基于TLI和PCA的贵州草海水质状况评价

doi:10.11988/ckyyb.20170236

长江科学院院报 ›› 2018, Vol. 35 ›› Issue (8): 40-45.DOI: 10.11988/ckyyb.20170236

基于TLI和PCA的贵州草海水质状况评价

陈丽华¹,喻记新^2,3,李丽¹,吕文强⁴,张建利⁴

1.黔西县环境监测站,贵州毕节 551500;
2.长江大学资源与环境学院,武汉 430100;
3.中国科学院水生生物研究所淡水生态与生物技术国家重点实验室, 武汉 430072;
4.贵州科学院贵州山地资源研究所, 贵阳 550001

收稿日期:2017-03-08 出版日期:2018-08-01 发布日期:2018-08-14
通讯作者: 张建利(1981-),男,甘肃庆阳人,副研究员,博士,主要从事植物生态和喀斯特退化森林生态系统恢复方面的研究。E-mail:zhangjl-z@163.com
作者简介:陈丽华(1977-),男,贵州毕节人,助理工程师,主要从事水质监测方面的研究工作。E-mail:2366388603@qq.com
基金资助:
贵州省重大专项(黔科合重大专项字[2016] 3022-03号)

Water Quality Assessment of Caohai Lake in Guizhou Based on Trophic State Index and Principal Component Analysis

CHEN Li-hua¹, YU Ji-xin^2,3, LI Li¹, L Wen-qiang⁴, ZHANG Jian-li⁴

1.Environmental Monitoring Station of Qianxi County, Bijie 551500, China;
2.College of Resources and Environment, Yangtze University, Wuhan 430100, China;
3.State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China;
4.Institute of Mountain Resources of Guizhou Province, Guiyang 550001, China

Received:2017-03-08 Online:2018-08-01 Published:2018-08-14

摘要/Abstract

摘要： 为了评价贵州草海水质状况,分别于2016年1,4,7,10月对草海阳关山和草海中部各3个样点的11项水体理化指标进行了监测,运用综合营养状态指数法(TLI)并结合《地表水环境质量标准》(GB 3838—2002)对监测区水质进行了评价,同时应用主成分分析(PCA)分析了水质的污染程度和主要影响因子。结果显示:①11项参数在2个区域均有显著的季节差异,其中透明度(SD)和溶氧(DO)为冬季最优,悬浮物(SPM)在秋季最高,pH几乎均呈弱碱性(除秋季);TN,TP,NH₄⁺-N,COD_Mn全年的水平分别为0.58～2.27,0.032～0.072,0.059～0.364,4.28～6.79 mg/L,Chl.a水平为5.62～9.95 mg/m³,其中TN在秋季最高,TP在秋冬季较高,COD_Mn和Chl.a在夏秋季较高。②TLI评价结果表明,阳关山和中部水体TLI值在秋季略超过50,为轻度污染状态,其他季节TLI值均为30～50,为中营养状态;《地表水环境质量标准》(GB 3838—2002)评价结果表明阳关山全年主要为Ⅱ—Ⅲ类水质,而中部较差,全年主要为Ⅱ— Ⅳ类水质,影响水质类别的主要因子是TN和COD_Mn,TP和NH₄⁺-N全年为Ⅱ类水平。③PCA结果显示阳关山水质最主要的影响因子包括离子、氮素、有机质、DO和Chl.a,而中部则包括Chl.a,NH₄⁺-N,SD,EC,TP,DO,WT,SPM。总之,氮素和有机质是影响草海阳关山和中部水质的最主要因子和根本原因,控制外源性的氮和有机质输入是管理和改善草海水质的关键所在。

关键词: 水质评价, 草海, 季节变化, 综合营养状态指数法(TLI), 主成分分析(PCA)

Abstract: Eleven indicators of water quality in two areas of the Caohai Lake (Yangguanshan and the middle part) in Guizhou Province were measured seasonally in 2016. Trophic state index (TLI) and Surface Water Quality Standard (GB 3838—2002) were used to evaluate the trophic status of water quality, and principal component analysis (PCA) was conducted to assess the pollution level and main influential factors. Results demonstrated that: (1) The eleven indicators in both areas displayed significant seasonal variations, among which transparency and dissolved oxygen (DO) were highest in winter; while suspended solids (SPM) was highest in autumn. Except that in autumn, pH remained at about 8. TN, TP, NH₄⁺-N and COD_Mnwere in 0.58-2.27, 0.032-0.072, 0.059-0.364 and 4.28-6.79 mg/L, respectively and Chl.a ranged from 5.62 to 9.95 mg/m³, with the highest TN in autumn, the highest TP in autumn and winter, and highest COD_Mn and Chl.a in summer and autumn. (2) The TLI of both areas were slightly higher than 50 in autumn, indicating slight pollution, while in other seasons, the TLI ranged between 30 and 50, implying medium trophic state. Evaluation results from GB 3838—2002 show that the water quality in Yangguanshan belonged to class Ⅱ and Ⅲ, except for a few occasions; whereas in the middle part, the water quality was mainly class Ⅱ and Ⅳ. TN and COD_Mnwere major influential factors, while TP and NH₄⁺-N were at level Ⅱ throughout the year. (3) Principle component analysis revealed that the most important influencing factors of water quality in Yangguanshan were ions, nitrogen, organic matter, DO and Chl.a, while in the middle part were Chl.a, NH₄⁺-N, SD, EC, TP, DO, WT and SPM. In conclusion, nitrogen and organic matters are the most important influencing factors and the fundamental causes of water quality in Yangguanshan and the middle part of Caohu Lake. Controlling the inputs of external nitrogen and organic matters would be the key to improving the water quality in Caohai Lake.

