Surface sediment samples were collected from 35 locations in Sulaibikhat Bay, Kuwait. Co, Cr, Cu, Ni, Pb and Zn concentrations were determined. Grain sizes, TOC （total organic carbon）, carbonate, mineralogical and e...Surface sediment samples were collected from 35 locations in Sulaibikhat Bay, Kuwait. Co, Cr, Cu, Ni, Pb and Zn concentrations were determined. Grain sizes, TOC （total organic carbon）, carbonate, mineralogical and environmental data were also determined. Multiple linear regression is applied to the data from the sediment sequential extractions to assess the relative importance of mineralogical and sedimentological factors in controlling heavy metal concentrations in individual chemical fractions （exchangeable, reducible, oxidizable, residual） under different environmental conditions. The analysis shows that grain size, TOC, calcium carbonate and minerals clearly influence heavy metal concentrations. For the exchangeable fraction, clay, grain size and the mineral pyrite are the main factors, whereas for the reducible fraction, TOC is the main factor influencing concentrations ofZn, Pb, Ni, Cu and Cr. For the oxidizable fraction, modelling shows that TOC is the main factor influencing Zn, Ni, Cu, Cr and Co concentrations. The residual fraction concentrations of Zn, Ni, Cr and Co were best predicted by the abundance of sand, with sand content having a negative effect on heavy metal concentrations in this fraction. The statistical techniques in environmental data interpretation are quite useful in cutting down the volume of the data and identifying identical classes which are statistically distinct.展开更多
文摘Surface sediment samples were collected from 35 locations in Sulaibikhat Bay, Kuwait. Co, Cr, Cu, Ni, Pb and Zn concentrations were determined. Grain sizes, TOC （total organic carbon）, carbonate, mineralogical and environmental data were also determined. Multiple linear regression is applied to the data from the sediment sequential extractions to assess the relative importance of mineralogical and sedimentological factors in controlling heavy metal concentrations in individual chemical fractions （exchangeable, reducible, oxidizable, residual） under different environmental conditions. The analysis shows that grain size, TOC, calcium carbonate and minerals clearly influence heavy metal concentrations. For the exchangeable fraction, clay, grain size and the mineral pyrite are the main factors, whereas for the reducible fraction, TOC is the main factor influencing concentrations ofZn, Pb, Ni, Cu and Cr. For the oxidizable fraction, modelling shows that TOC is the main factor influencing Zn, Ni, Cu, Cr and Co concentrations. The residual fraction concentrations of Zn, Ni, Cr and Co were best predicted by the abundance of sand, with sand content having a negative effect on heavy metal concentrations in this fraction. The statistical techniques in environmental data interpretation are quite useful in cutting down the volume of the data and identifying identical classes which are statistically distinct.
