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Significant achievements have been made in the control of point source pollution. However, agricultural non-point source pollution (AGNPSP) has become a serious threat to ecological environment quality and is now the main source of pollution in the Yangtze River Basin. The topographical features of the upper Yangtze River region are primarily characterised by hilly and mountainous terrain, marked by steep slopes and pronounced undulations. This renders the land susceptible to soil erosion, thereby becoming a significant conduit for the entry of AGNPSP into water bodies. Consequently, there is an urgent need to identify critical sources, areas and periods of AGNPSP and to promote the effective prevention and control of such pollution. The present study adopted the Yongchuan District of Chongqing, a region characterised by hilly and mountainous terrain in the upper reaches of the Yangtze River, as a case study. The research, conducted from 2018 to 2021, sought to identify the “critical sources—areas—periods“ of AGNPSP. In order to surmount the challenge posed by the absence of fundamental data, the study constructed and integrated three models. The export coefficient model was used to calculate the pollution load, the pollutant load intensity model was used for spatial analysis, and the equal-scale pollution load equation was used to assess the contribution degree of different pollutants. Furthermore, the study developed a monthly pollutant flux model to accurately identify the critical pollution periods within the year. In conclusion, the research results have indicated the necessity of a governance strategy that is to be implemented with utmost priority. This strategy is to be based on the following hierarchy: critical sources, areas, and periods. The results of the study indicate the following: (1) The pollutants that exhibit the greatest contribution in Yongchuan District are total nitrogen (TN)and chemical oxygen demand (COD), accounting for 34% and 33%, respectively. The primary source of pollution is attributed to livestock and poultry breeding, accounting for 49.7% of the total pollution load. (2) The critical area of AGNPSP in Yongchuan District is located in the south of the district and primarily comprises Zhutuo Town, Hegeng Town and Xianlong Town. Among the critical areas identified, livestock and poultry farming accounts for 68% of the pollution load. (3) The monthly variation of pollutant fluxes demonstrates a single peak pattern, with the peak occurring in June. The data indicates that the flux of pollutants in June and July accounted for 37% of the total, thus identifying these months as critical periods for the management of AGNPSP in Yongchuan District. The critical source–area–period analysis indicates that the comprehensive management strategy for AGNPSP should focus on critical sources, areas and periods. Furthermore, it should adopt a prioritised, zoned and phased management approach. This approach has the potential to promote cost-effective and efficient prevention and control, thereby facilitating the achievement of sustainable agricultural development.
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1. Introduction
Point source pollution is defined as an environmental pollution source with fixed discharge points. Common forms of discharge include industrial wastewater pipes, urban sewage networks and chimneys, which release pollutants into water bodies or the atmosphere through centralized outlets. It was during the 1970s that the global community began to take notice of the issue of point source pollution. The United States was the first to promulgate the Clean Water Act, with the aim of controlling point source pollution. Consequently, a multitude of nations worldwide initiated the implementation of pertinent policies and measures to address point source pollution. The implementation of stringent control standards, comprehensive environmental protection systems and rational industrial layout has yielded highly favourable outcomes in the treatment of point source pollution [1]. With the effective control of point source pollution in recent years, non-point source pollution has gained prominence and received global attention [2,3]. Non-point source pollution, otherwise referred to as surface source pollution, is primarily composed of soil particles, nutrients such as nitrogen and phosphorus, atmospheric particles, and pesticides. The substance in question is able to enter water, soil or the atmosphere via a variety of mechanisms, including surface runoff and farmland drainage. The sources of this pollution are diverse, including domestic sewage, fertilizer application, and household garbage. The pollution can be classified into two types: AGNPSP and urban non-point source pollution [4]. The characteristics include the absence of a fixed discharge outlet, an extensive pollution area, scattered pollution sources, and great difficulty in tracing and controlling them. Of these, the issue of AGNPSP is of particular concern. AGNPSP has caused water pollution and ecosystem degradation around the world, with crop cultivation and livestock breeding being major contributors [5,6,7,8]. In China, AGNPSP is a major source of water pollution [9]. According to the 2020 National Pollution Source Census Bulletin [10], COD, ammonia nitrogen (NH3-N), TN, and total phosphorus (TP) emissions from agricultural pollution sources account for 49.8%, 22.4%, 46.5%, and 67.2% of total surface water pollution loads, respectively. Livestock and poultry production account for more than 50% of COD, NH3-N, and TP, while agricultural cultivation accounts for more than half of TN.
The Yangtze River Basin is a key agricultural production area in China, with a significant strategic role in regional growth. However, according to data from the Yangtze River Economic Belt’s second pollution source survey, agricultural non-point sources of COD, TN, and TP account for 45.1%, 48.0%, and 66.6% of total pollutant emissions in the Yangtze River Economic Belt, respectively. Agricultural non-point sources have emerged as a major cause of pollution in the Yangtze River Basin. At the same time, AGNPSP is highly unpredictable, has wide spread, complicated formation sources, large latency, delayed generation, and is extremely difficult to control [11]. As a result, research into the prevention of AGNPSP is vital.
