تعیین عوامل انسانی و طبیعی آلودگی برخی فلزات سنگین در اراضی مرکزی استان زنجان (مطالعه بر اساس آنالیز چندمتغیره)

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد خاکشناسی، دانشکده کشاورزی

2 استاد گروه خاک‌شناسی

3 استادیار گروه خاک شناسی

چکیده

مطالعه حاضر با هدف بدست آوردن اطلاعات مقدماتی از وضعیت آلودگی برخی عناصر سنگین و منابع آلاینده ها با استفاده از آنالیز چندمتغیره در خاک های اراضی مرکزی استان زنجان انجام گرفت. برای این منظور تعداد 241 نمونه خاک سطحی (10-0 سانتی متر) در مرداد ماه 1391 برداشت گردید. خصوصیات فیزیکی و شیمیایی به همراه غلظت کل برخی فلزات سنگین اندازه گیری گردید. بر اساس نتایج به دست آمده، غلظت کل کادمیم 11/4-24/0، مس5/352-3/11، سرب5/1357-0/40، روی8/1353-3/86، نیکل8/86-8/12، کروم7/67-0/7، کبالت7/35-0/17، منگنز1761-338 و آهن27546-8437 میلی گرم بر کیلوگرم اندازه گیری گردید. آنالیز چندمتغیره (تجزیه به مؤلفه های اصلی، آنالیز خوشه ای و همبستگی) دو منبع مختلف فلزات را جدا کرد. عناصر سرب، روی، مس و کادمیم در یک گروه قرار گرفتند که با توجه به منابع مختلف و نتایج حاصل شده، متأثر از فعالیت های انسانی فرض شدند. عناصر آهن، کروم، کبالت، منگنز و نیکل در گروه دیگر جای گرفتند که به نظر می رسد از منابع زمین شناختی مشتق می شوند. با توجه به نتایج به دست آمده، کنترل ورودی های با منشا انسانی در منطقه ضروری است.

کلیدواژه‌ها


عنوان مقاله [English]

Determination of Natural and Anthropogenic Factors on Pollution of Heavy Metals in the Central Zanjan (Based on Multivariate Analysis)

نویسندگان [English]

  • A. Afshari 1
  • H. Khademi 2
  • P. Alamdari 3
1 Isfahan University of Technology
2 Isfahan University of Technology
3 University of Zanjan
چکیده [English]

