Non-point source pollution transport and photocatalytic degradation effect in the Mudong River basin of Huixian Wetland based on WASP model

Non-point source pollution transport and photocatalytic degradation effect in the Mudong River basin of Huixian Wetland based on WASP model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Non-point source pollution transport and photocatalytic degradation effect in the Mudong River basin of Huixian Wetland based on WASP model Yiyang Li, Zitao Li, Junfeng Dai, Saeed Rad, Xiaolan Xie, Shanshan Qi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5204210/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Agricultural activities are one of the important sources of water pollution in small agricultural watersheds. It is of great significance to understand the current status of water quality and non-point source pollution in small agricultural watersheds, estimate their pollution load, and explore pollutant reduction methods for sustainable water environment management and protection. In this paper, the small watershed of Mudong River in Huixian Wetland was taken as the study area, and the water quality monitoring indicators were dynamically simulated by the WASP model. Combined with the preparation of nano-titanium dioxide films and photocatalytic degradation experiments, the water quality reduction of each river section was systematically evaluated. Then, based on the simulation results, the reduction of pollution load into the river was estimated, which provided a scheme for the field reduction of pollutants in agricultural watersheds. The results showed that the WASP model was effective in simulating the water quality of the upper Mudong River in a typical karst area. The simulation inverted the reduction in pollution loads in the upper Mudong River for each indicator. Moreover, it calculated non-point source pollution reduction rates of ammonia nitrogen (NH 3 -N) (31%, 9.91%, 2.18%), total nitrogen (TN) (24.59%, 21.95%, 10.58%), total phosphorus (TP) (26.64%, 29.39%, 25.15%), dichromate oxidizability (CODcr) (46.46%, 13.39%, 0.99%), and biochemical oxygen demand (BOD 5 ) (37.67%, 31.04%, 23.09%) at 24, 12, and 6 h of the reaction, respectively. In short, this method will improve river water quality if nano-titanium dioxide material is promoted for outdoor use. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology small watershed WASP water quality modeling pollution load reduction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Water pollution sources can be divided into point source pollution and non-point source pollution. Non-point source pollution has become a major problem causing environmental pollution in the river basin 1 , 2 . There are many sources of non-point source pollution, including agricultural runoff, rainfall, and atmospheric deposition 3 . Among them, agricultural non-point source pollution is scattered, random, difficult to supervise, with high N and P content, and huge risks 4 – 6 . A variety of land use, tillage methods, fertilization management methods, and climate types all affect agricultural non-point source pollution in the operation and transformation processes 7 – 9 . Agricultural non-point source pollution 10 , 11 has been recognized as one of the main causes of water eutrophication 12 , 13 , and many countries are facing the problem of agricultural non-point source pollution 14 . For example, in the United States, agricultural non-point source pollution is considered to be a major source of nutrients in lakes and rivers 15 . In Europe, 50–80% of the total nitrogen and total phosphorus loads in freshwater and seawater are mainly due to agricultural pollution 16 . In China, the communiqué of the second national survey of pollution sources showed that agriculture emitted 10.6713 million tons of chemical oxygen demand (CODcr), 1.4149 million tons of total nitrogen (TN) and 212,000 tons of total phosphorus (TP), accounting for 49.8%, 46.5% and 67.2% of the total emissions, respectively 17 . Estimating non-point source pollution is an important way to quantify the pollution load, and commonly used methods include the output coefficient method 18 , production and discharge coefficient method, model estimation, and other methods. In the past, many researchers have used water quality models to estimate the non-point source pollution load in watersheds. For example, Zhou et al. 19 combined the one-dimensional water quality model with the improved output coefficient method to estimate the non-point source pollution load in the Chaohe River Basin, which verified the rationality and universality of this calculation method. Chen Wenjun and others 20 studied the pollutant load in the Taihu Lake Basin based on the water quality analysis simulation program (WASP) model and geographic information system (GIS) spatial analysis, providing a holistic framework for the analysis of the water qualities of various water bodies in the rural watershed in the humid region of southeast China. Over time, water quality modelling has expanded from an initial focus on basic parameters to a comprehensive assessment of aquatic ecosystems 21 , 22 . The WASP model, which is currently widely used around the world, has undergone significant advancements and updates, enhancing its flexibility and adaptability to simulate more complex environmental processes and various contaminants such as nutrients, organics, and heavy metals 23 . In recent years, studies have confirmed that the model has shown unique advantages in watershed pollution traceability, environmental capacity assessment and ecological risk early warning 24 , 25 . Zelazny et al. 26 revealed the compound impact mechanism of agriculture and urban pollution on the Dunayek River Basin through multi-scenario simulation. Obin's team 27 innovatively combined with the FLUX equation to construct a dynamic prediction system for water environment capacity in the Zhuzhou section of the Yangtze River. The sensitivity analysis of Mbongowo et al. 28 quantified the contribution of point source pollution to nutrient overload in the Shenandor River Basin. At the same time, the new water treatment technology based on the principle of semiconductor photocatalysis provides a new idea to solve the bottleneck of traditional process efficiency. Photocatalysts such as TiO₂ have attracted much attention because of their non-toxic, stable, and photoregenerative properties 29 . Due to its non-toxic, stable properties, chemical resistance and photocorrosion resistance, TiO2 has become the most promising semiconductor photocatalytic material 10 . TiO2 can convert 30 and remove heavy metal ions 31 , inorganic salts 31 , organic matter 32 , 33 , etc. in water through sunlight or ultraviolet photocatalytic reaction, which makes TiO2 photocatalytic technology widely used in improving water quality. Compared with the traditional physical treatment process, the use of TiO2 photocatalytic technology for water pollution control is more efficient 34 , environmentally friendly 35 , 36 and economical 37 , 38 . The Ag/TiO₂/PVA ternary composite system developed by Mohammad et al. 39 exhibited excellent heavy metal removal performance under ultraviolet excitation, while Nisereen's team 40 effectively alleviated the membrane fouling problem through a synergistic process of photocatalysis-membrane separation. Although the optical response range of TiO₂ can be extended to the visible region (from 3.2 eV to 2.4–2.8 eV) through elemental doping (e.g., N, Fe) and heterostructure, there are still multiple challenges in practical applications. The light scattering effect caused by water turbidity can reduce the photocatalytic efficiency by 30%-50% 41 , and the problem of reducing the photocatalytic efficiency due to complex water quality has not been effectively solved 42 . The light transmission efficiency and operating cost of large-scale reactors still need to be optimized. And current research focuses on the development of adaptive carrier materials 43 in order to break through the bottleneck of technology translation. It is worth noting that the synergistic application of WASP model and photocatalytic technology shows potential advantages. The former can provide accurate spatiotemporal dynamic simulation for pollution control projects, while the latter provides practical technical support for the pollutant reduction parameters in the model. In the future, it is necessary to focus on solving interdisciplinary problems such as the dynamic coupling mechanism of model parameters and the modular design of photocatalytic reactors, so as to promote the development of water environment treatment technology in the direction of precision and intelligence. The Huixian Karst Wetland is one of the largest subtropical low-altitude karst wetlands in China, and its surface water and groundwater are important foundations for the development of agriculture and animal husbandry in the basin 44 – 46 . However, overexploitation has led to severe eutrophication in this area. Among the sources of pollution, non-point source pollution from agricultural activities is the main cause of surface water and groundwater pollution in the wetlands 47 , 48 . Due to the complex hydrological conditions of karst wetlands and the inconvenience of monitoring non-point source pollution, few studies have been conducted on agricultural non-point source pollution in karst wetlands. In the past, research on the Huixian wetland has focused on the sources of pollutants and the differences in time and space 49 – 51 , but little work has focused on the estimation of the pollutant load, and the feasibility of combining models to simulate and calculate pollution transport and reduction in karst wetlands has not been proven. Therefore, it is particularly urgent to simulate and estimate the migration and reduction of pollutants in farmland runoff in karst wetlands 40 – 43 . In the past, most of the studies used the WASP model to estimate the non-point source pollution load of the watershed, but few of them were combined with the degradation effect of new materials. In order to find out the non-point source pollution of the Mudong River watershed in Huixian Wetland, estimate its pollution load and explore the possibility of photocatalytic degradation of pollutants in water by nanomaterials, this paper innovatively combines the WASP model with the photocatalytic degradation experiment of nano titanium dioxide films, estimates the inflow reduction of pollution load into the river based on the model, and discusses the application prospect of nano titanium dioxide materials in outdoor water. In the process of photocatalytic experiments, the low-iron ultra-white cullet reused by waste recycling and processing was selected as the coating substrate to prepare TiO2 films, and the packed-bed photocatalytic reactor was updated and upgraded to simulate the outdoor scenario, which advocated the idea of green development and provided solutions for the evaluation of pollutant reduction effect and pollutant reduction in small watersheds in the field. 2 Materials and methods The research ideas of this paper are shown in Fig. 1 . 2.1 Study area The small watershed of the Mudong River in the Huixian karst wetland is located at longitude 110°09′−110°14′E and latitude 25°04′−25°09′N, covering the entire area where the river flows into the core area of the wetland. The Huixian wetland is the transition zone between the watersheds of the Li River and the Liu River Basin, as well as the buffer zone between the Guilin Peak Forest Plain and the bottom of the Peak Thicket Valley, and it represents the typical characteristics of karst wetlands in China. It is typically used as a key area for the study of karst wetlands. The study area is located in the southern portion of the Nanling Mountains. The soil types include typical wetland soils and karst soils. The geological structure belongs to the South China Fold Belt, and it has a typical karst geomorphology. In addition, the study area is characterized by a typical subtropical monsoon climate, with an average annual temperature of 20°C and high precipitation 5 . The rainy season occurs between April and August, with precipitation accounting for approximately half of the annual total amount 50 . Furthermore, the dry season of the study area is from October to March, with frequent droughts in the rivers and in the wetland. In addition, the Mudong River small watershed is dominated by agriculture. Due to this, the primary source of agricultural non-point source pollution in the study area includes nitrogen and phosphorus nutrients lost from fertilizer applications on agricultural land, sewage from livestock and poultry farming, and domestic sewage from residential land. Currently, livestock and poultry in the watershed have both centralized breeding and scattered farming, but livestock and poultry wastewater is directly discharged, causing pollution to surface water, groundwater, and soil. The river segment covered by the model is the upper reach of the Mudong River (Fig. 2 ). 2.2 Data sources and analysis 2.2.1 Sample collection and Analysis Methods The field investigation and water sample collection time are July 2022 ~ December 2023, and the frequency is monthly sampling. Samples are collected 2 ~ 3 times a month in the rainy season and 1 time per month in the dry season. In addition, 9 sample collection points (No. 1 ~ 9) were set up near the main pollution sources such as farmland, duck farms, villages, and fish ponds along the Mudong River. The TN (HJ 636–2012) 52 , NH 3 -N (HJ 535–2009) 52 , TP (GB11893-89) 53 , chemical oxygen demand (CODcr) (HJ 05-2009) 54 , biochemical oxygen demand BOD 5 (GB 11914-89) 55 , and nitrate nitrogen (NO 3 ¯-N) (HJ/T 346–2007) 52 were measured in the laboratory in accordance with standard methods. In addition, the dissolved oxygen, pH, flow rate, and water temperature of the waterbody were monitored during the collection process. In addition, the dissolved oxygen, pH, flow rate, and water temperature of the water body were monitored using a portable dissolved oxygen meter, pH meter, and Doppler ultrasonic flow meter during the collection process. The data were processed using Microsoft Excel 2023, and Origin Pro 2024 was used to generate the charts. In addition, ArcGIS 10.6 was used for satellite image analysis and the creation of the maps of the study area. The WASP 8.23 tool allows for modeling of water resources and analysis of different scenarios in the studied basin, with the aim of ensuring the sustainable management of these resources. The meteorological data are from the National Meteorological Information Center, the Guilin Hydrological Station and the Doppler flow monitoring equipment. 2.2.2 Investigation and statistics of pollution sources We collected information on rural resident life, plantations, aquaculture, and livestock and poultry farming, as well as other land use information. Combined with the actual situation and referring to the relevant literature and calculation manuals 6 , 56 , the output coefficient method 57 , 58 was used to calculate the emissions of planting and other land use pollution sources. The expression of the output coefficient model is as Eq. (1) 59,60 : $$\:\begin{array}{c}L=\sum\:_{i=1}^{m}{E}_{i}{A}_{i}\#\left(1\right)\end{array}$$ where \(\:L\) is the total output of the contaminant, \(\:Ei\) is the output coefficient of this pollutant in the ith land use type, and \(\:i\) is the land use area in section i. The emission of rural domestic pollution needs to be calculated using Eq. (2) 59,60 . The amount of pollutants produced in domestic sewage can be expressed as follows: $$\:\begin{array}{c}Pollutant\:production\:=\:rural\:permanent\:population\:\times\:\:per\:capita\:pollution\:intensity\:\times\:\:365\#\left(2\right)\end{array}$$ Due to the lack of information about the addresses and sizes of the farms, in this study, we estimated the pollution emissions from aquaculture and poultry breeding through field visits and using data from the Guilin Economic and Social Statistical Yearbook, combined with the area proportion method and field survey data. 2.2.3 Calculation of emissions and input load to the river from non-point source pollution By referring to other studies conducted in China 61 , 62 and combining their conclusions with the previous research of our group, the river entry coefficient of the surface source pollution was determined to be 0.2 through repeated iterative trial calculations and the model according to comprehensive factors, such as the distribution of the karst, geological structures, and groundwater burial in the small watershed. In practice, it was found that the non-point source pollution load showed an increasing trend in the months with higher precipitation. Therefore, the annual average non-point source pollution entering the river coefficient was used to calculate the amount of non-point source pollution entering the river in each month based on the ratio of the total monthly precipitation to the total annual precipitation. The monthly cumulative rainfall and its percentage from January, 2023 to December, 2023 are shown in Supplementary tables. The average monthly input of non-point source pollution to the river was calculated as Eq. (3): $$\:\begin{array}{c}{L}_{nF\:}={W}_{F}\times\:{\gimel\:}_{F\:}\times\:P\#（3）\end{array}$$ Where \(\:{L}_{nF}\) is the monthly intake of non-point source pollution; \(\:{W}_{F}\) is the annual emission from non-point sources; \(\:{\gimel\:}_{F}\) is the non-point source pollution’s coefficient of entering the river; and \(\:P\) is the ratio of monthly rainfall to annual rainfall. 2.3 Constructing the WASP model 2.3.1 Overview and model principles of the WASP model The WASP model is a multifunctional water quality model developed by the U.S. Environmental Protection Agency (EPA). It integrates the DYNHYD (hydrodynamics) and WASP (water quality) modules. The WASP model is suitable for a variety of aquatic environments, such as wetlands, reservoirs, and rivers, as it simulates the interaction between pollutants and natural processes 26 , making it one of the more preferred tools in the field of water quality modeling 23 , 27 , 63 . The WASP model uses a series of mathematical equations based on the principle of conservation of mass to describe the migration and transformation of matter in aquaponic systems over time and space. The model takes the tiny water body as the basic analysis unit, simulates the dynamics of the pollutants, nutrients, and other dissolved substances in rivers, lakes, and reservoirs through equations, and integrates environmental factors such as river dynamics, temperature changes, dissolved oxygen levels, and biochemical reactions. The core function is to accurately simulate the transport of soluble components in the water body, and to realize dynamic analysis of the water quality using the mass conservation equation, i.e., Eq. (4) 4,64 . $$\:\begin{array}{c}\partial\:C/\partial\:t=-\frac{\partial\:\left({U}_{x}C\right)}{\partial\:x}+\frac{\partial\:\left({E}_{x}\frac{\partial\:C}{\partial\:x}\right)}{\partial\:x}-\frac{\partial\:\left({U}_{y}C\right)+\partial\:\left({E}_{y}\frac{\partial\:C}{\partial\:y}\right)}{\partial\:y}+\frac{\partial\:\left({E}_{z}\frac{\partial\:C}{\partial\:z}\right)}{\partial\:z}-\frac{\partial\:\left({U}_{z}C\right)}{\partial\:z}+{S}_{L}+{S}_{B}+{S}_{K}\#\left(4\right)\end{array}$$ where \(\:C\) is the concentration of the water quality index ( \(\:mg/L\) ); \(\:t\) is the time step ( \(\:s)\) ; \(\:{U}_{x}\) , \(\:{U}_{y}\) , and \(\:{U}_{z}\:\) are the longitudinal, transverse, and vertical convection velocities of the water body ( \(\:m/s),\:\) respectively; \(\:{E}_{x}\) , \(\:{E}_{y}\) , and \(\:{E}_{z}\) are the diffusion coefficient of the longitudinal, transverse, and vertical water body ( \(\:{m}^{2}/s)\) , respectively; \(\:{S}_{L}\) is the sum of the point source and non-point source pollution loads ( \(\:g/({m}^{3}\bullet\:d))\) ; \(\:{S}_{B}\) is the boundary load, including the upstream, downstream, bottom, and atmospheric environments ( \(\:g/({m}^{3}\bullet\:d)\) ) and \(\:{S}_{K}\) is the total dynamic conversion coefficient ( \(\:g/({m}^{3}\bullet\:d)\) ) . In the process of simulating water quality indicators, homogeneity is assumed in the horizontal and vertical directions, so the model is simplified to a one-dimensional mass balance Equ.(5) 4 , 64 : $$\:\begin{array}{c}\frac{\partial\:\left(AC\right)}{\partial\:t}=\frac{\partial\:\left(-{U}_{x}AC+{E}_{x}A\frac{\partial\:C}{\partial\:x}\right)}{\partial\:x}+A{S}_{L}+A{S}_{B}+A{S}_{K}\#\left(5\right)\end{array}$$ where \(\:A\) is the cross-sectional area of the simulated water body ( \(\:{m}^{2})\) . The rest of the variables are the same as above. 2.3.2 River generalization In this study, the upper reaches of the Mudong River were segmented as needed. Considering the field conditions and combining the principles of segmentation, the upper Mudong River segment was divided into five segments (Fig. 3 ). The segment-specific information is shown in Table 1 . Table 1 Mudong River small watershed reach information statistical table Segment Length/m Width/m Depth/m Velocity/m⚫s − 1 Slope Minimum depth/m Seg 1 1060 3 0.65 0.01 0.0034 0.1 Seg 2 830 4 0.6 0.05 0.00205 0.1 Seg 3 1500 3 0.53 0.12 0.00013 0.1 Seg 4 860 4 0.61 0.14 0.0007 0.1 Seg 5 1370 5 0.8 0.08 0.00036 0.1 2.3.3 Basic parameter setting and information entry In this study, the advanced eutrophication water quality module was used. Parameter rates were determined using water quality data from July 2022 to December 2023, and the hydrodynamic model was selected as a one-dimensional lattice kinematic fluctuation model with a time step of 1 day and solved using Euler's equation. The cell transport model was a one-dimensional kinematic wave transport model. Following this, the initial concentration inputs included NH 3 -N, TN, TP, CODcr, BOD 5 , and other water quality indicators. The model boundary conditions were based on an actual investigation where there was no point source pollution discharge in the Mudong River small watershed area; hence, the point source pollution load was zero. The pollutant loads calculated in the previous section were allocated to each stream section by combining with the different percentages for each month of rainfall, the length of each river section proportionally, and the number of days in each month. Afterwards, it was summarized for the entire ephemeral course of the different reaches (Table 2 ). Table 2 Pollution load of each indicator in each section of the upper Mudong River Segments NH 3 -N(kg/a) TN(kg/a) TP(kg/a) CODcr(kg/a) BOD 5 (kg/a) Seg 1 147.12 537.46 118.27 7530.84 3485.83 Seg 2 115.19 417.61 34.72 5656.47 2418.56 Seg 3 208.19 761.78 39.73 9845.63 4502.67 Seg 4 119.36 433.47 22.00 5741.36 2571.81 Seg 5 190.14 689.67 35.29 8595.69 3721.13 2.4 Photocatalytic degradation experiment 2.4.1 Preparation of the nanometer titanium dioxide film From a practical perspective, a TiO 2 colloid suitable for this study was prepared using the sol-gel method with simple reaction conditions and easy control of the reaction process to prepare the titanium dioxide nanoparticles. The equations for the hydrolysis and condensation reactions to the colloidal system are shown in Eq. (6) and Eq. (7) 65,66 : $$\:\begin{array}{c}-M-OR+{H}_{2}O\to\:-M-OR+ROH\#\left(6\right)\end{array}$$ $$\:\begin{array}{c}-M-OR+OR-M\to\:-M-O-M+ROH\#\left(7\right)\end{array}$$ Pure titanium dioxide powder (Fig. 4 (g)) was obtained after washing (Fig. 4 (a)), drying (Fig. 4 (b)), filtration (Figs. 4 (c), (d)), and high-temperature calcination (Fig. 4 (e), (f)). The steps are shown in Fig. 4 . 