AGNPSP study focuses on determining spatiotemporal distribution characteristics as well as identifying critical sources (CSs), critical areas (CAs), and critical periods (CPs). As a result, precisely identifying CSs, CAs, and CPs is vital for effectively managing AGNPSP [12]. The identification of critical source areas is also a significant approach for exploring the loss of pollutants [13]. At the same time, identifying CSs, CAs, and CPs lends scientific support to the development of prevention and control initiatives [14]. The complex spatiotemporal distribution of AGNPSP has made governance more difficult [15,16,17,18]. Some researchers have also investigated this topic, employing coupled models to assess spatiotemporal complexity [19,20]. However, identifying CSs, CAs, and CPs remains a substantial barrier for managing AGNPSP.
Models are an important tool in the research of AGNPSP [21], and they are now utilized to analyze the spatiotemporal distribution characteristics of AGNPSP. Mechanistic and empirical models are the two most popular types of models utilized both locally and globally. Typical mechanism models are the SWMM model [22,23,24], SWAT (Soil and Water Assessment Tool), HSPF (hydraulic simulation program fortran), the AnnAGNPS (Annualized Agricultural Non-Point Source) model [25,26], etc. The mechanistic model has performed well in simulating non-point source pollution processes, but it takes a large quantity of data and must be simulated over an extended time period. For example, the SWAT model is commonly used to detect non-point source locations and simulate pollutant transport [27,28]. However, breeding origin sources are rarely incorporated in overall load assessments [29], despite being a key source of AGNPSP. Typical empirical models include inventory analysis [30], the minimal cumulative resistance model, the nitrogen phosphorus index approach [31], the export coefficient model, etc. The empirical model requires far less data than the mechanistic model, making it appropriate for studying research areas where data volume is limited or difficult to collect. To rapidly identify the CSs and CAs of non-point source pollution, for instance, the export coefficient model creates pollution export coefficients based on various land use categories.
This study takes the typical hilly and mountainous area of Yongchuan District in Chongqing City, located in the Yangtze River Basin, as the research area and specifically utilizes data models to study critical sources, areas, and periods of AGNPSP. The primary objective of this study is (1) to conduct a load accounting simulation and spatio-temporal distribution characteristic analysis of major pollutants. This will be based on the output coefficient model, pollution intensity model and equivalent load equation. The identification of critical sources and areas will also be a key part of the study. (2) A flux analysis should be conducted on a monthly timescale in order to explore and analyse critical periods of AGNPSP, based on water quality pollutant data. (3) Priority governance strategies for AGNPSP should be proposed through a typical analysis of critical sources, areas and periods.
2. Materials and Methods
2.1. Study Area and Database
2.1.1. Overview of the Study Area
Yongchuan District (105°37′31″–106°05′06″ E, 28°56′16″–29°34′23″ N) is situated in the southeastern Sichuan Basin, on the north bank of the upper sections of the Yangtze River in the western portion of Chongqing. The district covers 1576 square kilometers and has an extended shape. Yongchuan District’s topography is low mountain and hilly, with an average elevation of 307 m, making it prone to soil erosion (Figure 1). The subtropical humid climate zone has four distinct seasons and an average annual temperature of 17.7 °C and 1015 mm of rainfall. Yongchuan District’s main land use type is arable land, which accounts for 49% of total land area. The region’s livestock and poultry production is mostly centered on swine and poultry, with aquaculture taking place primarily in ponds. Given the usual mountainous topography and farming methods in Yongchuan District, nitrogen and phosphorus pollutants from fertilizers, herbicides, cattle, and poultry farms pose a substantial threat to water bodies. Therefore, it is necessary to manage AGNPSP in Yongchuan District.
2.1.2. Introduction to Data Sources
The objective of this study was to estimate AGNPSP load in Yongchuan District and to identify critical periods. A variety of data sources were collated, and all of the data were collected and processed for the entire Yongchuan District and all of its towns. The data utilised in the estimation of pollutant load models were derived from relevant Yongchuan District and its subordinate town data from 2011 to 2022, encompassing the area of farmland and orchards, the number of various livestock (e.g., swine, cattle, poultry) in stock and the number of those that were sold, as well as the rural permanent population. The data presented herein were derived from the “Yongchuan Statistical Yearbook” and the statistical bulletins and agricultural and rural information released on the official website of the Yongchuan District Government of Chongqing (
2.2. Pollutant Load Estimation Model
Based on the study area’s characteristics, this study estimated and analyzed the AGNPSP load in Yongchuan District for three major pollution sources: farmland cultivation, livestock and poultry breeding, and rural life, as well as four major pollutants: TN, TP, COD, and NH3-N. The study employs the export coefficient model to determine the pollutant load of various pollution sources, as well as the pollutant intensity model and the equal standard pollutant load approach to assess the spatiotemporal distribution characteristics of pollutants in the study area.
