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Article

Pollution Source Apportionment of River Tributary Based on PMF Receptor Model and Water Quality Remote Sensing in Xinjian River, China

1
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
2
Beijing Institute of Space Mechanics & Electricity, Beijing 100094, China
3
Beijing Engineering Technology Research Center of Aerial Intelligence Remote Sensing Equipments, Beijing 100094, China
4
Research Institute of Frontier Science, Beihang University, Beijing 100191, China
5
Lishui Ecological and Environmental Monitoring Center of Zhejiang Province, Lishui 323050, China
*
Author to whom correspondence should be addressed.
Water 2023, 15(1), 7; https://doi.org/10.3390/w15010007
Submission received: 8 October 2022 / Revised: 3 December 2022 / Accepted: 13 December 2022 / Published: 20 December 2022
(This article belongs to the Section Water Quality and Contamination)

Abstract

:
Accurately identifying the source and controlling the total amount of pollutants are the basis for achieving regulation of pollution sources, which is critical for the prevention and control of surface water pollution. For this purpose, this study used the Xinjian River in Jinyun County, Lishui City, Zhejiang Province, China, as a case study to explore whether and how the tributary inflow impacts the downstream water quality. The main pollution sources in the upstream, midstream, and downstream of the Xinjian River were apportioned using the Positive Matrix Factorization (PMF) model based on the water quality data from four sample stations from January 2018 to September 2022. According to the unmatched factor in different sections, it is plausible to infer that the TN and TP are mainly caused by the tributaries. To enhance the reliability of pollution source apportionment based on the receptor model, a series of remote sensing images with high resolution were used to derive the water quality concentrations to present the spatial distribution and reveal the long-term trend of the local water environment. It is anticipated that the apportionment results could be of great assistance to local authorities for the control and management of pollution, as well as the protection of riverine water quality.

1. Introduction

1.1. Research Background and Significance

Water pollution poses a great threat to ecosystems and public safety. Due to its function in carrying agricultural runoff and municipal and industrial wastewater, rivers are susceptible to pollution [1], particularly in developing countries with a growing population and rapid industrialization and urbanization when fundamental infrastructure, such as sewage networks and treatment facilities, cannot keep up the pace of economic development [2]. The water quality in rivers is influenced by multiple factors. Complex pollution sources and highly fluctuating hydrological factors due to seasonal variations increase the uncertainty of water quality and pose a challenge for water resource management. Controlling pollution sources is an effective way for protecting water resources, but the prerequisite is to identify pollution sources and accurately quantify their contribution of each pollutant; however, determining pollutant sources in surface water is challenging due to their migration and dispersion, as well as the mixing effect of numerous sources [3].

