Sources, Spatial Distribution and Extent of Heavy Metals in Relation to Land Use, Lithology and Landform in Fuzhou City, China
Notice bibliographique
Résumé
Assessing the spatial distribution of soil heavy metals in urban areas in relation to land use, lithology and landform may provide insights for soil quality monitoring. This study evaluated the spatial distribution, the sources and the extent of heavy metal(loid)s in the topsoil of Fuzhou city, China. A combination of GIS and multivariate approaches was used to determine the spatial distribution and the sources of heavy metals. Additionally, analysis of variance was used to determine the variability of selected heavy metals across land use, landform, and lithology. The result show that the mean concentrations of Cd, Zn, As and Pb were higher than background values. Most of the heavy metals had significant correlations with each other. In particular, V and Fe (0.84 at p < 0.01) and Ni and Cr (0.74 at p < 0.01) had strong correlations, while Cu and Fe (0.68 at p < 0.01), Cu and V (0.63 at p < 0.01), Cu and Co (0.52 at p < 0.01), Zn and Ni (0.51 at p < 0.01), Co and Fe (0.54 at p < 0.01), and Cu and Zn (0.55 at p < 0.01) had moderate correlations. Arsenic, Cu, and Zn had significant positive correlations with total nitrogen (TN). Similarly, arsenic, Zn and Cr had positive correlations with total carbon (TC), while Co had negative correlations with TN and TC at p < 0.01. The peak values for Cr, Ni, Pb, Mn, and Zn were observed in the intensively urbanized central and eastern parts of the study area, suggesting that the main sources might be anthropogenic activities. Agricultural land use had the highest content of Cd, which may be attributed to the historical long-term application of agrochemicals in the area. Additionally, its content was significantly higher in agricultural land use with shale lithology, implying that shale lithology was a key geogenic source for Cd of soils in the study area. Pb content was affected by urban land use, which may be attributed to intensive human activities such as emissions from vehicles, industrial effluents, mining activities, and other discharges. The results show the high spatial variability of heavy metal(loid)s, implying that the soils in the study area were highly influenced by both geogenic variability and human activities. Moreover, land use and lithology had significant impacts on the variability of Cd, As and Pb. Sustainable agricultural practices and urban management are recommended to sustain the eco-environment of coastal city.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».