Key words: water quality assessment, Caohai Lake, seasonal variation, trophic state index (TLI), principal component analysis (PCA)

中图分类号:

X832

陈丽华,喻记新,李丽,吕文强,张建利. 基于TLI和PCA的贵州草海水质状况评价[J]. 长江科学院院报, 2018, 35(8): 40-45.

CHEN Li-hua, YU Ji-xin, LI Li, L Wen-qiang, ZHANG Jian-li. Water Quality Assessment of Caohai Lake in Guizhou Based on Trophic State Index and Principal Component Analysis[J]. JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI, 2018, 35(8): 40-45.

[1]	税永红, 卢永洪, 罗靖, 罗永军, 毛红梅, 彭黎帆. 冬春交替期沱江流域水质评价及差异分析[J]. 长江科学院院报, 2022, 39(4): 56-62.
[2]	赖文哲, 毛志勇, 岳立柱, 汤家喜. 基于熵权-偏序集的水质评价方法[J]. 长江科学院院报, 2021, 38(3): 32-38.
[3]	冯国平, 高宗军, 蔡五田, 吕永高, 郭林. 豫北山前修武地区地下水水化学特征及水质评价[J]. 长江科学院院报, 2021, 38(1): 27-34.
[4]	冯建国, 季德帅, 高宗军, 杨丽芝, 朱恒华, 刘治政. 山东淄博大武水源地地下水水化学特征及水质评价[J]. 长江科学院院报, 2020, 37(9): 18-23.
[5]	钱声源, 张乾坤, 陈从建, 杨国平. 焦作地区地下水水化学特征分析及水质评价[J]. 长江科学院院报, 2020, 37(4): 30-36.
[6]	尹晓爱,江波,洪江. 草海流域水质时空分布特征及污染评价[J]. 长江科学院院报, 2019, 36(9): 40-45.
[7]	褚一凡,赵闪闪,李杲光,靳同霞,马剑敏. 陈桥东湖浮游生物群落结构特征及水质评价[J]. 长江科学院院报, 2019, 36(8): 23-29.
[8]	王灿, 袁婷, 张建利, 喻记新, 徐萌, 刘勇. 贵州草海水质时空变化和水体营养状况[J]. 长江科学院院报, 2019, 36(6): 14-19.
[9]	张世旭, 王中美, 代天豪. 毕节地区岩溶地下水水化学特征与水质评价[J]. 长江科学院院报, 2019, 36(5): 28-33,41.
[10]	刚什婷, 贾涛, 邓英尔, 刘伟坡, 彭鑫. 基于熵权法的集对分析模型在蛤蟆通流域地下水水质评价中的应用[J]. 长江科学院院报, 2018, 35(9): 23-27.
[11]	张德彬, 刘国东, 王亮, 钟瑞. 基于博弈论组合赋权的TOPSIS模型在地下水水质评价中的应用[J]. 长江科学院院报, 2018, 35(7): 46-50.
[12]	闫滨, 黄万霞, 刘羽婷. 考虑时间维度的灰水足迹核算与评价——以浑河大伙房水库上游段为例[J]. 长江科学院院报, 2018, 35(7): 40-45.
[13]	黄瑞, 韩龙喜, 张防修. 强降雨过程中入库面源污染对库区水质的影响[J]. 长江科学院院报, 2018, 35(5): 12-16.
[14]	叶章蕊，卢毅敏. 组合权重模糊联系度模型在水质评价中的应用[J]. 长江科学院院报, 2016, 33(9): 33-39.
[15]	李鹏程，韩春，王月敏，王甲荣. 改进的RAGA-PPC模型及其在河流健康评价中的应用[J]. 长江科学院院报, 2016, 33(9): 18-22.

基于TLI和PCA的贵州草海水质状况评价

Water Quality Assessment of Caohai Lake in Guizhou Based on Trophic State Index and Principal Component Analysis

PDF

可视化

摘要/Abstract

引用本文

使用本文

相关文章 15

编辑推荐

Metrics

本文评价