Improving Water Environment in Water Source Area of Dabie Mountains Based on Investigation of Farmers’ Garbage Stacking Behavior

Abstract

The contradiction between ecological environment protection and economic development in the Yangtze River Basin has become increasingly prominent in recent years, which seriously limits the sustainable development of the basin. Research on water environment changes of the main tributaries of the Yangtze River helps explore measures to improve the ecological environment of the Yangtze River Basin. In this study, based on the theory of behavioral science in modern management, water quality data in the field were collected, and the farmers’ garbage stacking behavior was also investigated in the water source area of the Dabie Mountains. The results showed that ammonia nitrogen (AN), chemical oxygen demand (COD), total organic carbon (TOC), and total dissolved solids (TDS) in the water bodies showed an overall negative correlation with the distance of water quality collection sites from the garbage stacking point. AN was the most important pollution element affecting the rural water quality in the water source area of the Dabie Mountains. The unsuitable garbage stacking locations and the farmers’ behavior of dumping garbage along the riverbanks were the important causes of water pollution. The garbage stacking locations were optimized and designed by using a GIS spatial analysis tool and a developed suitability evaluation model for the garbage stacking points. The optimized garbage stacking locations were more suitable for improving the local water environment, and their average suitability values increased to 2.01 times and 2.94 times that of the original stacking locations in Kanxiawan and Lengshuigou, respectively. This study can be used as a scientific and methodological reference for improving the rural water environment in the water source area of the Dabie Mountains and in other similar regions in the world.

1. Introduction

The contradiction between ecological environment protection and economic development in the Yangtze River Basin has become increasingly prominent in recent years. The water quality in the middle reaches of the Yangtze River and its tributaries is not promising [1,2]. China’s national surface water environmental quality standard (GB3838-2002) [3] divides surface water quality into five classes from high to low (i.e., Classes I, II, III, IV, and V). Monitoring showed that in 2021, the water quality downstream of the main tributary of the Ba River Basin in the Wuhan section (the estuary of the Bahe Town) was Class II, and that of the upstream (the new bridge of Sanlifan Town) was also Class II (

Table 1. Water quality data of the Ba River’s main sections in 2021.

Date	Section	River	Type	Water Quality (Class)	PH	Dissolved Oxygen (mg/L)	COD (mg/L)	AN (mg/L)
August 2021	Ba River Estuary	Ba River	State-controlled section	II	7	7.60	−1	0.03
August 2021	Xinqiao, Sanlifan Town	Ba River	State-controlled section	II	7	7.80	−1	0.08
September 2021	Ba River Estuary	Ba River	State-controlled section	II	8	9.10	−1	0.03
September 2021	Xinqiao, Sanlifan Town	Ba River	State-controlled section	II	8	7.60	−1	0.02

), both of which had basically reached the water quality target state. The ecological environment protection in the middle reaches of the Yangtze River basin was an important link related to the sustainable development of the Yangtze River Economic Belt [4,5]. Changes in the water environment could effectively indicate the ecological status of the basin [6,7]. The main body of the Dabie Mountains region in eastern Hubei Province is located in Huanggang City, Hubei Province. The city is an important constituent of the middle reaches of the Yangtze River Basin, and the study of water environment protection in the Dabie Mountains is related to the ecological and economic sustainable development of the Yangtze River Economic Zone Belt [8]. Zhang et al. believed that due to the interception effect of shoals, the organic pollution of water in the upper reaches of the Yangtze River was significantly higher than that of the downstream estuaries and estuaries [9]; Gao et al. pointed out that the large total emission of pollutants such as nitrogen and phosphorus had caused serious environmental pollution in some areas and the main tributaries of the Yangtze River [10]; and Zhang et al. concluded that the serious pollution of the main tributaries of the Yangtze River was related to soil erosion in the upstream region, and also to the pollutant discharges from urban and rural areas in the middle and lower reaches of the Yangtze River [11]. Considering that the discharge of urban pollutants generally passed through sewage pipelines and had a relatively mature regulatory system, it was easy to detect and control [12], while the vast rural areas were far away from the administrative center, especially many mountainous areas were often deep in the upper reaches of rivers, where pollutants had a wider impact downstream and were more difficult to monitor [13]. Therefore, it is particularly urgent to conduct research on water environment management in mountainous rural areas, especially water sources, in the middle reaches of the Yangtze River.

