The spatial and temporal variations in regional aerosol optical thickness (AOT) over China during 2013 were
investigated in this study using Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol intermediate product (IP)
data obtained from the NOAA CLASS. It is found that high level AOT in China mainly occurs in the spring and
summer. The study compared the aerosols in the economically developed eastern China to those in the western region;
urban areas versus rural areas; inland versus coastal cities. Further investigation was also performed to validate VIIRS
derived AOT data and aerosol type with in situ ground measurements.
Global warming and climate change have gained more and more attention because the global mean surface temperature has increased since the late 19th century. With the progress of rapid urbanization, Jinan city has witnessed a significant urban thermal environment change. To investigate the relationship between urban heat islands and urban biophysical descriptors, the city’s biophysical properties along with land surface temperature (LST) in 1992 and 2011 were retrieved from the Landsat TM images. Additionally, three thematic indices were employed to extract the features of the impervious surface, water, and vegetation, respectively. The correlation and spatial overlay of these land surface features were then analyzed. The results show that the Jinan region has witnessed very fast urban sprawl. The total impervious surface area of the region in 2011 was 134.7% more than that in 1992. This increase significantly reduced the vegetation and open water coverage in the urban area. Simultaneously, the expansion of impervious surfaces was accompanied by an increased urban heat island (UHI) ratio index, which increased from 0.43 in 1992 to 0.55 in 2011, showing that the UHI in Jinan has developed from a weak level to a significant level over the 19-year period. The quantitative analysis between LST and indices revealed that impervious surfaces have a positive exponential relationship with LST, while the water and vegetation are both negatively correlated with temperature. A multifactor analysis also indicated that the contribution of impervious surfaces to the LST could equal or even exceed that of the sum of vegetation and water.
The pollution of surface soils caused by heavy metals has been a focus problem discussed. Instead of the acquisition of
the "best" estimation of unsampled points, the author paid much attention to the assessment of the spatial uncertainty
about unsampled values. The simulation method of Geostatistics, aimed at the same statistics (histogram, Variogram),
can generate a set of equally-probable realizations which is particularly useful for assessing the uncertainty in the spatial
distribution of attribute values. The case study was from an Urban - Rural transition zone of Shanghai, China. Six kinds
of heavy metals (Cu, Pb, Cd, Cr, Hg and As) in agricultural surface soils were analyzed in the paper. Based on the study
of spatial variation of different kind of heavy metal, the author got the different realization of the 6 kinds of heavy metals
respectively based on the sequential simulation methods. At last, the author drew the conclusion that Cu, Cd and Cr were
the dominant elements that influenced soil quality in the study area. At the end of the paper, the author gave the
uncertainty map of the six heave metals respectively.
The contents of heavy metals (Cu, Zn, Pb, Cd, Cr, Hg and As) in agricultural surface soils of Peri-Urban Area in Pudong of Shanghai were analyzed to investigate the heavy metal contents and spatial distribution. Different evaluation methods and assessment standards were also used for comparison. In addition, Kriging method based on GIS was also applied to study the spatial variability of heavy metal pollution. The result showed that mean concentrations of heavy metals were all higher than the natural-background values of them, respectively, except for Pb and As. Based on the national soil quality standard, Cu, Zn, Cd and Hg were determined in some regions, with the ratios of 3.8%, 2.1%, 9.2% and 0.8%, respectively. However, the contents of Pb, Cr and As were much lower than the values of national soil quality standard. The analysis of spatial distribution showed that the soil quality was influenced by different heavy metals at different levels. Cu, Zn, Cd and Hg were the dominant elements, causing soil heavy metal pollution in the area. Additionally, the regional differentiation of soil pollution was also obvious.