2.4.2 Photocatalytic reactor setup Considering that the objective of this study is to treat complex pollutants in outdoor ponds, a stationary photocatalytic reactor was selected as the reactor type for this study. The structure of the photocatalytic reactor 2.0 is shown in Figure 5 . Fishpond water from a certain fish pond close to the upper reaches of the Mudong River in the Mudong River small watershed was selected as the sample for the experiment. Following this, the blank control and the experiment were conducted at the same time, and the experimental group was treated in the photocatalytic reactor. In addition, the experiment was limited to a 24 h period to ensure that the water samples were stored for an optimal period. In addition, the room temperature of the laboratory was strictly controlled at 20°C. The NH 3 -N, TN, TP, CODcr, and BOD 5 concentrations of the samples that collected from the photocatalytic reactor were measured at 0, 6, 12, and 24 h. Eqs. (8)–(12) 43 , 67 are the specific photocatalytic reaction mechanism reaction equations. $$\:\begin{array}{c}{O}_{2}+{e}^{-}\to\:\bullet\:{O}_{2}^{-}\#\left(8\right)\end{array}$$ $$\:\begin{array}{c}{H}_{2}O+\bullet\:{O}_{2}^{-}\to\:\bullet\:OOH+O{H}^{-}\#\left(9\right)\end{array}$$ $$\:\begin{array}{c}\bullet\:OOH\to\:{{O}_{2}+H}_{2}{O}_{2}\#\left(10\right)\end{array}$$ $$\:\begin{array}{c}\bullet\:OOH+{H}_{2}O+{e}^{-}\to\:{H}_{2}{O}_{2}+O{H}^{-}\#\left(11\right)\end{array}$$ $$\:\begin{array}{c}{H}_{2}{O}_{2}+{e}^{-}\to\:\bullet\:OH+O{H}^{-}\#\left(12\right)\end{array}$$ 2.4.3 Estimation of pollutant reductions Based on the photocatalytic degradation experiments to derive the effect of degradation on the concentrations of water quality indicators, the coefficients (k) were multiplied by 1, 0.5, and 0.25 and applied to the water quality indicators of the waterbody during the monitoring period of the upstream of the Mudong River. The WASP water quality model was then applied using the trial-and-error method for simulation to estimate the reduction of the pollutant load flowing into the river 68 , 69 . 3 Results and analysis 3.1 Model parameter calibration and error analysis 3.1.1 Pollutant Emission Results There were about 250 pigs, 60 cattle, and 9,000 poultry animals in the study area, including about 7,000 meat poultry animals and 2,000 egg poultry animals. The total area of aquaculture is 2,055 hectares, and the total aquaculture production is 13,379 tons. In this study, we quantified the effects of precipitation and runoff on the non-point source pollution load in the Mudong River watershed. In the actual observation study, it was found that the non-point source pollution load exhibited an increasing trend during the months with a large amount of precipitation. We calculated the emissions of various pollution sources in the study area (Table 3 ). Table 3 Summary of the amounts of different non-point source pollution entering the river Water quality indicators Rural life Plantation Aquaculture Livestock breeding Other land uses Total BOD 5 (t/a) 0.39 13.99 1.44 2.05 0.39 18.25 CODcr (t/a) 0.66 34.97 0.41 2.69 0.97 39.69 TN (t/a) 0.05 2.64 0.02 0.13 0.15 2.97 NH 3 -N (t/a) 0.09 0.60 0.11 0.01 0.05 0.85 TP (t/a) 0.01 0.20 0.03 0.01 0.02 0.27 We calculated the proportion of the total regional pollutant emissions. The overall situation of the non-point source pollution load was as follows: cultivation (82.03% of the total load) > livestock and poultry farming (5.19% of the total load) > other land uses (4.48% of the total load) > aquaculture (4.35% of the total load) > rural life (3.57% of the total load). Further attention needs to be paid to the amount of fertilizer used during tillage. 3.1.2 Parameterization To ensure the accuracy of the simulation results, we used a trial algorithm to rate the parameter values of the water quality model of the upper Mudong River. The water quality concentration, flow data and non-point source pollution inflow from July 2022 to March 2023 were brought into the model, the simulated pollutant concentration value was calculated, the relevant parameters were continuously adjusted, and the error analysis was made with the measured pollutant concentration value, and the pollutant concentration calibration results are shown in the Supplementary tables. This algorithm combined the initial values of the parameter ranges suggested by the long-term on-site monitoring of the upper Mudong River and the WASP user’s manual. We then compared the monitoring data and simulation results from 2022 to 2023 and repeated the rate setting of the parameter values until all the parameters were determined, as shown in Table 4 . Table 4 Model parameter rates Parameters Unit Sign Values Nitrification rate constant at 20°C day-1 K12 0.12 K 12 temperature coefficient / K 12 T 1 Oxygen-limited nitrification half-saturation coefficient mg/L K NIT 0.1 Denitrification rate constant at 20°C day − 1 K 2D 0.045 K 2D temperature coefficient / K 2DT 1 Attenuation constant of biochemical oxygen demand at 20°C day − 1 K d 0.15 K d temperature coefficient / K dT 1.04 Oxygen limiting BOD half-saturation coefficient mgO 2 /L K BOD 0.5 Carbon ratio mgO 2 /mgC aoc 32/12 Reoxygenation coefficient at 20°C day − 1 K 2 4.5 Sediment oxygen demand g/m 2 d SOD 2.5 Temperature coefficient of sediment / θs 1 Mineralization rate of organic phosphorus at 20°C day − 1 K 83 0.2 Temperature coefficient of organic phosphorus mineralization / K 83T 1.08 Organic nitrogen mineralization rate at 20°C day − 1 K 71 0.08 K 17 temperature coefficient / K71T 1.08 Phytoplankton respiration rate coefficient at 20°C day-1 K1R 0.2 K 1R temperature coefficient / K1RT 1.05 3.1.2 Model simulation and error analysis Based on the completion rate of the model parameters, an error analysis of the model simulation results was conducted to calculate the average error and relative error between the measured and simulated values of the water quality indexes for each simulation period (Table 5 ). The error between the measured and simulated values during the simulation period was small, and the minimum relative error between the simulated and measured values was 6.67%. The maximum value was 11.96%(Fig. 6 ). Currently, the Technical Guidelines for Water Pollution Discharge Permit 4 indicate that the relative error of a model simulation should not exceed 40%. In addition, the Specification for Hydrological Intelligence Prediction (GB/T 22482 − 2008) states that the permitted error for a water quality simulation is 30% of the measured value 37 , making it a valid model. Therefore, it was determined that the established model could be used as a good tool to predict and regulate the water quality of the upper Mudong River, and this was based on the premise that the measured values had good fit to the simulated values. Table 5 Error analysis for the model Error parameter NH 3 -N TN TP CODcr BOD 5 Average error 0.06(mg/L) 0.24(mg/L) 0.01(mg/L) 0.55(mg/L) 0.18(mg/L) Relative error 9.21% 11.96% 9.89% 7.92% 6.67% Root mean square error 0.16(mg/L) 0.37(mg/L) 0.01(mg/L) 0.91(mg/L) 0.28(mg/L) Due to the limitations of the measured data and information collection, the effect of the substrate on the model simulation was not considered in this study, and the simulation accuracy may have been affected by using non-point source pollution as the primary research object. 3.2 Model Validation The WASP model was used to simulate the changes in the concentration of various indicators during the period of April 2023 ~ December 2023, and the measured values were compared with the simulated values (show in Supplementary table) for verification, and the results are as follows. As can be seen from Fig. 7 , the trend of the measured and simulated values of the indicators during the simulation validation period overlapped relatively well. In addition, the error between the measured and simulated values of the pollutant concentrations in the upstream of the Mudong River was relatively small, and the results of the model simulation could effectively reflect the trend of pollutant concentrations in the river over time. Besides, the discontinuity of the results in paragraphs 2 and 5 is due to the fact that the river was cut off during this period and sampling could not be carried out, so no data are available. From the viewpoint of the entire river section, the NH 3 -N and TP concentrations did not vary significantly. In addition, the NH 3 -N concentration was lower during the abundant water period than during the flat-water period and the dry water period. However, the TN, CODcr, and BOD 5 concentrations showed large changes, with the TN concentrations decreasing to varying degrees during the abundant water period and increasing to varying degrees during the dry water period. The CODcr concentrations increased to varying degrees during the abundant water period. The BOD 5 concentrations were lower during the abundant water period than those during the flat-water period and the dry water period, decreasing to varying degrees during the abundant water period. Furthermore, the BOD 5 concentrations increased to varying degrees during the flat water period and the dry water period. In summary, an overall seasonal variation was observed. According to the simulation results, the fit between the measured and modeled TP values was relatively poor in river section 4 (Fig. 7 ), which was anomalous. And the overall performance of CODcr was higher during the abundant water period than during the flat-water period and the dry water period (Fig. 7 ). However, each section of the river exceeded the Class III water quality standard by 20 mg/L only during April 2023. It is possible that April is the period of spring paddy crop greening and growing when farmers apply a certain amount of fertilizer to the field. In addition, the BOD 5 simulation of river section 3 and river section 5 exceeded the Class III water quality standard of 4 mg/L in more months than that of the other sections of the river (Fig. 7 ). In general, the pollutant concentrations of the main water quality indicators in the small watershed of the upper reaches of the Mudong River were CODcr > TN > BOD 5 > TP > NH 3 -N. The nitrogen content of the river water was significantly higher than its phosphorus content, and the TN monitoring concentration was high. During most of the testing period, the value exceeded the Class V water quality standard. 3.3 Experimental results of the photocatalytic degradation A blank control group (no TiO 2 ) and an experimental group (TiO 2 ) were set up (group A and group B, respectively) at 0 h, 6 h, 12 h, and 24 h to monitor the changes in the six water quality indicators during the 24 h experimental period (Fig. 8 ). As can be seen from the figure, there was a significant increase in the rate of pollutant elimination after photocatalytic degradation for all the pollutants. After 24 hours of the experiment, the concentrations of the water quality indicators in each experimental group were lower than those in the control group, and the concentrations dropped to more than half of the original values. Among them, the pollutant concentration decreased significantly for BOD 5 (54.2%), followed by NH 3 -N (47.3%) > NO 3 ¯-N (50.0%), TN (34.1%), CODcr (21.3%), and TP (11.9%). During the experiment, the NH 3 , TN, and TP concentrations gradually decreased as the photocatalytic reaction progressed. The CODcr concentration in the control group was slightly different, exhibiting a trend of initially decreasing and then slightly increasing. In the experiment, the NH 3 -N, TN, TP, and BOD 5 concentrations in the blank control group first decreased and then tended to stabilize, while in the experimental group, there was a continuous downward trend, and the downward trend was more obvious (Figs. 8 (a), (b), (c), (f)). This result indicated that the nano-titanium dioxide material using an ultraviolet (UV) irradiation photocatalytic reaction that occurred for the removal of ammonia and nitrogen in the waterbody had a more obvious effect compared to the situation without treatment. The NO 3 ¯-N blank control group and experimental group concentration value changes were more abnormal because the trend in their concentrations showed an overall decrease followed by a leveling off during the experiment (Fig. 8 (d)). This result could have been due to strong oxidizing radicals generated by the TiO 2 photocatalysis that promoted the conversion of NO 3 ¯-N to a more labile form of nitrogen. In addition, the concentration of nitrate-nitrogen in the water decreased with the prolongation of the reaction time, indicating that the photocatalytic reaction of the titanium dioxide nanomaterials after UV irradiation was effective for the removal of nitrate-nitrogen in the water body. The CODcr blank control group concentrations showed an overall decreasing trend followed by a slight rebound (Fig. 8 (e)) that was likely influenced by changes in the activity of the microbial community at different moments. 3.4 Pollutant load reduction estimation 3.4.1 Effect of the photocatalytic degradation experiments on water quality For the actual reduction of each water quality indicator, the concentration ratio was the initial concentration of each indicator after the actual reduction. In this study, based on the photodegradation experiments of the effect of pollutant reduction in water, the experimental data after 24 h of the experiment were used as a benchmark. Hence, the proportion of reduced concentration of the sample water in the experimental group from high to low was as follows: TN(75.4%) > TP(73.1%) > NH 3 -N(68.9%) > BOD 5 (62.4%) > NO 3 ¯-N(50%). According to the reduction of each water quality indicator (Table 6 ), the concentration of the water quality factors after photocatalytic degradation of the river was predicted by applying it to the field water quality data from the upper Mudong River. According to the water quality evaluation standard “Environmental Quality Standard for Surface Water” (GB3838-2002), a water quality evaluation and comparative analysis after the reduction of each monitoring section were conducted. The NO 3 ¯-N concentration was much lower than the limitation requirement (NO 3 ¯-N was less than 10 mg/L) in the Environmental Quality Standard for Surface Water, and the standard limit value does not specifically mention the different water quality level standards for NO 3 ¯-N. Therefore, five water quality indicators, namely, NH 3 -N, TN, TP, CODcr, and BOD 5 , were selected for water quality evaluation (Fig. 8 ). Table 6 Pollution load after photocatalytic degradation of each indicator in the upper Mudong River (k = 1) Segment Length (m) NH 3 -N (kg/a) TN (kg/a) TP (kg/a) CODcr (kg/a) BOD 5 (kg/a) Seg 1 1060 45.87 123.67 35.12 2250.25 1047.11 Seg 2 830 36.09 92.50 13.00 1690.03 716.36 Seg 3 1500 65.13 168.73 17.48 2888.11 1315.56 Seg 4 860 37.18 96.01 9.67 1684.17 751.27 Seg 5 1370 59.09 152.76 15.37 2521.68 1086.98 Total 5620 243.36 633.67 90.64 11034.2 4917.28 As can be seen from Fig. 9 , in the nine monitoring sections, the NH 3 -N concentration after photocatalytic degradation basically reached the Class II water quality standard (0.5 mg/L). In addition, the number of sections that met the standard increased by three compared with the number of sections prior to the degradation. In addition, the number of sections that met the Class I water quality standard (0.15 mg/L) accounted for 11.1% of the total number of monitoring sections, which means the effect of the NH 3 -N degradation as a whole was significantly improved. The TN concentration exceeded the standard of the Class IV water quality (1.15 mg/L), accounting for 66.7% of the total number of sections that met the standard prior to the photocatalytic degradation. The TN concentration before the photocatalytic degradation exceeded the standard of the poor class V water quality (2 mg/L), accounting for 66.7% of the initial concentration. After photocatalytic degradation of the water quality as a whole, there was a significant improvement that reached the standard of the class IV water quality (1.5 mg/L) of the number of sections increased by three. The photocatalytic degradation of the TP concentration met the standard of the class II water quality standard (0.10 mg/L) and met the water quality protection objectives of the Huixian wetland restoration. The photocatalytic degradation of the TP concentration was still an overall reduction. Prior to photocatalytic degradation, the average value of the CODcr concentration in each monitoring section was lower than the limit value of the Class I water quality standard (15 mg/L), and after photocatalytic degradation, the CODcr concentration was significantly reduced. Therefore, an overall improvement was achieved. The limit value of the Class I water quality standard and the limit value of the Class II water quality standard of BOD 5 were both 3 mg/L, and the number of sections that met the standard of the Class I water quality increased by two sections after degradation. In summary, the results demonstrated that if the nano-titanium dioxide material is promoted to outdoor waterbodies at a later stage, it will produce a water quality improvement effect. 3.4.2 Estimation of the pollution load reductions based on the WASP model The estimated results of the pollution load reduction in the Mudong River watershed after the photocatalytic degradation experiments were applied in the field are shown in Table 6 . Only the results of k = 1 are shown, and the rest are shown in Supplementary tables. Table 6 Pollution load after photocatalytic degradation of each indicator in the upper Mudong River (k = 1)(kg/a) Segment Length (m) NH 3 -N (kg/a) TN (kg/a) TP (kg/a) CODcr (kg/a) BOD 5 (kg/a) Seg 1 1060 45.87 123.67 35.12 2250.25 1047.11 Seg 2 830 36.09 92.50 13.00 1690.03 716.36 Seg 3 1500 65.13 168.73 17.48 2888.11 1315.56 Seg 4 860 37.18 96.01 9.67 1684.17 751.27 Seg 5 1370 59.09 152.76 15.37 2521.68 1086.98 Total 5620 243.36 633.67 90.64 11034.2 4917.28 In the actual reduction of each water quality indicator, the concentration ratio of the photocatalytic degradation experiment at 6, 12, and 24 h was the initial concentration ratio of each indicator after the actual reduction, and the results are shown in Table 7 . This concentration ratio was used as the input value of the outdoor simulation experiments to simulate the reductions of the different indicators of non-point source pollution into the river at 6 h and 12 h every day, the results are shown in Supplementary tables. The reduction rate is shown in Table 8 . Table 7 Actual abatement rates during the photocatalytic degradation experiments. Time NH 3 -N TN TP CODcr BOD 5 24 h 68.9% 75.4% 73.1% 53.5% 62.4% 12 h 90.1% 78% 70.6% 86.6% 69% 6 h 97.8% 89.4% 73.4% 99.9% 76.9% Table 8 The actual reduction rate of the outdoor photocatalytic degradation was simulated. Reduction time NH 3 -N TN TP CODcr BOD 5 24 h 31.00% 24.59% 26.64% 46.46% 37.67% 12 h 9.91% 21.95% 29.39% 13.39% 31.04% 6 h 2.18% 10.58% 25.15% 0.99% 23.09% 4 Discussion 4.1 WASP simulation feasibility analysis The study area is located in the karst irrigation area, and the area of the karst covered with soil of a certain thickness accounts for a large proportion of the total area. In the study area, karst landforms and non-karst landforms coexist. The runoff in the karst areas is more complex 31 , 70 , 71 than that in the non-karst areas, and the changes in the nitrogen and phosphorus migration paths and media affect their retention and regulation. The soil in the karst area is poor, the soil layer is shallow and discontinuous, the spatial distribution of the soil thickness is uneven, and the purification effect of the soil on nitrogen and phosphorus pollutants is weak. In terms of pollutant simulation and control, Mateusz et al. 26 showed that the WASP model has good adaptability in predicting pollutant changes under different land use patterns. This study also proves the effectiveness of the WASP model in the small watershed of the Mudong River. In this paper, we find that pollutant load shows seasonal variations, which may be related to hydrological characteristics and agricultural activity cycles. During the rainy season, frequent precipitation and surface runoff can lead to large amounts of non-point source pollutants entering water bodies. In the dry season, precipitation is scarce, surface runoff decreases, and the migration of non-point source pollution decreases, but the concentration of pollutants in the water body may increase due to the decrease in water volume 72 . Luo et al. 73 also found that rainfall runoff would increase the load of nitrogen and phosphorus pollution into the river, especially in wet season, where the concentration of nitrogen and phosphorus increased significantly. There was a significant difference in the concentration of pollutants in the Mudong River during the wet season and the dry season (Fig. 7 ) . The concentrations of total nitrogen (TN) and CODcr were higher during the wet season (April to August), while the concentrations of ammonia nitrogen (NH 3 -N) and BOD5 were higher during the dry season (October to March). At the same time, Gou et al. 58 showed that when the rainfall intensity exceeded 30 mm/h, the surface runoff was significantly enhanced, resulting in a large transport of agricultural pollutants, which was consistent with the rainfall-pollutant response relationship in this study. He et al. 74 further pointed out that the hydrological response speed of karst basins is fast, and rainfall events will quickly cause pollutant concentration fluctuations, which is consistent with the rapid increase of TN and CODcr in the Mudong River during the rainfall period in this study. In addition, Mateusz et al. 26 used the WASP model to study the impact of agricultural and urban pollutant input in the Dunajek River Basin in Poland, and found that land use type significantly affected the simulation accuracy of the model, and the pollutant concentration in the agriculture-dominated watershed varied greatly during the wet season. Luo et al. 73 found that the concentrations of nitrogen and phosphorus in different waters were higher in the high fertilization season than in the low fertilization season, and were closely related to rainfall characteristics. In section 4 of the upper reaches of the Mudong River, there is more agricultural land, and the concentration of total phosphorus (TP) increases significantly during the wet season. The reason for this may be that farmers add phosphorus fertilizer to their farmland during the wet season, and the pollution of the river near the farmland is more obvious. The results presented in this study are similar to those of Mateusz et al. and Luo et al. In addition, some studies have found that the low environmental capacity makes it easy to lose nutrients and sewage 49 from agricultural production, which aggravates the nitrogen and phosphorus pollution of the water and soil in this region. The diversion, transportation, distribution, and drainage channels in the study area regularly cross each other, the rice fields are dotted with ponds and weirs, the rainy season is strong, the rainfall is abundant, and the rice irrigation and fertilization period is within the rainy season. These may be the influencing factors 75 , 76 of the abnormal changes in the nitrogen and phosphorus concentrations. Therefore, the high CODcr concentration in this paper may be due to the fact that the rainfall in the upper reaches of the Mudong River during the wet season carries soil and pollutants rich in organic matter into the upper reaches of the Mudong River and its coastal areas, increasing the organic matter content of the upper reaches of the Mudong River, so the overall performance of the CODcr is higher in the wet season than in the normal and dry seasons. This is in contrast to the situation in April, when the spring rice crops are growing and farmers apply a certain amount of fertilizer to the fields. Moreover, April is the period with a high incidence of crop diseases and pests, and the use of pesticides for pest control, coupled with the increase in rainfall during the wet season, leads to the CODcr concentration of the water body being higher than in other months. This is similar to the BOD 5 concentration in sections 3 and 5 where there is more aquaculture along the river channel. The high concentration of BOD 5 in the fish ponds may be due to the accumulation of excrement and bait in the fish ponds. During the sampling process and the installation of real-time monitoring equipment for water quality indicators, it was found that the fish pond owner will exchange the fish pond water with the river water body, resulting in high monitoring data in this section of the river in some months. These conditions are similar to the conclusions of previous studies. 