Landscape patterns regulate non-point source nutrient pollution in an agricultural watershed

Quantifying variation of non-point source pollution and its impact factors: A study of Nansi Lake Basin

Agricultural non-point source (NPS) pollution directly affects the quality of soil and water, ecological balance and human health, and is a key challenge to achieve sustainable environmental development and efficient resource management. Taking the Nansi Lake Basin (NLB) as the study area, this study explores the main sources of agricultural NPS pollution and its influencing factors, aiming to provide scientific basis for the management of water resources in the basin. Current studies usually use the runoff pollution partitioning method to estimate agricultural NPS pollution loads in runoff, but the accuracy of the analyses is limited by the incompleteness of water quality monitoring data, especially the lack of complete runoff records in some years. To compensate for this deficiency, this study simulated the river runoff based on the Long-Term Hydrological Impact Assessment (L-THIA) model, and applied the simulation results to the quantitative calculation of agricultural NPS pollution loads after verifying the model reliability through accuracy calibration. Based on L-THIA model, the spatial and temporal distribution data of agricultural NPS pollution in the basin from 2010 to 2020 were obtained, the distribution characteristics of chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) were quantitatively assessed, and the impacts of natural and socio-economic factors on them were analyzed. A regression model was developed to simulate future agricultural NPS pollution through multiple regression analysis. The results showed that the total agricultural NPS pollution in the NLB showed an increasing trend during the study period. In particular, among the socio-economic factors, COD and NH3-N were significantly correlated with fertilizer application, pesticide use, rural employment and total population. Among the natural factors, topographic index, watershed area and gully density were positively correlated with pollutants, while slope and soil organic matter were negatively correlated. The results of this study raise awareness of the contribution of influencing factors and allow researchers and planners to focus on the most important NPS pollution sources and influencing factors. The study provides an important reference for the prevention and control of agricultural NPS pollution in the NLB, which is of great practical importance.

Tracking the optimal watershed landscape pattern for driving pollutant transport: Insights from the integration of mechanistic models and data-driven approaches

Identifying landscape patterns conducive to pollutant transport control is of vitally importance for water quality protection. However, it remains unclear which landscape patterns can weaken the transport capacity of pollutants entering water bodies. To fill this gap, this study proposes a new framework. This framework quantifies the contribution of landscape patterns to pollutant migration; it also identifies the optimal landscape patterns capable of reducing pollutants entering rivers. Furthermore, it analyzes the impact pathways of landscape patterns on pollutant migration by integrating mechanism models, machine learning techniques, and structural equation models (SEM). The results showed that on cultivated land and urban land, when the slope reached 35%, the terrestrial transport intensity of NH₃-N peaked at 34 kg/km2 and 45 kg/km2 respectively, with more pollutants entering the receiving water bodies. Meanwhile, in the forest with a DEM of 900 m, the terrestrial transport intensity of NH₃-N was the highest (50 kg/km2). The complexity of the landscape boundary shape in areas dominated by cultivated land and forest was verified to have a significant impact on the terrestrial migration intensity of NH₃-N, with a contribution rate of over 65%. From the comparison results of multiple land use combinations, it can be seen that the combination of woodland and grassland indirectly weakens the transport capacity of pollutants entering water bodies by directly influencing the connectivity among landscape units. In particular, when the proportion of woodland and grassland reaches 75%, it has a positive effect on improving river pollution and is the optimal landscape combination pattern for reducing the pollution load of the river. The outcomes can be used to develop more efficacious optimization and regulation tactics for landscape patterns and offer a decision - making foundation for the control of pollutant transport in large basins.

Integrating river transport processes and seasonal dynamics to assess watershed nitrogen export risk

Excessive nitrogen exported to water bodies affects the balance of ecosystem and poses a threat to human health. Although the concept of water purification service helps quantify nitrogen export, the impact of river transport remains unclear. This study focused on nitrogen as a pollutant by utilizing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess nitrogen export in the Dongting Lake Basin, taking into account both the processes of sub-basin nitrogen export and river transport. Additionally, the monthly variations within the year were further explored, together with the identification of the priority area of returning cropland to forest land. The results showed that in 2021, the total nitrogen load outside the Dongting Lake was 11.34 × 108 kg·year-1, with the sub-basins retaining a total of 9.02 × 108 kg·year-1, accounting for 79.57 % of the total nitrogen load. Notably, the total nitrogen retention by the river made up 16.87 % of the total nitrogen retention, up to 1.83 × 108 kg·year-1. In view of monthly variation, water purification service based on the entire process was higher in winter and late autumn, and lower in summer and early autumn. The priority areas for ecological project were mainly distributed around the Dongting Lake, achieving an improvement of 58.15 % in water purification service with approximately 7.02 % of the total returnable cropland. This study proposed a new approach of water purification service assessment through integrating river transport processes and seasonal dynamics, aiming to assess watershed nitrogen export risk more accurately.

Response of Water Quality to Land Use and Landscape Pattern in the Ganjiang River Watershed

Analysing the impact of landscape composition and structure on water quality at different scales is of great significance to water quality protection. The aim of this study was to determine scale-dependent impacts of land use/landscape patterns on water quality. The Ganjiang River, the largest water system in the Poyang Lake watershed, the largest freshwater lake in China. The response of water quality to land use and landscape patterns in the Ganjiang River watershed was explored based on land use and water quality data using redundancy and Spearman correlation analyses. Considering upstream monitoring of the entire Ganjiang River watershed; watersheds at the county level administrative region; and 1, 2, 5, 10, 15, 20, and 30 km-radius circular buffer zones, a total of nine scales of land use/landscape patterns that influence water quality in the Ganjiang River watershed were analysed. Results indicated that the county-level scale and the land use type of the 20 km-radius buffer zone upstream of the monitoring site were closely linked to water quality (96.28% and 93.23%, respectively). Among the land use types, construction land and cultivated land were the main output sources of pollutants. Regarding landscape pattern index, the greater the fragmentation of the landscape, the heavier was the water pollution load; the more the patches per unit area, the more stable was the ecosystem and the lower was the pollutant concentration. In addition, the eco-hydrological system of the Ganjiang River watershed was revealed to some extent through multi-angle analysis. These conclusions can serve as a reference for government departments to formulate land management and water quality protection measures.

Increasing human activity shifts the key spatial scale of landscape patterns on water quality from sub-basins to riparian zones

The relationship between landscape patterns and water quality has been extensively studied, yet the understanding of how human activity modulates the spatial scale effects of landscape patterns on water quality remains limited. Here, we investigated the water quality and landscape patterns of three rivers in the Poyang Lake Basin, China, subjected to different intensities of human activity, and analyzed the extent to which water quality parameters were influenced by human activity to unravel the spatial scale effects and identify critical landscape metrics that significantly influence water quality. The results showed that the influence of riparian zone landscape patterns on water quality progressively exceeded that of sub-basin landscape patterns as the intensity of human activity increased. For water quality parameters that were minimally affected by human activity, the influence of sub-basin landscape patterns slightly exceeded that of riparian zone landscape patterns at different intensities of human activity (differences were 0.63 %, 4.25 % and 7.65 %, respectively). Conversely, for water quality parameters significantly affected by human activity, the landscape patterns of the riparian zone had a significantly greater influence than the sub-basin landscape patterns (differences were 5.90 %, 13.00 % and 17.86 %, respectively). Furthermore, the discrepancy between the influence of riparian zone and sub-basin landscape patterns on water quality increased with increasing intensity of human activity, while the overall influence of landscape patterns on water quality showed a decreasing trend (decreasing from 60.35 % to 39.10 %). In addition, the proportions of construction land, farmland, and forestland, and the fragmentation of grassland, were identified as critical landscape metrics that significantly influenced water quality at different intensities of human activity. This study revealed that different intensities of human activity were key factors influencing the spatial scale effects of landscape patterns on water quality.

