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High spatial resolution and precision NanoSIMS for sulfur isotope analysis 期刊论文
Journal Analytical Atomic Spectrometry, 2022, 卷号: 37, 页码: 2529–2536
作者:  Youwei Chen;  Zhuojun Xie;  Shaohua Dong;  Quanliang Lei;  Jianfeng Gao
Adobe PDF(1669Kb)  |  收藏  |  浏览/下载：130/0  |  提交时间：2023/03/29
Spatial Characteristics and Risk Assessment of Heavy Metals in the Soil-Vegetation System of a Red Mud Slag Yard, SW China 期刊论文
Bulletin of Environmental Contamination and Toxicology, 2022, 卷号: 109, 期号: 1, 页码: 122-129
作者:  Runyu Zhang;  Zhi Zhang;  Jing Wu;  Liying Wang
Adobe PDF(2090Kb)  |  收藏  |  浏览/下载：138/0  |  提交时间：2023/03/14
Bauxite Residue Disposal Field  Heavy Metal  Soil-vegetation System  Potential Ecological Risk  Phytoremediation  
Arsenic accumulation and speciation in epilithic moss collected from an abandoned mercury mining area, south-western China 期刊论文
Atmospheric Pollution Research, 2019, 卷号: 10, 期号: 2, 页码: 455-461
作者:  Longchao Liang;  Zhidong Xu;  Guangle Qiu;  Pan Wu;  Ruixue Zhang
Adobe PDF(1315Kb)  |  收藏  |  浏览/下载：163/0  |  提交时间：2020/09/03
Arsenic  Arsenic Speciation  Characteristics And Spatial Distribution  Epilithic Moss  Potential Sources  
Assessment on the effects of aluminum-modified clay in inactivating internal phosphorus in deep eutrophic reservoirs 期刊论文
Chemosphere, 2019, 卷号: 215, 页码: 657-667
作者:  Jingfu Wang;  Jingan Chen;  Quan Chen;  Haiquan Yang;  Yan Zeng;  Pingping Yu;  Zuxue Jin
Adobe PDF(2195Kb)  |  收藏  |  浏览/下载：234/0  |  提交时间：2019/06/12
AlumInum-modified Clay  phosphorus  sediment  diffusive Gradients In thIn Films  high-resolution Dialysis Peeper  
Distribution characteristics of heavy metals in surface soils from the western area of Nansi Lake, China 期刊论文
Environmental Monitoring and Assessment, 2019, 卷号: 191, 期号: 5, 页码: 1-13
作者:  Huijuan Guo;  Liyuan Yang;  Xuemei Han;  Jierui Dai;  Xugui Pang;  Mingyi Ren;  Wei Zhang
Adobe PDF(2675Kb)  |  收藏  |  浏览/下载：194/0  |  提交时间：2020/05/11
Heavy Metal  Spatial Distribution  Environmental Impact Factor  Pollution Evaluation  
Metallogeny and temporal–spatial distribution of manganese mineralizations in Iran: Implications for future exploration 期刊论文
Ore Geology Reviews, 2019, 卷号: 115, 页码: 1-42
作者:  Sajjad Maghfouri;  Ebrahim Rastad;  Mehdi Movahednia;  David R. Lentz;  Mohammad Reza Hosseinzadeh;  Lin Ye;  Fardin Mousivand
Adobe PDF(55894Kb)  |  收藏  |  浏览/下载：161/0  |  提交时间：2020/11/17
Manganese Deposits  metallogeny  tectonic Setting  iran  
Geochemical characteristics of dissolved heavy metals in Zhujiang River, Southwest China: Spatial-temporal distribution, source, export flux estimation, and a water quality assessment 期刊论文
Peerj, 2019, 页码: 1-21
作者:  Jie Zeng;  Guilin Han;  Qixin Wu;  Yang Tang
Adobe PDF(1777Kb)  |  收藏  |  浏览/下载：146/0  |  提交时间：2020/09/25
Zhujiang River  water Quality Index  health Risk  Dissolved Heavy Metals  Southwest China  
Deposition flux and mass inventory of polychlorinated biphenyls in sediments of the Yangtze River Estuary and inner shelf, East China Sea: Implications for contributions of large-river input and e-was 期刊论文
Science of the Total Environment, 2019, 卷号: 647, 页码: 1222–1229
作者:  Tiange Zhao;  Zhigang Guo;  Peng Yao;  Limin Hu;  Zilan Wu;  Tian Lin
Adobe PDF(2216Kb)  |  收藏  |  浏览/下载：327/0  |  提交时间：2019/06/12
Coastal East China Sea  deposition Flux  mass Inventory  pcbs  
Large-river dominated black carbon flux and budget: A case study of the estuarine-inner shelf of East China Sea, China 期刊论文
Science of the Total Environment, 2019, 卷号: 651, 页码: 2489–2496
作者:  Yin Fang;  Yingjun Chen;  ming Luo;  Jun Li;  Gan Zhang;  Limin Hu;  Chongguo Tian;  YongMei Zheng;  Tian Lin
Adobe PDF(2173Kb)  |  收藏  |  浏览/下载：251/0  |  提交时间：2019/06/12
Carbon Cycle  black Carbon  riverine Discharge  yangtze River  east China Sea  
A machine learning approach to crater classification from topographic data 期刊论文
Remote Sensing, 2019, 卷号: 11, 期号: 21, 页码: 1-30
作者:  Qiangyi Liu;  Weiming Cheng;  Guangjian Yan;  Yunliang Zhao;  Jianzhong Liu
Adobe PDF(13418Kb)  |  收藏  |  浏览/下载：170/0  |  提交时间：2020/11/23
Moon  Distinguish Primary Craters From Secondary Craters  Machine Learning  Crater Characteristics