Article
文章

Regional Differences in the Impact of Land Use Pattern on
土地利用模式对区域差异的影响
Total Phosphorus Concentration in the Yangtze River Basin
长江流域总磷浓度

Fuliang Deng ¹, Wenhui Liu ¹, Wei Liu ¹, Yanxue Xu ^2,3,*, Yuanzhuo Sun ¹, Chen Zhang ¹, Mei Sun ¹ and Ying Yuan ¹
邓富良 ¹ 刘文辉 ¹ , 刘伟 ¹ , 许艳雪 ^{2, 3,} *, 孙元卓 ¹ , 张晨 ¹ , 孙梅 ¹ 和 袁颖 ¹

¹ School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China; fldeng8266@xmut.edu.cn (F.D.); 2322081026@stu.xmut.edu.cn (W.L.); devilweil@xmut.edu.cn (W.L.); 2422071039@stu.xmut.edu.cn (Y.S.); 2422071056@stu.xmut.edu.cn (C.Z.); sunm@xmut.edu.cn (M.S.); yuanying@xmut.edu.cn (Y.Y.)
厦门理工学院计算机与信息工程学院，厦门 361024，中国；fldeng8266@xmut.edu.cn (F.D.); 2322081026@stu.xmut.edu.cn (WL.); devilweil@xmut.edu.cn (W.L.); 2422071039@stu.xmut.edu.cn (YS.); 2422071056@stu.xmut.edu.cn (C.Z.); sunm@xmut.edu.cn (M.S.); yuanying@xmut.edu.cn (Y.Y.)

² United Center for Eco-Environment in Yangtze River Economic Belt 2, Chinese Academy of Environmental Planning, Beijing 100041, China
² 中国环境规划院长江经济带生态环境联合中心，北京 100041，中国

³ Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
清华大学水利工程系，中国北京 100084

* Correspondence: xuyx@caep.org.cn
* 通信: xuyx@caep.org.cn

Abstract: Accurately assessing the impact of land use patterns on total phosphorus (TP) concentration in surface water is crucial for protecting the water environment of the Yangtze River Basin (YRB). However, due to the heterogeneity of land use patterns, the regional differences in the intensity and direction of their impacts on TP concentrations in the YRB remain insufficiently understood. This study utilizes water quality monitoring data from state-controlled sections in 2021 and employs spatial autocorrelation analysis, geographic detectors, and Pearson correlation models to identify the impacts of land use on TP concentrations at multiple scales across the YRB. The results indicate that TP concentrations at 98.8% of the monitoring stations in the YRB exceed the Class III standard, with high concentrations of TP concentrated in the Pudu River Basin, Chengdu Plain, Jianghan Plain, and Yangtze River Delta regions. At the YRB scale, the spatial pattern of built-up land, cropland, and industrial and mining land significantly increases TP concentrations, while the pattern of forest and grassland areas exert mitigating effects. At the sub-basin scale, the impact of land use patterns on TP concentrations varies regionally. Specifically, TP concentrations in the Pudu River Basin are primarily attributed to the spatial pattern of industrial and mining land, in the Chengdu Plain to the spatial pattern of cropland and industrial-mining land, and in the Jianghan Plain to the spatial pattern of cropland, built-up land, and industrial-mining land. These results provided decision support for TP concentration control strategies and effective mitigation measures.
摘要：准确评估土地利用模式对长江流域（YRB）地表水中总磷（TP）浓度的影响对于保护水环境至关重要。然而，由于土地利用模式的异质性，其对长江流域 TP 浓度影响的强度和方向的区域差异仍然理解不足。本研究利用 2021 年国家控制段的水质监测数据，采用空间自相关分析、地理探测器和皮尔逊相关模型，识别土地利用对长江流域 TP 浓度的多尺度影响。结果表明，长江流域 98.8%的监测站 TP 浓度超过了三级标准，TP 浓度高的区域集中在普渡河流域、成都平原、江汉平原和长江三角洲地区。在长江流域尺度上，建设用地、农田和工业矿区的空间格局显著增加了 TP 浓度，而森林和草地的空间格局则起到缓解作用。 在子流域尺度上，土地利用模式对总磷浓度的影响因地区而异。具体而言，普渡河流域的总磷浓度主要归因于工业和采矿用地的空间格局，成都平原则与农田和工业-采矿用地的空间格局相关，而江汉平原则与农田、建设用地和工业-采矿用地的空间格局相关。这些结果为总磷浓度控制策略和有效缓解措施提供了决策支持。

Keywords: land use type; total phosphorus concentration; GeoDetector; regional
关键词：土地利用类型；总磷浓度；GeoDetector；区域
differences; Yangtze River Basin
差异；长江流域

1. Introduction
1. 引言

Phosphorus, a critical nutrient for algal growth in aquatic ecosystems, is the primary driver of water eutrophication [1]. Eutrophication significantly alters the ecological balance of aquatic systems and poses serious risks to ecosystem security [2]. Land use practices serve as a direct reflection of human activity impacts on the environment, shaping agricultural management, farming practices, and other phosphorus discharge pathways, which significantly influence the input and output of total phosphorus (TP) concentration in water bodies [3–5]. Regional disparities in land use practices create complex, non-linear relationships between land use and TP concentrations [6,7], complicating TP source traceability and hindering the effective management of aquatic ecosystems. Therefore, understanding watershed land use impacts on TP concentrations is critical for identifying TP sources and protecting aquatic ecosystems effectively.
磷是水生生态系统中藻类生长的关键营养素，是水体富营养化的主要驱动因素[1]。富营养化显著改变了水生系统的生态平衡，并对生态系统安全构成严重风险[2]。土地利用实践直接反映了人类活动对环境的影响，塑造了农业管理、耕作方式和其他磷排放途径，这些都显著影响了水体中总磷（TP）浓度的输入和输出[3–5]。土地利用实践的区域差异在土地利用与 TP 浓度之间形成了复杂的非线性关系[6,7]，这使得 TP 源追溯变得复杂，并阻碍了水生生态系统的有效管理。因此，理解流域土地利用对 TP 浓度的影响对于识别 TP 源和有效保护水生生态系统至关重要。