2.2.1. The Export Coefficient Model
In the early 1970s, the U.S. and Canada established the export coefficient model, which was initially used to explore the relationship between land use, nutrient load, and lake eutrophication [32]. However, the export coefficient model initially had several flaws, such as assuming that the export coefficients of all land use types were fixed and unchanged over time, which differed substantially from the actual situation [33]. To improve the shortcomings of the initial export coefficient model, in 1996, Johnes [34] established a more comprehensive classical export coefficient model based on the initial model, which considered the influence of complex factors such as livestock, poultry, land use, and population on the export coefficient. The Johnes export coefficient model has a simple structure, requires comparatively little data, and eliminates the impact of the arduous process of non-point source pollution on the load, significantly improving the dependability of the model results [35]. As a result, it is often employed in watershed-scale studies that lack data [36]. The study employed the export coefficient model to estimate the pollution load in the study area. The model equation is as follows (Equation (1)):
(1)
where L denotes the loss of nutrients (), which is the total pollutant load in the study area; denotes the export coefficient of the pollution source ( or or ); denotes the area of land use type (), or the number of livestock and poultry type , or the population of the study area; denotes the nutrient input to the pollution source (); P denotes the total amount of pollutants from precipitation input (). In this study, due to the absence of systematic monitoring data on atmospheric dry and wet deposition in the study area and in the hilly agricultural region, the pollutant load from atmospheric precipitation input contributes relatively little compared to strong anthropogenic sources (such as fertilisers and livestock breeding). Therefore, when quantifying the AGNPSP load in this study, the general model was simplified and the precipitation input term (i.e., setting P = 0) was not considered.2.2.2. Determination of Export Coefficient
The most important component of the export coefficient model is determining the export coefficient, which is influenced by various aspects such as topography, geomorphology, land use, climate, hydrology, etc. [37,38]. The land use type is strongly related to the pollutant export coefficient; therefore, when evaluating the load, the export coefficient must be calculated using land use.
The primary sources of AGNPSP include farmland cultivation, livestock and poultry breeding, and rural life. The primary focus of this study is to identify CSs and CAs, investigate changes in the overall load of various pollution sources, and choose these three categories of pollution sources for pollutant load and spatiotemporal analysis. According to the data from China’s second pollution census, the pollutants studied include TN, TP, COD, and NH3-N. In accordance with the requirements of agricultural pollution sources and domestic pollution sources Emission coefficient in the Manual of Accounting Methods and Coefficient of Emission Source Statistical Investigation issued by the Ministry of Ecology and Environment of China in 2021, the export coefficient of farmland pollution sources includes cultivated land and garden land. The export coefficient of livestock and poultry pollution sources is divided into four categories: large livestock, swine, sheep and poultry. It is important to note that the present study focuses on the primary sources of AGNPSP on land, including farmland cultivation, livestock and poultry breeding, and rural life. Aquaculture, defined as the practice of fish farming in ponds, is a prevalent agricultural production activity within the Yangtze River Basin. However, due to the absence of systematic and continuous statistics regarding the area of aquaculture at the township level, along with the lack of data on feed input and drainage volumes, this study was unable to incorporate aquaculture into the quantitative load estimation. The export coefficient of rural living sources is domestic sewage.
The prevailing methodologies employed for the determination of output coefficients encompass on-site monitoring and the consultation of extant literature values. The present study employs a comprehensive determination method, the specific process of which is outlined as follows: (1) Collection and screening of literature values: A comprehensive search strategy was employed to collate research findings from domestic regions exhibiting comparable hydrological, climatic, topographical and agricultural characteristics to Yongchuan District [33,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54]. The pollutant output coefficients of various pollution sources (such as different land use types and livestock) were extracted from these studies. (2) Localization correction and supplementation: The literature values were then compared and analysed with the specific topographic, geomorphic, hydrological, and meteorological characteristics of Yongchuan District. It was noted that studies conducted in hilly and mountainous terrain have demonstrated significant disparities in hydrological processes and pollutant migration mechanisms when compared to those observed in plain areas. For pollution sources with sufficient data (such as the main land use types), the literature values were directly used for statistics; for sources with insufficient data or significant variations, coefficients recommended by technical guidelines, such as the “National Pollution Source Census”, were supplemented and verified. (3) Statistical Determination and Uncertainty Representation: The final output coefficient was determined as the arithmetic mean of the available values selected through steps 1 and 2. The export coefficients of different pollutants are shown in Table A1, Table A2, Table A3 and Table A4, and the export coefficients determined by this study are shown in Table 1.
2.2.3. Supplement to Spatiotemporal Simulation Model
The analysis of the CSs and CAs cannot be supported by the pollutant load assessment based on the export coefficient model. This study introduces the pollution load intensity and equal scale pollution load equation for further simulation in order to examine the spatiotemporal variation characteristics of pollutants and determine the CSs and CAs.
The extent of pollution caused by agricultural non-point sources to the ecological environment is determined not only by the amount of pollutants emitted, but also by the land area harboring pollutants. The pollutant load intensity model can represent the amount of pollutants discharged per unit area, and the model formula is as follows (Equation (2)):
(2)
where denotes the pollutant load intensity of street town (); denotes the pollutant load of street town (); denotes the administrative area of street town ().The equal-scale pollution load equation is created to reflect the contamination level of contaminants [55]. It compares the total amount of a certain pollutant emitted by different pollution sources with the pollutant’s emission standard to obtain the amount that can be compared with each other on the same scale, making the comparison between different pollutants and pollution sources more comprehensive and scientific [56]. The formula is as follows (Equation (3)):
(3)
where denotes equal scale pollution load of pollutant (m3); denotes the emission of pollutant (t/a); denotes the standard concentration value of Class III pollutants in the Environmental Quality Standard for Surface Water (GB 3838-2002) [57], COD is 20 mg/L, NH3-N is 1.0 mg/L, TP is 0.2 mg/L, and TN is 1.0 mg/L.The pollutant load accounting model and the selection of indicators in the model take into account critical pollutants of AGNPSP, and also include the sources of livestock farming, which are less considered in other models. In light of the data deficiency situation in the designated study area, a range of empirical models were selected for multi-dimensional analysis of the temporal and spatial distribution of AGNPSP in the area. This approach facilitates precise comparison of the resulting data.
