Dynamic export coefficient model for evaluating the effects of environmental changes on non-point source pollution

Spatio-temporal characteristics and multi-scale risk identification of pollution load based on sensitivity analysis in small watersheds located in Tuojiang River Basin, China

High-quality development of water resources supports high-quality socio-economic development. High-quality development connects high-quality life, and clarifying the key management contents of small watersheds plays an important role in building ecologically clean small watersheds and promoting regional production and life. Previous research on pollution loads has focused on examining the impact of various external drivers on pollution loads but still lacks research on the impact of changes in pollution sources themselves on pollution loads. In this study, sensitivity analysis was used to determine the impact of changes from different sources on the total pollution loads, which can recognize the critical pollution sources. We first employed the pollutant discharge coefficient method to quantify non-point source pollution loads in the small watershed in the upstream Tuojiang River basin from 2010 to 2021. Then, combination sensitivity analysis with Getis-Ord Gi* was used to identify the critical sources and their crucial areas at the global, districts (counties), and towns (streets) scales, respectively. The results indicate: (1) The pollution loads of COD, NH3-N, TN, and TP all show a decreasing trend, reducing by 18.3%, 16.2%, 18.6%, and 28.1% from 2010 to 2021, respectively; (2) Livestock and poultry breeding pollution source is the most critical source for majority areas across watershed; (3) High-risk areas are mainly concentrated in Jingyang district and its subordinate towns (streets). There is a trend of low-pollution risk areas transitioning to high-pollution risk areas, with high-risk areas predominantly concentrated in the southeast and exhibiting a noticeable phenomenon of pollution load spilling around. This study can promote other similar small watersheds, holding significant importance for non-point source pollution control in small watersheds.

Pollution load estimation and influencing factor analysis in the Tuhai River Basin in Shandong Province of China based on improved output coefficient method

Estimating the pollution loads in the Tuhai River is essential for developing a water quality standard scheme. This study utilized the improved output coefficient method to estimate the total pollution loads in the river basin while analyzing the influencing factors based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Findings indicated that the projected point source pollution loads for total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (AN) would amount to 3937.22 ton, 335,523.25 ton, and 13,946.92 ton in 2021, respectively. Among these, COD pollution would pose the greatest concern. The primary contributors to the pollution loads were rural scattered life, large-scale livestock and poultry breeding, and surface runoff. Per capita GDP emerged as the most influential factor affecting the pollution loads, followed by cultivated land area, while the urbanization rate demonstrated the least impact.

Accounting for the annual variability when assessing non-point source pollution potential in Mediterranean regulated watersheds

The characterization of non-point source pollution at the watershed scale difficult owing to its distributed nature combined with the lack of suitable measurements for validation. This study proposes the classification of land within a Mediterranean watershed according to its potential source of non-point pollution, considering interannual precipitation variability and dam regulation effects. For this purpose, the potential non-point pollution index (PNPI) developed by the Italian Environmental Protection Agency was modified to include annual local precipitation behavior, named local annual PNPI (APNPI). PNPI and APNPI were computed for the Guadalquivir River (Spain), which has a drainage surface of 57,500 km2 and is highly regulated by >60 reservoirs. The results reflect the vulnerability along the Guadalquivir River in terms of the spatially variable non-point pollutant nature of its contributing watersheds. The annual average nitrate concentration values on the southern side exceeded the average value on the northern side by almost five times and showed a statistically significant power fit with the PNPI, with an R2 of 0.65. Long-term available nitrate data (1981/82-2006/07) on a monthly scale at the outlets of some watersheds allowed us to rank priority pollutant source areas within the watershed. The power fits between the annual average nitrate loads and the APNPI (R2 = 0.51-0.99) were statistically significant, which validated the utility of adding the variability of precipitation at an annual scale as a dynamic factor in the index. The APNPI can constitute a simple dynamic classification index for assessing the relative risk of non-point source pollution across a large area, especially in data-scarce situations.

Long-term control of non-point source pollution by adjusting human environmental behavior in watershed-a new perspective

The control of non-point source pollution is a major scientific and technological problem faced by mankind. We proposed a new approach to eliminate non-point source pollution, focusing on adjusting human environmental behavior. The implementation procedures are as follows: (1) Investigate the intention of pollution discharge behavior through interviews and questionnaires. (2) Carry out targeted intervention within the framework of social psychology to transform it into an environmentally friendly mode. (3) Calculate the amounts of pollutants produced and discharged before and after the intervention, and then evaluate the effect of the intervention on reducing pollution. (4) Based on successful interventions, a scheme can be developed to curb non-point source pollution. Aiming to reduce fertilizer use, a case study was conducted in Hetao Irrigation District, one of the three major Irrigation districts in China. The results showed that the interventions indirectly affected intention through attitude, subjective norm, and perceived behavioral control. The structural equation model explained 76.0% of the total variance of farmers' intention to reduce fertilizer application (SMC = 0.760), indicating effective intervention. Subsequently, a program to curb non-point source pollution was developed. This study can provide a key scientific and applied reference for the long-term control of non-point source pollution in watershed.

