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Record W3110268307 · doi:10.22067/econg.v12i1.70072

The Application of Heavy Metal Distribution and Pb Isotopic Ratios to Determine the Pollution Source of Surface Soils in Mashhad Parks

2020· article· en· W3110268307 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterPollutionEnvironmental chemistryEnvironmental scienceHeavy metalsMetalSoil scienceChemistryEcology

Abstract

fetched live from OpenAlex

Introduction Anthropogenic activities have a high impact on urban areas and lead to severe pollution in large cities. Urban soils are huge ‘basins’ for accumulation of various pollutants. Thus, they also serve as informative media. Therefore, the study of urban soils has developed in recent years. Mashhad is the second metropolitan city in Iran which has more than 3 million residences and over 20 million annual pilgrimage tourists. This city is surrounded by numerous factories and the high growth of urbanization has led to accelerated air pollution. In this research study, new analytical data for heavy metals (Cd, Co, Cr, Cu, Ni, Pb, Sn and Zn) in soil of parks in Mashhad are presented and they are compared with unpolluted non-urban soils. Then, the origin of soil pollution in park soils is discussed by statistical methods and Pb isotope values. Materials and methods The main aim of this work is to focus on the urban areas of Mashhad and study park soil geochemistry. Twenty three parks were selected for this purpose. At each selected park, top soils (5-20 cm) were collected. In addition to park samples, 4 soil samples were collected from areas which were far from the urban areas and they were considered to be unpolluted. The concentration of heavy metals was defined by ICP-MS at the accredited Activation Laboratories (Actlabs.), Canada. To determine available and bioavailable fractions of Co, Cr and Ni, soil samples were analyzed by the DTPA method (Lindsay and Norvell, 1978). Pearson correlation coefficients and PCA are used to investigate elemental associations and extract latent factors for analyzing relationships among the observed variables. In this regard, these statistical analyses of park soils data were done with the SPSS 16.0 software package for Windows. The geochemical maps were plotted by a geographical information system (GIS) using ArcMap v.10.0 (ArcGIS) to show the overall spatial distribution patterns of total and available heavy metal concentrations. The Pb isotopes were measured in all samples by a ThermoFinnigan Neptune MC-ICP-MS instrument. Isotopic measurements were performed by the procedure proposed by Álvarez-Iglesias et al.’s (2012). Results and discussion Park soil samples have a higher concentration of heavy metals than non-urban soils. PCA analysis and Pearson correlation coefficient indicate that anthropogenic sources are the main factors controlling the Cd, Pb, Sn and Zn concentrations (PC1 component) in park soils. In other words, both anthropogenic and natural sources are responsible for Cr, Co and Ni (PC2 component) distribution in these soils. The spatial variation of heavy metals in the soil of parks in Mashhad confirm the statistical results and PC1 and PC2 heavy metals show different spatial variations. Furthermore, there is clear correlation between metals distributions, so that central parks generally have a higher content of all heavy metals which could be related to the heavy traffic and many traffic jams and air pollution in these areas. The lead isotopic ratios of the sample studied show that park soils show a distinct composition from non-urban soils. These samples are less radiogenic than non-urban soils with lower 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, 206Pb/207Pb and higher 208Pb/206Pb ratios. The wide range of isotope ratios in park soils indicate that the Pb content in these soils is produced by the combination of different sources including natural and anthropogenic origins and that it has also been accumulating over time because of the enormous use of Pb in fuel, industrial activities, etc. (Galušková et al., 2014). The low 206Pb/204Pb values in park soils confirm a possible anthropogenic origin from the use of fossil fuels. The three-end-member model was used to determine possible Pb sources in the soil of parks in Mashhad. The average contribution was 6.6% and 93.4% for natural and anthropogenic (industrial and leaded petrol) sources, respectively. The detailed investigation of Pb isotope and Pb content of soil of parks suggests that industrial source is the main origin of samples with relatively low Pb content (<90 mg kg-1). The contribution of leaded petrol increases in the samples with high Pb. These soils were sampled from the central parks of Mashhad which have high traffic intensity. Conclusion The concentrations of potentially toxic elements in the soil of parks in the city of Mashhad are highly enriched relative to non-urban soils. The Pb isotope composition of non-urban soils indicate that they have a natural source while park soil samples have originated from anthropogenic sources. The soils which are sampled from the central parks of Mashhad have shown the highest heavy metal pollution due to high traffic congestion in these areas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.440
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
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