2.3. Identification and Analysis of CPs
Based on monitoring data for water quality and quantity from two representative areas of Ciba and Hongjiangzhaba in Yongchuan District, the section points are shown in Figure A1. With reference to existing methodologies for calculating river pollutant flux [58,59], a monthly pollution flux model was developed to predict the section’s data from 2018 to 2021 [60]. The model formula is as follows (Equation (4)):
(4)
where denotes the monthly flux of pollutant into the river (); denotes the monthly average pollutant mass concentration of the pollutant (). denotes the runoff of the hydrological station in the month ().It is possible to determine the trend of pollutant changes, the critical periods of pollution, and the correlation between the critical periods and rainfall by calculating the annual and monthly average fluxes, based on the estimated monthly pollutant flux data and rainfall data.
Figure 2 shows the research technical roadmap.
3. Results
3.1. Analysis of the Spatio-Temporal Characteristics of AGNPSP
3.1.1. Interannual Variation of AGNPSP
A spatio-temporal analysis of the agricultural non-point source pollutant load in Yongchuan District was conducted, with the pollutant load in the district being estimated from 2011 to 2022 (Figure 3 and Figure 4). From the perspective of long-term inter-annual variations, the trends of the four pollutants were roughly the same. Prior to 2017, there was a gradual upward trend in the equivalent load quantities of various pollutants. From 2017 to 2018, there was a significant decrease. This phenomenon may be associated with the policy initiated by China in 2017, entitled “Key Work Arrangements for the Battle Against Agricultural Non-point Source Pollution Prevention”. This policy aimed to enhance the governance of significant issues related to AGNPSP, promoting the reduction of fertilisers and pesticides, as well as the treatment of livestock manure and sewage. In the latter part of 2018, the strengthened governance of AGNPSP resulted in a slow downward trend in the pollutants. Within the Yongchuan District, COD and TN were identified as the primary pollutants, with their equivalent load levels exhibiting a consistent and persistent high. An analysis of the changes in pollutant sources reveals that the years in which significant changes in the pollutant load contributed by various sources occurred primarily between 2017 and 2022. The livestock and poultry breeding source exhibited an initial decrease, followed by an increase, while the rural life source demonstrated an initial increase, subsequently followed by a decrease. The farmland cultivation source, however, did not demonstrate a clear trend.
3.1.2. Spatial Changes of AGNPSP
An analysis of the spatial changes of the equivalent load in each town and sub-district of Yongchuan District in 2021 (Figure 5 and Figure 6) reveals that the two towns with relatively high equivalent load are Hegeng Town and Zhutuo Town. The load volume and load intensity of these two towns are also relatively high. With regard to the different types of pollutants, the following contribution is made to AGNPSP in Yongchuan District: TN > COD > TP > NH3-N. The contributions of TN and COD account for 34% and 33%, respectively. From the perspective of pollutant sources, the contribution of Yongchuan District to AGNPSP is as follows: livestock and poultry breeding sources > rural life sources > farmland cultivation sources. The contribution of livestock and poultry breeding sources accounts for 49.7%.
A spatial analysis of the area data for each town and sub-district in Yongchuan District was conducted to ascertain the distribution of pollutant load intensity within the study area. The results indicated that the street and sub-districts with high pollutant load intensity were predominantly located in the central and southern areas of Yongchuan District. From the standpoint of pollution sources in farmlands (Figure 7a), the street towns with high load intensity are principally located in the central and southern parts of Yongchuan District. Of these, Baofeng Town has the highest TP and NH3-N load intensity, Laisu Town has the highest TN load intensity, and this is close to the intensity of Baofeng Town. It is evident that the distribution of street towns exhibiting a low pollutant load intensity is predominantly concentrated in the northern region. This is related to the location of the street towns at the upstream and downstream of the river basin and the nature of the street towns. From the perspective of livestock and poultry breeding sources (Figure 7b), the street towns with high pollutant load intensity are mainly distributed in the south-central area of Yongchuan District. Among the sites investigated, Hegeng Town exhibited the highest load intensities of TP, TN, NH3-N, and COD. Songgai Town, located in the southeast of Yongchuan District, has been identified as the town with the lowest load intensity. With regard to the sources of pollution associated with rural life (Figure 7c), the distribution of areas with high levels of pollutant load intensity is relatively dispersed. It is evident that major towns are involved in both the southern and northern parts. The load intensity of TP, TN, NH3-N, and COD in the Shengli Road Town is the highest of all the sites under consideration. The distribution of towns exhibiting low load intensity is predominantly concentrated in the northern and western regions. The load intensity of TP, TN, NH3-N, and COD in the Zhongshan Road Town is the lowest of all the sites under consideration. The intensity of pollutants from living sources is closely related to the population density.