Regarding the treatment of rural water pollution, Yang Hong and Lin Yongbo believe that the leachate produced by rural domestic garbage seriously affects the quality of groundwater, and suggested that the garbage should be anti-seepage and concentrated before being transported to landfills for treatment [14]. Zhang et al. argued that harmful and toxic liquids produced by the random stacking of rural domestic waste pollute river water sources, and suggested a “promoting household classification, village collection, township (town) transportation, and county treatment” waste management model [15]. Li et al. found through model simulation that the impact of total nitrogen load on non-point source pollution in riverside areas was much greater than in areas far away from rivers [16]. Li Jie and Yang Wenxuan pointed out that due to the lack of specialized pollution-free waste disposal sites, rural domestic waste seeps into nearby water bodies through surface runoff, causing secondary pollution [17]. Dong Dianbo suggested adopting a model of “household classification, village concentration, township transfer and municipal treatment” to prevent the disorderly accumulation of garbage from contaminating groundwater sources [18]. The above studies showed that the seepage of harmful substances caused by the disorderly accumulation of domestic garbage in rural areas is an important reason for the pollution of nearby water bodies or water sources; although relevant studies have proposed the classification and centralized treatment of rural garbage, they were limited by the focus of the research, and no more in-depth research has been carried out on how to specifically solve the problem of disorderly accumulation of garbage.

Behavioral science theory in modern management believes that individual behavior will deviate from the rational person paradigm in many cases, but it can be transformed through low-cost behavioral intervention (positive guidance), such as empowering citizens with informed behavioral strategies or correction. Since human behavior is an important contributor to environmental problems, it is necessary to consider it as a key component of the solution [19]. The disorderly accumulation of garbage in mountainous rural areas is not only an environmental problem but also a scientific management issue. Therefore, farmers’ unreasonable environmental behaviors could be corrected by actively guiding them from the source [20]. Ge et al. pointed out through investigation of the current situation of rural domestic waste treatment in the Danjiangkou water source conservation area that in the process of rural waste treatment, farmers with different economic conditions had different willingness to pay for waste treatment [21]. Therefore, it was necessary to consider local specific economic and social realities. While farmers were eager for the village committee to effectively handle domestic waste and reduce environmental pollution. Tao et al. found that attitudes, subjective norms, and perceived behaviors had a significant positive impact on farmers’ environmental intentions [22]. Thus, it could be seen that combining local economic and social conditions and rationally guiding farmers’ environmental awareness and behavior were key prerequisites for solving the problem of garbage accumulation in mountainous rural areas.

In summary, scholars have reached a consensus on the water environment pollution caused by the disorderly accumulation of domestic garbage in mountainous rural areas. The monitoring technology and method of domestic garbage stacking points [23,24], the investigation and evaluation method of water environment quality around domestic garbage [25,26,27], and the investigation and analysis method of influencing factors of farmers’ participation in garbage classification and rural water pollution control behavior [28,29] have become mature. However, further research is still needed on how to guide farmers to reasonably pile garbage. Judging from the collected literature, there are relatively few studies on rural water environment pollution in the Dabie Mountains in eastern Hubei. The objective was to explore the specific scheme to improve the water environment of the Dabie Mountain water source area, and to provide technical support for promoting the protection of the Yangtze River. The Yangtze River Basin is a worldwide river, and the ecology of the middle and lower reaches of the Yangtze River Basin will also have a significant impact on the regional ecological environment. In addition, this study will also provide important reference values for developing regions similar to the middle and lower reaches of the Yangtze River, such as the Ganges River Basin in India and the Nile River Basin in Egypt. In view of the importance of this area as an ecological barrier and water resource in the middle reaches of the Yangtze River [30], in this study, based on a survey of local farmers’ garbage disposal behavior and willingness to improve, geographic information system (GIS) technology and some key factors affecting local farmers’ environmental behavior were combined to explore specific solutions to improve the water environment by constructing a garbage stacking buffer zone analysis model.

2. Study Area and Data Sources

2.1. Study Area

The Ba River is located in Huanggang City, Hubei Province, with a total length of 148 km and a multi-year average runoff of about 6.81 billion m³, which is a major tributary of the middle reaches of the Yangtze River (Figure 1a,b). The Ba River Basin covers 3489 km² at an elevation range of 4–1717 m asl. It is characterized by rich water resources, a dense river network, and an alpine gorge landscape. This area belongs to the subtropical monsoon climate zone. The average annual temperature is ~17 °C. The annual precipitation is ~1350 mm. In recent years, the water environment pollution in the Ba River Basin has become increasingly serious, and a large number of pollutants have entered the Yangtze River along the Ba River, which poses a serious threat to the water environment safety of the Yangtze River. Therefore, research on water environment improvement in this region is of great significance for the protection of water resources of the Yangtze River.