The impacts of spatio-temporal variation of natural and agricultural influences on the environmental water quality in a fluvial-lacustrine watershed in China

Despite the significant impacts of natural factors such as rainfall, topography, soil type, and river network as well as agricultural activities on the environmental water quality, little is known about the influence of their temporal and spatial variations in a fluvial-lacustrine watershed. In this study, a whole process accounting method based the export coefficient model (WP-ECM) was first developed to quantify how natural factors and agricultural activities distribution influenced water quality. A case study was performed in a typical fluvial-lacustrine area - Dongting basin, China. The simulated results indicated that the natural factors can promote and inhibit the migration and transformation of agricultural pollutants generated from the watershed and the spatial distribution of the natural factors displayed high variability. It should be priority to monitor the areas with greater natural impact in the basin. Moreover, the cultivated land area and the number of pig-breeding were positively correlated with the pollutant discharge. From the perspective of the spatial distribution of comprehensive influence, the comprehensive high-impact areas are mainly distributed in the Dongting Lake district in 2005-2010 and in Xiang River watershed in 2010-2020. A key strategy for controlling or reducing the cultivated land area and the intensity of livestock breeding in these high-impacts areas is recommended to reduce the impact of the environmental water quality for the entire basin.

Output characteristics and driving factors of non-point source nitrogen (N) and phosphorus (P) in the Three Gorges reservoir area (TGRA) based on migration process: 1995-2020

Although physical models at present have made important achievements in the assessment of non-point source pollution (NPSP), the requirement for large volumes of data and their accuracy limit their application. Therefore, constructing a scientific evaluation model of NPS nitrogen (N) and phosphorus (P) output is of great significance for the identification of N and P sources as well as pollution prevention and control in the basin. We considered runoff, leaching and landscape interception conditions, and constructed an input-migration-output (IMO) model based on the classic export coefficient model (ECM), and identified the main driving factors of NPSP using geographical detector (GD) in Three Gorges Reservoir area (TGRA). The results showed that, compared with the traditional export coefficient model, the prediction accuracy of the improved model for total nitrogen (TN) and total phosphorus (TP) increased by 15.46 % and 20.17 % respectively, and the error rates with the measured data were 9.43 % and 10.62 %. It was found that the total input volume of TN in the TGRA had declined from 58.16 × 10⁴ t to 48.37 × 10⁴ t, while the TP input volume increased from 2.76 × 10⁴ t to 4.11 × 10⁴ t, and then decreased to 4.01 × 10⁴ t. In addition Pengxi River, Huangjin River and the northern part of Qi River were high value areas of NPSP input and output, but the range of high value areas of migration factors has narrowed. Pig breeding, rural population and dry land area were the main driving factors of N and P export. The IMO model can effectively improve prediction accuracy, and has significant implications for the prevention and control of NPSP.

Applying water environment capacity to assess the non-point source pollution risks in watersheds

Comprehension of the spatial and temporal characteristics of non-point source (NPS) pollution risk in watersheds is essential for NPS pollution research and scientific management. Although the concept of water functional zones (WFZ) has been considered in the NPS pollution risk assessment process. However, no comprehensive study of the NPS pollution risk has been conducted to effectively protect water quality in watersheds with different water environment capacity. Therefore, this study proposes a new NPS pollution risk assessment method that integrates water functional zoning, receiving water body environmental capacity, and space-time distribution of pollution load for quantifying the impact of pollution discharge from sub-catchment on nearby water body quality. Based on the NPS nutrient loss process modeled by the Soil and Water Assessment Tool (SWAT), this method was used to assess the NPS pollution risk in the Le 'an River Watershed at annual and monthly scales. The results showed that the NPS pollution risk is characterized by seasonal and spatial variability and is influenced clearly by the water environment capacity. High NPS pollution loads are not necessarily high pollution risks. Conversely, a low NPS nutrient pollution load does not represent a low regional risk sensitivity. In addition, NPS risk assessment based on the water environment capacity could also distinguish the differences in risk levels that were masked by similar NPS pollutant loss and the same water function zoning to achieve accurate control of NPS pollution management in watersheds.

Identifying the critical areas and primary sources for agricultural non-point source pollution management of an emigrant town within the Three Gorges reservoir area

Agricultural non-point source pollution is threatening water environmental health of the Three Gorges reservoir. However, current studies for precision management of the agricultural non-point source pollution within this area are still limited. The objective of this study was identifying the critical areas and primary sources of agricultural non-point source pollution for precision management. Firstly, the inventory analysis approach was used to estimate the discharge amount of total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) from farmland fertilizer, crop residues, livestock breeding, and daily activities. Afterwards, the deviation standardization method was applied to evaluate the emission intensity of TN, TP, and COD, as well as calculating the comprehensive pollution index (CPI) of each village, based on which the critical areas for agricultural non-point source pollution management could be distinguished. Moreover, the equivalence pollution load method was conducted to identify the primary pollution sources within each critical zone. The above methods were implemented to an emigrant town within the Three Gorges reservoir area named Gufu. Results showed that agricultural non-point source pollution in Gufu town has been alleviated to a certain extent since 2016. Nevertheless, in four areas of the town (i.e., Longzhu, Fuzi, Shendu, and Maicang), the agricultural non-point source pollution still deserved attention and improvement. For the mentioned critical areas, farmland fertilizer and livestock breeding were the primary sources causing agricultural non-point source pollution. The emission amount of TN and TP from farmland fertilizer accounted for 60% and 48% of the total, respectively. And those from livestock breeding were 29% and 46%. Our research could provide definite targets to relieve agricultural non-point source pollution, which had great significance to protect water environment while coordinating regional economic growth after emigrant resettlement.