3.2. Identification of CSs, CAs and CPs of AGNPSP
3.2.1. Identification of CSs and CAs
The analysis of the temporal and spatial distribution of pollutant loads in Yongchuan District resulted in the division of the river basins according to the six main rivers in the area. The identification of discrepancies in pollutant load among the townships was undertaken to ascertain the CSs and CAs of pollutants throughout the district. As illustrated in Figure 8 and Figure 9, the spatial distribution of the total pollutant load in Yongchuan District exhibits significant variations across different river basins. Furthermore, there are areas with elevated levels of pollution from non-point sources. The Daluxi River Basin, located in the southern part of Yongchuan District, the southern part of Linjiang River Basin, and the main river basin of the Yangtze River have been identified as areas of concern due to their relatively high pollutant loads, irrespective of the type of pollutant or the method of assessment. The load volume within the basin has been observed to decrease in a westerly to easterly and northerly to southerly direction. Furthermore, within high-load areas, livestock and poultry breeding accounts for 68% of the total load, with rural life and farmland cultivation accounting for the remainder. The findings of in-depth research conducted in high-load river basins (Figure 9) indicate that areas exhibiting extremely high composite contributions in these basins are predominantly concentrated within three consecutive townships. Zhutuo Town, Hegeng Town, and Xianlong Town. The pollutant loads from the three townships accounted for 56% of the total load of the high-load river basin. Of these, 67% were attributable to livestock and poultry breeding. The investigation revealed that in the highly loaded townships, agricultural activities were predominantly concentrated on livestock and poultry breeding. Consequently, this became the primary source of AGNPSP.
3.2.2. Identification of CPs
AGNPSP is ultimately responsible for water contamination. The monthly flux of pollutants (COD, NH3-N, TP) was modelled for a typical section from 2018 to 2021. This was done using a monthly pollution flux model (Figure 10). It is evident from an analysis of the rainfall data from the relevant years that there is a consistency between the variations in monthly pollutant flux and those in rainfall. This consistency is indicative of long-term sequence changes. Conversely, during months with high rainfall, there is an observed increase in the flux of pollutants. The majority of the peak values of pollutant fluxes are observed to occur in months with high rainfall, with June being the most common. However, due to the lagging nature of AGNPSP, it is possible that some peak values may also occur with a delay. This synchronous change relationship is primarily attributable to the fact that AGNPSP generation and migration are highly dependent on hydrological processes. Intense rainfall, through the formation of surface runoff, becomes the main driving force for scouring and carrying the accumulated pollutants (such as residual fertilisers from farmland and livestock manure) within the watershed into the river network. This results in a significant increase in the pollutant flux at the cross-section during the rainy season. Consequently, ascertaining the period of high rainfall is imperative for determining the CPs for pollution output. An analysis of the data reveals that the variation range of pollutant fluxes at the Ciba section is more significant than that at the Hongjiangzhaba section. Furthermore, the peak values of each pollutant flux are also higher. In consideration of the section’s geographical location and the findings outlined in Section 3.1, it can be concluded that the Ciba section is situated within a high-load watershed, characterised by a heightened pollution load.
In order to eliminate the errors caused by unstable factors between different years, the monthly pollutant flux data from 2018 to 2021 were averaged over a period of four years. The weighted average monthly pollutant flux data of the Ciba and Hongjiangzhaba sections were utilised to identify the critical periods of surface water pollution in the high-load key source areas. As demonstrated in Figure 11, the annual variation of the monthly total pollutant flux at the Ciba and Hongjiangzhaba sections exhibits a single-peak pattern. The maximum peak is observed in June, with the pollutant flux in June being more than seven times higher than that in the month with the lowest flux, February. The period from June to July is characterised by a significant increase in precipitation in Yongchuan District, which also coincides with the peak of the annual flood season. It is during this period that the flux of pollutants attains its zenith, with two consecutive months of peak values being reached. The annual contribution of pollutants to the atmosphere during the months of June and July is responsible for 37% of the total annual flux. In conclusion, a critical period for AGNPSP in Yongchuan District has been identified. During this critical period, the flux of pollutants is concentrated. It is anticipated that there will be a rapid increase in the flux of pollutants in June and July, which will coincide with a corresponding increase in rainfall.
4. Discussion
The present study established an output coefficient model, which was then combined with the pollutant load intensity model and the equivalent load equation. A comprehensive analysis of the temporal and spatial characteristics of AGNPSP in Yongchuan District was conducted, resulting in the identification of critical pollutants. The analysis revealed that COD and TN were identified as the primary pollutants, with livestock and poultry breeding and rural life identified as significant sources of pollution. Of these, livestock and poultry breeding accounted for 49.7%. It is evident from the analysis of the temporal and spatial distribution of pollution load intensity that the high-load areas in Yongchuan District are predominantly situated in the central and southern parts.
