ABSTRACT
This study takes Guanshan River Basin, a typical inflow river of the Danjiangkou Reservoir, as the research area, and the nonpoint source (NPS) pollution characteristics of nitrogen and phosphorus from 2013 to 2018 were evaluated by establishing a Soil and Water Assessment Tool (SWAT) model. The pollution characteristics of heavy metals were also evaluated by combining the SWAT and simple output coefficient method. The results show that the pollutant load in Guanshan River Basin contributes significantly to the Danjiangkou Reservoir. Temporally, pollutant loads were primarily concentrated in the flood season, which had a significant positive correlation with precipitation, water yield, and sediments in the abundant period. Among them, sediments were the most important driving factor, followed by water yield. Spatially, pollutant loads were mainly concentrated downstream of the basin, where cultivated land is widespread. In this regard, it is suggested that the input of chemical fertilizers, pesticides, and livestock manure in cultivated land should be controlled and soil erosion downstream of the basin should be prevented to mitigate nitrogen, phosphorus, and heavy metal pollution. This research offers a basis for the formulation and implementation of watershed management for the Danjiangkou Reservoir.
HIGHLIGHTS
Based on the Soil and Water Assessment Tool model, the nonpoint source (NPS) pollution features and sources of nitrogen, phosphorus, and heavy metals were analyzed and discussed.
The NPS pollution is mainly concentrated in the flood season, and sediment is the most important driving factor, followed by water yield.
The input of fertilizers and pesticides into cultivated land should be controlled, and soil erosion should be prevented in the study's high-load area.
INTRODUCTION
The rapid development of economies in many parts of the world has resulted in serious pollution problems in rivers and lakes (Karaouzas et al. 2021; Qian et al. 2021), especially pollution caused by nitrogen, phosphorus, and heavy metals (Yang et al. 2020; Zhang et al. 2021). Government policies have gradually reduced point source pollution in recent years (Zou et al. 2020; Tang et al. 2022). However, nonpoint source (NPS) pollution remains extremely hard to control because of its randomness, universality, fuzziness, latency, and accumulation characteristics (Zhou et al. 2019; Cheng et al. 2020; Duan et al. 2021). NPS pollution can be attributed to erosion during heavy rainfall, runoff processes, arbitrary discharge of animal excreta, excessive application of farmland fertilizers, pesticides, herbicides, and emissions of urban pollutants such as organic contaminants, heavy metals, and microorganism contaminants (Chang et al. 2021). In particular, the main contributors to NPS in industrially underdeveloped areas are natural and rural activities (Ouyang et al. 2018; Zheng et al. 2021). Among the contaminants of NPS pollution, nitrogen, phosphorus, and heavy metals are the most prominent (Zhou et al. 2020b; Chang et al. 2021).
The Soil and Water Assessment Tool (SWAT) is a water simulation model with a wide application range and good simulation performance, and it has continuously been developed, improved, and widely used in many countries with good results (Cheng et al. 2020; Lai et al. 2020; Qian et al. 2021; Szalińska et al. 2021; Bridhikitti et al. 2022). SWAT can simulate runoff, sediment, and nutrient loads at various timescales and anticipate how management approaches and future climatic variables may affect watershed NPS pollutant loads. With these advantages, it has become an effective assessment tool for the in-depth study of NPS pollution at the watershed level (Heathman et al. 2008; Bridhikitti et al. 2022).