1.2. Research Status

To this end, various pollution source analysis methods, including qualitative identification, quantitative identification, and a combination of qualitative and quantitative analysis, have been proposed [4,5,6].
Emission inventory is an important tool for identifying the source of pollutants and quantifying the pollution load in a specific area at a particular time; however, the emission inventory is generally based on the gross national level and rarely accounts for microlevel activity data [7,8]. The isotope tracer method has also been widely employed to resolve pollution sources and their contributions towards an environmental impact [9,10], but it is limited to some extent by high equipment requirements and a complex analysis process [11].
In addition, numerical modeling based on the diffusion model, a traditional quantitative source apportionment technique, has been utilized to simulate the output and transport process of pollutants to determine the pollution sources and their contributions [12,13,14]. This technique can predict the temporal and spatial variations of pollutants, but it requires a comprehensive understanding of the transportation and transformation mechanisms of the pollutants; moreover, it heavily relies on priority pollutant emission inventories and hydrology data, as well as model parameter optimization, which is often constrained in practical applications [15]. The field, as a whole, suffers from a lack of measurements of source emission compositions. Therefore, the lack of profiles is a serious problem in effective source apportionment [16]. The complexity and the accuracy of the numerical model has been improving as more information can be input; however, there remain limitations to the ability to fully and accurately represent the variability of species and concentrations observed in the environment. Thus, alternative approaches based on measurements for understanding the nature of the relationship between the source and receptor that exists in the environment can be essential for providing the most effective and efficient approach to apportion the pollutants to a specific source type.
Widely used in source apportionment in a variety of environmental media, receptor models are mathematical procedures that use optimal regression techniques for identifying and quantifying the sources of ambient pollutants and their effects at a site (the receptor), primarily on the basis of measured species concentration at the receptor, and generally without the requirement of emissions and meteorological or hydrological data [17].
The receptor model quantifies the contribution rate of each potential pollution source through the chemical analysis of the receptor samples. It is the only one of the three source apportionment methods that does not rely on the emission conditions of pollution sources and does not require tracking of the migration process of target pollutants. As a result, it has become the most prevalent source analysis technology, and is applicable to both qualitative and quantitative analyses of pollution sources. The receptor model mainly consists of a composition/ratio method, chemical mass balance model (CMB), and multivariate statistical method, etc. Multivariate statistical methods are typically represented by a principal component analysis/factor analysis (PCA/FA) [18], principal component/factor analysis-multiple linear regression (PCA/FA-MLR) model [4,19,20], absolute principal component score-multiple linear regression (APCS-MLR) models, and positive definite matrix factor decomposition model (PMF).
The fundamental principle of the receptor model is the mass conservation, and a mass balance analysis can be used to identify and apportion sources of contaminants. If the source and the profile are known, then CMB is the appropriate model [21]. Among the above methods, the CMB model requires a complete profile of emission components, which is difficult to ascertain in practice. Few or no profiles have been measured in developing countries, although good fits can be obtained by using a given set of source profiles from the literature or the United States, though this approach raises serious concerns regarding the accuracy of the apportionment [16].
However, if the number and nature of the sources are unknown and have to be derived from the ambient data, then a multivariate statistical model can be applied. PCA/FA are powerful tools for exploring intrinsic relationships and are widely utilized for providing qualitative information about the nature of the source profile compositions and the relative importance of a given source to the measured concentrations, but they alone cannot quantify the contribution of each variable to the identified pollution sources; moreover, it does not account for data uncertainty, and the data interpretation is usually uncertain and subjective. This can be accomplished using APCS-MLR [1,22,23] and positive matrix factorization (PMF), etc. [4,5].
The PMF receptor model was primarily used to identify and apportion the potential pollution sources in an atmospheric environment [24,25,26,27]. This method has been increasingly utilized to identify the contamination sources in a water environment [28,29,30,31], it does not rely on the component profile of pollution sources, but rather on the number and type of pollution sources. PMF is specifically appropriate for environmental data for the consideration of data uncertainty and can cope with missing and below-detection-limit data by associating them with a higher uncertainty; moreover, it forces all of the values in the solutions profiles and contributions to be positive, which can lead to a more environmentally interpretable solution than that derived from other multivariate methods such as PCA/FA [32]. Therefore, when the environment is relatively simple, the pollution source type and composition profile are unknown, and the sample data is normal, thereby making PMF an excellent choice for water pollution apportionment.
In contrast, the PMF and PCA/FA-MLR models mainly use the variation of water quality parameters to analyze the potential pollution sources and their contributions while relying less on the source component profile [4,33]. However, in the process of source apportionment analysis, the identification of source types depends on prior information. Therefore, this method requires researchers to judge the number and types of pollution sources, which may cause bias in the pollution source analysis based on different perceptions of the research [34,35]. In view of the weakness of individual models, the comparison of various methods can help to reduce this subjectivity [36]. Additionally, it is feasible to integrate multiple models to achieve more accurate and reliable results, which can be termed the ensemble method [16,37].
However, all the aforementioned models have the issue that they can only identify the types of contaminants and cannot achieve the spatial location of pollution sources. Specific treatment measures remain difficult to implement. In fact, only when the spatial location of pollution sources is identified can the source apportionment play a definite role in environmental enforcement, which could result in insights into appropriate water management policies [2]. Consequently, obtaining the spatial location of pollutant sources is of critical importance.
Some efforts have been made to analyze the spatial distribution of pollution sources through a geostatistics approach with spatial interpolation on sampling points. Zanotti et al. [32] used PMF to analyze the pollution sources of groundwater and surface water in the Oglio River, and marked the distribution characteristics of various pollution sources with a GIS method that combined a factor score and sampling point location. Agyeman et al. [38] combined the PMF model and geostatistical model, such as ordinary Kriging and empirical Bayesian Kriging, and developed the hybridized geostatistical-based receptor model: ordinary Kriging-positive matrix factorization (OK-PMF) and empirical Bayesian Kriging-positive matrix factorization (EBK-PMF). The limitation of this method is that the interpolation method can only be used to estimate the pollution distribution characteristics in the space within the sampling point and not in the space outside the sampling point.

1.3. Focus of This Study

As an effective means of obtaining spatial information, remote sensing can increase the deficiency of conventional monitoring. With the improvement of spatial resolution and the rapid increase of satellite numbers, remote sensing has been used for operational monitoring for small rivers. This study intends to conduct the sources apportionment using monthly water quality data collected from the upstream, midstream, and downstream of the Xinjian River in Jinyun County, Zhejiang Province, China, based on the PMF receptor model. The spatial location of river pollution sources can be further determined based on the significant concentration differences based on remote sensing inversion.
In the main part of the article, the geographical situation of the study area is analyzed, and the water quality characteristics of the sampling sites are analyzed before the identification of water pollution sources based on the PMF model in the upstream and downstream, respectively. Finally, the pollution characteristics of the upstream and downstream are compared and the tributaries’ contribution has been addressed.

2. Materials and Methods

2.1. Site Description

The study area is located within the Xinjian sub-watershed in Jinyun County, Lishui City, Zhejiang Province, China (Figure 1a). The main river in the study area is the Xinjian River with the inflow of four tributaries. Considering the difference in land use and population, the study area is divided into three sections: upstream, midstream, and downstream, with the Xinjian Town and Zhaiji monitoring station serving as the boundary. In the upstream, Xinanxi River originates from Xuefeng Village in Xinjian Town, and the land on both sides of the river is predominantly undeveloped, with a tributary (Bihexi River) flowing into the main river. There are more villages and farmland on both sides of the midstream than that in the upstream, and some factories are distributed along the banks. Two tributaries (Xinanxi River and Qianyangxi River) flow into the main river. In the downstream area, the economic development zone of Jinyun County is located in Xinbi Street. The predominant land use is industrial zoning and a large number of residential areas. A tributary (Qiaoxi River) flows into the main river in this section. The basic characteristics of the Xinjian River and its main tributaries are shown in Figure 1b.
The study area belongs to the subtropical monsoon climate zone, which is warm and humid with sufficient heat and abundant precipitation. The water quality of the Xinjian River basin has been mostly at Grade III, with a few instances of Grade II (GB 3838-2002), which is worse than the majority of the rivers in Lishui. Figure 1 shows the location of the study area and the water quality monitoring sites in the Xinjian River. The water quality was monitored at four representative sites. As indicated in Figure 1b, Site 1, the drinking water source for Xinjian Town, is located upstream of the river. Site 2, Zhaiji, is where the outflow of Xinjian Town and the inflow of Xinbi Street are located. Site 3, Xiaoxiaoxixia, is located downstream of the Jinyun Economic Development Zone. Site 4, Guangyao, is the exit of the Xinjian River in Lishui City and is the entrance to neighboring Jinhua City.