The Yangtze River, the largest river in China, contributes to around 20% of the nation’s wetland area, 35% of its total water resources, and sustains over 40% of its population and GDP [8,9]. Since 2016, TP concentration has been recognized as a key pollutant in the Yangtze River Basin (YRB) aquatic environment [10,11]. Accurately assessing the impact of land use on TP concentrations in the basin has become a key research priority. An increasing body of evidence indicates that land use patterns within a watershed significantly affect water quality [12–14]. Numerous studies have examined the relationship between land use and TP concentrations across different scales, including control units, sub-basins, and the entire basin [15–17]. However, the conclusions of these studies also exhibit discrepancies to some extent. For example, Li et al. [12] identified agricultural land as the primary source of TP concentrations in the upper Yangtze River, whereas Wang et al. [14] found that both construction and agricultural lands are the major sources in the Gan River Basin. Similarly, Zhang et al. [18] demonstrated that construction land is the primary contributor to TP concentrations in the Dianchi Basin of the Jinsha River system. These discrepancies highlight that multi-scale, systematic studies are essential for understanding the mechanisms and regional differences of land use impacts on TP concentrations in the YRB. However, current research often focuses on specific river segments or individual sub-watersheds [19–21].
长江是中国最大的河流，约占全国湿地面积的 20%，总水资源的 35%，并维持着超过 40%的人口和 GDP [8,9]。自 2016 年以来，TP 浓度被认定为长江流域水域环境中的关键污染物 [10,11]。准确评估土地利用对流域内 TP 浓度的影响已成为关键研究优先事项。越来越多的证据表明，流域内的土地利用模式显著影响水质 [12–14]。许多研究考察了不同尺度下土地利用与 TP 浓度之间的关系，包括控制单元、子流域和整个流域 [15–17]。然而，这些研究的结论在某种程度上也存在差异。例如，李等 [12] 确定农业用地是上游长江 TP 浓度的主要来源，而王等 [14] 发现在赣江流域，建设用地和农业用地都是主要来源。类似地，张等。 [18] 证明了建设用地是金沙江系统滇池流域总磷浓度的主要贡献者。这些差异突显出多尺度、系统性研究对于理解土地利用对黄河流域总磷浓度影响的机制和区域差异是至关重要的。然而，目前的研究往往集中在特定的河段或单个子流域 [19–21]。

In addition, the land use types examined in previous studies typically encompass broad categories, including agricultural land, construction land, grassland, and forest land. However, the influence of industrial and mining land on TP concentrations must not be underestimated. For instance, Guan et al. [22] observed a significant correlation between industrial and mining land and TP concentrations, while Cao et al. [23] identified that sub-basins dominated by industrial and mining land tend to have the worst water quality. The YRB is a major region for phosphate mining, phosphorus chemical industries, and phosphogypsum disposal sites, especially in the upper and middle reaches [10]. However, the extent and regional variations of the impact of industrial and mining land on TP concentrations remain unclear.
此外，以往研究中考察的土地利用类型通常包括农业用地、建设用地、草地和森林用地等广泛类别。然而，工业和矿业用地对总磷浓度的影响不容小觑。例如，Guan 等人[22]观察到工业和矿业用地与总磷浓度之间存在显著相关性，而 Cao 等人[23]则指出，以工业和矿业用地为主的子流域往往水质最差。黄河流域是磷矿开采、磷化工产业和磷石膏处置场的主要区域，尤其是在上游和中游[10]。然而，工业和矿业用地对总磷浓度的影响程度及区域差异仍不清楚。

This study aimed to investigate the impact of land use on TP concentrations in the YRB at multiple scales, along with regional variations of this influence. Specifically, the study aims were as follows: (1) to analyze the spatial distribution characteristics of TP concentrations across the entire YRB and its secondary water resource divisions, and identify the spatial hot and cold zones of total TP concentrations; (2) to examine the spatial distribution of different land use types at three scales (the entire basin, hot and cold zones of TP concentrations, and secondary water resource divisions); (3) to explore the impact of land use on TP concentrations at the above three scales and identify regional differences in the dominant factors influencing TP concentrations. The results provide decision support for regional management and the control of TP concentrations in the YRB.
本研究旨在调查土地利用对黄河流域（YRB）总磷（TP）浓度的影响，涉及多个尺度及该影响的区域差异。具体研究目标如下：（1）分析整个黄河流域及其二级水资源分区的 TP 浓度空间分布特征，识别总 TP 浓度的空间热点和冷点；（2）考察在三个尺度（整个流域、TP 浓度的热点和冷点、二级水资源分区）下不同土地利用类型的空间分布；（3）探讨土地利用对上述三个尺度 TP 浓度的影响，并识别影响 TP 浓度的主导因素的区域差异。研究结果为区域管理和黄河流域 TP 浓度控制提供决策支持。

2. Materials and Methods
2. 材料与方法

2.1. Study Area
2.1. 研究区域

The YRB is a key strategic economic region in China, comprising 16 provinces and municipalities, including Qinghai, Zhejiang, and Shanghai, etc. The Yangtze River’s main stem is divided into three sections: the upstream, extending from Yichang to the river’s upper reaches, with a catchment area of approximately ; the midstream, from Yichang to Hukou, covering around ; and the downstream, from Hukou onwards, with a catchment area of about [24] (Figure 1). The YRB spans China’s eastern, central, and western economic zones, linking the north and south, and plays a pivotal role in national economic development. The upper and middle reaches of the YRB are rich in the country’s phosphate mineral resources and are the regions most impacted by the “three phosphates” issue. In recent years, TP has emerged as the primary pollutant in the YRB, posing a significant barrier to water quality improvement in the region [25]
长江经济带是中国一个关键的战略经济区域，包括青海、浙江、上海等 16 个省市。长江的主干部分分为三个区段：上游，从宜昌延伸至河流的上游，流域面积约为 ；中游，从宜昌到湖口，覆盖约 ；下游，从湖口往下，流域面积约为 [24]（图 1）。长江经济带跨越中国东部、中部和西部经济区，连接南北，在国家经济发展中发挥着关键作用。长江经济带的上游和中游富含国家的磷矿资源，是受“三磷”问题影响最严重的地区。近年来，总磷已成为长江经济带的主要污染物，成为该地区水质改善的重大障碍 [25]。.

Figure 1. Overview of the Yangtze River Basin.
图 1. 长江流域概览。

2.2. Data Source
2.2. 数据来源

2.2.1. State-Controlled Sections and Water Resource Allocation
2.2.1. 国家控制的部门与水资源分配

TP concentration monitoring data were obtained from the China National Environmental Monitoring Center (https://www.cnemc.cn). The monitoring parameters encompass water temperature, pH, dissolved oxygen, conductivity, turbidity, permanganate index, ammonia nitrogen, TP, and total nitrogen. In accordance with the “Surface Water Environmental Quality Assessment Method (Trial)” issued by the Ministry of Environmental Protection in 2011, the annual TP concentration indicator for the 2021 monitoring data was derived using the arithmetic mean of TP concentrations. During the 14th Five-Year Plan, the Ministry of Ecology and Environment set up 3641 state-controlled sections (monitoring stations) nationwide [26]. These stations monitor the main streams and major tributaries of key national river basins, vital water bodies at provincial and municipal boundaries, cities at or above the prefecture level, and significant river and lake water functional zones across the country. Specifically, the YRB includes 1252 state-controlled sections and 12 secondary water resource divisions.
TP 浓度监测数据来自中国国家环境监测中心（https://www.cnemc.cn）。监测参数包括水温、pH 值、溶解氧、电导率、浊度、锰酸盐指数、氨氮、TP 和总氮。根据 2011 年环境保护部发布的《地表水环境质量评估方法（试行）》，2021 年监测数据的年度 TP 浓度指标是通过 TP 浓度的算术平均值得出的。在第十四个五年规划期间，生态环境部在全国设立了 3641 个国家控制断面（监测站）[26]。这些站点监测国家重点流域的主要河流和主要支流、省市交界的重要水体、地级及以上城市以及全国重要的河湖水功能区。具体而言，黄河流域包括 1252 个国家控制断面和 12 个二级水资源分区。

