Where does land use matter most? Contrasting land use effects on river quality at different spatial scales

Distinct influences of altitude on microbiome and antibiotic resistome assembly in a glacial river ecosystem of Mount Everest

The profound influences of altitude on aquatic microbiome were well documented. However, differences in the responses of different life domains (bacteria, microeukaryotes, viruses) and antibiotics resistance genes (ARGs) in glacier river ecosystems to altitude remain unknown. Here, we employed shotgun metagenomic and amplicon sequencing to characterize the altitudinal variations of microbiome and ARGs in the Rongbu River, Mount Everest. Our results indicated the relative influences of stochastic processes on microbiome and ARGs assembly in water and sediment were in the following order: microeukaryotes < ARGs < viruses < bacteria. Moreover, distinct assembly patterns of the microbiome and ARGs were found in response to differences in altitude, the latter of which shift from deterministic to stochastic processes with increasing differences in altitude. Partial least squares path modeling revealed that mobile genetic elements (MGEs) and viral β-diversity were the major factors influencing the ARG abundances. Taken together, our work revealed that altitude-caused environmental changes led to significant changes in the composition and assembly processes of the microbiome and ARGs, while ARGs had a unique response pattern to altitude. Our findings provide novel insights into the impacts of altitude on the biogeographic distribution of microbiome and ARGs, and the associated driving forces in glacier river ecosystems.

Using surface runoff to reveal the mechanisms of landscape patterns driving on various forms of nitrogen in non-point source pollution

Non-point source (NPS) pollution directly threatens river water quality, constrains sustainable economic development, and poses hazards to human health. Comprehension of the impact factors on NPS pollution is essential for scientific river water quality management. Despite the landscape pattern being considered to have a significant impact on NPS pollution, the driving mechanism of landscape patterns on NPS pollution remains unclear. Therefore, this study coupled multi-models including the Soil and Water Assessment Tool (SWAT), Random Forest, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to construct the connection between landscape patterns, NPS pollution, and surface runoff. The results suggested that increased runoff during the wet season enhances the link between landscape patterns and NPS pollution, and the explained NPS pollution variation by landscape pattern increased from 59.6 % (dry season) to 84.9 % (wet season). Furthermore, from the impact pathways, we find that the sink landscape pattern can significantly and indirectly influence NPS pollution by regulating surface runoff during the wet season (0.301*). Meanwhile, the sink and source landscape patterns significantly and directly impact NPS pollution during different seasons. Moreover, we further find that the percentage of paddy land use (Pad_PLAND) and grassland patch density (Gra_PD) metrics can significantly predict the dissolved total nitrogen (DTN) and nitrate nitrogen (NO3--N) variation. Thus, controlling the runoff migration process by guiding the rational evolution of watershed landscape patterns is an important development direction for watershed NPS pollution management.

Spatial distribution characteristics of natural ecological resilience in China

High-intensity exploitation of land resources and the natural environment can upset the balance of ecosystems, causing multiple ecological problems and affecting regional sustainable development. Recently, China has carried out integrated regional ecosystem protection and restoration governance. Ecological resilience (ER) is the foundation of and key to achieving sustainable regional development. Based on the significance of ER in ecological protection and restoration efforts and the necessity of conducting large-scale studies, we conducted relevant research on the ER in China. In this study, we selected typical impact factors to construct an assessment model of ER in China and quantitatively measured its large-scale spatial and temporal distribution characteristics, while also exploring the relationship between ER and land-use types. The country was zoned according to the ER contributions of each land-use type, and ER enhancement and ecological protection were discussed based on the characteristics of different regions. The ER in China shows clear spatial heterogeneity and spatial agglomeration, roughly represented by high and low ER in the southeast and northwest regions. The mean ER values of woodland, arable land, and construction land were all greater than 0.6, with more than 97% of the ER values at levels of medium or above. The country can be divided into three regions based on the degree of ER contributions of various land-use types, each with different ecological problems. This study provides a detailed understanding of and explores the important role of ER on the regional development process, and provides support and reference for regional ecological protection and restoration as well as sustainable development.