The spatial scale and threshold effects of the relationship between landscape metrics and water quality in the Hutuo River Basin

The impact of landscape patterns on river water quality has been widely studied; however, it remains unclear which spatial scale has the greatest impact on water quality. Here, we analyzed the spatial scale and threshold impacts of the link between landscape metrics and water quality in a large-scale basin using the random forest (RF) model and nonparametric change point analysis (nCPA) method. The concentrations of nitrate nitrogen (NO3--N) and total nitrogen (TN) were comparatively high in winter and relatively low during spring and summer, whereas the total phosphorus (TP) concentrations were comparatively low during winter and summer and relatively high during spring. The R2 values of the RF models at the sub-basin scale were generally higher than those at the riparian zone scale. Moreover, the R2 of water quality modelling at the riparian zone scale demonstrated a declining tendency from a riparian zone 30 m-210 m wide in the majority of seasons. This shows that landscape metrics at the subbasin scale provide a better explanation for the variability in water quality than those at the riparian zone scale in the Hutuo River Basin. The results of the RF model indicated that landscape metrics of landscape configuration were more important in determining water quality during winter, whereas landscape metrics of landscape composition or physiography were more important in determining water quality during summer. Furthermore, several abrupt thresholds were estimated by nCPA; for example, the summertime slope abrupt threshold was 10.79° in the relationship between the slope and NO3--N. This study contributes to the understanding of the debate regarding the scale effects of landscape patterns on water quality, emphasizing the significance of the basin area and offering managers valuable insights into the control of non-point source pollution.

Interactive impact of landscape composition and configuration on river water quality under different spatial and seasonal scales

Currently, the comprehensive effect of the landscape pattern on river water quality has been widely studied. However, the interactive influences of landscape type, namely composition (COM) and configuration (CON) on water quality variations, as well as the specific landscape driving types affecting water quality variations under different spatial and seasonal scales remain unclear. To further improve the effectiveness of landscape planning and water quality protection, this study collected monthly water samples from the Fengyu River Watershed in southwestern China from 2018 to 2021, the Biota-Environment Matching Analysis (Bioenv) was used to identify key metrics representing landscape COM and CON, respectively. Then, the multiple regression (MLR) and redundancy analysis (RDA) were used to explore the relationship between these landscape metrics and water quality. In addition, this study used a variation partitioning analysis (VPA) to quantify the interactive and independent influence of landscape COM and CON on water quality. Results revealed that construction land and the Shannon's diversity index (SHDI) were the key metrics of landscape COM and CON, respectively, for predicting water pollution concentrations. The interactive contribution was particularly sensitive to seasonal changes in riparian buffer areas (27.66 % to 48.73 %), while it remained relatively stable at the sub-watershed scale (38.22 % to 40.51 %). Moreover, landscape CON had a higher independent contribution to variations on water quality across most spatio-temporal scales. Overall, identifying and managing key landscape type and consequential metrics, matching with the spatio-temporal scale, holds promise for enhancing water quality conservation. Furthermore, this study provides valuable insights into the identification and selection of core landscape metrics.

Spatiotemporal drivers of agricultural non-point source pollution: A case study of the Huang-Huai-Hai Plain, China

Agricultural non-point source pollution (ANPSP) poses a severe threat to ecological environments, especially in China's major grain-producing regions. Despite the increasing attention, existing studies often overlook the spatial heterogeneity and driving mechanisms of ANPSP within different functional regions. This study addresses this research gap by constructing a bottom-up regional inventory of ANPSP for the Huang-Huai-Hai Plain (HHHP) and applying the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyse the spatio-temporal patterns of ANPSP from 2000 to 2020. Spatial econometric models were further applied to examine the spatial spillover effects of driving factors from the perspective of Major Function-oriented Zoning (MFZ). The results show that while ANPSP emissions in the HHHP have generally increased over the past two decades, a slight decrease has been observed since 2015. Grain yield capacity and cropping intensity were identified as the primary drivers of ANPSP growth, particularly in urbanised zones (UZs) and main agricultural production zones (MAPZs). The study also highlights significant spatial heterogeneity in the impact of driving factors on ANPSP across different MFZs, with marked differences in both the direct and spatial spillover effects of these factors. This underlines the need for differentiated environmental protection policies tailored to the functions and characteristics of each region. By integrating the LMDI decomposition method with spatial econometric models, this study offers a new framework for understanding the ANPSP dynamics within the context of MFZs, providing policymakers with valuable insights for designing effective, regionally coordinated governance strategies.

Fertilization intensities at the buffer zones of ponds regulate nitrogen and phosphorus pollution in an agricultural watershed

The sudden increase in water nutrients caused by environmental factors have always been a focus of attention for ecologists. Fertilizer inputs with spatio-temporal characteristics are the main contributors to water pollution in agricultural watersheds. However, there are few studies on the thresholds of nitrogen (N) and phosphorus (P) fertilization rates that affect the abrupt deterioration of water quality. This study aims to investigate 28 ponds in Central China in 2019 to reveal the relationships of basal and topdressing fertilization intensities in surrounding agricultural land with pond water N and P concentrations, including total N (TN), nitrate (NO3--N), ammonium (NH4+-N), total P (TP), and dissolved P (DP). Abrupt change analysis was used to determine the thresholds of fertilization intensities causing sharp increases in the pond water N and P concentrations. Generally, the observed pond water N and P concentrations during the high-runoff period were higher than those during the low-runoff period. The TN, NO3--N, TP, DP concentrations showed stronger positive correlations with topdressing intensities, while the NH4+-N concentrations exhibited a higher positive correlation with basal intensities. On the other hand, the NO3--N concentrations had a significant positive correlation with the topdressing N, basal N, and catchment slope interactions. Significant negative correlations were observed between all water quality parameters and pond area. Spatial scale analysis indicated that fertilization practices at the 50 m and 100 m buffer zone scales exhibited greater independent effects on the variations in the N and P concentrations than those at the catchment scale. The thresholds analysis results of fertilization intensities indicated that pond water N concentrations increased sharply when topdressing and basal N intensities exceeded 163 and 115 kg/ha at the 100 and 50 m buffer zone scales, respectively. Similarly, pond water P concentrations rose significantly when topdressing and basal P intensities exceeded 117 and 78 kg/ha at the 50 m buffer zone scale, respectively. These findings suggest that fertilization management should incorporate thresholds and spatio-temporal scales to effectively mitigate pond water pollution.