Many SWAT model studies have examined NPS contamination of nitrogen and phosphorus, but heavy metals have gotten less attention. This is due to SWAT's heavy metal module's weaknesses (Meng et al. 2018) and the requirement of massive amounts of data. Compared with the study of the NPS pollution of nitrogen and phosphorus using the SWAT model, in addition to complete flow, sediment, meteorological data, etc., data on soil heavy metals are also required for studying the heavy metal pollution in NPS (Cheng et al. 2020; Song et al. 2020; Chueh et al. 2021). This model is not convenient for studying the NPS pollution of heavy metals in large basins because of the large amount of time required for sampling and analysis. Moreover, as the emigration mechanism for various heavy metals is not completely understood, the theoretical basis is inadequate, and it is challenging to replicate the complicated emigration process (Lin et al. 2012; Ouyang et al. 2016; Qiao et al. 2019). Therefore, most existing research focused only on one or two heavy metals, and a comparative study of NPS pollution for various heavy metals is still lacking (Zhang et al. 2018; Du et al. 2019; Zhou et al. 2020a). Even so, the NPS pollution of various heavy metals is very important for watershed pollution control, and related research studies need to be promoted.
Known as a national first-class water source protection area, the Danjiangkou Reservoir is a crucial component of China's South-to-North Water Transfer Project. Therefore, its ecological environment and water quality safety have attracted broad attention (Chen et al. 2017; Bi et al. 2021). In recent years, the Danjiangkou Reservoir has been affected by human activities, such as industrial emissions, pesticide and fertilizer usage, and livestock and poultry waste, posing huge risks to the well-being of millions of individuals (Zheng et al. 2021). Among many pollutants, nitrogen, phosphorus, and heavy metals are the most prominent (Li et al. 2020; Wang et al. 2022a). Existing research on the pollution sources in Danjiangkou showed that NPS pollution cannot be neglected. For example, the total nitrogen (TN) and total phosphorus (TP) in the Danjiangkou Reservoir from NPS was 18,100 and 2,900 t/a, respectively, in 2010 (Zhuang et al. 2016). The TN load of the Danjiangkou Reservoir was dominated by NPS pollution, with high contributions from NPS in the wet periods and point sources in the dry periods (Wei et al. 2020). Thus, it is imperative to strengthen the study on NPS pollution in the Danjiangkou Reservoir to offer a scientific foundation for water quality protection.
As one of the typical inflow rivers of the Danjiangkou Reservoir, the water condition of the Guanshan River directly affects the water environment of the Danjiangkou Reservoir. However, despite its high significance to this area, NPS pollution in the Guanshan River Basin has not been investigated thus far. To overcome this shortcoming, this study selected the Guanshan River Basin and investigated NPS pollution using a SWAT model, focusing on nitrogen, phosphorus, and heavy metals. This investigation sought to (1) assess the NPS pollution load of nitrogen, phosphorus, and heavy metals in the basin to identify their contributions to the Danjiangkou Reservoir; and (2) analyze the spatial and temporal distribution of nitrogen, phosphorus, and heavy metal loads and possible source to provide a reference for NPS pollution management in Guanshan River. This research can offer a scientific foundation for the formulation and implementation of watershed management measures in the Danjiangkou Reservoir as well as serve as a guide for NPS pollution control in other analogous watersheds.
MATERIALS AND METHODS
Study area description
Location of the study area, including terrain, land use, and sample locations.
Datasets used
The SWAT model 10.5 used in this research is based on the ArcGIS 10.5 platform, and the powerful spatial analysis ability of GIS and the management ability of geographic data were used to collect, extract, and store the geographic parameters required for the operation of the model. The basic database required includes a spatial database and an attribute database (Table 1). The spatial database utilized in this study comprises several key components, including the digital elevation model (DEM), land use type map, soil type map, and river network map of the study area (Li et al. 2021b). The attribute database encompasses a range of data types, including meteorological, water quality, and hydrological data, as well as soil physical and chemical attribute data, and information on pollution sources (Ouyang et al. 2012; Szalińska et al. 2021).