The identification of CSs and CAs was based on the temporal and spatial characteristics of pollutant loads. The results indicated the presence of high-load river basin areas and townships in Yongchuan District that were affected by AGNPSP. The contribution of livestock and poultry breeding sources in the high-load river basin areas accounted for 68%. Xu et al. [61] also arrived at a comparable conclusion when identifying critical source areas in the Three Gorges Reservoir Area by employing the list analysis method in conjunction with equivalent pollution load. The CSs and CAs in the study were predominantly sourced from the domains of livestock and poultry breeding, as well as farmland cultivation. Within the designated high-load water basin area, three southern townships have been identified as key contributors, accounting for 56% of the total. Fang et al. [62] found that integrating the Soil and Water Assessment Tool (SWAT) model and multivariate statistical analysis based on land use and land cover (LULC) revealed the presence of small critical source areas in the Choctawhatchee basin with high pollutant load contributions.
In the course of the identification of CPs of AGNPSP in Yongchuan District, it was established that the monthly flux of pollutants exhibited a congruence with the trend of rainfall. The pollutant fluxes at the cross-sections in the high-load river basin areas were found to be even higher. Concurrently, the pollutant fluxes exhibited a single-peak pattern, with the peak occurring in June. It is evident that the months of June and July have been designated as CPs for the management of AGNPSP. During this period, the contribution rate of pollutant fluxes is 37%. During this period, there is an increase in the concentration of pollutants, and as rainfall levels rise, there is a corresponding rise in the levels of pollutants in June and July. Consequently, the implementation of effective prevention and control measures during this period is of paramount importance in order to reduce AGNPSP. This finding is consistent with the conclusions of Wen et al. [63], who utilised the distributed dual-structure IPB empirical output model (DSEEMIPB) in the Luhu River Basin and determined that the period with the greatest contribution was June and July.
Based on the findings, the following remedies are proposed to prioritize the management of AGNPSP in CSs, CAs and CPs.
4.1. Priority Control Strategy of AGNPSP in CSs and CAs
Agricultural non-point source pollutants in Yongchuan District are primarily COD and TN, with livestock and poultry breeding sources being the most important, followed by rural life sources and farmland cultivation sources, which is consistent with the findings of many researchers using various methods to study AGNPSP [61,64,65,66,67,68]. Based on basin-level research, it is clear that Yongchuan District has high pollution load basins, which are primarily spread in the Linjiang River Basin, Daluxi River Basin, and Yangtze main stream basin. As a result, we should prioritize and control these basins, as well as pay attention to the critical pollutants and pollution sources in the basin, from the point to the surface, and manage the interaction between the administrative region and the basin itself. To improve resource utilization and achieve sustainable agricultural development, first focus on livestock and poultry breeding, standardize manure treatment in livestock and poultry breeding, encourage the allocation of supporting equipment for manure treatment, and promote ecological farming technologies [69], including aquaponics [70], circular farming, and farming and breeding combinations [63]. The second category is rural life sources. The pollution caused by rural life sources is frequently underestimated. Most villages and towns do not have fully equipped sewage pipe networks, and the building of pipe network facilities should be enhanced to meet the individual needs of each village and town. Domestic sewage discharge ditches have been established in villages and cities, but the removal rate of pollutants is insufficient, resulting in poor water quality in the lake. The ditches must be converted into ecological ditches to improve interception and purification capacity [71]. Finally, Yongchuan District is a hilly and mountainous terrain with a high pollution load, which is detrimental to soil and water conservation and has the potential to exacerbate AGNPSP. Consider slope modification, terraced field building, contour planting [72,73], and ecological ditch slope protection [74,75], etc. Furthermore, fertilization strategies should be adjusted [76] to increase utilization rates. Reduce the impact of AGNPSP caused by farmland cultivation.
4.2. Priority Control Strategy of AGNPSP in CPs
In the management of agricultural non-point source pollution, CPs need to be taken into consideration. The CPs of agricultural non-point source pollution in Yongchuan District are consistent with the periods of concentrated rainfall. Therefore, for the main flood season period, the first step is to strengthen the monitoring of pollutants at the monitoring sections. It is necessary to conduct more frequent and dynamic monitoring to grasp the entire process of pollutant changes during the main flood season, trace back to the sources for treatment, and reduce the infiltration of pollutants due to rainfall erosion. Secondly, during CPs, it is also necessary to improve the ecological ditches and slope protection, and enhance the interception effect of the river channels. For the drainage facilities of towns and rural areas, they also need to be strengthened and repaired to prevent pollutants from overflowing into rivers and lakes [77].
In addition, it is important to note that the CSs and CAs of AGNPSP identified in this study are based on the potential for pollutant load generation estimated by the export coefficient model. This potential is indicative of the theoretical capacity of the source areas to produce and lose pollutants per unit area or per unit activity intensity. However, due to the absence of continuous water quality and quantity monitoring at the outlets or ditches of each sub-watershed, it was not possible to directly obtain the actual pollutant loads transported from these critical areas to the river channels. The potential estimated by the model and the actual loads that eventually enter the water body may be affected by complex migration, transformation and retention processes within the watershed. Consequently, in future management practices and research, it is recommended to establish monitoring points in these areas to verify the model estimates and quantify the actual pollutant transport fluxes, thereby more accurately assessing the effectiveness of control measures. At the same time, we identified the challenges posed by a lack of data at the AGNPSP prevention research center, as well as an insufficient model simulation in this study. As a result, in future research, we will improve the monitoring module, collect long-term AGNPSP data, develop the model for deeper analysis, and increase the applicability and reliability of the model results.
