Towfiqul Islam Abu Reza Md, Hasanuzzaman Md, Touhidul Islam H M, Mia Md Uzzal, Khan Rahat, Habib Md Ahosan, Rahman Md Mostafizur, Siddique Md Abu Bakar, Moniruzzaman Md, Rashid Md Bazlar
Department of Disaster Management, Begum Rokeya University, Rangpur, Bangladesh.
Institute of Nuclear Science & Technology, Bangladesh Atomic Energy Commission, Savar, Dhaka, Bangladesh.
Environ Toxicol Chem. 2020 Oct;39(10):2041-2054. doi: 10.1002/etc.4814. Epub 2020 Aug 18.
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, co-occurrence, 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. Environ Toxicol Chem 2020;39:2041-2054. © 2020 SETAC.
首次使用正定矩阵因子分解（PMF）受体模型，在国家流域尺度上，对孟加拉国上游、中游和下游河流（蒂斯塔河、科托亚 - 希塔拉卡河、梅克纳 - 鲁普沙河和帕苏尔河）沉积物中重金属污染的来源贡献进行量化。还调查了金属污染状况、共现情况和生态毒理风险。从30个地点采集了深度范围为0至20厘米的沉积物样本，使用电感耦合等离子体质谱法分析9种金属。上游、中游下游河段的金属平均浓度各不相同。结果表明，铬（Cr）与其他金属（如锰[Mn]、铁[Fe]和镍[Ni]）呈现出强显著共现网络。地累积指数（Igeo；63.3%）和风险指数（48.5%）的蒙特卡罗模拟结果表明，镉（Cd）是沉积物污染的主要贡献者。然而，沉积物被金属污染的累积概率被列为“中度至重度污染”（Igeo为46.6%；风险指数为16.7%）。毒性单位结果显示，锌（Zn）和Cd是沉积物的主要毒性贡献者。PMF模型预测了金属浓度并识别出4个潜在来源。农业源（因子1）主要贡献了铜（Cu；78.9%）和砷（As；62.8%）；镍（96.9%）和锰（83.5%）呈现出工业点源（因子2），在西北部和西南部地区有2个热点。镉（93.5%）有人为点源（因子3），铁（64.3%）和铬（53.5%）有混合源（因子4）。在空间上，识别出了PMF分配因子与预测金属来源之间的相似模式，表明该模型对河流系统分析的有效性。河段中的金属污染程度表明生态系统的生物成分处于令人担忧的状况。《环境毒理学与化学》2020年；39：2041 - 2054。© 2020 SETAC。