SWAT model input data table
Type of data . | Data sources . | Data description . |
---|---|---|
DEM diagram | Geospatial Data Cloud (http: //www.gscloud.cn) | 30 m × 30 m grid diagram |
Land use map | Institute of Aerospace Information Innovation, Chinese Academy of Sciences (https://data.casearth.cn/sdo/detail/5fbc7904819aec1ea2dd7061) | 30 m × 30 m grid diagram |
Soil type map | HWSDv1.2 (https: //www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/) | 1:1 million soil type data |
Meteorological data | CMADS V1.2 (http: //www.cmads.org/) | Daily precipitation, maximum and minimum temperature, humidity, radiation, and wind speed from 2013 to 2018 |
Water quality and hydrological data | Hubei Provincial Bureau of Hydrology and Shiyan Municipal Bureau of Hydrology and Water Resources | TN, ammonia, TP, runoff |
Soil attribute data | HWSDv1.2 (https: //www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/) | Physical and chemical properties of soil |
Type of data . | Data sources . | Data description . |
---|---|---|
DEM diagram | Geospatial Data Cloud (http: //www.gscloud.cn) | 30 m × 30 m grid diagram |
Land use map | Institute of Aerospace Information Innovation, Chinese Academy of Sciences (https://data.casearth.cn/sdo/detail/5fbc7904819aec1ea2dd7061) | 30 m × 30 m grid diagram |
Soil type map | HWSDv1.2 (https: //www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/) | 1:1 million soil type data |
Meteorological data | CMADS V1.2 (http: //www.cmads.org/) | Daily precipitation, maximum and minimum temperature, humidity, radiation, and wind speed from 2013 to 2018 |
Water quality and hydrological data | Hubei Provincial Bureau of Hydrology and Shiyan Municipal Bureau of Hydrology and Water Resources | TN, ammonia, TP, runoff |
Soil attribute data | HWSDv1.2 (https: //www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/) | Physical and chemical properties of soil |
From 86 sampling sites (including farmland soil and undisturbed soil, as well as river surface sediments) in the Guanshan River Basin (Figure 1), 118 samples were obtained to represent the distribution of heavy metal concentration in the soil (Figure S1). Each sampling point uses a gravity-grab sediment sampler to collect soil and sediment samples. The samples are wrapped in a plastic wrap to isolate the air, wrapped in tin paper to avoid light, and placed in a polyethylene sealed bag. The samples are labeled back to the laboratory for analysis as soon as possible. The 0.50 g sample (error within 0.001 g) was weighed in the Teflon digestion tube, and 8 mL nitric acid, 4 mL perchloric acid, and 6 mL hydrofluoric acid were added for digestion; the acid was thoroughly expelled, and then 5 mL 1:1 aqua regia was used for dissolution. After dissolution, the volume was diluted to 50 mL with deionized water to obtain the digestion solution. After the digestion solution was filtered through a 0.45 μm filter membrane, the contents of Zn, Cr, As, Pb, Cu, and Cd were determined by inductively coupled plasma mass spectrometry. The experiment used parallel samples and blank samples for quality control, and the relative standard deviation of all heavy metals in three parallel samples was less than 10% to ensure the reliability of the obtained data.
Construction of SWAT model
The SWAT model is combined with GIS software to extract watershed water system based on the DEM, and watersheds are divided by setting the watershed threshold and watershed outlet points (Zhang et al. 2018). In this research, the watershed threshold of 1,300 ha was taken and 15 sub-basins were divided; simulation thresholds were set at 5, 10, and 10%, respectively, for land use, soil area, and slope grade. In the construction of the SWAT model, it is necessary to evaluate the uncertainty of parameters. In this study, the sequential uncertainty fitting (SUFI-2) algorithm of the SWAT-CUP tool was used to analyze the sensitivity and uncertainty of parameters from two aspects of runoff and water quality. The final sensitivity parameters and calibration values are shown in Table 2.
