Estimates of heavy metals pollution in parishan wetland sediments using pollution indices
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".