2.2. Sampling Strategies

There are four water quality monitoring stations in the Xinjian River from southwest to northwest, including the Xinjian Town water source, Zhaiji, Xiaxiaoxixia, and Guangyao. There are two tributaries between the Xinjian Town water source and Zhaiji monitoring station. The monitoring frequency of water quality monitoring stations along the Xinjian River is shown in Table 1. The sampling, preservation, transportation, and analysis of the water samples followed the standard methods (State Environment Protection Bureau of China 2002). There are a total of 29 water quality samples in the site of the Xinjian Town water source, Zhaji, and Xiaxiaxixia, respectively, and were recorded during the odd months from January 2018 to September 2022. The Guanyao station started in January 2021 with a monthly sampling frequency. A total of 108 samples were collected and used in the source identification analysis. The water quality analysis follows the standards methods, as shown in Table 2.
The 24 measured water quality parameters include water temperature, pH value, electrical conductivity, dissolved oxygen, permanganate index, COD, BOD5, NH3-N, TN, TP and F, oils, volatile phenol, mercury, lead, copper, zinc, selenium, arsenic, cadmium, Cr (VI), cyanide, anionic surfactant, and sulfide. Most of the concentration of metal ions, cyanide, anionic surfactant, and sulfide were below the detection limit, so they were not selected for source apportionment analysis. The criteria for selecting water quality monitoring indicators were that each station have measured data and the selected parameters can effectively indicate the local water quality. Therefore, six water quality evaluation indices, including DO, CODMn, NH3-N, TN, and TP and F, were determined. Table 2 shows the detection methods, detection limits, and relative standard deviations for each water quality index.

2.3. Methods

Unlike conventional factor analysis methods, the PMF model can distinguish between different sources in a mixture and has a better physical meaning through non-negative constraint on the factor score. The PMF model is an effective factorization method based on a least iterative square algorithm that divides the concentration matrix of the sample into a factor contribution matrix, a factor distribution matrix, and a residual matrix as follows:
X(n×m) = G(n × p)F(p × m) + E(n × m)
where the sample concentration matrix (X) includes n samples and m chemical substances; chemical profile matrix (F) describes P factors or sources; the G-matrix describes the contribution of each factor to any given sample; and E is a residual matrix.
The PMF model is based on a weighted least square method for finite and iterative calculations, and constantly decomposes the matrix to achieve the optimal result. The objective of PMF optimization seeks to minimize the objective function Q:
Q = i = 1 n j = 1 m ( x ij k = 1 p g ik f kj / u ij ) 2
where uij is the concentration uncertainty of species j in the ith sample; xij is the observed concentration with jth chemical species of ith sample; gik is the kth factor contribution of ith sample; and fkj is the jth factor profile of factor kth.
The concentration data and uncertainty data must both be loaded during the operation. There are two ways to provide uncertainty: observation-based and equation-based. The observation-based method should provide an estimate of the uncertainty for each species in a sample. The equation-based method should provide species-specific parameters that the PMF model uses to calculate uncertainties for each sample. When the target concentration is less than or equal to the corresponding method detection limit (MDL), the value of uncertainty is [39]:
Unc = 5MDL/6
When the target concentration is greater than the corresponding MDL, the uncertainty value is:
Unc = [(Urel × xij)2 + (0.5MDL)2]1/2
where Urel is the relative standard deviation of the monitoring project. Neither method detection limits nor relative standard deviations are allowed to be zero and negative. A detailed discussion of calculating uncertainties can be referred to in Reff et al. [39].
The factor number must be determined for the PMF model. If there are too many factors, a source may be divided into two or more parts. Otherwise, various sources of pollution will be merged into one. Meanwhile, the residual matrix should be minimized to ensure the correlation between the calculated results and the observations. Therefore, when determining the number of factors, it is critical to consider the minimization of the Q value and the control of the residual matrix.
The conventional source apportionment analysis can only provide the category information of pollution sources without a spatial location. Therefore, satellite remote sensing inversion was conducted in this study. The series of Sentinel-2 satellite images that were synchronized or quasi-synchronized with ground-based water quality data were acquired. Since most of the water quality images are not optically active matter, the classic bio-optical model in ocean color remote sensing is invalid, and it is also difficult to establish the correlation between water quality parameters and image reflectance using conventional statistical methods. Consequently, a data-driven machine learning method was employed in this study. Machine learning can utilize complicated networks and structures to extract explicit relationships between the input variable and the desired output variable [40]. Hence, the random forest (RF) was utilized to estimate the water quality using the Sentinel-2 images in this study. Since the main role of the source identification is based on the receptor model and the remote sensing mainly acts in an assistance role, the details for the inversion model can be referred to in the related studies [41,42].

