The physical structure of rivers and lakes is an important component of the indicator system for river and lake health assessment. This study focuses on the Yellow River and Nansi Lake basins, carries out the interpretation of physical form remote sensing images, and evaluates and analyzes their physical form characteristics. The results show that: (1) The expansion of urban construction and water conservancy planning and construction in the water systems of the Yellow River and Nansi Lake basins have seriously affected the connectivity of the rivers. (2) The vegetation coverage around the Yellow River and Nansi Lake basins is relatively low. More than half of the rivers and lakes (reservoirs) have vegetation coverage less than 5%, and most of them are vegetation-free rivers and lakes (reservoirs). (3) The average value of the ecological buffer zone index in the Nansi Lake Basin is greater than that in the Yellow River Basin. The main reasons for the decline in the ecological buffer index are the large width of the water level fluctuation zone, the small width of the land buffer zone, and insufficient delineation and protection of the ecological buffer zone. (4) River coastlines are dominated by natural coastlines, and the proportion of natural coastlines on both sides of most rivers is greater than 80%. The proportion of natural coastlines in the Yellow River Basin is lower than that in the Nansi Lake Basin. Coastline protection and utilization are important for ecological protection and protection in the Yellow River Basin.
It is of great significance to explore the spatial-temporal variations and estimate the relative importance of the influencing factors of PM2.5 and O3 pollution. The study established nationwide surface O3, NO2, and SO2 estimation models using the extreme gradient boosting model and the data fusion method. The cross-validation results indicated that the forecasted models performed well (R-values from 0.86 to 0.95). The results revealed that the pollution levels of O3, PM2.5, NO2, and SO2 in the North China Plain (NCP) were the highest in China. Subsequently, a multi-task learning model was utilized to estimate the relative importance of influential factors on the PM2.5 and O3 pollution in the NCP. The sensitivity analysis results indicated that the O3 pollution from 2010–2020 in the NCP was susceptible to meteorological factors such as ultraviolet radiation and temperature, as well as anthropogenic precursors such as NOX, and PM2.5 pollution in the NCP was constrained by both meteorological factors (44.62%) and anthropogenic emissions (16.86%). The impact of NO2 on PM2.5 pollution was similar to its impact on O3 pollution; therefore, the importance of NO2 emission reduction to PM2.5 pollution is as important as that of O3 pollution, whereas the impact of SO2 on PM2.5 was much greater than its impact on O3 pollution, so SO2 emission reduction is more important for PM2.5.
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 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.
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.
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