Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales

Correlations between catchment-scale farm infrastructure densities and stocking rate to stream nutrient concentrations in dairy-dominant catchments

Nitrogen and phosphorus pollution is a critical environmental issue that causes eutrophication of water bodies. High concentrations of these nutrients primarily come from agricultural areas and are driven by catchment characteristics such as climate, hydrology, topography, geology, land use, and land cover. In addition to these factors, specific farming practices - particularly, the use of dairy farm infrastructure and management of stocking rate - also influence stream nutrient concentrations. However, the extent of the influence of specific farming practices and their relative importance in determining nutrient concentrations in waterways remain unknown. In this paper, we used data from an agriculturally-intensive dairy farming region to investigate these relationships. We used statistical analyses and modelling to determine relationships between concentrations of ammonium (NH4+), filterable reactive phosphorus (FRP), nitrogen oxides (NOx), total phosphorus (TP), and total nitrogen (TN) with 26 predictors which include farm infrastructure density and stocking rate. We found that farm infrastructure and operational characteristics such as effluent pond density, dairy shed density, and stocking rate are consistently important predictors that influence concentrations of NH4+, FRP, NOx, TP, and TN during both wet and dry weather periods. This paper has shown that in addition to established factors such as land use and land cover, specific farming practices also play a role in influencing stream nutrient concentrations. By identifying key infrastructure and stocking rate as drivers of stream nutrient concentrations, this research emphasized the need for targeted management strategies to mitigate the impacts of agricultural activities on water quality.

A social-ecological-technological vulnerability approach for assessing urban hydrological risks

In the context of urban population growth and climate change, and ever greater number of people are anticipated to face severe risks associated with extreme climate events; major ones are due to stormwater-related hazards. This study offers novel understanding of the complex nature of water-related risks in urban geographies by employing a Social-Ecological-Technological Systems (SETS) framework to assess vulnerabilities. Hydrology-informed urban risk index was developed, quantifying seventeen indicators from historical and modeled data on sewer overflow and flood events. The spatially explicit SETS-based approach identifies high-risk communities and hotspots where multiple vulnerabilities intersect and can serve as a valuable tool for guiding policy and decision-making to support more resilient urban futures. Our findings reveal that social vulnerability plays a critical role in determining the overall risk (R = 0.4), with the greatest impacts imposed on socially vulnerable communities. However, insights from the ecological (R = 0.2) and technological (R = 0.1) domains provide essential guidance for future risk reduction strategies-such as upgrading outdated sewer infrastructure and exploring green space potential for run-off mitigation. The framework proposed is generalizable to other cities facing similar environmental challenges, highlighting its potential as a foundational tool for policymaking to reduce risks associated with extreme climate events.

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.

Distributions of Fecal Indicators at Aquaculture Areas in a Bay of Republic of Korea

Aquaculture products, such as clams, scallops, and oysters, are major vectors of fecal-derived pathogens. Male-specific and somatic coliphages are strongly correlated with human noroviruses, the major enteric viruses worldwide. Geographic information system with local land-use patterns can also provide valuable information for tracking sources of fecal-derived pathogens. We examined distributions of four fecal indicator microorganisms, i.e., male-specific and somatic coliphage, total coliform, and Escherichia coli (E. coli) in three river and seawater sampling sites located on the coast of Gomso Bay in the Republic of Korea during the sampling period (from March 2015 to January 2016). Geospatial analyses of fecal indicators and correlations between environmental parameters and fecal indicators or among fecal indicators were also performed. Overall, river water samples showed highest concentrations of both types of coliphage in summer (July 2015). High concentrations of both total coliform and E. coli were detected in river water during the period from July to September 2015. High concentrations of all fecal indicators were found at site GL02, located in the innermost part of Gomso Bay, which has high-density agriculture and residential areas. Environmental factors related to precipitation-cumulative precipitation on and from 3 days before the sampling day (Prep-0 and Prep-3, respectively)-and salinity were strongly correlated with the concentrations of all fecal indicators. The present results suggest that investigations of multiple fecal indicators with systemic geospatial information are necessary for precisely tracking fecal contaminations of aquaculture products.