The evaluation of N/P fate using the SPARROW model: a case study in an arid and semi-arid region, northern China

The assessment of nutrients' fate from source to sink is critical to water quality control. As an important ecological reserve in the arid and semi-arid regions of China, the Luanhe River Basin (LRB) has suffered from the deterioration of water quality, thus leading to the urgent management and control. However, few studies have devoted to exploring the fate of N/P contaminations for the entire watershed, due possibly to the large drainage area and heterogeneous watershed composition. Here, we attempt to illustrate N/P contaminations delivery and retention processes using the SPAtially Referenced Regression On Watershed attributes (SPARROW) model. The model reveals 97% of the spatial variability in the TN load and 81% in the TP load, verifying its availability and credibility. The results indicate that anthropogenic sources are dominating the N/P load, which account for 68.5% of N and 74.6% of P inputs. The results highlight the significant retention effects of streams and reservoirs, with 16.4% of N and 13.4% of P removals by streams and 24.3% of N and 10.7% of P removals by reservoirs, respectively. Ultimately, only 49,045.2 t yr^-1 (or 16.9%) of N and 1668.7 t yr^-1 (or 17.1%) of P being transported to the Bohai Sea. In addition, the analysis of influencing factors showed that regional characteristics (e.g., topography, rainfall), stream size, and delivery distance are potential factors affecting the riverine transport, whereas flow rate and surface area are primarily affecting the reservoirs attenuation. In the future, the watershed water quality management should pay more attention to source management and pollution legacy risks to achieve sustainable and healthy watershed development.

Improved export coefficient model for identification of watershed environmental risk areas

As a complex system under the joint action of man and nature, land use/cover directly or indirectly affects the environmental quality of the freshwater ecosystem. Studying the response of water environment quality to land use/cover change was significant to accurately simulate lake water quality and effectively enhance the management level. As an empirical model, the classical export coefficient model has been widely used and developed in agricultural non-point source pollution research because of its simple structure and convenient application. However, it assumes that the export coefficient of a particular type of land use/cover was constant, ignoring the influence of surface runoff and interception on the output intensity of pollutants in pollutant migration. This study improved the classical export coefficient model by adding factors such as precipitation, surface cover, and topography, evaluated the contribution of land use/cover to total nitrogen load into the lake in Dianchi Lake Basin, and applied the pollution assessment results to the identification of watershed environmental risk areas. The results showed that the improved export coefficient model could better simulate the relationship between land use/cover and total nitrogen load into Dianchi Lake from the basin. At the same time, spatial characteristics of the total nitrogen load contribution of the terrestrial could be represented. The high-risk areas in the basin were mainly cultivated land and construction areas with low vegetation coverage around lakes or downstream. The contribution per unit area to the TN load into the lake from areas with a high risk was 14.28 t/km², which was 3.47 times that of medium-high-risk areas and 52.28 times that of the medium-risk area. Land use control measures in high-risk areas in the basin should be further strengthened, especially in the lakeside zone.

High-resolution mapping of the rainfall runoff pollution: case study of Shiwuli River watershed, China

Rainfall runoff is the key factor of water quality deterioration in highly urbanized area, which is characterized by intensive human activities and frequent extreme weather events. Urban landscape system is composed of highly diverse and heterogeneous land patches, which makes the effective management of urban runoff pollution difficult. Therefore, high-resolution land-use data is imperative for the identification and analysis of spatial-temporal characteristics of urban runoff pollution. In this study, Shiwuli River watershed, a rapidly urbanizing area in China, is selected as the study area. We first interpret nine kinds of land-use types with a high-resolution remote sensing data of 2 m [Formula: see text] 2 m. Then, a localized Soil Conservation Service model based on field observation and rainfall experiments is applied to map the spatial-temporal pattern of runoff pollution. The results indicate that the COD, NH₃-N, TP, and TN load generated by the runoff in the watershed accounted for 23.4%, 3.7%, 8.2%, and 9.0% of the total pollution load in 2016, respectively. Furthermore, the spatial-temporal pattern of the assessed runoff pollution was mainly subject to the distribution of rainfall and land-use patterns. We suggest that the sponge city construction combined with surface pollution control is an effective way to reduce the runoff pollution. This study highlights the necessity to identify spatial-temporal hotspots in developing precise pollution control measures, which provides valuable information for pollution control policy-making in Shiwuli River watershed and could serve as a reference for other river watersheds undergoing rapid urbanization.