The sensitivity parameters and their calibration values
. | Parameter . | Method . | Range . | Calibrated value . |
---|---|---|---|---|
1 | CN2 | R | −0.5 to 0.5 | −0.548456 |
2 | SFTMP | V | −5 to 5 | −1.272 |
3 | SMTMP | V | −5 to 5 | −1.607 |
4 | SMFMX | V | 0 to 10 | 5.2993 |
5 | TLAPS | V | 0 to 50 | 25.8588 |
6 | HRU_SLP | V | 0 to 0.6 | 0.42087 |
7 | CANMX | V | 0 to 100 | 17.58535 |
8 | GW_DELAY | V | 0 to 500 | 23.87364 |
9 | CH_K2 | V | 0 to 150 | 4.907545 |
10 | NPERCO | V | 0 to 1 | 0.455373 |
11 | N_UPDIS | V | 20 to 100 | 63.866806 |
12 | SDNCO | V | 0 to 1 | 0.504027 |
13 | ERORGN | V | 0 to 5 | 2.782589 |
14 | AI1 | V | 0.07 to 0.09 | 0.084105 |
15 | CDN | V | 0 to 3 | 1.717586 |
16 | PSP | V | 0.01 to 0.7 | 0.165204 |
17 | ERORGP | V | 0 to 5 | 0.001262 |
18 | USLE_P | V | 0 to 1 | 0.181434 |
. | Parameter . | Method . | Range . | Calibrated value . |
---|---|---|---|---|
1 | CN2 | R | −0.5 to 0.5 | −0.548456 |
2 | SFTMP | V | −5 to 5 | −1.272 |
3 | SMTMP | V | −5 to 5 | −1.607 |
4 | SMFMX | V | 0 to 10 | 5.2993 |
5 | TLAPS | V | 0 to 50 | 25.8588 |
6 | HRU_SLP | V | 0 to 0.6 | 0.42087 |
7 | CANMX | V | 0 to 100 | 17.58535 |
8 | GW_DELAY | V | 0 to 500 | 23.87364 |
9 | CH_K2 | V | 0 to 150 | 4.907545 |
10 | NPERCO | V | 0 to 1 | 0.455373 |
11 | N_UPDIS | V | 20 to 100 | 63.866806 |
12 | SDNCO | V | 0 to 1 | 0.504027 |
13 | ERORGN | V | 0 to 5 | 2.782589 |
14 | AI1 | V | 0.07 to 0.09 | 0.084105 |
15 | CDN | V | 0 to 3 | 1.717586 |
16 | PSP | V | 0.01 to 0.7 | 0.165204 |
17 | ERORGP | V | 0 to 5 | 0.001262 |
18 | USLE_P | V | 0 to 1 | 0.181434 |
Calculation model of NPS pollution load










Factor analysis
Factor analysis (FA) is a useful tool to provide information of heavy metal behavior and sources. Kaiser–Meyer–Olkin and Bartlett tests showed that the dataset of six heavy metals (Zn, Cr, As, Pb, Cu, and Cd) concentration was suitable to conduct FA, so FA was conducted based on SPSS26.0. The principal component analysis method was used to constructing factor variables, and the Kaiser normalized maximum variance method was used to rotate factor variables.
RESULTS AND DISCUSSION
Model calibration and verification

Measurement and simulation of monthly runoff, TN, ammonia nitrogen, and TP load in Guanshan River during calibration and verification period.
Measurement and simulation of monthly runoff, TN, ammonia nitrogen, and TP load in Guanshan River during calibration and verification period.
Nitrogen and phosphorus pollution characteristics
Temporal variation of nitrogen and phosphorus loads

(a–c) Nitrogen and phosphorus changes in Guanshan River Basin in different periods from 2013 to 2018. (d) Time series of average precipitation, water yield, and sediment in Guanshan River Basin from 2013 to 2018. (e) Monthly average runoff of Gushan Hydrological Station from 2013 to 2018. (f) Monthly average nitrogen and phosphorus change in Guanshan River Basin from 2013 to 2018.
(a–c) Nitrogen and phosphorus changes in Guanshan River Basin in different periods from 2013 to 2018. (d) Time series of average precipitation, water yield, and sediment in Guanshan River Basin from 2013 to 2018. (e) Monthly average runoff of Gushan Hydrological Station from 2013 to 2018. (f) Monthly average nitrogen and phosphorus change in Guanshan River Basin from 2013 to 2018.
