Probabilistic risk assessment of heavy metals sources by integrating interpretable machine learning with receptor models

Journal:

Environmental Pollution

Published:

2026-05-26

DOI:

10.1016/j.envpol.2026.128411

Affiliations:

6

Authors:

6

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Institutions	Authors	Share
Chang'an University, China	2.000000	0.33
Department of Agriculture and Rural Affairs of Xizang Autonomous Region, China	2.000000	0.33
Institute of Geographic Sciences and Natural Resources Research (IGSNRR), CAS, China	1.000000	0.17
Tibet University, China	1.000000	0.17

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