Distribution, sources, and pollution levels of toxic metal(loid)s in an urban river (Ichamati), Bangladesh using SOM and PMF modeling with GIS tool

Abstract

Indexical assessment coupled with a self-organizing map (SOM) and positive matrix factorization (PMF) modeling of toxic metal(loid)s in sediment and water of the aquatic environment provides valuable information from the environmental management perspective. However, in northwest Bangladesh, indexical and modeling assessments of toxic metal(loid)s in surface water and sediment are still rare. Toxic metal(loid)s were measured in sediment and surface water from an urban polluted river (Ichamati) in northwest Bangladesh using an atomic absorption spectrophotometer to assess distribution, pollution levels, sources, and potential environmental risks to the aquatic environment. The mean concentrations (mg/kg) of metal(loid)s in water are as follows: Fe (871) > Mn (382) > Cr (72.4) > Zn (34.2) > Co (20.8) > Pb (17.6) > Ni (16.7) > Ag (14.9) > As (9.0) > Cu (5.63) > Cd (2.65), while in sediment, the concentration follows the order, Fe (18,725) > Mn (551) > Zn (213) > Cu (47.6) > Cr (30.2) > Ni (24.2) > Pb (23.8) > Co (9.61) > As (8.23) > Cd (0.80) > Ag (0.60). All metal concentrations were within standard guideline values except for Cr and Pb for water and Cd, Zn, Cu, Pb, and As for sediment. The outcomes of eco-environmental indices, including contamination and enrichment factors and geoaccumulation index, differed spatially, indicating that most of the sediment sites were moderately to highly polluted by Cd, Zn, and As. Cd and Zn content can trigger ecological risks. The positive matrix factorization (PMF) model recognized three probable sources of sediment, i.e., natural source (49.39%), industrial pollution (19.72%), and agricultural source (30.92%), and three possible sources of water, i.e., geogenic source (45.41%), industrial pollution (22.88%), and industrial point source (31.72%), respectively. SOM analysis identified four spatial patterns, e.g., Fe-Mn-Ag, Cd-Cu, Cr-Pb-As-Ni, and Zn-Co in water and three patterns, e.g., Mn-Co-Ni-Cr, Cd-Cu-Pb-Zn, and As-Fe-Ag in sediment. The spatial distribution of entropy water quality index values shows that the southwestern area possesses "poor" quality water. Overall, the levels of metal(loid) pollution in the investigated river surpassed a critical threshold, which might have serious consequences for the river's aquatic biota and human health in the long run.

Appraisal of trace metals toxicity and human health risk using a novel approach in produced water of four gas fields, Bangladesh.

“…The uncertainty can be obtained from the equation: and (if the concentration (C ) is below or equivalent to the detection limit of the method (MDL), otherwise, uncertainty can be obtained from the equation: (if C is higher than MDL. Here, EF is the error fraction during measurement (Nasiruddin et al, 2022).…”

“…Besides, pollution management techniques need macro-policy assistance. In addition, the authorities should raise residents' knowledge and conduct campaigns to prevent pollution and protect the aquatic ecosystem (Nasiruddin et al, 2022).…”

“…Nasiruddin et al, 2022). The anticipated trace element concentrations by the PMF method were compared to quanti ed metal concentrations.…”

Spatial–temporal distributions, probable health risks, and source identification of organic pollutants in surface waters of an extremely hypoxic river basin in Türkiye

“…Principle Component Analysis (PCA) reduces the dimensionality of a multivariate data set, while Cluster Analysis (CA) classifies the objects characterized by the values of variables into groups. Therefore, PCA and CA have been frequently used all over the world while dealing with water pollution risk assessment data (Men et al, 2022 ; Mutlu, et al, 2021 ; Nasiruddin et al, 2022 ; Saleem et al, 2019 ; Ustaoğlu et al, 2020 ; Varol, 2020 ).…”

Indexical and statistical approaches to investigate the integrated origins of elements in the sediment of Teesta River, Bangladesh: sediment quality and ecological risk assessment

“…[1][2][3][4] Besides, rapid urbanization, unplanned industrialization, and random expansion of agricultural chemicals are continuously discharging a wide variety of extraneous pollutants into the uvial system, which may create severe sediment contamination. [5][6][7][8] Sediment, in contrast, has the capacity to accumulate toxins existing in the environmental system (soil and water) at a faster rate. 7,[9][10][11] Hence, enrichment a Industrial Ecology in Energy Research Center, Faculty of Environmental Management, Prince of Songkla University, Songkhla 90112, Thailand.…”