UNMIXING OF LEAD, CS-137, AND PAH RECORDS IN LAKE-SEDIMENTS USING CURVE-FITTING WITH FIRST-ORDER AND 2ND-ORDER CORRECTIONS

Citation
Er. Christensen et Jf. Karls, UNMIXING OF LEAD, CS-137, AND PAH RECORDS IN LAKE-SEDIMENTS USING CURVE-FITTING WITH FIRST-ORDER AND 2ND-ORDER CORRECTIONS, Water research, 30(11), 1996, pp. 2543-2558
Citations number
21
Categorie Soggetti
Engineering, Civil","Environmental Sciences","Water Resources
Journal title
ISSN journal
00431354
Volume
30
Issue
11
Year of publication
1996
Pages
2543 - 2558
Database
ISI
SICI code
0043-1354(1996)30:11<2543:UOLCAP>2.0.ZU;2-K
Abstract
Two methods to unmix pollutant records obtained from sediment core dat a are investigated. A first-order method is expanded to include polyno mial curve fitting to the original data. In addition, a new second-ord er method is developed; this method is based on the advection-diffusio n equation for a tracer with constant mixing in a layer of thickness z (m) and no mixing below. The original data are fitted with a polynomia l. The first-order method effectively unmixes lead and Cs-137 profiles in a core from northern Lake Michigan that was collected in 1984. Wit hout unmixing, a 1969 maximum in the lead record would have been misse d, and the 1963 Cs-137 maximum would have been underestimated, and inc orrectly dated as 1956. Unmixing profiles of polycyclic aromatic hydro carbons (PAHs) in two cores collected from central and northern Lake M ichigan in 1988 produces more equivocal results, probably because of s maller z(m) values in these latter cores. The second-order method give s profiles of similar shapes as the first-order method, but the time s cales are only identical for z(m) < 0.5-1.0 cm. For larger z(m), the f irst-order method gives extrema at more recent and correct times. Equi valent mixing parameters in the two methods are obtained by maintainin g the Peclet number constant for conservative tracers. Errors in the r econstructed profiles are smaller for the first than for the second-or der method, Accurate error estimates may be obtained by a Monte-Carlo method that takes into account the correlation between adjacent data p oints. Copyright (C) 1996 Elsevier Science Ltd