Spatiotemporal and vertical variability of water quality in lentic small water bodies: implications of varying rainfall and land use conditions

Lentic small water bodies (LSWBs) deteriorate owing to anthropogenic activities, such as untreated domestic and agricultural waste disposal. Moreover, different turnover mechanisms occur during different seasons, contributing to nutrient enrichment and consequent degradation of LSWBs. However, understanding their spatial, temporal, and vertical variations during different seasons is understudied. In addition, studies on the variation in water quality under varying rainfall and land-use conditions are limited. Therefore, in this study, three LSWBs located in Northern India were studied during the pre-monsoon and monsoon seasons (December 2022 to October 2023). Total nitrogen (TN), chlorophyll-a (Chl-a), total phosphorus (TP), temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), chemical oxygen demand (COD), secchi disk depth (SDD), and water level (WL) were measured monthly. Sentinel-2 and CHIRPS pentad data were used for land use, land cover classification, and rainfall analysis. The spatial analysis indicates that the seasonal shift affects the water quality distribution, especially near the inlets and at the edges. The overall concentrations of TN and TP decreased during the monsoon season; however, they increased significantly at the inlets of the LSWBs. On the other hand, the Chl-a concentration shifted towards the edges due to the inflow during the monsoon. Temporal analysis also suggests that the arrival of the monsoon lowers pH, DO, and TDS. However, the concentrations of TN and TP increased because of agricultural runoff. Chl-a and COD show distinct variations due to the individual LSWBs' local conditions. Vertical variability analysis demonstrated pH, temperature, and TN stratification during the pre-monsoon period. However, during the monsoon, stratification is less significant due to intermixing. Redundancy analysis (RDA) showed that land use and rainfall patterns affected the water quality of LSWB 1, 2, and 3 by 53.49%, 81.62%, and 92.64%, respectively. This shows that land use, land cover, and rainfall changes affect the water quality of LSWBs. This study highlights the negative impact of runoff from agricultural land use as the main factor responsible for increased nutrient levels in the LSWBs.

Spatiotemporal smoothing of water quality in a complex riverine system with physical barriers

Freshwater ecosystems offer a variety of ecosystem services, and water quality is essential information for understanding their environment, biodiversity, and functioning. Interpolation by smoothing methods is a widely used approach to obtain temporal and/or spatial patterns of water quality from sampled data. However, when these methods are applied to freshwater systems, ignoring terrestrial areas that act as physical barriers may affect the structure of spatial autocorrelation and introduce bias into the estimates. In this study, we applied stochastic partial differential equation (SPDE) smoothing methods with barriers to spatial interpolation and spatiotemporal interpolation on water quality indices (chemical oxygen demand, phosphate phosphorus, and nitrite nitrogen) in a freshwater system in Japan. Then, we compared the estimation bias and accuracy with those of conventional non-barrier models. The results showed that the estimation bias of spatial interpolations of snapshot data was improved by considering physical barriers (5.8 % for (chemical oxygen demand, 22.5 % for phosphate phosphorus, and 21.6 % for nitrite nitrogen). The prediction accuracy was comparable to that of the non-barrier model. These were consistent with the expectation that accounting for physical barriers would capture realistic spatial correlations and reduce estimation bias, but would increase the variance of the estimates due to the limited information that can be gained from the neighbourhood. On the other hand, for spatiotemporal smoothing, the barrier model was comparable to the non-barrier model in terms of both estimation bias and prediction accuracy. This may be due to the availability of information in the time direction for interpolation. These results demonstrate the advantage of considering barriers when the available data are limited, such as snapshot data. SPDE smoothing methods can be widely applied to interpolation of various environmental and biological indices in river systems and are expected to be powerful tools for studying freshwater systems spatially and temporally.

Response of water quality in major tributaries to the difference of multi-scale landscape indicators in Dongting Lake basin, China

River water quality has been increasingly deteriorated because of the influence of natural process and anthropogenic activities. Quantifying the influence of landscape metrics, namely topography and land use pattern, which encompass land use composition and landscape configuration, across different spatial and seasonal scales that reflect natural process and anthropogenic activities, is highly beneficial for water quality protection. In this study, we focused on investigating the effects of topography, landscape configuration and land use composition on water quality at different spatial scales, including 1-km buffer and sub-watershed, and seasonal scales, including wet and dry season, based on the monthly water quality data in 2016 of Dongting Lake in China. Multivariate statistical analysis of redundancy analysis and partial redundancy analysis was used to quantify the contributions of these factors under different scales. Our results showed that among the three environmental groups, topography made the greatest pure contribution to water quality, accounting for 11.4 to 30.9% of the variation. This was followed by landscape configuration, which accounted for 9.4 to 23.0%, and land use composition, which accounted for 5.9 to 15.7%. More specifically, water body made the greatest contribution to the water quality variation during dry season at both spatial scales, contributing 16.6 to 17.2% of the variation. In contrast, edge density was the primary interpreter of the variability in water quality during wet season at both spatial scales, accounting for 9.9 to 11.1% of the variation. The spatial variability in the influence of landscape metrics on water quality was not markedly distinct. However, these metrics have a minimal impact difference on water quality at the buffer scale and the sub-watershed scale. Moreover, the contribution of landscape configuration varied the most from the buffer to sub-watershed scales, indicating its importance for the spatial scale difference in water quality. The findings of this study offer useful insights into enhancing water quality through improved handling of landscape metrics.

