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基于GIS的中国近岸海域1950—2020年赤潮时空变异格局 被引量:1
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作者 李志文 左秀玲 滕君灿 《环境科学学报》 CAS CSCD 北大核心 2023年第6期203-214,共12页
基于1950—2020年的赤潮公报等数据,采用GIS方法分析了中国近岸海域赤潮的时空分布规律与赤潮优势种演变.结果表明,过去70年中国近岸海域赤潮事件共发生1701次,累计赤潮面积超过25万km^(2),整体呈先增后减的趋势.赤潮高发期主要在4—8月... 基于1950—2020年的赤潮公报等数据,采用GIS方法分析了中国近岸海域赤潮的时空分布规律与赤潮优势种演变.结果表明,过去70年中国近岸海域赤潮事件共发生1701次,累计赤潮面积超过25万km^(2),整体呈先增后减的趋势.赤潮高发期主要在4—8月,其中5月赤潮发生次数最多,累计面积最大.赤潮发生月份由南到北呈季节性变化趋势,南部海域赤潮发生月份要早于北方.赤潮事件主要以180 km^(2)以内的小面积赤潮为主,大部分在11 d以内.赤潮高发区集中在河口、海湾、港湾及污染排放量较大的海域.诱发赤潮次数较多的赤潮优势种包括夜光藻、中肋骨条藻、东海原甲藻和米氏凯伦藻等.其中,夜光藻、中肋骨条藻和米氏凯伦藻在中国近岸海域均有分布,而东海原甲藻均发生在东海海域.夜光藻和中肋骨条藻一年四季均具备生长条件,米氏凯伦藻和东海原甲藻则在5—6月最为频繁.海水水质状况分析表明,中国近岸海域赤潮发生次数与海水水质污染程度密切相关;赤潮优势种的发生月份也与温度和盐度相关,赤潮优势种的季节性生长和空间分布可直接影响中国近岸海域的赤潮发生规模. 展开更多
关键词 赤潮 时空分布 赤潮优势种 影响因素 中国近岸海域
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富营养化指数法在中国近岸海域的应用 被引量:24
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作者 黄亚楠 吴孟孟 《海洋环境科学》 CAS CSCD 北大核心 2016年第2期316-320,共5页
系统研究了40多年来海水富营养化指数E定义的发展,进而整理了近10 a来应用富营养指数法评价中国近岸海域水质状态的富营养化指数E值,计算了我国Ⅰ、Ⅱ、Ⅲ和Ⅳ类海水水质标准所对应的E的最大临界值分别为:1.3、6、10.7和25;并阐明了河... 系统研究了40多年来海水富营养化指数E定义的发展,进而整理了近10 a来应用富营养指数法评价中国近岸海域水质状态的富营养化指数E值,计算了我国Ⅰ、Ⅱ、Ⅲ和Ⅳ类海水水质标准所对应的E的最大临界值分别为:1.3、6、10.7和25;并阐明了河口近岸海域环境中海水产生富营养化E高值的原因。结果显示,中国近岸海域富营养化指数E值的范围在0~137.84;中国近岸海域出现E的高值不仅与港口海湾所处的自然地理环境有关,也与人为因素有关;人为排放的氮磷营养盐是导致海水富营养化的主要因素。 展开更多
关键词 海水水质 富营养化 富营养化指数 中国近岸海域
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Impact of climatic change on sea surface temperature variation in Subei coastal waters,East China 被引量:2
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作者 王然 于非 《Chinese Journal of Oceanology and Limnology》 SCIE CAS CSCD 2014年第6期1406-1413,共8页
Sea surface temperature (SST) variation in the Subei coastal waters, East China, which is important for the ecological environment of the Yellow Sea where Enteromorphaprolifera blooms frequently, is affected by the ... Sea surface temperature (SST) variation in the Subei coastal waters, East China, which is important for the ecological environment of the Yellow Sea where Enteromorphaprolifera blooms frequently, is affected by the East Asian winter monsoon (EAWM), El Nifio-Southem Oscillation (ENSO), and Pacific Decadal Oscillation (PDO). In this study, correlations between climatic events and SST anomalies (SSTA) around the Subei (North Jiangsu Province, East China) Coast from 1981-2012 are analyzed, using empirical orthogonal function (EOF) and correlation analyses. First, a key region was determined by EOF analysis to represent the Subei coastal waters. Then, coherency analyses were performed on this key region. According to the correlation analysis, the EAWM index has a positive correlation with the spring and summer SSTA of the key region. Furthermore, the Nifio3.4 index is negatively correlated with the spring and summer SSTA of the key region 1 year ahead, and the PDO has significant negative coherency with spring SSTA and negative coherency with summer SSTA in the key region 1 year ahead. Overall, PDO exhibits the most significant impact on SSTA of the key region. In the key region, all these factors are correlated more significantly with SSTA in spring than in summer. This suggests that outbreaks ofEnteromorpha prolifera in the Yellow Sea are affected by global climatic changes, especially the PDO. 展开更多
