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A Modified Model for Quantitative Heavy Metal Source Apportionment and Pollution Pathway Identification.

Maodi Wang¹, Pengyue Yu¹, Zhenglong Tong¹

¹National Engineering Laboratory of High Efficient Use on Soil and Fertilizer, College of Resources, Hunan Agricultural University, Changsha 410128, China.

|June 26, 2024

Summary

This study enhances soil heavy metal source apportionment models to pinpoint pollution pathways like traffic and irrigation. The improved model accurately identifies non-metal factories as key sources, guiding contamination management.

Keywords:

GeoDetector grouped principal components heavy metals receptor model source apportionment

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Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies

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Area of Science:

Environmental Science
Geochemistry
Soil Science

Background:

Accurate soil heavy metal source apportionment is crucial for effective environmental management.
Existing models require refinement to account for migration patterns in soil contamination assessments.
Understanding pollution pathways is key to mitigating heavy metal impacts.

Purpose of the Study:

To enhance the principal component analysis and multiple linear regression with distance (PCA-MLRD) model for improved heavy metal source apportionment in soil.
To accurately identify and quantify pollution pathways, including traffic emissions, irrigation water, and atmospheric deposition.
To provide a more precise method for managing soil heavy metal contamination.

Main Methods:

Soil heavy metal data collection from the Chang-Zhu-Tan region, Hunan, China.
Utilized enrichment factors and crustal reference elements to identify soil parent material contributions.
Applied principal component analysis (PCA) and GeoDetector for anthropogenic emission identification and spatial analysis.

Main Results:

Non-metal manufacturing factories identified as significant anthropogenic sources of soil contamination.
Pollution pathways determined to be primarily rivers and atmospheric deposition.
Irrigation water influence on heavy metals was significant within 1000 m, decreasing with distance.

Conclusions:

The enhanced PCA-MLRD model offers improved accuracy in heavy metal source apportionment and pathway identification.
Non-metal factories pose a considerable risk through riverine and atmospheric transport.
Findings provide valuable technical guidance for soil heavy metal contamination management and remediation strategies.