The impact of land use on stream macroinvertebrates: a bibliometric analysis for 2010-2021

Changes in stream biodiversity are now mainly driven by land-use development. However, a literature review on the impact of land use on stream macroinvertebrates is lacking, especially a scientometric review. Here, we bibliometrically analyzed the literature on land use and stream macroinvertebrates that were published in 2010-2021 and listed in the Web of Science database. We found that the impact of land use on stream macroinvertebrates had been increasingly studied and that these studies were distributed across the globe and had multi-national collaborations. Through co-citation analysis and high-frequency keyword analysis, we found that land use and some environmental factors, especially water quality and habitat, affected macroinvertebrate community biodiversity, biotic integrity, and patterns. Macroinvertebrate traits, analytical methods or models, evaluation index development, and riparian vegetation were the research hotspots. Using historical direct citation network analysis, we also found that the analytical methods in this field and the macroinvertebrate evaluation index had clear development trends from 2010 to 2021. Our findings can help researchers quickly grasp the background of the impact of land use on stream macroinvertebrates and inform future research.

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.

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.

Insights from the Niger Delta Region, Nigeria on the impacts of urban pollution on the functional organisation of Afrotropical macroinvertebrates

Anthropogenic activities, including urbanisation and industrialisation threaten stream ecological integrity, ecosystem community structure and ecosystem functioning of rivers and streams worldwide. However, developing sustainable monitoring strategies for ecological health remains a critical challenge in Africa. We examined the effects of urban disturbance on macroinvertebrate Functional Feeding Groups in selected streams in the Niger Delta Region of Nigeria. We sampled 11 sites between 2008 and 2012 and grouped into three site groups (Site groups 1 > 2 > 3). The groups represent an increasing gradient of urban pollution. Our result showed that urban-induced disturbances affected physicochemical variables in the study area (PERMANOVA; p < 0.05), with nutrients NO₂-N, PO₄-P, and electrical conductivity being significantly higher in impacted Site group 3 (ANOVA, p < 0.05). Predators and gatherers were the most dominant Functional Feeding Group recorded in the study area, while shredders were the least abundant macroinvertebrate Functional Feeding Groups. The multivariate RLQ analysis revealed that shredders, predators, and scrapers were tolerant of urban pollution, whereas gatherers were sensitive to increasing urban pollution. Overall, macroinvertebrates Functional Feeding Groups responded differentially to urban pollution in the Niger Delta Region. Identifying pollution indicator Functional Feeding Groups is seen as an important step towards developing a reliable, low-cost tool for riverine monitoring of urban pollution effects in Africa.

Unravelling the spatiotemporal variation of pCO2 in low order streams: Linkages to land use and stream order

Headwater streams make the majority of cumulative stream length in a river basin, carbon dioxide (CO₂) emission from headwater (low order) streams is thus an essential component. Anthropogenic activities in headwater areas such as land use change and land use practices can strongly modify terrestrial carbon and nutrient input, which could affect the level of partial pressure of dissolved carbon dioxide (pCO₂) and CO₂ degassing from streams. However, there are large uncertainties in estimates due to the lack of data in subtropical rivers of rapidly developing rural regions. The spatiotemporal variation and driving factors of the pCO₂ and CO₂ degassing from low-order streams remain to be explored. In this study, we assess multi-spatial scale effects of land use on pCO₂ dynamics in seven headwater tributary rivers in Central China during 2016, 2017 and 2018 in rainy and dry seasons. Our results reveal that the stream pCO₂ level consistently increases as the stream order increases from 1 to 3 under apparent seasonal variations. Riverine pCO₂ is positively related to the percentage of urban land and cropland surrounding the river segments, but is negatively related to the percentage of forest land. The stream pCO₂ is more closely correlated with the 1000 and 2000 m diameters of circular buffers at upstream sampling sites than the circular buffers with 100 and 500 m diameters. There exist significant relationships of pCO₂ with the concentrations of TN, TP, DO, and DOC in the low-order streams. The partial redundancy analysis quantifies the relative importance of anthropogenic land uses, natural factors and water chemical variables in mediating stream pCO₂, showing that influences of anthropogenic land uses (urban and cropland) on pCO₂ decrease, with a percentage role of 34%, 14%, and 4% in the 1st-, 2nd- and 3rd-order streams, respectively. The impact of nutrients on pCO₂, however, increases as the stream order increases. Urban influence on stream pCO₂ also decreases as stream order increases. Our study highlights the effect of land use/land cover types and stream order on riverine pCO₂ and provides new insight into estimating CO₂ emission in headwater streams. Future studies are needed on the linkage between riverine CO₂ degassing and stream orders under changing land use conditions.

