大亚湾水质对人类活动响应的关键控制指标识别和量化解析

doi:10.11978/2022120

热带海洋学报 ›› 2023, Vol. 42 ›› Issue (1): 182-191.doi: 10.11978/2022120CSTR: 32234.14.2022120

大亚湾水质对人类活动响应的关键控制指标识别和量化解析

姜迅¹(), 武文¹^,²(), 宋德海³

1.中国海洋大学, 海洋与大气学院, 山东青岛 266100
2.中国海洋大学, 海洋发展研究院, 山东青岛 266100
3.中国海洋大学, 物理海洋教育部重点实验室, 山东青岛 266100

收稿日期:2022-05-25 修回日期:2022-07-06 出版日期:2023-01-10 发布日期:2022-07-06
通讯作者: 武文(1985—), 女, 山东枣庄人, 副教授, 博士, 主要研究方向为海洋管理。email: wenwu1985@ouc.edu.cn
作者简介:
姜迅(1996—), 女, 山东青岛人, 博士研究生, 主要研究方向为海洋管理。email: jiangxun@stu.ouc.edu.cn
基金资助:
国家自然科学基金项目(41806132)

Identification and quantitative analysis of key controlling factors of water quality response to human activities in the Daya Bay, China

JIANG Xun¹(), WU Wen¹^,²(), SONG Dehai³

1. College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
2. Institute of Marine Development of Ocean University of China, Qingdao 266100, China
3. Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, 266100, China

Received:2022-05-25 Revised:2022-07-06 Online:2023-01-10 Published:2022-07-06
Contact: WU Wen. emails: wenwu1985@ouc.edu.cn
Supported by:
National Natural Science Foundation of China(41806132)

摘要/Abstract

摘要：

随着沿海地区经济和人口的不断发展, 人类活动对海湾水质状况的影响日渐加剧。本文以大亚湾为研究对象, 以近20年的调查统计数据为基础, 综合运用变异系数评价、双变量相关性分析、主成分分析、线性回归分析等方法, 对影响大亚湾水质的人为压力要素和海域承压要素所包含的指标进行筛选; 将自身变动幅度较大且对大亚湾水质产生主要影响的指标识别为关键控制指标, 并通过承载力占比的计算对关键控制指标进行量化解析, 继而对指标产生的水质效应进行定量评价。结果表明, 人为压力要素中的围填海面积、生活污水排放量和工业废水排放量这3个指标的变异性大、主成分综合载荷数较高且与大亚湾海域的主要污染物溶解无机氮含量的年际变化有显著相关性, 是影响大亚湾水质的关键控制指标。对这3个指标进行承载力占比计算的结果显示, 围填海面积、生活污水排放量和工业废水排放量的承载力占比分别为6.19%、5.07%和17.51%, 其中工业废水排放的占比最高, 对海湾承载力的影响最大。以上研究显示, 人类活动是造成大亚湾水质恶化的主要原因, 为有效改善大亚湾的海洋环境质量、提高管理水平, 应从岸线整治修复和陆源排污(尤其是工业废水的排放)控制入手, 科学制定管理方案, 推动大亚湾地区的可持续发展。

关键词: 大亚湾, 水质, 指标识别, 量化解析, 人类活动

Abstract:

With the rapid development of economy and population in coastal areas, the water quality of the main bays around the world has been affected by human activities which results in the deterioration of the ecological environment. Based on the survey and statistical data from 1995 to 2014 in the Daya Bay, Guangdong Province, China, coefficient of variation method, bivariate correlation analysis, principal component analysis and linear regression analysis were used to identify the key controlling indicators from the anthropogenic pressure indicators and coastal carrying indicators, which have significant impact on the water quality of the Daya Bay. The proportion of the carrying capacity of each important controlling factor was used to quantify the water quality effect. The results showed that the key controlling indicators contained three anthropogenic pressure indicators, including land reclamation, domestic sewage discharge and industrial wastewater discharge. The key controlling indicators had remarkable variation and greater loading values, and they were significantly correlated to the C_DIN which was the main pollutant in the Daya Bay. The quantitative assessment results showed that the carrying capacity of key controlling indicators (land reclamation, domestic sewage discharge and industrial wastewater discharge) were 6.19%, 5.07% and 17.51%, respectively, of which the proportion of industrial wastewater discharge is the highest and has the greatest impact on the Daya Bay. Therefore, human activities were the main cause for the deterioration of water quality in the Daya Bay. These results illustrated that the control of land-based pollution and the regulation of the coastline should be implemented to promote the sustainable development of social economy around the Daya Bay.