Development, validation, and application of a microbial community-based index of biotic integrity for assessing the ecological status of a peri-urban watershed in China

This study represents the pioneering effort in employing 16S rRNA-bacteria and 18S rRNA-microeukaryotes to construct the microbial community-based index of biotic integrity (MC-IBI) for assessing the ecological health of riverine ecosystems. The MC-IBI was developed, validated, and implemented using water samples from the Changle River watershed, encompassing both wet and dry seasons. A total of 205 metrics, containing microbial diversity, composition, pollution tolerance/sensitivity, and functional categories, were selected as candidates for evaluation. Following a rigorous screening process, five core metrics were identified as key indicators, namely Pielou's evenness of microeukaryotes, %Cryptophyceae, %Proteobacteria, %Oxyphotobacteria, and % 16S rRNA gene-human pathogens. Moreover, redundancy analysis revealed three metrics (i.e., Pielou's evenness, % 16S rRNA gene-human pathogens, and % Proteobacteria) were positively correlated with impairment conditions. In contrast, two metrics (i.e., %Oxyphotobacteria and %Cryptophyceae) were associated positively with reference conditions. Notably, the developed MC-IBI demonstrates clear discrimination between reference and impaired sites and significantly correlates with environmental parameters and land use patterns. A path model analysis revealed that land use patterns (i.e., build-up land, cropland) negatively impacted the MC-IBI scores. The application of the MC-IBI method yielded an assessment of the ecological conditions at the 73 sampling locations within the Changle River watershed, assigning them into categories of "Very good" (4.1 %), "Good" (4.1 %), "Moderate" (5.5 %), "Poor" (21.9 %), and "Very poor" (64.4 %). This bioassessment framework presents an innovative approach toward the preservation, maintenance, and management of riverine ecosystems.

Effects of environmental factors on the river water quality on the Tibetan Plateau: a case study of the Xoirong River, China

Climate, topography, and landscape patterns affect river water quality through processes that influence non-point source pollution. However, little is known about the response of the water quality of rivers on China's Tibetan Plateau to these environmental factors. Based on the water quality parameters data of the Xoirong River on the Tibetan Plateau in western China, the redundancy analysis and variation partitioning analysis were adopted to determine the main influencing factors affecting river water quality and their spatial scale effects. The major water pollutants were further analyzed using the partial least square-structural equation modeling (PLS-SEM). Another mountainous river with a similar latitude, the same stream order, and low anthropogenic disturbance in central China, the Jinshui River, was also selected for comparative discussion. The results indicated that the overall river water quality on the Tibetan Plateau was superior to that of the Jinshui River. At the catchment scale, the cumulative explanatory powers of the influencing factors of both rivers were greatest. Landscape composition and configuration were the determinant factors for the overall water quality of the two rivers, while the river on the Tibetan Plateau was also significantly affected by climatic and topographical factors. Regarding the main water quality issue, i.e., total nitrogen, agricultural production activities might be the main cause of the river on the Tibetan Plateau. This study unveiled that the river water quality on the Tibetan Plateau is sensitive to climate and topography through comparative studies.

Evaluating the relationship between groundwater quality and land use in an urbanized watershed

Understanding the impact of urbanization on groundwater quality is critical. Effective water management requires understanding the relationship between land use and water quality. The study's goals were to compare the effects of land use, identify the types of land that impact hydrochemistry, and define how different land use affects water quality. For this purpose, the comparative relationship between groundwater quality, land use classes and landscape metrics were established for the years 2016 and 2021. Water samples were collected from 42 wells, and different hydro-chemical variables were considered to calculate the water quality index (WQI). The WQI value in 2016 ranged from 26.49 to 151.03 and 29.65 to 155.62 in 2021. The results indicate that the water quality in most parts of the study area is moderate for drinking and domestic purpose use. The google earth engine platform was used and radiometrically corrected and orthorectified Sentinel-2 satellite images were processed to classify land use classes for selected years. Five buffer zones were established within a 2-km watershed along each well site, and the effects of land use types and landscape metrics on water quality in the buffer zones were analyzed. Results revealed that the effects of land use types on water quality were mainly reflected in buffer 1 (B1), buffer 4 (B4), buffer 5 (B5) in 2016 and B1, buffer 3 (B3), and B5 in 2021. The impacts of landscape-level metrics on water quality are mainly reflected in buffer 2 (B2) and B3 in 2021, while at the class-level, they are mainly reflected in B1 and B4 in 2021. The redundancy analysis revealed that different hydro-chemical variables behaved differently with the land use classes and landscape metrics in the various buffer zones.

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.

Effects of land use and slope on water quality at multi-spatial scales: a case study of the Weihe River Basin

Exploring the impact of land use and slope on basin water quality can effectively contribute to the protection of the latter at the landscape level. This research concentrates on the Weihe River Basin (WRB). Water samples were collected from 40 sites within the WRB in April and October 2021. A quantitative analysis of the relationship between integrated landscape pattern (land use type, landscape configuration, slope) and basin water quality at the sub-basin, riparian zone, and river scales was conducted based on multiple linear regression analysis (MLR) and redundancy analysis (RDA). The correlation between water quality variables and land use was higher in the dry season than in the wet season. The riparian scale was the best spatial scale model to explain the relationship between land use and water quality. Agricultural and urban lands had a strong correlation with water quality, which was most affected by land use area and morphological indicators. In addition, the greater the area and aggregation of forest land and grassland, the better the water quality, while urban land presented larger areas with poorer water quality. The influence of steeper slopes on water quality was more remarkable than that of plains at the sub-basin scale, while the impact of flatter areas was greater at the riparian zone scale. The results indicated the importance of multiple time-space scales to reveal the complex relationship between land use and water quality. We suggest that watershed water quality management should focus on multi-scale landscape planning measures.

Influences of landscape pattern on water quality at multiple scales in an agricultural basin of western China

Determining the associations between landscape pattern and river water quality and quantifying the abrupt change points of landscape metrics are vital to optimize landscape planning and improve basin water quality. This study took an agricultural basin in western China as a case study. River water quality of 61 sub-basin outlets were monitored during wet and dry seasons from 2020 to 2021. Landscape metrics were extracted at 100 m, 300 m, 500 m riparian buffer and sub-basin scales, respectively. Relationships between water quality and landscape pattern at multiple scales were explored by using redundancy analysis (RDA). Results showed that urban-related landscape metrics served as the primary contributor to degrade water quality during both seasons, followed by cropland-related metrics, which might be attributed to the increase of urban land and reduction of agricultural chemical fertilizer use. Landscape metrics could better explain the water quality variations during wet season than dry season. The explanatory abilities of landscape metrics to overall water quality appeared little difference among spatial scales during wet season, whereas landscape metrics within 100 m riparian buffer had much larger explanatory rate than other spatial scales during dry season. Results of abrupt change point analysis revealed that the abrupt change interval values (ACIVs) of percentage of urban land (PLAND_urban) and the largest patch index of urban land (LPI_urban) differed among COD, TN, and TP. The recommended threshold values of PLAND_urban and LPI_urban for COD, TN, and TP management were smaller than 11.0%, 2.5%, and 1.0%, respectively. When the PLAND_urban or LPI_urban exceeded 19.0%, the TN, TP, and COD pollution would all significantly accelerate. Therefore, a limit value of 19% of PLAND_urban and LPI_urban, respectively is put forward. From dry season to wet season, the ACIVs of PLAND_urban and LPI_urban for COD concentration increased, whereas they decreased for TN and TP concentrations. Our results can provide scientific insights into sustainable landscape planning and effective water quality protection in agricultural basins.

