Assessment and mapping of environmental quality in agricultural soils of Zhejiang Province, China. J Environ Sci China

Institute ofAgricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310029, China.
Journal of Environmental Sciences (Impact Factor: 2). 02/2007; 19(1):50-4. DOI: 10.1016/S1001-0742(07)60008-4
Source: PubMed


Heavy metal concentrations in agricultural soils of Zhejiang Province were monitored to indicate the status of heavy metal contamination and assess environmental quality of agricultural soils. A total of 908 soil samples were collected from 38 counties in Zhejiang Province and eight heavy metal (Cd, Cr, Pb, Hg, Cu, Zn, Ni and As) concentrations had been evaluated in agricultural soil. It was found 775 samples were unpolluted and 133 samples were slightly polluted and more respectively, that is approximately 14.65% agricultural soil samples had the heavy metal concentration above the threshold level in this province by means of Nemerow's synthetical pollution index method according to the second grade of Standards for Soil Environmental Quality of China (GB15618-1995). Contamination of Cd was the highest, followed by Ni, As and Zn were lower correspondingly. Moreover, Inverse Distance Weighted (IDW) interpolation method was used to make an assessment map of soil environmental quality based on the Nemerow's pollution index and the soil environmental quality was categorized into five grades. Moreover, ten indices were calculated as input parameters for principal component analysis (PCA) and the principal components (PCs) were created to compare environmental quality of different soils and regions. The results revealed that environmental quality of tea soils was better than that of paddy soils, vegetable soils and fruit soils. This study indicated that GIS combined with multivariate statistical approaches proved to be effective and powerful tool in the mapping of soil contamination distribution and the assessment of soil environmental quality on provincial scale, which is beneficial to environmental protection and management decision-making by local government.

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Available from: Zhou Shi, Feb 14, 2015
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    • "GIS-based predictive modeling of heavy metal concentrations Following the approach of previous studies (for example, Liu et al. 2006; Wang and Qin 2006; Cheng et al. 2007; Zhang et al. 2008; Hani and Pazira 2011), a geographical information system (GIS) was utilized for modeling the spatial concentrations of heavy metals in the study area. "
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    ABSTRACT: Soils from different land use areas in Lahore City, Pakistan, were analyzed for concentrations of heavy metals-cadmium (Cd), chromium (Cr), nickel (Ni), and lead (Pb). One hundred one samples were randomly collected from six land use areas categorized as park, commercial, agricultural, residential, urban, and industrial. Each sample was analyzed in the laboratory with the tri-acid digestion method. Metal concentrations in each sample were obtained with the use of an atomic absorption spectrophotometer. The statistical techniques of analysis of variance, correlation analysis, and cluster analysis were used to analyze all data. In addition, kriging, a geostatistical procedure supported by ArcGIS, was used to model and predict the spatial concentrations of the four heavy metals-Cd, Cr, Ni, and Pb. The results demonstrated significant correlation among the heavy metals in the urban and industrial areas. The dendogram, and the results associated with the cluster analysis, indicated that the agricultural, commercial, and park areas had high concentrations of Cr, Ni, and Pb. High concentrations of Cd and Ni were also observed in the residential and industrial areas, respectively. The maximum concentrations of both Cd and Pb exceeded world toxic limit values. The kriging method demonstrated increasing spatial diffusion of both Cd and Pb concentrations throughout and beyond the Lahore City area.
    Environmental Monitoring and Assessment 09/2015; 187(10):636. DOI:10.1007/s10661-015-4855-1 · 1.68 Impact Factor
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    • "Finally, heavy metals Ag, Co, Pb, Tl, Be, Ni, Cd, Ba, Cu, V, Zn and Cr were analyzed by inductively coupled plasma (ICP) optical emission spectroscopy(ICP-OES). In order to show the relative magnitudes of soil pollution, Nemerow's synthetical pollution index P n for all the soil sampling points with respect to the following equations was calculated (Liang et al. 2007). "
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    ABSTRACT: The levels of 12 heavy metals (Ag, Ba, Be, Cd, Co, Cr, Cu, Ni, Pb, Tl, V, Zn) were considered in 229 soil samples in Semnan Province, Iran. To discriminate between natural and anthropogenic inputs of heavy metals, factor analysis was used. Seven factors accounting for 90.5 % of the total variance were extracted. The mining and agricultural activities along with geogenic sources have been attributed as the main causes of the levels of heavy metals in the study area. The partial least squares regression was utilized to predict the level of soil pollution index (SPI) considering the concentrations of 12 heavy metals. The eigenvectors from the first three PLS represented more than 98 % of the overall variance. The correlation coefficient between the observed and predicted SPI was 0.99 indicating the high efficiency of this method. The resultant coefficient of determination for three PLS components was 0.984 confirming the predictive ability of this method.
    Bulletin of Environmental Contamination and Toxicology 08/2015; DOI:10.1007/s00128-015-1632-3 · 1.26 Impact Factor
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    • "The topsoils (0–20 cm) were collected using a soil corer. A total of 5 soil cores were collected at each grid node within a 2-m square area following the quincunx-sampling method (Cheng et al., 2007): four cores were collected from the four corners and one from the center of the square. All five cores were bulked together to prepare a composite sample for laboratory analysis. "
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    ABSTRACT: In the recent past, much of the marshland in China has been reclaimed for agriculture and to create urban buffer zones. Information about the variability of soil properties (e.g. salinity) in these reclaimed areas is mostly limited, but is crucial for improving agricultural production as well as sustaining their environmental benefits. Characterization of variations in soil salinity and their correlation with other soil properties at different scales can improve the use of these reclaimed areas, as well as assess the efficacy of the reclamation process. In this study, we examined variations in soil salinity at different directions using anisotropic analysis (AA) and separated the variations at different scales using empirical mode decomposition (EMD). The AA identified two pairs of major and minor axes along which variations in soil salinity were dominant. The EMD separated the variations in soil salinity along these axes into six intrinsic mode functions according to the dominant scales of variations. The importance of these scale components were assessed based on their percent variance contribution towards the overall variance. Large variations in soil salinity were observed mainly at 200–500 m and 3500–4500 m scales. The scale-specific variations in soil salinity and their correlation with other soil properties were direction-specific. A strong correlation between the large scale components of soil salinity (N 5 km) and soil total nitrogen and clay content were identified along the directions from inland to the ocean. This may be attributed to the distance from the ocean as well as the length of time for which reclaimed land has been cultivated. Information about this variability at different scales and directions acquired by combining AA and EMD provided a unique way to characterize reclaimed lands for agricultural or environmental use. This information will be helpful in developing future sampling strategies and preparing detailed digital soil maps for improved management decisions.
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