Water and sediment samples were collected from 20 location of the Buriganga river of Bangladesh during Summer and Winter 2009 to determine the spatial distribution, seasonal and temporal variation of different heavy metal contents. Sequential extraction procedure was employed in sediment samples for the geochemical partitioning of the metals. Total trace metal content in water and sediment samples were analyzed and compared with different standard and reference values. Concentration of total chromium, lead, cadmium, zinc, copper, nickel, cobalt and arsenic in water samples were greatly exceeded the toxicity reference values in both season. Concentration of chromium, lead, copper and nickel in sediment samples were mostly higher than that of severe effect level values, at which the sediment is considered heavily polluted. On average 72 % chromium, 92 % lead, 88 % zinc, 73 % copper, 63 % nickel and 68 % of total cobalt were associated with the first three labile sequential extraction phases, which portion is readily bioavailable and might be associated with frequent negative biological effects. Enrichment factor values demonstrated that the lead, cadmium, zinc, chromium and copper in most of the sediment samples were enriched sever to very severely. The pollution load index value for the total area was as high as 21.1 in Summer and 24.6 in Winter season; while values above one indicates progressive deterioration of the sites and estuarine quality. The extent of heavy metals pollution in the Buriganga river system implies that the condition is much frightening and may severely affect the aquatic ecology of the river.
The Tsurumi, a class-one Japanese river, has a significant metal loading originating from urban environment. Water and sediment samples were collected from 20 sites in winter and summer, 2009 and were analyzed to determine and compare the extent of different trace element enrichment. A widely used five-step sequential extraction procedure was also employed for the fractionation of the trace elements. Concentrations of zinc, copper, lead, chromium, and cadmium were three to four times higher than that of reference values and downstream sediments are much more polluted than the upstream sites. Geochemical partitioning results suggest that the potential trace metal mobility in aquatic environment was in the order of: cadmium > zinc > lead > copper > cobalt > chromium > molybdenum > nickel. About 80.2% zinc, 77.9% molybdenum, 75.3% cobalt, 63.7% lead, 60.9% copper, 55.1% chromium, and 39.8% nickel in the sediment were contributed anthropogenically. According to intensity of pollution, Tsurumi river sediments are moderately to heavily contaminated by zinc, lead, and cobalt. Enrichment factor values demonstrated that zinc, lead, and molybdenum have minor enrichment in both the season. The pollution load index (PLI) has been used to access the pollution load of different sampling sites. The area load index and average PLI values of the river were 7.77 and 4.93 in winter and 7.72 and 4.89 in summer, respectively. If the magnitude of pollution with trace metal in the river system increases continuously, it may have a severe impact on the river's aquatic ecology.
Geochemical discrimination has recently been recognised as a potentially useful proxy for identifying tsunami deposits in addition to classical proxies such as sedimentological and micropalaeontological evidence. However, difficulties remain because it is unclear which elements best discriminate between tsunami and non-tsunami deposits. Herein, we propose a mathematical methodology for the geochemical discrimination of tsunami deposits using machine-learning techniques. The proposed method can determine the appropriate combinations of elements and the precise discrimination plane that best discerns tsunami deposits from non-tsunami deposits in high-dimensional compositional space through the use of data sets of bulk composition that have been categorised as tsunami or non-tsunami sediments. We applied this method to the 2011 Tohoku tsunami and to background marine sedimentary rocks. After an exhaustive search of all 262,144 (= 218) combinations of the 18 analysed elements, we observed several tens of combinations with discrimination rates higher than 99.0%. The analytical results show that elements such as Ca and several heavy-metal elements are important for discriminating tsunami deposits from marine sedimentary rocks. These elements are considered to reflect the formation mechanism and origin of the tsunami deposits. The proposed methodology has the potential to aid in the identification of past tsunamis by using other tsunami proxies.
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