Managing landscape patterns at the riparian zone and sub-basin scale is equally important for water quality protection

Widespread attention has been given to understanding the effect of the landscape pattern on river water quality. However, which spatial scale (riparian zone versus sub-basin) has the greater impact on water quality has long been controversial, since the key metrics that affect water quality varied with spatial scale. Thus, quantifying the spatial scale effects of key landscape metrics on water quality is critical to clarifying which scale of landscape pattern is more conducive to water quality conservation. Here, we adopted variation partitioning analysis (VPA) and random forest models to quantify the landscape pattern impact on water quality at northern Erhai Lake during the 2019 rainy season (early, mid, and late), and comprehensively analyze the key landscape metrics on different scales. The results revealed that the riparian zone and sub-basin scale landscape patterns explained similar water quality variations (difference only 0.9%) in the mid (August) and late rainy season (October), but exhibited a large difference (24.1%) during the early rainy season (June). Furthermore, rivers were primarily stressed by nitrogen pollution. Maintaining the Grassland_ED > 27.99 m/ha, Grassland_LPI > 4.19%, Farmland_LSI < 3.2 in the riparian zone, and Construction_ED < 1.69 m/ha, Construction_LSI < 2.46, Farmland_PLADJ < 89.0% at the sub-basin scale could significantly reduce the TN concentration in the stream. Meanwhile, managing of these metrics can effectively prevent rapid increases of TN in rivers. Moreover, due to the low phosphorus concentration in the rivers, none of the landscape metrics significantly explained the variation in TP. This study explored the spatial scale effect of landscape patterns on water quality and revealed the driving factors of nutrient variation. This study will provide a scientific basis for aquatic environmental management in plateau watersheds.

A source-sink landscape approach to mitigation of agricultural non-point source pollution: Validation and application

Optimizing landscape pattern to reduce the risk of non-point source (NPS) pollution is an effective measure to improve river water quality. The "source-sink" landscape theory is a recent research tool for landscape pattern analysis that can effectively integrate landscape type, area, spatial location, and topographic features to depict the spatial heterogeneity of NPS pollution. Based on this theory, we quantitatively analyzed the influence of "source-sink" landscape pattern on the river water quality in one of the most intensive agricultural watersheds in Southeastern China. The results indicated that the proportion of "sink" landscape (68.59%) was greater than that of "source" landscape (31.41%) in the study area. In addition, when elevation and slope increased, the "source" landscape proportion decreased, and the "sink" landscape proportion increased. Nitrogen (N) and phosphorus (P) pollutants in rivers showed significant seasonal and spatial variations. Farmland was the primary source of nitrate nitrogen (NO₃^--N) and total nitrogen (TN) pollution, whereas residential land was the primary source of ammonium nitrogen (NH₄⁺-N) and total phosphorus (TP) pollution. Intensively cultivated areas and densely inhabited areas degraded water quality despite high proportions of forest land. The four "source-sink" landscape indices (LWLI, LWLI'e, LWLI's, LWLI'd) had significant positive correlations with NO₃^--N and TN and weak correlations with NH₄⁺-N and TP. The capacity of LWLI to quantify the NPS pollution was greater in agricultural areas than in residential areas. The "source-sink" landscape thresholds resulted in abrupt changes in water quality. When LWLI was ∼0.35, the probability of river water quality degradation increased sharply. The results suggest the importance of optimizing the "source-sink" landscape pattern for mitigating agricultural NPS pollution and provide policy makers with adequate new information on the agroecosystem-environmental interface in highly developed agricultural watersheds.

Effects of multi-scale landscape pattern changes on seasonal water quality: a case study of the Tumen River Basin in China

Landscape patterns affect river water quality by influencing hydrological processes. However, with changes in spatial scale and season, landscape factors may have different effects on water pollution. Therefore, quantitative analysis of the scale effect of each landscape index was carried out to determine the mutation point of river water quality and its index relationship, which is of great significance to landscape planning and water quality protection. Based on the water quality monitoring data of 19 sampling points in the Tumen River Basin, we used redundant methods to quantify the spatial scale effects and seasonal dependencies of various landscape indicators on river water quality, then determined the mutation point of the water quality along the landscape-scale gradient. The results showed that different types of landscape indicators have different effects on river water quality, and the spatial-scale effect of landscape composition affects a river's water quality, while landscape configuration indicators had the highest sensitivity. The landscape characteristics of river straps better explained the overall water quality, a phenomenon that is more obvious in the wet season than the dry season. We identified a key landscape indicator of urban area proportion (Urban%) and a contagion index (CONTAG) as the river strap scale. An Urban% < 30% and a CONTAG > 70% suggest effective landscape planning parameters that effectively protect water quality. The results indicated that, to protect water quality, landscape regulation should follow scale-adaptability measures and consider landscape thresholds, which cause abrupt changes in water quality.

Domestic wastewater causes nitrate pollution in an agricultural watershed, China

Excessive quantities of nitrates in the aquatic environment can cause eutrophication and raise water safety concerns. Therefore, identification of the sources of nitrate is crucial to mitigate nitrate pollution and for better management of the water resources. Here, the spatiotemporal variations and sources of nitrate were investigated by stable isotopes (δ¹⁵N and δ¹⁸O), hydrogeochemical variables (e.g., NO₃^- and Cl^-), and exogenous microbial signals (i.e., sediments, soils, domestic and swine sewage) in an agricultural watershed (Changle River watershed) in China. The concentration ranges of δ¹⁵N- and δ¹⁸O-NO₃^- between 3.03‰-18.97‰ and -1.55‰-16.47‰, respectively, suggested that soil nitrogen, chemical fertilizers, and manure and sewage (M&S) were the primary nitrate sources. Bayesian isotopic mixing model suggested that the major proportion of nitrate within the watershed (53.12 ± 10.40% and 63.81 ± 15.08%) and tributaries (64.43 ± 5.03% and 76.20 ± 4.34%) were contributed by M&S in dry and wet seasons, respectively. Community-based microbial source tracking (MST) showed that untreated and treated domestic wastewater was the major source (>70%) of river microbiota. Redundancy analysis with the incorporation of land use, hydrogeochemical variables, dual stable isotope, and exogenous microbial signals revealed domestic wastewater as the dominant cause of nitrate pollution. Altogether, this study not only identifies and quantifies the spatiotemporal variations in nitrate sources in the study area but also provides a new analytical framework by combining nitrate isotopic signatures and community-based MST approaches for source appointment of nitrate in other polluted watersheds.