4.2 Feasibility analysis of photocatalytic degradation experiments The simulation results show that the pollutants such as ammonia nitrogen, total nitrogen, total phosphorus, CODcr and BOD 5 are reduced to a certain extent in different river sections, especially after the application of photocatalytic degradation technology, the pollutant concentration decreases as a whole, indicating that this method has potential application value in pollution control in agricultural watersheds. The pollutant reduction effect observed in indoor photocatalytic degradation experiments differs from the actual reduction effect in field conditions. Several studies have reported that water quality indicators monitored in the field exhibit a wider range of variation. In the study conducted on the Willamette River 77 , nitrate concentrations increased by 516.7%, from 1.2 mg/L to 7.4 mg/L, and phosphate concentrations increased by 866.7%, from 0.03 mg/L to 0.29 mg/L. Total nitrogen concentrations in agricultural watersheds ranged from less than 1 mg/L to more than 10 mg/L. This variation can be attributed to higher biodiversity in wild river ecosystems, where biological activities have a more pronounced impact on water quality. The growth and death of algae, the metabolic activities of microorganisms, and other ecological processes can all influence water quality parameters 77 . These complex ecological interactions are challenging to fully simulate in a laboratory setting. In addition, seasonal changes are difficult to simulate in the laboratory. Marin et al. 78 found that ammonia and phosphorus concentrations in rivers increased by 50% and 80%, respectively, during an event with precipitation exceeding 25.4 mm. Frequent precipitation during the rainy season triggers surface runoff, bringing more pollutants into rivers. At the same time, there are seasonal variations in the degree to which agricultural areas are affected by pollutants such as fertilizers. N Shiferaw et al. argue that rivers in agricultural areas may be directly affected by agricultural activities from time to time 79 – 81 . The amount of fertilizer, tillage and production habits in the rainy and dry seasons will be different 82 , 83 , and the emissions of chemical fertilizers, pesticides and manure used by farmers in the study area will change accordingly, and the water quality may change through surface runoff and soil infiltration into the river. Unlike laboratory experiments, these variables are difficult to accurately estimate in terms of their effect on water quality changes. In this study, photocatalytic degradation was tested in a laboratory, where the water sample was stationary, maintained at a constant temperature, and unaffected by external pollution sources. As a result, microbial activity was diminished, and the range of pollutant concentration changes was narrower than in the field. However, we applied the results of the laboratory photocatalytic degradation experiment to the WASP water quality model, using specific coefficients to estimate pollutant reduction in the river. The model results indicated a reduction in pollutant concentrations (NH 3 -N, TN, TP, CODcr, and BOD 5 ) after 24 hours, with reductions of 37.9%, 50.81%, 46.46%, 7.04%, and 24.73%, respectively. Although the photocatalytic degradation test has been continuously improved in this study, it remains an indoor experiment, and its application to field conditions may introduce some uncertainty in the reduction effect. Therefore, outdoor experimental research is currently underway, with efforts to adapt the photocatalytic reactor for outdoor use while ensuring photocatalytic efficiency. This will allow for a more accurate assessment of pollutant reduction effects and the feasibility of large-scale implementation, providing valuable data to support water environment management and treatment measures in the agricultural watershed of the Huixian wetland. This research aims to provide a basis for water quality assessment in small agricultural watersheds. 4.3 Deficiencies and prospects of research This study found that there are differences in the reduction potential of different types of pollutants, and the total phosphorus reduction rate can reach more than 25%, which suggests that we need to adopt differentiated treatment strategies according to the characteristics of pollutants. At the same time, the study proves that the nano-titanium dioxide thin film photocatalytic technology is effective in improving water quality, and lowers the application threshold through low-cost substrate materials (low-iron ultra-white cullet glass), advocates green development, and has the potential for technology promotion. Despite some success, there are still uncertainties and limitations. The climatic characteristics of Guilin are significantly affected by extreme weather events 84 – 86 , such as heavy rainfall, drought and cold waves, which are difficult to accurately predict through conventional meteorological data. The suddenness and uncertainty of these events add to the complexity of the study. In addition, global climate change has led to changes in precipitation patterns in Guilin, making traditional seasonal divisions no longer applicable. In recent years, the precipitation concentration period in Guilin has tended to be postponed 87 , 88 , which may affect the division of the rainy season and the dry season. At the same time, the study spanned from July 2022 to December 2023, and the relatively short time span may not fully reflect the long-term trends of climate change and water quality changes. In addition, photocatalytic degradation experiments are conducted indoors, which can provide a certain reference, but the results may be different when applied outdoors. These problems may affect the accuracy and generalizability of the research results, and future research needs to further optimize the data source and improve the model method. Attempts can be made to introduce high-resolution meteorological models that combine historical data and future climate change scenarios to make more accurate predictions of extreme climate events. The scope and time span of the study were expanded, and outdoor experiments were carried out to evaluate the photocatalytic degradation effect under natural light conditions, so as to improve the reliability and scientificity of the research. There are many challenges in the process of promoting outdoor photocatalytic degradation technology in the future, including the limitation of natural light conditions, the low utilization rate of visible light, the complexity of the outdoor environment, the stability and recovery of catalysts, the design challenges of photocatalytic reactors, and the complexity of pollutants. Optical components such as mirrors and focusing lenses can be added to the photocatalytic reactor design to improve the utilization rate of light 89 , 90 . Optimize the water flow design in the reactor to ensure that the contaminants are in full contact with the catalyst and improve the degradation efficiency. In addition, small-scale field trials were conducted in a real-world environment to evaluate the performance of photocatalytic technology in different seasons and under different weather conditions. Long-term monitoring of field trials to collect data to assess the long-term effects and environmental impact of the technology. Perform a detailed cost-benefit analysis to evaluate the economic viability of photocatalytic technology for applications of different scales. Seek support from governments and environmental agencies to obtain funding and policy incentives to lower the barriers to technology adoption. Although the model framework of this study does not specifically characterize the karst landform characteristics, its construction method can provide a baseline reference for the study of pollutant migration in karst areas. As a typical karst wetland, the complete water quality simulation of Huixian Wetland needs to consider the special karst hydrological process, and the relevant in-depth research will be the focus of follow-up work. 5 Conclusions 1. The use of the WASP model was found to be feasible to simulate the water quality of the upper Mudong River. This study was based on the water quality index data of the Mudong River from July 2022 to December 2023 for parameter rate setting, and after adjusting the parameter values many times, the simulation results met the requirements for model accuracy. Hence, the overall effect of the model simulation was good. 2. In the indoor experiments, of NH 3 -N, TN, TP, CODcr, and BOD 5 via photocatalytic degradation reduction resulted in the following: NH 3 -N accounted for 68.9% of the initial concentration, the nitrate nitrogen concentration accounted for 50%, the TN concentration accounted for 75.4%, the TP concentration accounted for 73.1%, the CODcr concentration accounted for 53.5%, and the BOD 5 concentration accounted for 62.4%. 3. Based on the results of the photocatalytic degradation experiments, the actual reduction effect in the field was estimated by multiplying with the coefficients of 1, 0.5, and 0.25. The calculation results showed that the reductions of the upstream NH 3 -N input into the river at 24 h, 12 h, and 6 h were 243.36 kg/a, 127.04 kg/a, and 47.70 kg/a, respectively; those of TN were 633.67 kg/a, 330.71 kg/a, and 124.17 kg/a; those of of TP were 90.64 kg/a, 47.13 kg/a, and 17.68 kg/a; those of CODcr were 11034.20 kg/a, 5762.43 kg/a, and 2163.99 kg/a; and those of BOD 5 were 4917.28 kg/a, 2566.52 kg/a, and 963.64 kg/a. The concentration ratios of NH 3 -N at 24 h, 12 h, and 6 h were 31%, 9.91%, and 2.18%, respectively; those of TN were 24.59%, 21.95%, and 10.58%; those of TP were 26.64%, 29.39%, and 25.15%; those of CODcr were 46.46%, 13.39%, and 0.99%; and those of BOD 5 were 537.67%, 31.04%, and 23.09%. Overall, if this nano titanium dioxide material is promoted to outdoor waterbodies, it will be effective in improving the water quality of the rivers. There are also limitations in this study. When running the model simulation, due to the limited experiments and information, the influencing factors considered in this study were not sufficiently comprehensive. For example, the spatial and temporal differences of the rainfall runoff, the role of the substrate on the pollutants, and pollutants during complex migration and transformation processes may have been the cause of the water quality simulation error. Declarations Acknowledgments This research was funded by the National Natural Science Foundation of China (No. 52269010), the Science and Technology Planning Project of Guangxi, China (No. AB AB22035075) and the Science and Technology Plan Project of Guilin (No. 20220114-2). Author contrubutions Yiyang Li contributed to the investigation, data collection and analysis, research idea, methodology and model design as well as the writing of the first draft of the manuscript. Zitao Li contributed to the data collection and analysis, research idea, methodology and model design and the writing of the paper. Junfeng Dai contributed to the research idea, methodology, model design and paper review. Saeed Rad contributed to the material preparation, data collection and analysis. Xiaolan Xie contributed to the data collection and analysis. Shanshan Qi contributed to the data collection and analysis. Baoli Xu contributed to the data collection and analysis. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Additional information Ethical Approval This paper does not contain any studies with human participants or animals performed by any of the authors. Consent to Publish Applicable. Consent to Participate Consent. Data availability The data used to support the findings of this study are available from the corresponding author upon request. References E.D. Ongley, X. L. Z., T. Yu. Current status of agricultural and rural non-point source pollution assessment in China. Environmental Pollution , 158 , 1159–1168, doi:10.1016/j.envpol.2009.10.047 (2010) Shen, Z. Y., Zhong, Y.C., Huang, Q., Chen, L. Identifying non-point source priority management areas in watersheds with multiple functional zones. Water Research , 68 , 563–571 (2015).https://doi.org/10.1016/j.watres.2014.10.034. Zhang, T., Ni, J.P., Xie, D.T. Assessment of the relationship between rural non-point source pollution and economic development in the Three Gorges Reservoir area. Environment Science and Pollution Research , 23 , 8125–8132 (2016).https://doi.org/10.1007/s11356-016-6344-7. Zitao Li, J. D., Zhangnan Li, Yan Liu, Jingxuan Xu, Zhenyu Zhang, Baoli Xu. Simulation Study on the Effect of Non-Point Source Pollution on Water Quality in the Upper Reaches of the Lijiang River. Water , 14 (2022).https://doi.org/10.3390/w14243995 Pan L, D. J., Wu Z, Liangliang Huang, Zupeng Wan, Junlei Han, Zhangnan Li. Spatial and temporal variations of nitrogen and phosphorus in surface water and groundwater of Mudong river watershed in Huixian Karst wetland, southwest China. Sustainability , 13 (2021).https://doi.org/10.3390/su131910740 CHANG Xiaomin, W. S., GUAN Xiaoyan, HUANG Jiasheng, JIA Haifeng, YOU Lijun. Spatiotemporal Distribution of Nonpoint Source Pollution from Agriculture in the Huangshui River Basin. Journal of Irrigation and Drainage , 42 , 81-88, doi:10.13522/j.cnki.ggps.2022468 (2023) Boardman, E., Danesh-Yazdi, M., Foufoula-Georgiou, E.,Dolph, C. L., Finlay, J. C. Fertilizer, landscape features and climate regulate phosphorus retention and river export in diverse Midwestern watersheds. Biogeochemistry , 146 , 293–309 (2019).https://doi.org/10.1007/s10533-019-00623-z Zhang, S., Hou, X., Wu, C., Zhang, C. Impacts of climate and planting structure changes on watershed runoff and nitrogen and phosphorus loss. Science of the Total Environment , 706 , 134489 (2020).https://doi.org/10.1016/j.scitotenv.2019.134489 Carver, R. E., Nelson, N. O., Roozeboom, K. L., Kluitenberg,G. J., Tomlinson, P. J., Kang, Q., David S. Abel. Cover crop and phosphorus fertilizer management impacts on surface water quality from a no-till corn-soybean rotation. Journal of Environmental Management , 301 , 113818 (2022).https://doi.org/10.1016/j.jenvman.2021.113818 Jianqiang Wang, J. H., Yuling Ma, Ming Ma. Research progress of modified titanium dioxide photocatalytic technology in water pollution control. Modern Salt and Chemical Industry , 48 , 9-11, doi:10.19465/j.cnki.2095-9710.2021.06.005 (2021) Fleming P.M., S. K., Collick A.S. & Easton Z.M. Targeting for nonpoint source pollution reduction: A synthesis of lessons learned, remaining challenges, and emerging opportunities. Journal of Environmental Management , 308 , 114649-114649 (2022).https://doi.org/10.1016/j.jenvman.2022.114649 Wang H, H. P., Shen C, Wu Z Effect of irrigation amount and fertilization on agriculture non-point source pollution in the paddy field. Environmental Science Pollution Research , 26 , 10363–10373 (2019).https://doi.org/10.1007/s11356-019-04375-z Zhang T, Y. Y., Ni J, Xie D. Adoption behavior of cleaner production techniques to control agricultural non-point source pollution: a case study in the three Gorges Reservoir Area. Journal of Cleaner Production , 223 , 897-906 (2019).https://doi.org/10.1016/j.jclepro.2019.03.194 Tahmasebi Nasab, M., Grimm, K., Bazrkar, M. H., Zeng, L.,Shabani, A., Zhang, X., & Chu, X. SWAT modeling of non-point source pollution in depression-dom-inated basins under varying hydroclimatic conditions. International Journal of Environmental Research and Public Health , 15 , 2492 (2018).https://doi.org/10.3390/ijerph15112492 Jabbar FK, G. K. Statistical assessment of nonpoint source pollution in agricultural watersheds in the Lower Grand River watershed, MO, USA. Environment Science and Pollution Research , 26 , 1487–1506 (2019).https://doi.org/10.1007/s11356-018-3682-7 Lankoski, J. a. O., M. . Innovations in nonpoint source pollution policy - European perspectives. Choice , 28 , 1-5 (2013).https://www.jstor.org/stable/choices.28.3.08 Mei Luo, X. L., Nebiyou Legesse, Ying Liu, Sheng Wu, Fengxiang X. Han, Youhua Ma. Evaluation of Agricultural Non-point Source Pollution: a Review. Water Air Soil Pollution , 234,657 (2023).https://doi.org/10.1007/s11270-023-06686-x Lijun, G. Pollution source investigation and pollution load estimation in Kashgar River Basin. Water Resources Development Research , 1-6 (2023).https://link.cnki.net/urlid/11.4655.TV.20231201.1452.006 Zhou Rui, W. B., Lin Haodong, Yang Jizhou, Wei Daming. Application of 1-Dimensional Water Quality Model Combined with the Improved Export Coefficient Method in Polluting Load Estimation of Non-Point Source Pollution. Journal of Jilin University(Earth Science Edition) , 51 , 505-515 (2021).http://dx.doi.org/10.13278/j.cnki.jjuese.20190208 CHEN Wenjun, D. W., HE Bin, CHEN Wen. Water quality modeling for typical rural watershed based on the WASP model in Mountain Mao Region，upper Taihu Basin. Lake Science , 29 , 836-847, doi:10. 18307 /2017. 0407 (2017) Rekha, P., Yadav, S. & Singh, L. A review on cobalt phosphate-based materials as emerging catalysts for water splitting. Ceramics International , 47 , 16385-16401 (2021).https://doi.org/10.1016/j.ceramint.2021.02.215 Huang, S. et al. Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning. Water Research , 263,112191 (2024).https://doi.org/10.1016/j.watres.2024.122191 Wool, T., Jr., R. B. A., Martin, J. L. & Comer, A. WASP 8/ The Next Generation in the 50-year Evolution of USEPA’s Water Quality Model. Water , 12 , doi:10.3390/w12051398 (2020) Lin YP, M. H., Huang KT, Petway J R, Lin CM, Chou C F, Liao SW. Real-time identification of irrigation water pollution sources and pathways with a wireless sensor network and blockchain framework. Sensors , 20 (2020).https://doi.org/10.3390/s20133634 MAMANI LARICO A J & A, Z. M. S. Application of WASP model for assessment of water quality for eutrophication control for a reservoir in the Peruvian Andes. Lakes & Reservoirs: Science, Policy and Management for Sustainable Use , 24 , 37-47 (2019).https://doi.org/10.1111/lre.12256 Mateusz Żelazny, M. B., Bogdan Ozga-Zielinski, Tomasz Walczykiewi. Applicability of the WASP Model in an Assessment of the Impact of Anthropogenic Pollution on Water Quality—Dunajec River Case Study. Sustainbility , 15 , 2444 (2023).https://doi.org/10.3390/su15032444 Nicolas Obin, H. T., Fei Ge, Xingwang Liu. Research on Water Quality Simulation and Water Environmental Capacity in Lushui River Based on WASP Model. Water , 13 , 2819 (2021). https://doi.org/10.3390/w13202819 MBUH M J, MBIH R & C., W. Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed. Physical Geography , 40 , 127-148 (2019).https://doi.org/10.1080/02723646.2018.1507339 Petronella, F. et al. Nanocomposite materials for photocatalytic degradation of pollutants. Catalysis Today , 281 , 85-100 (2017).https://doi.org/10.1016/j.cattod.2016.05.048 MEHDILO, A. & IRANNAJAD, M. Surface Modification of Ilmenite and Its Accompanied Gangue Minerals by Thermal Pretreatment: Application in Flotation Process. Transactions of Nonferrous Metals Society of China , 31 , 2836-2851 (2021).https://doi.org/10.1016/S1003-6326(21)65697-2 J Kogovsek, M. P. Solute transport processes in a karst vadose zone characterized by long-term tracer tests (the cave system of Postojnska Jama, Slovenia). Journal of Hydrology , 519 , 1205-1213 (2014).https://doi.org/10.1016/j.jhydrol.2014.08.047 Soares, O. S. G. P., Pereira, M. F. R., Órfão, J. J. M., Faria, J. L. & Silva, C. G. Photocatalytic nitrate reduction over Pd–Cu/TiO2. Chemical Engineering Journal , 251 , 123-130 (2014).https://doi.org/10.1016/j.cej.2014.04.030 Drosos, M., Ren, M. & Frimmel, F. H. The efect of NOM to TiO2:interactions and photocatalytic behavior. Applied Catalysis B: Environmental , 165 , 328-334 (2015).https://doi.org/10.1016/j.apcatb.2014.10.017 Mouele, E. S. M. et al. A Critical Review on Ozone and Co-Species, Generation and Reaction Mechanisms in Plasma Induced by Dielectric Barrier Discharge Technologies for Wastewater Remediation. Journal of Environmental Chemical Engineering , 9 (2021).https://doi.org/10.1016/j.jece.2021.105758 Chen, W., Wang, Y., Sun, Y., Fang, G. & Li, Y. Release of Soluble Ions and Heavy Metal During Fly Ash Washing by Deionized Water and Sodium Carbonate Solution. Chemosphere , 307 (2022).https://doi.org/10.1016/j.chemosphere.2022.135860 GUERRERO-FLORES, Á. D., ELIZONDO-ÁLVAREZ, M. A., FLORES-ÁLVAREZ, J. M. & URIBE-SALAS, A. Comparative Study on Simultaneous Removal of Calcium and Sulfate Ions from Flotation Recycling Water by Aluminum Hydroxide. Transactions of Nonferrous Metals Society of China , 32 , 2379-2390 (2022).https://doi.org/10.1016/S1003-6326(22)65954-5 Yadav, A., Sharma, P., Panda, A. B. & Shahi, V. K. Photocatalytic TiO2 incorporated PVDF-co-HFP UV-cleaning mixed matrix membranes for effective removal of dyes from synthetic wastewater system via membrane distillation. Journal of Environmental Chemical Engineering , 9 (2021).https://doi.org/10.1016/j.jece.2021.105904 Sahar A. Mousa, H. A., S. A. Khairy. Low-cost photocatalytic membrane modified with green heterojunction TiO2/ZnO nanoparticles prepared from waste. Scientific Report , 13 (2023).https://doi.org/10.1038/s41598-023-49516-0 Lindi, A. M., Falah, S., Sadeghnezhad, M. & Ghorbani, M. Optimization of fenugreek seed mucilage extraction for the synthesis of a novel bio-nano composite for efficient removal of cadmium ions from aqueous environments. International Journal of Biological Macromolecules , 261 (2024).https://doi.org/10.1016/j.ijbiomac.2024.129882 Nisreen S. Ali, K. R. K., Amer N. Ahmed, Talib M. Albayati Performance of a solar photocatalysis reactor as pretreatment for wastewater via UV, UV/TiO2, and UV/H2O2 to control membrane fouling. Scientific Report , 12 (2022).https://doi.org/10.1038/s41598-022-20984-0 Afshin Shabani et al. A coupled hydrodynamic (HEC-RAS 2D) and water quality model (WASP) for simulating flood-induced soil, sediment, and contaminant transport. Journal of Flood Risk Management , 14 , e12747 (2021).https://doi.org/10.1111/jfr3.12747 J., Z., F., C., J., Z., H., C. & M, A. Fe3+-TiO2photocatalysts prepared by combining sol-gel method with hydrothermal treatment and their characterization. J. Photochem. Photobiol. A Chem , 180 , 196–204, doi:10.1016/j.jphotochem.2005.10.017. (2006) Cheng Chang, S. R., Lei Gan, Zitao Li, Junfeng Dai, Asfandyar Shahab. Review of the sol–gel method in preparing nano TiO2 for advanced oxidation process. Nanotechnology Reviews , 12 (2023).https://doi.org/10.1515/ntrev-2023-0150 Dan-ni, Z. et al. Pollution and Irrigation Applicability of Surface Water from Wet，Normal, and Dry Periods in the Huixian Karst Wetland，China. Environmental Science , 42 , 2240-2250, doi:10.13227/j.hjkx.202010087 (2021) Han J, D. J., Xie X. Spatial and temporal distribution characteristics of soil nutrients in small watershed of Huixian Wetland. Yangtze River , 70-77 , doi:10.16232/j.cnki.1001-4179.2022.07.011 (2022) Li, J. et al. Application of the hydrochemistry, stable isotopes and MixSIAR model to identify nitrate sources and transformations in surface water and groundwater of an intensive agricultural karst wetland in Guilin, China. Ecotoxicology and Environmental Safety , 231 (2022).https://doi.org/10.1016/j.ecoenv.2022.113205 Zupeng Wan, J. D., Linyan Pan, Junlei Han, Zhangnan Li, Kun Dong. Simulation study on nitrogen pollution in shallow groundwater in small agricultural watersheds in the Huixian wetland. Water , 14 (2022).https://doi.org/10.3390/w14223657 Zhao Yi, Z. S., Shen Haoyong, Lan Funing, Xie Hao, Li Jun. Source and control factors of main ions and dissolved inorganic carbon in the lakes of Huixian Karst Wetland. Geology in China , 51 , 1701−1713, doi:10.12029/gc20221025002 (2024) Peng, C., Pan, X.-D., Jiao, Y.-J., Ren, K., & Zeng, J. Identification of Nitrate Pollution Sources Through Various Isotopic Methods: A Case Study of the Huixian Wetland. ENVIRONMENTAL SCIENCE , 39 , 0250-3301, doi:10.13227/j.hjkx.201803212 (2018) Xiao, H., Shahab, A., Li, J., Xi, B., Sun, X., He, H., Yu, G. Distribution, ecological risk assessment and source identification of heavy metals in surface sediments of Huixian karst wetland, China. Ecotoxicology and Environmental Safety , 185 , 1090-2414 (2019).https://doi.org/10.1016/j.ecoenv.2019.109700 Sun, X., Lu, M., & Ren, Z. Dynamic changes and sensitivity of water Area of Huixiankarst wetland. IOP Conference Series: Earth and Environmental Science , 512 (2020).https://doi.org/10.1088/1755-1315/512/1/012033 Liu, D. et al. Research on microbial structures, functions and metabolic pathways in an advanced denitrification system coupled with aerobic methane oxidation based on metagenomics. Bioresource Technology , 332 (2021).https://doi.org/10.1016/j.biortech.2021.125047 Yang, H. et al. Removal of phosphate by aluminum-modified clay in a heavily polluted lake, Southwest China: Effectiveness and ecological risks. Science of the Total Environment , 705 (2020).https://doi.org/10.1016/j.scitotenv.2019.135850 Yu, E., Li, Y., Li, F., He, C. & Feng, X. Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector. Environmental Research , 263 (2024).https://doi.org/10.1016/j.envres.2024.120168 Ma, J., Rene, E. R., Chen, Z. & Ma, W. Fate of PAHs in treated wastewater reused as irrigation water: Environmental risks in water-soil-ryegrass multimedia system. Journal of Hazardous Materials , 424 (2022).https://doi.org/10.1016/j.jhazmat.2021.127500 Dong Hongmin, Z. Z., Huang Hongkun, Yongxing Chen, Bin Shang, Xiuping Tao, Zhongkai Zhou. Pollutant generation coefficient and discharge coefficient in animal production. Transactions of the CSAE , 27 , 303-308, doi:10.3969/j.issn.1002-6819.2011.01.049 (2011) Zhu, Z., Huang, H., Tian, G. & Fang, P. Review of Empirical Models in Agricultural Non-Point Source Pollution Simulation. Geographical Science Research , 11 , 210-219, doi:10.12677/gser.2022.112022 (2022).https://doi.org/10.12677/gser.2022.112022 GOU Ting, P. D.