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Purpose <p>The purpose of this study was to compare and evaluate the applicability of two widely used receptor models, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF), in the source apportionment of heavy metals in farmland soil.</p> Methods <p>A total of 40 heavy metal concentration data collected from a typical polluted farmland were used to evaluate the accuracy of APCS-MLR and PMF in identifying the sources of heavy metals in farmland soil by analyzing the fitting degree between predicted and observed values and the characteristics of sources.</p> Results <p>Through APCS-MLR, three sources of heavy metals in farmland soil were obtained: industrial and vehicle emission (44.2%), natural source (33.8%), and agricultural activity (22.0%). PMF further clarified the contributions of industrial and vehicle emissions, identifying four sources: industrial production (32.0%), natural source (28.2%), agricultural activity (25.8%), and vehicle emission (14.0%). Moreover, the correlation coefficient between predicted and observed values in PMF was higher than that in APCS-MLR, and the error of PMF for simulating the predicted values was lower than that of APCS-MLR, indicating that PMF was more accurate compared to APCS-MLR.</p> Conclusions <p>PMF was more effective in the application of source apportionment of heavy metals in farmland soil. However, there is a certain degree of uncertainty in the factor contributions obtained in PMF, as its estimation performance for heavy metals with lower contribution percentages in the factor is limited.</p>

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Application evaluation of APCS-MLR and PMF in source apportionment of heavy metals in farmland soil

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Xufeng Zhang,
  • Luyou Wang,
  • Shaohua Feng,
  • Yunze Gao,
  • Tingting Shang,
  • Xiang-Zhou Meng

摘要

Purpose

The purpose of this study was to compare and evaluate the applicability of two widely used receptor models, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF), in the source apportionment of heavy metals in farmland soil.

Methods

A total of 40 heavy metal concentration data collected from a typical polluted farmland were used to evaluate the accuracy of APCS-MLR and PMF in identifying the sources of heavy metals in farmland soil by analyzing the fitting degree between predicted and observed values and the characteristics of sources.

Results

Through APCS-MLR, three sources of heavy metals in farmland soil were obtained: industrial and vehicle emission (44.2%), natural source (33.8%), and agricultural activity (22.0%). PMF further clarified the contributions of industrial and vehicle emissions, identifying four sources: industrial production (32.0%), natural source (28.2%), agricultural activity (25.8%), and vehicle emission (14.0%). Moreover, the correlation coefficient between predicted and observed values in PMF was higher than that in APCS-MLR, and the error of PMF for simulating the predicted values was lower than that of APCS-MLR, indicating that PMF was more accurate compared to APCS-MLR.

Conclusions

PMF was more effective in the application of source apportionment of heavy metals in farmland soil. However, there is a certain degree of uncertainty in the factor contributions obtained in PMF, as its estimation performance for heavy metals with lower contribution percentages in the factor is limited.