3. Results and Discussion

3.1. Characteristics of Water Quality at Sampling Points

The statistical description of the water quality in the upstream, midstream, and downstream were presented in Table 3, Table 4 and Table 5. According to the surface water environmental quality standard of China (GB 3838-2002), a water quality of Grade I and II is considered to be clean or have a low pollution status; furthermore, Grade III corresponds to moderate pollution and Grade IV and V are considered to be high pollution. [35]. The majority of water quality parameters in the study area ranged from Grade I–II, with the exception of TN in upstream (Table 3) and both TN and TP in midstream (Table 4) and downstream (Table 5). The mean mass concentration of downstream TP was 0.16 mg/L, which corresponded to Grade II–III, and the mean mass concentration of TN was 2.62 mg/L, which corresponded to an inferior Grade V. The major pollutants in this study area are TN, followed by TP.
The coefficient of variation (CV) is used to describe the variation degree of samples over time or space, which can eliminate the influence caused by the difference of units and mean value [2]. Specifically, CV < 20% means low variability, 20% ≤ CV ≤ 50% means moderate variability, CV > 50% means high variability, and CV > 100% means exceptionally high variability. Since the water quality indexes in this study are discussed separately from upstream, midstream, and downstream, they are primarily used to describe the variations in water quality indexes over time, and the coefficient of variation also reflects the temporal differences between various water quality indexes.
In the Xinjian River, the CV of DO, CODMn, and TN were less than 0.3, which belongs to weak variability, thereby indicating low variability and the resulting in the concentration steadily fluctuating around the mean value. In contrast, the CV of NH3-N was greater than 0.5, indicating that there is significant time variation. Combined with the skewness value, the skewness values of NH3-N in the upstream, midstream, and downstream were all greater than 1, showing a positive skewness, and indicating that there was a high peak concentration of NH3-N.
Figure 2 depicts the variation of the selected water quality at four sites during the study period, and Figure 3 presents the average concentration of water quality in the upstream, midstream, and downstream in the Xinjian River. It can be found that the water quality gradually declines from the upstream to the midstream and then to the downstream.
All parameters in the upstream were superior to those in the midstream and downstream because the river originates from the mountain area, which has less impact from human activities and pollution. Specifically, according to the DO concentration, the upstream belongs to Grade I water, and the midstream and downstream belong to Grade II water during most of the study period, and dropped to Grade II water during some period of 2019. According to the judgement of the CODMn concentration, the upstream water quality fluctuates between Grade I and Grade II water, whereas the midstream and downstream water quality fluctuate between Grade II water and Grade III water. According to the NH3-N concentration, the upstream water quality belongs to Grade I water and seldom belongs to Grade II water. According to the judgement of the TN concentration, the TN concentration in the upstream is generally significantly lower than that in the midstream and downstream. Similarly, the TP concentration in the upstream is generally significantly lower than that in the midstream and downstream. The midstream and downstream belong to Grade III water most of the time, with seldom Grade II. The F concentration is lower in the upstream than that in the midstream and downstream. According to the classification criteria of F, the water for all of the Xinjian River belongs to Grade I water (<1).
From the aforementioned analysis, we can find that the overall water quality of the Xinjian River is in good condition. Most of the water quality in the upstream is Grade I water, which meets the standards for drinking water sources, while the indicators exceeding the standard are TP and TN.
When the average concentrations of TN and TP in the Xinjian River and in Lishui City with 100 sites during the study period were compared, the former is evidently inferior to that of the latter (Figure 4). The TP in the upstream is comparable to that of Lishui’s average condition, which is significantly lower than that in the midstream and downstream.

3.2. Source Apportionment

The source apportionment and various factor contributions were estimated by the EPA PMF software, which incorporate the concentration and uncertainty factors as model inputs. The resulting input parameters then generated a signal-to-noise ratio (S/N) for each W/Q parameter. The parameters were classified as Strong (S/N > 2), Weak (0.2 < S/N < 2), and Bad (S/N < 2), respectively, based on the S/N ratio. Based on the iterations for each factor obtaining the minimum Q (Robust)/Q(Exp) value, four factors were considered for the PMF analysis to assess each of their contributions to the water pollution of the Xinjian River. (Table 6). The model was then simulated, considering all of the input parameters and the four factors, thereby providing the results shown in Figure 5, Figure 6 and Figure 7.
Table 6 shows the goodness-of-fit parameters of the PMF model operations in the upstream, midstream, and downstream, respectively. Q is the critical parameter for PMF, where Q(true) is the goodness-of-fit parameter that is calculated including all points and Q(robust) is the goodness-of-fit parameter that is calculated excluding points not fit by the model, which are defined as samples for which the uncertainty-scaled residual is greater than 4 [25].
Figure 4, Figure 5 and Figure 6 show the fitting comparison diagrams of measurement and prediction of water quality parameters in the upstream, midstream, and downstream, respectively. The prediction is mainly evaluated using statistics such as the intercept, slope, and R2. It can be found that the correlation coefficient between the simulated and predicted values of most water quality parameters is high, and R2 is close to 1 with the exception of CODMn and TP in the upstream, and CODMn and TN in both the midstream and downstream. It can be seen that the PMF has good fitting performance for these water quality parameters, and the selected factors can explain the information contained in the original data.

3.3. Identification of Pollution Sources

According to the water quality concentrations in the upstream, midstream, and downstream, the source profile and contribution rate of the corresponding pollution factors can be identified by the PMF model. The pollution source corresponding to the factor can be determined according to the significant identified pollutants in the source profile of each factor and the relative relationship between the factors.

3.3.1. Identification of Pollution Sources in Upstream

The PMF model resolved the component profile and contribution rates of four pollution factors in the upstream of the Xinjian River (Figure 8). These factor profiles need to be interpreted to identify the source types that may be contributing to the samples.
The Factor (F1) showed high loading on F, which accounted for 44.1% of the total variance (Table 7). F is an important identification element of F1, which can be inferred to industrial sources; however, there are no big factories in the upstream area. Considering the local conditions, rural family workshops and small factories are prevalent in Zhejiang Province, and rural family workshops for the processing industry might contribute to this source. TN and TP significantly contribute to Factor 2 (F2), accounting for 49.5% and 48.8% of the total variance, respectively. Specifically, the contribution to TP is significantly more than that of other factors, and potential sources include the use of chemical fertilizers, pesticides, livestock and poultry wastewater, and waste discharge. Therefore, F2 can be considered as an agricultural non-point source of wastewater. Factor 3 is primarily dominated by NH3-N (74.2%) and TP (29.0). Consequently, NH3-N can be considered as an essential identification element for F3. This factor might be interpreted as urban and rural domestic sewage. The concentration of water quality in Factor 4 (F4) is much lower than the other three factors—except DO; hence, F4 can be considered as the natural background source, such as soil.