Two representative villages in Luotian County, Kanxiawan and Lengshuigou, both traversed by rivers, were selected as study areas (Figure 1c). The two villages are located in the source area of the upper reaches of the Ba River, as well as deeply in the Dabie Mountains Ecological Reserve where the farming households live. The area has approximately 300 residents. There are no industrial or mining enterprises in the two villages. Farmers’ household garbage has caused great pollution to the water environment, which has also attracted a lot of attention from relevant government departments. Therefore, there is an urgent need to conduct research on the water pollution caused by rural garbage, in order to improve the quality of the water environment.

2.2. Data Acquisition

At the time of sampling, the weather conditions were cloudy, and the crops had already been harvested. The absence of agricultural activities, such as fertilization, ensured minimal interference from cropland non-point source pollution. This sampling period provided an optimal scenario to accurately assess the impact of farmers’ garbage stacking behavior on the water environment. By obtaining and analyzing the contents of pollution indicators in water bodies, technical support could be provided for water environment protection [31]. In this study, four representative indicators, namely, total organic carbon (TOC), chemical oxygen demand (COD), total dissolved solids (TDS), and ammonia nitrogen content (AN), were selected for water quality sampling in the two tributaries of Lengshuigou and Kanxiawan. Among those, TOC represents the total amount of carbon contained in organic matter in water bodies, which could fully reflect the pollution level of organic matter in water bodies [32]. COD refers to the amount of oxygen required when oxidizable substances in water are oxidized by chemical oxidants. It represents a comprehensive indicator of pollution by reducing substances in water, and is measured in milligrams/liter of oxygen [33]; COD is a mandatory item in environmental monitoring in China [34], and it is one of the most commonly used indicators for measuring the content of organic matter in water bodies. TDS indicates how many milligrams of dissolved solids are dissolved in 1 L of water. The higher the TDS value, the more dissolved substances are contained in the water [35].

The testing and analysis of water quality samples were carried out using relevant instruments and equipment from the Geographic Process Laboratory of Huanggang Normal University. The determination of TOC, COD, and TDS was carried out using the “Yaming Water Quality Detector 2” produced by Xuzhou Yaming Instrument Co., Ltd., located in Shenzhen, Guangdong Province, China. This instrument uses the principle of light induction to measure these three indicators. The AN determination was carried out using the “Portable ammonia nitrogen tester AD-82B” manufactured by Hangzhou Qiwei Instrument Co., Ltd., located in Hangzhou, Zhejiang Province, China. This instrument uses the principle of photoelectron colorimetry to replace the traditional visual colorimetry. The artificial error is eliminated, so the measurement resolution is greatly improved.

The survey data on farmers’ willingness to dispose garbage were obtained from household surveys and interviews with local farmers in mid-October 2021. Land-use data were extracted by visual interpretation method based on unmanned aerial vehicle remote sensing data obtained in mid-October 2021. Land-use types were divided into woodland, cropland, residential land, bare land, river, and road.

3. Field Work and Theoretical Analysis

3.1. Optimal Water Quality Indicator Identifying for Rationality Analysis of Garbage Stacking Points

Combined with the distribution of residential houses and the current situation of garbage stacking, 8 water quality data collection points were set up for Kanxiawan Village, and 7 water quality data collection points were set up for Lengshuigou Village along the river flow direction. During water quality sampling, the distances between sampling points and garbage stacking points varied. This variation was designed for two primary purposes. First, it allowed for the analysis of the influence of garbage stacking proximity on river water quality. Second, due to the rugged mountain terrain in the study area, sampling points were strategically positioned downstream of the nearest garbage stacking points to ensure both the representativeness of the data and the safety and accessibility of the sampling personnel. The location of the collection points and garbage stacking points is shown in Figure 2, and the collected data information is shown in

Table 2. Summary of water quality data in Kanxiawan and Lengshuigou.

