不同富营养化评价方法在典型采煤沉陷积水区的应用
Application of Various Eutrophication Assessment Methods in Typical Coal-Mining Subsidence Water Areas
DOI: 10.12677/hjcet.2025.155025, PDF,   
作者: 李 兵, 胡 林, 王庆刚：平安煤炭开采工程技术研究院有限责任公司，安徽 淮南；淮南矿业(集团)有限责任公司，安徽 淮南；吴 康, 范廷玉：安徽理工大学地球与环境学院，安徽 淮南
关键词: 采煤沉陷积水区；富营养化评价；综合营养状态指数；卡尔森营养状态指数；BP神经网络；Coal Mining Subsidence Waterlogged Area； Eutrophication Assessment； Comprehensive Trophic State Index； Carlson Trophic State Index； BP Neural Network
摘要: 水体富营养化评价一直是环境研究的热点问题，为找到适宜采煤沉陷积水区富营养化评价的方法，本研究选取淮南矿区潘集、谢桥采煤沉陷积水区作为研究对象，利用综合营养状态指数法(TLI)、卡尔森营养状态指数法(TSI)及BP神经网络法进行富营养化评价。结果表明：1) 潘集、谢桥沉陷积水区，丰水期Chl.a浓度显著高于枯水期(P < 0.05)，表现出显著的季节性差异，总氮(TN)、总磷(TP)、高锰酸盐指数(CODMn)和透明度(SD)等指标表现为丰水期高于枯水期；2) TLI及BP神经网络法富营养化评价结果具有高度一致性，即丰水期营养等级高于枯水期(P < 0.05)，TSI评价结果却与其相反，这与TSI构建机制、适用水体和沉陷积水区特殊水文特征有关；3) TLI及BP神经网络法更适合采煤沉陷积水区的富营养化评价。
Abstract: Eutrophication assessment of water bodies has long been a focal issue in environmental research. To establish an appropriate evaluation method for eutrophication in coal mining subsidence waterlogged areas, this study investigated the Panji and Xieqiao subsidence water accumulation zones within the Huainan mining area. Three methodologies were applied: the Comprehensive Trophic Level Index (TLI), Carlson’s Trophic State Index (TSI), and Backpropagation (BP) neural network. Key findings include: 1) Chlorophyll-a (Chl.a) concentrations in both Panji and Xieqiao areas were significantly higher during the wet season than in the dry season (P < 0.05), indicating pronounced seasonal variation. Other parameters—total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), and transparency (SD)—also exhibited elevated wet-season values. 2) TLI and BP neural network assessments demonstrated high consistency, both identifying higher trophic levels during the wet season (P < 0.05). In contrast, TSI yielded opposing results, likely attributable to its inherent structural limitations, applicability constraints for specific water bodies, and the unique hydrological dynamics of subsidence-induced water accumulation zones. 3) Comparative analysis confirmed the superior suitability of TLI and BP neural network methods for eutrophication evaluation in coal mining subsidence water environments.
文章引用：李兵, 胡林, 吴康, 王庆刚, 范廷玉. 不同富营养化评价方法在典型采煤沉陷积水区的应用[J]. 化学工程与技术, 2025, 15(5): 254-264. https://doi.org/10.12677/hjcet.2025.155025

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