摘要
基于水口水库2015—2019年的水质监测数据,采用主成分分析法(PCA)识别研究区域主要污染因子,分别对汛期/非汛期水质进行综合评价,测算研究区域主成分综合污染指数(PC-WPI)及动态度分析水库不同期污染变化特征,并通过水质的正矩阵因子分解模型(PMF)进行污染溯源解析.研究结果表明:(1)PCA识别氨氮、高锰酸盐指数、DO和COD是区域主要污染因子,库区自上游到下游水质逐渐改善,2015—2016汛期水质优于非汛期,2017—2019则相反;(2)研究区域PC-WPI为0.057~0.115,为轻度-中度污染,除2018年外,汛期的PC-WPI均值均低于前一个非汛期.2015—2019年区域水质呈现“好转-持续恶化-有所好转”趋势;(3)PMF解析表明汛期污染源为生活污水和养殖废水(39.47%)>农业面源(33.01%)>工业废水排放(27.52%),非汛期污染源为生活污水排放(39.62%)>养殖废水(25.54%)>农业面源(18.33%)>工业废水排放(16.51%).本研究可为缺少污染源统计资料区域的水环境污染识别和溯源提供技术支撑.
In this study,5-year(2015—2019)water quality measurements were collected to identify the main pollution factors of Shuikou Reservoir by using principal component analysis(PCA).The overall water quality in flooding and non-flooding seasons was evaluated as well.The principal component comprehensive pollution index(PC-WPI)and its dynamic degree were then calculated to analyze the pollution variability spatially and temporally.The positive matrix factor decomposition model(PMF)was used for pollution-tracing analysis.Our results suggested that ammonia nitrogen,permanganate index,dissolved oxygen and chemical oxygen demand were the four main pollution indexes in the study area.Water quality was observed to improve steadily from upstream to downstream.Water quality in flooding seasons of 2015 and 2016 was better than that in non-flooding seasons,however,the opposite trend was noted during 2017—2019.The PC-WPI from 2015 to 2019 was calculated between 0.057 to 0.115,indicating that the pollution was not severe,either light or moderate in the study area.The average PC-WPI in flooding seasons was always lower compared to that in previous non-flooding seasons with the exception of 2018.The dynamic degree of PC-WPI showed water quality from 2015 to 2019was featured with much deterioration after overall improvement,then followed by slight improvement again.The PMF analysis revealed that the contribution rates of pollution sources during flooding seasons were ranked as follows:domestic sewage and aquaculture wastewater(39.47%)>agricultural non-point source pollution(33.01%)>industrial wastewater(27.52%).The contribution rates during non-flooding seasons were domestic sewage(39.62%)>aquaculture wastewater(25.54%)>agricultural non-point source pollution(18.33%)>industrial wastewater(16.51%).This study could provide technical support for identifying and tracing water environment pollution in those areas with inadequate pollution source data.
作者
胡容华
谢蓉蓉
李家兵
石成春
刘继辉
陈锦
吴贤忠
江华
HU Ronghua;XIE Rongrong;LI Jiabing;SHI Chengchun;LIU Jihui;CHEN Jin;WU Xianzhong;JIANG Hua(College of Environmental and Resource Sciences,Fujian Normal University,Fuzhou 350007;Fujian Key Laboratory of Pollution Control&Resource Reuse,Fujian Normal University,Fuzhou 350007;Digital Fujian Environmental Monitoring Internet of Things Laboratory,Fuzhou 350007;Fujian Academy of Environmental Sciences,Fuzhou 350013;Fuzhou Environmental Science Research Institute,Fuzhou 350013)
出处
《环境科学学报》
CAS
CSCD
北大核心
2022年第12期136-146,共11页
Acta Scientiae Circumstantiae
基金
福建省省属公益类科研院所基本科研专项项目(No.2021R1015002,2021R1015001)
国家自然科学基金(No.42007343)
福建省自然科学基金(No.2021J01195)。
关键词
水库
水环境
主成分综合污染指数
溯源解析
reservoir
water environment
principal component comprehensive pollution index
source apportionment
