Geochemistry of reduced inorganic sulfur, reactive iron, and organic carbon in fluvial and marine surface sediment in the Laizhou Bay region, China

Understanding the geochemical cycling of sulfur in sediments is important because it can have implications for both modern environments (e.g., deterioration of water quality) and interpretation of the ancient past (e.g., sediment C/S ratios can be used as indicators of palaeodepositional environment). This study investigates the geochemical characteristics of sulfur, iron, and organic carbon in fluvial and coastal surface sediments of the Laizhou Bay region, China. A total of 63 sediment samples were taken across the whole Laizhou Bay marine region and the 14 major tidal rivers draining into it. Acid volatile sulfur, chromium (II)-reducible sulfur and elemental sulfur, total organic carbon, and total nitrogen were present in higher concentrations in the fluvial sediment than in the marine sediment of Laizhou Bay. The composition of reduced inorganic sulfur in surface sediments was dominated by acid volatile sulfur and chromium (II)-reducible sulfur. In fluvial sediments, sulfate reduction and formation of reduced inorganic sulfur were controlled by TOC and reactive iron synchronously. High C/S ratios in the marine sediments indicate that the diagenetic processes in Laizhou Bay have been affected by rapid deposition of sediment from the Yellow River in recent decades

Information Loss-Guided Multi-Resolution Image Fusion

Spatial downscaling is an ill-posed, inverse problem, and information loss (IL) inevitably exists in the predictions produced by any downscaling technique. The recently popularized area-to-point kriging (ATPK)-based downscaling approach can account for the size of support and the point spread function (PSF) of the sensor, and moreover, it has the appealing advantage of the perfect coherence property. In this article, based on the advantages of ATPK and the conceptualization of IL, an IL-guided image fusion (ILGIF) approach is proposed. ILGIF uses the fine spatial resolution images acquired in other wavelengths to predict the IL in ATPK predictions based on the geographically weighted regression (GWR) model, which accounts for the spatial variation in land cover. ILGIF inherits all the advantages of ATPK, and its prediction has perfect coherence with the original coarse spatial resolution data which can be demonstrated mathematically. ILGIF was validated using two data sets and was shown in each case to predict downscaled images more accurately than the compared benchmark methods