Overall, the nitrogen and phosphorus pollution loads followed the order TN > -N > TP. From 2013 to 2018, the mean yearly pollutant loads of NPS TN,
-N, and TP in the Guanshan River Basin were 5.71 × 105, 5.38 × 104, and 2.20 × 104 kg/a, respectively. The yearly pollutant loads of nitrogen and phosphorus widely varied, and the maximum values of TN (1.30 × 106 kg),
-N (1.56 × 105 kg), and TP (4.68 × 104 kg) all appeared in 2016. The minimum values of TN (2.51 × 105 kg) and TP (7.20 × 103 kg) appeared in 2013, while the minimum value of
-N (1.36 × 104 kg) appeared in 2018.
Analysis of the correlation between nitrogen and phosphorus loads and precipitation, water yield, and sediments revealed a significant positive correlation (Table 3), indicating that precipitation, water yield, and sediments were closely related to the generation of NPS pollutant loads, which is consistent with previous studies (White et al. 2015; Cheng et al. 2020; Szalińska et al. 2021). The correlation between nitrogen and phosphorus loads and sediments was found to be the most significant, followed by water yield and precipitation, indicating that nitrogen and phosphorus pollutants in the study area are mainly transported through sediments. Although precipitation has a significant correlation with the NPS pollution of nitrogen and phosphorus, these pollutants primarily migrate with sediments and water flow. Precipitation acts as more of a driving factor for sediment and water yield, thus indirectly affecting the migration of nitrogen and phosphorus. This result also could be supported by the finding from a previous study that particulate nitrogen accounts for 71.2% of TN and particulate phosphorus accounts for 90.3% of TP in NPS pollution around the Danjiangkou Reservoir (Zhuang et al. 2016). Therefore, strengthening the control of sediment loss in the watershed is crucial to decreasing the NPS pollution of nitrogen and phosphorus.
Correlation analysis of nitrogen and phosphorus with precipitation, water yield, and sediment
. | Precipitation . | Water yield . | Sediment . | TN . | ![]() | TP . |
---|---|---|---|---|---|---|
Precipitation | 0.766a | 0.605a | 0.593a | 0.545a | 0.689a | |
Water yield | 0.841a | 0.819a | 0.786a | 0.920a | ||
Sediment | 0.926a | 0.962a | 0.940a | |||
TN | 0.962a | 0.925a | ||||
![]() | 0.913a | |||||
TP |
. | Precipitation . | Water yield . | Sediment . | TN . | ![]() | TP . |
---|---|---|---|---|---|---|
Precipitation | 0.766a | 0.605a | 0.593a | 0.545a | 0.689a | |
Water yield | 0.841a | 0.819a | 0.786a | 0.920a | ||
Sediment | 0.926a | 0.962a | 0.940a | |||
TN | 0.962a | 0.925a | ||||
![]() | 0.913a | |||||
TP |
aAt the 0.01 level (two-tailed), the correlation is significant.
As shown in Figure 3(d) and Figure S3, precipitation, water yield, and sediments in the basin were higher in 2016 than in other years, which may explain the highest nitrogen and phosphorus loads in 2016. The lower nitrogen and phosphorus loads in 2013, 2015, and 2018 are attributable to the generally low precipitation, water yield, and sediments in these years. Compared to 2016, the nitrogen and phosphorus loads were much smaller in 2017 although precipitation and water yield were similar between these two years. It is understood that the government began to rectify the environment of the Guanshan River from 2014 until 2017 and implemented source control and pollution reduction (such as decreasing the usage of chemical fertilizers) and ecological management (such as returning farmland to forests and grasslands). In 2017, as the last year of rectification, the reduction of sediment volume and the gradual reduction of related agricultural pollution input that year resulted in a significant decrease in the NPS pollution of nitrogen and phosphorus. In contrast, although the amount of sediments in 2014 was much lower than that in 2017, the nitrogen and phosphorus loads were not low, which may be because 2014 was the first year of the remediation of watershed soil pollution.