Source appointment at large-scale and ungauged catchment using physically-based model and dynamic export coefficient

Data scarcity has caused enormous problems in non-point pollution predictions and the related source apportionment. In this study, a new framework was developed to undertake the source apportionment at a large-scale and ungauged catchment, by integrating the physically-based model and a surrogate model. The improvements were made, in terms of the application of a physically-based model in an ungauged area for the transfer process and the parametric transplantation process. The new framework was then tested in the Chaohu Lake basin, China. The result suggested that there has been a good match between simulated and observed data. Although the planting industry was the largest emission source with 48.16% of nitrogen (N), itonly contributed 12.61% of N flux to the Chaohu Lake. The ungauged catchments surrounding the Chaohu Lake were identified as non-negligible sources with 8.46% of phosphorus (P) contribution. The rainfall conditions could have great impacts on source apportionment results; e.g., the planting industry contributed from 68.17t of P in dry year to 436.02t in wet year. The new framework could be extended to other large-scale watersheds for source apportionment with data limitations.

Distribution of antibiotic resistance genes in the sediments of Erhai Lake, Yunnan-Kweichow Plateau, China: Their linear relations with nonpoint source pollution discharges from 26 tributaries

Erhai Lake, a typical plateau deep water lake, experienced long-term nonpoint source (NPS) pollution discharge from 26 tributaries, which significantly affected the abundance and spread of resistance genes. In this study, 25 antibiotic resistance genes (ARGs), classified into six types, and NPS pollution discharges were investigated throughout around the Erhai basin. FCA (mexF) and sulfonamide resistance genes (sul1, sul2 and sul3) were the most common. Although the absolute overall abundance of ARGs there was low so far, the individual gene like sulfonamide resistance gene was high. Regression analysis using an ordinary least squares model (OLS) showed that the discharge of NPS pollution into Erhai Lake would have an obvious effect on the distribution of ARGs. And the relations between them were linear. Concretely speaking, the total nitrogen (TN) pollution input from tributaries could significantly correlated with the increasing of ARG abundance, while the total phosphorus (TP) pollution input showed the opposite correlation, and ultimately affect the distribution of ARGs. Moreover, the effect of TP on ARG distribution was more significant than TN. This study provides a geographical profile of ARG distribution in a subtropical deep lake on Yunnan-Kweichow Plateau. The results are beneficial for predicting the distribution characteristics of ARGs and controlling their pollution in plateau lakes.

Water quality status response to multiple anthropogenic activities in urban river

Water quality evaluation and degrading factors identification are crucial for predicting water quality evolution trends in an urban river. However, under the coupling of multiple factors, these targets face great challenges. The water quality status response to multiple anthropogenic activities in an urban river was evaluated and predicted based on comprehensive assessment methods and random forest (RF) model. We found that the distribution of each physicochemical parameter exhibits an obvious spatial clustering. The mean pollution level and trophic status of the urban river are medium pollution (water quality index = 59.79; Nemerow's pollution index = 2.00) and light eutrophication (trophic level index = 57.30). The water quality status is sensitive to anthropogenic activities, showing the following order of TLI and NPI values: residential district > industrial district > agricultural district and downtown > suburbs > countryside. According to the redundancy analysis, constructed land (F = 15.90, p < 0.01) and domestic sewage (F = 14.20, p < 0.01) evinced as the crucial factors that aggravated the water quality pollution level. Based on the simulation results of the RF model (variation explained = 94.91%; R² = 0.978), improving domestic sewage treatment standards is the most effective measure to improve the water quality (increased by 40.3-49.3%) in residential and industrial districts. While in a suburban district, improving the domestic sewage collection rate has more effectively (23%) than those in the residential and industrial districts. Conclusively, reducing exogenous pollution input and improving domestic sewage treatment standards are vital to urban river restoration. Clinical trial registration Not applicable.

Integrating source apportionment and landscape patterns to capture nutrient variability across a typical urbanized watershed

Effective integrated watershed management requires models that can characterize the sources and transport processes of pollutants at the watershed with multiple landscape patterns. However, few studies have investigated the influence of landscape spatial configuration on pollutant transport processes. In this study, the SPARROW_TN and SPARROW_TP models were constructed by combining direct pollution source data and landscape pattern data to investigate the source composition and nutrient transport processes and to reveal the influence of landscape patterns on nutrient transport in the urbanized Beiyun River Watershed. The introduction of landscape metrics significantly improved the simulation results of both models, with R² increasing from 0.89 to 0.85 to 0.93 and 0.91, respectively. Spatial variations existed in TN and TP loads and yields, as well as the source compositions. Pollution hotspots were effectively identified. Source apportionment showed that for the entire watershed, TN came from atmospheric nitrogen deposition (35.25%), untreated sewage (28.23%), agricultural sources (22.60%), and treated sewage (13.92%). In comparison, TP came from untreated sewage (44.94%), agricultural sources (40.22%), and treated sewage (11.51%). In addition, the largest patch index of grassland correlated positively with both TN and TP, whereas the largest shape index of buildup land and interspersion and juxtaposition index of forest were negatively correlated with TN and TP, respectively. The results of this study will provide insight into effective nutrient control measures that consider spatially varying nutrient sources and associated nutrient transport processes.