2.2.2. Land Use Type Data
2.2.2. 土地利用类型数据

This study utilizes land use type data from the WorldCover dataset released by the European Space Agency (ESA) in 2021, which has a resolution of 10 m. The WorldCover v200 product provides a global land cover map for 2021, generated from Sentinel-1 and Sentinel-2 data (https://viewer.esa-worldcover.org/worldcover/). It includes 11 land cover classes aligned with the UN-FAO Land Cover Classification System, developed within the ESA WorldCover project. Based on existing research and the land use types in the YRB, six land use categories were selected as potential determinants of phosphorus concentrations: forest, grassland, cropland, built-up land, bareland, and wetland
本研究利用欧洲航天局（ESA）在 2021 年发布的 WorldCover 数据集中的土地利用类型数据，该数据集的分辨率为 10 米。WorldCover v200 产品提供了 2021 年的全球土地覆盖图，基于 Sentinel-1 和 Sentinel-2 数据生成（https://viewer.esa-worldcover.org/worldcover/）。它包括与联合国粮农组织（UN-FAO）土地覆盖分类系统对齐的 11 个土地覆盖类别，该系统是在 ESA WorldCover 项目中开发的。基于现有研究和黄河流域的土地利用类型，选择了六个土地利用类别作为磷浓度的潜在决定因素：森林、草地、农田、建筑用地、裸地和湿地。.

Industrial and mining land use data were obtained from the National Land Use/Cover Change Dataset, provided by the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (https://www.resdc.cn). This dataset represents a secondary classification within land use/cover change data, encompassing areas designated for industrial and mining activities, including factories, large industrial zones, oil fields, salt fields, quarries, transportation infrastructure, airports, and specialized land uses. To assess the distribution of industrial and mining land, this study employed 2022 Point of Interest (POI) data (https://lbs.amap.com/), filtering records based on keywords such as “phosphorus,” “chemicals,” “phosphate,” “fertilizer,” and “mining”. A total of 19,609 POI records corresponding to industrial and mining land were retrieved. Since the POI data are point-based, a 3000 m buffer was applied to extract areas within transportation infrastructure related to industrial and mining land use, thereby delineating the land use areas corresponding to industrial and mining activities.
工业和矿业用地数据来自中国科学院遥感与数字地球研究所提供的国家土地利用/覆盖变化数据集（https://www.resdc.cn）。该数据集代表了土地利用/覆盖变化数据中的二级分类，涵盖了指定用于工业和矿业活动的区域，包括工厂、大型工业区、油田、盐田、采石场、交通基础设施、机场和专业用地。为了评估工业和矿业用地的分布，本研究采用了 2022 年兴趣点（POI）数据（https://lbs.amap.com/），根据“磷”、“化学品”、“磷酸盐”、“肥料”和“矿业”等关键词过滤记录，共检索到 19,609 条与工业和矿业用地相关的 POI 记录。由于 POI 数据是基于点的，因此应用了 3000 米缓冲区，以提取与工业和矿业用地相关的交通基础设施内的区域，从而划定与工业和矿业活动相对应的用地区域。

2.3. Methods
2.3. 方法

This study is comprised of two primary components: the first involves employing hotspot analysis to investigate the spatial aggregation characteristics of TP concentrations, and the second focuses on constructing a land use impact index to identify the mechanisms by which land use influences TP concentrations across multiple scales. The technical approach is illustrated in Figure 2
本研究由两个主要部分组成：第一部分涉及使用热点分析来研究 TP 浓度的空间聚集特征，第二部分则侧重于构建土地利用影响指数，以识别土地利用在多个尺度上影响 TP 浓度的机制。技术方法如图 2 所示。.

Figure 2. Technical Scheme
图 2. 技术方案.

2.3.1. Getis-Ord Gi* Hot Spot Analysis
2.3.1. Getis-Ord Gi* 热点分析

Getis-Ord Gi* is a spatial clustering method, used to reveal the spatial distribution patterns of statistically significant hot and cold spots [27]. Specifically, it identifies regions of high (hot spots) and low (cold spots) concentrations of TP concentrations in the YRB. The specific calculation formula of Getis-Ord Gi* is as follows:
Getis-Ord Gi*是一种空间聚类方法，用于揭示统计显著的热点和冷点的空间分布模式[27]。具体而言，它识别了 YRB 中 TP 浓度的高（热点）和低（冷点）浓度区域。Getis-Ord Gi*的具体计算公式如下：

where i is the central section, j is the adjacent section within the distance d from the central section i, is the TP concentration of section j, is the spatial weight between section i and section j, and n is the total number of sections within the distance d. Moreover:
其中 i 是中心部分，j 是距离中心部分 i 在 d 范围内的相邻部分， 是部分 j 的 TP 浓度， 是部分 i 和部分 j 之间的空间权重，n 是距离 d 内的部分总数。此外：

where is the Z-score and no further calculations are required.
其中 是 Z 分数，无需进一步计算。

2.3.2. Land Use Impact Factor (LUIF)
2.3.2. 土地利用影响因子 (LUIF)

To quantify the strength and direction of the impact of land use type on TP concentration and to comprehensively evaluate the impact of land use type on TP concentration, we proposed an innovative index, Land Use Impact Factor (LUIF). LUIF combines the q-value (interpretability) of GeoDetector and Pearson correlation (directionality) as follows:
为了量化土地利用类型对总磷浓度的影响强度和方向，并全面评估土地利用类型对总磷浓度的影响，我们提出了一种创新指标——土地利用影响因子（LUIF）。LUIF 结合了 GeoDetector 的 q 值（可解释性）和 Pearson 相关性（方向性），具体如下：

where q is the GeoDetector statistic, which characterizes the influence of land use type on TP concentration, and is the Pearson correlation coefficient.
其中 q 是 GeoDetector 统计量，表征土地利用类型对 TP 浓度的影响，而 是皮尔逊相关系数。

Pearson correlation analysis is a statistical method used to measure the strength and direction of a linear relationship between two continuous variables. It has a value between −1 and 1, where 1 is a perfect positive correlation, −1 is a complete negative correlation, and 0 is no correlation. The Pearson correlation calculation is based on the covariance and standard deviation of the two variables, which are calculated as follows:
皮尔逊相关分析是一种统计方法，用于测量两个连续变量之间线性关系的强度和方向。其值介于−1 和 1 之间，其中 1 表示完全正相关，−1 表示完全负相关，0 表示无相关性。皮尔逊相关的计算基于两个变量的协方差和标准差，计算方法如下：

where and are the observed values of the two variables, and are the mean values of the two variables.
其中 和 是两个变量的观测值， 和 是两个变量的均值。