As shown in Figure 3(f), from the change of pollution load proportion in each year, the overall nitrogen and phosphorus loads from NPS pollution followed the order abundant period > normal period > dry period. TN, -N, and TP loads accounted for 56.63, 79.67, and 71.02% of the whole year in the abundant period, respectively. This was closely related to the high precipitation, water yield, and sediment in the flood seasons. Therefore, strengthening the management of soil and water loss in flood season is key to preventing the NPS pollution of nitrogen and phosphorus.
According to the above analysis, it is known that the nitrogen and phosphorus pollution load is mainly related to the change of sediment and water yield. In the process of comparing the monthly average nitrogen and phosphorus pollution load change and the change of precipitation, water yield, and sediment in the basin (Figure 3(f) and Figure S4), we found that the change of nitrogen and phosphorus pollution load in most months is consistent with the change of sediment and water yield. However, the water yield in June, July, and November (24.77, 25.20, and 20.12 mm) was slightly higher than in April and May (15.54 and 17.89 mm), and the sediment in June, July, and November (0.31, 0.36, and 0.035 t/ha) was lower than in April and May (0.76 and 0.44 t/ha), with some months even showing a difference in sediment of over 20 times. According to the above analysis (Table 3), the positive correlation between sediment yield and nitrogen and phosphorus is most significant; theoretically, the overall nitrogen and phosphorus pollution load in April and May should be higher, but the overall nitrogen and phosphorus pollution load in April and May is lower than in June, July, and November. For example, the water yield in June increased by 38.4% compared with May, and the sediment load decreased by 29.20%, but the TN and TP loads increased by 157.81 and 90.69%, respectively. The most fertile months are from June to July and from October to November. The application of chemical fertilizers may have been the primary cause of the basin's increased pollution burden at this time.
Spatial variation of nitrogen and phosphorus loads






Spatial distribution of average pollution load in Guanshan River Basin from 2013 to 2018: (a) TN; (b) -N; and (c) TP.
Spatial distribution of average pollution load in Guanshan River Basin from 2013 to 2018: (a) TN; (b) -N; and (c) TP.
The average loss intensity of TN, -N, and TP pollution loads in each sub-basin was 24.29, 1.48, and 1.00 kg/ha a, respectively (Figure 4). Sub-basin 10 (TN: 162.07 kg/ha a;
-N: 2.21 kg/ha a; TP: 6.89 kg/ha a) had the highest pollution load loss intensity. Although the output load of sub-basin 10 was not high, the pollution load loss intensity was particularly high due to the small area. Sub-basin 2 (TN: 52.18 kg/ha a;
-N: 7.05 kg/ha a; TP: 2.32 kg/ha a) had the second highest pollution load loss intensity. The pollution load loss intensity of sub-basin 13 was the lowest (TN: 0.32 kg/ha a;
-N: 0.0014 kg/ha a; TP: 0.0062 kg/ha a).



Spatial distribution of (a) sediment, (b) precipitation, and (c) water yield.
Previous studies showed that the TN and TP fluxes of the Danjiangkou Reservoir in 2018 were 48,100 and 1,303.4 tons, respectively (Zhang et al. 2019), while the SWAT simulation showed TN and TP fluxes of 266.06 and 8.2 tons, respectively, for the Guanshan River Basin in 2018. A rough calculation showed that the annual contributions of nitrogen and phosphorus in the Guanshan River Basin to the TN and TP fluxes in the Danjiangkou Reservoir are 0.55 and 0.63%, respectively. However, the area of the Guanshan River Basin comprises only 0.49% of the catchment area of the Danjiangkou Reservoir. Therefore, the risk of nitrogen and phosphorus input from the Guanshan River Basin to the Danjiangkou Reservoir is still of concern.