Village	No.	Distance from Garbage Stacking Point (m)	TOC (mg/L)	COD (mg/L)	TDS (mg/L)	AN (mg/L)
Kanxiawan	1-1	70	2.60	2.23	32.67	0.09
	2-1	2	4.05	4.35	75.00	0.36
	2-2	5	3.30	3.05	33.50	0.35
	3-1	8	2.67	2.37	27.00	0.35
	4-1	10	3.03	2.87	27.00	0.33
	5-1	25	2.80	2.53	37.00	0.15
	5-2	45	2.70	2.43	22.67	0.12
	5-3	55	2.47	2.03	37.67	0.11
Lengshuigou	1-1	10	2.30	1.97	23.33	0.37
	1-2	16	2.80	2.50	34.50	0.30
	2-1	18	3.30	1.90	25.50	0.21
	2-2	22	3.33	1.93	22.33	0.15
	3-1	35	2.30	1.93	22.33	0.08
	4-1	12	2.30	1.90	22.33	0.32
	5-1	30	3.20	1.80	25.67	0.11

Note: Numbers 2-1 and 2-2 indicated that these two water quality collection points were controlled by the garbage stacking point 2, and other similar numbers in this table had the same meanings.

. The fitting analysis between TOC, COD, TDS, and AN and the distance from the garbage stacking points to data collection points were respectively carried out using Microsoft Excel software (version 2021). Linear simulation, exponential simulation, logarithmic simulation, and polynomial simulation were carried out in turn to obtain the fitting equation, and the goodness of fit (R²) was used to evaluate the best fitting method. The value range of R² is between 0 and 1. The closer the value is to 1, the better the fitting is, and the closer the value is to 0, the worse the fitting is.

TOC is an important indicator for evaluating the degree of organic matter pollution in water. Through fitting analysis (Figure 3), it could be intuitively reflected that the TOC content in Kanxiawan showed a relatively strong negative correlation with the distance from the garbage stacking points, with a fitting degree of 0.7559. In contrast, there was a weak negative correlation between the TOC content in Lengshuigou and the distance from the garbage stacking points, with a fitting degree of only 0.0811 and a relatively gentle slope of the fitting curve. In addition, the TOC content of the water body in Lengshuigou was relatively stable, except for one slightly larger outlier.

Through fitting analysis (Figure 4), it was found that the COD content in the rivers of Kanxiawan showed a relatively strong negative correlation with the distance from the garbage stacking points, with a fitting degree of 0.7249. In contrast, the COD content in the rivers of Lengshuigou showed a weak negative correlation with the distance from the garbage stacking points, with a fitting degree of only 0.087. In addition, the COD content of the water body in Lengshuigou was relatively stable, except for one slightly larger outlier.

TDS is an important indicator reflecting water quality. Through fitting analysis (Figure 5), the TDS content in the rivers of Kanxiawan showed a relatively strong negative correlation with the distance from the garbage stacking points, with a fitting degree of 0.3294. In contrast, the TDS content in the rivers of Lengshuigou showed a weak negative correlation with the distance from the garbage stacking points, with a fitting degree of only 0.0273. In addition, the TDS content of the water body in Lengshuigou was relatively stable, except for one slightly larger outlier.

Through the collection of water quality data from the tributaries of Kanxiawan and Lengshuigou, it was found that the AN content in the river water bodies of the two villages showed a strong negative correlation with the distance from the garbage stacking points, and the logarithmic fitting for the two villages was higher than the linear fitting (Figure 6). This indicated that the closer the river was to the garbage stacking points, the higher the AN content; and the farther the river was from the garbage stacking points, the smaller and more stable the change in the content of the water bodies. It has been shown that the total amount of pollutants such as nitrogen and phosphorus discharged was large, which usually caused serious pollution of water bodies [36]. It could be seen that compared with the other three indicators, AN could reflect the water quality condition more significantly and was the main water pollution element.

Through the comparison and analysis of water quality data collected in Kanxiawan Village and Lengshuigou Village, it could be seen that the content of TOC, COD, and TDS in the river of Kanxiawan Village was negatively correlated with the distance between the data collection point and the garbage stacking points, while in Lengshuigou Village, this negative correlation was not significant. Only the AN content showed a significant negative correlation with the distance between the data collection points and the garbage stacking points in both the rivers of Kanxiawan Village and Lengshuigou Village. Therefore, the AN content in the water body can be selected as the main indicator to analyze whether the location of the garbage stacking points was reasonable.

