The contents of eight heavy metals （Cr, Ni, Cu, Zn, Cd, Pb, As, and Hg） were determined based on the surface soil samples of sewage irrigation and industrial complex in Kaifeng City. The absolute factor analysis-multiple linear regression （APCS-MLR） model and positive matrix factorization （PMF） model were used to analyze the sources and contribution rates of heavy metals in soil combined with correlation analysis and systematic cluster analysis. The results showed that： ① The average values of ω（Cr）, ω（Ni）, ω（Cu）, ω（Zn）, ω（Cd）, ω（Pb）, ω（As）, and ω（Hg） in the study area were 52.19, 25.00, 42.03, 323.53, 1.79, 53.45, 9.43, and 0.20 mg·kg-1, respectively, and Cr, Ni, and As are lower than the background values of tidal soil. Cu, Zn, Cd, Pb, and Hg are higher than the background values of the tidal soil. ② There were four sources of the eight heavy metals： natural sources, agricultural sewage irrigation sources, industrial atmospheric sedimentation sources, and transportation sources. Cr and Ni were mainly from natural sources； Cu, Zn, Cd, and Pb were mainly from agricultural sewage irrigation and transportation sources； As was mainly from natural sources and agricultural sewage irrigation； and Hg was mainly from industrial atmospheric sedimentation. ③ The APCS-MLR and PMF source analysis results indicated that industrial and agricultural activities were the main sources of heavy metals in the soil of the study area. The average contribution rates of APCS-MLR in the nine sampling areas of the research area were 76.01% （natural sources and agricultural sewage irrigation sources）, 22.71% （industrial atmospheric sedimentation sources and transportation sources）, and 1.28% （unknown sources）. The average contribution rates of PMF were 59.66% （natural sources and agricultural sewage irrigation sources） and 40.34% （industrial atmospheric sedimentation sources and transportation sources）. The source analysis results of the LZ, XZ, NLT, PT, YLZ, and BC models were basically consistent, and WL was better in the APCS-MLR model, whereas SG and QT were better in the PMF model. The research results can provide a scientific basis for the prevention and control of soil heavy metal pollution and environmental remediation.
Keywords: absolute factor analysis-multiple linear regression （APCS-MLR）; farmland soil; positive matrix factorization （PMF）; sewage irrigation and industrial complex area; source apportionment.