-f., LIANG Rong-chang, SHE Lei, YANG Jun, MA Qian-li, ZHAO Xue-min, ZHAO Rui, YAO Ling-ai. Analysis of nitrogen and phosphorus emissions from agricultural non-point sources based on improved output coefficient method. CHINA ENVIRONMENTAL SCIENCECE , 43 , 6539-6550, doi:10.19674/j.cnki.issn1000-6923.2023.0212 (2023) Xuda, Z. M. W. L. M. C. H. Water quality change and pollution source accounting of Licun River under long-term governance. Scientific Reports , 12 , 2779-2770, doi:10.1038/S41598-022-06803-6 (2022) Niraula, R., Kalin, L., Srivastava, P. & Anderson, C. J. Identifying critical source areas of nonpoint source polution with SWAT and GWLF. Ecological Modelling , 268 , 123-133 (2013).https://doi.org/10.1016/j.ecolmodel.2013.08.007 Tan Y, X. J., Xu L, Zhao WY. Estimation and Application of Non-point Source Pollutant Inflow Coefficient in Typical Area of Minjiang River ( Inner Ｒiver Section). Sichuan Environment , 40 , 138-144, doi:10.14034/j.cnki.schj.2021.04.019 (2021) Jing-lin, Z., Yan-jun, Z., Su-miao, W., Fei, Q. & Lan, L. An improved export coefficient model with a spatially modified river load ratio:A case study of the Yangtze River Basin. China Rural Water and Hydropower , 78-85+95, doi:10.12396/znsd.231029 (2024) Villota-López, C., Rodríguez-Cuevas, C., Torres-Bejarano, F., Cisneros-Pérez, R., Cisneros-Almazán, R., & Couder-Castañeda, C. Applying EFDC Explorer model in the Gallinas River, Mexico to estimate its assimilation capacity for water quality protection. Scientific Reports , 11 (2021).https://doi.org/10.1038/s41598-021-92453-z Mbongowo J. Mbuh, R. M., Comfort Wendi. Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed. Physical Geography , 40 , 127-148 (2016).https://doi.org/10.1080/02723646.2018.1507339 XIAO Huaqing, W. K., QIU Junjie, XIAO Yongjun, HE Wuguang, ZENG Qingdong, WANG Boyun, XIONG Liangbin. The Preparation and Characterization of TiO2 Film Prepared by a Sol-gel Meth- od. JournalofChongqingUniversityofTechnology(NaturalScience) , 32 , 93-99, doi:10.3969/j.isn.1674-8425(z). (2018) Chang, C. et al. Preparation and application of nano TiO2 filmcoated recycled low-iron crushed glass in a novel packed-bed photocatalytic reactor for efficient removal of biodegradable contaminants. Environmental Technology & Innovation , 33 (2024).https://doi.org/10.1016/j.eti.2024.103541 Pichat, P. Fundamentals of TiO2 Photocatalysis. Consequences for Some Environmental Applications. Heterogeneous photocatalysis: from fundamentals to green applications , 321-359 (2016).https://doi.org/10.1007/978-3-662-48719-8_10 Wang Ni, L. J., Xie Jiancang. Dynamic research of Urban river water environment capac. Environmental Engineering , 32 , 50-54, doi:10.13205/j.hjgc.201410013 (2014) Zhu, Y., Drake, S., Lü, H. & Xia, J. Analysis of Temporal and Spatial Differences in Eco-environmental Carrying Capacity Related to Water in the Haihe River Basins, China. Water Resources Management , 24 , 1089–1105 (2010).https://doi.org/10.1007/s11269-009-9487-1 Zhuang Yiling, Z. Q., Zhang Jianyun, Li Jieyou. Application of Xinanjiang Model to Karst Regions. Journal of Hohai University 17 (1989) Guang-hui, J., chun, W. J.-i., Fang, G., Hua-ju, L. & long, S. H.-i. Threshold value of epikarst runoff in forest karst mountain area. Advances in Water Science , 19 , 72-77, doi:10.14042/j.cnki.32.1309.208.01.03 (2008) Wenjie Zhou, Zhihua Zhu, Yulei Xie & Cai, Y. Impacts of rainfall spatial and temporal variabilities on runoff quality and quantity at the watershed scale. Journal of Hydrology , 603 , 127057 (2021).https://doi.org/10.1016/j.jhydrol.2021.127057 Luo, G. et al. Distribution，Influencing Factors and Eutrophication Evaluation of Nitrogen and Phosphorus in Different Waters of Karst Basin. CHINA RURAL WATER AND HYDROPOWER , 94-99+105. (2020) He, R., Sun, P.-a., YU, S. & He, S. A High-Resolution Monitoring Research of the Carbon Flux Dynamic Behaviors of Rivers in Karst Areas in Different Periods of the Year——A Case Study in the Lijiang River Basin. Journal of Southwest University Natural Science Edition , 40 , 150-158, doi:10.13718/j.cnki.xdzk.2018.03.022 (2018) Peng, T. & Wang, S.-j. Effects of land use, land cover and rainfall regimes on the surface runoff and soil loss on karst slopes in southwest China. CATENA , 90 , 53-62 (2012).https://doi.org/10.1016/j.catena.2011.11.001 Jie, Z., Qixin, W., Siliang, L. & Fujun, Y. Characteristics of Nitrogen Export and the Efects of Rainfal during the Rainy Season in a Karst Smal Catchment. Journal of Soil and Water Conservation , 31 , doi:10.13870/j.cnki.stbcxb.2017.03.013 (2017) Fisheries, D. o., Agriculture, F. o., Harcourt, U. o. P., Choba, R. S. & Nigeria. The influence of agricultural runoff on river water quality. International Journal of Fisheries and Aquatic Research 9 , 22-24 (2024).www.fishjournals.com Marin Skidmore, Tihitina Andarge & Foltz, J. The impact of extreme precipitation on nutrient runoff. 2 , 769-785 (2023).https://doi.org/10.1002/jaa2.90 Shiferaw, N., Habte, L. & Waleed, M. Land use dynamics and their impact on hydrology and water quality of a river catchment: a comprehensive analysis and future scenario. Environmental Science and Pollution Research (2025).https://doi.org/10.1007/s11356-025-35946-y Chen, D. et al. Photocatalytic degradation of organic pollutants using TiO2-based photocatalysts: A review. Journal of Cleaner Production , 268 , doi:https://doi.org/10.1016/j.jclepro.2020.121725 (2020) Wenying Liu et al. Small water bodies influence river water quality in agricultural watersheds. Agricultural Water Management , 304 , 109070 (2024).https://doi.org/10.1016/j.agwat.2024.109070 Monica Pinardi et al. Agricultural practices regulate the seasonality of groundwater-river nitrogen exchanges. Agricultural Water Management , 271 , 107904 (2022).https://doi.org/10.1016/j.agwat.2022.107904 Youen Grusso, Ingrid Wesstrom & Joel, A. Impact of Climate Change on Swedish Agriculture: Growing Season Rain Deficit and Irrigation Need. Agricultural Water Management , 251 , 106858 (2021).https://doi.org/10.1016/j.agwat.2021.106858 Zilan, L. Temporal and Spatial Distribution Characteristics of Summer Extreme Climate Events in Guangxi Region. Climate Change Research Letters , 9 , 494-507 (2020).https://doi.org/10.12677/CCRL.2020.95055 HUANG Fen, YIN Weilu, HU Xiaonong & Jianhua, C. A quantitative analysis of tracing tests for the Maocun subterranean river basin of Guilin during rainy and dry seasons. CARSOLOGICA SINICA , 35 , 648-658, doi:10.11932/karst2017y32 (2017) Xianda Bai, Yaxin Zhang & Wang, C. Analysis on Characteristic of Drought of Guilin. Climate Change Research Letters , 2 , 20-24 (2013).http://dx.doi.org/10.12677/CCRL.2013.21004 Zhi Rongzhirong & Zhihai, Z. Characteristics of Climate Anomalies over China in Winter 2021/2022 and Causes for Precipitation Anomalies over Southern China. Meteorological Monthly , 48 , 925-935, doi: 10.7519/j.issn.1000-0526.2022.061701 (2022) Li Xiang & Yongguang, W. Atmospheric Circulation Characteristics of Northern Hemisphere in Winter 2022/2023 and Its Impact on Weather and Climate of China. Meteorological Monthly , 49 , 881-891, doi:10.7519/j.issn.1000-0526.2023.060801 (2023) S. Bouguecha, B. Hamrouni & Dhahbi, M. Small scale desalination pilots powered by renewable energy sources: case studies. Desalination , 183 , 151-165 (2005).https://doi.org/10.1016/j.desal.2005.03.032 Vishnu Pareek, Siewhui Chong, Moses Tadé & Adesina, A. A. Light intensity distribution in heterogeneous photocatalytic reactors. Chemical Engineering , 3 , 171-201 (2008).https://doi.org/10.1002/apj.129 Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5204210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":446535343,"identity":"bdeffcae-c4ec-4791-a94e-f579ba27e6b3","order_by":0,"name":"Yiyang Li","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yiyang","middleName":"","lastName":"Li","suffix":""},{"id":446535344,"identity":"068c13ac-3b20-40b7-a27c-15209d58792b","order_by":1,"name":"Zitao Li","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zitao","middleName":"","lastName":"Li","suffix":""},{"id":446535346,"identity":"24ac74d9-2328-4263-a88f-1d6f17182a6f","order_by":2,"name":"Junfeng Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYDACCSCSsLHh4edvIElLWpqM5IwDpGhhSDtsY9CQQKQOg9s9hjcsEs7zGDAcYPzwMYcYLXfOGFtIJNzmMWduYJacuY0ILWY3cswkJH/c5rFsOMDGzEu0FomEczwGBxJI03KABC32N9KKgX5J5pGccbCZOL9IzkjeeFsiwc6en7/54IePxGgBAWYJMMXYQKR6kNoPxKsdBaNgFIyCkQgAMTUzs3RLJ7sAAAAASUVORK5CYII=","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Dai","suffix":""},{"id":446535348,"identity":"91e682d6-eba3-4c1c-ab4f-27002d3d3e9a","order_by":3,"name":"Saeed Rad","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Saeed","middleName":"","lastName":"Rad","suffix":""},{"id":446535349,"identity":"4a3a8beb-5317-47a1-9489-a3a4b7536185","order_by":4,"name":"Xiaolan Xie","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaolan","middleName":"","lastName":"Xie","suffix":""},{"id":446535351,"identity":"1447ad48-1166-42c4-bd3f-ac5fa8aa4087","order_by":5,"name":"Shanshan Qi","email":"","orcid":"","institution":"Guilin Institute for Environmental Science Research","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Qi","suffix":""},{"id":446535353,"identity":"5b1858a4-3a74-4f5e-98db-2b445b12018d","order_by":6,"name":"Baoli Xu","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Baoli","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-10-04 13:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5204210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5204210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81205057,"identity":"e202099a-1d46-4b6a-b4fa-bca627fa0541","added_by":"auto","created_at":"2025-04-23 12:05:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77239,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow diagram. Conceptual diagram of the overall research framework and workflow.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/1abc9b4a40ac4dc249167bd4.png"},{"id":81206440,"identity":"7561dd08-321c-4987-992c-3f2af6c3b0ad","added_by":"auto","created_at":"2025-04-23 12:21:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":326108,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic map of the study area: (a) Map showing the location of the study area; (b) elevation of the study area and distribution of sampling points; (c) land use types in the study area; and (d) three-dimensional schematic diagram of the study area. Map created using ArcGIS 10.6( https://www.esri.com/)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/508b32399ca6cf9b625a2aa7.png"},{"id":81205021,"identity":"d2f60e95-3e69-4c82-9f3d-8648f1acebbd","added_by":"auto","created_at":"2025-04-23 12:05:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122786,"visible":true,"origin":"","legend":"\u003cp\u003eStream generalization map of the Mudong River small watershed\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/37981d149b13fce42bb053b1.png"},{"id":81206169,"identity":"eb4dd866-8acd-4e58-9324-cc93fbae4c60","added_by":"auto","created_at":"2025-04-23 12:13:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151005,"visible":true,"origin":"","legend":"\u003cp\u003ePreparation of the nano-titanium dioxide film: (a) Sonication after washing and soaking; (b) drying after spraying with acetone; (c) coating with gels; (d) filtration; (e) drying to form a dry gel; (f) muffle furnace annealing; and (g) crystalline nano-titanium dioxide.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/22bc0a64c8f3c9507bc9dcf3.png"},{"id":81206161,"identity":"49e18f14-7395-4228-bd1f-745d8252df9b","added_by":"auto","created_at":"2025-04-23 12:13:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181245,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the photocatalytic degradation experiment: (a) Outdoor collection of water samples; (b) indoor water samples to be processed; (c) photocatalytic reactor for water sample processing; (d) filling and reaction inside the third water tank; and € effluent discharged.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/4ecb47eb2ccddf51ccd0c401.png"},{"id":81205050,"identity":"23244d97-0b1d-4735-8be2-011129f7e088","added_by":"auto","created_at":"2025-04-23 12:05:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":167377,"visible":true,"origin":"","legend":"\u003cp\u003eThe error between the measured value and the simulated value in different river sections: (a) NH\u003csub\u003e3\u003c/sub\u003e-N; (b) TN; (c) TP; (d) BOD\u003csub\u003e5\u003c/sub\u003e; and (e) CODcr\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/310b81b893bdde7791e1aa03.png"},{"id":81205025,"identity":"0c99a034-ded8-4355-868b-df376b4233c1","added_by":"auto","created_at":"2025-04-23 12:05:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":441112,"visible":true,"origin":"","legend":"\u003cp\u003eThe fluctuation of the simulated and measured values of pollutants in each month: (a) segment 1; (b) segment 2; (c) segment 3; (d) segment 4; and (e) segment 5.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/2a627b8cc3de0b57934a8848.png"},{"id":81205048,"identity":"5ce9002c-2a71-403e-8211-e1da1aa14d53","added_by":"auto","created_at":"2025-04-23 12:05:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":239560,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in pollutant concentrations in the experimental group and the blank control group under photocatalytic degradation experiments: (a) NH\u003csub\u003e3\u003c/sub\u003e-N; (b) TN; (c) TP; (d) NO\u003csub\u003e3\u003c/sub\u003e¯-N; (e) CODcr; and (f) BOD\u003csub\u003e5\u003c/sub\u003e.\u003cem\u003e k \u003c/em\u003eis the reduction rate.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/7807b2ee4f863d76b7dc7c0e.png"},{"id":81206175,"identity":"4ff10416-5eef-4b09-a046-4220847e0e63","added_by":"auto","created_at":"2025-04-23 12:13:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":52564,"visible":true,"origin":"","legend":"\u003cp\u003eThe reduction of pollutant concentrations after photocatalytic degradation in 9 monitoring sections. The dotted and dashed lines represent the water quality standards corresponding to each water quality standards\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/3582e45924033eb310d55484.png"},{"id":81793067,"identity":"3d958b2f-a19b-4475-afd2-542ea8959db8","added_by":"auto","created_at":"2025-05-02 02:16:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3668658,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/80f396c8-2c66-4d5d-b4e4-b0339fe7c963.pdf"},{"id":81206164,"identity":"220cd23f-5469-4603-bfd0-b2dae09fa573","added_by":"auto","created_at":"2025-04-23 12:13:49","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":278016,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.doc","url":"https://assets-eu.researchsquare.com/files/rs-5204210/v1/d4a6e71964e29c93e876ef04.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-point source pollution transport and photocatalytic degradation effect in the Mudong River basin of Huixian Wetland based on WASP model","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWater pollution sources can be divided into point source pollution and non-point source pollution. Non-point source pollution has become a major problem causing environmental pollution in the river basin\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. There are many sources of non-point source pollution, including agricultural runoff, rainfall, and atmospheric deposition\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Among them, agricultural non-point source pollution is scattered, random, difficult to supervise, with high N and P content, and huge risks\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A variety of land use, tillage methods, fertilization management methods, and climate types all affect agricultural non-point source pollution in the operation and transformation processes\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Agricultural non-point source pollution\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e has been recognized as one of the main causes of water eutrophication\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and many countries are facing the problem of agricultural non-point source pollution\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. For example, in the United States, agricultural non-point source pollution is considered to be a major source of nutrients in lakes and rivers\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In Europe, 50\u0026ndash;80% of the total nitrogen and total phosphorus loads in freshwater and seawater are mainly due to agricultural pollution\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In China, the communiqu\u0026eacute; of the second national survey of pollution sources showed that agriculture emitted 10.6713\u0026nbsp;million tons of chemical oxygen demand (CODcr), 1.4149\u0026nbsp;million tons of total nitrogen (TN) and 212,000 tons of total phosphorus (TP), accounting for 49.8%, 46.5% and 67.2% of the total emissions, respectively\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEstimating non-point source pollution is an important way to quantify the pollution load, and commonly used methods include the output coefficient method\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, production and discharge coefficient method, model estimation, and other methods. In the past, many researchers have used water quality models to estimate the non-point source pollution load in watersheds. For example, Zhou et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e combined the one-dimensional water quality model with the improved output coefficient method to estimate the non-point source pollution load in the Chaohe River Basin, which verified the rationality and universality of this calculation method. Chen Wenjun and others\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e studied the pollutant load in the Taihu Lake Basin based on the water quality analysis simulation program (WASP) model and geographic information system (GIS) spatial analysis, providing a holistic framework for the analysis of the water qualities of various water bodies in the rural watershed in the humid region of southeast China.\u003c/p\u003e \u003cp\u003eOver time, water quality modelling has expanded from an initial focus on basic parameters to a comprehensive assessment of aquatic ecosystems\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The WASP model, which is currently widely used around the world, has undergone significant advancements and updates, enhancing its flexibility and adaptability to simulate more complex environmental processes and various contaminants such as nutrients, organics, and heavy metals\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In recent years, studies have confirmed that the model has shown unique advantages in watershed pollution traceability, environmental capacity assessment and ecological risk early warning\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Zelazny et al.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e revealed the compound impact mechanism of agriculture and urban pollution on the Dunayek River Basin through multi-scenario simulation. Obin's team\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e innovatively combined with the FLUX equation to construct a dynamic prediction system for water environment capacity in the Zhuzhou section of the Yangtze River. The sensitivity analysis of Mbongowo et al.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e quantified the contribution of point source pollution to nutrient overload in the Shenandor River Basin.\u003c/p\u003e \u003cp\u003eAt the same time, the new water treatment technology based on the principle of semiconductor photocatalysis provides a new idea to solve the bottleneck of traditional process efficiency. Photocatalysts such as TiO₂ have attracted much attention because of their non-toxic, stable, and photoregenerative properties\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Due to its non-toxic, stable properties, chemical resistance and photocorrosion resistance, TiO2 has become the most promising semiconductor photocatalytic material\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. TiO2 can convert\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and remove heavy metal ions\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, inorganic salts\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, organic matter\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, etc. in water through sunlight or ultraviolet photocatalytic reaction, which makes TiO2 photocatalytic technology widely used in improving water quality. Compared with the traditional physical treatment process, the use of TiO2 photocatalytic technology for water pollution control is more efficient\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, environmentally friendly\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and economical\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The Ag/TiO₂/PVA ternary composite system developed by Mohammad et al.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e exhibited excellent heavy metal removal performance under ultraviolet excitation, while Nisereen's team\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e effectively alleviated the membrane fouling problem through a synergistic process of photocatalysis-membrane separation. Although the optical response range of TiO₂ can be extended to the visible region (from 3.2 eV to 2.4\u0026ndash;2.8 eV) through elemental doping (e.g., N, Fe) and heterostructure, there are still multiple challenges in practical applications. The light scattering effect caused by water turbidity can reduce the photocatalytic efficiency by 30%-50%\u003csup\u003e41\u003c/sup\u003e, and the problem of reducing the photocatalytic efficiency due to complex water quality has not been effectively solved\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The light transmission efficiency and operating cost of large-scale reactors still need to be optimized. And current research focuses on the development of adaptive carrier materials\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e in order to break through the bottleneck of technology translation.\u003c/p\u003e \u003cp\u003eIt is worth noting that the synergistic application of WASP model and photocatalytic technology shows potential advantages. The former can provide accurate spatiotemporal dynamic simulation for pollution control projects, while the latter provides practical technical support for the pollutant reduction parameters in the model. In the future, it is necessary to focus on solving interdisciplinary problems such as the dynamic coupling mechanism of model parameters and the modular design of photocatalytic reactors, so as to promote the development of water environment treatment technology in the direction of precision and intelligence.\u003c/p\u003e \u003cp\u003eThe Huixian Karst Wetland is one of the largest subtropical low-altitude karst wetlands in China, and its surface water and groundwater are important foundations for the development of agriculture and animal husbandry in the basin\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. However, overexploitation has led to severe eutrophication in this area. Among the sources of pollution, non-point source pollution from agricultural activities is the main cause of surface water and groundwater pollution in the wetlands\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Due to the complex hydrological conditions of karst wetlands and the inconvenience of monitoring non-point source pollution, few studies have been conducted on agricultural non-point source pollution in karst wetlands. In the past, research on the Huixian wetland has focused on the sources of pollutants and the differences in time and space\u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, but little work has focused on the estimation of the pollutant load, and the feasibility of combining models to simulate and calculate pollution transport and reduction in karst wetlands has not been proven. Therefore, it is particularly urgent to simulate and estimate the migration and reduction of pollutants in farmland runoff in karst wetlands\u003csup\u003e\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the past, most of the studies used the WASP model to estimate the non-point source pollution load of the watershed, but few of them were combined with the degradation effect of new materials. In order to find out the non-point source pollution of the Mudong River watershed in Huixian Wetland, estimate its pollution load and explore the possibility of photocatalytic degradation of pollutants in water by nanomaterials, this paper innovatively combines the WASP model with the photocatalytic degradation experiment of nano titanium dioxide films, estimates the inflow reduction of pollution load into the river based on the model, and discusses the application prospect of nano titanium dioxide materials in outdoor water. In the process of photocatalytic experiments, the low-iron ultra-white cullet reused by waste recycling and processing was selected as the coating substrate to prepare TiO2 films, and the packed-bed photocatalytic reactor was updated and upgraded to simulate the outdoor scenario, which advocated the idea of green development and provided solutions for the evaluation of pollutant reduction effect and pollutant reduction in small watersheds in the field.