The characteristics of nitrogen and phosphorus output in China's highly urbanized Pearl River Delta region

The nitrogen (N) and phosphorus (P) transportation due to the anthropogenic activities have strong correlations to the water pollution events. In the highly urbanized Pearl River Delta (PRD) region of China, the main input pathways for N and P have been changed. However, their main output pathways have not yet been understood. Based on the modified export coefficient model (ECM), we have quantified the N and P outputs and identified the main factors affecting the N and P outputs in highly urbanized areas such as PRD. The results showed that the N output intensity of the PRD has increased from 3010 to 3970 kg km^-2·a^-1 from 2008 to 2016. The P output exhibited a similar trend, from 549 to 769 kg km^-2·a^-1. In terms of spatial distribution, the output intensity gradually increased from economically underdeveloped regions to economically developed regions. N and P emissions in urban wastewater increased significantly with increasing urbanization rates, with output intensities increasing by 640 kg km^-2·a^-1 and 141 kg km^-2·a^-1 from 2008 to 2016, respectively. The correlation analysis showed that population density and urbanization rate were the most relevant factors with N and P outputs intensity in highly urbanized areas. This indicates that improving the effluent standards and utilization rates of wastewater treatment plants in these regions are effective measures to control N and P output. Our findings provide some new theoretical basis for the identification and management of pollution sources in highly urbanized areas for other regions, especially developing countries.

Improving the accuracy of nonpoint-source pollution estimates in inland waters with coupled satellite-UAV data

Quantitatively and accurately analyzing nonpoint-source (NPS) pollution is essential for efficiently preventing the input of NPS loads into inland waters. However, the accuracy of previous NPS pollution models is limited by the accuracy of ground parameter data. In addition, there are few effective methods that thoroughly verify modeling results at large scales. This paper presents a framework for accurate NPS pollution estimation by coupling satellite and unmanned aerial vehicle (UAV) monitoring data, and the results are verified by both field sampling and a newly developed inlet NPS pollution "observation" simulation method. Fractional vegetation coverage (FVC) data obtained by satellite were used to improve the accuracy of the runoff module of the framework. Satellite and UAV data were coupled to acquire livestock data, determine inlets, and identify reservoir buffer zones and vegetation types. These new data were then used to improve the accuracy of the livestock and runoff modules in the framework. The results show that the estimation accuracy of total nitrogen, total phosphorus, ammonia nitrogen, and chemical oxygen demand with FVC were improved by 39.96%, 69.29%, 54.05% and 47.22% (in relative error), respectively. The high-resolution livestock data acquisition improved the estimation accuracy of the NPS pollution load by 7-53%. The high-resolution inlet extraction improved the accuracy by 3-24%. The high-resolution buffer zone identification improved the accuracy with the estimated NPS pollutant concentration into reservoir decreasing by 60-99%. Finally, the high-resolution vegetation type identification improved the accuracy by 10-72%. The framework performs satisfactorily, which was verified based on the simulated NPS observations with an average relative error of 11.54-24.31%. We found that the FVC, livestock number, and inlet number are key parameters for NPS pollution modeling; the introduction of monthly variation in the FVC makes the modeled NPS pollution load much higher in areas with mature complex forested ecosystems or densely distributed vegetation but much lower in areas with sparsely distributed vegetation. The above methods provide a scientific reference for high-efficiency NPS pollution prevention in inland waters, laying a solid basis for decision-making regarding water quality management in data-scarce regions around the world.

Nitrate pollution source apportionment, uncertainty and sensitivity analysis across a rural-urban river network based on δ15N/δ18O-NO3- isotopes and SIAR modeling

Nitrate pollution is of considerable global concern as a threat to human health and aquatic ecosystems. Nowadays, δ¹⁵N/δ¹⁸O-NO₃^- combined with a Bayesian-based SIAR model are widely used to identify riverine nitrate sources. However, little is known regarding the effect of variations in pollution source isotopic composition on nitrate source contributions. Herein, we used δ¹⁵N/δ¹⁸O-NO₃^-, SIAR modeling, probability statistical analysis and a perturbing method to quantify the contributions and uncertainties of riverine nitrate sources in the Wen-Rui Tang River of China and to further investigate the model sensitivity of each nitrate source. The SIAR model confirmed municipal sewage (MS) as the major nitrate source (58.5-75.7%). Nitrogen fertilizer (NF, 8.6-20.9%) and soil nitrogen (SN, 7.8-20.1%) were also identified as secondary nitrate sources, while atmospheric deposition (AD, <0.1-7.9%) was a minor source. Uncertainties associated with NF (UI₉₀ = 0.32) and SN (UI₉₀ = 0.30) were high, whereas those associated with MS (UI₉₀ = 0.14) were moderate and AD low (UI₉₀ = 0.0087). A sensitivity analysis was performed for the SIAR modeling and indicated that the isotopic composition of the predominant source (i.e., MS in this study) had the strongest effect on the overall riverine nitrate source apportionment results.

