Spatial distribution and source identification of heavy metal pollution in roadside surface soil: a study of Dhaka Aricha highway, Bangladesh

Ahmed, Fahad; Fakhruddin, A. N. M.; Imam, M. D. Toufick; Khan, Noor; Khan, Tanzir Ahmed; Rahman, Md. Mahfuzur; Abdullah, Abu Tareq Mohammad

doi:10.1186/s13717-016-0045-5

Cited by 76 publications

(20 citation statements)

References 50 publications

(51 reference statements)

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“…It gives us indication about the anthropogenic inputs of heavy metals in the agricultural soils (Ahmed et al, 2016). It was calculated by following the equation given by Hakanson (1980): where HM s and HM b are the concentrations of heavy metals in sampling sites and background environment.…”

Section: Cfmentioning

confidence: 99%

“…In order to determine the pollution and ecological risks of heavy metals, various indices were applied. Scientists applied various factors, i.e., contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), ecological risk index (RI), and modified ecological risk index (MRI) for assessment of pollution and ecological risks (Ahmed et al, 2016;Kumar, Sharma, Minakshi, Bhardwaj, & Thukral, 2018;Tian et al, 2017). The EF and Igeo are based on relative assessment of heavy metals in polluted and unpolluted soil conditions Sakram, Machender, Dhakate, Saxena, & Prasad, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Spatial distribution and potential ecological risk assessment of heavy metals in agricultural soils of Northeastern Iran

Keshavarzi

Kumar

2019

Geology, Ecology, and Landscapes

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Illustrating the spatial distribution and potential sources of soil properties, and heavy metals, viz., Fe, Zn, Mn, and Cu, are the vital prerequisites for decreasing their pollution. The 68 composite agricultural soil samples in triplicates were collected by employing grid method to evaluate the concentration of pH, sand, silt, clay, organic carbon, P, K, and heavy metals. Multivariate techniques (Pearson's correlation, heatmap, principal component analysis, and nonmetric multidimensional scaling), geostatistcal techniques, and contamination indices were employed. The contents of Fe, Zn, Mn, and Cu were lower than the limits for Iran EPA guidelines and Earth's crust. The results of contamination factor and potential ecological risk index (RI) showed that agricultural soils have less contamination and low ecological risks. The enrichment factor (EF), geoaccumulation index (Igeo) and modified ecological risk index (MRI) indicated that 99%, 86.7% and 52.4% agricultural soil samples showed very high enrichment and ecological risks of heavy metals. Both anthropogenic activities and natural factors were responsible for heavy metal contents. The results of geostatistcal analysis revealed that Zn is accumulated more in Central regions, whereas Cu and Mn accumulated more in South and Northeastern regions of the studied area for EF, Igeo, and modified potential ecological RI. ARTICLE HISTORY

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Section: Cfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial distribution and potential ecological risk assessment of heavy metals in agricultural soils of Northeastern Iran

Keshavarzi

Kumar

2019

Geology, Ecology, and Landscapes

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“…Complex analysis methods, such as multidimensional statistical methods, are typically used to assess the contribution of anthropogenic sources to the overall pollution levels of natural ecosystems (Esakkimuthu, Vinod Kumar, & Ponram, ; Zamani, Yaftian, & Parizanganeh, ), in combination with GIS technologies (Ahmed et al, ) and modelling (Zhang & Xin, ). The results of the present study, however, suggest a simpler means of solving a main ecology; namely, identification of the potential sources of pollution, and how the pollutants enter the aquatic ecosystems.…”

Section: Discussionmentioning

confidence: 99%

“…Since it is not feasible to control all pollutants entering waterbodies, one can only consider a limited set of the most significant pollutants within the process of environmental monitoring. Major attention is given to heavy metals (Ojekunle et al, ; Rajkowska & Protasowicki, ), which represent a particular hazard to the health of natural ecosystems and humans (Ahmed et al, ; Javed & Usmani, ; Kawser et al, ).…”

Section: Introductionmentioning

confidence: 99%

Tracking pollution and its sources in the catchment‐lake system of major waterbodies in Kazakhstan