Study on the Water Quality Characteristics of the Baoan Lake Basin in China under Different Land Use and Landscape Pattern Distributions

Land use and landscape pattern highly affect water quality. Their relationship can assist in land-use management and improve land-use efficiency. In this study, a water quality survey of rivers and lakes was performed in 2020 to analyze the effects of land use and the landscape pattern on the water quality of the rivers and lakes in the Baoan Lake basin and is expected to provide a reference for land use planning. The results demonstrated that the effects of land use on water quality were generally higher during the dry season than during the wet season; however, the opposite was demonstrated for the landscape pattern index. Cropland and urban land were closely correlated with deteriorating water quality, with contributions to total nitrogen, total phosphorous, and ammonia nitrogen in the basin. The impact of the landscape pattern of the basin on water quality was controlled by the original land-use type. In addition, the landscape configuration formed different land-use types to produce different effects on water quality. The basin scale better explained the changes in water quality, especially for construction land, followed by the 250 m and 500 m scales in the buffer zone.

Prediction and optimization of regional land-use patterns considering nonpoint-source pollution control under conditions of uncertainty

Socioeconomic development, leading to significant changes in land-use patterns, has further influenced the output of regional nonpoint-source (NPS) pollution. Multiple uncertainties exist in the processes of land-use changes and NPS pollution export. These uncertainties can deeply affect the management of regional land-use patterns and control of NPS pollution. In this research, an integrated land-use prediction and optimization (ILUPO) model based on system dynamics, export coefficient, interval linear programming, and fuzzy parameter programming models was proposed. The ILUPO model can provide future land-use patterns and NPS pollution loads, and also help optimize the patterns under multiple pollution reduction scenarios. Interval and fuzzy uncertainties in the processes of land-use changes and NPS pollution output can be effectively addressed. The developed model was applied to a water source area in the central part of northern Guangdong Province in South China. For the prediction period 2020-2030 under the high-speed development scenario, results show that cropland area would decrease, while grassland and waterbody areas would increase. In contrast, these three types of land-use would show opposite variation trends under the low-speed development scenario. Construction land area would decrease, while forestland area would increase under both the low-speed and high-speed development scenarios. Variation of the predicted land-use patterns would lead to an increase of total nitrogen loads under each of the scenario, while the total phosphorus loads would show relatively complex variation trends. Regional land-use patterns should be further optimized to mitigate NPS pollution. However, the pollution loads in the study area cannot be reduced by >5% through land-use adjustment. Because cropland would still be the critical source of NPS pollution after optimization, strictly controlling the areas of cropland would be important for the management of such pollution in the research area. In addition, certain areas of grassland and waterbody would need to be converted into cropland and construction land to balance the economic benefit of the system and NPS pollution control. Multiple results obtained from the model under different scenarios of pollution reduction targets and α-cut levels can provide decision-making supports for the local policy makers. The developed ILUPO model can yield insights useful for the planning and adjustment of regional land-use patterns while considering NPS pollution control under conditions of uncertainty.

Influence of the catchment area use on the water quality in the Utrata River

The present paper discusses the impact of land use and seasons on the concentration of nutrients in the waters of the Utrata River (Pruszków Poviat, Mazowieckie Voivodeship) from April 2018 to March 2019. The pollution of rivers by nutrients is a major problem for society. Surface water is a source of drinking water, water used for industrial and agricultural purposes. With the increasing pollution of rivers, the purification process for these purposes becomes more expensive and more challenging. To assist in carrying out activities aimed at reducing the inflow of biogenic substances into large river systems and then down to the Baltic Sea, we analyzed the spatial and temporal dynamics of loads from the entire Utrata River catchment area. We divided the entire catchment area into three impact zones: grasslands and wastelands, urbanized areas, and agricultural land and examined changes in nutrient concentrations (total phosphorus, nitrate nitrogen, ammonium nitrogen) in each of the zones. The results were statistically processed using the 1-factor ANOVA method with the p-value of significance below 0.05. Research indicates an increase in the concentration of total phosphorus and nitrogen forms down the course of the river in urban and agricultural areas with persistently low concentrations of these biogenic substances in grasslands.

Tracking microeukaryotic footprint in a peri-urban watershed, China through machine-learning approaches

Microeukaryotes play a significant role in biogeochemical cycling and can serve as bioindicators of water quality in freshwater ecosystems. However, there is a knowledge gap on how freshwater microeukaryotic communities are assembled, especially that how terrestrial microeukaryotes influence freshwater microeukaryotic assemblages. Here, we used a combination of 18S rRNA gene amplicon sequencing and community-based microbial source tracking (MST) approaches (i.e., SourceTracker and FEAST) to assess the contribution of microeukaryotes from surrounding environments (i.e., soils, river sediments, swine wastewater, influents and effluents of decentralized wastewater treatment plants) to planktonic microeukaryotes in the main channel, tributaries and reservoir of a peri-urban watershed, China in wet and dry seasons. The results indicated that SAR (~ 49% of the total communities), Opithokonta (~ 34%), Archaeplastida (~ 9%), and Amoebozoa (~ 2%) were dominant taxa in the watershed. The community-based MST analysis revealed that sewage effluents (7.96 - 21.84%), influents (2.23 - 13.97%), and river sediments (2.56 - 11.71%) were the major exogenous sources of riverine microeukaryotes. At the spatial scale, the downstream of the watershed (i.e., main channel and tributaries) received higher proportions of exogenous microeukaryotic OTUs compared to the upstream reservoirs, while at the seasonal scale, the sewage effluents and influents contributed higher exogenous microeukaryotes to river water in wet season than in dry season. Moreover, the swine and domestic wastewater led to the presence of Apicomplexa in wet season only, implying rainfall runoff may enhance the spread of parasitic microeukaryotes. Taken together, our study provides novel insights into the immigration patterns of microeukaryotes and their dominant supergroups between terrestrial and riverine habitats.

Contract farming, social trust, and cleaner production behavior: field evidence from broiler farmers in China

Pollution from livestock and poultry is the main source of rural pollution, which directly affects the rural ecological environment as well as the quality and safety of agricultural products. Based on field experiment data on broiler farmers in China, this paper analyzes farmers' cleaner production behavior from the perspective of incomplete contracts and social trust. We find that social trust can promote farmers' cleaner production behavior. Moreover, our evidence suggests that contract farming (CF) has a significant positive effect on farmers' social trust and cleaner production behaviors. Further analysis indicates that CF not only directly promotes farmers' cleaner production, but can also indirectly promote farmers' cleaner production by improving their interpersonal trust and institutional trust. Overall, this paper offers a new point of view for improving the rural environment and sheds light upon how the government can formulate relevant policies to promote farmers' cleaner production behavior.

Linking river sediment arsenic to catchment spatial attributes in agricultural landscapes

Understanding of the sources and processes involved in the heavy metal accumulation in river sediments is important for measuring the risks associated with human exposure. Hence, an integrated modeling approach was designed to study the linkage between landscape-related natural and anthropogenic features and high arsenic levels at the outlet of six catchments on the Ghareh-Ajagh River, central Iran. Sediment arsenic levels were measured in 8 months from October 2018 to November 2019 when the river sediment and water conditions were stable and ranged from 16.3 to 78.5 mg/kg. Monthly catchment-level agricultural areas were extracted from Landsat 8-OLI images. Predictive variables included NDVI values; area and spatial patterns of agriculture measured using four landscape metrics of NP, PD, MPS, and ENN; length and slope of the streams extended from main agricultural areas to the catchment outlet; and four hydrologic soil groups. The best-fitted multiple regression model (r2 = 0.763, p≤ 0.05) with the Akaike information criteria of 105.07 was developed using stream length, soil group C, and area and PD of agricultural areas. Results showed that sediment arsenic levels increase with increasing quantity and density of agricultural activities that were close to the river outlet and increasing proportion of silty loam or loamy soils but are relatively less dependent on agricultural structural patterns. These insights are helpful to inform policy decisions regarding the processes involved in river contamination in central Iran.

