Quantifying Source Apportionment, Co‐occurrence, and Ecotoxicological Risk of Metals from Upstream, Lower Midstream, and Downstream River Segments, Bangladesh

Abstract

The positive matrix factorization (PMF) receptor model was used for the first time to quantify the source contributions to heavy metal pollution of sediment on a national basin scale in the upstream, midstream, and downstream rivers (Teesta and Kortoya-Shitalakkah and Meghna-Rupsha and Pasur) of Bangladesh. The metal contamination status, cooccurrence, and ecotoxicological risk were also investigated. Sediment samples were collected from 30 sites at a depth range of 0 to 20 cm for analysis of 9 metals using inductively coupled plasma-mass spectrometry. The mean concentrations of metals varied for upstream, lower midstream, and downstream river segments. The results showed that chromium (Cr) exhibited a strong significant co-occurrence network with other metals (e.g., manganese [Mn], iron [Fe], and nickel [Ni]). Monte Carlo simulation results of the geo-accumulation index (Igeo; 63.3%) and risk indices (48.5%) showed that cadmium (Cd) was the main contributor to sediment pollution. However, the cumulative probabilities of sediments being polluted by metals were ranked as "moderate to heavily polluted" (Igeo 46.6%; risk index 16.7%). Toxicity unit results revealed that zinc (Zn) and Cd were the key toxic contributors to sediments. The PMF model predicted metal concentrations and identified 4 potential sources. The agricultural source (factor 1) mostly contributed to copper (Cu; 78.9%) and arsenic (As; 62.8%); Ni (96.9%) and Mn (83.5%) exhibited industrial point sources (factor 2), with 2 hot spots in northwestern and southwestern regions. Cadmium (93.5%) had anthropogenic point sources (factor 3), and Fe (64.3%) and Cr (53.5%) had a mixed source (factor 4). Spatially, similar patterns between PMF apportioning factors and predicted metal sources were identified, showing the efficiency of the model for river systems analysis. The degree of metal contamination in the river segments suggests an alarming condition for biotic components of the ecosystem.

Application of Multivariate Statistical Techniques and Water Quality Index for the Assessment of Water Quality and Apportionment of Pollution Sources in the Yeongsan River, South Korea

“…It is an approach to factor analysis that specifically addresses the problem of non-optimal scaling. PMF is also one of the most important receptor models, and as such has been suggested for use in source apportionment by the US EPA and widely applied over the years in numerous research areas [8,13,24,32]. The following Equation ( 1) is used to describe the model:…”

“…As rivers provide the leading inland water resources for domestic, industrial, and irrigation purposes, it is vital to have reliable information about river water quality. Despite the dramatic development of water quality monitoring programs over the past few decades, representative and reliable water quality assessment remains difficult; therefore, gathering reliable information on river water quality, assessing spatial and temporal changes, performing source apportionment of pollution, and determining and controlling water pollution in rivers are indispensable tasks [9,[12][13][14].…”

“…CA has been employed in river water quality datasets to group similar sampling sites (spatial variability), while DA supports the statistical classification of samples and helps to group samples with common properties [24]. Network analysis is commonly used to identify relationships among river water quality variables [13]. PCA/FA is a dimensionality reduction technique that provides useful information about the most significant factors, resulting in a straightforward representation of the data [8].…”

“…The PMF technique has been used mainly for pollution source identification and apportionment in studies of the atmospheric environment [32]. The application of this technique to the apportionment of pollution sources in aqueous environments has increased in recent years [8,13,14].…”

Distribution and risk appraisal of dissolved trace elements in Begnas Lake and Rupa Lake, Gandaki Province, Nepal

“…The components are classified as strong, moderate, and weak corresponding to absolute loading values of > 0.75, 0.75-0.50, and < 0.50, respectively. These strong and moderate associations among the TEs were considered to have a major influence on the lake water quality [46]. In the Begnas Lake, two principal components (PCs) with eigenvalue > 1, explained about 76.32% of the total variances.…”