GeoDetector is a statistical method proposed by Wang Jinfeng in 2017 to detect spatial heterogeneity and reveal the driving factors behind it [28]. The q statistic of GeoDetector can be used to measure spatial heterogeneity, detect explanatory factors, and analyze the interaction between variables. The specific formula for calculating the q value is as follows:
GeoDetector 是一种由王金凤于 2017 年提出的统计方法，用于检测空间异质性并揭示其背后的驱动因素[28]。GeoDetector 的 q 统计量可用于测量空间异质性、检测解释因素以及分析变量之间的相互作用。计算 q 值的具体公式如下：

where is the strata of the variable Y or factor X, i.e., classification or partitioning; and N are the number of units in layer h and the whole area, respectively; $$ and are the variances of the Y values for layer h and the whole area, respectively. The value ranges from [0, 1], and the larger the value, the more obvious is the spatial heterogeneity of Y. If the stratification is generated by the independent variable X, a higher value indicates that the independent variable X is more explanatory to the dependent variable Y, and vice versa. In the extreme case, a value of 1 indicates that factor X fully controls the spatial distribution of Y; a value of 0 indicates that factor X has no relationship to Y; and a value indicates that X explains 100 ×% of Y.
其中 是变量 Y 或因素 X 的层次，即分类或分区； 和 N 分别是层 h 中的单位数量和整个区域的单位数量； $$ 和 分别是层 h 和整个区域 Y 值的方差。 值的范围是[0, 1]，值越大，Y 的空间异质性越明显。如果分层是由自变量 X 生成的，则更高的 值表明自变量 X 对因变量 Y 的解释能力更强，反之亦然。在极端情况下， 值为 1 表示因素 X 完全控制 Ya 的空间分布， 值为 0 表示因素 X 与 Y 没有关系，而 值表示 X 解释了 Y 的 100 × %。

Interactive detectors are an important part of the GeoDetector method, which can study the effects of any two land use types on the TP concentration, and the following five situations may occur when the two factors are interacted (Table 1).
交互探测器是 GeoDetector 方法的重要组成部分，可以研究任意两种土地利用类型对 TP 浓度的影响，当这两个因素相互作用时，可能会出现以下五种情况（表 1）。

Table 1. The type of interaction between two independent variables on the dependent variable
表 1. 两个自变量对因变量的交互作用类型.

3. Results
3. 结果

3.1. Spatial Distribution Pattern of TP Concentration in the YRB
3.1. 黄河流域 TP 浓度的空间分布模式

3.1.1. Spatial Distribution Characteristics of TP Concentration
3.1.1. TP 浓度的空间分布特征

The spatial distribution of TP concentration in the YRB shows a fragmented pattern, with areas of high TP concentration predominantly located in low-elevation plains, such as the Chengdu Plain, Jianghan Plain, and the Yangtze River Delta (Figure 3). TP concentrations in the middle reaches of the Yangtze River are significantly lower than those in both the upper and lower reaches. Among the 12 secondary water resource divisions, areas such as the downstream of the Jinsha River, the mainstream from Yibin to Yichang, the Han River, the mainstream from Yichang to Hukou, and the Dongting Lake water system exhibit more frequent extreme TP concentration values, marking them as high-risk zones for TP concentration (Figure A1). According to the water quality classification standards issued by the Ministry of Ecology and Environment, TP concentration in the YRB ranges from Class I to Class V. Notably, 98.8% of state-controlled sections report TP concentration above the Class III standard, indicating that the overall water quality in the basin is predominantly classified as good or better. However, certain areas in the basin exhibit TP concentrations that exceed the standard. The highest recorded TP concentration is at the Muguodian Village section in Wuding County, Chuxiong Yi Autonomous Prefecture, Yunnan Province, where the TP concentration is 0.45 mg/L, corresponding to the Class V standard, indicating poor water quality.
YRB 中 TP 浓度的空间分布呈现出碎片化的模式，高 TP 浓度区域主要位于低海拔平原，如成都平原、江汉平原和长江三角洲（图 3）。长江中游的 TP 浓度显著低于上下游。在 12 个二级水资源分区中，金沙江下游、宜宾至宜昌的主流、汉江、宜昌至湖口的主流以及洞庭湖水系等区域的极端 TP 浓度值更为频繁，标志着这些区域为 TP 浓度的高风险区（图 A1）。根据生态环境部发布的水质分类标准，YRB 的 TP 浓度范围为 I 类至 V 类。值得注意的是，98.8%的国家控制段报告的 TP 浓度超过 III 类标准，表明流域内的整体水质主要被分类为良好或更好。然而，流域内某些区域的 TP 浓度超过了标准。 在云南省楚雄彝族自治州武定县木果甸村段，记录到的最高总磷浓度为 0.45 毫克/升，符合 V 类标准，表明水质较差。

Figure 3. Spatial distribution map of TP concentration in the Yangtze River Basin based on national control cross-sections
图 3. 基于国家控制断面的长江流域 TP 浓度空间分布图.

3.1.2. The Analysis of Hotspot Areas for TP Concentration
3.1.2. TP 浓度热点区域分析

TP concentration in the YRB displays distinct spatial clustering, predominantly concentrated in low-lying plains, including the Chengdu Plain in the Sichuan Basin, the Pu River Basin in Yunnan, the Jianghan Plain, the Central Anhui Plain, and the Yangtze River Delta (Figure 4). TP concentration in hotspot areas ranges from 0.02 mg/L to 0.45 mg/L, which are classified below the Class II water quality standard. In contrast, TP concentration in cold spot areas remains consistently below 0.3 mg/L, all surpassing the Class III water quality standard. In non-hotspot regions, TP concentrations range from 0.005 mg/L to 0.25 mg/L, with pollution levels meeting or surpassing the Class IV standard.
YRB 中的 TP 浓度显示出明显的空间聚集，主要集中在低洼平原，包括四川盆地的成都平原、云南的普河流域、江汉平原、安徽中部平原和长江三角洲（图 4）。热点区域的 TP 浓度范围为 0.02 mg/L 至 0.45 mg/L，均低于 II 类水质标准。相比之下，冷点区域的 TP 浓度始终保持在 0.3 mg/L 以下，均超过 III 类水质标准。在非热点区域，TP 浓度范围为 0.005 mg/L 至 0.25 mg/L，污染水平达到或超过 IV 类标准。

Figure 4. Hotspot analysis and secondary water resource zoning-based TP concentration hot and cold zoning map in the Yangtze River Basin
图 4. 基于热点分析和二级水资源分区的长江流域 TP 浓度冷热区划图.

3.2. Spatial Distribution Characteristics of Land Use Patterns in the YRB
3.2. 黄河流域土地利用模式的空间分布特征

The land use in the YRB is predominantly composed of forests, grasslands, and croplands (Figure 5). The distribution of land use types is as follows: forests (53.5%), grasslands (22.1%), croplands (14.5%), built-up lands (2.9%), barelands (3.2%), and wetlands (0.2%). Forests are predominantly located in the middle reaches of the YRB, forming a concentric pattern around the Chengdu Plain. Grasslands are mainly found in the upper reaches of the basin, while croplands are concentrated in the middle and lower reaches, including the Sichuan Basin. Built-up lands, which occupy a significant proportion, are mainly located in the downstream sections of the basin and around the Chengdu Plain. Barelands are primarily located in the upper and lower reaches, with limited presence in the middle reaches. Wetlands are predominantly distributed in the lower reaches, especially within the Poyang Lake and Dongting Lake water systems.
YRB 的土地利用主要由森林、草地和农田组成（图 5）。土地利用类型的分布如下：森林（53.5%）、草地（22.1%）、农田（14.5%）、建设用地（2.9%）、裸地（3.2%）和湿地（0.2%）。森林主要分布在 YRB 的中游，形成围绕成都平原的同心模式。草地主要位于流域的上游，而农田则集中在中游和下游，包括四川盆地。占有相当比例的建设用地主要位于流域的下游和成都平原周边。裸地主要分布在上游和下游，中游的分布有限。湿地主要分布在下游，特别是在鄱阳湖和洞庭湖水系内。

Figure 5. Spatial distribution map of the 6 main land use types in the Yangtze River Basin: (a) forest; (b) grassland; (c) cropland; (d) built-up land; (e) bareland; (f) wetland
图 5. 长江流域 6 种主要土地利用类型的空间分布图：(a) 森林；(b) 草地；(c) 耕地；(d) 建成区；(e) 裸地；(f) 湿地.