Risk assessment of non-point source pollution based on landscape pattern in the Hanjiang River basin, China

Non-point source (NPS) pollution has become a vital contaminant source affecting the water environment because of its wide distribution, hydrodynamic complexity, and difficulty in prevention and control. In this study, the identification and evaluation of NPS pollution risk based on landscape pattern were carried out in the Hanjiang River basin above Ankang hydrological section, Shaanxi province, China. Landscape distribution information was obtained through land use data, analyzing the contribution of "source-sink" landscape to NPS pollution through the location-weighted landscape contrast index. Using the NPS pollution risk index to identify and evaluate the regional NPS pollution risk considering the slope, cost distance, soil erosion, and precipitation erosion affect migration of pollutants. The results showed that (i) the pollution risk was generally high in the whole watershed, and the sub-watersheds dominated by "source" landscapes account for 74.61% of the whole basin; (ii) the high-risk areas were distributed in the central, eastern, and western regions of the river basin; the extremely high-risk areas accounted for 12.7% of the whole watershed; and the southern and northern regions were dominated by forestland and grassland with little pollution risk; (iii) "source" landscapes were mostly distributed in areas close to the river course, which had a great impact on environment, and the landscape pattern units near the water body needed to be further adjusted to reduce the influence of NPS pollution.

Monitoring the Landscape Pattern and Characteristics of Non-Point Source Pollution in a Mountainous River Basin

This study aimed to assess the relationship between the landscape patterns and non-point source (NPS) pollution distribution in Qixia County, China. The sub-basin classification was conducted based on a digital elevation model and Landsat8 satellite images. Water samples were collected from each sub-basin, andtheir water quality during the wet and dry seasons was estimated. The correlation between the landscape indices and water pollution indicators was determined by Pearson analysis. The location-weighted landscape contrast index (LWLCI) was calculated based on the "source-sink" theory. Qixia was further divided into five sections based on the LWLCI score to illustrate the potential risk of NPS pollution. The results showed that the water quality in Qixia County was generally good. Cultivated land, orchards, construction areas, and unused land were positively correlated with the water pollution index and weredesignated as the "source" landscape categories, while forests, grasslands, and water bodies, which were negatively correlated with water pollution, were the "sink" landscapes; the LWCI was high in 36.94% of the study area. In these areas, measures such as increasing vegetation buffer zones are necessary to decrease the sediment and nutrient loads carried by precipitation.

Sources, transformations of suspended particulate organic matter and their linkage with landscape patterns in the urbanized Beiyun river Watershed of Beijing, China

This study explored the sources, transformations of suspended particulate organic matter (POM), and the influence of landscape patterns on POM within the Beiyun River Watershed by applying the stable carbon and nitrogen isotope technique combined with multiple statistical analyses. The POM variables showed great spatial fluctuations under different urban development gradients. Analysis of multiple isotopes revealed that assimilation of phytoplankton might exist in the rainy season, while nitrification occurs in the dry season. SIAR modeling results indicated that the sewage debris and phytoplankton were the main sources of POM in both seasons, accounting for 52.58% and 38.39% in the rainy season, 33.17% and 31.95% in the dry season, respectively. Spatiotemporal variations of POM sources existed in the study watershed, probably due to urbanization and human disturbance. The multiple linear stepwise regression and redundant analysis results indicated that landscape metrics reflecting contagion and fragmentation at the class level correlated well with the POM variables over seasons. Interspersion and juxtaposition indices of grassland and water were negatively related to POM variables in the rainy season, whereas the landscape division index of buildup land showed negative correlations with POM parameters in the dry season. Increasing the adjacency of grassland and water to other land uses, while reducing the aggregation of buildup lands would be an efficient way for urban river water quality improvement.

Optimal control of nonpoint source pollution in the Bahe River Basin, Northwest China, based on the SWAT model

This study conducts in the Bahe River Basin, an agricultural basin in Northwest China. We use the Soil and Water Assessment Tool (SWAT) model to identify the spatial distribution characteristics of non-point source (NPS) pollution and determine the critical source areas (CSA). Then the relationship between landscape pattern and NPS pollution is analyzed by spearman correlation analysis and redundancy analysis (RDA). On this basis, we set up eight landscape management practices in the CSA and evaluate their reduction effects on NPS pollution loads. The results show that the spatial distribution of nitrogen and phosphorus loss intensity has a certain correlation with rainfall and runoff, and the correlation between phosphorus loss intensity and sediment loss intensity is more significant. The NPS pollution load is closely related to the landscape pattern of the river basin, and is affected by the fragmentation, aggregation and complexity of the landscape. Farmland, forest land, and grassland are the main landscape components of the river basin. Farmland is the main source of NPS pollution, whereas forest land and grassland can effectively inhibit the output of NPS pollution, and the reduction effect of forest land is significantly better than that of grassland. The largest patch index (LPI), landscape shape index (LSI), patch density (PD) are the main landscape factors that affect the output of NPS pollution load. Among all the scenarios, the reduction effect of returning farmland to forest land in slopes above 15° is the best, and the reduction rates of total nitrogen (TN) and total phosphorus (TP) loads have reached about 25%. This study provides some reference for the management of NPS pollution in the Bahe River Basin and other similar basins.

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.

Seasonal variations of water quality response to land use metrics at multi-spatial scales in the Yangtze River basin

Land use pattern is increasingly regarded as an important determinant of environmental quality and regional ecosystems. Understanding the correlation between land use metrics and water quality is essential to improve water pollution prediction and provide guidance for land use planning. Here, we examined the land use metrics and water quality parameters (i.e., dissolved oxygen, DO; pH; ammonia nitrogen NH₄⁺-N; permanganate index, COD_Mn), as well as their relationships in the Yangtze River basin. The DO and pH exhibited the notable spatio-temporal variability, suggesting that anthropogenic land uses (farmland and urban land) greatly impacted riverine water quality. The catchment and riparian scales respectively showed a high potential in explaining water quality in the dry and wet seasons. The land use metrics were tightly linked to water quality in the dry season, indicating that intensive farming activities led to high loadings of agriculture-related chemicals and thus water quality deterioration. Our results provided useful information regarding riverine water quality response to land use metrics at multi-spatial scales.