Modelling relationships between land use and water quality using statistical methods: A critical and applied review

Land use/land cover (LULC) can have significant impacts on water quality and the health of aquatic ecosystems. Consequently, understanding and quantifying the nature of these impacts is essential for the development of effective catchment management strategies. This article provides a critical review of the literature in which the use of statistical methods to model the impacts of LULC on water quality is demonstrated. A survey of these publications, which included hundreds of original research and review articles, revealed several common themes and findings. However, there are also several persistent knowledge gaps, areas of methodological uncertainty, and questions of application that require further study and clarification. These relate primarily to appropriate analytical scales, the significance of landscape configuration, the estimation and application of thresholds, as well as the potentially confounding influence of extraneous variables. Moreover, geographical bias in the published literature means that there is a need for further research in ecologically and climatically disparate regions, including in less developed countries of the Global South. The focus of this article is not to provide a technical review of statistical techniques themselves, but to examine important practical and methodological considerations in their application in modelling the impacts of LULC on water quality.

Influence of watershed characteristics and human activities on the occurrence of organophosphate esters related to dissolved organic matter in estuarine surface water

Organophosphate esters (OPEs) are widespread in aquatic environments and pose potential threats to ecosystem and human health. Here, we profiled OPEs in surface water samples of heavily urbanized estuaries in eastern China and investigated the influence of watershed characteristics and human activities on the spatial distribution of OPEs related to dissolved organic matter (DOM). The total OPE concentration ranged from 22.3 to 1201 ng/L, with a mean of 162.6 ± 179.8 ng/L. Chlorinated OPEs were the predominant contaminant group, accounting for 27.4-99.6 % of the total OPE concentration. Tris(2-chloroisopropyl) phosphate, tris(1,3-dichloro-2-propyl) phosphate, and tributyl phosphate were the dominant compounds, with mean concentrations of 111.2 ± 176.0 ng/L, 22.6 ± 21.5 ng/L, and 14.8 ± 14.9 ng/L, respectively. Variable OPE levels were observed in various functional areas, with significantly higher concentrations in industrial areas than in other areas. Potential source analysis revealed that sewage treatment plant effluents and industrial activities were the primary OPE sources. The total OPE concentrations were negatively correlated to the mean slope, plan curvature, and elevation, indicating that watershed characteristics play a role in the occurrence of OPEs. Individual OPEs (triisobutyl phosphate, tris(2-butoxyethyl) phosphate, tris(2-chloroethyl) phosphate, and tricresyl phosphate) and Σalkyl-OPEs were positively correlated to the night light index or population density, suggesting a significant contribution of human activity to OPE pollution. The co-occurrence of OPEs and DOM was also observed, and the fluorescence indices of DOM were found to be possible indicators for tracing OPEs. These findings can elucidate the potential OPE dynamics in response to DOM in urbanized estuarine water environments with intensive human activities.

Evaluation of impact of land use and landscape metrics on surface water quality in the northeastern part along Lake Tanganyika, Africa