Development of modified integrated water quality index to assess the surface water quality: a case study of Tuo River, China

Water quality evaluation is an important step in water environment control and management. The water quality index (WQI) is considered to be an effective method for water quality evaluation. However, when constructing the WQI, the contribution of the lower threshold limits of water quality parameters to water quality has received little attention. The principle of the modified integrated water quality index (IWQI) is that the concentration of any water quality parameter below the lower threshold limits as well as above the upper threshold limits will lead to an increase in the overall index value. Based on the concentration of water quality parameters, the modified IWQI classified water quality into five categories, i.e., bad (> 8), poor (5-8), medium (2-5), good (1-2), and excellent (< 1). Tuo River plays a crucial role in potable and irrigation water sources of Sichuan Province, and the assessment result of modified IWQI reveals that 67.8% of samples were classified as "medium," 29% "poor," and 3.2% "bad." The high concentrations of N and P from agricultural activities and industrial wastewater are the main contributors to the deterioration of water quality in the Tuo River. Additionally, the Tuo River presents the characteristics of worse water quality in the midstream. The evaluation results of the modified IWQI are consistent with that of the conventional WQI, which proves the accuracy of the modified IWQI as a surface water quality evaluation method.

An ensemble deep neural network approach for predicting TOC concentration in lakes along the middle-lower reaches of Yangtze River

In recent years, lakes pollution has become increasingly serious, so water quality monitoring is becoming increasingly important. The concentration of total organic carbon (TOC) in lakes is an important indicator for monitoring the emission of organic pollutants. Therefore, it is of great significance to determine the TOC concentration in lakes. In this paper, the water quality dataset of the middle and lower reaches of the Yangtze River is obtained, and then the temperature, transparency, pH value, dissolved oxygen, conductivity, chlorophyll and ammonia nitrogen content are taken as the impact factors, and the stacking of different epochs’ deep neural networks (SDE-DNN) model is constructed to predict the TOC concentration in water. Five deep neural networks and linear regression are integrated into a strong prediction model by the stacking ensemble method. The experimental results show the prediction performance, the Nash-Sutcliffe efficiency coefficient (NSE) is 0.5312, the mean absolute error (MAE) is 0.2108 mg/L, the symmetric mean absolute percentage error (SMAPE) is 43.92%, and the root mean squared error (RMSE) is 0.3064 mg/L. The model has good prediction performance for the TOC concentration in water. Compared with the common machine learning models, traditional ensemble learning models and existing TOC prediction methods, the prediction error of this model is lower, and it is more suitable for predicting the TOC concentration. The model can use a wireless sensor network to obtain water quality data, thus predicting the TOC concentration of lakes in real time, reducing the cost of manual testing, and improving the detection efficiency.

Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. Thus, quantitative analysis of the relationship between different land cover types and river water quality contributes to a better understanding of the effects of land cover on water quality, the landscape planning of water quality protection, and integrated water resources management. Based on water quality data of 2006–2018 at 18 typical water quality stations in the Yangtze River basin, this study analyzed the spatial and temporal variation characteristics of water quality by using the single-factor water quality identification index through statistical analysis. Furthermore, the Spearman correlation analysis method was adopted to quantify the spatial-scale and temporal-scale effects of various land uses, including agricultural land (AL), forest land (FL), grassland (GL), water area (WA), and construction land (CL), on the stream water quality of dissolved oxygen (DO), chemical oxygen demand (CODMn), and ammonia (NH3-N). The results showed that (1) in terms of temporal variation, the water quality of the river has improved significantly and the tributaries have improved more than the main rivers; (2) in the spatial variation respect, the water quality pollutants in the tributaries are significantly higher than those in the main stream, and the concentration of pollutants increases with the decrease of the distance from the estuary; and (3) the correlation between DO and land use is low, while that between NH3-N, CODMn, and land use is high. CL and AL have a negative effect on water quality, while FL and GL have a purifying effect on water quality. In particular, AL and CL have a significant positive correlation with pollutants in water. Compared with NH3-N, CODMn has a higher correlation with land use at a larger scale. The results highlight the spatial scale and seasonal dependence of land use on water quality, which can provide a scientific basis for land management and seasonal pollution control.