3.3. Effects of Land Use Types on TP Concentration at Different Scales
3.3. 不同尺度下土地利用类型对总磷浓度的影响

3.3.1. The Impact of Land Use Patterns on TP Concentrations at the Whole Basin Scale
3.3.1. 土地利用模式对整个流域 TP 浓度的影响

Land use types in the YRB, including built-up lands, croplands, forests, grasslands, industrial and mining lands, and barelands, show significant correlations with TP concentration (Table 2). The top three land use types influencing TP concentration in the basin are built-up lands (q = 0.19, corr = 0.43, p < 0.001), croplands (q = 0.18, corr = 0.40, p < 0.001), and forests (q = 0.16, corr = −0.38, p < 0.001). Built-up lands, croplands, industrial and mining lands, and barelands significantly increase TP concentration, whereas forests and grasslands significantly reduce them. Wetlands have a negative effect on TP concentrations; however, the p-value exceeds 0.05, indicating statistical insignificance. Due to the limited distribution of wetlands in the YRB and the absence of a significant correlation, wetlands are excluded from the analysis of the impact of land use types on TP concentration in subsequent analyses of secondary water resource divisions and hotspot/coldspot areas.
在黄河流域的土地利用类型，包括建设用地、农田、森林、草地、工业和矿业用地以及裸地，与总磷浓度（TP 浓度）显示出显著的相关性（表 2）。影响流域 TP 浓度的前三种土地利用类型是建设用地（q = 0.19，corr = 0.43，p < 0.001）、农田（q = 0.18，corr = 0.40，p < 0.001）和森林（q = 0.16，corr = −0.38，p < 0.001）。建设用地、农田、工业和矿业用地以及裸地显著增加 TP 浓度，而森林和草地则显著降低 TP 浓度。湿地对 TP 浓度有负面影响；然而，p 值超过 0.05，表明统计上不显著。由于黄河流域湿地的分布有限且缺乏显著相关性，湿地在后续的二级水资源划分和热点/冷点区域分析中被排除在土地利用类型对 TP 浓度影响的分析之外。

Table 2. Land use impact factor (LUIF) of land use type and TP concentration across the Yangtze River Basin in 2021
表 2. 2021 年长江流域土地利用类型的土地利用影响因子（LUIF）与总磷浓度.

Note: “***” indicates a significance level of p < 0.001, “+” indicates a positive correlation, “-“ indicates a negative correlation
注意：“***”表示显著性水平为 p < 0.001，“+”表示正相关，“-”表示负相关。.

The interaction between any two land use types leads to either bilinear or nonlinear increases in explanatory power (Figure 6). Notably, the interaction between built-up lands and croplands demonstrates the highest explanatory power, reaching 0.30. The TP concentration increases under the influence of any two land use types, with a notable strengthening in the explanatory power of the interaction factor. This further validates the impact of combined land use types on TP concentration.
任何两种土地利用类型之间的相互作用导致了解释力的双线性或非线性增加（图 6）。值得注意的是，建设用地与农田之间的相互作用表现出最高的解释力，达到 0.30。在任何两种土地利用类型的影响下，TP 浓度增加，且相互作用因子的解释力显著增强。这进一步验证了组合土地利用类型对 TP 浓度的影响。

Figure 6. Interaction q value of different land use types and TP concentration in the Yangtze River Basin. Mining land refers to industrial and mining land.
图 6. 长江流域不同土地利用类型与总磷浓度的交互 q 值。矿业用地指工业和矿业用地。

3.3.2. The Impact of Land Use Patterns on TP Concentrations at the Sub-Basin Scale
3.3.2. 土地利用模式对子流域尺度总磷浓度的影响

The impact of land use patterns on TP concentrations varies across the sub-basins of the YRB. Figure 7 illustrates that TP concentration in the upper reaches of the Jinsha River, upstream of Shigu, primarily originates from grasslands. In contrast, TP concentration in the lower reaches of the Jinsha River, downstream of Shigu, primarily originates from industrial and mining lands. In the Minjiang and Tuojiang Rivers, TP concentration is predominantly derived from croplands, built-up lands, and industrial and mining lands, with croplands exerting the most significant impact on TP concentrations in these regions. In the Jialing River, TP concentration is primarily derived from croplands. Along the mainstem from Yibin to Yichang, TP concentration is driven primarily by croplands, built-up lands, and barelands, with croplands having the most significant impact on TP concentrations. In the Wujiang River, TP concentration originates mainly from built-up lands, industrial and mining lands, and barelands, with industrial and mining lands exerting the greatest impact on TP concentrations in this area. In the Han River, TP concentration is primarily derived from croplands, built-up lands, industrial and mining lands, and barelands, with built-up lands having the greatest impact on TP concentrations in this region. Along the mainstem from Yichang to Hukou, TP concentration is driven by grasslands, croplands, built-up lands, industrial and mining lands, and barelands, with croplands exerting the greatest impact on TP concentrations. In the Dongting Lake water system, TP concentration primarily originates from croplands, built-up lands, industrial and mining lands, and barelands, with croplands exerting the greatest impact on TP concentrations. Below Hukou, TP concentration along the mainstem originates from grasslands, croplands, built-up lands, industrial and mining lands, and barelands, with built-up lands exerting the greatest impact on TP concentrations. In the Taihu Lake water system, TP concentration is primarily derived from grasslands, croplands, and built-up lands, with croplands having the greatest impact on TP concentrations.
土地利用模式对长江上游各子流域总磷浓度的影响各不相同。图 7 显示，金沙江上游（石鼓上游）的总磷浓度主要来源于草地。相比之下，金沙江下游（石鼓下游）的总磷浓度主要来源于工业和采矿用地。在岷江和沱江，总磷浓度主要来源于农田、建设用地以及工业和采矿用地，其中农田对这些地区的总磷浓度影响最大。在嘉陵江，总磷浓度主要来源于农田。在从宜宾到宜昌的主干流中，总磷浓度主要受农田、建设用地和裸地的驱动，其中农田对总磷浓度的影响最为显著。在乌江，总磷浓度主要来源于建设用地、工业和采矿用地以及裸地，其中工业和采矿用地对该地区的总磷浓度影响最大。 在汉江中，总磷（TP）浓度主要来源于农田、建设用地、工业和矿业用地以及裸地，其中建设用地对该地区 TP 浓度的影响最大。在从宜昌到湖口的主干流中，TP 浓度受草地、农田、建设用地、工业和矿业用地以及裸地的驱动，其中农田对 TP 浓度的影响最大。在洞庭湖水系中，TP 浓度主要来源于农田、建设用地、工业和矿业用地以及裸地，其中农田对 TP 浓度的影响最大。在湖口以下，主干流的 TP 浓度来源于草地、农田、建设用地、工业和矿业用地以及裸地，其中建设用地对 TP 浓度的影响最大。在太湖水系中，TP 浓度主要来源于草地、农田和建设用地，其中农田对 TP 浓度的影响最大。