Krupa¹,

Barınova

Aubakirova

2020

Lakes & Reservoirs

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The objective of the present study was to identify the sources and routes of entry of heavy metals and nutrients into several large waterbodies in the arid zone of Kazakhstan, namely Lake Balkhash and Samarkan and Shardara reservoirs. A data visualization method was used for this purpose. Statistical mapping, based on referencing coordinates of the material selection points, was performed with the Statistics 12.0 program. Matching statistical maps with the waterbody contours was performed in Adobe Photoshop. Maps of the spatial distribution of heavy metals (cadmium, nickel, cobalt, lead, zinc and copper) and nutrients were constructed for all three study waterbodies. Data visualization clearly illustrated the relationship between the distribution of pollutants in the study waterbodies, as well as the sources of anthropogenic pollution. Based on the mapping exercise, it was established that Cu and Zn entered the waterbodies mainly in agricultural waste waters, while Cd, Ni, Co, Pb and partly Zn entered the waterbodies mainly in industrial and surface run‐off. Nutrients enter the waterbodies mainly in river and municipal waters. The constructed maps demonstrate the role of the constant flow in the distribution of pollutants in the waters of Lake Balkhash. The results also indicate that statistical mapping is an effective tool for identifying the sources and routes of pollutant entry into waterbodies. The imposition of statistical maps on the contours of waterbodies of concern provides a mapping method allowing for more visually compelling data and information for a wide circle of users and managers.

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“…PCA, FA, and CA will be excellently used in future studies to find inter-parameter associations existing between different pollutants. This data-mining technique will further help in reducing the number of pollution parameters to be tested and subsequent cost of analysis (Ahmed et al 2016). …”

Section: Introductionmentioning

confidence: 99%

Assessment of roadside surface water quality of Savar, Dhaka, Bangladesh using GIS and multivariate statistical techniques

et al. 2017

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In this study, multivariate statistical techniques in collaboration with GIS are used to assess the roadside surface water quality of Savar region. Nineteen water samples were collected in dry season and 15 water quality parameters including TSS, TDS, pH, DO, BOD, Cl -, F -, NO 3 2-, NO 2 -, SO 4 2-, Ca, Mg, K, Zn and Pb were measured. The univariate overview of water quality parameters are TSS 25.154 ± 8.674 mg/l, TDS 840.400 ± 311.081 mg/l, pH 7.574 ± 0.256 pH unit, DO 4.544 ± 0.933 mg/l, BOD 0.758 ± 0.179 mg/l, Cl -51.494 ± 28.095 mg/l, F -0.771 ± 0.153 mg/l, NO 3 2-2.211 ± 0.878 mg/l, NO 2 -4.692 ± 5.971 mg/l, SO 4 2-69.545 ± 53.873 mg/l, Ca 48.458 ± 22.690 mg/l, Mg 19.676 ± 7.361 mg/l, K 12.874 ± 11.382 mg/l, Zn 0.027 ± 0.029 mg/l, Pb 0.096 ± 0.154 mg/l. The water quality data were subjected to R-mode PCA which resulted in five major components. PC1 explains 28% of total variance and indicates the roadside and brick field dust settle down (TDS, TSS) in the nearby water body. PC2 explains 22.123% of total variance and indicates the agricultural influence (K, Ca, and NO 2 -). PC3 describes the contribution of nonpoint pollution from agricultural and soil erosion processes (SO 4 2-, Cl -, and K). PC4 depicts heavy positively loaded by vehicle emission and diffusion from battery stores (Zn, Pb). PC5 depicts strong positive loading of BOD and strong negative loading of pH. Cluster analysis represents three major clusters for both water parameters and sampling sites. The site based on cluster showed similar grouping pattern of R-mode factor score map. The present work reveals a new scope to monitor the roadside water quality for future research in Bangladesh.

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Spatial distribution and source identification of heavy metal pollution in roadside surface soil: a study of Dhaka Aricha highway, Bangladesh

Abstract: Introduction: In this study, metal pollution and their sources in surface soils were evaluated by pollution indices and multivariate statistical techniques in association with a geographical information system (GIS).

Cited by 76 publications

References 50 publications

Spatial distribution and potential ecological risk assessment of heavy metals in agricultural soils of Northeastern Iran

Spatial distribution and potential ecological risk assessment of heavy metals in agricultural soils of Northeastern Iran

Tracking pollution and its sources in the catchment‐lake system of major waterbodies in Kazakhstan

Assessment of roadside surface water quality of Savar, Dhaka, Bangladesh using GIS and multivariate statistical techniques

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