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003eThe research ideas of this paper are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe small watershed of the Mudong River in the Huixian karst wetland is located at longitude 110\u0026deg;09\u0026prime;\u0026minus;110\u0026deg;14\u0026prime;E and latitude 25\u0026deg;04\u0026prime;\u0026minus;25\u0026deg;09\u0026prime;N, covering the entire area where the river flows into the core area of the wetland. The Huixian wetland is the transition zone between the watersheds of the Li River and the Liu River Basin, as well as the buffer zone between the Guilin Peak Forest Plain and the bottom of the Peak Thicket Valley, and it represents the typical characteristics of karst wetlands in China. It is typically used as a key area for the study of karst wetlands. The study area is located in the southern portion of the Nanling Mountains. The soil types include typical wetland soils and karst soils. The geological structure belongs to the South China Fold Belt, and it has a typical karst geomorphology. In addition, the study area is characterized by a typical subtropical monsoon climate, with an average annual temperature of 20\u0026deg;C and high precipitation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The rainy season occurs between April and August, with precipitation accounting for approximately half of the annual total amount\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Furthermore, the dry season of the study area is from October to March, with frequent droughts in the rivers and in the wetland. In addition, the Mudong River small watershed is dominated by agriculture. Due to this, the primary source of agricultural non-point source pollution in the study area includes nitrogen and phosphorus nutrients lost from fertilizer applications on agricultural land, sewage from livestock and poultry farming, and domestic sewage from residential land. Currently, livestock and poultry in the watershed have both centralized breeding and scattered farming, but livestock and poultry wastewater is directly discharged, causing pollution to surface water, groundwater, and soil.\u003c/p\u003e \u003cp\u003eThe river segment covered by the model is the upper reach of the Mudong River (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources and analysis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Sample collection and Analysis Methods\u003c/h2\u003e \u003cp\u003eThe field investigation and water sample collection time are July 2022\u0026thinsp;~\u0026thinsp;December 2023, and the frequency is monthly sampling. Samples are collected 2\u0026thinsp;~\u0026thinsp;3 times a month in the rainy season and 1 time per month in the dry season. In addition, 9 sample collection points (No. 1\u0026thinsp;~\u0026thinsp;9) were set up near the main pollution sources such as farmland, duck farms, villages, and fish ponds along the Mudong River. The TN (HJ 636\u0026ndash;2012)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, NH\u003csub\u003e3\u003c/sub\u003e-N (HJ 535\u0026ndash;2009)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, TP (GB11893-89)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, chemical oxygen demand (CODcr) (HJ 05-2009)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, biochemical oxygen demand BOD\u003csub\u003e5\u003c/sub\u003e (GB 11914-89)\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, and nitrate nitrogen (NO\u003csub\u003e3\u003c/sub\u003e\u0026macr;-N) (HJ/T 346\u0026ndash;2007)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e were measured in the laboratory in accordance with standard methods. In addition, the dissolved oxygen, pH, flow rate, and water temperature of the waterbody were monitored during the collection process. In addition, the dissolved oxygen, pH, flow rate, and water temperature of the water body were monitored using a portable dissolved oxygen meter, pH meter, and Doppler ultrasonic flow meter during the collection process.\u003c/p\u003e \u003cp\u003eThe data were processed using Microsoft Excel 2023, and Origin Pro 2024 was used to generate the charts. In addition, ArcGIS 10.6 was used for satellite image analysis and the creation of the maps of the study area. The WASP 8.23 tool allows for modeling of water resources and analysis of different scenarios in the studied basin, with the aim of ensuring the sustainable management of these resources. The meteorological data are from the National Meteorological Information Center, the Guilin Hydrological Station and the Doppler flow monitoring equipment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Investigation and statistics of pollution sources\u003c/h2\u003e \u003cp\u003eWe collected information on rural resident life, plantations, aquaculture, and livestock and poultry farming, as well as other land use information. Combined with the actual situation and referring to the relevant literature and calculation manuals\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, the output coefficient method\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e was used to calculate the emissions of planting and other land use pollution sources.\u003c/p\u003e \u003cp\u003eThe expression of the output coefficient model is as Eq.\u0026nbsp;(1)\u003csup\u003e59,60\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}L=\\sum\\:_{i=1}^{m}{E}_{i}{A}_{i}\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:L\\)\u003c/span\u003e\u003c/span\u003e is the total output of the contaminant, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ei\\)\u003c/span\u003e\u003c/span\u003e is the output coefficient of this pollutant in the ith land use type, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e is the land use area in section i.\u003c/p\u003e \u003cp\u003eThe emission of rural domestic pollution needs to be calculated using Eq.\u0026nbsp;(2) \u003csup\u003e59,60\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe amount of pollutants produced in domestic sewage can be expressed as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}Pollutant\\:production\\:=\\:rural\\:permanent\\:population\\:\\times\\:\\:per\\:capita\\:pollution\\:intensity\\:\\times\\:\\:365\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDue to the lack of information about the addresses and sizes of the farms, in this study, we estimated the pollution emissions from aquaculture and poultry breeding through field visits and using data from the Guilin Economic and Social Statistical Yearbook, combined with the area proportion method and field survey data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Calculation of emissions and input load to the river from non-point source pollution\u003c/h2\u003e \u003cp\u003eBy referring to other studies conducted in China\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and combining their conclusions with the previous research of our group, the river entry coefficient of the surface source pollution was determined to be 0.2 through repeated iterative trial calculations and the model according to comprehensive factors, such as the distribution of the karst, geological structures, and groundwater burial in the small watershed.\u003c/p\u003e \u003cp\u003eIn practice, it was found that the non-point source pollution load showed an increasing trend in the months with higher precipitation. Therefore, the annual average non-point source pollution entering the river coefficient was used to calculate the amount of non-point source pollution entering the river in each month based on the ratio of the total monthly precipitation to the total annual precipitation. The monthly cumulative rainfall and its percentage from January, 2023 to December, 2023 are shown in Supplementary tables.\u003c/p\u003e \u003cp\u003eThe average monthly input of non-point source pollution to the river was calculated as Eq.\u0026nbsp;(3):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{L}_{nF\\:}={W}_{F}\\times\\:{\\gimel\\:}_{F\\:}\\times\\:P\\#（3）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{L}_{nF}\\)\u003c/span\u003e\u003c/span\u003e is the monthly intake of non-point source pollution; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{F}\\)\u003c/span\u003e\u003c/span\u003eis the annual emission from non-point sources; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gimel\\:}_{F}\\)\u003c/span\u003e\u003c/span\u003eis the non-point source pollution\u0026rsquo;s coefficient of entering the river; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\)\u003c/span\u003e\u003c/span\u003e is the ratio of monthly rainfall to annual rainfall.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Constructing the WASP model\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Overview and model principles of the WASP model\u003c/h2\u003e \u003cp\u003eThe WASP model is a multifunctional water quality model developed by the U.S. Environmental Protection Agency (EPA). It integrates the DYNHYD (hydrodynamics) and WASP (water quality) modules. The WASP model is suitable for a variety of aquatic environments, such as wetlands, reservoirs, and rivers, as it simulates the interaction between pollutants and natural processes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, making it one of the more preferred tools in the field of water quality modeling\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe WASP model uses a series of mathematical equations based on the principle of conservation of mass to describe the migration and transformation of matter in aquaponic systems over time and space. The model takes the tiny water body as the basic analysis unit, simulates the dynamics of the pollutants, nutrients, and other dissolved substances in rivers, lakes, and reservoirs through equations, and integrates environmental factors such as river dynamics, temperature changes, dissolved oxygen levels, and biochemical reactions. The core function is to accurately simulate the transport of soluble components in the water body, and to realize dynamic analysis of the water quality using the mass conservation equation, i.e., Eq.\u0026nbsp;(4)\u003csup\u003e4,64\u003c/sup\u003e.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\partial\\:C/\\partial\\:t=-\\frac{\\partial\\:\\left({U}_{x}C\\right)}{\\partial\\:x}+\\frac{\\partial\\:\\left({E}_{x}\\frac{\\partial\\:C}{\\partial\\:x}\\right)}{\\partial\\:x}-\\frac{\\partial\\:\\left({U}_{y}C\\right)+\\partial\\:\\left({E}_{y}\\frac{\\partial\\:C}{\\partial\\:y}\\right)}{\\partial\\:y}+\\frac{\\partial\\:\\left({E}_{z}\\frac{\\partial\\:C}{\\partial\\:z}\\right)}{\\partial\\:z}-\\frac{\\partial\\:\\left({U}_{z}C\\right)}{\\partial\\:z}+{S}_{L}+{S}_{B}+{S}_{K}\\#\\left(4\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:C\\)\u003c/span\u003e\u003c/span\u003e is the concentration of the water quality index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:mg/L\\)\u003c/span\u003e\u003c/span\u003e); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e is the time step (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s)\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{x}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{y}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{z}\\:\\)\u003c/span\u003e\u003c/span\u003eare the longitudinal, transverse, and vertical convection velocities of the water body (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:m/s),\\:\\)\u003c/span\u003e\u003c/span\u003erespectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{x}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{y}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{z}\\)\u003c/span\u003e\u003c/span\u003e are the diffusion coefficient of the longitudinal, transverse, and vertical water body (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{m}^{2}/s)\\)\u003c/span\u003e\u003c/span\u003e, respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{L}\\)\u003c/span\u003e\u003c/span\u003e is the sum of the point source and non-point source pollution loads (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:g/({m}^{3}\\bullet\\:d))\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{B}\\)\u003c/span\u003e\u003c/span\u003e is the boundary load, including the upstream, downstream, bottom, and atmospheric environments (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:g/({m}^{3}\\bullet\\:d)\\)\u003c/span\u003e\u003c/span\u003e) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{K}\\)\u003c/span\u003e\u003c/span\u003e is the total dynamic conversion coefficient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:g/({m}^{3}\\bullet\\:d)\\)\u003c/span\u003e\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eIn the process of simulating water quality indicators, homogeneity is assumed in the horizontal and vertical directions, so the model is simplified to a one-dimensional mass balance Equ.(5)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\frac{\\partial\\:\\left(AC\\right)}{\\partial\\:t}=\\frac{\\partial\\:\\left(-{U}_{x}AC+{E}_{x}A\\frac{\\partial\\:C}{\\partial\\:x}\\right)}{\\partial\\:x}+A{S}_{L}+A{S}_{B}+A{S}_{K}\\#\\left(5\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:A\\)\u003c/span\u003e\u003c/span\u003e is the cross-sectional area of the simulated water body (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{m}^{2})\\)\u003c/span\u003e\u003c/span\u003e. The rest of the variables are the same as above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 River generalization\u003c/h2\u003e \u003cp\u003eIn this study, the upper reaches of the Mudong River were segmented as needed. Considering the field conditions and combining the principles of segmentation, the upper Mudong River segment was divided into five segments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The segment-specific information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMudong River small watershed reach information statistical table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength/m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWidth/m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepth/m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVelocity/m⚫s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMinimum depth/m\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Basic parameter setting and information entry\u003c/h2\u003e \u003cp\u003eIn this study, the advanced eutrophication water quality module was used. Parameter rates were determined using water quality data from July 2022 to December 2023, and the hydrodynamic model was selected as a one-dimensional lattice kinematic fluctuation model with a time step of 1 day and solved using Euler's equation. The cell transport model was a one-dimensional kinematic wave transport model. Following this, the initial concentration inputs included NH\u003csub\u003e3\u003c/sub\u003e-N, TN, TP, CODcr, BOD\u003csub\u003e5\u003c/sub\u003e, and other water quality indicators. The model boundary conditions were based on an actual investigation where there was no point source pollution discharge in the Mudong River small watershed area; hence, the point source pollution load was zero.\u003c/p\u003e \u003cp\u003eThe pollutant loads calculated in the previous section were allocated to each stream section by combining with the different percentages for each month of rainfall, the length of each river section proportionally, and the number of days in each month. Afterwards, it was summarized for the entire ephemeral course of the different reaches (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePollution load of each indicator in each section of the upper Mudong River\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCODcr(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e537.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7530.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3485.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e417.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5656.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2418.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e761.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9845.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4502.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5741.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2571.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e689.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8595.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3721.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Photocatalytic degradation experiment\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Preparation of the nanometer titanium dioxide film\u003c/h2\u003e \u003cp\u003eFrom a practical perspective, a TiO\u003csub\u003e2\u003c/sub\u003e colloid suitable for this study was prepared using the sol-gel method with simple reaction conditions and easy control of the reaction process to prepare the titanium dioxide nanoparticles. The equations for the hydrolysis and condensation reactions to the colloidal system are shown in Eq.\u0026nbsp;(6) and Eq.\u0026nbsp;(7)\u003csup\u003e65,66\u003c/sup\u003e:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}-M-OR+{H}_{2}O\\to\\:-M-OR+ROH\\#\\left(6\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}-M-OR+OR-M\\to\\:-M-O-M+ROH\\#\\left(7\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePure titanium dioxide powder (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e (g)) was obtained after washing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e (a)), drying (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b)), filtration (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e (c), (d)), and high-temperature calcination (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e (e), (f)). The steps are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Photocatalytic reactor setup\u003c/h2\u003e \u003cp\u003eConsidering that the objective of this study is to treat complex pollutants in outdoor ponds, a stationary photocatalytic reactor was selected as the reactor type for this study. The structure of the photocatalytic reactor 2.0 is shown in\u003cb\u003eFigure 5\u003c/b\u003e. Fishpond water from a certain fish pond close to the upper reaches of the Mudong River in the Mudong River small watershed was selected as the sample for the experiment. Following this, the blank control and the experiment were conducted at the same time, and the experimental group was treated in the photocatalytic reactor. In addition, the experiment was limited to a 24 h period to ensure that the water samples were stored for an optimal period. In addition, the room temperature of the laboratory was strictly controlled at 20\u0026deg;C. The NH\u003csub\u003e3\u003c/sub\u003e-N, TN, TP, CODcr, and BOD\u003csub\u003e5\u003c/sub\u003e concentrations of the samples that collected from the photocatalytic reactor were measured at 0, 6, 12, and 24 h.\u003c/p\u003e \u003cp\u003eEqs.\u0026nbsp;(8)\u0026ndash;(12)\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e are the specific photocatalytic reaction mechanism reaction equations.\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{O}_{2}+{e}^{-}\\to\\:\\bullet\\:{O}_{2}^{-}\\#\\left(8\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{H}_{2}O+\\bullet\\:{O}_{2}^{-}\\to\\:\\bullet\\:OOH+O{H}^{-}\\#\\left(9\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\bullet\\:OOH\\to\\:{{O}_{2}+H}_{2}{O}_{2}\\#\\left(10\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equk\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\bullet\\:OOH+{H}_{2}O+{e}^{-}\\to\\:{H}_{2}{O}_{2}+O{H}^{-}\\#\\left(11\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equl\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equl\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{H}_{2}{O}_{2}+{e}^{-}\\to\\:\\bullet\\:OH+O{H}^{-}\\#\\left(12\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Estimation of pollutant reductions\u003c/h2\u003e \u003cp\u003eBased on the photocatalytic degradation experiments to derive the effect of degradation on the concentrations of water quality indicators, the coefficients (k) were multiplied by 1, 0.5, and 0.25 and applied to the water quality indicators of the waterbody during the monitoring period of the upstream of the Mudong River. The WASP water quality model was then applied using the trial-and-error method for simulation to estimate the reduction of the pollutant load flowing into the river\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results and analysis","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Model parameter calibration and error analysis\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Pollutant Emission Results\u003c/h2\u003e \u003cp\u003eThere were about 250 pigs, 60 cattle, and 9,000 poultry animals in the study area, including about 7,000 meat poultry animals and 2,000 egg poultry animals. The total area of aquaculture is 2,055 hectares, and the total aquaculture production is 13,379 tons. In this study, we quantified the effects of precipitation and runoff on the non-point source pollution load in the Mudong River watershed. In the actual observation study, it was found that the non-point source pollution load exhibited an increasing trend during the months with a large amount of precipitation. We calculated the emissions of various pollution sources in the study area (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the amounts of different non-point source pollution entering the river\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater quality indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural life\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlantation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAquaculture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLivestock breeding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOther land uses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e (t/a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCODcr (t/a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTN (t/a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N (t/a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP (t/a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe calculated the proportion of the total regional pollutant emissions. The overall situation of the non-point source pollution load was as follows: cultivation (82.03% of the total load)\u0026thinsp;\u0026gt;\u0026thinsp;livestock and poultry farming (5.19% of the total load)\u0026thinsp;\u0026gt;\u0026thinsp;other land uses (4.48% of the total load)\u0026thinsp;\u0026gt;\u0026thinsp;aquaculture (4.35% of the total load)\u0026thinsp;\u0026gt;\u0026thinsp;rural life (3.57% of the total load). Further attention needs to be paid to the amount of fertilizer used during tillage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Parameterization\u003c/h2\u003e \u003cp\u003eTo ensure the accuracy of the simulation results, we used a trial algorithm to rate the parameter values of the water quality model of the upper Mudong River. The water quality concentration, flow data and non-point source pollution inflow from July 2022 to March 2023 were brought into the model, the simulated pollutant concentration value was calculated, the relevant parameters were continuously adjusted, and the error analysis was made with the measured pollutant concentration value, and the pollutant concentration calibration results are shown in the Supplementary tables. This algorithm combined the initial