To better understand the relationship between prenatal exposure to heavy metals and trace elements and the risk of adverse pregnancy outcomes, we investigated the status of heavy metals and trace elements level in a Chinese population by collecting umbilical cord blood. Umbilical cord blood heavy metals and trace elements concentrations were determined by inductively coupled plasma-mass spectrometry. No differences with statistical significance in the median arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), strontium (Sr), thallium (Tl), vanadium (V), and zinc (Zn) concentrations were observed between the adverse pregnancy outcome group and the reference group. Titanium (Ti) and antimony (Sb) were found at higher levels with statistical significance in the cord blood samples with adverse pregnancy group when compared to the ones in the reference group. The association between Ti levels and the risk of adverse pregnancy outcomes remained significant after adjusting for potential confounding factors, including newborn weight. These results indicated that environmental exposure to Ti may increase the risk of adverse pregnancy outcomes in Chinese women without occupational exposure.

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The main objective of this research is to determine the capacity of land cover classification combining spectral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM image texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS information (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to implement and should be applicable in other settings and over larger extents.