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中国科学院重庆绿色智能技术研究院机构知识库
KMS Chongqing Institute of Green and Intelligent Technology, CAS
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Changes in CO2 concentration drive a succession of toxic and non-toxic strains of
Microcystis
blooms
期刊论文
WATER RESEARCH, 2024, 卷号: 250, 页码: 10
作者:
Jiang, Jingyu
;
Zeng, Jiaying
;
Wang, Jingkai
;
Zuo, Jun
;
Wei, Nian
;
Song, Lirong
;
Shan, Kun
;
Gan, Nanqin
收藏
  |  
浏览/下载:186/0
  |  
提交时间:2024/03/01
CO2
Toxic Microcystis
Non-toxic Microcystis
Succession
XGBoost model
Microcystis blooms
Improved Deep Learning Predictions for Chlorophyll Fluorescence Based on Decomposition Algorithms: The Importance of Data Preprocessing
期刊论文
WATER, 2023, 卷号: 15, 期号: 23, 页码: 19
作者:
Wang, Lan
;
Xie, Mingjiang
;
Pan, Min
;
He, Feng
;
Yang, Bing
;
Gong, Zhigang
;
Wu, Xuke
;
Shang, Mingsheng
;
Shan, Kun
收藏
  |  
浏览/下载:200/0
  |  
提交时间:2024/01/17
chlorophyll fluorescence
deep learning
online monitoring
time-series decomposition
Dianchi Lake
Response of soil aggregate stability to plant diversity loss along an inundation stress gradient in a reservoir riparian zone
期刊论文
CATENA, 2023, 卷号: 233, 页码: 13
作者:
Wang, Xiaoxiao
;
Shan, Kun
;
Huang, Ping
;
Ma, Maohua
;
Wu, Shengjun
收藏
  |  
浏览/下载:14/0
  |  
提交时间:2023/12/25
Soil aggregate stability
Plant diversity
Root traits
Inundation stress
Three Gorges Reservoir
Combined Physical Process and Deep Learning for Daily Water Level Simulations across Multiple Sites in the Three Gorges Reservoir, China
期刊论文
WATER, 2023, 卷号: 15, 期号: 18, 页码: 16
作者:
Xie, Mingjiang
;
Shan, Kun
;
Zeng, Sidong
;
Wang, Lan
;
Gong, Zhigang
;
Wu, Xuke
;
Yang, Bing
;
Shang, Mingsheng
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2023/12/25
water level prediction
Three Gorges Reservoir
deep learning
hybrid model
Temperature-Dependent Growth Characteristics and Competition of Pseudanabaena and Microcystis
期刊论文
WATER, 2023, 卷号: 15, 期号: 13, 页码: 15
作者:
Hu, Lili
;
Wang, Haiyan
;
Cui, Jingzhen
;
Zou, Wansheng
;
Li, Jie
;
Shan, Kun
收藏
  |  
浏览/下载:206/0
  |  
提交时间:2023/12/25
Pseudanabaena
Microcystis
temperature
growth
competition
Nutrient reduction mitigated the expansion of cyanobacterial blooms caused by climate change in Lake Taihu according to Bayesian network models
期刊论文
WATER RESEARCH, 2023, 卷号: 236, 页码: 10
作者:
Deng, Jianming
;
Shan, Kun
;
Shi, Kun
;
Qian, Song S.
;
Zhang, Yunlin
;
Qin, Boqiang
;
Zhu, Guangwei
收藏
  |  
浏览/下载:12/0
  |  
提交时间:2023/12/25
Global warming
Cyanobacterial blooms
Internal loading
Shallow lakes
Data-intensive statistical models
Harmful cyanobacteria-diatom/dinoflagellate blooms and their cyanotoxins in freshwaters: A nonnegligible chronic health and ecological hazard
期刊论文
WATER RESEARCH, 2023, 卷号: 233, 页码: 14
作者:
Zhang, Yanyan
;
Whalen, Joann K.
;
Cai, Chen
;
Shan, Kun
;
Zhou, Hongxu
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2023/12/25
Harmful algal bloom
Ecophysiological roles
Species compositions
Cyanotoxins
Human and ecological health
Harmful Cyanobacterial Blooms: Biological Traits, Mechanisms, Risks, and Control Strategies
期刊论文
ANNUAL REVIEW OF ENVIRONMENT AND RESOURCES, 2023, 卷号: 48, 页码: 123-147
作者:
Song, Lirong
;
Jia, Yunlu
;
Qin, Boqiang
;
Li, Renhui
;
Carmichael, Wayne W.
;
Gan, Nanqin
;
Xu, Hai
;
Shan, Kun
;
Sukenik, Assaf
收藏
  |  
浏览/下载:12/0
  |  
提交时间:2024/01/17
harmful cyanobacterial blooms
climate change
eutrophication
cyanotoxins
freshwaters
Water depth and land-use intensity indirectly determine phytoplankton functional diversity and further regulate resource use efficiency at a multi-lake scale
期刊论文
SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 卷号: 834, 页码: 13
作者:
Zhou, Qichao
;
Zhang, Yun
;
Tao, Juan
;
Ye, Lin
;
Wang, Haijun
;
Shan, Kun
;
Jeppesen, Erik
;
Song, Lirong
收藏
  |  
浏览/下载:46/0
  |  
提交时间:2022/10/14
Phytoplankton
Biodiversity-ecosystem functioning (BEF)
Lake depth
Land use
Total phosphorus
Bayesian network
Revealing Physiochemical Factors and Zooplankton Influencing Microcystis Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning
期刊论文
TOXINS, 2022, 卷号: 14, 期号: 8, 页码: 16
作者:
Wang, Xiaoxiao
;
Wang, Lan
;
Shang, Mingsheng
;
Song, Lirong
;
Shan, Kun
收藏
  |  
浏览/下载:113/0
  |  
提交时间:2022/10/14
Microcystis blooms
microcystins
nutrient
zooplankton
machine learning
risk management
Lake Taihu