Changes in Heavy Metal Accumulation Depending
on Traffic Density in Some Landscape Plants

Türkyilmaz, Aydın; Şevik, Hakan; Çetin, Mehmet; Saleh, Elnaji A. Ahmaida

doi:10.15244/pjoes/78620

Cited by 93 publications

(57 citation statements)

References 36 publications

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“…Heavy metal accumulation changes according to the structure of plant canopy, leaf inclination angle, branch density, leaf lamina morphologic anatomical structure, and leaf area (Shahid et al, 2017) Moreover, heavy metal accumulation hinges on plant species, genetic structure and plant age. Results of an experiment conducted by (Turkyilmaz et al, 2018) also supported the differences in heavy metal accumulation based on both plant species and traffic density. In traffic-dense areas, they measured the greatest concentrations of Cu, Ni, and Fe in Prunus cerasifera; Ca, Mg, and Mn in Ailanthus altissima; Cr and Zn in Elaeagnus angustifolia; and Pb and Cd in Tilia tomentosa.…”

Section: Traffic Effect On Heavy Metal Concentration Of Leavesmentioning

confidence: 62%

Heavy metal accumulation potential in pomegranate fruits and leaves grown in roadside orchards

Aydın

Pakyürek

2020

PeerJ

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This study was carried out to determine the possible heavy metal accumulation in fruits and leaves of Zivzik pomegranate (Punica granatum L.) grown in two different roadside orchards located in Pirinçli and Kapılı villages of Siirt province, Turkey. Leaf and fruit samples were collected from trees located at 0, 50, 100 m distances from the main roads. Plant samples were analyzed for cobalt (Co), nickel (Ni), cadmium (Cd), lead (Pb) and chromium (Cr) concentrations. The Co, Ni, Cd, Pb and Cr concentrations of fruit samples collected from Pirinçli village were ranged from 0.082 to 0.238 mg kg−1, from 1.160 to 1.559 mg kg−1, from 0.087 to 0.179 mg kg−1, 0.326 to 0.449 mg kg−1 and 0.606 to 1.054 mg kg−1, respectively. The Co, Ni, Cd, Pb and Cr concentrations of fruit samples from Kapılı village were between 0.085 and 0.137 mg kg−1, 1.042 and 1.123 mg kg−1, 0.037 and 0.076 mg kg−1, 0.277 and 0.520 mg kg−1 and 0.762 and 0.932 mg kg−1, respectively. Heavy metal concentrations of leaf samples from Pirinçli village varied from 0.191 to 0.227 mg Co kg−1, 2.201 to 3.547 mg Ni kg−1, 0.051 to 0.098 mg Cd kg−1, 0.535 to 0.749 mg Pb kg−1 and from 1.444 to 2.017 mg Cr kg−1. Similarly, the heavy metal concentration of leaf samples from Kapılı villages were between 0.213 and 0.217 mg Co kg−1, 2.160 and 2.511 mg Ni kg−1, 0.058 and 0.114 mg Cd kg−1, 0.579 and 0.676 mg Pb kg−1 and 1.688 and 1.518 mg Cr kg−1. The Co, Ni and Cr concentrations in fruit samples collected from 0, 50 and 100 meters to the main road in Pirinçli village were at statistically significant level, while only Ni concentration in leaf samples collected from 0, 50 and 100 meters to the main road was at significant level. In contrast, heavy metal concentrations in fruit and leaf samples collected from 0, 50 and 100 m to the main road in Kapılı village were not statistically significant level.

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Section: Traffic Effect On Heavy Metal Concentration Of Leavesmentioning

confidence: 62%

Heavy metal accumulation potential in pomegranate fruits and leaves grown in roadside orchards

Aydın

Pakyürek

2020

PeerJ

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“…Plants accumulate some of the heavy metals in their bodies via soil or air; and by determining the level of this accumulation, data on heavy metal pollution in soil or air can be obtained [4,12]. Therefore, the leaves of large plants [16][17][18][19], body shells [20], woods [21] and fruits [22] are used as biomonitors.…”

Section: Introductionmentioning

confidence: 99%

The Usability of Scotch Pine (Pinus sylvestris) as a Biomonitor for Traffic-Originated Heavy Metal Concentrations in Turkey

Arıcak¹,

Çetin²,

Erdem³

et al. 2020

Pol. J. Environ. Stud.

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Heavy metals are one of the most infamous air pollutants. They do not deteriorate easily in nature and they tend to bioaccumulate in nature. Because of their significance in terms of potential damage to human and environmental wellbeing, the monitoring of heavy metal pollution and identifying risk-prone regions is of great importance. Bioindicators are the most important determinants of the change in the concentration of heavy metals in the atmosphere. While plants play the biggest and most important role in reducing pollution in all of its types, they are also the ideal bioindicators. However, some plant species are better equipped to detect heavy metal accumulation than others. This study aims to determine the usability potential of Scotch pine (Pinus sylvestris) in monitoring traffic-based heavy metal concentrations. For this purpose, samples of Scotch pine individuals were collected from one of the busiest highways in Turkey (along the Ankara-Istanbul route) from refuges at roadsides and at distances of 3m, 10m, 30m, 50m, and 100m from the roadside. Some of the branches and needles of the samples were also subjected to washing processes and the changes of Ni, Cr and Zn concentrations were determined for these samples. The change of Ni, Cr and Zn concentrations depending on the distance to the road, washing conditions and organelle were evaluated separately. We determined that Scotch pine is a good biomonitor-especially for monitoring changes in Cr concentrations.

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“…Human health is affected by all air pollution, but some emissions have more severe atmospheric conditions. In particular, carbon dioxide (CO 2 ) and other pollutants, which provide global warming, have recently attracted attention; because CO 2 is one of the most searched gases [7][8][9][10][11][12][13]. Recent studies show PM10 and PM2.5 CO 2 air quality indices.…”

Section: Introductionmentioning

confidence: 99%

Predicting and Analyzing CO2 Emissions Based on an Improved Least Squares Support Vector Machine

Sun¹,

Huang²,

Wang³

2019

Pol. J. Environ. Stud.

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Carbon dioxide (CO 2) is the main cause of the greenhouse effect. With the rapid development of the economy in China, CO 2 emissions have increased dramatically. To reduce CO 2 emissions, ensure the sustainability of China's economy and implement the Paris International Convention, it is important to investigate the main factors affecting CO 2 emissions and use those factors to accurately forecast CO 2 emissions. In order to achieve accurate prediction of CO 2 , this paper proposes a CO 2 emission prediction model based on principal component analysis (PCA) and particle swarm optimization for least squares support vector machine (PSO-LSSVM). Through data 1990-2016 in Hebei Province of China, this paper identifies 24 influencing factors though the bivariate correlation analysis. After applying PCA to reduce the dimensions of the influencing factors, two principal components were extracted as input variables. Then the parameters of the LSSVM model are obtained by PSO and the forecast model is established. By comparing the prediction results with actual values, it is proved that the prediction error of the PSO-LSSVM prediction model is 0.663%, which is smaller than that of the traditional BPNN and LSSVM models.

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Changes in Heavy Metal Accumulation Depending on Traffic Density in Some Landscape Plants

Cited by 93 publications

References 36 publications

Heavy metal accumulation potential in pomegranate fruits and leaves grown in roadside orchards

Heavy metal accumulation potential in pomegranate fruits and leaves grown in roadside orchards

The Usability of Scotch Pine (Pinus sylvestris) as a Biomonitor for Traffic-Originated Heavy Metal Concentrations in Turkey

Predicting and Analyzing CO2 Emissions Based on an Improved Least Squares Support Vector Machine

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