Estimates of heavy metals pollution in parishan wetland sediments using pollution indices
Notice bibliographique
Résumé
Introduction: Parishan wetland is one of the most valuable and ecologically important ecosystems in the world which has completely dried up in recent years due to successive droughts (Dehghani, 2007). Considering that heavy metals are among the most important pollutants of the environment, their survival time is high in sediments (Ullah et al., 2017). Also, these metals have harmful effects on aquatic ecosystems, and sediments are both a source of and a place to store these elements. Acquiring information about these metals is important (Bhuiyana et al., 2010). Materials and methods: The purpose of this study was to estimate the level of contamination of Perishan wetland sediments with the heavy metals copper, iron, manganese and zinc using the enrichment index, geochemical accumulation index, degree of contamination and the comprehensive pollution factor index. In this study, 40 sediment samples with a weight of 200 g were systematically harvested. After acid digestion and filtering to read the concentration of toxic metals liberated in each sample, an AAS atomic absorption device by the Thermo Company (UK) was used. In this study, because of the lack of any specific standard for soil contamination in our country, standards from other countries were used. The average concentration of the metals studied in the region is lower than the maximum acceptable concentration (mg / kg) for the countries of Poland, Canada and Australia.Results and discussion: The changes in pH and electrical conductivity (Ec) were calculated in the range of 2.7-9.2 and 4.2-34, respectively, and the concentration of heavy metals based on mg/kg was found in most samples including manganese (2.18-3.71), iron (3.6-4.26), zinc (2.15-8.3) and copper (5.3-8.13), respectively. The background concentration for manganese, copper, iron and zinc was 950, 50, 41000 and 75 mg/kg, respectively. The geochemical accumulation index, the pollution factor and the comprehensive pollution factorindex were all used to determine contamination levels in the area. The geochemical accumulation index was less than zero for all metals, which shows the non-contamination class of the area. In addition, the factor of contamination index for most of the samples was in the class unpolluted, and the contamination factor for all metals was less than one, so that all data were placed in the non-contaminated class. The results also showed that the elements of manganese, copper, iron and zinc have a geological and agricultural origin, while the concentration of metals in the soil was related to their natural origin.Conclusion: Finally, it can be stated that the studied area has the lowest risk of contamination with heavy metals. In fact, it can be said that the copper, iron, manganese and zinc found in this region have a geological origin and they change through the natural processes of geology and through springs in the wetland.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,002 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».