Figure 7. Histogram of land use impact factor (LUIF) of secondary water resources in the Yangtze River Basin. Mine refers to industrial and mining land.
图 7.长江流域二级水资源的土地利用影响因子（LUIF）直方图。矿地指工业和采矿用地。

3.3.3. The Impact of Land Use Patterns on TP Concentrations at the Regional Scale
3.3.3. 土地利用模式对区域尺度总磷浓度的影响

Significant differences in the impact of land use patterns on TP concentrations exist across different hotspot zones (Figure 8a–g). In the three high-value hotspot areas, forests play a mitigating role, with TP concentration primarily originating from built-up lands and industrial and mining lands. In the three low-value coldspot areas, grasslands exert a mitigating effect, with TP concentration primarily stemming from built-up lands, croplands, and industrial and mining lands. In non-hotspot areas, both forests and grasslands provide a mitigating effect, with TP concentration primarily emanating from built-up lands, croplands, and industrial and mining lands. According to Figure 8h, TP concentration hotspots are predominantly located in areas with a higher proportion of croplands and built-up lands, while coldspot areas are more prevalent in regions dominated by forests and grasslands. From coldspot low-value areas to hotspot high-value areas, the proportion of forests decreases progressively (area proportion from 70.4% to 40.7%), while the proportion of croplands increases (area proportion from 7.2% to 33.7%), along with a rise in the proportion of built-up lands (area proportion from 1.2% to 10.2%).
不同热点区域的土地利用模式对总磷（TP）浓度的影响存在显著差异（图 8a–g）。在三个高价值热点区域，森林发挥了缓解作用，TP 浓度主要来源于建设用地和工业及矿业用地。在三个低价值冷点区域，草地发挥了缓解作用，TP 浓度主要源于建设用地、农田以及工业和矿业用地。在非热点区域，森林和草地均提供了缓解作用，TP 浓度主要来自建设用地、农田和工业及矿业用地。根据图 8h，TP 浓度热点主要位于农田和建设用地比例较高的区域，而冷点区域则更常见于以森林和草地为主的地区。从低价值冷点区域到高价值热点区域，森林的比例逐渐减少（面积比例从 70.4%降至 40.7%），而农田的比例则增加（面积比例从 7.2%增至 33%）。7%），同时建成区土地的比例也有所上升（面积比例从 1.2%上升至 10.2%）。

Figure 8. Impact index of land use type in hot and cold zones in the Yangtze River Basin
图 8.长江流域热区和冷区土地利用类型的影响指数.

4. Discussion
4. 讨论

4.1. Significant Spatial Clustering of TP Concentration in the YRB
4.1. 黄河流域 TP 浓度的显著空间聚集

This study reveals the spatial clustering of TP pollution in the YRB using hotspot analysis. Hotspot regions of TP concentration include the Pudu River Basin, Chengdu Plain, Jianghan Plain, and the Yangtze River Delta. In comparison to the studies by Ji et al. [29,30] and Chen et al. [31], this study also identifies the Chengdu and Hanjiang Plains as hotspots for TP concentration. This distribution corresponds to areas rich in China’s renowned phosphate mineral deposits, such as the Jinhua-Qingping mines in Sichuan, Dianchi mines in Yunnan, Yichang mines in Hubei, and Fuquan mines in Guizhou [10,32,33]. These areas, rich in phosphate resources, experience elevated TP concentrations due to intensive mining activities and high-load emissions from phosphate chemical industries, which significantly contribute to persistent pollution levels.
本研究通过热点分析揭示了黄河流域 TP 污染的空间聚集。TP 浓度的热点区域包括普渡河流域、成都平原、江汉平原和长江三角洲。与季等人[29,30]和陈等人[31]的研究相比，本研究还将成都平原和汉江平原确定为 TP 浓度的热点。这一分布与中国著名的磷矿资源丰富地区相对应，如四川的金华-青平矿、云南的滇池矿、湖北的宜昌矿和贵州的福泉矿[10,32,33]。这些富含磷资源的地区由于集中的采矿活动和磷化工行业的高负荷排放，导致 TP 浓度升高，显著加剧了持续的污染水平。

In August 2019, the Ministry of Ecology and Environment published a rectification report, revealing that 276 out of 692 “three-phosphate” enterprises (phosphate mines, storage, and chemical industries) were found to have ecological and environmental issues (Figure 9). Upstream of the four primary hotspots for elevated TP concentrations, a dense cluster of “three-phosphate” enterprises requiring rectification is observed. TP concentrations from these enterprises accumulate through tributaries, eventually flowing into the main rivers, forming high-value hotspot areas along the river. Research by Yan et al. [34] also highlights significant spatial variations in phosphorus loads entering the river from “three-phosphate” enterprises in the YRB. Sub-basins such as the downstream of the Jinsha River, Wujiang River, Min and Tuo Rivers, and Han River contribute 32.8% to the TP flux in the Yangtze River, marking them as key sources of phosphorus in the basin. In addition, in the 12 secondary water resource zones within the basin, TP concentration in the Jinsha River (below Shigu) and Wujiang River is primarily attributed to industrial and mining lands. Industrial and mining lands are also key contributors to TP concentration in the Min-Tuo River, Han River, the stretch from Yichang to Hukou, the Dongting Lake system, and downstream of Hukou. Furthermore, in the high-value areas identified through hotspot analysis, TP concentration primarily originates from built-up and industrial-mining lands, emphasizing that the major sources of TP concentration in these hotspots are mining activities and high-load emissions from phosphate mining and chemical industries.
2019 年 8 月，生态环境部发布了一份整改报告，揭示在 692 家“三磷”企业（磷矿、储存和化工行业）中，有 276 家存在生态和环境问题（见图 9）。在四个主要的总磷浓度升高热点的上游，观察到需要整改的“三磷”企业密集聚集。这些企业的总磷浓度通过支流累积，最终流入主河流，形成沿河的高价值热点区域。严等人的研究[34]也强调了来自“三磷”企业进入长江的磷负荷存在显著的空间变化。金沙江、乌江、岷江和沱江、汉江等下游子流域对长江的总磷通量贡献了 32.8%，标志着它们是流域内磷的主要来源。此外，在流域内的 12 个二级水资源区中，金沙江（石鼓以下）和乌江的总磷浓度主要归因于工业和矿区土地。 工业和矿业用地也是长江、汉江、宜昌至湖口段、洞庭湖系统以及湖口下游 TP 浓度的主要贡献者。此外，在热点分析中识别出的高价值区域，TP 浓度主要来源于城市建设和工业矿业用地，强调了这些热点中 TP 浓度的主要来源是矿业活动以及磷矿和化工行业的高负荷排放。

Figure 9. Distribution map of enterprises that need to be rectified in the self-examination of “three phosphorus” in the Yangtze River Basin in 2019
图 9. 2019 年长江流域“三磷”自查中需要整改的企业分布图.