5. Conclusions
The intricacy of the characteristics and temporal-spatial distribution of AGNPSP has resulted in a considerable increase in the complexity of its control. This study provides a novel perspective on the comprehensive management strategies for the critical source–area–period of AGNPSP. The objective of this study was to facilitate strategy formulation and enhance scientificity. To this end, the spatiotemporal distribution of load quantities was simulated based on the output coefficient model. Furthermore, the pollutant fluxes were calculated by combining the pollutant flux equation. In addition to this, a joint analysis of fluxes and rainfall was conducted, and a detailed analysis of critical source–area–period of AGNPSP in Yongchuan District was conducted.
(1) The inter-annual fluctuation of pollutant load in Yongchuan District signifies that the critical pollutants in this area are COD and TN, with CSs of pollution originating from livestock and poultry breeding and rural life. Further analysis, based on the equivalent pollution load, indicates that the contribution degree of pollutants in Yongchuan District is ranked as follows: TN > COD > TP > NH3-N. The contributions of TN and COD account for 34% and 33%, respectively. The degree to which pollution sources contribute to AGNPSP can be categorised as follows: livestock and poultry breeding sources > rural life sources > farmland cultivation sources. The contribution of livestock and poultry breeding sources accounts for 49.7%.
(2) The temporal and spatial distribution characteristics were analysed at the township and river basin scales, with the pollutant load volume and load intensity being taken into consideration. The results demonstrated that the changes observed in the townships of Yongchuan District were distinct, exhibiting significant disparities between the high and low pollution load townships. The high-load river basin areas are principally located in the southern part of the region. It is evident that the areas which have made significant contributions are predominantly located within Zhutuo Town, Hegeng Town and Xianlong Town. In the context of high-load river basins, it has been determined that the pollution load resulting from livestock and poultry breeding is responsible for 68% of the total.
(3) The analysis of pollutant fluxes in typical sections reveals a single-peak pattern in the monthly pollutant fluxes, with the peak occurring in June. The annual pollutant flux contributions in June and July account for 37%, which is the CPs for AGNPSP in Yongchuan District. Concurrently, the occurrence of CPs corresponds with the regional flood season, thereby suggesting a positive correlation between CPs of AGNPSP and rainfall.
In conclusion, it is recommended that the comprehensive management strategy for AGNPSP should focus on CSs, CAs, and CPs. In the course of the governance process, a management strategy that is both differentiated and prioritised, based on a variety of regions and levels, should be proposed. Through precise identification of the governance areas and time periods, the governance technologies for CSs and critical pollutants should be carried out within CAs. It is imperative that prevention and control measures be strengthened during CPs. The strategy is designed to achieve three key objectives: the reduction in costs, the enhancement of governance efficiency, and the attainment of sustainable agricultural development.
Y.L.: methodology, writing—original draft, validation. R.Y.: writing—review and editing, supervision. M.S.: writing—review and editing, validation. L.Z.: formal analysis. Y.Z.: writing—review and editing, data curation. Y.Y.: data curation. X.L.: conceptualization, methodology. All authors have read and agreed to the published version of the manuscript.
The original contributions presented in this study are included in the article.
The authors declare no conflicts of interest.
Footnotes
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Figure 1 Overview of the Study Area. (a) Location and elevation map (b) Location of each town and sub-district.
Figure 2 Research on the technical roadmap.
Figure 3 Inter-annual variations of pollutant equivalent load in Yongchuan District.
Figure 4 The proportion of major pollutant loads in Yongchuan District.
Figure 5 Equivalent load quantities of different pollutants in Yongchuan District.
Figure 6 Equivalent load quantities of different pollutant sources in Yongchuan District.
Figure 7 Spatial distribution of pollutant load intensity in each town and sub-district of Yongchuan District in 2021: (a) farmland cultivation source, (b) livestock and poultry breeding source, and (c) rural life source.
Figure 8 Spatial distribution of the total pollutant load in Yongchuan District.
Figure 9 Distribution of pollutant load in CAs.
Figure 10 Annual variation chart of pollutant fluxes at typical sections in Yongchuan District and rainfall amounts.
Figure 11 Average monthly pollutant flux change chart from 2018 to 2021.
Determines the export coefficient of each pollutant under different factors.
| Main Pollutants | Land Use Factors | Non Land Use Factors (kg·ca−1·a−1 or kg·per−1·a−1) | |||||
|---|---|---|---|---|---|---|---|
| Cultivated Land | Orchard Land | Draught Animal | Swine | Sheep | Poultry | Domestic Sewage | |
| TP (Total phosphorus) | 0.090 | 0.050 | 0.218 | 0.142 | 0.045 | 0.005 | 0.214 |
| TN (Total nitrogen) | 1.352 | 0.585 | 5.690 | 1.043 | 0.700 | 0.046 | 1.690 |
| NH3-N (Ammonia nitrogen) | 0.040 | 0.020 | 1.320 | 0.100 | 0.060 | 0.003 | 1.200 |
| COD (Chemical oxygen demand) | / | / | 300.000 | 30.000 | 4.400 | 1.170 | 25.000 |
Appendix A
TP export coefficient under different factors.