Introduction: Soil forms a thin layer over the surface of the earth that performs many essential life processes . Soil has always been important to humans and their health, providing a resource that can be used for shelter and food production. Higher heavy metals concentration within soils has beenshown to be toxic for human bodies, since they might be broken out easily via dust or direct contact through trophic levels. In addition long term heavy metals recalcitrance in soil results in low potential of soil productivity . Heavy metals interact complicatedly in soil. The present study was conducted in large scale by analyzing elements Mn, Co, Ni, Zn, Pb, Cd and Cu in soils in central lands of Zanjan province. The main objectives of present research were to investigate heavy metals diffusion and total contamination status in soil and determination of their possible origin using multivariate analysis.
Materials and Methods: This research was conducted in central lands located in Zanjan province (northwest of Iran). In terms of the main land uses, study area may involve farmlands, rangelands, urbanized and industrial lands. Study sites are totally covered 2000 km2 in coordinates of 36.20 to 36.41 N latitude and 48.19 to 48.53 E longitude. Sampling was conducted based on gridding method in randomized manner in August 2011. Urban lands affected by much anthropogenic activities and farm and rangelands together were placed in grids of 1.5×x 1.5 and 3×3 km2 respectively. Totally number of 241 soil samples (0-10 cm depth) was taken. Soil specimen's digestions were conducted using nitric acid 5 normal. Total elements concentration of Pb, Zn, Ni, Mn, Cu, Cr, Fe and Co were measured using Atomic adsorption device Perkin-Elmer: AA 200 and Cd concentration was determined by Atomic adsorption device equipped with graphic furnace model Rayleigh: WF-1E. Mean separation analysis of parameters (Pearson and spearman) was conducted using Duncan test in probability level of 5%. Principle component analysis (PCA) and hierarchical cluster analysis (HCA) were used to classify metals group. Achieving a simple structure and better results interpretation, data rotation in varimax type was conducted in PCA algorithm. Before cluster analysis, data were standardized and subsequently exposed to cluster analysis and plotting dendrogram, Euclidean approach was applied.
Results and Discussion Multivariate analysis (PCA, CA and CM) have been shown as an efficient tool to identify heavy metals origins, helping us in better data comprehension and interpretation. Results obtained on multivariate analysis approaches might are promising to distinguish polluted area and heavy metals potential origin, in turns indicating soil environmental quality.
PCA is known as an efficient method to determine anthropogenic impacts on a spatial scale and it may be essential to specify heavy metals contamination degree in respect to anthropogenic and litogenic contribution. As it illustrated, heavy metals are categorized in three-component model framework, accounting for 67% of total data variations. In rotated component matrix the first PC (PC1, 30% of variance) involves Ni, Cr, Co, Mn and Fe, while the second PC (PC2, 19% of variance) involves Zn and Pb and eventually the third one (PC3, 18% of variance) covers Cu and Cd among others. PC1 can be introduced as geological component because of its less coefficient of variations than others, skewedness less than 1 and normalized data status. It denotes lithogenic distribution of these metals in area. Furthermore,as above mentioned, the average heavy metalconcentrations werefound to be less than calculated background threshold. Because of their increased concentration in soil, high coefficient of variations and very high concentration than background threshold level as well as positive skewedness in heavy metals, PC2 and PC3 can be defined to antropogenical components. Atmospheric precipitation (deposition) serves as one of the main anthropogenic source of heavy metals pollution in the soil samples near theintense transportation traffic roads, accumulation site of factories solidwaters, energy generation process, metal melting, construction and traffic breaking out in whole area. Our results are in line with enormous findings on farming operations as the main factor that cause changes in Cd concentration in soils. although Pb, Cu, Zn and Cd have been shown to have anthropogenic origin inputs, however in respect to PCA analysis, the main origins for Lead and Zn may be municipal and industrial operations specially Pb processing factory as well as industrial complexes. At the same time, Cu and Cd stems from farming operations as well as municipal activities. The main municipal input origins for elements Pb, Cu and Cd include all components used in automobile industry, batteries, engines oils, fossil fuels and construction materials (like cement).
Cluster analysis is used to classifying those samples having common and similar characteristics as well as evaluating information obtained from PCA analysis. Cluster analysis gave the same groups. So two majororigins can be identified. First (CI) involves prior interpreted lithogenic elements (Cr, Co, Mn and Fe), while two later clusters (C2, C3) contain anthropogenic elements (Pb, Cu, Zn and Cd). Many researchers believed that Mn, Cr, Ni and Fe are controlled by parent material. In contrast, they attributed any increases of Pb, Cu, Cd and Zn accumulation to anthropogenical operations. Cluster analysis gives the same results as derived from PCA, enabling us to identify two major origins on which heavy metals break out hence, multivariate analysis results confirms the presence of two different heavy metals origins within soils.
Conclusion: The aim of this research was to provide some preliminary information on heavy metals (Pb,Zn,Cd,Cu,Ni,Co,Cr,FeandMn) contamination status in soils in Zanjan province. Metal contamination cannot be assessed by common metal concentration test, hence useful and promising tools were applied to evaluate heavy metals pollution. The highest level of heavy metals pollution and accumulation was related to Cd, Pb and followed by then Cu. Multivariate analysis showed that Fe, Mn, Cr, Co and Ni are controlled by parent materials, while Pb, Cu and Zn originate from anthropogenic sources. In the second class, anthropogenic activity did not seem to significantly affect their accumulation which is strongly supported the lithogenicaly origin of these heavy metals in this study.

کلیدواژه‌ها [English]

  • Anthropogenic pollution
  • Cluster analysis
  • Kriging map
  • Principal component analysis
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