Comparative Research of Water Environmental Carrying Capacity in Xiangshan Bay
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摘要: 近年来，海洋经济的快速发展导致了沿海区域环境污染问题加剧，特别是象山港作为重要的水产养殖基地，其水环境状况直接影响当地的经济发展和生态环境。本文以DO、COD、DIN、DIP四项水质监测指标构建BP神经网络模型，研究了象山港2020至2023年水环境承载力的空间分布和时间变化特征。结果显示，象山港的水环境承载力指数（WECCI）呈现出显著的年际波动，并在2022年达到峰值。象山港内湾由于水体交换能力较弱，污染物滞留时间较长，其水环境承载力显著低于外湾。本文还结合NQI、A、E三个水环境评价指数对象山港的水环境状况进行研究，结果同样发现2022年象山港的水环境质量有所改善。分析2022年干旱背景下的水环境变化，发现河流径流量的减少和盐水入侵导致象山港内营养盐浓度降低，从而提高了当年的水环境承载力值。相比水环境指数评价法，BP神经网络模型在综合评估水环境承载力及其空间差异方面表现出显著优势。Abstract: In recent years, the rapid development of the marine economy has led to intensified environmental pollution in coastal areas. As an important aquaculture base, the water environment status of Xiangshan Bay directly affects local economic development and the ecological environment. This study established a BP neural network model based on four water quality monitoring indicators DO, COD, DIN and DIP to analyze the spatial distribution and variation characteristics of the water environmental carrying capacity in Xiangshan Bay from 2020 to 2023. Results show that the Water Environmental Carrying Capacity Index (WECCI) exhibited significant interannual fluctuations, reaching its peak in 2022. Spatially, the inner bay exhibited significantly lower water environmental carrying capacity than the outer bay, attributable to diminished hydrodynamic exchange capacity and prolonged pollutant retention duration. Additional analysis incorporating three water quality evaluation indices NQI, A, and E also indicated improved water environmental quality in 2022. Analysis of the 2022 drought conditions revealed that reduced river runoff and saltwater intrusion led to decreased nutrient concentrations, consequently enhancing the water environmental carrying capacity. Compared to traditional water quality index evaluation methods, the BP neural network model demonstrated superior performance in comprehensively assessing water environmental carrying capacity and its spatial heterogeneity.
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图 1 象山港地理位置
a、 象山港地理位置及其邻近区域 b、象山港区域划分图（据文献4修改）
Fig. 1 Geographical location of Xiangshan Bay
a. The geographical location of Xiangshan Port and its adjacent areas b. Regional division map of Xiangshan Port(Modified according to Reference 4)
图 2 象山港监测站位分布（2010−2014年共9个站位数据，2015−2017年共10个站位数据，2018−2023年共8个站位数据）
Fig. 2 Distribution of monitoring stations in Xiangshan Bay(2010−2014: 9 monitoring stations; 2015−2017: 10 monitoring stations; 2018−2023: 8 monitoring stations)
图 4 象山港2010年至2019年夏季DO（a）、COD（b）、DIN（c）、DIP（d）数据正态拟合
Fig. 4 Normal fitting of DO (a), COD (b), DIN (c), DIP (d) data in Xiangshan Bay from 2010 to 2019 summer
图 5 2020−2023年象山港年平均WECCI时间变化
Fig. 5 Annual average WECCI time change in Xiangshan Bay from 2020 to 2023
图 6 2020−2023年象山港WECCI空间分布（a−d为2020−2023年各年份WECCI空间分布，e为2020−2023年4年平均WECCI空间分布）