| Research Area | Land Use Factors | Non Land Use Factors (kg·ca−1·a−1 or kg·per−1·a−1) | |||||
|---|---|---|---|---|---|---|---|
| Cultivated Land | Orchard Land | Draught | Swine | Sheep | Poultry | Domestic | |
| Qiongjiang River Basin (Anju Section) | 0.006 | / | 0.186 | 0.085 | 0.027 | 0.003 | 0.128 |
| Yangtze river basin | 0.009 | 0.005 | 0.310 | 0.142 | 0.045 | 0.005 | 0.214 |
| Xiaoanxi River Basin | 0.006 | 0.003 | 0.186 | 0.085 | 0.027 | 0.003 | 0.128 |
| Huaihe River Basin (Wang–Beng Section) | 0.017 | 0.001 | 0.218 | 0.041 | 0.014 | 0.005 | 0.112 |
| Upper Reach of Yangtze River Basin | 0.090 | / | 0.218 | 0.142 | 0.045 | 0.005 | 0.214 |
| Mountainous area of Sichuan | 0.090 | 0.090 | / | / | / | / | 0.200 |
| Nenjiang Watershed | 0.161 | / | 0.748 | 0.111 | 0.069 | / | / |
| Baoxianghe watershed | 0.161 | / | 0.587 | 0.443 | 0.054 | 0.005 | / |
| Poyang Lake Watershed | 0.003 | 0.008 | 0.003 | 0.003 | / | 0.010 | / |
| Yanghua River Basin | / | / | 0.218 | 0.142 | 0.045 | 0.005 | 0.214 |
| Hanjiang Watershed (Jingmen Region) | 0.079 | 0.108 | 0.624 | 0.179 | 0.085 | 0.009 | 0.214 |
| Jialing River Watershed | 0.117 | / | 0.624 | 0.179 | 0.085 | 0.009 | 0.214 |
| Three Gorges Reservoir Area (Chongqing) | 0.250 | 0.262 | 1.010 | 0.170 | 0.045 | 0.012 | 0.160 |
TN export coefficient under different factors.
| Research Area | Land Use Factors | Non Land Use Factors (kg·ca−1·a−1 or kg·per−1·a−1) | |||||
|---|---|---|---|---|---|---|---|
| Cultivated Land | Orchard Land | Draught | Swine | Sheep | Poultry | Domestic Sewage | |
| Yangtze river basin | 0.190 | 0.080 | 7.320 | 1.390 | 1.400 | 0.060 | 1.870 |
| Xiaoanxi River Basin | 0.095 | 0.040 | 3.660 | 0.695 | 0.700 | 0.030 | 0.935 |
| Huaihe River Basin (Wang–Beng Section) | 0.167 | 0.194 | 7.893 | 1.290 | 0.730 | 0.043 | 1.087 |
| Upper Reach of Yangtze River Basin | 2.900 | / | 11.318 | 2.667 | 1.513 | 0.005 | 1.955 |
| Mountainous area of Sichuan | 2.750 | 1.500 | / | / | / | / | 2.470 |
| Nenjiang Watershed | 1.303 | / | 3.203 | 0.356 | 0.069 | / | / |
| Baoxianghe watershed | 2.320 | / | 16.226 | 0.740 | 0.400 | 0.046 | / |
| Poyang Lake Watershed | 0.025 | 0.020 | 0.025 | 0.025 | / | 0.010 | / |
| Hanjiang Watershed (Jingmen Region) | 1.121 | 1.021 | 10.210 | 0.740 | 0.400 | 0.040 | 2.140 |
| Jialing River Watershed | 2.200 | / | 10.348 | 2.038 | 0.968 | 0.055 | 1.955 |
| Three Gorges Reservoir Area (Chongqing) | 1.800 | 1.240 | 6.110 | 0.450 | 0.230 | 0.028 | 1.580 |
COD export coefficient under different factors.
| Research Area | Land Use Factors | Non Land Use Factors (kg·ca−1·a−1 or kg·per−1·a−1) | |||||
|---|---|---|---|---|---|---|---|
| Cultivated Land | Orchard Land | Draught Animal | Swine | Sheep | Poultry | Domestic Sewage | |
| Huaihe River Basin (Wang–Beng Section) | 2.800 | 2.450 | 300 | 30 | 4.400 | 1.170 | 10.100 |
| Yanghua River Basin | / | / | / | 30 | / | / | 25 |
| Hanjiang Watershed (Jingmen Region) | 1.800 | 2.450 | / | 30 | / | / | 25 |
NH3-N export coefficient under different factors.
| Research Area | Land Use Factors | Non Land Use Factors (kg·ca−1·a−1 or kg·per−1·a−1) | |||||
|---|---|---|---|---|---|---|---|
| Cultivated Land | Orchard Land | Draught Animal | Swine | Sheep | Poultry | Domestic Sewage | |
| Huaihe River Basin (Wang–Beng Section) | 0.042 | 0.027 | 1.320 | 0.100 | 0.060 | 0.003 | 0.163 |
| Yanghua River Basin | / | / | / | 1.500 | / | / | 1.200 |
| Hanjiang Watershed (Jingmen Region) | 0.800 | 0.400 | / | 1.500 | / | / | 1.200 |
Figure A1 Typical section point distribution.
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