• 关键词：水库  水环境  主成分综合污染指数  溯源解析

• 基金项目：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）

• 胡容华
• 福建师范大学环境与资源学院，福州 350007

• 谢蓉蓉
• 福建师范大学环境与资源学院，福州 350007;福建师范大学福建省污染控制与资源循环利用重点实验室，福州 350007;数字福建环境监测物联网实验室，福州 350007

• 李家兵
• 福建师范大学环境与资源学院，福州 350007;福建师范大学福建省污染控制与资源循环利用重点实验室，福州 350007;数字福建环境监测物联网实验室，福州 350007

• 石成春
• 福建省环境科学研究院，福州 350013

• 刘继辉
• 福建省环境科学研究院，福州 350013

• 陈锦
• 福建省环境科学研究院，福州 350013

• 吴贤忠
• 福州市环境科学研究院，福州 350013

• 江华
• 福州市环境科学研究院，福州 350013

• 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：①PCA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；③PMF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.

• Abstract：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 2019 was 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.

• Key words：reservoir  water environment  principal component comprehensive pollution index  source apportionment

• 
  摘要点击次数： 169 全文下载次数： 263