For sub-basins 1, 2, 3, and 7, a vegetation buffer zone and grass waterway could be set up, and the use of chemical fertilizers in farmland in the above sub-basins can be reduced to mitigate NPS pollution (Zhou et al. 2019; Li et al. 2021a). For sub-basin 5, the direct discharge of domestic sewage and livestock waste should be controlled.
Variation of heavy metal loads
Table 4 presents a summary of the content of heavy metals in the soil of Guanshan watershed, and the content of heavy metals widely varies, which is related to the different environments around the sampling points. Except for Zn and As, other heavy metal mean concentrations were below background levels, and all heavy metal mean concentrations were below the national risk screening levels. Therefore, the heavy metal contamination in the basin was weak, and attention should be paid to the input of anthropogenic Zn and As.
Statistical summary of heavy metal concentrations in soil in the study area (mg/kg)
Items . | Zn . | Cr . | As . | Pb . | Cu . | Cd . |
---|---|---|---|---|---|---|
Minimum | 12.37 | 1.93 | 4.21 | 2.63 | 3.07 | 0.01 |
Maximum | 1,181.59 | 242.34 | 129.07 | 86.79 | 61.93 | 0.60 |
BV | 83.6 | 86 | 12.3 | 26.7 | 30.7 | 0.172 |
GB | 250 | 200 | 30 | 120 | 100 | 0.3 |
Average | 149.55 | 25.73 | 26.37 | 22.4 | 23.47 | 0.0506 |
S.D. | 143.48 | 25.7 | 20.1 | 9.26 | 9.23 | 0.0558 |
CV (%) | 95.94 | 99.88 | 76.22 | 41.34 | 39.33 | 110.28 |
Items . | Zn . | Cr . | As . | Pb . | Cu . | Cd . |
---|---|---|---|---|---|---|
Minimum | 12.37 | 1.93 | 4.21 | 2.63 | 3.07 | 0.01 |
Maximum | 1,181.59 | 242.34 | 129.07 | 86.79 | 61.93 | 0.60 |
BV | 83.6 | 86 | 12.3 | 26.7 | 30.7 | 0.172 |
GB | 250 | 200 | 30 | 120 | 100 | 0.3 |
Average | 149.55 | 25.73 | 26.37 | 22.4 | 23.47 | 0.0506 |
S.D. | 143.48 | 25.7 | 20.1 | 9.26 | 9.23 | 0.0558 |
CV (%) | 95.94 | 99.88 | 76.22 | 41.34 | 39.33 | 110.28 |
BV, background values of Hubei (China National Environmental Monitoring Center 1990); GB, Chinese standard (Soil environmental quality – Risk control standard for soil contamination of agricultural land GB 15618-2018) (6.5 < pH ≤ 7.5); SD, standard deviation; CV, coefficient of variation; n = 118.
Changes of heavy metal pollution load in Guanshan River Basin from 2013 to 2018.
Changes of heavy metal pollution load in Guanshan River Basin from 2013 to 2018.
Pollution load distribution of heavy metals in the study area: (a) Cd, (b) Cr, (c) Cu, (d) Zn, (e) As, and (f) Pb.
Pollution load distribution of heavy metals in the study area: (a) Cd, (b) Cr, (c) Cu, (d) Zn, (e) As, and (f) Pb.
Soil heavy metal sources include natural and anthropogenic sources (Wang et al. 2022b). Natural sources include geological processes, such as rock formation, weathering, desertification, and erosion, while anthropogenic sources involve human activities, such as mining, processing of ore, refining, automobile exhaust, atmospheric deposition, waste treatment, sewage, and fertilization (Zamora-Ledezma et al. 2021).
To further investigate the pollution source of heavy metals, FA was carried out (Huang et al. 2015). Table S1 and Figure S5 show that the six heavy metals in the soil can be explained by four factors, accounting for 85.264% of the total variance. Factor 1 comprised 29.33% of the total variance, mainly including As, Pb, and Cu; factor 2 comprised 19.584% of the total variance, mainly Cr; factor 3 comprised 19.433% of the total variance, mainly Zn and As; factor 4 comprised 16.917% of the total variance, mainly Cd.
