- Title
- Assessment of trace elements in urban topsoils of Rawalpindi-Pakistan: a principal component analysis approach
- Creator
- Shehzad, Muhammad Tahir; Murtaza, Ghulam; Shafeeque, Muhammad; Sabir, Muhammad; Nawaz, Haq; Khan, Muhammad Jamal
- Relation
- Environmental Monitoring and Assessment Vol. 191, Issue 2, no. 65
- Publisher Link
- http://dx.doi.org/10.1007/s10661-019-7212-y
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2019
- Description
- Assessment of trace elements is inevitable to reduce stress on environment due to urbanization and industrialization. Rawalpindi, the fourth largest city of Pakistan, rapidly moving towards industrialization and has a large number of automobiles. In the present study, the urban area of Rawalpindi was divided into five parts: Gawal Mandi, Pir Wadhai, Soan Adda, Chah Sultan, and Central Ordinance Depot (COD), to determine distribution of trace elements. Soil samples were collected from 5 to 20 cm depth. After drying and sieving, samples were digested using di-acid (HNO3 and HClO4 at 2:1). Concentrations of heavy metals were determined using atomic absorption spectrophotometer (AAS). Principal component analysis (PCA) was performed to reduce multidimensional space of variables and samples. Observed mean concentrations of Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn were 164, 33.4, 295, 336, 634, 236, 1572, and 546 mg kg−1, respectively. Mean concentrations of all the heavy metals in urban area soil were higher than the WHO permissible limits. Correlation coefficient analysis showed positive correlation among Cd, Co, Cu, Ni, and Pb, whereas no obvious correlation for Cr and Mn was found with any other heavy metal. Zn was positively correlated with Co, Ni, and Mn, whereas negative correlation was found with Cr. Results showed that Pir Wadhai and COD were the most and least contaminated parts of the city, respectively, and this is attributed to the presence and absence of heavy traffic loads and industrial effluents.
- Subject
- heavy metal; principal component analysis; di-acid digestion; correlation coefficient analysis; SDG 11; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1436006
- Identifier
- uon:39889
- Identifier
- ISSN:0167-6369
- Language
- eng
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Goal 11: Sustainable Cities and Communities
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