values of the parameter ranges suggested by the long-term on-site monitoring of the upper Mudong River and the WASP user\u0026rsquo;s manual. We then compared the monitoring data and simulation results from 2022 to 2023 and repeated the rate setting of the parameter values until all the parameters were determined, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel parameter rates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrification rate constant at 20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eday-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csub\u003e12\u003c/sub\u003e temperature coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003e12\u003c/sub\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen-limited nitrification half-saturation coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003eNIT\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenitrification rate constant at 20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003e2D\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csub\u003e2D\u003c/sub\u003e temperature coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003e2DT\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttenuation constant of biochemical oxygen demand at 20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csub\u003ed\u003c/sub\u003e temperature coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003edT\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen limiting BOD half-saturation coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emgO\u003csub\u003e2\u003c/sub\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003eBOD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emgO\u003csub\u003e2\u003c/sub\u003e/mgC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaoc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32/12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReoxygenation coefficient at 20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSediment oxygen demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/m\u003csup\u003e2\u003c/sup\u003ed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature coefficient of sediment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eθs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMineralization rate of organic phosphorus at 20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003e83\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature coefficient of organic phosphorus mineralization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003e83T\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic nitrogen mineralization rate at 20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003e71\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csub\u003e17\u003c/sub\u003e temperature coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK71T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytoplankton respiration rate coefficient at 20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eday-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csub\u003e1R\u003c/sub\u003e temperature coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK1RT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Model simulation and error analysis\u003c/h2\u003e \u003cp\u003eBased on the completion rate of the model parameters, an error analysis of the model simulation results was conducted to calculate the average error and relative error between the measured and simulated values of the water quality indexes for each simulation period (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The error between the measured and simulated values during the simulation period was small, and the minimum relative error between the simulated and measured values was 6.67%. The maximum value was 11.96%(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Currently, the Technical Guidelines for Water Pollution Discharge Permit\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e indicate that the relative error of a model simulation should not exceed 40%. In addition, the Specification for Hydrological Intelligence Prediction (GB/T 22482\u0026thinsp;\u0026minus;\u0026thinsp;2008) states that the permitted error for a water quality simulation is 30% of the measured value\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, making it a valid model. Therefore, it was determined that the established model could be used as a good tool to predict and regulate the water quality of the upper Mudong River, and this was based on the premise that the measured values had good fit to the simulated values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eError analysis for the model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCODcr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18(mg/L)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot mean square error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28(mg/L)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDue to the limitations of the measured data and information collection, the effect of the substrate on the model simulation was not considered in this study, and the simulation accuracy may have been affected by using non-point source pollution as the primary research object.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Model Validation\u003c/h2\u003e \u003cp\u003eThe WASP model was used to simulate the changes in the concentration of various indicators during the period of April 2023\u0026thinsp;~\u0026thinsp;December 2023, and the measured values were compared with the simulated values (show in Supplementary table) for verification, and the results are as follows.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the trend of the measured and simulated values of the indicators during the simulation validation period overlapped relatively well. In addition, the error between the measured and simulated values of the pollutant concentrations in the upstream of the Mudong River was relatively small, and the results of the model simulation could effectively reflect the trend of pollutant concentrations in the river over time. Besides, the discontinuity of the results in paragraphs 2 and 5 is due to the fact that the river was cut off during this period and sampling could not be carried out, so no data are available.\u003c/p\u003e \u003cp\u003eFrom the viewpoint of the entire river section, the NH\u003csub\u003e3\u003c/sub\u003e-N and TP concentrations did not vary significantly. In addition, the NH\u003csub\u003e3\u003c/sub\u003e-N concentration was lower during the abundant water period than during the flat-water period and the dry water period. However, the TN, CODcr, and BOD\u003csub\u003e5\u003c/sub\u003e concentrations showed large changes, with the TN concentrations decreasing to varying degrees during the abundant water period and increasing to varying degrees during the dry water period. The CODcr concentrations increased to varying degrees during the abundant water period. The BOD\u003csub\u003e5\u003c/sub\u003e concentrations were lower during the abundant water period than those during the flat-water period and the dry water period, decreasing to varying degrees during the abundant water period. Furthermore, the BOD\u003csub\u003e5\u003c/sub\u003e concentrations increased to varying degrees during the flat water period and the dry water period. In summary, an overall seasonal variation was observed.\u003c/p\u003e \u003cp\u003eAccording to the simulation results, the fit between the measured and modeled TP values was relatively poor in river section \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which was anomalous. And the overall performance of CODcr was higher during the abundant water period than during the flat-water period and the dry water period (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, each section of the river exceeded the Class III water quality standard by 20 mg/L only during April 2023. It is possible that April is the period of spring paddy crop greening and growing when farmers apply a certain amount of fertilizer to the field. In addition, the BOD\u003csub\u003e5\u003c/sub\u003e simulation of river section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e3\u003c/span\u003e and river section \u003cspan refid=\"Sec30\" class=\"InternalRef\"\u003e5\u003c/span\u003e exceeded the Class III water quality standard of 4 mg/L in more months than that of the other sections of the river (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general, the pollutant concentrations of the main water quality indicators in the small watershed of the upper reaches of the Mudong River were CODcr\u0026thinsp;\u0026gt;\u0026thinsp;TN\u0026thinsp;\u0026gt;\u0026thinsp;BOD\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;TP\u0026thinsp;\u0026gt;\u0026thinsp;NH\u003csub\u003e3\u003c/sub\u003e-N. The nitrogen content of the river water was significantly higher than its phosphorus content, and the TN monitoring concentration was high. During most of the testing period, the value exceeded the Class V water quality standard.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Experimental results of the photocatalytic degradation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA blank control group (no TiO\u003csub\u003e2\u003c/sub\u003e) and an experimental group (TiO\u003csub\u003e2\u003c/sub\u003e) were set up (group A and group B, respectively) at 0 h, 6 h, 12 h, and 24 h to monitor the changes in the six water quality indicators during the 24 h experimental period (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e). As can be seen from the figure, there was a significant increase in the rate of pollutant elimination after photocatalytic degradation for all the pollutants. After 24 hours of the experiment, the concentrations of the water quality indicators in each experimental group were lower than those in the control group, and the concentrations dropped to more than half of the original values. Among them, the pollutant concentration decreased significantly for BOD\u003csub\u003e5\u003c/sub\u003e (54.2%), followed by NH\u003csub\u003e3\u003c/sub\u003e-N (47.3%)\u0026thinsp;\u0026gt;\u0026thinsp;NO\u003csub\u003e3\u003c/sub\u003e\u0026macr;-N (50.0%), TN (34.1%), CODcr (21.3%), and TP (11.9%). During the experiment, the NH\u003csub\u003e3\u003c/sub\u003e, TN, and TP concentrations gradually decreased as the photocatalytic reaction progressed. The CODcr concentration in the control group was slightly different, exhibiting a trend of initially decreasing and then slightly increasing.\u003c/p\u003e \u003cp\u003eIn the experiment, the NH\u003csub\u003e3\u003c/sub\u003e-N, TN, TP, and BOD\u003csub\u003e5\u003c/sub\u003e concentrations in the blank control group first decreased and then tended to stabilize, while in the experimental group, there was a continuous downward trend, and the downward trend was more obvious (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e (a), (b), (c), (f)). This result indicated that the nano-titanium dioxide material using an ultraviolet (UV) irradiation photocatalytic reaction that occurred for the removal of ammonia and nitrogen in the waterbody had a more obvious effect compared to the situation without treatment.\u003c/p\u003e \u003cp\u003eThe NO\u003csub\u003e3\u003c/sub\u003e\u0026macr;-N blank control group and experimental group concentration value changes were more abnormal because the trend in their concentrations showed an overall decrease followed by a leveling off during the experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e(d)). This result could have been due to strong oxidizing radicals generated by the TiO\u003csub\u003e2\u003c/sub\u003e photocatalysis that promoted the conversion of NO\u003csub\u003e3\u003c/sub\u003e\u0026macr;-N to a more labile form of nitrogen. In addition, the concentration of nitrate-nitrogen in the water decreased with the prolongation of the reaction time, indicating that the photocatalytic reaction of the titanium dioxide nanomaterials after UV irradiation was effective for the removal of nitrate-nitrogen in the water body.\u003c/p\u003e \u003cp\u003eThe CODcr blank control group concentrations showed an overall decreasing trend followed by a slight rebound (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e(e)) that was likely influenced by changes in the activity of the microbial community at different moments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Pollutant load reduction estimation\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Effect of the photocatalytic degradation experiments on water quality\u003c/h2\u003e \u003cp\u003eFor the actual reduction of each water quality indicator, the concentration ratio was the initial concentration of each indicator after the actual reduction. In this study, based on the photodegradation experiments of the effect of pollutant reduction in water, the experimental data after 24 h of the experiment were used as a benchmark. Hence, the proportion of reduced concentration of the sample water in the experimental group from high to low was as follows: TN(75.4%)\u0026thinsp;\u0026gt;\u0026thinsp;TP(73.1%)\u0026thinsp;\u0026gt;\u0026thinsp;NH\u003csub\u003e3\u003c/sub\u003e-N(68.9%)\u0026thinsp;\u0026gt;\u0026thinsp;BOD\u003csub\u003e5\u003c/sub\u003e(62.4%)\u0026thinsp;\u0026gt;\u0026thinsp;NO\u003csub\u003e3\u003c/sub\u003e\u0026macr;-N(50%).\u003c/p\u003e \u003cp\u003eAccording to the reduction of each water quality indicator (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the concentration of the water quality factors after photocatalytic degradation of the river was predicted by applying it to the field water quality data from the upper Mudong River. According to the water quality evaluation standard \u0026ldquo;Environmental Quality Standard for Surface Water\u0026rdquo; (GB3838-2002), a water quality evaluation and comparative analysis after the reduction of each monitoring section were conducted. The NO\u003csub\u003e3\u003c/sub\u003e\u0026macr;-N concentration was much lower than the limitation requirement (NO\u003csub\u003e3\u003c/sub\u003e\u0026macr;-N was less than 10 mg/L) in the Environmental Quality Standard for Surface Water, and the standard limit value does not specifically mention the different water quality level standards for NO\u003csub\u003e3\u003c/sub\u003e\u0026macr;-N. Therefore, five water quality indicators, namely, NH\u003csub\u003e3\u003c/sub\u003e-N, TN, TP, CODcr, and BOD\u003csub\u003e5\u003c/sub\u003e, were selected for water quality evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePollution load after photocatalytic degradation of each indicator in the upper Mudong River (k\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCODcr\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2250.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1047.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1690.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e716.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2888.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1315.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1684.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e751.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2521.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1086.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e243.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e633.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11034.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4917.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e, in the nine monitoring sections, the NH\u003csub\u003e3\u003c/sub\u003e-N concentration after photocatalytic degradation basically reached the Class II water quality standard (0.5 mg/L). In addition, the number of sections that met the standard increased by three compared with the number of sections prior to the degradation. In addition, the number of sections that met the Class I water quality standard (0.15 mg/L) accounted for 11.1% of the total number of monitoring sections, which means the effect of the NH\u003csub\u003e3\u003c/sub\u003e-N degradation as a whole was significantly improved. The TN concentration exceeded the standard of the Class IV water quality (1.15 mg/L), accounting for 66.7% of the total number of sections that met the standard prior to the photocatalytic degradation. The TN concentration before the photocatalytic degradation exceeded the standard of the poor class V water quality (2 mg/L), accounting for 66.7% of the initial concentration. After photocatalytic degradation of the water quality as a whole, there was a significant improvement that reached the standard of the class IV water quality (1.5 mg/L) of the number of sections increased by three. The photocatalytic degradation of the TP concentration met the standard of the class II water quality standard (0.10 mg/L) and met the water quality protection objectives of the Huixian wetland restoration. The photocatalytic degradation of the TP concentration was still an overall reduction. Prior to photocatalytic degradation, the average value of the CODcr concentration in each monitoring section was lower than the limit value of the Class I water quality standard (15 mg/L), and after photocatalytic degradation, the CODcr concentration was significantly reduced. Therefore, an overall improvement was achieved. The limit value of the Class I water quality standard and the limit value of the Class II water quality standard of BOD\u003csub\u003e5\u003c/sub\u003e were both 3 mg/L, and the number of sections that met the standard of the Class I water quality increased by two sections after degradation. In summary, the results demonstrated that if the nano-titanium dioxide material is promoted to outdoor waterbodies at a later stage, it will produce a water quality improvement effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Estimation of the pollution load reductions based on the WASP model\u003c/h2\u003e \u003cp\u003eThe estimated results of the pollution load reduction in the Mudong River watershed after the photocatalytic degradation experiments were applied in the field are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Only the results of k\u0026thinsp;=\u0026thinsp;1 are shown, and the rest are shown in Supplementary tables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePollution load after photocatalytic degradation of each indicator in the upper Mudong River (k\u0026thinsp;=\u0026thinsp;1)(kg/a)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength\u003c/p\u003e \u003cp\u003e(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCODcr\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(kg/a)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2250.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1047.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1690.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e716.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2888.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1315.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1684.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e751.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2521.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1086.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e243.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e633.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11034.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4917.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the actual reduction of each water quality indicator, the concentration ratio of the photocatalytic degradation experiment at 6, 12, and 24 h was the initial concentration ratio of each indicator after the actual reduction, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e7\u003c/span\u003e. This concentration ratio was used as the input value of the outdoor simulation experiments to simulate the reductions of the different indicators of non-point source pollution into the river at 6 h and 12 h every day, the results are shown in Supplementary tables. The reduction rate is shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e8\u003c/span\u003e .\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActual abatement rates during the photocatalytic degradation experiments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCODcr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe actual reduction rate of the outdoor photocatalytic degradation was simulated.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduction time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCODcr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.1 WASP simulation feasibility analysis\u003c/h2\u003e \u003cp\u003eThe study area is located in the karst irrigation area, and the area of the karst covered with soil of a certain thickness accounts for a large proportion of the total area. In the study area, karst landforms and non-karst landforms coexist. The runoff in the karst areas is more complex\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e than that in the non-karst areas, and the changes in the nitrogen and phosphorus migration paths and media affect their retention and regulation. The soil in the karst area is poor, the soil layer is shallow and discontinuous, the spatial distribution of the soil thickness is uneven, and the purification effect of the soil on nitrogen and phosphorus pollutants is weak. In terms of pollutant simulation and control, Mateusz et al.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e showed that the WASP model has good adaptability in predicting pollutant changes under different land use patterns. This study also proves the effectiveness of the WASP model in the small watershed of the Mudong River.\u003c/p\u003e \u003cp\u003eIn this paper, we find that pollutant load shows seasonal variations, which may be related to hydrological characteristics and agricultural activity cycles. During the rainy season, frequent precipitation and surface runoff can lead to large amounts of non-point source pollutants entering water bodies. In the dry season, precipitation is scarce, surface runoff decreases, and the migration of non-point source pollution decreases, but the concentration of pollutants in the water body may increase due to the decrease in water volume\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Luo et al.\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e also found that rainfall runoff would increase the load of nitrogen and phosphorus pollution into the river, especially in wet season, where the concentration of nitrogen and phosphorus increased significantly. There was a significant difference in the concentration of pollutants in the Mudong River during the wet season and the dry season (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The concentrations of total nitrogen (TN) and CODcr were higher during the wet season (April to August), while the concentrations of ammonia nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N) and BOD5 were higher during the dry season (October to March). At the same time, Gou et al.\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e showed that when the rainfall intensity exceeded 30 mm/h, the surface runoff was significantly enhanced, resulting in a large transport of agricultural pollutants, which was consistent with the rainfall-pollutant response relationship in this study. He et al.\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e further pointed out that the hydrological response speed of karst basins is fast, and rainfall events will quickly cause pollutant concentration fluctuations, which is consistent with the rapid increase of TN and CODcr in the Mudong River during the rainfall period in this study. In addition, Mateusz et al.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e used the WASP model to study the impact of agricultural and urban pollutant input in the Dunajek River Basin in Poland, and found that land use type significantly affected the simulation accuracy of the model, and the pollutant concentration in the agriculture-dominated watershed varied greatly during the wet season. Luo et al.\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e found that the concentrations of nitrogen and phosphorus in different waters were higher in the high fertilization season than in the low fertilization season, and were closely related to rainfall characteristics. In section \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e4\u003c/span\u003e of the upper reaches of the Mudong River, there is more agricultural land, and the concentration of total phosphorus (TP) increases significantly during the wet season. The reason for this may be that farmers add phosphorus fertilizer to their farmland during the wet season, and the pollution of the river near the farmland is more obvious. The results presented in this study are similar to those of Mateusz et al. and Luo et al.