4.2. Regional Differences in the Impact of Land Use Patterns on TP Concentration in the YRB
4.2. 土地利用模式对黄河流域 TP 浓度影响的区域差异

At the basin-wide scale, the primary sources of TP concentration are ranked as follows: built-up land, cropland, industrial and mining land, and bareland. However, the main sources of TP concentration vary significantly across the sub-basins. Specifically, TP concentration in seven sub-basins is primarily attributed to cropland, in two sub-basins it is predominantly sourced from built-up land, and in two others, industrial and mining land are the major contributors (Figure 10). These findings align with the general trend observed by Zou et al. [35], which indicates that 83% of TP concentrations in China originate from agricultural non-point source pollution. However, discrepancies between the basin-wide results and those at the sub-basin scale may be related to the scale-dependent nature of land use impacts on water quality [19]
在流域范围内，总磷浓度的主要来源排名如下：建设用地、农田、工业和矿业用地以及裸地。然而，总磷浓度的主要来源在各个子流域之间差异显著。具体而言，七个子流域的总磷浓度主要归因于农田，两个子流域主要来源于建设用地，而另外两个子流域则以工业和矿业用地为主要贡献者（图 10）。这些发现与 Zou 等人[35]观察到的一般趋势一致，表明中国 83%的总磷浓度源于农业非点源污染。然而，流域范围内的结果与子流域尺度的结果之间的差异可能与土地利用对水质影响的尺度依赖性特征有关[19]。.

In this study, the sources of TP pollution in the sub-basins of the Yangtze River exhibit considerable complexity, with four predominant types of pollution sources identified: industrial-mining land type, cropland type, cropland-dominant type, and built-up land-dominant type. In the downstream section of the Jinsha River, below Shigu, TP concentration primarily originates from industrial-mining land. This is primarily due to the region’s status as a major phosphate mining area in the Dianchi region, where the proven P₂O₅ reserves amount to 8.94 × 10⁸ tons. The extraction and processing of phosphate ores have caused significant vegetation degradation, contributing to persistently high TP concentrations in this sub-basin [18,33,36]. Therefore, the focus for TP concentration control in this region should be directed towards the management of mining areas and regulation of phosphorus emissions.
在本研究中，长江流域子水系的 TP 污染源表现出相当复杂性，识别出四种主要的污染源类型：工业矿业用地类型、农田类型、农田主导类型和建设用地主导类型。在金沙江下游，石鼓以下，TP 浓度主要来源于工业矿业用地。这主要是由于该地区作为滇池地区主要的磷矿开采区，已探明的 P ₂ O ₅ 储量达到 8.94 × 10 ⁸ 吨。磷矿石的开采和加工导致了显著的植被退化，导致该子水系 TP 浓度持续偏高[18,33,36]。因此，该地区 TP 浓度控制的重点应放在矿区管理和磷排放的监管上。

In the Jialing River, TP concentration arises predominantly from cropland, a finding consistent with the results of Wu et al. [37], which indicates that cropland contributes the most to TP concentration in the Jialing River. Consequently, TP control efforts in this sub-basin should prioritize preventing soil erosion and implementing ecological fertilization techniques to reduce pollution in agricultural lands.
在嘉陵江，TP 浓度主要来源于农田，这一发现与吴等人的研究结果一致[37]，表明农田对嘉陵江 TP 浓度的贡献最大。因此，该支流的 TP 控制工作应优先考虑防止土壤侵蚀和实施生态施肥技术，以减少农业用地的污染。

The sources of TP concentration in the Min-Tuo River, the Dongting Lake system, the mainstream from Yibin to Hukou, and the Taihu Lake system are characterized as cropland-dominant. Within these regions, the influence of built-up and industrial-mining lands is also substantial, further exacerbating TP concentration levels. Notable examples include the phosphate mines in the Jinhe-Qingping area of Sichuan and the dense concentration of phosphorus chemical industries along the Yichang section of the Yangtze River [38,39]. In these cropland-dominant sub-basins, TP concentration control should focus on large-scale agricultural management practices, including optimizing fertilizer use and promoting conservation tillage.
在岷沱河、洞庭湖系统、从宜宾到湖口的主流以及太湖系统中，TP 浓度的来源以农田为主。在这些地区，城市建设和工业矿区的影响也相当显著，进一步加剧了 TP 浓度水平。显著的例子包括四川金河-青平地区的磷矿和长江宜昌段沿线的磷化工产业的密集分布。在这些以农田为主的子流域中，TP 浓度控制应重点关注大规模农业管理实践，包括优化施肥和推广保护性耕作。

In the Han River and the downstream section below Hukou, the primary source of TP concentration is built-up land, although cropland and industrial-mining lands also significantly influence the TP concentrations in these areas, such as the concentration of phosphorus enterprises below the Danjiangkou section of the Han River [24]. For sub-basins dominated by built-up land, the control of TP concentration should focus on the management of domestic sewage discharge and waste treatment.
在汉江及其下游的壶口以下段落，TP 浓度的主要来源是建设用地，尽管农田和工业矿区也显著影响这些地区的 TP 浓度，例如汉江丹江口段下游的磷企业浓度[24]。对于以建设用地为主的子流域，TP 浓度的控制应重点关注生活污水排放和废物处理的管理。

In the Wujiang River, TP concentration is predominantly from industrial-mining land, with substantial contributions from built-up and bare lands. The basin contains strategically significant national-level phosphate mines, such as the Kaiyang and Wengfu mines, which have been in continuous development, contributing notably to TP concentration due to mining activities and the associated phosphate chemical industries [40]. Therefore, TP concentration control in these sub-basins should focus on the regulation of mining areas, emissions from phosphate production, and the management of domestic wastewater and solid waste disposal.
在吴江河中，总磷浓度主要来自工业采矿用地，建设用地和裸地也有显著贡献。该流域包含具有战略意义的国家级磷矿，如开阳矿和翁福矿，这些矿山一直在持续开发，由于采矿活动及相关的磷化学工业，显著增加了总磷浓度。因此，这些子流域的总磷浓度控制应重点关注采矿区的管理、磷生产的排放以及生活污水和固体废物处置的管理。