3.3.2. Identification of Pollution Sources in Midstream

Figure 9 shows the identification results of pollution sources in the midstream. F1 showed high loading on F, accounting for 67.1% of the total variance (Table 8). F is an important identifier for F1. Concurrently, CODMn, TN, and TP substantially contributed to F1 with 29%, 27.6%, and 28.6%, respectively. Consequently, F1 can be inferred to industrial sources. The pollution level of this factor is relatively heavy, which is consistent with the fact that the existence of the factory and also the number of family workshops are more than that in the upstream (Figure 1). F2 indicates a low pollution level. TP and TN have a slightly higher contribution rate in this factor, which are 14.2% and 24.6%, respectively. It is judged that the source is the local small livestock and poultry breeding. F3 has a high loading on NH3-N, accounting for 99.6% of the total variance. NH3-N can be considered as a crucial identifier for F3. This factor can be interpreted as nutrient pollution from strong anthropogenic impacts such as domestic sewage. TN and TP make substantial contributions to F4, accounting for 41.8% and 28.1%, respectively. In particular, their contribution to TN is much greater than that of other factors, and possible sources could be agricultural non-point sources.

3.3.3. Identification of Pollution Sources in Downstream

Figure 10 shows the identification results of pollution sources in the downstream. In the downstream, F3 showed a significant loading on NH3-N, accounting for 76.4% of the total variance (Table 9). NH3-N can be considered as a crucial identifier for F3. This factor can be interpreted as domestic sewage. F2 showed high loading on F, accounting for 55.6%. F is an important identifier for F1. Concurrently, CODMn, TN, and TP substantially contributed to F1 with 30%, 19.3%, and 16.6%, respectively. Given this, F1 can be inferred to industrial sources. TN and TP significantly contribute to F3, accounting for 40.4% and 36.9% of the total variance, respectively. In particular, their contribution to TN and TP were much greater than that of other factors, and possible sources could be agricultural non-point sources. F4 showed relatively high loading on TN, TP, and F−, accounting for 24.2%, 36%, and 34.5% of the total variance. Concurrently, the contribution of CODMn and NH3-N could not be ignored, which can be regarded as the integrated source from local emissions and the tributary inflow.

4. Discussion

When the source apportionment results are compared, it is evident that F1 in the upstream, F1 in the midstream, and F2 in the downstream are similar (Figure 11), which can be referred to as Matched Factor 1 (MF1) (Table 10). The identification element for this factor is F, which can be identified as the industrial sources.
F2 in the upstream, F4 in the midstream, and F3 in the downstream are similar, which is referred to as Matched Factor 2 (MF2). This factor contributes significantly to CODMn, TN, and TP, which are indicative of nutrient pollution and can be identified as agricultural non-point pollution. The pollutants from agricultural planting directly enter the body of water as the rain runoff. Agricultural planting pollution cannot effectively be reduced due to the absence of effective interception or isolation facilities along the rivers. According to the survey, the planting mode of Zizania latifolia is extensive, and the degree of intensification and refinement is not high, with the number of pesticides being relatively large and a large number of organic pollutants being discharged from farmland runoff, thereby exerting significant pressure on the water ecological environment in this basin.
F3 in the upstream and midstream and F1 in the downstream are similar, which is referred to as Matched Factor 3 (MF3). This factor has a high contribution rate in NH3-N, which can be identified as domestic sewage. According to the local conditions, although a majority of the rural domestic sewage along the rivers has been piped for treatment, problems still exist in some villages, such as aging and deteriorated pipelines and direct discharge of sewage; moreover, most of the terminal treatment processes utilize anaerobic and artificial wetlands, so the efficiency of pollutant treatment is insufficient. Furthermore, there are numerous terminals that are widely distributed, making standardized operation and maintenance difficult.
In addition to the aforementioned pollution factors, there exists a group of factors without similar characteristics, which are called Unmatched Factor (UMF) and are F4 with a lower pollution degree in the upstream, F2 with a certain contribution to TN and TP in the midstream, and F4 with a higher pollution degree in the downstream.
Matched Factor 1 is identified as industrial sources. Its important identification element is F. The contribution of the downstream is slightly greater than that in the midstream, and the contributions of the midstream and downstream are significantly greater than that of the upstream, which is consistent with the distribution of factories in the Xinjian River basin. The average concentrations of F in the upstream, midstream, and downstream are 0.202 mg/L, 0.242 mg/L, and 0.320 mg/L, respectively, which are far lower than 1.0 mg/L—the national standard of Grade II water. The level of pollution is relatively low, and the pollution sources in the Xinjian River basin are mainly agricultural non-point sources and domestic sewage sources.
The receptor model can only identify the potential source but cannot locate the specific position of the source. Satellite remote sensing can compensate for the deficiency of the receptor model in space description. Therefore, the land cover and land use and the spatial distribution of the quantitative water quality information based on high-resolution satellite images can assist in locating the source and verifying the performance of the receptor model. To match the in situ measurements, we inversed the water quality concentration for every odd month from November 2020 to September 2022 based on Sentinel-2 images. Then, the accumulated concentration was derived by the total 12 months to reduce the errors caused by the inversion algorithm and image noise, and to reveal the general trend for the specific parameter in the past two years (Figure 12). The BOD5 and COD present similar spatial patterns in different sections of the river. The tributary presents a low concentration compared with the main channel according to the BOD5, COD, CODMn, NH3-N, and TN, which is consistent with the distribution of DO, which has high concentrations in the tributaries. The concentration of CODMn is high at the intersection of tributaries (Bihe River, Xinanxi River, and Qianyang River) and the Xinjian River.
In the upstream, the concentration of NH3-N of the tributary is higher than that of the Xinjian River, while the TN and TP concentration of the tributary is lower than that of the Xinjian River. This can be attributed to the domestic sewage discharge of residents on both banks of the river. The tributary aggravates the TN concentration in the Xinjian River.
The high concentration in the midstream is closely related to emissions from the factories in the south of river. TP in the tributary from the Xinanxi River and Qianyang River cannot be ignored, and the emissions from the farmland adjacent to the river contribute significantly. The TN concentration of the tributaries is low, which plays a role in diluting the Xinjian River. Currently, Xinbi Street has completed the construction project of “Zero Direct Sewage Discharge Area”. Nevertheless, satellite monitoring reveals that the TP and TN concentration in some downstream sections of the Xinjian River is still high (Figure 12), thereby indicating the presence of pollution sources entering the river. Consequently, this project should be strengthened and improved.
As we know, metal elements have very good identifiability, but the body of water in Lishui City is in good condition as a whole. Among the tested indicators of water quality, metal elements are below the detection limit, so they are not used in this study.
To reduce the error caused by the image noise or inversion algorithm of a single period image, 12 inversed water quality concentrations were composed to reveal the overall water quality trend of the whole basin; however, due to the limited resolution (10 m) of the Sentinel-2 image, errors still exist in some areas for rivers with complicated conditions, including shallow terrain, mixed pixel phenomena, and shadows of the trees along the river, etc. High precision remote sensing for narrow rivers remains a challenge.
It should be noted that the amount of monitoring data in this study is not very large, and more reliable source apportionment results could be obtained if the data is accumulated for a longer period of time.