\u003c/p\u003e \u003cp\u003eIn addition, some studies have found that the low environmental capacity makes it easy to lose nutrients and sewage\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e from agricultural production, which aggravates the nitrogen and phosphorus pollution of the water and soil in this region. The diversion, transportation, distribution, and drainage channels in the study area regularly cross each other, the rice fields are dotted with ponds and weirs, the rainy season is strong, the rainfall is abundant, and the rice irrigation and fertilization period is within the rainy season. These may be the influencing factors\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e of the abnormal changes in the nitrogen and phosphorus concentrations. Therefore, the high CODcr concentration in this paper may be due to the fact that the rainfall in the upper reaches of the Mudong River during the wet season carries soil and pollutants rich in organic matter into the upper reaches of the Mudong River and its coastal areas, increasing the organic matter content of the upper reaches of the Mudong River, so the overall performance of the CODcr is higher in the wet season than in the normal and dry seasons. This is in contrast to the situation in April, when the spring rice crops are growing and farmers apply a certain amount of fertilizer to the fields. Moreover, April is the period with a high incidence of crop diseases and pests, and the use of pesticides for pest control, coupled with the increase in rainfall during the wet season, leads to the CODcr concentration of the water body being higher than in other months. This is similar to the BOD\u003csub\u003e5\u003c/sub\u003e concentration in sections \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Sec30\" class=\"InternalRef\"\u003e5\u003c/span\u003e where there is more aquaculture along the river channel. The high concentration of BOD\u003csub\u003e5\u003c/sub\u003e in the fish ponds may be due to the accumulation of excrement and bait in the fish ponds. During the sampling process and the installation of real-time monitoring equipment for water quality indicators, it was found that the fish pond owner will exchange the fish pond water with the river water body, resulting in high monitoring data in this section of the river in some months. These conditions are similar to the conclusions of previous studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Feasibility analysis of photocatalytic degradation experiments\u003c/h2\u003e \u003cp\u003eThe simulation results show that the pollutants such as ammonia nitrogen, total nitrogen, total phosphorus, CODcr and BOD\u003csub\u003e5\u003c/sub\u003e are reduced to a certain extent in different river sections, especially after the application of photocatalytic degradation technology, the pollutant concentration decreases as a whole, indicating that this method has potential application value in pollution control in agricultural watersheds.\u003c/p\u003e \u003cp\u003eThe pollutant reduction effect observed in indoor photocatalytic degradation experiments differs from the actual reduction effect in field conditions. Several studies have reported that water quality indicators monitored in the field exhibit a wider range of variation. In the study conducted on the Willamette River\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, nitrate concentrations increased by 516.7%, from 1.2 mg/L to 7.4 mg/L, and phosphate concentrations increased by 866.7%, from 0.03 mg/L to 0.29 mg/L. Total nitrogen concentrations in agricultural watersheds ranged from less than 1 mg/L to more than 10 mg/L. This variation can be attributed to higher biodiversity in wild river ecosystems, where biological activities have a more pronounced impact on water quality. The growth and death of algae, the metabolic activities of microorganisms, and other ecological processes can all influence water quality parameters\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. These complex ecological interactions are challenging to fully simulate in a laboratory setting. In addition, seasonal changes are difficult to simulate in the laboratory. Marin et al.\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e found that ammonia and phosphorus concentrations in rivers increased by 50% and 80%, respectively, during an event with precipitation exceeding 25.4 mm. Frequent precipitation during the rainy season triggers surface runoff, bringing more pollutants into rivers. At the same time, there are seasonal variations in the degree to which agricultural areas are affected by pollutants such as fertilizers. N Shiferaw et al. argue that rivers in agricultural areas may be directly affected by agricultural activities from time to time\u003csup\u003e\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The amount of fertilizer, tillage and production habits in the rainy and dry seasons will be different\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, and the emissions of chemical fertilizers, pesticides and manure used by farmers in the study area will change accordingly, and the water quality may change through surface runoff and soil infiltration into the river. Unlike laboratory experiments, these variables are difficult to accurately estimate in terms of their effect on water quality changes.\u003c/p\u003e \u003cp\u003eIn this study, photocatalytic degradation was tested in a laboratory, where the water sample was stationary, maintained at a constant temperature, and unaffected by external pollution sources. As a result, microbial activity was diminished, and the range of pollutant concentration changes was narrower than in the field. However, we applied the results of the laboratory photocatalytic degradation experiment to the WASP water quality model, using specific coefficients to estimate pollutant reduction in the river. The model results indicated a reduction in pollutant concentrations (NH\u003csub\u003e3\u003c/sub\u003e-N, TN, TP, CODcr, and BOD\u003csub\u003e5\u003c/sub\u003e) after 24 hours, with reductions of 37.9%, 50.81%, 46.46%, 7.04%, and 24.73%, respectively. Although the photocatalytic degradation test has been continuously improved in this study, it remains an indoor experiment, and its application to field conditions may introduce some uncertainty in the reduction effect. Therefore, outdoor experimental research is currently underway, with efforts to adapt the photocatalytic reactor for outdoor use while ensuring photocatalytic efficiency. This will allow for a more accurate assessment of pollutant reduction effects and the feasibility of large-scale implementation, providing valuable data to support water environment management and treatment measures in the agricultural watershed of the Huixian wetland. This research aims to provide a basis for water quality assessment in small agricultural watersheds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Deficiencies and prospects of research\u003c/h2\u003e \u003cp\u003eThis study found that there are differences in the reduction potential of different types of pollutants, and the total phosphorus reduction rate can reach more than 25%, which suggests that we need to adopt differentiated treatment strategies according to the characteristics of pollutants. At the same time, the study proves that the nano-titanium dioxide thin film photocatalytic technology is effective in improving water quality, and lowers the application threshold through low-cost substrate materials (low-iron ultra-white cullet glass), advocates green development, and has the potential for technology promotion.\u003c/p\u003e \u003cp\u003eDespite some success, there are still uncertainties and limitations. The climatic characteristics of Guilin are significantly affected by extreme weather events\u003csup\u003e\u003cspan additionalcitationids=\"CR85\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e, such as heavy rainfall, drought and cold waves, which are difficult to accurately predict through conventional meteorological data. The suddenness and uncertainty of these events add to the complexity of the study. In addition, global climate change has led to changes in precipitation patterns in Guilin, making traditional seasonal divisions no longer applicable. In recent years, the precipitation concentration period in Guilin has tended to be postponed\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, which may affect the division of the rainy season and the dry season. At the same time, the study spanned from July 2022 to December 2023, and the relatively short time span may not fully reflect the long-term trends of climate change and water quality changes. In addition, photocatalytic degradation experiments are conducted indoors, which can provide a certain reference, but the results may be different when applied outdoors.\u003c/p\u003e \u003cp\u003eThese problems may affect the accuracy and generalizability of the research results, and future research needs to further optimize the data source and improve the model method. Attempts can be made to introduce high-resolution meteorological models that combine historical data and future climate change scenarios to make more accurate predictions of extreme climate events. The scope and time span of the study were expanded, and outdoor experiments were carried out to evaluate the photocatalytic degradation effect under natural light conditions, so as to improve the reliability and scientificity of the research. There are many challenges in the process of promoting outdoor photocatalytic degradation technology in the future, including the limitation of natural light conditions, the low utilization rate of visible light, the complexity of the outdoor environment, the stability and recovery of catalysts, the design challenges of photocatalytic reactors, and the complexity of pollutants. Optical components such as mirrors and focusing lenses can be added to the photocatalytic reactor design to improve the utilization rate of light\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Optimize the water flow design in the reactor to ensure that the contaminants are in full contact with the catalyst and improve the degradation efficiency. In addition, small-scale field trials were conducted in a real-world environment to evaluate the performance of photocatalytic technology in different seasons and under different weather conditions. Long-term monitoring of field trials to collect data to assess the long-term effects and environmental impact of the technology. Perform a detailed cost-benefit analysis to evaluate the economic viability of photocatalytic technology for applications of different scales. Seek support from governments and environmental agencies to obtain funding and policy incentives to lower the barriers to technology adoption.\u003c/p\u003e \u003cp\u003eAlthough the model framework of this study does not specifically characterize the karst landform characteristics, its construction method can provide a baseline reference for the study of pollutant migration in karst areas. As a typical karst wetland, the complete water quality simulation of Huixian Wetland needs to consider the special karst hydrological process, and the relevant in-depth research will be the focus of follow-up work.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003e1. The use of the WASP model was found to be feasible to simulate the water quality of the upper Mudong River. This study was based on the water quality index data of the Mudong River from July 2022 to December 2023 for parameter rate setting, and after adjusting the parameter values many times, the simulation results met the requirements for model accuracy. Hence, the overall effect of the model simulation was good.\u003c/p\u003e\n\u003cp\u003e2. In the indoor experiments, of NH\u003csub\u003e3\u003c/sub\u003e-N, TN, TP, CODcr, and BOD\u003csub\u003e5\u003c/sub\u003e via photocatalytic degradation reduction resulted in the following: NH\u003csub\u003e3\u003c/sub\u003e-N accounted for 68.9% of the initial concentration, the nitrate nitrogen concentration accounted for 50%, the TN concentration accounted for 75.4%, the TP concentration accounted for 73.1%, the CODcr concentration accounted for 53.5%, and the BOD\u003csub\u003e5\u003c/sub\u003e concentration accounted for 62.4%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e3. Based on the results of the photocatalytic degradation experiments, the actual reduction effect in the field was estimated by multiplying with the coefficients of 1, 0.5, and 0.25. The calculation results showed that the reductions of the upstream NH\u003csub\u003e3\u003c/sub\u003e-N input into the river at 24 h, 12 h, and 6 h were 243.36 kg/a, 127.04 kg/a, and 47.70 kg/a, respectively; those of TN were 633.67 kg/a, 330.71 kg/a, and 124.17 kg/a; those of of TP were 90.64 kg/a, 47.13 kg/a, and 17.68 kg/a; those of CODcr were 11034.20 kg/a, 5762.43 kg/a, and 2163.99 kg/a; and those of BOD\u003csub\u003e5\u003c/sub\u003e were 4917.28 kg/a, 2566.52 kg/a, and 963.64 kg/a. The concentration ratios of NH\u003csub\u003e3\u003c/sub\u003e-N at 24 h, 12 h, and 6 h were 31%, 9.91%, and 2.18%, respectively; those of TN were 24.59%, 21.95%, and 10.58%; those of TP were 26.64%, 29.39%, and 25.15%; those of CODcr were 46.46%, 13.39%, and 0.99%; and those of BOD\u003csub\u003e5\u003c/sub\u003e were 537.67%, 31.04%, and 23.09%. Overall, if this nano titanium dioxide material is promoted to outdoor waterbodies, it will be effective in improving the water quality of the rivers.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThere are also limitations in this study. When running the model simulation, due to the limited experiments and information, the influencing factors considered in this study were not sufficiently comprehensive. For example, the spatial and temporal differences of the rainfall runoff, the role of the substrate on the pollutants, and pollutants during complex migration and transformation processes may have been the cause of the water quality simulation error.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eAcknowledgments\u003c/h1\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (No. 52269010), the Science and Technology Planning Project of Guangxi, China (No. AB AB22035075) and the Science and Technology Plan Project of Guilin (No. 20220114-2).\u003c/p\u003e\n\u003ch1\u003eAuthor contrubutions\u003c/h1\u003e\n\u003cp\u003eYiyang Li contributed to the investigation, data collection and analysis, research idea, methodology and model design as well as the writing of the first draft of the manuscript. Zitao Li contributed to the data collection and analysis, research idea, methodology and model design and the writing of the paper. Junfeng Dai contributed to the research idea, methodology, model design and paper review. Saeed Rad contributed to the material preparation, data collection and analysis. Xiaolan Xie contributed to the data collection and analysis. Shanshan Qi contributed to the data collection and analysis. Baoli Xu contributed to the data collection and analysis.\u003c/p\u003e\n\u003ch1\u003eCompeting Interests\u003c/h1\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch1\u003eAdditional information\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003eThis paper does not contain any studies with human participants or animals performed by any of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003eApplicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003eConsent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eE.D. Ongley, X. L. Z., T. Yu. Current status of agricultural and rural non-point source pollution assessment in China. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e,\u003cstrong\u003e158\u003c/strong\u003e, 1159\u0026ndash;1168, doi:10.1016/j.envpol.2009.10.047 (2010)\u003c/li\u003e\n\u003cli\u003eShen, Z. Y., Zhong, Y.C., Huang, Q., Chen, L. Identifying non-point source priority management areas in watersheds with multiple functional zones. \u003cem\u003eWater Research\u003c/em\u003e,\u003cstrong\u003e68\u003c/strong\u003e, 563\u0026ndash;571 (2015).https://doi.org/10.1016/j.watres.2014.10.034.\u003c/li\u003e\n\u003cli\u003eZhang, T., Ni, J.P., Xie, D.T. Assessment of the relationship between rural non-point source pollution and economic development in the Three Gorges Reservoir area. \u003cem\u003eEnvironment Science and Pollution Research\u003c/em\u003e,\u003cstrong\u003e23\u003c/strong\u003e, 8125\u0026ndash;8132 (2016).https://doi.org/10.1007/s11356-016-6344-7.\u003c/li\u003e\n\u003cli\u003eZitao Li, J. D., Zhangnan Li, Yan Liu, Jingxuan Xu, Zhenyu Zhang, Baoli Xu. Simulation Study on the Effect of Non-Point Source Pollution on Water Quality in the Upper Reaches of the Lijiang River. \u003cem\u003eWater\u003c/em\u003e,\u003cstrong\u003e14\u003c/strong\u003e (2022).https://doi.org/10.3390/w14243995\u003c/li\u003e\n\u003cli\u003ePan L, D. J., Wu Z, Liangliang Huang, Zupeng Wan, Junlei Han, Zhangnan Li. Spatial and temporal variations of nitrogen and phosphorus in surface water and groundwater of Mudong river watershed in Huixian Karst wetland, southwest China. \u003cem\u003eSustainability\u003c/em\u003e,\u003cstrong\u003e13\u003c/strong\u003e (2021).https://doi.org/10.3390/su131910740\u003c/li\u003e\n\u003cli\u003eCHANG Xiaomin, W. S., GUAN Xiaoyan, HUANG Jiasheng, JIA Haifeng, YOU Lijun. Spatiotemporal Distribution of Nonpoint Source Pollution from Agriculture in the Huangshui River Basin. \u003cem\u003eJournal of Irrigation and Drainage\u003c/em\u003e,\u003cstrong\u003e42\u003c/strong\u003e, 81-88, doi:10.13522/j.cnki.ggps.2022468 (2023)\u003c/li\u003e\n\u003cli\u003eBoardman, E., Danesh-Yazdi, M., Foufoula-Georgiou, E.,Dolph, C. L., Finlay, J. C. Fertilizer, landscape features and climate regulate phosphorus retention and river export in diverse Midwestern watersheds. \u003cem\u003eBiogeochemistry\u003c/em\u003e,\u003cstrong\u003e146\u003c/strong\u003e, 293\u0026ndash;309 (2019).https://doi.org/10.1007/s10533-019-00623-z\u003c/li\u003e\n\u003cli\u003eZhang, S., Hou, X., Wu, C., Zhang, C. Impacts of climate and planting structure changes on watershed runoff and nitrogen and phosphorus loss.\u003cem\u003e Science of the Total Environment\u003c/em\u003e,\u003cstrong\u003e706\u003c/strong\u003e, 134489 (2020).https://doi.org/10.1016/j.scitotenv.2019.134489\u003c/li\u003e\n\u003cli\u003eCarver, R. E., Nelson, N. O., Roozeboom, K. L., Kluitenberg,G. J., Tomlinson, P. J., Kang, Q., David S. Abel. Cover crop and phosphorus fertilizer management impacts on surface water quality from a no-till corn-soybean rotation. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e,\u003cstrong\u003e301\u003c/strong\u003e, 113818 (2022).https://doi.org/10.1016/j.jenvman.2021.113818\u003c/li\u003e\n\u003cli\u003eJianqiang Wang, J. H., Yuling Ma, Ming Ma. Research progress of modified titanium dioxide photocatalytic technology in water pollution control. \u003cem\u003eModern Salt and Chemical Industry\u003c/em\u003e,\u003cstrong\u003e48\u003c/strong\u003e, 9-11, doi:10.19465/j.cnki.2095-9710.2021.06.005 (2021)\u003c/li\u003e\n\u003cli\u003eFleming P.M., S. K., Collick A.S. \u0026amp; Easton Z.M. Targeting for nonpoint source pollution reduction: A synthesis of lessons learned, remaining challenges, and emerging opportunities. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e,\u003cstrong\u003e308\u003c/strong\u003e, 114649-114649 (2022).https://doi.org/10.1016/j.jenvman.2022.114649\u003c/li\u003e\n\u003cli\u003eWang H, H. P., Shen C, Wu Z Effect of irrigation amount and fertilization on agriculture non-point source pollution in the paddy field. \u003cem\u003eEnvironmental Science Pollution Research\u003c/em\u003e,\u003cstrong\u003e26\u003c/strong\u003e, 10363\u0026ndash;10373 (2019).https://doi.org/10.1007/s11356-019-04375-z\u003c/li\u003e\n\u003cli\u003eZhang T, Y. Y., Ni J, Xie D. Adoption behavior of cleaner production techniques to control agricultural non-point source pollution: a case study in the three Gorges Reservoir Area. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e,\u003cstrong\u003e223\u003c/strong\u003e, 897-906 (2019).https://doi.org/10.1016/j.jclepro.2019.03.194\u003c/li\u003e\n\u003cli\u003eTahmasebi Nasab, M., Grimm, K., Bazrkar, M. H., Zeng, L.,Shabani, A., Zhang, X., \u0026amp; Chu, X. SWAT modeling of non-point source pollution in depression-dom-inated basins under varying hydroclimatic conditions. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e,\u003cstrong\u003e15\u003c/strong\u003e, 2492 (2018).https://doi.org/10.3390/ijerph15112492\u003c/li\u003e\n\u003cli\u003eJabbar FK, G. K. Statistical assessment of nonpoint source pollution in agricultural watersheds in the Lower Grand River watershed, MO, USA. \u003cem\u003eEnvironment Science and Pollution Research\u003c/em\u003e,\u003cstrong\u003e26\u003c/strong\u003e, 1487\u0026ndash;1506 (2019).https://doi.org/10.1007/s11356-018-3682-7\u003c/li\u003e\n\u003cli\u003eLankoski, J. a. O., M. . Innovations in nonpoint source pollution policy - European perspectives. \u003cem\u003eChoice\u003c/em\u003e,\u003cstrong\u003e28\u003c/strong\u003e, 1-5 (2013).https://www.jstor.org/stable/choices.28.3.08\u003c/li\u003e\n\u003cli\u003eMei Luo, X. L., Nebiyou Legesse, Ying Liu, Sheng Wu, Fengxiang X. Han, Youhua Ma. Evaluation of Agricultural Non-point Source Pollution: a Review. \u003cem\u003eWater Air Soil Pollution\u003c/em\u003e,\u003cstrong\u003e234,657\u003c/strong\u003e (2023).https://doi.org/10.1007/s11270-023-06686-x\u003c/li\u003e\n\u003cli\u003eLijun, G. Pollution source investigation and pollution load estimation in Kashgar River Basin. \u003cem\u003eWater Resources Development Research\u003c/em\u003e, 1-6 (2023).https://link.cnki.net/urlid/11.4655.TV.20231201.1452.006\u003c/li\u003e\n\u003cli\u003eZhou Rui, W. B., Lin Haodong, Yang Jizhou, Wei Daming. Application of 1-Dimensional Water Quality Model Combined with the Improved Export Coefficient Method in Polluting Load Estimation of Non-Point Source Pollution. \u003cem\u003eJournal of Jilin University(Earth Science Edition)\u003c/em\u003e,\u003cstrong\u003e51\u003c/strong\u003e, 505-515 (2021).http://dx.doi.org/10.13278/j.cnki.jjuese.20190208\u003c/li\u003e\n\u003cli\u003eCHEN Wenjun, D. W., HE Bin, CHEN Wen. Water quality modeling for typical rural watershed based on the WASP model in Mountain Mao Region，upper Taihu Basin. \u003cem\u003eLake Science\u003c/em\u003e,\u003cstrong\u003e29\u003c/strong\u003e, 836-847, doi:10. 18307 /2017. 0407 (2017)\u003c/li\u003e\n\u003cli\u003eRekha, P., Yadav, S. \u0026amp; Singh, L. A review on cobalt phosphate-based materials as emerging catalysts for water splitting. \u003cem\u003eCeramics International\u003c/em\u003e,\u003cstrong\u003e47\u003c/strong\u003e, 16385-16401 (2021).https://doi.org/10.1016/j.ceramint.2021.02.215\u003c/li\u003e\n\u003cli\u003eHuang, S.\u003cem\u003e et al.\u003c/em\u003e Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning. \u003cem\u003eWater Research\u003c/em\u003e,\u003cstrong\u003e263,112191\u003c/strong\u003e (2024).https://doi.org/10.1016/j.watres.2024.122191\u003c/li\u003e\n\u003cli\u003eWool, T., Jr., R. B. A., Martin, J. L. \u0026amp; Comer, A. WASP 8/ The Next Generation in the 50-year Evolution of USEPA\u0026rsquo;s Water Quality Model. \u003cem\u003eWater\u003c/em\u003e,\u003cstrong\u003e12\u003c/strong\u003e, doi:10.3390/w12051398 (2020)\u003c/li\u003e\n\u003cli\u003eLin YP, M. H., Huang KT, Petway J R, Lin CM, Chou C F, Liao SW. Real-time identification of irrigation water pollution sources and pathways with a wireless sensor network and blockchain framework. \u003cem\u003eSensors\u003c/em\u003e,\u003cstrong\u003e20\u003c/strong\u003e (2020).https://doi.org/10.3390/s20133634\u003c/li\u003e\n\u003cli\u003eMAMANI LARICO A J \u0026amp; A, Z. M. S. Application of WASP model for assessment of water quality for eutrophication control for a reservoir in the Peruvian Andes. \u003cem\u003eLakes \u0026amp; Reservoirs: Science, Policy and Management for Sustainable Use\u003c/em\u003e,\u003cstrong\u003e24\u003c/strong\u003e, 37-47 (2019).https://doi.org/10.1111/lre.12256\u003c/li\u003e\n\u003cli\u003eMateusz Żelazny, M. B., Bogdan Ozga-Zielinski, Tomasz Walczykiewi. Applicability of the WASP Model in an Assessment of the Impact of Anthropogenic Pollution on Water Quality\u0026mdash;Dunajec River Case Study. \u003cem\u003eSustainbility\u003c/em\u003e,\u003cstrong\u003e15\u003c/strong\u003e, 2444 (2023).https://doi.org/10.3390/su15032444\u003c/li\u003e\n\u003cli\u003eNicolas Obin, H. T., Fei Ge, Xingwang Liu. Research on Water Quality Simulation and Water Environmental Capacity in Lushui River Based on WASP Model. \u003cem\u003eWater\u003c/em\u003e,\u003cstrong\u003e13\u003c/strong\u003e, 2819 (2021). https://doi.org/10.3390/w13202819\u003c/li\u003e\n\u003cli\u003eMBUH M J, MBIH R \u0026amp; C., W. Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed. \u003cem\u003ePhysical Geography\u003c/em\u003e,\u003cstrong\u003e40\u003c/strong\u003e, 127-148 (2019).https://doi.org/10.1080/02723646.2018.1507339\u003c/li\u003e\n\u003cli\u003ePetronella, F.\u003cem\u003e et al.\u003c/em\u003e Nanocomposite materials for photocatalytic degradation of pollutants. \u003cem\u003eCatalysis Today\u003c/em\u003e,\u003cstrong\u003e281\u003c/strong\u003e, 85-100 (2017).https://doi.org/10.1016/j.cattod.2016.05.048\u003c/li\u003e\n\u003cli\u003eMEHDILO, A. \u0026amp; IRANNAJAD, M. Surface Modification of Ilmenite and Its Accompanied Gangue Minerals by Thermal Pretreatment: Application in Flotation Process. \u003cem\u003eTransactions of Nonferrous Metals Society of China\u003c/em\u003e,\u003cstrong\u003e31\u003c/strong\u003e, 2836-2851 (2021).https://doi.org/10.1016/S1003-6326(21)65697-2\u003c/li\u003e\n\u003cli\u003eJ Kogovsek, M. P. Solute transport processes in a karst vadose zone characterized by long-term tracer tests (the cave system of Postojnska Jama, Slovenia). \u003cem\u003eJournal of Hydrology\u003c/em\u003e,\u003cstrong\u003e519\u003c/strong\u003e, 1205-1213 (2014).https://doi.org/10.1016/j.jhydrol.2014.08.047\u003c/li\u003e\n\u003cli\u003eSoares, O. S. G. P., Pereira, M. F. R., \u0026Oacute;rf\u0026atilde;o, J. J. M., Faria, J. L. \u0026amp; Silva, C. G. Photocatalytic nitrate reduction over Pd\u0026ndash;Cu/TiO2. \u003cem\u003eChemical Engineering Journal\u003c/em\u003e,\u003cstrong\u003e251\u003c/strong\u003e, 123-130 (2014).https://doi.org/10.1016/j.cej.2014.04.030\u003c/li\u003e\n\u003cli\u003eDrosos, M., Ren, M. \u0026amp; Frimmel, F. H. The efect of NOM to TiO2:interactions and photocatalytic behavior. \u003cem\u003eApplied Catalysis B: Environmental\u003c/em\u003e,\u003cstrong\u003e165\u003c/strong\u003e, 328-334 (2015).https://doi.org/10.1016/j.apcatb.2014.10.017\u003c/li\u003e\n\u003cli\u003eMouele, E. S. M.\u003cem\u003e et al.\u003c/em\u003e A Critical Review on Ozone and Co-Species, Generation and Reaction Mechanisms in Plasma Induced by Dielectric Barrier Discharge Technologies for Wastewater Remediation. \u003cem\u003eJournal of Environmental Chemical Engineering\u003c/em\u003e,\u003cstrong\u003e9\u003c/strong\u003e (2021).https://doi.org/10.1016/j.jece.2021.105758\u003c/li\u003e\n\u003cli\u003eChen, W., Wang, Y., Sun, Y., Fang, G. \u0026amp; Li, Y. Release of Soluble Ions and Heavy Metal During Fly Ash Washing by Deionized Water and Sodium Carbonate Solution. \u003cem\u003eChemosphere\u003c/em\u003e,\u003cstrong\u003e307\u003c/strong\u003e (2022).https://doi.org/10.1016/j.chemosphere.2022.135860\u003c/li\u003e\n\u003cli\u003eGUERRERO-FLORES, \u0026Aacute;. D., ELIZONDO-\u0026Aacute;LVAREZ, M. A., FLORES-\u0026Aacute;LVAREZ, J. M. \u0026amp; URIBE-SALAS, A. Comparative Study on Simultaneous Removal of Calcium and Sulfate Ions from Flotation Recycling Water by Aluminum Hydroxide. \u003cem\u003eTransactions of Nonferrous Metals Society of China\u003c/em\u003e,\u003cstrong\u003e32\u003c/strong\u003e, 2379-2390 (2022).https://doi.org/10.1016/S1003-6326(22)65954-5\u003c/li\u003e\n\u003cli\u003eYadav, A., Sharma, P., Panda, A. B. \u0026amp; Shahi, V. K. Photocatalytic TiO2 incorporated PVDF-co-HFP UV-cleaning mixed matrix membranes for effective removal of dyes from synthetic wastewater system via membrane distillation. \u003cem\u003eJournal of Environmental Chemical Engineering\u003c/em\u003e,\u003cstrong\u003e9\u003c/strong\u003e (2021).https://doi.org/10.1016/j.jece.2021.105904\u003c/li\u003e\n\u003cli\u003eSahar A. Mousa, H. A., S. A. Khairy. Low-cost photocatalytic membrane modified with green heterojunction TiO2/ZnO nanoparticles prepared from waste. \u003cem\u003eScientific Report\u003c/em\u003e,\u003cstrong\u003e13\u003c/strong\u003e (2023).https://doi.org/10.1038/s41598-023-49516-0\u003c/li\u003e\n\u003cli\u003eLindi, A. M., Falah, S., Sadeghnezhad, M. \u0026amp; Ghorbani, M. Optimization of fenugreek seed mucilage extraction for the synthesis of a novel bio-nano composite for efficient removal of cadmium ions from aqueous environments. \u003cem\u003eInternational Journal of Biological Macromolecules\u003c/em\u003e,\u003cstrong\u003e261\u003c/strong\u003e (2024).https://doi.org/10.1016/j.ijbiomac.2024.129882\u003c/li\u003e\n\u003cli\u003eNisreen S. Ali, K. R. K., Amer N. Ahmed, Talib M. Albayati Performance of a solar photocatalysis reactor as pretreatment for wastewater via UV, UV/TiO2, and UV/H2O2 to control membrane fouling. \u003cem\u003eScientific Report\u003c/em\u003e,\u003cstrong\u003e12\u003c/strong\u003e (2022).https://doi.org/10.1038/s41598-022-20984-0\u003c/li\u003e\n\u003cli\u003eAfshin Shabani\u003cem\u003e et al.\u003c/em\u003e A coupled hydrodynamic (HEC-RAS 2D) and water quality model (WASP) for simulating flood-induced soil, sediment, and contaminant transport. \u003cem\u003eJournal of Flood Risk Management\u003c/em\u003e,\u003cstrong\u003e14\u003c/strong\u003e, e12747 (2021).https://doi.org/10.1111/jfr3.12747\u003c/li\u003e\n\u003cli\u003eJ., Z., F., C., J., Z., H., C. \u0026amp; M, A. Fe3+-TiO2photocatalysts prepared by combining sol-gel method with hydrothermal treatment and their characterization. J. Photochem. \u003cem\u003ePhotobiol. A Chem\u003c/em\u003e,\u003cstrong\u003e180\u003c/strong\u003e, 196\u0026ndash;204, doi:10.1016/j.jphotochem.2005.10.017. (2006)\u003c/li\u003e\n\u003cli\u003eCheng Chang, S. R., Lei Gan, Zitao Li, Junfeng Dai, Asfandyar Shahab. Review of the sol\u0026ndash;gel method in preparing nano TiO2 for advanced oxidation process. \u003cem\u003eNanotechnology Reviews\u003c/em\u003e,\u003cstrong\u003e12\u003c/strong\u003e (2023).https://doi.org/10.1515/ntrev-2023-0150\u003c/li\u003e\n\u003cli\u003eDan-ni, Z.\u003cem\u003e et al.\u003c/em\u003e Pollution and Irrigation Applicability of Surface Water from Wet，Normal, and Dry Periods in the Huixian Karst Wetland，China. \u003cem\u003eEnvironmental Science\u003c/em\u003e,\u003cstrong\u003e42\u003c/strong\u003e, 2240-2250, doi:10.13227/j.hjkx.202010087 (2021)\u003c/li\u003e\n\u003cli\u003eHan J, D. J., Xie X. Spatial and temporal distribution characteristics of soil nutrients in small watershed of Huixian Wetland. \u003cem\u003eYangtze River\u003c/em\u003e,\u003cstrong\u003e70-77\u003c/strong\u003e, doi:10.16232/j.cnki.1001-4179.2022.07.011 (2022)\u003c/li\u003e\n\u003cli\u003eLi, J.\u003cem\u003e et al.\u003c/em\u003e Application of the hydrochemistry, stable isotopes and MixSIAR model to identify nitrate sources and transformations in surface water and groundwater of an intensive agricultural karst wetland in Guilin, China. \u003cem\u003eEcotoxicology and Environmental Safety\u003c/em\u003e,\u003cstrong\u003e231\u003c/strong\u003e (2022).https://doi.org/10.1016/j.ecoenv.2022.113205\u003c/li\u003e\n\u003cli\u003eZupeng Wan, J. D., Linyan Pan, Junlei Han, Zhangnan Li, Kun Dong. Simulation study on nitrogen pollution in shallow groundwater in small agricultural watersheds in the Huixian wetland. \u003cem\u003eWater\u003c/em\u003e,\u003cstrong\u003e14\u003c/strong\u003e (2022).https://doi.org/10.3390/w14223657\u003c/li\u003e\n\u003cli\u003eZhao Yi, Z. S., Shen Haoyong, Lan Funing, Xie Hao, Li Jun. Source and control factors of main ions and dissolved inorganic carbon in the lakes of Huixian Karst Wetland. \u003cem\u003eGeology in China\u003c/em\u003e,\u003cstrong\u003e51\u003c/strong\u003e, 1701\u0026minus;1713, doi:10.12029/gc20221025002 (2024)\u003c/li\u003e\n\u003cli\u003ePeng, C., Pan, X.-D., Jiao, Y.-J., Ren, K., \u0026amp; Zeng, J. Identification of Nitrate Pollution Sources Through Various Isotopic Methods: A Case Study of the Huixian Wetland. \u003cem\u003eENVIRONMENTAL SCIENCE\u003c/em\u003e,\u003cstrong\u003e39\u003c/strong\u003e, 0250-3301, doi:10.13227/j.hjkx.201803212 (2018)\u003c/li\u003e\n\u003cli\u003eXiao, H., Shahab, A., Li, J., Xi, B., Sun, X., He, H., Yu, G. Distribution, ecological risk assessment and source identification of heavy metals in surface sediments of Huixian karst wetland, China. \u003cem\u003eEcotoxicology and Environmental Safety\u003c/em\u003e,\u003cstrong\u003e185\u003c/strong\u003e, 1090-2414 (2019).https://doi.org/10.1016/j.ecoenv.2019.109700\u003c/li\u003e\n\u003cli\u003eSun, X., Lu, M., \u0026amp; Ren, Z. Dynamic changes and sensitivity of water Area of Huixiankarst wetland. \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e,\u003cstrong\u003e512\u003c/strong\u003e (2020).https://doi.org/10.1088/1755-1315/512/1/012033\u003c/li\u003e\n\u003cli\u003eLiu, D.\u003cem\u003e et al.\u003c/em\u003e Research on microbial structures, functions and metabolic pathways in an advanced denitrification system coupled with aerobic methane oxidation based on metagenomics. \u003cem\u003eBioresource Technology\u003c/em\u003e,\u003cstrong\u003e332\u003c/strong\u003e (2021).https://doi.org/10.1016/j.biortech.2021.125047\u003c/li\u003e\n\u003cli\u003eYang, H.\u003cem\u003e et al.\u003c/em\u003e Removal of phosphate by aluminum-modified clay in a heavily polluted lake, Southwest China: Effectiveness and ecological risks. \u003cem\u003eScience of the Total Environment\u003c/em\u003e,\u003cstrong\u003e705\u003c/strong\u003e (2020).https://doi.org/10.1016/j.scitotenv.2019.135850\u003c/li\u003e\n\u003cli\u003eYu, E., Li, Y., Li, F., He, C. \u0026amp; Feng, X. Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector. \u003cem\u003eEnvironmental Research\u003c/em\u003e,\u003cstrong\u003e263\u003c/strong\u003e (2024).https://doi.org/10.1016/j.envres.2024.120168\u003c/li\u003e\n\u003cli\u003eMa, J., Rene, E. R., Chen, Z. \u0026amp; Ma, W. Fate of PAHs in treated wastewater reused as irrigation water: Environmental risks in water-soil-ryegrass multimedia system. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e,\u003cstrong\u003e424\u003c/strong\u003e (2022).https://doi.org/10.1016/j.jhazmat.2021.127500\u003c/li\u003e\n\u003cli\u003eDong Hongmin, Z. Z., Huang Hongkun, Yongxing Chen, Bin Shang, Xiuping Tao, Zhongkai Zhou. Pollutant generation coefficient and discharge coefficient in animal production. \u003cem\u003eTransactions of the CSAE\u003c/em\u003e,\u003cstrong\u003e27\u003c/strong\u003e, 303-308, doi:10.3969/j.issn.1002-6819.2011.01.049 (2011)\u003c/li\u003e\n\u003cli\u003eZhu, Z., Huang, H., Tian, G. \u0026amp; Fang, P. Review of Empirical Models in Agricultural Non-Point Source Pollution Simulation. \u003cem\u003eGeographical Science Research\u003c/em\u003e,\u003cstrong\u003e11\u003c/strong\u003e, 210-219, doi:10.12677/gser.2022.112022 (2022).https://doi.org/10.12677/gser.2022.112022\u003c/li\u003e\n\u003cli\u003eGOU Ting, P. D.-f., LIANG Rong-chang, SHE Lei, YANG Jun, MA Qian-li, ZHAO Xue-min, ZHAO Rui, YAO Ling-ai. Analysis of nitrogen and phosphorus emissions from agricultural non-point sources based on improved output coefficient method. \u003cem\u003eCHINA ENVIRONMENTAL SCIENCECE\u003c/em\u003e,\u003cstrong\u003e43\u003c/strong\u003e, 6539-6550, doi:10.19674/j.cnki.issn1000-6923.2023.0212 (2023)\u003c/li\u003e\n\u003cli\u003eXuda, Z. M. W. L. M. C. H. Water quality change and pollution source accounting of Licun River under long-term governance. \u003cem\u003eScientific Reports\u003c/em\u003e,\u003cstrong\u003e12\u003c/strong\u003e, 2779-2770, doi:10.1038/S41598-022-06803-6 (2022)\u003c/li\u003e\n\u003cli\u003eNiraula, R., Kalin, L., Srivastava, P. \u0026amp; Anderson, C. J. Identifying critical source areas of nonpoint source polution with SWAT and GWLF. \u003cem\u003eEcological Modelling\u003c/em\u003e,\u003cstrong\u003e268\u003c/strong\u003e, 123-133 (2013).https://doi.org/10.1016/j.ecolmodel.2013.08.007\u003c/li\u003e\n\u003cli\u003eTan Y, X. J., Xu L, Zhao WY. Estimation and Application of Non-point Source Pollutant Inflow Coefficient in Typical Area of Minjiang River ( Inner Ｒiver Section). \u003cem\u003eSichuan Environment\u003c/em\u003e,\u003cstrong\u003e40\u003c/strong\u003e, 138-144, doi:10.14034/j.cnki.schj.2021.04.019 (2021)\u003c/li\u003e\n\u003cli\u003eJing-lin, Z., Yan-jun, Z., Su-miao, W., Fei, Q. \u0026amp; Lan, L. An improved export coefficient model with a spatially modified river load ratio:A case study of the Yangtze River Basin. \u003cem\u003eChina Rural Water and Hydropower\u003c/em\u003e, 78-85+95, doi:10.12396/znsd.231029 (2024)\u003c/li\u003e\n\u003cli\u003eVillota-L\u0026oacute;pez, C., Rodr\u0026iacute;guez-Cuevas, C., Torres-Bejarano, F., Cisneros-P\u0026eacute;rez, R., Cisneros-Almaz\u0026aacute;n, R., \u0026amp; Couder-Casta\u0026ntilde;eda, C. Applying EFDC Explorer model in the Gallinas River, Mexico to estimate its assimilation capacity for water quality protection. \u003cem\u003eScientific Reports\u003c/em\u003e,\u003cstrong\u003e11\u003c/strong\u003e (2021).https://doi.org/10.1038/s41598-021-92453-z\u003c/li\u003e\n\u003cli\u003eMbongowo J. Mbuh, R. M., Comfort Wendi. Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed. \u003cem\u003ePhysical Geography\u003c/em\u003e,\u003cstrong\u003e40\u003c/strong\u003e, 127-148 (2016).https://doi.org/10.1080/02723646.2018.1507339\u003c/li\u003e\n\u003cli\u003eXIAO Huaqing, W. K., QIU Junjie, XIAO Yongjun, HE Wuguang, ZENG Qingdong, WANG Boyun, XIONG Liangbin. The Preparation and Characterization of TiO2 Film Prepared by a Sol-gel Meth- od. \u003cem\u003eJournalofChongqingUniversityofTechnology(NaturalScience)\u003c/em\u003e,\u003cstrong\u003e32\u003c/strong\u003e, 93-99, doi:10.3969/j.isn.1674-8425(z). (2018)\u003c/li\u003e\n\u003cli\u003eChang, C.\u003cem\u003e et al.\u003c/em\u003e Preparation and application of nano TiO2 filmcoated recycled low-iron crushed glass in a novel packed-bed photocatalytic reactor for efficient removal of biodegradable contaminants. \u003cem\u003eEnvironmental Technology \u0026amp; Innovation\u003c/em\u003e,\u003cstrong\u003e33\u003c/strong\u003e (2024).https://doi.org/10.1016/j.eti.2024.103541\u003c/li\u003e\n\u003cli\u003ePichat, P. Fundamentals of TiO2 Photocatalysis. Consequences for Some Environmental Applications. \u003cem\u003eHeterogeneous photocatalysis: from fundamentals to green applications\u003c/em\u003e, 321-359 (2016).https://doi.org/10.1007/978-3-662-48719-8_10\u003c/li\u003e\n\u003cli\u003eWang Ni, L. J., Xie Jiancang. Dynamic research of Urban river water environment capac. \u003cem\u003eEnvironmental Engineering\u003c/em\u003e,\u003cstrong\u003e32\u003c/strong\u003e, 50-54, doi:10.13205/j.hjgc.201410013 (2014)\u003c/li\u003e\n\u003cli\u003eZhu, Y., Drake, S., L\u0026uuml;, H. \u0026amp; Xia, J. Analysis of Temporal and Spatial Differences in Eco-environmental Carrying Capacity Related to Water in the Haihe River Basins, China. \u003cem\u003eWater Resources Management\u003c/em\u003e,\u003cstrong\u003e24\u003c/strong\u003e, 1089\u0026ndash;1105 (2010).https://doi.org/10.1007/s11269-009-9487-1\u003c/li\u003e\n\u003cli\u003eZhuang Yiling, Z. Q., Zhang Jianyun, Li Jieyou. Application of Xinanjiang Model to Karst Regions. \u003cem\u003eJournal of Hohai University \u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e (1989)\u003c/li\u003e\n\u003cli\u003eGuang-hui, J., chun, W. J.-i., Fang, G., Hua-ju, L. \u0026amp; long, S. H.-i. Threshold value of epikarst runoff in forest karst mountain area. \u003cem\u003eAdvances in Water Science\u003c/em\u003e,\u003cstrong\u003e19\u003c/strong\u003e, 72-77, doi:10.14042/j.cnki.32.1309.208.01.03 (2008)\u003c/li\u003e\n\u003cli\u003eWenjie Zhou, Zhihua Zhu, Yulei Xie \u0026amp; Cai, Y. Impacts of rainfall spatial and temporal variabilities on runoff quality and quantity at the watershed scale. \u003cem\u003eJournal of Hydrology\u003c/em\u003e,\u003cstrong\u003e603\u003c/strong\u003e, 127057 (2021).https://doi.org/10.1016/j.jhydrol.2021.127057\u003c/li\u003e\n\u003cli\u003eLuo, G.\u003cem\u003e et al.\u003c/em\u003e Distribution，Influencing Factors and Eutrophication Evaluation of Nitrogen and Phosphorus in Different Waters of Karst Basin. \u003cem\u003eCHINA RURAL WATER AND HYDROPOWER\u003c/em\u003e, 94-99+105. (2020)\u003c/li\u003e\n\u003cli\u003eHe, R., Sun, P.-a., YU, S. \u0026amp; He, S. A High-Resolution Monitoring Research of the Carbon Flux Dynamic Behaviors of Rivers in Karst Areas in Different Periods of the Year\u0026mdash;\u0026mdash;A Case Study in the Lijiang River Basin. \u003cem\u003eJournal of Southwest University Natural Science Edition\u003c/em\u003e,\u003cstrong\u003e40\u003c/strong\u003e, 150-158, doi:10.13718/j.cnki.xdzk.2018.03.022 (2018)\u003c/li\u003e\n\u003cli\u003ePeng, T. \u0026amp; Wang, S.-j. Effects of land use, land cover and rainfall regimes on the surface runoff and soil loss on karst slopes in southwest China. \u003cem\u003eCATENA\u003c/em\u003e,\u003cstrong\u003e90\u003c/strong\u003e, 53-62 (2012).https://doi.org/10.1016/j.catena.2011.11.001\u003c/li\u003e\n\u003cli\u003eJie, Z., Qixin, W., Siliang, L. \u0026amp; Fujun, Y. Characteristics of Nitrogen Export and the Efects of Rainfal during the Rainy Season in a Karst Smal Catchment. \u003cem\u003eJournal of Soil and Water Conservation\u003c/em\u003e,\u003cstrong\u003e31\u003c/strong\u003e, doi:10.13870/j.cnki.stbcxb.2017.03.013 (2017)\u003c/li\u003e\n\u003cli\u003eFisheries, D. o., Agriculture, F. o., Harcourt, U. o. P., Choba, R. S. \u0026amp; Nigeria. The influence of agricultural runoff on river water quality. \u003cem\u003eInternational Journal of Fisheries and Aquatic Research \u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 22-24 (2024).www.fishjournals.com\u003c/li\u003e\n\u003cli\u003eMarin Skidmore, Tihitina Andarge \u0026amp; Foltz, J. The impact of extreme precipitation on nutrient runoff.\u003cstrong\u003e2\u003c/strong\u003e, 769-785 (2023).https://doi.org/10.1002/jaa2.90 \u003c/li\u003e\n\u003cli\u003eShiferaw, N., Habte, L. \u0026amp; Waleed, M. Land use dynamics and their impact on hydrology and water quality of a river catchment: a comprehensive analysis and future scenario.\u003cem\u003e Environmental Science and Pollution Research\u003c/em\u003e (2025).https://doi.org/10.1007/s11356-025-35946-y\u003c/li\u003e\n\u003cli\u003eChen, D.\u003cem\u003e et al.\u003c/em\u003e Photocatalytic degradation of organic pollutants using TiO2-based photocatalysts: A review. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e,\u003cstrong\u003e268\u003c/strong\u003e, doi:https://doi.org/10.1016/j.jclepro.2020.121725 (2020)\u003c/li\u003e\n\u003cli\u003eWenying Liu\u003cem\u003e et al.\u003c/em\u003e Small water bodies influence river water quality in agricultural watersheds. \u003cem\u003eAgricultural Water Management\u003c/em\u003e,\u003cstrong\u003e304\u003c/strong\u003e, 109070 (2024).https://doi.org/10.1016/j.agwat.2024.109070\u003c/li\u003e\n\u003cli\u003eMonica Pinardi\u003cem\u003e et al.\u003c/em\u003e Agricultural practices regulate the seasonality of groundwater-river nitrogen exchanges. \u003cem\u003eAgricultural Water Management\u003c/em\u003e,\u003cstrong\u003e271\u003c/strong\u003e, 107904 (2022).https://doi.org/10.1016/j.agwat.2022.107904\u003c/li\u003e\n\u003cli\u003eYouen Grusso, Ingrid Wesstrom \u0026amp; Joel, A. Impact of Climate Change on Swedish Agriculture: Growing Season Rain Deficit and Irrigation Need. \u003cem\u003eAgricultural Water Management\u003c/em\u003e,\u003cstrong\u003e251\u003c/strong\u003e, 106858 (2021).https://doi.org/10.1016/j.agwat.2021.106858\u003c/li\u003e\n\u003cli\u003eZilan, L. Temporal and Spatial Distribution Characteristics of Summer Extreme Climate Events in Guangxi Region. \u003cem\u003eClimate Change Research Letters\u003c/em\u003e,\u003cstrong\u003e9\u003c/strong\u003e, 494-507 (2020).https://doi.org/10.12677/CCRL.2020.95055\u003c/li\u003e\n\u003cli\u003eHUANG Fen, YIN Weilu, HU Xiaonong \u0026amp; Jianhua, C. A quantitative analysis of tracing tests for the Maocun subterranean river basin of Guilin during rainy and dry seasons. \u003cem\u003eCARSOLOGICA SINICA\u003c/em\u003e,\u003cstrong\u003e35\u003c/strong\u003e, 648-658, doi:10.11932/karst2017y32 (2017)\u003c/li\u003e\n\u003cli\u003eXianda Bai, Yaxin Zhang \u0026amp; Wang, C. Analysis on Characteristic of Drought of Guilin. \u003cem\u003eClimate Change Research Letters\u003c/em\u003e,\u003cstrong\u003e2\u003c/strong\u003e, 20-24 (2013).http://dx.doi.org/10.12677/CCRL.2013.21004\u003c/li\u003e\n\u003cli\u003eZhi Rongzhirong \u0026amp; Zhihai, Z. Characteristics of Climate Anomalies over China in Winter 2021/2022 and Causes for Precipitation Anomalies over Southern China. \u003cem\u003eMeteorological Monthly\u003c/em\u003e,\u003cstrong\u003e48\u003c/strong\u003e, 925-935, doi: 10.7519/j.issn.1000-0526.2022.061701 (2022)\u003c/li\u003e\n\u003cli\u003eLi Xiang \u0026amp; Yongguang, W. Atmospheric Circulation Characteristics of Northern Hemisphere in Winter 2022/2023 and Its Impact on Weather and Climate of China. \u003cem\u003eMeteorological Monthly\u003c/em\u003e,\u003cstrong\u003e49\u003c/strong\u003e, 881-891, doi:10.7519/j.issn.1000-0526.2023.060801 (2023)\u003c/li\u003e\n\u003cli\u003eS. Bouguecha, B. Hamrouni \u0026amp; Dhahbi, M. Small scale desalination pilots powered by renewable energy sources: case studies. \u003cem\u003eDesalination\u003c/em\u003e,\u003cstrong\u003e183\u003c/strong\u003e, 151-165 (2005).https://doi.org/10.1016/j.desal.2005.03.032\u003c/li\u003e\n\u003cli\u003eVishnu Pareek, Siewhui Chong, Moses Tad\u0026eacute; \u0026amp; Adesina, A. A. Light intensity distribution in heterogeneous photocatalytic reactors. \u003cem\u003eChemical Engineering\u003c/em\u003e,\u003cstrong\u003e3\u003c/strong\u003e, 171-201 (2008).https://doi.org/10.1002/apj.129\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"small watershed, WASP, water quality modeling, pollution load reduction","lastPublishedDoi":"10.21203/rs.3.rs-5204210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5204210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgricultural activities are one of the important sources of water pollution in small agricultural watersheds. It is of great significance to understand the current status of water quality and non-point source pollution in small agricultural watersheds, estimate their pollution load, and explore pollutant reduction methods for sustainable water environment management and protection. In this paper, the small watershed of Mudong River in Huixian Wetland was taken as the study area, and the water quality monitoring indicators were dynamically simulated by the WASP model. Combined with the preparation of nano-titanium dioxide films and photocatalytic degradation experiments, the water quality reduction of each river section was systematically evaluated. Then, based on the simulation results, the reduction of pollution load into the river was estimated, which provided a scheme for the field reduction of pollutants in agricultural watersheds. The results showed that the WASP model was effective in simulating the water quality of the upper Mudong River in a typical karst area. The simulation inverted the reduction in pollution loads in the upper Mudong River for each indicator. Moreover, it calculated non-point source pollution reduction rates of ammonia nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N) (31%, 9.91%, 2.18%), total nitrogen (TN) (24.59%, 21.95%, 10.58%), total phosphorus (TP) (26.64%, 29.39%, 25.15%), dichromate oxidizability (CODcr) (46.46%, 13.39%, 0.99%), and biochemical oxygen demand (BOD\u003csub\u003e5\u003c/sub\u003e) (37.67%, 31.04%, 23.09%) at 24, 12, and 6 h of the reaction, respectively. In short, this method will improve river water quality if nano-titanium dioxide material is promoted for outdoor use.\u003c/p\u003e","manuscriptTitle":"Non-point source pollution transport and photocatalytic degradation effect in the Mudong River basin of Huixian Wetland based on WASP model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 12:05:42","doi":"10.21203/rs.3.rs-5204210/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"747d1364-73bf-4b74-b7d5-2f8d40bded2b","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47531248,"name":"Earth and environmental sciences/Environmental sciences"},{"id":47531249,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2025-05-02T02:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-23 12:05:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5204210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5204210","identity":"rs-5204210","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}