Assessment of trace elements in urban topsoils of Rawalpindi-Pakistan: a principal component analysis approach

Shehzad, Muhammad Tahir; Murtaza, Ghulam; Shafeeque, Muhammad; Ṣābir, Muḥammad; Nawaz, Haq; Khan, Muhammad Jamal

doi:10.1007/s10661-019-7212-y

Cited by 31 publications

(13 citation statements)

References 49 publications

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“…Shehzad et al also found that Cr was more likely to be adsorbed to soils with high Mn concentrations. 47 Besides, Mn oxides were proven to be the only naturally occurring oxidant of Cr(III). 48 Soil elements (e.g.…”

Section: Removal Of Heavy Metals From Different Particle-size Fractiomentioning

confidence: 99%

Simultaneous removal of multiple heavy metals from soil by washing with citric acid and ferric chloride

et al. 2020

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Section: Removal Of Heavy Metals From Different Particle-size Fractiomentioning

confidence: 99%

Simultaneous removal of multiple heavy metals from soil by washing with citric acid and ferric chloride

et al. 2020

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“…(Froger et al, 2018). However, Pb and Cr show a weak correlation with each other, suggesting that the contents of Pb and Cr may originate from different sources (Shehzad et al, 2019). The studies carried out by Turer et al 01:50 PM have shown that the atmospheric deposition constitutes a vital source of Pb in soil and sediment (Sun et al, 2019).…”

Section: Potential Ecological Risk Assessment Of Heavy Metals In Soilmentioning

confidence: 99%

Impact Imposed by Urbanization on Soil Heavy Metal Content of Lake Wetland and Evaluation of Ecological Risks in East Dongting Lake in China

Yang

Kai

et al. 2021

Preprint

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The urbanization is regarded as the major factor related to human activities that may interfere with the natural ecosystem. In this study, we have selected the wetland of East Dongting Lake as the research area. We have collected 180 soil samples (within the range of 0–20 cm, and 20cm-40cm), and we have measured the contents of their physicochemical properties (including salinity, pH value, soil particle composition, soil organic carbon, nitrate nitrogen and rapidly available phosphorus) as well as heavy metal elements (including As, Hg, Cd, Pb, Ni and Cr). We have adopted the methods of multivariate statistical analysis and inverse distance weighted (IDW) interpolation, so as to to reveal the sources and distribution characteristics of heavy metal content in soil in the research area. By adopting the potential ecological risk index (PERI) method proposed by Hakanson, we intend to assess the PERI values of heavy metals. Our research findings have shown that: (1) 8 sorts of heavy metals have shown positive correlation with each other. As, Hg and Zn have shown a significantly positive correlation with SOC (P < 0.01); As, Ni, Cr and Zn have shown a significantly positive correlation with AP (P < 0.01); As and Pb have shown a significantly positive correlation with Clay (P < 0.01); whereas Hg and Zn have shown a significantly negative correlation with Silt (P < 0.01); As and Pb have shown a significantly negative correlation with Sand (P < 0.01). (2) During urbanization, the elements of Cd, Ni, As, Hg and Pb might be enriched due to agricultural and industrial activities, whereas the use of fertilizers and pesticides constitute one of the major factors contributing to the increase of Cd and Pb contents in soil. (3) Influenced by the varying land patterns and with exception to Cu, the Fe-normalized concentrations have shown significant variations among different types of land use (P < 0.05). Specifically, there is a significantly higher level of Cd, Zn, Pb and Hg contents in the agricultural land than other types of land use, whereas there is a slightly higher level of heavy metal content in the mudflats than that in the grassland. In addition, the content of heavy metals in woodland remains relatively stable, and with exception to As, the content of heavy metals in woodland is the lowest among the five types of land. (4) The average value of the comprehensive PERI in heavy metals amounts to 555.03, representing a strong degree of ecological risks. Specifically, the proportion of sampling points of Cd that have a high level of ecological risks amounts to 36.51%, which is the primary element contributing to heavy metal pollution in the region, especially in the agricultural land.

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“…In this study, the PCA/FA method is used to reduce the data dimensionality and extract the most information from the original dataset based on the correlation of water quality variables [41,42]. Several new factors are generated to explain the variance of the whole dataset, and each component is identified as a pollution source [14,15].…”

Section: The Pca/fa-mlr Receptor Modelmentioning

confidence: 99%

Pollution Source Apportionment and Water Quality Risk Evaluation of a Drinking Water Reservoir during Flood Seasons

Qin

Liu

et al. 2021

IJERPH

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Reservoirs play an important role in the urban water supply, yet reservoirs receive an influx of large amounts of pollutants from the upper watershed during flood seasons, causing a decline in water quality and threatening the water supply. Identifying major pollution sources and assessing water quality risks are important for the environmental protection of reservoirs. In this paper, the principal component/factor analysis-multiple linear regression (PCA/FA-MLR) model and Bayesian networks (BNs) are integrated to identify water pollution sources and assess the water quality risk in different precipitation conditions, which provides an effective framework for water quality management during flood seasons. The deterioration of the water quality of rivers in the flood season is found to be the main reason for the deterioration in the reservoir water quality. The nonpoint source pollution is the major pollution source of the reservoir, which contributes 53.20%, 48.41%, 72.69%, and 68.06% of the total nitrogen (TN), phosphorus (TP), fecal coliforms (F.coli), and turbidity (TUB), respectively. The risk of the water quality parameters exceeding the surface water standard under different hydrological conditions is assessed. The results show that the probability of the exceedance rate of TN, TP, and F.coli increases from 91.13%, 3.40%, and 3.34%, to 95.75%, 25.77%, and 12.76% as the monthly rainfall increases from ≤68.25 mm to >190.18 mm. The risk to the water quality of the Biliuhe River reservoir is found to increase with the rising rainfall intensity, the water quality risk at the inlet during the flood season is found to be much greater than that at the dam site, and the increasing trend of TP and turbidity is greater than that of TN and F.coli. The risk of five-day biochemical oxygen demand (BOD5) does not increase with increasing precipitation, indicating that it is less affected by nonpoint source pollution. The results of this study can provide a research basis for water environment management during flood seasons.

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Assessment of trace elements in urban topsoils of Rawalpindi-Pakistan: a principal component analysis approach

Cited by 31 publications

References 49 publications

Simultaneous removal of multiple heavy metals from soil by washing with citric acid and ferric chloride

Simultaneous removal of multiple heavy metals from soil by washing with citric acid and ferric chloride

Impact Imposed by Urbanization on Soil Heavy Metal Content of Lake Wetland and Evaluation of Ecological Risks in East Dongting Lake in China

Pollution Source Apportionment and Water Quality Risk Evaluation of a Drinking Water Reservoir during Flood Seasons

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