5. Suggestions

(1)
Strengthen the prevention and control of rural domestic pollution.
An emphasis should be placed on harnessing rural domestic waste by classification, harmlessness treatment, and resource utilization to improve the rural environment; moreover, the rural domestic sewage treatment facilities should be improved and supporting pipe network facilities should be constructed; the number of sewage treatment households should be increased; and the sewage treatment levels and coverage should be further enhanced.
(2)
Strengthen the prevention and control of agricultural non-point source pollution.
Technology should be adopted to promote the model of reducing fertilizer and pesticide consumption while increasing efficiency. Development of ecological ditches to intercept the nitrogen and phosphorus from farmland is a promising option. The combined use of physics, chemistry, and biology to strengthen the purification and advanced treatment of the total nitrogen and total phosphorus should be used.
(3)
Strengthen environmental management and ecological restoration
First, the comprehensive management of the river should be improved. Specific countermeasures for source control and sewage interception and ecological restoration include the development, restoration, and reinforcement of embankments, as well as the development of green land to protect the bank and improve the quality of the water and ecology and living environment. The second way is to upgrade the dam. The retaining dam at the intersection of the Bihe River and Xinjian River was built so long ago that an environmental assessment was not conducted. To improve the downstream water quality, it is proposed that an environmental assessment be undertaken to improve the dam, discharge water regularly, and increase the discharge water volume.
(4)
Check the area of direct sewage discharge
A mechanism for standardizing the operation and maintenance of sewage wells should be established. It is necessary to inspect the state of rainfall and sewage diversion in the residential community and commercial district in Xinjian Town and Xinbi Street. Whether all the sewage is intercepted and disposed of, and whether the rainwater and sewage pipe network are wrongly connected or not should be emphasized.

6. Conclusions

In this study, the PMF receptor model was used to identify the water pollution source in the Xinjian River, Lishui City, Zhejiang Province, China. Remote sensing technology was also utilized to aid in the analysis of source apportionment and to provide the spatial distribution of the water condition.
According to the location of the monitoring station, the whole basin is divided into three sections: upstream, midstream, and downstream. By comparing the source apportionment results in different sections, we can distinguish the local source and the extent input by tributary inflow. Accordingly, some anti-pollution measures are proposed. TN, followed by TP, are the major pollutants in the study area, and non-point source pollution is the major pollution source; they can be traced to tributary inflow, particularly in the Xinan River and Qianyang River. It is still essential to take prevention measures to control non-point source pollutants to preserve the natural ecology and environment. Some ecological measures should be implemented to mitigate the impact of tributaries’ pollution by TN and TP in the midstream and downstream. The industrial pollution resolved by F exists from upstream to downstream. Therefore, it is imperative to strengthen the management of untreated pollutant discharge in family workshops and factories.

Author Contributions

G.Z.: Conceptualization, Methodology, Writing—original draft, Writing—review & editing. S.C.: Methodology, Formal analysis, Writing—review & editing. A.L.: Investigation, Software, Writing—original draft. C.X.: Conceptualization, Resources, Writing—review & editing. G.J.: Conceptualization, Supervision, Writing—review & editing. Q.C.: Data curation, Formal analysis, Writing—review & editing. Y.H.: Formal analysis, visualization, Validation. S.T.: Investigation, Validation, Formal analysis. M.L.: Validation, Formal analysis. K.X.: Validation, Formal analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (grant number 2021YFB3901000), the National Natural Science Foundation of China (grant number 41971320), and Beijing Natural Science Foundation (Grant No. 162028).