In the study area, only the concentrations of Zn and As in the soil exceeded the soil background values of Hubei Province. The concentrations of other heavy metals were below the background value, and the average concentrations of all heavy metals were below the risk screening value of the national standard, indicating that the heavy metals in the soil of the region of work were mainly derived from rock weathering. Based on the results of the FA, factor 3 attributed to human sources, and factors 1, 2, and, 4 could be regarded as natural sources. The study area has weak industrial activities, and the primary land use type is forest land and farmland. Therefore, the primary anthropogenic source in the study area is agricultural activities. The agricultural sources of Zn in the study area include the use of fertilizers and livestock manure and irrigation using sewage water (Kuziemska et al. 2016; Lei et al. 2020). Besides, as Guanshan Town has a gas station, the frequent traffic activity and zinc dust produced by the wear of automobile tires are also one of the sources of Zn (Miazgowicz et al. 2020). The origins of As include the application of arsenic-containing pesticides and insecticides, irrigation using sewage water, and the input of fertilizers and livestock manure (Zamora-Ledezma et al. 2021). Overall, the input of fertilizer, livestock manure, and sewage irrigation may be the common sources of Zn and As in the study area.
According to the analysis presented above, to regulate the NPS pollution of heavy metals in the basin, the focus should first be on reinforcing the management and prevention of soil erosion (especially during flood season) in the basin. Despite the anthropogenic sources of heavy metals in the Guanshan River Basin account for only a minor proportion, the input of heavy metals from agricultural activities (fertilization, pesticides, and livestock manure, etc.) and other anthropogenic sources (automobile exhaust emissions, tire wear, emission of harmful gases and dust containing heavy metals) also need attention. On the whole, sub-basins 1, 2, 3, 7, and 11 demand special attention. Specifically, as sub-basins 1, 2, and 7 have a large area of cultivated land, human-caused soil degradation in these sub-basins should be monitored. Furthermore, soil erosion under natural conditions should be considered in sub-basins 3 and 11.
CONCLUSIONS
Using the spatial database and attribute database gathered in the study, a SWAT model for the Guanshan River Basin was constructed. The results demonstrated that R2 and NS satisfy the precision requirements of the model and can be applied to investigate the features of NPS pollution in this basin. This research revealed the spatial and temporal distribution of nitrogen, phosphorus, and heavy metals in the Guanshan River Basin and found that the loads of nitrogen, phosphorus, and heavy metals were larger in the flood season than in other periods, and they showed significant positive correlation with precipitation, water yield, and sediments in this period. At the same time, high nitrogen, phosphorus, and heavy metal loads could be mainly attributed to natural processes and agricultural activities. The findings suggest that the flood season is the key period of NPS pollution control. Specifically, controlling the input of fertilizers, pesticides, and livestock manure in agricultural land and preventing soil erosion downstream of the basin are key to reducing nitrogen, phosphorus, and heavy metal pollution. Although this study has certain limitations and more data are required to improve the overall quality, the assessment of nitrogen, phosphorus, and heavy metal loads based on the spatiotemporal analysis provides valuable information for the development of effective watershed management strategies.
AUTHOR CONTRIBUTIONS
WC contributed to conceptualization, data curation, investigation, writing the original draft, and methodology. TM contributed to funding acquisition, reviewing and editing the manuscript, and conceptualization. LC contributed to conceptualization, supervision, reviewing and editing the manuscript, and investigation. WL contributed to data curation, investigation, and reviewing and editing the manuscript. RS contributed to data curation and investigation. ZC contributed to investigation. All authors read and approved the final manuscript.
FUNDING
This work was funded by the Project of China Geological Survey (No. 121201001000150121).
DATA AVAILABILITY STATEMENT
Data cannot be made publicly available; readers should contact the corresponding author for details.
CONFLICT OF INTEREST
The authors declare there is no conflict.