Spatial distribution characteristics of heavy metals in the soil of coal chemical industrial areas

2018 ◽  
Vol 18 (5) ◽  
pp. 2044-2052 ◽  
Author(s):  
Kai Zhang ◽  
Changdi Qiang ◽  
Jing Liu
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Distribution Characteristics ◽  
Industrial Areas ◽  
Spatial Distribution Characteristics

scholarly journals Spatial distribution characteristics and health risk assessment of heavy metals in surface sediment of the Hai River and its Tributaries in Tianjin, China

2021 ◽  
Author(s):  
Mengxin Kang ◽  
Yimei Tian ◽  
Haiya Zhang ◽  
Cheng Wan
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Spatial Distribution ◽  
Health Risk ◽  
Health Risk Assessment ◽  
Surface Sediment ◽  
Self Organizing Map ◽  
Distribution Characteristics ◽  
Mean Values ◽  
Spatial Distribution Characteristics

Abstract To assess the spatial distribution characteristics and health risk of heavy metals (Cu, Zn, Ni, Cd, Pb, and Cr) in surface sediment of the Hai River and its tributaries in Tianjin, China, 32 surface sediment samples were collected. All the heavy metals mainly occurred in residue, except Cd. Cd primarily existed in exchangeable fraction and posed a high risk to the aquatic environment. The mean values of pollution index followed a decreasing trend of Cu > Cd > Ni > Pb > Cr > Zn. The results of health risk assessment showed that the heavy metals were not a threat to local residents and Cr and Pb were the main contributors to the health risk. The carcinogenic risk posed by Cr was two orders of magnitude higher than that posed by Cd. A self-organizing map divided the 32 sites into three clusters and more attention should be paid to cluster 3. The results will be conducive to understanding the heavy metal pollution patterns and implementing effective and accurate management programs.

scholarly journals Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Function Method ◽  
Kernel Density ◽  
Fire Occurrence ◽  
Distribution Characteristics ◽  
Fire Point ◽  
Spatial Distribution Characteristics ◽  
Grassland Fire

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.

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