It was found through on-site investigation that the number of farmers, the scale of garbage stacking points, and the terrain slope in Kanxiawan Village were all larger than those in Lengshuigou Village, resulting in a much more severe river pollution level in Kanxiawan Village than in Lengshuigou Village. In addition, the reason for the large outliers for TOC, COD, and TDS at the same site was the presence of residual pesticide buildup in nearby cropland.

Considering that the factors affecting the level of pollutants in water bodies were not only related to the proximity of the water bodies to the garbage stacking points, but also to the behavioral habits of human beings in disposing of garbage, it was necessary to investigate the relevant factors affecting the farmers’ behavior in dumping garbage.

3.2. Investigation and Analysis of Farmers’ Garbage Stacking Behavior

3.2.1. Purpose and Object of Investigation

In this study, the form of on-site investigation was adopted to understand the daily ways of garbage disposal by local farmers, in order to analyze the relevant factors that affect their garbage stacking behavior and explore solutions to improve their garbage stacking behavior. The survey objects in this paper involved 233 farmers in Kanxiawan and Lengshuigou (

Table 3. Summary of detailed information of survey subjects.

Village	Gender		Education Level				Total
	Males	Females	Primary School	Junior Middle School	High School	College and Above
Kanxiawan	69	58	96	29	2	0	127
Lengshuigou	56	50	58	47	1	0	106

).

3.2.2. Existing Problems and Cause Analysis

Farmers living by the riverbank preferred to discard garbage on the riverbank nearby. It was found through the survey that farmers usually preferred to locate their houses closer to rivers. Due to insufficient centralized garbage disposal facilities in the village, farmers often choose to discard garbage on the nearby river beach. Once it rained, this garbage would be washed away with the stream, making the water source more susceptible to pollution (Figure 7a,b).

Farmers were more likely to discard or burn garbage instead of using dustbins. In some residential areas where dustbins were placed, the farmers were more likely to throw garbage next to the dustbins, causing some dustbins to be covered with spider webs (Figure 7c); at the same time, due to the unpleasant smell of the piled garbage, the garbage was often discarded for a long time and then burned in piles (Figure 7d). This phenomenon showed that on the one hand, farmers had not developed an awareness of centralized garbage recycling, and on the other hand, it also reflected that the garbage management department failed to deal with the piled garbage in a timely manner.

Randomly discarding garbage in an ecological zone. Through on-site investigation, it was found that in some shrubs and forests not far from the farmers’ houses, garbage was also piled up (Figure 7e,f). Not only did this cause pollutants in the garbage to seep down with the rainwater and pollute groundwater during rainy days, but it was also easy for garbage to be washed into nearby streams when it rained heavily, thereby polluting downstream water. The reasons for this behavior were not only related to the weak environmental awareness of farmers, but also to inadequate management and publicity.

Overall, the garbage stacking points in the two villages were generally close to the river. According to statistics, there were five garbage stacking points located closer to the river in Kanxiawan and Lengshuigou, respectively. By water quality testing, it was also found that the water quality in waters closer to garbage stacking points was relatively worse (Table 2).

4. Model Construction and Application

It has been shown by relevant research that geographic information system technology was beneficial in revealing the spatial variation characteristics of water environment problems [24].In order to improve farmers’ garbage stacking behavior, it was necessary to combine existing water quality detection data and build a spatial analysis evaluation model based on geographic information system (GIS). Thus, the garbage stacking points suitable for improving the local water environment were designed through evaluation and calculating suitability garbage stacking points.

4.1. Construction of Suitability Evaluation Model for Garbage Stacking Points

Based on previous analyses of water quality testing data, combined with field investigations, it was found that the specific factors affecting water quality in garbage stacking behavior could be attributed to two aspects: (1) the distance between garbage stacking points and the water area; and (2) the garbage disposal habits of farmers. Therefore, based on the above two major factors, a suitability evaluation model for garbage stacking points could be constructed as follows:

$$Q=T\cdot R\cdot {\displaystyle \sum _{i=1}^n(A_i\times W_i})$$

(1)

where Q is the suitability value (dimensionless) of the garbage stacking points, T is the terrain factor, R is the water distance factor, A_i is the i-th key factor that affects farmers’ behavior, and W_i is the weight coefficient of the i-th key factor that affects farmers’ behavior.

