COMPREHENSIVE ANALYSIS AND MODEL PREDICTION OF WATER QUALITY IN THE SEA-ENTRY CHANNELS OF YANGTZE ESTUARY
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摘要: 以中国最大河口地区为例,2004—2018年对包括徐六泾、启东港、南港、北港在内的4个长江口主要入海通道断面进行定期监测。对水样中的高锰酸盐指数、氨氮(NH3-N)、总磷(TP)等水质参数进行测定,运用综合水质标识指数法(WQII)对入海通道断面水质进行综合分析。选择溶解氧(DO)作为响应变量,建立各断面逐步回归方程、季节性差分自回归移动平均模型(SARIMA),并对2018年10—12月进行预测以检验模型精度,比较2种模型在各断面的适用性。结果显示:1)长江口各入海通道断面DO浓度在空间上呈现徐六泾最高、北支其次、南支最低的规律,平均值为8.73 mg/L;2)各断面综合水质标识指数从相对最优开始排列依次为启东港(3.110)>徐六泾(3.120)>北港(3.220)>南港(3.420);3)除南港断面外,SARIMA模型相较逐步回归方程对长江口断面DO浓度预测的相对偏差更小。Abstract: Taking the largest estuary in China as an example, regular monitoring was carried out at four major cross-sections, including Xuliujing, Qidonggang, Nangang and Beigang in Yangtze Estuary from 2004 to 2018. Water quality parameters such as permanganate index, ammonia nitrogen(NH3-N) and total phosphorus(TP) were measured in water samples. A comprehensive analysis of the water quality at the cross-sections was conducted via the comprehensive water quality identification index(WQII) method; dissolved oxygen(DO) was selected as the response variable to establish stepwise regression equations and seasonal autoregressive integrated moving average model(SARIMA) for each cross-section, and predictions were made from October to December 2018 to test the accuracy of the models and compare the applicability of the two models at each cross-section. The results showed that:1) based on the monthly data from 2004 to 2018, the average DO concentration at the representative cross-sections of the main sea-entry channels of the Yangtze Estuary was 8.73 mg/L; spatially, the DO concentration in the Yangtze Estuary showed a pattern that Xuliujing was the highest and the northern branch was higher than the southern branch. 2) based on the comprehensive water quality marker index, the sections, water quality was in the order of Qidonggang(3.110)>Xuliujing(3.120)>Beigang(3.220)>Nangang(3.420);3) when predicting DO, the SARIMA model was more accurate than the stepwise regression equation in predicting DO at the Xuliujing, Qidonggang and Beigang section, while the stepwise regression equation performed better at the Nangang section.
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Key words:
- Yangtze Estuary /
- sea-entry channel /
- WQII method /
- SARIMA model /
- stepwise regression
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[1] 中共中央国务院印发《长江三角洲区域一体化发展规划纲要》[J].中华人民共和国国务院公报,2019(35):10-34. [2] JIANG J P,WANG P,LUNG W S,et al.A GIS-based generic real-time risk assessment framework and decision tools for chemical spills in the river basin[J].Journal of Hazardous Materials,2012,227/228:280-291. [3] WANG H,YAN H Y,ZHOU F N,et al.Dynamics of nutrient export from the Yangtze River to the East China sea[J].Estuarine Coastal and Shelf Science,2019,229:106415. [4] CLOERN J E,FOSTER S Q,KLECKNER A E.Phytoplankton primary production in the world's estuarine-coastal ecosystems[J].Biogeosciences,2014,11(9):2477-2501. [5] ROY E D,NGUYEN N T,WHITE J R.Changes in estuarine sediment phosphorus fractions during a large-scale Mississippi River diversion[J].Science of the Total Environment,2017,609:1248-1257. [6] VEGA F A,WENG L.Speciation of heavy metals in River Rhine[J].Water Research,2013,47(1):363-372. [7] KOTWICKI V,ALLAN R.La niňa de australia-contemporary and palaeo-hydrology of Lake