study　area,　with　contributions　of　38.03　%　for　the　noncarcinogenic　risk　and　28.68　%　for　the　carcinogenic　risk.　The　results　indicated　that　PC-PMF　performed　better　at　the　source　apportionment　of　soil　heavy　metals　than　PMF.　This　study　provides　a　good　example　of　how　the　spatial　variability　can　be　utilized　to　reduce　the　uncertainty　in　source　apportionment.