Considering that gentle slopes and steep slopes act differently on waste washout with rainfall, it is necessary to take the effect of the slope into account. Based on these factors, the modeling framework described above can be further specified into a more accurate algorithm as follows:

$$Q=[R-H\times \tan (\alpha )]\times {\displaystyle \sum _{i=1}^n(A_i\times W_i})$$

(2)

where H is the relative height difference between the garbage stacking point and the river, and α is the slope where the garbage stacking point is located. Based on the on-site investigation and expert suggestions, 10 key factors and weights that affected farmers’ garbage stacking behavior were proposed, as shown in

Table 4. Key influencing factors and weight coefficients of farmers’ garbage stacking behaviors.

No.	Key Factors Affecting Farmers’ Behavior	Weight Coefficient of Key Factors
1	Kitchen waste	0.08
2	Timely disposal of garbage	0.20
3	Habitual waste stacking	0.06
4	Suitable waste treatment facilities	0.20
5	Stacking garbage on the back of the bank slope	0.10
6	Pesticide residues in soil near river banks	0.05
7	Waste sorting treatment	0.20
8	Garbage is stacked toward the riverbank	0.06
9	Garbage contains feces	0.03
10	Direct stacking of garbage into the river	0.02

. If the corresponding behavior of the relevant factors caused adverse effects, the weight coefficient would be less than 0.1. The larger the weight coefficient, the higher the suitability.

4.2. Simulation Results and Analysis

According to Table 4 and field investigations, through the analysis and calculation of the weight values of the existing garbage stacking points in Kanxiawan and Lengshuigou, the suitability values of the garbage stacking points corresponding to eight water quality data collection points in Kanxiawan and seven water quality data collection points in Lengshuigou were calculated, as shown in

Table 5. Suitability value of garbage stacking points in Kanxiawan and Lengshuigou.

Village	No.	H (m)	Tan(α)	R (m)	W	Q
Kanxiawan	1-1	9.2	0.43	70	0.39	25.76
	2-1	2.0	0.58	2	0.50	0.42
	2-2	2.3	0.58	5	0.50	1.83
	3-1	2.8	0.49	8	0.42	2.78
	4-1	2.9	0.45	10	0.30	2.61
	5-1	4.8	0.36	25	0.66	15.36
	5-2	8.5	0.36	45	0.66	27.68
	5-3	12.4	0.36	55	0.66	33.35
Lengshuigou	1-1	1.5	0.32	10	0.42	4.00
	1-2	2.0	0.32	16	0.42	6.45
	2-1	2.7	0.27	18	0.52	8.98
	2-2	3.0	0.27	22	0.52	11.02
	3-1	5.0	0.36	35	0.58	19.26
	4-1	2.0	0.27	12	0.52	5.96
	5-1	4.0	0.36	30	0.40	12.00

. The results showed that both Kanxiawan and Lengshuigou had four garbage stacking points with corresponding suitability values below 10. The suitability values of the garbage stacking points in Kanxiawan ranged from 0.42 to 33.35, with an average value of 13.72. The suitability values of the garbage stacking points in Lengshuigou ranged from 4.00 to 19.26, with an average value of 9.67.

According to the previous analysis conclusion, the AN content in the water showed the most significant positive correlation with the distance between the garbage stacking points and the water area, which indicated that AN content was the most active factor among the four water quality indicators. Therefore, using the suitability value of the garbage stacking point as the independent variable and AN data as the dependent variable, the correlation between the AN content of rivers and the suitability of the garbage stacking point was analyzed.

Through the analysis of Figure 8, as well as Table 5, it was found that there were four garbage stacking points with suitability values less than 10 in each of Kanxiawan and Lengshuigou, and the AN content of their corresponding water bodies was greater than 0.15 mg/L, which did not comply with China national surface water quality Class I standard. It was revealed by fieldwork that of the three main factors in Equation (1), the magnitude of change in the topographic drop was usually not very large on a small regional scale; it also took a relatively long period of time to change the behavior of farmers’ garbage stacking. Therefore, adjusting the distance between the garbage stacking points and the river, as well as the slope of the garbage stacking points, was the most direct way to improve the situation under conditions of little change in elevation and farmer behavior. For this purpose, the relationship between the distance factor and the suitability and AN content of the garbage stacking points could be further analyzed based on the survey values.