Identification and Regulation of Critical Source Areas of Non-Point Source Pollution in Medium and Small Watersheds Based on Source-Sink Theory

The identification and regulation of the critical source areas (CSAs) of non-point source (NPS) pollution have been proven as economical and effective ways to control such pollution in watersheds. However, the traditional models for the identification of CSAs have complex operation processes, and comprehensive systematic methods for the regulation of CSAs are still lacking. This study systematically developed a new methodological framework for the identification and regulation of CSAs in medium and small watersheds based on source-sink theory, which included the following: (1) a grid-based CSAs identification model involving the evaluation of the rationality of the source-sink landscape pattern and three geographical factors (landscape slope, relative elevation, and the distance from the river), and identifying CSAs by the calculation and division of the integrated grid pollution index (IGPI); (2) a comprehensive CSAs regulation strategy that was formulated based on three landscape levels/regulation intensities—including the optimization of the overall source-sink landscape pattern, the conversion of the landscape type or landscape combination, and local optimization for single source landscape—to meet various regulatory intensity requirements in watersheds. The Jiulong River watershed in Fujian Province of China was taken as a case study. The results indicate that: (1) the identified CSAs of the Jiulong River watershed covered 656.91 km2, equivalent to 4.44% of the watershed, and through adopting multiple-intensity regulation measures for 10 key control zones that had spatially concentrated high values of the IGPI among the CSAs, the watershed IGPIs were predicted to be generally reduced and the area of CSAs was predicted to decrease by 23.84% (31.43% in Zhangzhou, the major city in the watershed); (2) the identification model can identify the CSAs with easy data access and simple operation, and the utilization of neighborhood impact analysis makes the grid-based research more scientific in the evaluation of the rationality of the source-sink landscape pattern; (3) the application of multi-scale landscape planning framework and the principle of source-sink landscape pattern regulation make the CSAs regulation strategy systematic and cost-effective, and the provision of different intensity regulation strategies makes the regulation strategy easy to implement and relatively lower cost. The proposed methodological framework can provide technical support for governments to quickly and accurately identify the CSAs of NPS pollution and effectively control such CSAs in medium and small watersheds.

Evaluating the Relationships between Riparian Land Cover Characteristics and Biological Integrity of Streams Using Random Forest Algorithms

The relationships between land cover characteristics in riparian areas and the biological integrity of rivers and streams are critical in riparian area management decision-making. This study aims to evaluate such relationships using the Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI), Fish Assessment Index (FAI), and random forest regression, which can capture nonlinear and complex relationships with limited training datasets. Our results indicate that the proportions of land cover types in riparian areas, including urban, agricultural, and forested areas, have greater impacts on the biological communities in streams than those offered by land cover spatial patterns. The proportion of forests in riparian areas has the greatest influence on the biological integrity of streams. Partial dependence plots indicate that the biological integrity of streams gradually improves until the proportion of riparian forest areas reach about 60%; it rapidly decreases until riparian urban areas reach 25%, and declines significantly when the riparian agricultural area ranges from 20% to 40%. Overall, this study highlights the importance of riparian forests in the planning, restoration, and management of streams, and suggests that partial dependence plots may serve to provide insightful quantitative criteria for defining specific objectives that managers and decision-makers can use to improve stream conditions.

Spatial scale effects of landscape metrics on stream water quality and their seasonal changes

Physiography and land use patterns influence streams water quality by affecting non-point source (NPS) pollution process. However, each landscape factor may affect the NPS pollution process differently with the variations of the spatial scale and season. Thus, quantitative analysis of each landscape metrics scale effect and determination of the abrupt change-point in the relationship between stream water quality and the metrics is very helpful for landscape planning of water quality protection. Based on water quality monitoring data for four years in 12 sub-watersheds of a typical headwater watershed in Eastern China, we adopted regular and partial redundancy methods to quantify the spatial scale effects and seasonal dependence of various landscape metrics impact on stream water quality, and then to identify the abrupt change-point of the water quality along the gradient of landscape metrics. Results revealed that the pure effects of different categories of landscape metrics on stream water quality were in the following order: landscape configuration metrics (20.5-31.6%) > physiographic metrics (4.0-15.9%) >landscape composition metrics (3.2-7.5%). The spatial scale effect of physiography impact on stream water quality was the most significant, while the impact of landscape configuration on water quality had the highest seasonal sensitivity. The overall water quality variation was better explained by buffer zone scale than by catchment scale landscape characteristics, and this phenomenon was more obvious during the wet season than during the dry season. In the studied watershed, we identified the largest patch index of farmland (LPI_far) and the landscape shape index of forest (LSI_for) as the key landscape metrics at sub-watershed scale and buffer zone scale, respectively. The LPI_far > 7.0% at the sub-watershed scale and LSI_for < 5.5 at the buffer zone scale were suggested as the preferred landscape planning parameters to protect the stream water quality efficiently. Results indicated that, to protect water quality, landscape regulation should follow the scale-adaptability measures and consider the landscape thresholds, which cause abrupt changes in water quality.

Identification of the control factors affecting water quality variation at multi-spatial scales in a headwater watershed

Understanding the effect of landscape characteristics on water quality can provide insight into mitigating water quality impairment. However, there is no consensus about the key controlling factors influencing water quality. This paper examined the combined effects of land use and topography on water quality across multi-scale, and identified the key controlling factors determining water quality variation in the headwater watershed of the Hengxi reservoir in Eastern China. Water quality impairment (WQI), expressed as a composite variable, was established to measure the overall water quality. We used the partial least squares (PLSR) method to explore the combination of landscape metrics and identify the key controlling factors. Results showed that the optimal PLSR model at 50-m, 100-m, and 150-m buffer scales and catchment scale explained 77%, 63%, 60%, and 56% of variability in WQI, respectively. At catchment scale, patch density, the percentage of paddy field, and hypsometric integral were the key controlling factors impacting water quality. At buffer scales, the slope gradient, the percentage of forest land, and topographic wetness index were more effectively determined WQI variation. Thus, the key controlling factors depend on spatial scales. Both spatial scales and corresponding key controlling factors should be considered in the adjustment of land use composition and planning of landscape configuration to better protect water quality.

Impact of Land Use Change on Non-Point Source Pollution in a Semi-Arid Catchment under Rapid Urbanisation in Bolivia

Changes in pollution pressure exerted on the Rocha River in Bolivia from diffuse sources were assessed using potential non-point pollution indexes (PNPI) for 1997 and 2017. PNPI is a simple, low-effort, time- and resource-saving method suitable for data-scarce regions, as it works at catchment level with commonly available geographical data. Land use type (obtained by Landsat imagery classification), runoff (determined by runoff coefficient characterisation) and distance to river network (calculated at perpendicular distance) were each transformed into corresponding indicators to determine their relative importance in generating pollution. Weighted sum, a multi-criteria analysis tool in the GIS environment, was used to combine indicators with weighting values. Different weighting values were assigned to each of the indicators resulting in a set of six equations. The results showed that higher PNPI values corresponded to human settlements with high population density, higher runoff values and shorter distance to river network, while lower PNPI values corresponded to semi-natural land use type, lower runoff coefficient and longer distances to river. PNPI values were positively correlated with measured nitrate and phosphate concentrations at six sub-catchment outlets. The correlation was statistical significant for phosphate in 2017. Maps were produced to identify priority source areas that are more likely to generate pollution, which is important information for future management.

