D
Da Chen
Researcher at Jinan University
Publications - 179
Citations - 7127
Da Chen is an academic researcher from Jinan University. The author has contributed to research in topics: Polybrominated diphenyl ethers & Chemistry. The author has an hindex of 34, co-authored 162 publications receiving 4521 citations. Previous affiliations of Da Chen include Carleton University & Southern Illinois University Carbondale.
Papers
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Journal ArticleDOI
Bisphenol Analogues Other Than BPA: Environmental Occurrence, Human Exposure, and Toxicity-A Review.
Da Chen,Da Chen,Kurunthachalam Kannan,Hongli Tan,Zhengui Zheng,Yong-Lai Feng,Yan Wu,Margaret Widelka +7 more
TL;DR: In this paper, a review of the current state of knowledge on the occurrence of bisphenol analogues (other than BPA) in the environment, consumer products and foodstuffs, human exposure and biomonitoring, and toxicity.
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Interaction of toxic chemicals with microplastics: A critical review.
TL;DR: The role of marine microplastic as a novel medium for environmental partitioning of chemicals in the ocean, which can cause toxic effects in the ecological environment is systematically demonstrated.
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Occurrence of bisphenol S in the environment and implications for human exposure: A short review.
Liu-Hong Wu,Xue-Mei Zhang,Fei Wang,Chong-Jing Gao,Da Chen,Jillian R. Palumbo,Ying Guo,Eddy Y. Zeng +7 more
TL;DR: The current data supports that exposure to BPS may have adverse effects on reproductive systems, endocrine systems, and nervous systems in animals and humans, and may trigger oxidative stress.
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A global review of polybrominated diphenyl ether flame retardant contamination in birds.
Da Chen,Robert C. Hale +1 more
TL;DR: This review summarizes most available PBDE data in birds and emphasizes several specific aspects, i.e., inter-regional differences in PBDE contamination, the extent of BDE-209 contamination, differences in congener composition patterns between piscivorous and terrestrial-feeding birds, trophic biomagnification and temporal changes inPBDE contamination.
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Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data.
Kangyang Chen,Hexia Chen,Chuanlong Zhou,Yichao Huang,Xiangyang Qi,Ruqin Shen,Fengrui Liu,Min Zuo,Xinyi Zou,Jinfeng Wang,Yan Zhang,Da Chen,Xingguo Chen,Xingguo Chen,Yongfeng Deng,Yongfeng Deng,Hongqiang Ren +16 more
TL;DR: Compared to other 7 models, decision tree (DT), random forest (RF) and deep cascade forest (DCF) trained by data sets of pH, DO, CODMn, and NH3-N had significantly better performance in prediction of all 6 Levels of water quality recommended by Chinese government.