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基于Monte Carlo模拟的河流水质评价——以温瑞塘河为例
引用本文:黄宏,商栩,梅琨,王振峰,夏芳,黄树辉,张明华,纪晓亮.基于Monte Carlo模拟的河流水质评价——以温瑞塘河为例[J].中国环境科学,2019,39(5):2210-2218.
作者姓名:黄宏  商栩  梅琨  王振峰  夏芳  黄树辉  张明华  纪晓亮
作者单位:1. 温州医科大学公共卫生与管理学院, 浙江 温州 325035; 2. 浙江省流域水环境与健康风险研究重点实验室, 浙江 温州 325035; 3. 浙南水科学研究院, 浙江 温州 325035
基金项目:国家自然科学基金资助项目(41601554,41807495);温州市基础性科研项目(S20180005)
摘    要:基于水质评价的综合污染指数(CWQI)法和水质指标实测含量的统计分析,应用Monte Carlo模拟方法,建立了河流水质评价的Monte Carlo-CWQI耦合模型并进行实例研究.通过建立的耦合模型和温瑞塘河流域14个监测断面2004~2010年的水质监测数据,定量分析各监测断面隶属于不同污染等级的概率水平和各水质指标对水体污染的影响程度.结果表明:温瑞塘河水系水质污染十分严重,勤奋、九山、东水厂、十字河、南白象、灰桥、新桥、米筛桥、仙门、光明、郭溪、瞿溪、西岙和梧田监测断面处于重度污染的概率分别为28.50%,0.55%,92.71%,59.73%,78.85%,39.38%,78.87%,83.09%,65.32%,78.08%,0.00%,0.96%,68.09%,86.06%;处于严重污染的概率分别为71.28%,0.01%,4.33%,39.76%,21.07%,60.59%,4.42%,12.41%,11.02%,21.24%,0.00%,0.02%,1.42%,13.12%.各监测断面总氮(TN),氨氮(NH3-N)和溶解氧(DO)的Spearman等级相关系数范围分别是0.41~0.76、0.25~0.63和0.14~0.66,是其他指标的2倍以上,表明影响该地区水质达标的主要因子是TN,NH3-N和DO.本研究拓宽了河流水质评价的研究视角,能够为流域水环境管理提供丰富的决策依据.

关 键 词:Monte  Carlo模拟  水质评价  不确定性  温瑞塘河  
收稿时间:2018-10-26

River water quality assessment based on Monte Carlo simulation: A case study of Wen-Rui Tang River
HUANG Hong,SHANG Xu,MEI Kun,WANG Zhen-feng,XIA Fang,HUANG Shu-hui,ZHANG Ming-hua,JI Xiao-liang.River water quality assessment based on Monte Carlo simulation: A case study of Wen-Rui Tang River[J].China Environmental Science,2019,39(5):2210-2218.
Authors:HUANG Hong  SHANG Xu  MEI Kun  WANG Zhen-feng  XIA Fang  HUANG Shu-hui  ZHANG Ming-hua  JI Xiao-liang
Institution:1. School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China; 2. Key Laboratory of Watershed Science and Health of Zhejiang Province, Wenzhou 325035, China; 3. Southern Zhejiang Water Research Institute(iWATER), Wenzhou 325035, China
Abstract:Accurate evaluation of the pollution magnitude in a water body is an important premise for effective water pollution prevention and control. This study used Monte Carlo simulation method together with comprehensive water quality index (CWQI) method and statistical analysis of measured water quality parameters to assess water quality. The Wen-Rui Tang River watershed was used as the study site. Through the combined model and water quality data from 14 monitoring sites at Wen-Rui Tang River during 2004 to 2010, the probability of each site for every pollution level and the influence of each water quality parameter on water pollution were quantified. The results of Monte Carlo-CWQI coupled model indicated that the water quality of Wen-Rui Tang River was highly impaired. The probabilities of the water impairments at sites of Qinfen, Jiushan, Dongshuichang, Shizihe, Nanbaixiang, Huiqiao, Xinqiao, Mishaiqiao, Xianmen, Guangming, Guoxi, Quxi, Xi-ao, and Wutian being at the heavy pollution level were 28.50%, 0.55%, 92.71%, 59.73%, 78.85%, 39.38%, 78.87%, 83.09%, 65.32%, 78.08%, 0.00%, 0.96%, 68.09%, and 86.06%, respectively. The probabilities of the water impairments at these monitoring sites being worse than heavy pollution level were 71.28%, 0.01%, 4.33%, 39.76%, 21.07%, 60.59%, 4.42%, 12.41%, 11.02%, 21.24%, 0.00%, 0.02%, 1.42%, and 13.12%, respectively. The spearman rank correlation coefficient for total nitrogen (TN), ammonium-nitrogen (NH3-N) and dissolved oxygen (DO) respectively ranged from 0.41 to 0.76, 0.25 to 0.63 and 0.14 to 0.66, which were more than twice on the values for other parameters. This result implied that TN, NH3-N and DO were the dominant factors affecting the rate of reaching water quality standard in Wen-Rui Tang River. This investigation can broaden the viewpoints for researches and managers on river water quality evaluation and can provide abundant information for decision-making on water environment management.
Keywords:Monte Carlo simulation  water quality assessment  uncertainty  Wen-Rui Tang River  
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