The distribution of hexachlorocyclohexanes(HCHs) and dichlorodiphenyltrichloroethanes(DDTs) in the surface seawater and sediment of Jincheng Bay mariculture area were investigated in the present study. The concentrati...The distribution of hexachlorocyclohexanes(HCHs) and dichlorodiphenyltrichloroethanes(DDTs) in the surface seawater and sediment of Jincheng Bay mariculture area were investigated in the present study. The concentration of total HCHs and DDTs ranged from 2.98 to 14.87 ng L-1 and were < 0.032 ng L-1, respectively, in surface seawater, and ranged from 5.52 to 9.43 and from 4.11 to 6.72 ng g-1, respectively, in surface sediment. It was deduced from the composition profile of HCH isomers and DDT congeners that HCH residues derived from a mixture of technical-grade HCH and lindane whereas the DDT residues derived from technical-grade DDT and dicofol. Moreover, both HCH and DDT residues may mainly originate from historical inputs. The hazard quotient of α-HCH, β-HCH, γ-HCH and δ-HCH to marine species was 0.030, 0.157, 3.008 and 0.008, respectively. It was estimated that the overall probability of adverse biological effect from HCHs was less than 5%, indicating that its risk to seawater column species was low. The threshold effect concentration exceeding frequency of γ-HCH, p,p'-DDD, p,p'-DDE and p,p'-DDT in sediment ranged from 8.3% to 100%, and the relative concentration of the HCH and DDT mixture exceeded their probable effect level in sediment. These findings indicated that the risk to marine benthos was high and potentially detrimental to the safety of aquatic products, e.g., sea cucumber and benthic shellfish.展开更多
The abundance of spectral information provided by hyperspectral imagery offers great benefits for many applications.However,processing such high-dimensional data volumes is a challenge because there may be redundant b...The abundance of spectral information provided by hyperspectral imagery offers great benefits for many applications.However,processing such high-dimensional data volumes is a challenge because there may be redundant bands owing to the high interband correlation.This study aimed to reduce the possibility of“dimension disaster”in the classification of coastal wetlands using hyperspectral images with limited training samples.The study developed a hyperspectral classification algorithm for coastal wetlands using a combination of subspace partitioning and infinite probabilistic latent graph ranking in a random patch network(the SSP-IPLGR-RPnet model).The SSP-IPLGR-RPnet approach applied SSP techniques and an IPLGR algorithm to reduce the dimensions of hyperspectral data.The RPnet model overcame the problem of dimension disaster caused by the mismatch between the dimensionality of hyperspectral bands and the small number of training samples.The results showed that the proposed algorithm had a better classification performance and was more robust with limited training data compared with that of several other state-of-the-art methods.The overall accuracy was nearly 4%higher on average compared with that of multi-kernel SVM and RF algorithms.Compared with the EMAP algorithm,MSTV algorithm,ERF algorithm,ERW algorithm,RMKL algorithm and 3D-CNN algorithm,the SSP-IPLGR-RPnet algorithm provided a better classification performance in a shorter time.展开更多
基金supported by the Marine Special Scientific Fund for the Non-profit Public Industry of China (200805031)Fund of Key Laboratory of Fishery Ecology and Environment, Guangdong Province (LFE-20144)Scientific Research Foundation for the Third Institute of Oceanography, State Oceanic Administration (No. 2013031)
文摘The distribution of hexachlorocyclohexanes(HCHs) and dichlorodiphenyltrichloroethanes(DDTs) in the surface seawater and sediment of Jincheng Bay mariculture area were investigated in the present study. The concentration of total HCHs and DDTs ranged from 2.98 to 14.87 ng L-1 and were < 0.032 ng L-1, respectively, in surface seawater, and ranged from 5.52 to 9.43 and from 4.11 to 6.72 ng g-1, respectively, in surface sediment. It was deduced from the composition profile of HCH isomers and DDT congeners that HCH residues derived from a mixture of technical-grade HCH and lindane whereas the DDT residues derived from technical-grade DDT and dicofol. Moreover, both HCH and DDT residues may mainly originate from historical inputs. The hazard quotient of α-HCH, β-HCH, γ-HCH and δ-HCH to marine species was 0.030, 0.157, 3.008 and 0.008, respectively. It was estimated that the overall probability of adverse biological effect from HCHs was less than 5%, indicating that its risk to seawater column species was low. The threshold effect concentration exceeding frequency of γ-HCH, p,p'-DDD, p,p'-DDE and p,p'-DDT in sediment ranged from 8.3% to 100%, and the relative concentration of the HCH and DDT mixture exceeded their probable effect level in sediment. These findings indicated that the risk to marine benthos was high and potentially detrimental to the safety of aquatic products, e.g., sea cucumber and benthic shellfish.
基金supported by the National Natural Science Foundation of China (Grant Nos. 42106179 and 42076189)the Pilot Project of Monitoring Evaluation of Spartina Alterniflora in Shandong Province in 2021 “Remote Sensing Monitoring of Spartina Alterniflora”
文摘The abundance of spectral information provided by hyperspectral imagery offers great benefits for many applications.However,processing such high-dimensional data volumes is a challenge because there may be redundant bands owing to the high interband correlation.This study aimed to reduce the possibility of“dimension disaster”in the classification of coastal wetlands using hyperspectral images with limited training samples.The study developed a hyperspectral classification algorithm for coastal wetlands using a combination of subspace partitioning and infinite probabilistic latent graph ranking in a random patch network(the SSP-IPLGR-RPnet model).The SSP-IPLGR-RPnet approach applied SSP techniques and an IPLGR algorithm to reduce the dimensions of hyperspectral data.The RPnet model overcame the problem of dimension disaster caused by the mismatch between the dimensionality of hyperspectral bands and the small number of training samples.The results showed that the proposed algorithm had a better classification performance and was more robust with limited training data compared with that of several other state-of-the-art methods.The overall accuracy was nearly 4%higher on average compared with that of multi-kernel SVM and RF algorithms.Compared with the EMAP algorithm,MSTV algorithm,ERF algorithm,ERW algorithm,RMKL algorithm and 3D-CNN algorithm,the SSP-IPLGR-RPnet algorithm provided a better classification performance in a shorter time.