Discussing on "source-sink" landscape theory and phytoremediation for non-point source pollution control in China

Water pollution is exacerbated due to irrational human activities in China. Restoring and rebuilding river basin ecosystems are major ecological strategies at present. Controlling the non-point source pollution (NPSP) by reasonable management of land use in the basin and phytoremediation of contaminated waters is the optimum approach. Thus, it is significant to study on the relationship that between landscape change and the aquatic environment, as well as further to analyze on the combined effect of the landscape and water quality. This paper describes the application and development of the "source-sink" landscape theory in China, and the role of the theory in controlling NPSP. From this perspective, a landscape capable of generating NPSP would be a "source" landscape, such as farmland, while another capable of preventing NPSP would be a "sink" landscape, such as forests and wetland. Applying the source-sink landscape theory, it is possible to exert the ecological benefits of the landscape while playing the esthetic value of the landscape. Also, the purification mechanism of plants in contaminated water is discussed. Besides, it is vital that research on water body restoration should focus not only on single discipline but also on integration and coordination between various ones such as ecology, environmental science, and geography to jointly push up researches related to water body phytoremediation. Hopefully, this paper could help to control water pollution from a new perspective, also to improve water environment and benefit human lives.

Characterizing the heterogeneous correlations between the landscape patterns and seasonal variations of total nitrogen and total phosphorus in a peri-urban watershed

Landscape patterns in a watershed potentially have significant influence on the occurrence, migration, and transformation of pollutants, such as nitrogen (N) and phosphorus (P) in rivers. Human activities can accelerate the pollution and complicate the problem especially in a peri-urban watershed with different types of land use. To characterize the heterogeneous correlations between landscape patterns and seasonal variations of N and P in a peri-urban watershed located upstream of Tianjin metropolis, China, observations of total nitrogen (TN) and total phosphorus (TP) at 33 locations were performed in the wet and dry seasons from 2013 to 2016. The data from individual locations were averaged for the wet and dry seasons and analyzed with geographical detector to identify influential landscape indices on seasonal water quality variations. The geographically weighted regression method, capable of analyzing heterogeneous correlations, was used to evaluate the integrated effects from different landscape indices. The results demonstrated that the location-weighted landscape contrast index (LWLI), the ratio of urban areas, and the ratio of forest areas were major influential indicators that affected TN and TP in river water. These indices also had integrated effects on variations of TN and TP together with other indices such as Shannon diversity index, landscape shape index, largest patch index, and contagion index. The integrated effects were different in the wet and dry seasons because of different effects of flushing and dilution by rainwater and the heterogeneity in landscape patterns. The LWLI had a positive relationship to water quality in the areas with high ratio of urban areas, indicating that domestic wastewater can be a major source of N and P pollution. The approaches and findings of this study may provide a reference for characterizing the major factors and integrated effects that control nonpoint source pollution in a watershed.

An Evolutionary Game Model for the Multi-agent Co-Governance of Agricultural Non-Point Source Pollution Control under Intensive Management Pattern in China

This paper focuses on the sustainable development dilemma of agricultural production in China under the pattern of intensive management, which is seriously challenged by agricultural non-point source pollution. The key to effectively break through the dilemma is to promote the co-governance of agricultural non-point source pollution control by stakeholders including local governments, new agricultural operators and traditional farmers. Accordingly, this paper discusses the interactive decision-making relationships between new agricultural operators and traditional farmers under the guidance of local governments, by constructing a trilateral evolutionary game model, as well as analyzing evolutionary cooperative stability strategies and realizing the simulation of evolution processes in different scenarios by MATLAB. The results show that new agricultural operators play a leading role in agricultural non-point source pollution control, whose strategies have effects such as technology spillover. The rewards from the superior government will support local governments in taking proactive action in the co-governance of agricultural non-point source pollution control, and then local governments can offer technical support and subsidies to new agricultural operators and traditional farmers for reducing their costs. Furthermore, this paper also finds that there are green synergy effects among the groups, where the variations of parameters and strategies by one group would affect the two others. Additionally, agricultural land operation rights transfers would cause traditional farmers to take more time to cooperate in the co-governance of agricultural non-point source pollution control. In order to promote the multi-agent co-governance of agricultural non-point source pollution control under intensive management pattern, this paper suggests that it should be necessary to reduce their costs and improve incentives, as well as to increase the common interests among groups and enhance their green synergy effects.

The Influence of Different Forest Characteristics on Non-point Source Pollution: A Case Study at Chaohu Basin, China

Forestland is a key land use/land cover (LULC) type that affects nonpoint source (NPS) pollution, and has great impacts on the spatiotemporal features of watershed NPS pollution. In this study, the forestland characteristics of the Chaohu Basin, China, were quantitatively represented using forestland types (FLTs), watershed forest coverage (WFC) and forest distance from the river (DFR). To clarify the impact of forests on NPS pollution, the relationship between forestland characteristics and watershed nutrient outputs (TN and TP) was explored on a monthly scale using SWAT (Soil and Water Assessment Tool) and the period simulation was 2008-2016. The results showed that: (1) the TN and TP showed similar output characteristics and the rainy season was the peak period of nitrogen and phosphorus output. (2) Among the forestland characteristics of forestland types, watershed forest coverage and forest distance from the river, watershed forest coverage and forest distance from the river had greater effects than forestland types on the control of watershed nutrient outputs (TN and TP). (3) In different forestland types, the watershed nutrient outputs intensity remained at the lowest level when the FLTs was mixed forest, with a TN output of 1244.73kg/km² and TP output of 341.39 kg/km². (4) The watershed nutrient outputs and watershed forest coverage were negatively correlated, with the highest watershed forest coverage (over 75%) reducing the TN outputs by 56.69% and the TP outputs by 53.46% compared to the lowest watershed forest coverage (below 25%), it showed that in areas with high forest land coverage, the non-point source pollution load in the watershed is smaller than in other areas. (5) forest distance from the river had an uncertain effect on the TN and TP output of the basin, the forestland itself is a source of pollution, but it also has the function of intercepting pollution movement; the forest distance from the river in the range of 500-1000 m had the lowest NPS pollution. Considering the different forest characteristics and topographical factors, an optimal allocation mode of differentiated forest land was proposed, these suggestions will provide a scheme for surface source pollution prevention and control in the basin. This research gap is the basis of real forestland optimization. We may optimize the forestland layout for NPS pollution prevention and control by clarifying the internal mechanism.

Impacts of Land Use and Land Cover on Water Quality at Multiple Buffer-Zone Scales in a Lakeside City

Understanding the effect of land use/land cover (LULC) on water quality is essential for environmental improvement, especially in urban areas. This study examined the relationship between LULC at buffer-zone scales and water quality in a lakeside city near Poyang Lake, which is the largest freshwater lake in China. Representative indicators were selected by factor analysis to characterize the water quality in the study area, and then the association between LULC and water quality over space and time was quantified by redundancy analysis. The results indicated that the influence of LULC on water quality is scale-dependent. In general, the LULC could explain from 56.9% to 31.6% of the variation in water quality at six buffer zones (from 500 m to 1800 m). Forest land had a positive effect on water quality among most buffer zones, while construction land and bare land affected the representative water quality indicators negatively within the 1200 m and 1500 m buffer zones, respectively. There was also a seasonal variation in the relationship between LULC and water quality. The closest connection between them appeared at the 1000 m buffer zone in the dry season, whereas there was no significant difference among the buffer zones in the wet season. The results suggest the importance of considering buffer-zone scales in assessing the impacts of LULC on water quality in urban lakeshore areas.