关键词 sea surface temperature (SST) Pacific Decadal Oscillation (PDO) El Nifio-Southem Oscillation(ENSO) East Asian winter monsoon (EAWM) Subei coastal waters
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Profile of candidate microsatellite markers in Sebastiscus marmoratus using 454 pyrosequencing 被引量:2
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作者 SONG Na 《Chinese Journal of Oceanology and Limnology》 SCIE CAS CSCD 2017年第1期198-202,共5页
Sebastiscus marmoratus is an important sedentary ovoviparous fish distributed in near-shore coastal waters from the coast of China to Japan. Candidate S. marmoratus microsatellite markers were developed in the present... Sebastiscus marmoratus is an important sedentary ovoviparous fish distributed in near-shore coastal waters from the coast of China to Japan. Candidate S. marmoratus microsatellite markers were developed in the present study using 454 pyrosequencing, and the marker profile was analyzed. A total of 2 000 000 raw sequence reads were assembled to reduce redundancy. Among them, 1 043 dinucleotide, 925 trinucleotide, 692 tetranucleotide, and 315 pentanucleotide repeats were detected. AC repeats were the most frequent motifs among the dinucleotide repeats, and AAT was the most abundant among the trinucleotide repeats. AAAT, ATAG, and ATCC were the three most common tetranucleotide motifs, and AAGAT and AATAT were the most dominant pentanucleotide motifs. The greatest numbers of loci and potentially amplifiable loci were found in dinucleotide repeats, whereas trinucleotide repeats had the fewest. In summary, a wide range of candidate microsatellite markers were identified in the present study using a rapid and efficient 454 pyrosequencing approach. 展开更多
关键词 marbled rockfish microsatellite 454 FLX pyrosequencing genomic DNA
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Quantitative Seismic Risk Evaluation on Blind Faults in Offshore Areas of Tianjin,China
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作者 Liu Hongyan Chen Yukun +1 位作者 Yan Chengguo Yang Fei 《Earthquake Research in China》 2013年第4期542-552,共11页
Based on faults surveying and research data in the Tianjin offshore areas,through studying tectonic structure,Quaternary activity,deep structure,stress and strain fields and seismicity in the Tianjin offshore areas,th... Based on faults surveying and research data in the Tianjin offshore areas,through studying tectonic structure,Quaternary activity,deep structure,stress and strain fields and seismicity in the Tianjin offshore areas,the activity and tectonic features of the faults are determined synthetically.Using seismo-geological data,and the historical and modern seismicity data,the frequency-magnitude relationship model normalized by 500a is established and based on the relationship between the upper limit of maximum magnitude M u and a t/b,the maximum magnitudes of the sea section of the Haihe river fault and the Haiyi fault are calculated.Then Poisson probability model is adopted and the quantitative parameters,such as the maximum magnitude,occurrence probability,recurrence cycle of the faults in the south Tianjin offshore areas in the coming 50~200a,are calculated. 展开更多
关键词 Seismic risk Quantitative evaluation Fault sub-area Frequency-magnituderelationship Maximum magnitude Occurrence probability Recurrenceperiod
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