Response Model for Urban Area Source Pollution and Water Environmental Quality in a River Network Region

With the development of cities, urban area source pollution has become more severe and a significant source of water pollution. To study the relationship between urban area source pollution and water environmental quality in a river network, this study uses a city in the Yangtze River Delta, China, as an example. The Storm Water Management Model (SWMM) model and the MIKE11 model were combined into a unified modeling framework and used to simulate dynamic changes in the water quality of a river network under light rain, moderate rain, and heavy rain. In the study period, the annual urban area source input loads of potassium permanganate (COD_Mn), total phosphorus (TP), and ammonia nitrogen were 29.8, 0.9, and 4.8 t, respectively. The influence of light rain on the water quality of the river network was lagging and temporary, and rainfall area pollution was the primary contributor. Under the scenario of moderate rain, overflow from a pipeline network compounded rainfall runoff, resulting in a longer duration of impact on the water quality in the river. Additionally, the water quality in the river course was worse under moderate rain than under light or heavy rain. Under the scenario of heavy rain, rain mainly served a dilutive function. This research can provide support for urban area source pollution control and management.

A review of non-point source water pollution modeling for the urban-rural transitional areas of China: Research status and prospect

China has experienced a rapid period of urbanization since the 1980s. Many traditional agricultural areas were transformed into the urban-rural transitional areas, in which both urban and rural characteristics exist. Non-point source pollution (NPSP) has become a major side effect of urbanization and agricultural production which caused wide public concerns. It is crucial to carry out research on identifying the spatiotemporal variation in NPSP in the urban-rural transitional area (especially in developing countries, e.g., in China), which is a prerequisite for improving water quality and guiding NPSP control efforts. Modeling approaches are great tools to provide quantitative information on NPSP and optimize the best management practices for NPSP control. We reviewed over twenty years of publications on NPSP modeling and applications in urban, rural and its transitional areas. The strengths and limitations of 20 commonly used NPSP models in China were concluded based on a brief introduction and the evolution history. Reporting the strengths and weaknesses of each NPSP model could enhance its utility in practice. In terms of the unique characteristics of urban-rural transitional areas, which are neither strictly urban nor rural, non-point source pollutants are often distinctly different between traditional pollutants from urban and agricultural areas since the great differences in the hydrological processes, and none of existing NPSP models are fully applicable to urban-rural transitional areas. Based on limited NPSP modeling studies in urban-rural transitional areas, the existing research insufficiency were technical and mechanism limitations of the model despite of numerous improvements in the past, concerns about simulation accuracy, limited investigations on new pollutants, and lack of monitoring data. Future development trend and concerns of NPSP models for urban-rural transitional areas were discussed, which could be of great help to the development of NPSP models and their applications in water quality management in the rapid urbanized China.

Phenology–Gross Primary Productivity (GPP) Method for Crop Information Extraction in Areas Sensitive to Non-Point Source Pollution and Its Influence on Pollution Intensity

The excessive use of pesticides and fertilizers during agricultural production causes water pollution, which is an important type of non-point source pollution (NSP). Large amounts of harmful substances, such as nitrogen and phosphorus, flow into surface water along with farmland runoff, leading to eutrophication and other problems. However, the pollutant discharge capacity of different types of cultivated land varies greatly. Areas sensitive to NSP are areas with rich crop types, large spatial differences in crop growth, and complex planting patterns. These factors can cause different amounts of fertilizer used in and absorbed by the crops to influence the emission intensity of pollutants. NSP intensity mapping can reflect the spatial distribution of lands’ pollutant discharge capacity and it can provide a basis for pollution control. However, when estimating NSP intensity, existing methods generally treat cultivated land as a category and ignore how complex crop conditions impact pollution intensity. Remote sensing technology enables the classification and monitoring of ground objects, which can provide rich geographical data for NSP intensity mapping. In this study, we used a phenology–GPP (gross primary productivity) method to extract the spatial distribution of crops in the Yuecheng reservoir catchment area from Sentinel-2 remote sensing images and the overall accuracy reached 85%. Moderate resolution imaging spectroradiometer (MODIS) GPP data were used to simulate the spatial distribution of crop growth. Finally, a new model that is more suitable for farmland was obtained by combining this large amount of remote sensing data with existing mapping models. The findings from this study highlight the differences in spatial distributions between total nitrogen and total phosphorous; they also provide the means to improve NSP intensity estimations.

Evaluating the risks of spatial and temporal changes in nonpoint source pollution in a Chinese river basin

In watershed management, it is of great importance to evaluate the risks of nonpoint source (NPS) pollution. In this study, the Nonpoint Source Pollution Risk Index (NSPRI), a multi-factor NPS risk assessment model that was based on the source-sink landscape theory, was proposed and applied in Muzhuhe River Basin, Shandong, China to (1) highlight spatial and temporal variations in the risks from nitrogen and phosphorus losses, and (2) identify how the basin characteristics influenced the risk of nutrient loss. According to the analysis on land use change, the study area is featured with high proportions of forest and agricultural land uses; the area of urban and industrial land had increased considerably from 2000 and 2018. Based on the division of the calculated risk indices on subbasin scale, the area with extremely high risks has decreased from 56,442 ha to 43,922 ha. The average and coefficient of variation (CV) values of NSPRI in the river basin have dropped from 1.3 to 1.1, and from 78.2% to 48.9%, respectively. The distribution of NSPRI suggested an increase in spatial clustering and improvements in the ecological balance. Correlation analysis of the Soil and Water Assessment Tool (SWAT) model (R² > 0.68, ENS > 0.59) and NSPRI indicated the applicability of the method used (r > 0.84, p < 0.01). Analysis on the impact of metrics of land use composition, landscape, and environmental settings on NSPRI indicated that the water quality was more significantly correlated with land use composition, landscape pattern and vegetation cover than with flow path distance, soil erodibility, and rainfall erosivity. Moreover, results of redundancy analysis revealed that nutrient loss risk was better explained by land use compositions than by landscape configuration. The assessment method provided scientific support for NPS pollution control from the perspective of source-sink landscape theory.