Eyre[J].Palaeogeography Palaeoclimatology Palaeoecology,1998,144(3/4):265-280. [8] NYAMWEYA C,STURLUDOTTIR E,TOMASSON T,et al.Exploring Lake Victoria ecosystem functioning using the Atlantis modeling framework[J].Environmental Modelling& Software,2016,86:158-167. [9] YANG T,ZHANG Q,WAN X H,et al.Comprehensive ecological risk assessment for semi-arid basin based on conceptual model of risk response and improved TOPSIS model-a case study of Wei River Basin,China[J].Science of the Total Environment,2020,719:137502. [10] LIU Y,HU Y C,HU Y M,et al.Water quality characteristics and assessment of Yongding New River by improved comprehensive water quality identification index based on game theory[J].Journal of Environmental Sciences,2021,104:40-52. [11] HIEN T N,DINH L C,VAN T P.The performance of classification and forecasting Dong Nai River water quality for sustainable water resources management using neural network techniques[J].Journal of Hydrology,2021,596:126099. [12] 刘贤梅,周忠发,张昊天,等.基于主成分分析的喀斯特山区河流水质评价及水质时空特征分析:以贵州省张维河为例[J].环境工程,2019,37(10):49-54,132. [13] NONG X Z,SHAO D G,ZHONG H,et al.Evaluation of water quality in the South-to-North Water Diversion Project of China using the water quality index (WQI) method[J].Water Research,2020,178:115781. [14] 王磊,汪文东,刘懂,等.象山港流域入湾河流水体中重金属风险评价及其来源解析[J].环境科学,2020,41(7):3194-3203. [15] 徐祖信.我国河流综合水质标识指数评价方法研究[J].同济大学学报(自然科学版),2005,33(4):482-488. [16] 徐祖信.我国河流单因子水质标识指数评价方法研究[J].同济大学学报(自然科学版),2005,33(3):321-325. [17] 谢建磊,王寒梅,何中发,等.上海市长江口及邻近海域地质调查现状及展望[J].上海国土资源,2008,29(4):17-23. [18] 黄俊,衣俊,程金平.长江口及近海水环境中新型污染物研究进展[J].环境化学,2014,33(9):1484-1494. [19] 惠军,陈银川,林剑波,等.长江口地区水环境风险分析[J].人民长江,2016,46(13):24-27. [20] 高华斌,唐兵.应对长江口咸潮入侵的临界流量经验模型研究[J].长江科学院院报,2020,37(4):25-29. [21] 杨颖,刘鹏霞,周红宏,等.近15年长江口海域海洋生物变化趋势及健康状况评价[J].生态学报,2020,40(24):8892-8904. [22] 马京久,喻婷,陈燕飞,等.基于综合水质标识指数法的汉江中下游水质评价[J].人民珠江,2020,41(9):63-69. [23] LIU H,TIAN H Q,LI Y F.Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction[J].Applied Energy,2012,98:415-424. [24] BOUZERDOUM M,MELLIT A,MASSI P A.A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant[J].Solar Energy,2013,98:226-235. [25] FARUK D O.A hybrid neural network and ARIMA model for water quality time series prediction[J].Engineering Applications of Artificial Intelligence,2009,23(4):586-594. [26] 范海梅,蒋晓山,纪焕红,等.长江口及其邻近海域生态环境综合评价[J].生态学报,2019,39(13):4660-4675. [27] WANG H,YUAN W H,YAN H Y,et al.Separating the driving force on estuary nutrient evolution[J].Clean-Soil,Air,Water,2020,48(10):13. [28] 范明源,李俊花,万远扬,等.径流量变化对长江口水动力特性的影响[J].海洋工程,2020,38(4):81-90. [29] 韩非非,崔冬.长江口污水超标排放对水质影响的数值模拟研究[J].人民长江,2018,49(14):17-23. [30] AKAIKE H.A new look at the statistical model identification[J].IEEE Transactions on Automatic Control,1974,6:716-723. [31] 吴晓敏,郝瑞娟,潘宏博,等.黄浦江浮游动物群落结构及其与环境因子的关系[J].生态环境学报,2018,27(6):1128-1137. [32] 杨盼,卢路,王继保,等.基于主成分分析的spearman秩相关系数法在长江干流水质分析中的应用[J].环境工程,2019,37(8):76-80. [33] 汤玉强,李清伟,左婉璐,等.内梅罗指数法在北戴河国家湿地公园水质评价中的适用性分析[J].环境工程,2019,37(8):195-199,189. [34] 李娜,李勇,冯家成,等.太湖水体Chl-a预测模型ARIMA的构建及应用优化[J].环境科学,2021,42(5):2223-2231. [35] KUROYANAGI A,DA ROCHA R E,BIJMA J,et al.Effect of dissolved oxygen concentration on planktonic foraminifera through laboratory culture experiments and implications for oceanic anoxic events[J].Marine Micropaleontology,2013,101(1):28-32. [36] 王兆群,张宁红,张咏.洪泽湖藻类与环境因子逐步回归统计和蓝藻水华初步预测[J].中国环境监测,2012,28(4):17-20.
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