Linking Land Use Metrics Measured in Aquatic–Terrestrial Interfaces to Water Quality of Reservoir-Based Water Sources in Eastern China

Multi-scale response relationship between water quality of rivers entering lakes from different pollution source areas and land use intensity: a case study of the three lakes in central Yunnan

As the main supply source of lakes, the water quality of the rivers entering the lakes directly determines the water safety and sustainable development of the lakes. Human activities are the direct cause of changes in the water quality of rivers entering lakes, and land use intensity is the direct manifestation of human activities on the land surface. Although significant progress has been made in studying the relationship between land use changes and water quality in lakes, there is still a lack of research on exploring the relationship between land use intensity and water quality at multiple scales, especially in comparative studies of different pollution source areas. To address this problem, this study used Pearson's correlation analysis and land use intensity index method to explore the response relationship between river water quality and land use intensity at different spatial and temporal scales and different pollution source areas using three lakes in central Yunnan as examples. The results showed that land use intensity was generally positively correlated with water quality, but the response relationship between land use intensity and different water quality indicators was significantly different at different scales and for different pollution source areas. Compared to non-urban areas, the impact of land use intensity on water quality is more significant in urban areas. Compared to the rainy season, the correlation between CODNa, TP, and NH3-N values and land use intensity is stronger during the dry season, while the correlation between COD, TN, and land use intensity is weaker during the dry season. When viewed at different scales, different water quality indicators have different scale effects, but overall, the larger the scale, the stronger the correlation. Therefore, in the work of lake water environmental governance, it is necessary to consider comprehensively from multiple scales and perspectives and adopt measures that are more suitable for regional water pollution prevention and control.

Effects of Land-Use Type and Flooding on the Soil Microbial Community and Functional Genes in Reservoir Riparian Zones

Ecological processes (e.g., nutrient cycling) in riparian zones are often affected by land-use type and flooding. The extent to which land-use types and flooding conditions affect soil microorganisms and their ecological functions in riparian zones is not well known. By using high-throughput sequencing and quantitative PCR (q-PCR), we tested the effects of three land-use types (i.e., forest, wetland, and grassland) and two flooding conditions (i.e., landward locations and waterward locations within the land-use types) on soil microbial communities and microbial functional genes in the riparian zones of a reservoir. Land-use type but not flooding significantly affected soil microbial community composition at the phylum level, while both land-use type and flooding significantly affected the orders Nitrosotaleales and Nitrososphaerales. Alpha diversity was higher in the wetland and forest regardless of flooding conditions. Functional gene abundance differed among the three land-use types. Archaeal amoA (AOA) and nirS genes were more abundant in the wetland than in the grassland or forest. Bacterial amoA (AOB), nirK, nirS, and nosZ genes were more abundant in the waterward location than in the landward location but only in the wetland. Soil pH, moisture, and concentrations of soil organic matter and total soil nitrogen were significantly associated with the composition of archaeal and bacterial communities as well as with their gene abundance. This study revealed that soil microorganisms putatively involved in nitrogen cycling in riparian zones were more affected by land-use type than flooding.

A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

Urban stormwater runoff represents a significant challenge for the practical assessment of diffuse pollution sources on receiving water bodies. Given the high dimensionality of the problem, the main goal of this study was the comparison of linear and non-linear machine learning (ML) methods to characterize urban nutrient runoff from impervious surfaces. In particular, the principal component analysis (PCA) for the linear technique and the self-organizing map (SOM) for the non-linear technique were chosen and compared considering the high number of successful applications in the water quality field. To strengthen this comparison, these techniques were supported by well-known linear and non-linear methods. Those techniques were applied to a complete dataset with precipitation, flow rate, and water quality (sediments and nutrients) records of 577 events gathered for a watershed located in Southern Italy. According to the results, both linear and non-linear techniques can represent build-up and wash-off, the two main processes that characterize urban nutrient runoff. In particular, non-linear methods are able to capture and represent better the rainfall-runoff process and the transport of dissolved nutrients in urban runoff (dilution process). However, their computational time is higher than the linear technique (0.0054 s vs. 15.24 s, for linear and non-linear, respectively, in our study). The outcomes of this study provide significant insights into the application of ML methods for the water quality field.

The direct and indirect effects of land use and water quality on phytoplankton communities in an agriculture-dominated basin

Understanding the factors that control biodiversity in rivers is challenging due to the variety of potential sources, linkages, and processes. This research assesses the effects of land use on phytoplankton communities across water quality gradients. By employing abiotic and biotic datasets of 149 catchments in Lake Chaohu basin, China, and a structural equation model (SEM), the direct and indirect effects of land use and water quality on phytoplankton dynamic were analyzed. Both land use and water quality had statistically significant direct effects on phytoplankton community attribute and diversity, although these effects differed among these indices. For instance, farmland was found to positively affect the abundance and diversity indices, while total nitrogen (TN) had significant positive effects on species richness and abundance. Importantly, the average indirect effects strengthened the effects of land use (e.g., built-up land and woodland) up to 82.4% mainly through nutrients, while the average indirect effects weakened the effects of land use (e.g., farmland) by as much as 49.9% mainly due to nutrients, thus indicating the prevailing role of the effects of land use on phytoplankton based on nutrient concentrations. The results suggest that nutrients can regulate the effect of land use on phytoplankton community attribute and diversity indices. This study highlights the advantages of using an SEM because the potential linkages for phytoplankton diversities are more likely to be identified with this method than with a classical linear regression model. Therefore, SEM has wide application prospects in the field of the conservation of biodiversity in freshwater rivers.