Fig. 6 The spatial distribution of WECCI in Xiangshan Bay from 2020 to 2023 (a−d show the spatial distribution of WECCI in each year from 2020 to 2023, and e shows the average spatial distribution of WECCI in the four years from 2020 to 2023)
图 7 2020−2023年NQI、A、E指数时间变化
Fig. 7 Temporal variations of NQI, A, and E indices in Xiangshan Bay (2020−2023)
图 8 2020−2023年象山港NQI指数空间分布(a−d为2020−2023年各年份NQI空间分布，e为2020−2023年4年平均NQI空间分布)
Fig. 8 The spatial distribution of NQI index in Xiangshan Bay from 2020 to 2023 (a−d show the spatial distribution of NQI in each year from 2020 to 2023, and e shows the average spatial distribution of NQI in the four years from 2020 to 2023)
图 9 2020−2023年象山港A指数空间分布（a−d为2020−2023年各年份A空间分布，e为2020−2023年4年平均A空间分布）
Fig. 9 The spatial distribution of A index in Xiangshan Bay from 2020 to 2023 (a−d show the spatial distribution of A in each year from 2020 to 2023, and e shows the average spatial distribution of A in the four years from 2020 to 2023)
图 10 2020−2023年象山港E指数空间分布（a−d为2020−2023年各年份E空间分布，e为2020−2023年4年平均E空间分布）
Fig. 10 The spatial distribution of E index in Xiangshan Bay from 2020 to 2023 (a−d show the spatial distribution of E in each year from 2020 to 2023, and e shows the average spatial distribution of E in the four years from 2020 to 2023)
表 1 象山港2010年至2019年DO、COD、DIN、DIP数据正态分布拟合均值μ和方差σ
Tab. 1 Normal distribution fitting mean (μ) and variance (σ) of DO, COD, DIN, DIP data in Xiangshan Bay from 2010 to 2019
数据 指标数据 DO/mg/L COD/mg/L DIN/mg/L DIP/mg/L 均值μ 6.7209 0.9503 0.6797 0.0494 方差σ 0.9551 0.3617 0.1509 0.0190 
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表 2 BP神经网络模型样本数据输入输出取值
Tab. 2 BP neural network model sample data input and output values
数据 样本数据输入 样本数据输出（WECCI） DO COD 1/COD DIN 1/DIN DIP 1/DIP （mg/L） （mg/L） （L/mg） （mg/L） （L/mg） （mg/L） （L/mg） 最优值 8.6312 0.2269 4.4070 0.3779 2.6463 0.0114 87.4583 0.977 较优值 7.6760 0.5886 1.6990 0.5288 1.8911 0.0304 32.8588 0.841 中间值 6.7209 0.9503 1.0523 0.6797 1.4713 0.0494 20.2296 0.500 较差值 5.7658 1.3119 0.7622 0.8306 1.2040 0.0684 14.6131 0.159 最差值 4.8106 1.6736 0.5975 0.9815 1.0188 0.0874 11.4376 0.023
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表 3 2020-2023年模型输出WECCI
Tab. 3 Model output WECCI from 2020 to 2023
站位 年份 站位平均 2020 2021 2022 2023 S1 0.8026 0.4054 0.9759 0.5783 0.5889 S2 0.9490 0.4727 0.9768 0.5854 0.7849 S3 0.5949 0.6412 0.9757 0.5005 0.6069 S4 0.9762 0.4839 0.9765 0.7717 0.9700 S5 0.8448 0.5798 0.9768 0.6970 0.9002 S6 0.9148 0.8941 0.9768 0.9762 0.9738 S7 0.5180 0.9755 0.9658 0.0258 0.9746 S8 0.5842 0.9764 0.9726 0.6953 0.9729 年平均 0.9249 0.6120 0.9765 0.8507 0.8978
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表 4 年平均NQI、A、E环境指数分析
Tab. 4 Analysis of annual average NQI, A, E environmental index
年份 数据 DO/mg/L COD/mg/L DIN/mg/L DIP/mg/L NQI A E 2020 6.8450 1.2063 0.0211 0.4468 4.2452 3.1044 2.5298 2021 6.7050 0.8700 0.0388 0.5563 5.7996 4.6821 4.1672 2022 6.3238 1.0763 0.0335 0.3316 4.4296 3.3756 2.6570 2023 6.8775 1.3538 0.0255 0.4666 4.7100 3.5638 3.5796 平均 6.6878 1.1266 0.0297 0.4503 4.7961 3.6815 3.3503
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