Search

Abstract: Distinguishing between natural and human-induced sources of estuarine sedimentary elemental concentrations often relies on mathematical normalisation (using geochemical or granulometric variables) and multivariate statistical techniques (e.g. Principle Component Analysis (PCA)). However, the extent to which these mathematical treatments can influence the interpretation of the data is not well constrained. We report here the highest resolution mapping of surface sediment elemental concentrations in the Pearl River Estuary (PRE) to date (n = 77) and detail an investigation on the effect of “pre-defining” elements of interest before statistical analysis, a comparison of common normalisation techniques and discuss implications for the identification and interpretation of sediment elemental concentrations enriched by human activities. Our analysis indentifies 4 distinct patterns of distribution in the PRE: North River, Western Shoal, Macau and Coastal Water distribution patterns. Mapping of these raw distribution patterns underpins a conceptual understanding of the interacting processes driving these patterns of deposition in the PRE, aiding the implementation of PCA and mathematical normalisation. However, we find evidence that (i) different normalisation elements/parameters can significantly alter the distribution patterns in elemental concentrations when applied across the entire PRE and may not reflect the true distribution (i.e. an artefact), and (ii) developing a reduced dataset for PCA can also alter the underlying trends in the spatial data; both of which significantly impact upon the subsequent environmental interpretation of source and human-induced pollution effects. However, we demonstrate that through interpolation and mapping of raw geochemical data, zonation of the PRE can allow for much more accurate normalisation within discrete regions using the most appropriate conservative tracer element for each specific region identified. [Copyright &y& Elsevier]

The article presents a study on the water salinity history during the late Quaternary period in the Pearl River estuary in China. The study indicates the major factors that affect the estuary's water salinity including freshwater discharge, sea level, and deltaic shoreline movement. It demonstrates the advantage of salinity reconstructions in improving reconstruction quality in estuaries and observing salinity values.

Abstract: We collected 77 modern diatom samples from sites across the Pearl River estuary, China to analyze the relationship between diatom assemblages and environmental parameters including water salinity, water depth and sediment particle size. Results showed that marine diatoms were dominant in the high salinity environment around Hong Kong and the outer part of the estuary. Brackish water diatoms were found in high abundance in the central part of the estuary. Both marine and brackish water diatoms were predominantly planktonic taxa. Freshwater diatoms dominated in low salinity environments, with planktonic taxa in the deep tidal channel and benthic species in the shallow deltaic distributaries. Statistical tests indicated that the modern diatom distribution is strongly correlated with salinity but is also influenced by several other environmental variables including sand content and water depth. Transfer functions relating diatom assemblages and water salinity had high r 2 (0.94–0.98 for WA-PLS, 0.95 for MA and 0.98 for MAT) and relatively low RMSEP (2.66–1.63‰ for WA-PLS, 2.35‰ for MA and 2.70‰ for MAT). Due to the geographical distribution of samples, some spatial autocorrelation is likely present in the dataset. When this effect is considered, r 2 decreases to 0.90 and RMSEP increases to 5.41‰, although the diatom–salinity relationship remains appropriate for reconstructing paleosalinity. Based on this estimate, a diatom-based salinity transfer function with high accuracy and precision is developed and successfully applied to a sediment core for quantitative reconstruction of the Holocene paleosalinity in the Pearl River estuary. [Copyright &y& Elsevier]