The land use types in the Poyang Lake watershed did not pass the significance test, and the main factors driving TP pollution have yet to be identified. However, studies by Sun et al. [41] and Wang et al. [42] indicated that the TP concentration in Poyang Lake is primarily influenced by five rivers: Xiu River, Gan River, Fu River, Xin River, and Rao River. The pollution load from these rivers, along with hydrological changes, are key factors influencing the TP concentration in the lake’s waters. Therefore, the control and mitigation of TP concentration in the Poyang Lake watershed should focus on the management of these five rivers. In conclusion, effective TP concentration management in the YRB requires a tailored approach, with zonal management and systematic governance based on regional characteristics.
鄱阳湖流域的土地利用类型未通过显著性检验，导致总磷（TP）污染的主要驱动因素尚未确定。然而，孙等人[41]和王等人[42]的研究表明，鄱阳湖的 TP 浓度主要受五条河流的影响：秀水、赣江、抚河、新河和饶河。这些河流的污染负荷以及水文变化是影响湖水 TP 浓度的关键因素。因此，控制和减轻鄱阳湖流域的 TP 浓度应重点关注这五条河流的管理。总之，黄河流域的有效 TP 浓度管理需要量身定制的方法，基于区域特征进行分区管理和系统治理。

Figure 10. The distribution map of the dominant land use types for TP concentration sources in the 12 sub-basins of the Yangtze River.
图 10. 长江 12 个子流域 TP 浓度源的主要土地利用类型分布图。

4.3. Limitations and Future Works
4.3. 限制与未来工作

Although this study has made significant progress in examining the impact of land use on TP pollution, several limitations need to be addressed in future research. First, the water quality data used in this study consist solely of national monitoring data from 2021, which limits the ability to analyze the spatiotemporal variations and trends of TP concentration in the YRB. Second, the land use types considered in this study, except for industrial and mining land, are based on the primary classification of land use, which may obscure spatial heterogeneity within these categories. For example, the influence of different crop types within cropland on TP concentration can vary significantly. In this study, TP concentration in many sub-basins primarily originates from croplands, representing agricultural non-point source pollution. Future research could explore the impact of different farming practices on TP concentration.
尽管本研究在考察土地利用对总磷（TP）污染的影响方面取得了显著进展，但未来研究中仍需解决几个局限性。首先，本研究使用的水质数据仅包括 2021 年的国家监测数据，这限制了对黄河流域（YRB）TP 浓度时空变化和趋势的分析。其次，本研究考虑的土地利用类型，除了工业和矿业用地外，均基于土地利用的主要分类，这可能掩盖了这些类别内的空间异质性。例如，农田内不同作物类型对 TP 浓度的影响可能存在显著差异。在本研究中，许多子流域的 TP 浓度主要来源于农田，代表了农业非点源污染。未来的研究可以探讨不同农业实践对 TP 浓度的影响。

5. Conclusions
5. 结论

This study provides a systematic analysis of the impact of land use on TP concentration at various scales, including both watershed and sub-watershed levels. In 2021, 98.8% of the state-controlled sections in the YRB were classified with TP concentration as “good” or better. TP concentration showed clear spatial clustering, with notable pollution detected in the Pudu River Basin, Chengdu Plain, Jianghan Plain, and the Yangtze River Delta. At the basin-wide scale, built-up land and cropland were identified as the most significant land use types influencing TP concentration levels. A significant regional variation exists in the sources of TP concentration within the YRB, with over half of the sub-basins identifying cropland as the primary source of pollution. Notably, phosphorus concentrations in hotspot regions, including the Pudu River Basin, Chengdu Plain, and Jianghan Plain, are strongly correlated with the distribution of industrial and mining lands. Therefore, TP concentration management in the YRB should implement targeted strategies that address the specific sources of pollution in individual sub-basins.
本研究对土地利用对总磷（TP）浓度的影响进行了系统分析，涵盖了流域和子流域两个层面。2021 年，雅鲁藏布江流域（YRB）中 98.8%的国家控制区域被分类为“良好”或更好的 TP 浓度，且 TP 浓度显示出明显的空间聚集，普渡河流域、成都平原、江汉平原和长江三角洲等地的污染尤为显著。在流域范围内，建设用地和农田被确定为影响 TP 浓度水平的最重要土地利用类型。YRB 内 TP 浓度的来源存在显著的区域差异，超过一半的子流域将农田识别为主要污染源。值得注意的是，普渡河流域、成都平原和江汉平原等热点地区的磷浓度与工业和矿业用地的分布密切相关。因此，YRB 的 TP 浓度管理应实施针对性的策略，以解决各个子流域特定的污染源。

Author Contributions: Methodology, F.D. and Y.X.; Investigation, F.D. and Y.Y.; Data curation, W.L.(Wenhui Liu)and W.L.(Wei Liu); Writing—original draft, F.D., W.L.(Wenhui Liu), W.L.(Wei Liu), Y.X., Y.S., C.Z., M.S., and Y.Y.; Writing—review & editing, F.D., Y.X., W.L.(Wenhui Liu), W.L.(Wei Liu), and Y.S.; Visualization, W.L.(Wei Liu) and M.S. All authors have read and agreed to the published version of the manuscript.
作者贡献：方法论，F.D. 和 Y.X.；调查，F.D. 和 Y.Y.；数据整理，W.L.(刘文辉) 和 W.L.(刘伟)；撰写—初稿，F.D.，W.L.(刘文辉)，W.L.(刘伟)，Y.X.，Y.S.，C.Z.，M.S. 和 Y.Y.；撰写—审阅与编辑，F.D.，Y.X.，W.L.(刘文辉)，W.L.(刘伟) 和 Y.S.；可视化，W.L.(刘伟) 和 M.S. 所有作者均已阅读并同意发表的手稿版本。

Funding: This research was funded by the Xiamen Natural Science Foundation Project (3502Z202372044) and the Xiamen Natural Science Foundation Project (3502Z202471079)
资助：本研究由厦门市自然科学基金项目（3502Z202372044）和厦门市自然科学基金项目（3502Z202471079）资助.

Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.
数据可用性声明：支持本文结论的原始数据将由作者根据请求提供。

Conflicts of Interest: The authors declare no conflict of interest.
利益冲突：作者声明没有利益冲突。

Appendix A
附录 A

Figure A1. Box plot of TP concentration in 2021 for secondary water resources in the Yangtze River Basin
图 A1. 2021 年长江流域二级水资源中 TP 浓度的箱线图.

Figure A2. Accumulation histogram of land use area proportion and TP concentration curve of secondary water resources in the Yangtze River Basin. ASJR is the above Shigu of the Jinsha River; BSJR is the below Shigu of the Jinsha River; MTR is the Minjiang and Tuojiang River; YTY is the mainstream from Yibin to Yichang; WR is the Wujiang River; JR is the Jialing River; HR is the Han River; YTH is the mainstream from Yibin to Yichang; DTL is the Dongting Lake Water System; PYL is the Poyang Lake Water System; BHMS is the Below Hukou of Main Stream; THL is the Taihu Lake Water System
图 A2. 长江流域二级水资源的土地利用面积比例累积直方图和 TP 浓度曲线。ASJR 是金沙江上游的石鼓；BSJR 是金沙江下游的石鼓；MTR 是岷江和沱江；YTY 是从宜宾到宜昌的主流；WR 是乌江；JR 是嘉陵江；HR 是汉江；YTH 是从宜宾到宜昌的主流；DTL 是洞庭湖水系；PYL 是鄱阳湖水系；BHMS 是主流的下游湖口；THL 是太湖水系。.

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