As the second deepest lake in Africa, Lake Tanganyika plays an important role in supplying fish protein for the catchment's residents and is irreplaceable in global biodiversity. However, the lake's water environment is threatened by socioeconomic development and rapid population growth along the lake. This study analyzed the spatial scale effects and seasonal dependence of land use types and landscape metrics on water quality in 16 sub-basins along northeastern Lake Tanganyika at different levels of urbanization. The results revealed that land use types had a higher influence on water quality in urban areas than that in rural areas; the explanatory variance in the urban area was 0.78-0.96, while it was 0.21-0.70 in the rural area. The explanatory ability of land use types on water quality was better at the buffer scale than at the sub-watershed scale, and the 500 m buffer scale had the highest explanatory ability in the urban area and rural area both in the rainy season and dry season, and artificial surface and arable land were the main contributing factors. And this phenomenon was more obvious in dry season than in rainy season. We identified that CONTAG was the key landscape metric in urban area and was positively correlated with nutrient variables, indicating that water quality degraded in less fragmented landscapes. The sub-watershed scale had the highest explained ability, while in rural area, the 1500 m buffer scale had the highest explained ability and IJI had the highest explanatory variance, which had a negative effect on water quality. Research on the relationship between land use and water quality would help assess the water quality in the unmonitored watershed as monitoring is expensive and time-consuming in low-income area. This knowledge would provide guideline to watershed managers and policymakers to prioritize the future land use development within Lake Tanganyika basin.

Guiding the landscape patterns evolution is the key to mitigating river water quality degradation

Consensus has emerged that landscape pattern evolution significantly impacts the river environment. However, there remains unclear how the landscape pattern evolves possible to achieve a balance between land resource use and water conservation. Thus, simulating future landscape patterns under different scenarios to predict river eutrophication level is critical to propose targeted landscape planning programs and alleviate river water quality degradation. Here, we coupled five water quality parameters (TOC, TN, NO3--N, NH4+-N, TP), collected from October 2020 to September 2021, to construct the river eutrophication index (EI) to assess river water quality. Meanwhile, based on redundancy analysis, patch-generating land use simulation model, and stepwise multiple linear regression model comprehensively analyze the Fengyu River watershed landscape patterns evolution and their impact on river eutrophication. Results indicated that current rivers reach eutrophic levels, and EI reaches 40.7. The landscape patterns explain 88.2 % of river eutrophication variation, while the LPI_Con metric is critical and individually explained 21.5 %. Furthermore, eutrophication in the watershed will increase in 2040 under the natural development (ND) scenario, and the EI will reach 44.4. In contrast, farmland protection (FP) scenarios and environmental protection (EP) scenarios contribute to mitigating eutrophication, the EI values are 38.2 and 38.1, respectively. The results provide a potential mechanistic explanation that river eutrophication is a consequence of unreasonable landscape pattern evolution. Guiding the landscape patterns evolution based on critical driver factors from a planning perspective is conducive to mitigating river water quality degradation.

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.

Effects of comprehensive landscape patterns on water quality and identification of key metrics thresholds causing its abrupt changes

Comprehensive landscape patterns influence water quality with multiple factors, complex processes, and scale dependence. However, studies identifying landscape thresholds causing abrupt water quality changes and characterizing the contribution of topography to water quality are still limited. Exploring the impact mechanisms of natural geographical and landscape characteristics on spatial and seasonal water quality variations is conducive to watershed water resource protection and ecosystem restoration. Based on water quality monitoring data of Minjiahe River in the typical headwater area of the upstream Dan River in China from 2019 to 2021, we employed redundancy analysis, partial redundancy analysis, and nonparametric change-point analysis to analyze the relationship between stream water quality and multi-spatial scale comprehensive landscape patterns, to obtain the interactive and independent contributions of different landscape categories at multi-spatial scales on water quality, and to find the key landscape threshold leading to abrupt changes in water quality. Results showed that landscape configuration, landscape composition, and topographic factors collectively explain over 89.1% of water quality variation. Most seasonal variations in water quality were primarily caused by landscape configuration. The landscape composition was mainly responsible for the differences in water quality variations among spatial scales. The topographic factors made the least independent contribution and had a potential impact on overall water quality variation. In order to protect the water quality of streams, it is more reasonable to regulate the landscape at different scales. At the sub-catchment scale, interspersion and juxtaposition index (IJI) and landscape shape index (LSI) should be controlled below 82% and 22. At the 100 m riparian scale, farmland, urban land, IJI, and LSI should be controlled below 29%, 6.5%, 92%, and 26, respectively. Our results provide important guidance for optimizing landscape patterns and water conservation in the watershed.

