引用本文:	邓雪,胡玉斌,刘春颖,杨桂朋,陆小兰,张洪海.胶州湾表层海水碳酸盐体系的季节变化.海洋与湖沼,2016,47(1):234-244.

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胶州湾表层海水碳酸盐体系的季节变化
邓雪,胡玉斌,刘春颖,杨桂朋,陆小兰,张洪海
中国海洋大学化学化工学院海洋化学理论与工程技术教育部重点实验室青岛 266100,中国海洋大学化学化工学院海洋化学理论与工程技术教育部重点实验室青岛 266100,中国海洋大学化学化工学院海洋化学理论与工程技术教育部重点实验室青岛 266100,中国海洋大学化学化工学院海洋化学理论与工程技术教育部重点实验室青岛 266100,中国海洋大学化学化工学院海洋化学理论与工程技术教育部重点实验室青岛 266100,中国海洋大学化学化工学院海洋化学理论与工程技术教育部重点实验室青岛 266100

摘要:

根据2007年8月(夏季),11月(秋季),2008年1月(冬季)和2010年4月(春季)在胶州湾海域测得的pH、溶解无机碳(DIC)、总碱度(Alk),以及通过以上参数计算得到的二氧化碳分压(pCO₂)的数据,结合现场的化学、水文、生物等参数,探讨和分析了该海域的二氧化碳各参数的分布特征、季节变化和影响因素。结果表明:胶州湾pH、DIC、Alk和pCO₂的年变化范围分别为:7.77-8.30,1949.2-2201.8μmol/kg,2033.9-2382.5μmol/kg和89.9-745.3μatm,均呈现明显的时空变化。温度是影响胶州湾碳酸盐体系的主要影响因素之一,同时陆地径流和降水会降低海水碳酸盐体系中各参数的含量,但是人类活动和生物活动也会在一定程度上增加DIC、Alk和pCO₂的含量。

关键词: 胶州湾无机碳体系分布季节变化影响因素

DOI：10.11693/hyhz20150800221

分类号:

基金项目:国家自然科学基金项目,41176062号,40706040号。

附件

DISTRIBUTIONS AND SEASONAL VARIATIONS OF CARBONATE SYSTEM IN THE JIAOZHOU BAY, CHINA

dengxue,HuYubin,LiuChunying,YangGuipeng,LuXiaolan and ZhangHonghai

Ocean University of China,Ocean University of China,Ocean University of China,Ocean University of China,Ocean University of China,Ocean University of China

Abstract:

Data of pH, dissolved inorganic carbon (DIC), and alkalinity (Alk) were collected from the Jiaozhou Bay, Shandong, China during four field observations in August 2007, November 2007, January 2008, and April 2010, and partial pressure of CO₂ (pCO₂) was calculated. The distributions, seasonal variations, and influencing factors of carbon dioxide system with in-situ chemical, hydrological, and biological parameters were studied. The result showed that the values of pH and pCO₂, concentrations of DIC and Alk, in the surface waters of the Jiaozhou Bay were in the range of 7.77-8.30, 89.9-745.3 μatm, 1949.2-2201.8 μmol/kg, and 2033.9-2382.5 μmol/kg, respectively, and all of parameters presented obvious seasonal variations. The CO₂ parameters were found affected by seawater temperature, land runoff, biological activity, and human activity.

Key words: Jiaozhou Bay carbonate system distributions season variations influencing factors