Monthly and Interannual Variations in Winter Positive Surface‐Bottom Temperature Difference in Northeastern Coastal Waters of the Shandong Peninsula in the Yellow Sea

Author(s):  
Fangguo Zhai ◽  
Zizhou Liu ◽  
Peiliang Li ◽  
Yanzhen Gu ◽  
Luoyu Hu ◽  
...  
2019 ◽  
Vol 70 (11) ◽  
pp. 1611 ◽  
Author(s):  
Xiaoyun Bai ◽  
Congcong Guo ◽  
Mamun Abdullah Al ◽  
Alan Warren ◽  
Henglong Xu

Multifunctional trait analysis is increasingly recognised as an effective tool for assessing ecosystem function and environmental quality. Here, a baseline study was performed at four depths (i.e. 1, 2, 3.5 and 5m) in Yellow Sea coastal waters of northern China in order to determine the optimal depth for bioassessment using biological traits of biofilm-dwelling ciliates. Community-weighted means (CWM) from functional traits system were used to summarise the trait distribution and functional diversity of ciliates among the four depths during a 1-month colonisation period. Functional trait distribution revealed a clear temporal variation among the four depths. In total, 3 of 17 functional traits (i.e. feeding type, body size and flexibility) showed significant temporal patterns. Bootstrapped averaging and permutational multivariate analysis of variance (PERMANOVA) tests demonstrated that the colonisation pattern of biofilm-dwelling ciliates as expressed by CWM at 1 and 2m differed significantly from those at 3.5 and 5m. Functional diversity indices showed lower variability at 1 and 2m than at 3.5 and 5m. These results suggest that 1 and 2m are the preferred sampling depths for bioassessment of marine water quality using biological traits of biofilm-dwelling ciliates.


2015 ◽  
Vol 34 (12) ◽  
pp. 147-153 ◽  
Author(s):  
Gang Xu ◽  
Jian Liu ◽  
Shaofeng Pei ◽  
Xianghuai Kong ◽  
Gang Hu ◽  
...  

2017 ◽  
Author(s):  
Chun-Ying Liu ◽  
Wei-Hua Feng ◽  
Ye Tian ◽  
Gui-Peng Yang ◽  
Pei-Feng Li ◽  
...  

Abstract. We developed a new method for the determination of dissolved nitric oxide (NO) in discrete seawater samples based on a combination of a purge-and-trap set-up and fluorometric detection of NO. 2,3-diaminonaphthalene (DAN) reacts with NO in seawater to form the highly fluorescent 2,3-naphthotriazole (NAT). The fluorescence intensity was linear for NO concentrations in the range from 0.14 nmol L−1 to 19 nmol L−1. We determined a detection limit of 0.068 nmol L−1, an average recovery coefficient of 83.8 % (80.2–90.0 %), and a relative standard deviation of ±7.2 %. With our method we determined for the first time the temporal and spatial distributions of NO surface concentrations in coastal waters of the Yellow Sea off Qingdao and in Jiaozhou Bay during a cruise in November 2009. The concentrations of NO varied from below the detection limit to 0.50 nmol L−1 with an average of 0.26 ± 0.14 nmol L−1. NO surface concentrations were generally enhanced significantly during daytime implying that NO formation processes such as NO2− photolysis are much higher during daytime than chemical NO consumption which, in turn, lead to a significant decrease of NO concentrations during nighttime. In general, NO surface concentrations and measured NO production rates were higher compared to previously reported measurements. This might be caused by the high NO2− surface concentrations encountered during the cruise. Moreover, additional measurements of NO production rates implied that the occurrence of particles and a temperature increase can enhance NO production rates. With the method introduced here we have a reliable and comparably easy to use method at hand to measure oceanic NO surface concentrations which can be used to decipher both its temporal and spatial distributions as well as its biogeochemical pathways in the oceans.


2011 ◽  
Vol 29 (1) ◽  
pp. 118-127 ◽  
Author(s):  
Kuidong Xu ◽  
Joong Ki Choi ◽  
Yanli Lei ◽  
Eun Jin Yang

2018 ◽  
Vol 63 ◽  
pp. 34-43 ◽  
Author(s):  
Mamun Abdullah Al ◽  
Yangyang Gao ◽  
Guangjian Xu ◽  
Zheng Wang ◽  
Alan Warren ◽  
...  

2020 ◽  
Vol 2 (3) ◽  
pp. 292-301 ◽  
Author(s):  
Mohammad Nurul Azim Sikder ◽  
Henglong Xu ◽  
Alan Warren

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