As seen in Figure 9, both in Kanxiawan and Lengshuigou, the proximity of the garbage stacking points to water quality testing sites showed a significant negative correlation with the AN content, and a significant positive correlation with the suitability of the garbage stacking points. This indicated that the further the garbage stacking points were from water, the lower the AN content, and that the further the garbage stacking points were from water, the higher the suitability. In addition, if the slope of the garbage stacking point could be adjusted to zero, then the effect of rainwater washout would be greatly reduced. Although increasing the distance between garbage stacking points and rivers may reduce the risk of pollutants being directly carried into water bodies by surface runoff, pollutants can still migrate into rivers through groundwater. Studies have shown that pollutants in landfill leachate (such as ammonia nitrogen, heavy metals, and organic pollutants) can infiltrate through soil layers into groundwater and migrate toward rivers with groundwater flow [37]. Therefore, even if the garbage stacking point is farther from the river, pollutants may still enter the river through groundwater pathways, albeit with a potentially longer migration time [38]. Additionally, the speed and direction of groundwater flow are influenced by factors such as geological conditions, hydrological characteristics, and rainfall. In highly permeable soils, pollutants can migrate more quickly, potentially reaching rivers in a shorter time [39]. Thus, merely increasing the distance between garbage stacking points and rivers cannot completely eliminate the risk of pollution. Other measures, such as installing impermeable layers and constructing leachate collection systems, are necessary to effectively control pollutant migration [40].

5. Optimization Design and Suggestions

5.1. Optimization of Garbage Stacking Location

The two villages are located in the Dabie Mountains National Nature Reserve, and the Chinese National Surface Water Environmental Quality Standard stipulates that the surface water quality of national nature reserves should be Class I. It was found that the shortest distance between the data collection points with the AN level below 0.15 mg/L (the upper limit value specified in the national surface water quality Class I standard) and the garbage stacking points was 25 m in Kanxiawan, and the shortest distance between the data collection points with the AN level below 0.15 mg/L and the garbage stacking points was 22 m in Lengshuigou (Figure 9). Therefore, it was necessary to extend 25 m from the river to the riverbank as a buffer zone when the improvement plan for garbage stacking points was designed, so that the garbage stacking points would be outside of the buffer zone. According to the results of the model calculation, combined with the location of farmers’ houses and farmers’ living habits, based on the GIS buffer zone analysis, the locations of the optimized garbage stacking points were designed as shown in Figure 10.

The calculation results of the suitability values for the optimized garbage stacking points in Kanxiawan and Lengshuigou are shown in

Table 6. Suitability values for optimized garbage stacking points in Kanxiawan and Lengshuigou.

Village	No.	H (m)	Tan(α)	R (m)	W	Q
Kanxiawan	1-1	10.5	0	110	0.39	42.90
	2-1	3.0	0	55	0.50	27.50
	2-2	3.1	0	56	0.50	28.00
	3-1	3.0	0	40	0.42	16.80
	4-1	3.5	0	53	0.30	15.90
	5-1	4.6	0	30	0.66	19.80
	5-2	8.3	0	48	0.66	31.68
	5-3	12.2	0	57	0.66	37.62
Lengshuigou	1-1	1.7	0	52	0.42	21.84
	1-2	2.2	0	54	0.42	22.68
	2-1	3.2	0	56	0.52	29.12
	2-2	3.5	0	58	0.52	30.16
	3-1	5.5	0	45	0.58	26.10
	4-1	3.0	0	92	0.52	47.84
	5-1	4.0	0	50	0.42	21.00

. The results showed that the suitability values of the garbage stacking points in Kanxiawan and Lengshuigou were both greater than 10. The suitability values of the garbage stacking points in Kanxiawan ranged from 15.90 to 42.90, with an average value of 27.53, which was 2.01 times the average value before optimization. The suitability values of the garbage stacking points in Lengshuigou ranged from 21.00 to 47.84, with an average value of 28.39, which was 2.94 times the average value before optimization. The results indicated that the optimized garbage stacking locations were more suitable for improving the local water environment.

5.2. Suggestions for Improving Farmers’ Garbage Disposal Behavior

In response to the problems found in the investigation regarding farmers’ garbage stacking behavior, the following suggestions for improvement were put forward:

(1)

Enhancing environmental awareness of farmers through publicity and other means.

It was found through investigation that the existing promotional methods such as slogans were relatively single. This single form of publicity had a limited impact on improving farmers’ environmental awareness. It was recommended to regularly carry out environmental knowledge readings and distribute promotional brochures with illustrations and text. Considering that television and mobile phones are currently the main ways for middle-aged and elderly farmers in rural areas to obtain information, multimedia means such as television, mobile phones, and the Internet should also be used for promotion.

