| 不同缓冲区的土地利用方式对地表水水质的影响：以海河流域天津段为例 |
| 摘要点击 613 全文点击 165 投稿时间：2023-03-07 修订日期：2023-05-23 |
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| 中文关键词 地表水 水质 土地利用 偏最小二乘回归(PLSR) 天津 |
| 英文关键词 surface water water quality land use partial least squares regression (PLSR) Tianjin |
| 作者 | 单位 | E-mail | | 代孟均 | 天津师范大学天津市水资源与水环境重点实验室, 天津 300387
天津师范大学地理与环境科学学院, 天津 300387 | 2110080051@stu.tjnu.edu.cn | | 张兵 | 天津师范大学天津市水资源与水环境重点实验室, 天津 300387 | zhangbing@tjnu.edu.cn | | 杜倩倩 | 天津师范大学天津市水资源与水环境重点实验室, 天津 300387
天津师范大学地理与环境科学学院, 天津 300387 | | | 孙季珲 | 天津师范大学天津市水资源与水环境重点实验室, 天津 300387
天津师范大学地理与环境科学学院, 天津 300387 | | | 田蕾 | 天津师范大学天津市水资源与水环境重点实验室, 天津 300387
天津师范大学地理与环境科学学院, 天津 300387 | | | 王义东 | 天津师范大学天津市水资源与水环境重点实验室, 天津 300387 | |
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| 中文摘要 |
| 探明土地利用方式与水质的关系对改善地表水环境具有重要意义.基于2021年天津市16个国家地表水水质监测站的月数据及土地利用数据，利用GIS空间分析和数理统计方法研究不同尺度缓冲区的土地利用方式对地表水水质的影响.结果表明：①研究区土地利用类型以建设用地、耕地和水域为主，对河流水质影响显著.除水温（WT）和pH外，耕地、建设用地和水域与各水质指标均呈负相关；林地和草地与溶解氧（DO）和总氮（TN）呈正相关，与其他水质指标均呈负相关.②水质指标在不同季节表现出明显的空间差异.pH、DO与TN浓度在旱季较高，而高锰酸盐指数、氨氮（NH4+-N）与总磷（TP）浓度在雨季较高.③冗余分析（RDA）结果表明，800 m缓冲区土地利用对旱季水质变化具有最大的解释能力（50.4%），而3 000 m缓冲区土地利用可以最大程度解释雨季水质变化情况（49.6%）；从旱雨季的平均解释率来看，3 000 m缓冲区是天津市土地利用对水质指标的最佳影响尺度（50.0%）；④偏最小二乘回归（PLSR）分析可知，3 000 m缓冲区内建设用地、耕地和水域是影响地表水水质变化最显著的地类.旱季大多数水质指标PLSR模型的预测能力比雨季强.在旱季，除WT和pH外，其余水质指标均受耕地的影响最大.在雨季，建设用地对WT和NH4+-N浓度的影响最大，其余水质指标的最重要影响因子仍是耕地.研究表明合理规划河流或湖库3 000 m内的土地利用方式有利于改善地表水水环境质量. |
| 英文摘要 |
| It is important to explore the relationship between land use types and water quality to improve the surface water environment. Based on monthly water quality monitoring data from 16 nationally controlled surface water quality monitoring stations in Tianjin and land use data in 2021, GIS spatial analysis and mathematical and statistical methods were used to study the influence of land use types on surface water quality in buffer zones at different scales. The results showed that:① the land use types in the study area were mainly construction land, farmland, and water areas, which had significant effects on river water quality. Except for water temperature (WT) and pH, the farmland, construction land, and water areas were negatively correlated with each water quality indicator; forest land and grassland were positively correlated with dissolved oxygen (DO) and total nitrogen (TN) and negatively correlated with other water quality indicators. ② The water quality indicators showed obvious spatial differences in different seasons. The pH, DO and TN concentrations were higher in the dry season, whereas the permanganate index, ammonia nitrogen (NH4+-N), and total phosphorus (TP) concentrations were higher in the rainy season. ③ The results of the RDA analysis showed that the 800 m buffer zone land use had the greatest explanatory power for water quality changes in the dry season (50.4%), whereas the 3 000 m buffer zone land use could explain the water quality changes in the rainy season to the greatest extent (49.6%); from the average explanation rate of the dry and rainy seasons, the 3 000 m buffer zone was the best impact scale (50.0%) on water quality indicators in Tianjin. ④ The partial least squares regression (PLSR) analysis showed that the most important variables affecting surface water quality changes were construction land, farmland, and water areas. The predictive ability of the PLSR model of most water quality indicators was stronger in the dry season than that in the rainy season. In the dry season, all water quality indicators, except WT and pH, were most influenced by farmland. In the rainy season, construction land had the greatest influence on WT and NH4+-N concentrations, and the most important influencing factor for the remaining water quality indicators was still farmland. This study showed that the rational planning of land use types within 3 000 m of rivers or lakes was beneficial to improving the water quality of surface water. |
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