Non-point source pollution loads estimation in Three Gorges Reservoir Area based on improved observation experiment and export coefficient model

The non-point source (NPS) pollution has become an important limitation to the sustainable development of the Three Gorges Reservoir Area (TGRA) water resources. NPS load estimation research has theoretical and realistic significance for water environment security and water pollution control. Therefore, the TGRA was chosen to be the study area, and the export coefficients of different land-use type were calculated through literature consultation method combined with improved observation experiment. The load of total nitrogen (TN) and total phosphorus (TP) of NPS from different pollution sources including farmland, decentralized livestock and poultry breeding and domestic pollution sources were estimated. The results are shown as follows: the order of TN load of different sources in the TGRA from high to low was land use, livestock and poultry breeding, rural life; the TN from land use was 372% higher than that of rural; the order of TP load of different sources in the TGRA from high to low was livestock and poultry breeding, rural life, land use; the TP from livestock and poultry breeding was 114.5% higher than that of land use. Therefore, control of livestock and poultry sewage discharges was the key practice to limit the TP loss, while the optimization of agricultural management was the key practice to control the loss of TN.

Development and Assessment of a New Framework for Agricultural Nonpoint Source Pollution Control

The transport of agricultural nonpoint source (NPS) pollutants in water pathways is affected by various factors such as precipitation, terrain, soil erosion, surface and subsurface flows, soil texture, land management, and vegetation coverage. In this study, based on the transmission mechanism of NPS pollutants, we constructed a five-factor model for predicting the path-through rate of NPS pollutants. The five indices of the hydrological processes, namely the precipitation index (α), terrain index (β), runoff index (TI), subsurface runoff index (LI), and buffer strip retention index (RI), are integrated with the pollution source data, including the rural living, livestock and farmland data, obtained from the national pollution source census. The proposed model was applied to the headwater of the Miyun Reservoir watershed for identifying the areas with high path-through rates of agricultural NPS pollutants. The results demonstrated the following. (1) The simulation accuracy of the model is acceptable in mesoscale watersheds. The total nitrogen (TN) and total phosphorus (TP) agriculture loads were determined as 705.11 t and 3.16 t in 2014, with the relative errors of the simulations being 19.62% and 24.45%, respectively. (2) From the spatial distribution of the agricultural NPS, the TN and TP resource loads were mainly distributed among the upstream of Dage and downstream of Taishitun, as well as the towns of Bakshiying and Gaoling. The major source of TN was found to be farmland, accounting for 47.6%, followed by livestock, accounting for 37.4%. However, the path-through rates of TP were different from those of TN; rural living was the main TP source (65%). (3) The path-through rates of agricultural NPS were the highest for the towns of Wudaoying, Dage, Tuchengzi, Anchungoumen, and Huodoushan, where the path-through rate of TN ranged from 0.17 to 0.26. As for TP, it was highest in Wudaoying, Kulongshan, Dage, and Tuchengzi, with values ranging from 0.012 to 0.019. (4) A comprehensive analysis of the distribution of the NPS pollution load and the path-through rate revealed the towns of Dage, Wudaoying, and Tuchengzi as the critical source areas of agricultural NPS pollutants. Therefore, these towns should be seriously considered for effective watershed management. In addition, compared with field monitoring, the export coefficient model, and the physical-based model, the proposed five-factor model, which is based on the path-through rate and the mechanism of agricultural NPS pollutant transfer, cannot only obtain the spatial distribution characteristics of the path-through rate on a field scale but also be applicable to large-scale watersheds for estimating the path-through rates of NPS pollutants.

Preferred hierarchical control strategy of phosphorus from non-point source pollution at regional scale

Spatiotemporal heterogeneity poses challenges on prevention and control of non-point source (NPS) pollution. Treating pollution sources sequentially by prioritizing the critical periods (CPs) and critical source areas (CSAs) is essential for effective control of regional NPS pollution. In this study, the gird-based dual-structure export empirical model (DSEEM) was used to simulate phosphorus losses in the Danjiangkou Reservoir Basin (DRB) on a monthly scale. Based on the co-analysis of CPs and CSAs coupled with the point density analysis (PDA), a preferred hierarchical control strategy, which was connected with regional management units, was proposed to improve the pertinence for phosphorus loss control. CPs, sub-CPs, and non-CPs were identified on the temporal scale; CSAs, sub-CSAs, and non-CSAs were identified on the spatial scale. The results showed that CPs (July, April, and September), sub-CPs (May, March, and August), and non-CPs contributed 62.8%, 31.1%, and 6.1% of the annual TP loads, respectively. Furthermore, we proposed a hierarchical control strategy for NPS pollution: class I (CSAs in CPs) → class II (sub-CSAs in CPs, CSAs in sub-CPs) → class III (non-CPs, non-CSAs, sub- and non-CSAs in sub-CPs). Class I covered the periods and areas with the highest loads, contributing 26.2% of the annual loads within 14.5% of the area and 25.0% of the time. This study provides a reference for the targeted control of NPS pollution at regional scale, especially in environmental protection with limited funds.