Key words: Daya Bay, water quality, indicators identification, quantitative analysis, human activities

姜迅, 武文, 宋德海. 大亚湾水质对人类活动响应的关键控制指标识别和量化解析[J]. 热带海洋学报, 2023, 42(1): 182-191.

JIANG Xun, WU Wen, SONG Dehai. Identification and quantitative analysis of key controlling factors of water quality response to human activities in the Daya Bay, China[J]. Journal of Tropical Oceanography, 2023, 42(1): 182-191.

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指标	围填海(C1)	渔业(C2)	人口(C3)	生活污水(C4)	人均GDP(C5)	工业废水(C6)	第三产业(C7)	流速(C8)	风速(C9)	DO(C12)	叶绿素a (C13)	浮游动物(C14)
围填海(C1)	1
渔业(C2)	0.104	1
人口(C3)	0.955**	0.021	1
生活污水(C4)	0.941**	0.008	0.948**	1
人均GDP(C5)	0.962**	0.09	0.937**	0.966**	1
工业废水(C6)	0.891**	-0.158	0.930**	0.892**	0.889**	1
第三产业(C7)	0.874**	-0.28	0.890**	0.846**	0.868**	0.916**	1
流速(C8)	-0.033	-0.08	-0.023	-0.074	0.007	-0.127	0.083	1
风速(C9)	0.635*	-0.101	0.659**	0.611**	0.551**	0.726**	0.650**	-0.416	1
DO(C12)	-0.289	0.089	-0.181	-0.087	-0.216	-0.297	-0.397	0.172	-0.398	1
叶绿素a (C13)	0.004	0.396	-0.066	0.003	0.114	-0.087	-0.183	0.308	-0.35	0.088	1
浮游动物(C14)	-0.282	0.129	-0.357	-0.218	-0.187	-0.304	-0.482*	-0.06	-0.596**	0.27	0.476*	1

成分	初始特征值			旋转载荷平方和
成分	总计	方差百分比/%	累积/%	总计	方差百分比/%	累积/%
1	3.776	41.952	41.952	3.366	37.402	37.402
2	1.754	19.488	61.44	1.797	19.971	57.373
3	1.162	12.912	74.353	1.528	16.98	74.353
4	0.926	10.288	84.641
5	0.832	9.245	93.886
6	0.341	3.786	97.672
7	0.115	1.275	98.947
8	0.071	0.793	99.741
9	0.023	0.259	100

指标	主成分			Fsum
指标	F1	F2	F3	Fsum
围填海(C1)	0.972	0.03	-0.07	0.951
渔业(C2)	0.044	0.742	-0.2	0.593
生活污水(C4)	0.953	0.025	-0.018	0.909
工业废水(C6)	0.935	-0.151	-0.116	0.910
流速(C8)	0.024	-0.084	0.925	0.863
风速(C9)	0.678	-0.358	-0.526	0.865
溶解氧(C12)	-0.258	0.203	0.41	0.276
叶绿素a (C13)	0.078	0.767	0.389	0.746
浮游动物(C14)	-0.323	0.677	0.134	0.581

大亚湾水质对人类活动响应的关键控制指标识别和量化解析

Identification and quantitative analysis of key controlling factors of water quality response to human activities in the Daya Bay, China

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