(2)

Enhancing the dominant position of farmers in household garbage disposal.

It was suggested that the government guide farmers to actively carry out relevant activities through various methods and channels, and gradually become the main actors in household garbage disposal. Firstly, with farmers as the core, the government should adopt rewards and punishments to fully mobilize the enthusiasm and initiative of farmers. Secondly, the government should encourage more environmental organizations to pay attention to rural environmental issues. Thirdly, the government should collaborate with the market and farmers to negotiate and solve environmental problems for farmers.

(3)

Utilizing external conditions to improve farmers’ behavior habits.

It was recommended to cultivate farmers’ good living habits through external incentives, and rely on the formation of their own environmental awareness to maintain good habits of garbage disposal. To this end, it was first necessary to improve rural infrastructure construction to meet the needs of farmers’ domestic waste. Secondly, special environmental protection projects should be implemented, and targeted reward policies should be implemented to guide farmers in garbage classification and recycling, avoiding arbitrary discarding.

6. Conclusions

Under the background of the water environment protection of the Yangtze River, this study conducted an in-depth study on the impact of farmers’ garbage stacking behavior in the water source area of Dabie Mountains on the local water environment through the field collection of water quality data and a household survey. The conclusions were as follows:

(1)

Compared with other indicators, AN was the main source of pollutants affecting the water quality of rural garbage in the Dabie Mountains water source area.

(2)

The contents of AN, COD, TOC, and TDS in the water of the Dabie Mountains water source area were generally negatively correlated with the distance between the water quality collection points and the garbage stacking points.

(3)

The garbage stacking locations were optimized by using GIS spatial analysis and a developed suitability evaluation model for the garbage stacking points. The optimized garbage stacking locations were more suitable for improving the local water environment, and their average suitability values increased to 2.01 times and 2.94 times that of the original stacking locations in Kanxiawan and Lengshuigou, respectively.

(4)

Inappropriate farmers’ garbage dumping behavior was an important cause of water pollution, for which the following improvement measures were proposed: enhancing the farmers’ environmental awareness through publicity and other means; enhancing the farmers’ dominant position in domestic waste treatment; and utilizing external conditions to improve farmers’ behavior habits.

In this study, water environment improvement was innovatively conducted from a new perspective of the theory of behavioral science in modern management. A suitability evaluation model for the garbage stacking points was developed based on the analyses of both water quality data and farmers’ garbage stacking behavior acquired by a field survey in the water source area of the Dabie Mountains. The garbage stacking locations were optimized and designed by using the GIS spatial analysis tool and a suitability evaluation model was developed for the garbage stacking points. However, the method proposed in this study has some limitations. Firstly, the model is suitable for rural areas without industrial and mining enterprises pollution and high-intensity agricultural non-point source pollution. Secondly, when the model is applied to other areas outside the Dabie Mountains, the model parameters may need to be adjusted. In different regions, farmers’ education level and environmental protection publicity are different, and farmers’ garbage stacking behavior will also be different. The key influencing factors and weight coefficients of farmers’ garbage stacking behavior in the Dabie Mountain water source areas given in this study will no longer be applicable in these areas. When applying the model in similar areas, it is necessary to carry out extensive investigations of the local farmers’ garbage stacking behavior and combine it with expert advice to determine the localized model parameters, such as the key factors and weights that affect the farmers’ garbage stacking behavior. Finally, the optimal water quality indicator for the rationality analysis of the garbage stacking points may change. The shortest distance between the data collection point and the garbage stacking point determined by this indicator is 25 m (that is, the buffer distance of the improved garbage stacking point position) will also change. In this study, through the collection and analysis of water quality data, it was determined that AN is the most significant indicator to indicate the distance of garbage stacking point affecting water quality. Affected by the differences in the dominant types of rural domestic garbage in different regions and the differences in the types of water quality indicators adopted by different national surface water environmental quality standards, the most significant indicator indicating that the distance of the garbage stacking point affecting the water quality may no longer be AN. The shortest distance between the data collection point and the garbage stacking point determined according to the standard of AN content below 0.15 mg/L will be different from the 25 m determined in this study. Considering the limitations of the model, more potential environmental factors will be considered to improve the application effect of the model, and the model will be applied to more regions to test its applicability in the future.