Modeling water quantity and quality for a typical agricultural plain basin of northern China by a coupled model

Water crisis across the globe has placed high pressure on social development due to the need to balance the water consumption between sustainable economy and functioning ecosystem. Integrated process-based modeling has been reported as an effective tool to better understand the complex mechanisms of water issues on a basin scale. Considering that it is still relatively difficult to simulate the water quantity-quality processes simultaneously, this study proposed an integrated modeling framework by coupling a hydrological model with a water quality model. Taking the Xiaoqing River Basin in the Shandong Province of northern China as an example, this study coupled a distributed hydrological model, SWAT, with a one-dimensional hydrodynamic-water quality model, HEC-RAS, to investigate its ability to simulate water quality and quality at the basin scale. The coupling of the two models adopted the "output-input" scheme, where the runoff modeling results from SWAT are input into HEC-RAS for hydrodynamic and water quality simulations of the river channel. The results show that the SWAT model can adequately reproduce runoff with accepted accuracy for the calibration and validation periods with acceptable R² and Nash-Sutcliffe coefficients for the two hydrological stations. Further analysis also shows that the coupled model can simulate the concentration of ammonia nitrogen (NH₄-N) and the chemical oxygen demand (COD) in the middle and upper stream of the river for both low and high flow periods. The coupling of the hydrological and hydraulic models in this study provides a good tool for identifying the spatial patterns of the water pollutants over the basin and, thus, helps simplify precision water management.

Critical source areas' identification for non-point source pollution related to nitrogen and phosphorus in an agricultural watershed based on SWAT model

Water eutrophication caused by the extensive expansion of slope farming has caused the high attention of the Chinese government. We choose Lake Tianmu basin as the study area because it can represent vast majority of basins plagued by water eutrophication derived from slope tillage in southern China. The water ecosystem in the reservoir Daxi and Shahe within the basin has been seriously threatened by multiple pollution sources related to many intricate human activities especially agricultural production. For the first time, we identified the critical source areas (CSAs) within the basin based on nutrient load and nutrient load intensity (NLI), and on this basis, we further excavated the main causes of pollution and proposed pertinent remediation measures. The results based on the calibrated Soil and Water Assessment Tool model indicated that the TN load of each reservoir remarkably exceeded their respective water environmental capacity from 2014 to 2018. Accordingly, six main tributaries with great nutrient contributions and their corresponding sub-basins were then identified. Overall, tea and rice plantations appear to be the major nutrient contributors to reservoir Daxi. And the main nutrient sources for reservoir Shahe are tea plantations, orchards, farmland, forestland, and point sources. Regarding the CSAs identified only by nutrient load, agronomic measures such as reducing fertilizer amount, biochar application, straw incorporation, and plastic mulch coverage can be employed to improve soil water retention and curb soil erosion. Regarding the CSAs identified by nutrient load intensity (NLI), the CSAs with narrow areas should be turned directly into forestland. For the CSAs with large areas, engineering measures such as constructing ecological riparian zone, filtration, and sedimentation tank can be employed to prevent pollutants from entering downstream reaches. Overall, the present results can provide the decision-making support for the safe and efficient management of watershed land use in southern China.

Using soil erosion to locate nonpoint source pollution risks in coastal zones: A case study in the Yellow River Delta, China

Soil erosion contributes greatly to nonpoint source pollution (NSP). We built a coastal NSP risk calculation method (CNSPRI) based on the Revised Universal Soil Loss Equation (RUSLE) and geospatial methods. In studies on the formation and transport of coastal NSP, we analysed the pollution impacts on the sea by dividing subbasins into the sea and monitoring the pollutant flux. In this paper, a case study in the Yellow River Delta showed that the CNSPRI could better predict the total nitrogen (TN) and total phosphorus (TP) NSP risks. The value of the soil erodibility factor (K) was 0.0377 t h·MJ^-1·mm^-1, indicating higher soil erodibility levels, and presented an increased trend from the west to the east coast. The NSP risk also showed an increased trend from west to east, and the worst status was found near the Guangli River of the south-eastern region. The contributions of the seven influencing factors to CNSPRI presented an order of vegetation cover > rainfall erosivity > soil content > soil erodibility > flow > flow path > slope. The different roles of source and sink landscapes influenced the pollutant outputs on a subbasin scale. Arable land and saline-alkali land were the two land-use types with the greatest NSP risks. Therefore, in coastal zones, to reduce NSP output risks, we should pay more attention to the spatial distribution of vegetation cover, increase its interception effect on soil loss, and prioritize the improvement of saline-alkali land to reduce the amount of bare land.