摘要
为了解胶州湾湿地的土壤质量情况,于2014年7月9日和10月10日,分别采集光滩、碱蓬（Suaeda glauca）盐沼、芦苇（Phragmites australis）盐沼和互花米草（Spartina alterniflora）盐沼采样地土壤样品,测定了12种土壤理化指标;运用主成分分析法,确定评价土壤质量的指标;采用土壤质量综合评价指数法,评价胶州湾湿地土壤质量。研究结果表明,溶解性有机碳含量、微生物量碳含量、全氮含量和铵态氮含量可以作为评价土壤质量评价的指标;4处采样地土壤综合质量指数为0.17～0.42,其中,随着与海洋距离的增加,大沽河口采样地土壤质量越来越差,即光滩、碱蓬盐沼和芦苇盐沼土壤质量逐渐变差,而由于互花米草的入侵,洋河口采样地的土壤质量显著提高;7月9日各采样地土壤质量高于10月10日。植物、水文条件、采样时间等可以通过影响土壤质量评价指标,影响湿地土壤质量。
In order to understand the soil quality of Jiaozhou Bay wetland, four typical sample sites around Jiaozhou Bay were chosen, including bare flat, Suaeda glauca salt marshes, Phragmites australis salt marshes and Spartina anglica salt marshes on July 9 and October 10 of 2014. Twelve kinds of soil property indexes were collected and determined. The principal component analysis（PCA） was used to determine the minimum data set（MDS）, and the soil quality index（SQI） were obtained by the sum operation with coefficient. The results showed that soil quality evaluation indexes included contents of dissolved organic carbon, microbial biomass carbon, total nitrogen and ammonia nitrogen. The soil quality indexes of the four kinds of sampling sites were 0.17-0.42, and the soil quality in Dagu Estuary plots decreased with the increasing distance from the sea, the soil quality of bare flat was the best and followed by those of Suaeda glauca salt marshes and Phragmites australis salt marshes. The invasion of Spartina anglica in Yang estuary significantly improved the soil quality of the wetlands. The performance of soil quality on July 9 was larger than that of October 10.Vegetation, hydrologic condition and seasonal changes could affect the quality of wetland soil by influencing the content of evaluation indexes, which was closely related to the soil quality.
作者
郗敏
仙旋旋
孔范龙
李悦
于雪
XI Min;XIAN Xuanxuan;KONG Fanlong;LI Yue;YU Xue(College of Environmental Science and Engineering,Qingdao University,Qingdao 266071,Shandong,P.R.China)
出处
《湿地科学》
CSCD
北大核心
2018年第5期604-611,共8页
Wetland Science
基金
国家自然科学基金项目(41771098)资助
关键词
土壤
质量评价
主成分分析
胶州湾
soil
quality evaluation
principal component analysis
Jiaozhou Bay
