Stable isotopes, PCBs and pesticide concentrations in zooplankton, seawater and POM from Kongsfjorden and Liefdefjorden, Svalbard

Differences in bioaccumulation of persistent organic pollutants (POPs) between fjords characterized by different water masses were investigated by comparing POP concentrations, patterns and bioaccumulation factors (BAFs) in seven species of zooplankton from Liefdefjorden (Arctic water mass) and Kongsfjorden (Atlantic water mass), Svalbard, Norway. No difference in concentrations and patterns of POPs was observed in seawater and POM; however higher concentrations and BAFs for certain POPs were found in species of zooplankton from Kongsfjorden. The same species were sampled in both fjords and the differences in concentrations of POPs and BAFs were most likely due to fjord specific characteristics, such as ice cover and timing of snow/glacier melt. These confounding factors make it difficult to conclude on water mass (Arctic vs. Atlantic) specific differences and further to extrapolate these results to possible climate change effects on accumulation of POPs in zooplankton. The present study suggests that zooplankton do biomagnify POPs, which is important for understanding contaminant uptake and flux in zooplankton, though consciousness regarding the method of evaluation is important.

Concentrations of brominated and chlorinated contaminants in adipose tissue of polar bears (Ursus maritimus) between 1991-2007

Adipose tissue was sampled from the western Hudson Bay (WHB) subpopulation of polar bears at intervals from 1991 to 2007 to examine temporal trends of PCB and OCP levels both on an individual and sum-contaminant basis. We also determined levels and temporal trends of emerging polybrominated diphenyl ethers (PBDEs), hexabromocyclododecane (HBCD), polybrominated biphenyls (PBBs) and other current-use brominated flame retardants. Over the 17-year period, sum DDT (and p,p'-DDE, p,p'-DDD, p,p'-DDT) decreased (-8.4%/year); alpha-hexachlorocyclohexane (alpha-HCH) decreased (-11%/year); beta-HCH increased ( + 8.3%/year); and sum PCB and sum chlordane (CHL), both contaminants at highest concentrations in all years (>1 ppm), showed no distinct trends even when compared to previous data for this subpopulation dating back to 1968. Some of the less persistent PCB congeners decreased significantly (-1.6%/year to -6.3%/year), whereas CB153 levels tended to increase (+ 3.3%/year). Parent CHLs (c-nonachlor, t-nonachlor) declined, whereas non-monotonic trends were detected for metabolites (heptachlor epoxide, oxychlordane). sum chlorobenzene, octachlorostyrene, sum mirex, sum MeSO2-PCB and dieldrin did not significantly change. Increasing sum PBDE levels (+13%/year) matched increases in the four consistently detected congeners, BDE47, BDE99, BDE100 and BDE153. Although no trend was observed, total-(alpha)-HBCD was only detected post-2000. Levels of the highest concentration brominated contaminant, BB153, showed no temporal change. As long-term ecosystem changes affecting contaminant levels may also affect contaminant patterns, we examined the influence of year (i.e., aging or "weathering" of the contaminant pattern), dietary tracers (carbon stable isotope ratios, fatty acid patterns) and biological (age/sex) group on congener/metabolite profiles. Patterns of PCBs, CHLs and PBDEs were correlated with dietary tracers and biological group, but only PCB and CHL patterns were correlated with year. DDT patterns were not associated with any explanatory variables, possibly related to local DDT sources. Contaminant pattern trends may be useful in distinguishing the possible role of ecological/diet changes on contaminant burdens from expected dynamics due to atmospheric sources and weathering.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.