Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 839, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.scitotenv.2022.156057
Keywords
Soil heavy metals; Diffusion and deposition; Spatial distribution; Accumulative contributions; Source identi fication
Categories
Funding
- National Key R&D Program of China [2018YFB0605504]
- National Natural Science Foundation of China [51878272]
- Science and Technology Projects of Suzhou City [SYG201913, SYG201914]
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In this research, a dynamics-simulation-based source apportionment approach (DSSA) was developed to quantify the accumulative contributions of soil heavy metals (SHMs). A case study in a complex industrialized region in southeast China revealed that SHMs distributions were influenced by seasonal variation and near-surface meteorology. The DSSA approach provided valuable insights into the migration process of SHMs and proved to be advantageous compared to existing methods.
Existing receptor-model-based source apportionment methods failed to derive source contributions to accumulation of soil heavy metals (SHMs). In this research, a dynamics-simulation-based source apportionment approach (DSSA) was developed by integrating mathematical models of source release, diffusion and deposition pathway, and receptor accumulation, to quantify accumulative contributions of SHMs. The case study was carried out in a complex industrialized region in southeast China to investigate pollution situation of SHMs (Zn, Pb, Ni, As, Cd, and Cr). The results showed that SHMs distributions were affected by seasonal variation and near-surface meteorology, which could be sequenced by correlation coefficient as temperature (0.968) > humidity (0.552) > precipitation (0.389) > wind speed (0.386). The source categories and corresponding contribution rates were identified as: i) battery plant to Zn (72.32%) and Pb (71.73%), ii) traffic to Ni (64.55%), iii) traffic and agriculture to Cd (43.26%, 41.63%), iv) agriculture to As (75.30%) and Cr (60.05%), which was similar to the results of positive matrix factorization (PMF). Furthermore, DSSA could illustrate SHMs migration process from source to receptor. The uncertainty analysis further proved the distinct advantages of DSSA. The results of this research could predict pollutant enrichment and could provide new perspective for environment and public health management.
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