Data Availability Statement

The Sentinel-2 data can be available online at: https://scihub.copernicus.eu/ (accessed on 1 June 2022).

Acknowledgments

The authors acknowledge the European Space Agency (ESA) (https://scihub.copernicus.eu/ (accessed on 1 June 2022)) for providing the Sentinel-2 data.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. The location of the study area. (a) Study area in Lishui, Zhejiang Province, China; (b) The location of the study area and the water quality monitoring sites. Site 1: Xinjian Town water source; Site 2: Zhaiji; Site 3: Xiaxiaoxixia; Site 4: Guangyao. R1: Xinjian River; R2: Qiaoxi River; R3: Qianyangxi River; R4: Xinanxi River.
Figure 1. The location of the study area. (a) Study area in Lishui, Zhejiang Province, China; (b) The location of the study area and the water quality monitoring sites. Site 1: Xinjian Town water source; Site 2: Zhaiji; Site 3: Xiaxiaoxixia; Site 4: Guangyao. R1: Xinjian River; R2: Qiaoxi River; R3: Qianyangxi River; R4: Xinanxi River.
Water 15 00007 g001
Figure 2. Variation of water quality concentration in the Xinjian River from January 2018 to September 2022. (af) represent DO, CODMn, NH3-N, TN, TP and F, respectively. The water quality type is labeled according to the surface water environmental quality standard of China (GB 3838-2002).
Figure 2. Variation of water quality concentration in the Xinjian River from January 2018 to September 2022. (af) represent DO, CODMn, NH3-N, TN, TP and F, respectively. The water quality type is labeled according to the surface water environmental quality standard of China (GB 3838-2002).
Water 15 00007 g002
Figure 3. Average concentration of water quality in the upstream, midstream, and downstream in the Xinjian River.
Figure 3. Average concentration of water quality in the upstream, midstream, and downstream in the Xinjian River.
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Figure 4. Variation of TN and TP concentrations in the Xinjian River. (a) shows the average concentration of TN in Lishui City is much lower than that in Xijianxi River, even for the upstream water source. (b) shows the TP concentration in upstream water source is comparable to Lishui average condition, which is much lower than midstream and downstream.
Figure 4. Variation of TN and TP concentrations in the Xinjian River. (a) shows the average concentration of TN in Lishui City is much lower than that in Xijianxi River, even for the upstream water source. (b) shows the TP concentration in upstream water source is comparable to Lishui average condition, which is much lower than midstream and downstream.
Water 15 00007 g004
Figure 5. Upstream fitting comparison diagram of observed and predicted concentrations. Green line is the 1:1 reference line and red line is the regression line.
Figure 5. Upstream fitting comparison diagram of observed and predicted concentrations. Green line is the 1:1 reference line and red line is the regression line.
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Figure 6. Midstream fitting comparison diagram of observed and predicted concentrations. Green line is the 1:1 reference line and red line is the regression line.
Figure 6. Midstream fitting comparison diagram of observed and predicted concentrations. Green line is the 1:1 reference line and red line is the regression line.
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Figure 7. Downstream fitting comparison diagram of observed and predicted concentrations. Green line is the 1:1 reference line and red line is the regression line.
Figure 7. Downstream fitting comparison diagram of observed and predicted concentrations. Green line is the 1:1 reference line and red line is the regression line.
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Figure 8. Source profile and contribution rate in the upstream.
Figure 8. Source profile and contribution rate in the upstream.
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Figure 9. Source profile and contribution rate in the midstream.
Figure 9. Source profile and contribution rate in the midstream.
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Figure 10. Source profile and contribution rate in the downstream.
Figure 10. Source profile and contribution rate in the downstream.
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Figure 11. Factor figure prints resulting from PMF model. (a) Upstream, (b) Midstream, (c) Downstream.
Figure 11. Factor figure prints resulting from PMF model. (a) Upstream, (b) Midstream, (c) Downstream.
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Figure 12. Cumulative concentration of water quality with 12 months from November 2020 to September 2022 in odd month. (ah) represent BOD, COD, CODMn, DO, NH3-N, pH, TN, and TP, respectively.
Figure 12. Cumulative concentration of water quality with 12 months from November 2020 to September 2022 in odd month. (ah) represent BOD, COD, CODMn, DO, NH3-N, pH, TN, and TP, respectively.
Water 15 00007 g012aWater 15 00007 g012b
Table 1. Frequency of water quality monitoring in the Xinjian River.
Table 1. Frequency of water quality monitoring in the Xinjian River.
Monitoring StationMonitoring PeriodMonitoring Frequency
Xinjian Town water sourceJanuary 2018–September 2022monthly issue in odd months (29 issues)
