4.7 Article

Effects of land-use patterns on in-stream nitrogen in a highly-polluted river basin in Northeast China

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 553, 期 -, 页码 232-242

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.scitotenv.2016.02.104

关键词

Land use; Landscape metrics; Nitrogen pollution; Nitrate isotopes; Nitrogen source contributions; Isotope mixing models

资金

  1. Open Funding Project of State Key Laboratory of Environmental Criteria and Risk Assessment
  2. Chinese Research Academy of Environmental Sciences [SKLECRA2014OFP12]
  3. China Scholarship Council [201404910228]

向作者/读者索取更多资源

This study investigated the effects of land-use patterns on nitrogen pollution in the Haicheng River basin in Northeast China during 2010 by conducting statistical and spatial analyses and by analyzing the isotopic composition of nitrate. Correlation and stepwise regressions indicated that land -use types and landscape metrics were correlated well with most river nitrogen variables and significantly predicted them during different sampling seasons. Built-up land use and shape metrics dominated in predicting nitrogen variables over seasons. According to the isotopic compositions of river nitrate in different zones, the nitrogen sources of the river principally originated from synthetic fertilizer, domestic sewage/manure, soil organic matter, and atmospheric deposition. isotope mixing models indicated that source contributions of river nitrogen significantly varied from forested headwaters to densely populated towns of the river basin. Domestic sewage/manure was a major contributor to river nitrogen with the proportions of 76.4 f 6.0% and 62.8 2.1% in residence and farmland-residence zones, respectively. This research suggested that regulating built-up land uses and reducing discharges of domestic sewage and industrial wastewater would be effective methods for river nitrogen control. (C) 2016 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据