Spatial variations in the associations of surface water quality with roads and traffic across an urbanization gradient in northern Georgia, USA

Roads and traffic are important elements of urbanization, but their spatial associations with surface water quality in watersheds have been seldom studied. In this study, the spatially varying associations of three urbanization indicators, including road density, traffic density, and percentages of urban land, with twenty water quality indicators, including dissolved oxygen (DO), specific conductance (SC), dissolved solids (DS), suspended solids (SS), biochemical oxygen demand (BOD), dissolved nutrients, dissolved ions, heavy metals, and coliform bacteria, across the watersheds in the northern part of the state of Georgia, USA, have been examined by a conventional statistical method, ordinary least squares regression (OLS), and a spatial statistical method, geographically weighted regression (GWR). The results from OLS show that the urbanization indicators all have significant positive associations with the majority of the studied water pollutants, indicating that water pollution is significantly contributed by human activities related to urbanization in northern Georgia. In contrast, GWR results show that the associations vary across the watersheds affected by their urbanization levels. Significant positive associations are found between each urbanization indicator and each of the studied water pollutants, but not in all watersheds. The associations of suspended solids, nitrogen nutrients, and coliform bacteria with all three urbanization indicators are more significant in less-urbanized watersheds, while the associations of dissolved ions, BOD, and orthophosphate (PO4) with road density and traffic density are more significant than those with urban land in more-urbanized watersheds, indicating that those water pollutants are more contributed by human activities associated with roads and traffic than other activities in more-urbanized areas. As a pilot study to explore how and why the associations of surface water quality with roads and traffic change across watersheds with different urbanization levels, its findings suggest that the policies of watershed management, land-use planning, and transportation planning should be tailored in local areas based on the locally important water pollutants and their associated urbanization indicators.

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

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

The effects of land use on water quality of alpine rivers: A case study in Qilian Mountain, China

Land use influences the variation of river water quality. This effect varies depending on the region of the river and the spatial scale at which land use is calculated. This study investigated the influence of land use on river water quality in Qilian Mountain, an important alpine river region in northwestern China, on different spatial scales in the headwaters and mainstem areas. Redundancy analysis and multiple linear regression were used to identify the optimal scales of land use for influencing and predicting water quality. Nitrogen and organic carbon parameters were more influenced by land use than phosphorus. The impact of land use on river water quality varied according to regional and seasonal differences. Water quality in headwater streams was better influenced and predicted by land use types on the natural surface at the smaller buffer zone scale, while water quality in mainstream rivers was better influenced and predicted by land use types associated with human activities at the larger catchment or sub-catchment scale. The impact of natural land use types on water quality differed with regional and seasonal variations, while the impact of land types associated with human activities on water quality parameters mainly resulted in elevated concentrations. The results of this study suggested that different land types and spatial scales needed to be considered to assess water quality influences in different areas of alpine rivers in the context of future global change.

Determining the main contributing factors to nutrient concentration in rivers in arid northwest China using partial least squares structural equation modeling

Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.

Seasonal and longitudinal water quality dynamics in three effluent-dependent rivers in Arizona

Effluent-fed streams, which receive inputs from wastewater treatment plants, are becoming increasingly common across the globe as urbanization intensifies. In semi-arid and arid regions, where many natural streams have dried up due to over extraction of water, many streams rely completely on treated effluent to sustain baseflow during dry seasons. These systems are often thought of as ‘second-class’ or highly disturbed stream ecosystems, but they have the potential to serve as refuges for native aquatic biota if water quality is high, especially in areas where few natural habitats remain. In this study, we investigated seasonal and longitudinal water quality dynamics at multiple sites across six reaches of three effluent-dependent rivers in Arizona (USA) with the objective (1) to quantify changes in effluent water quality due to distance traveled and season/climate and (2) to qualify whether water quality conditions in these systems are sufficient to support native aquatic species. Study reaches ranged in length from 3 to 31 km and in geographic setting from low desert to montane conifer forest. We observed the lowest water quality conditions (e.g., elevated temperature and low dissolved oxygen) during the summer in low desert reaches, and significantly greater natural remediation of water quality in longer vs. shorter reaches for several factors, including temperature, dissolved oxygen and ammonia. Nearly all sites met or exceeded water quality conditions needed to support robust assemblages of native species across multiple seasons. However, our results also indicated that temperature (max 34.2 °C), oxygen levels (min 2.7 mg/L) and ammonia concentrations (max 5.36 mg/L N) may occasionally be stressful for sensitive taxa at sites closest to effluent outfalls. Water quality conditions may be a concern during the summer. Overall, effluent-dependent streams have the capacity to serve as refuges for native biota in Arizona, and they may become the only aquatic habitat available in many urbanizing arid and semi-arid regions.