ZhaijiJanuary 2018–September 2022monthly issue in odd months (29 issues)
XiaxiaoxixiaJanuary 2018–September 2022monthly issue in odd months (29 issues)
GuangyaoJanuary 2018–September 2022monthly (21 issues)
Table 2. Detection methods, detection limits, and relative standard deviations of water quality indexes.
Table 2. Detection methods, detection limits, and relative standard deviations of water quality indexes.
ElementsMethodDetection Limit/mg·L−1RSD/%
DOIodimetry method (GB 7489-89)0.25
CODMnPotassium permanganate method (GB 11892-89)0.55
NH3-NSalicylic acid spectrophotometric method (GB 7481-87)0.015
TNAlkaline potassium persulfate digestion UV spectrophotometry (GB 11894-89)0.056
TPAmmonium molybdate spectrophotometric method (GB 11893-89)0.012
FFluoride ion chromatography (HJ/T 84-2001)0.0064
Table 3. Statistical description of upstream water quality indicators (concentration units in mg/L).
Table 3. Statistical description of upstream water quality indicators (concentration units in mg/L).
ParameterMaxMinMeanNational Water Standard of Grade IIStd. Dev.Coef. of VariabilitySkewness
DO12.87.289.671≥61.6140.1670.273
CODMn3.60.92.186≤40.6300.2880.401
NH3-N0.490.020.127≤0.50.1120.8851.518
TN2.360.851.723≤0.50.3080.178−0.249
TP0.090.020.043≤0.10.0160.3791.613
F0.3550.1480.202≤1.00.0580.2881.704
Table 4. Statistical description of midstream water quality indicators (concentration units in mg/L).
Table 4. Statistical description of midstream water quality indicators (concentration units in mg/L).
ParameterMaxMinMeanNational Water Standard of Grade IIStd. Dev.Coef. of VariabilitySkewness
DO10.86.38.503≥61.2700.1490.291
CODMn4.91.43.269≤40.8930.2730.032
NH3-N0.670.050.181≤0.50.1821.0051.816
TN3.761.682.449≤0.50.4490.1830.672
TP0.190.120.161≤0.10.0230.141−0.255
F0.5330.1450.242≤1.00.0840.3471.878
Table 5. Statistical description of downstream water quality indicators (concentration units in mg/L).
Table 5. Statistical description of downstream water quality indicators (concentration units in mg/L).
ParameterMaxMinMeanNational Water Standard of Grade IIStd. Dev.Coef. of VariabilitySkewness
DO12.36.19.030≥61.6440.1820.029
CODMn5.81.73.482≤41.0030.2880.332
NH3-N0.810.040.259≤0.50.1900.7341.196
TN3.231.182.575≤0.50.3620.141−0.971
TP0.190.080.156≤0.10.0290.185−0.913
F0.6680.1360.320≤1.00.1330.4140.974
Table 6. Operation parameters in upstream, midstream, and downstream.
Table 6. Operation parameters in upstream, midstream, and downstream.
Q (Robust)Q (True)Iterations
Upstream203.947210.037151
Midstream211.383224.598182
Downstream558.318587.321160
Table 7. Pollution source profile and contribution rate in the upstream.
Table 7. Pollution source profile and contribution rate in the upstream.
ParameterConcentration of Species (mg/L)Contribution of Species (%)
F1F2F3F4F1F2F3F4
DO2.073.230.993.3721.433.510.234.9
CODMn0.660.770.40.3230.835.818.714.7
NH3-N0.010.010.10.017.69.174.29
TN0.30.840.340.211849.52012.5
TP0.010.020.01022.248.8290
F0.090.060.040.0244.127.919.68.3
Table 8. Pollution source profile and contribution rate in the midstream.
Table 8. Pollution source profile and contribution rate in the midstream.
ParameterConcentration of Species (mg/L)Contribution of Species (%)
F1F2F3F4F1F2F3F4
DO2.770.711.443.4633.18.517.141.3
CODMn0.930.350.990.942910.930.829.4
NH3-N000.1900.3099.60
TN0.660.340.39127.614.216.441.8
TP0.050.040.030.0528.624.618.728.1
F0.160.020.050.0167.18.222.52.1
Table 9. Pollution source profile and contribution rate in the downstream.
Table 9. Pollution source profile and contribution rate in the downstream.
ParameterConcentration of Species (mg/L)Contribution of Species (%)
F1F2F3F4F1F2F3F4
DO2.473.073.42027.534.338.20
CODMn0.7311.190.4221.93035.612.5
NH3-N0.20.0200.0476.46.70.416.6
TN0.410.481.020.6116.119.340.424.2
TP0.020.030.060.0610.516.636.936
F0.030.1800.119.955.6034.5
Table 10. Pollution factors matching in upstream, midstream, and downstream.
Table 10. Pollution factors matching in upstream, midstream, and downstream.
Matched
Factors
UpstreamMidstreamDownstreamCharacters
MF1F1F1F2A large contribution to F
MF2F2F4F3High pollution and a large contribution to TN and TP
MF3F3F3F1A large contribution to NH3-N
UMFF4//Low pollution
/F2/Low pollution and a slight contribution to TN and TP
//F4High pollution and a large contribution to TN, TP and F
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Zhou, G.; Chen, S.; Li, A.; Xu, C.; Jing, G.; Chen, Q.; Hu, Y.; Tang, S.; Lv, M.; Xiao, K. Pollution Source Apportionment of River Tributary Based on PMF Receptor Model and Water Quality Remote Sensing in Xinjian River, China. Water 2023, 15, 7. https://doi.org/10.3390/w15010007

AMA Style

Zhou G, Chen S, Li A, Xu C, Jing G, Chen Q, Hu Y, Tang S, Lv M, Xiao K. Pollution Source Apportionment of River Tributary Based on PMF Receptor Model and Water Quality Remote Sensing in Xinjian River, China. Water. 2023; 15(1):7. https://doi.org/10.3390/w15010007

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Zhou, Guanhua, Sizhong Chen, Anqi Li, Chongbin Xu, Guifei Jing, Qian Chen, Yinbo Hu, Shunjie Tang, Meile Lv, and Kejian Xiao. 2023. "Pollution Source Apportionment of River Tributary Based on PMF Receptor Model and Water Quality Remote Sensing in Xinjian River, China" Water 15, no. 1: 7. https://doi.org/10.3390/w15010007

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