Source availability and hydrological connectivity determined nitrate-discharge relationships during rainfall events in karst catchment as revealed by high-frequency nitrate sensing

Karst terrain seasonal monsoonal rainfall is often associated with high concentrations of nitrate-N in streams draining agricultural land. Such high concentrations can pose problems for environmental and human health. However, the relationship between rainfall events that mobilize nitrate and resulting nitrate export remains poorly understood in karst terrain. To better understand the processes that drive nitrate dynamics during rainfall events, the characteristics of individual rainfall events were analyzed using sensor technology. Thirty-eight rainfall events were separated from the high-frequency dataset spanning 19 months at a karst spring site. The results revealed that nitrate-discharge (N-Q) hysteresis in 79% of rainfall events showed anticlockwise hysteresis loop patterns, indicating nitrate export from long distances within short event periods. Karstic hydrological connectivity and source availability were considered two major determining factors of N-Q hysteresis. Gradual increase in hydrological connectivity during intensive rainfall period accelerated nitrate transportation by karst aquifer systems. Four principal components (PCs, including antecedent conditions PC1&3 and rainfall characteristics PC2&4 explained 82% of the cumulative variance contribution to the rainfall events. Multiple linear regression of four PCs explained more than 50% of the variation of nitrate loading and amplitude during rainfall events, but poorly described nitrate concentrations and hydro-chemistry parameters, which may be influenced by other factors, e.g., nitrate transformation, fertilization time and water-rock interaction. Although variation of N concentration during event flow is evident, accounting for antecedent conditions and rainfall factors can help to predict rainfall event N loading during rainfall events. Pollution of the karstic catchment occurred by a flush of nitrate input following rainfall events; antecedent and rainfall conditions are therefore important factors to consider for the water quality management. Reducing source availability during the wet season may facilitate to reduction of nitrogen loading in similar karst areas.

Land use and hydrological factors control concentrations and diffusive fluxes of riverine dissolved carbon dioxide and methane in low-order streams

We analyzed the impacts of land use/land cover types on carbon dioxide (CO₂) and methane (CH₄) concentration and diffusion in 1^st to 4^th Strahler order tributaries of the Longchuan River to the upper Yangtze River in China by using headspace equilibration method and CO₂SYS program. Field sampling and measurements were conducted during the dry and wet seasons from 2017 to 2019. The average of calculated CO₂ partial pressure (pCO₂, mean ± SD: 2389 ± 3220 μatm) by CO₂SYS program was 1.9-fold higher than the value (mean ± SD: 1230 ± 1440 μatm) 10 years ago in the Longchuan River basin, where the urban land area increased by a factor of 7 times. Further analysis showed that corrected pCO₂ by headspace method and dissolved CH₄ (dCH₄) decrease as the stream order and flow velocity increase. The pCO₂ and dCH₄ in the wet season was lower than that in the dry season. The explanatory ability of land use types on the variation of corrected pCO₂ and dCH₄ was stronger at the reach scale than at the riparian and catchment scales in two seasons. Urban land at reach scale further showed much higher explanation on corrected pCO₂ and dCH₄ than cropland, grassland and forest land in the wet season. The Longchuan River emits approximately 112.5 kt CO₂-C and 1.0 kt CH₄-C per year, being 1.7-fold of the total lateral export of dissolved inorganic and dissolved organic carbon (68.3 kt C y^-1). The findings highlight the scale effects of land use on the observed seasonality in dissolved carbon gases in low-order streams.

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.

Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density

Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R² =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.

Effect of multiple spatial scale characterization of land use on water quality

Land use in uplands is an important factor affecting water quality in its respective catchment, and its influences at the different spatial scales and configurations warrant further investigation. Here, we selected 26 catchments in the upper Han River (China) and sampled the surface water at the outlet of each catchment in four seasons during 2019. Multivariate statistics were used to identify the relationships between land use characteristics in uplands and water quality in river system. The results indicated that chemical oxygen demand (COD_Mn); pH; dissolved oxygen; electrical conductivity; nutrient, i.e., NH₄⁺-N, NO₃^--N; and dissolved phosphorus (DP) in rivers displayed significant seasonal variations. Stepwise regression revealed that landscape metrics such as patch density, landscape shape index, and splitting index were important factors influencing water quality in rivers regardless of their spatiality and seasonality. Urban was the most frequently chosen land-use type in the best prediction models, and forest area showed a negative correlation with water quality parameters in most cases for example, DP. Overall, the influence of land use on river water quality was slightly stronger at reach scale than at catchment and riparian scales. Also, nutrients (i.e., NH₄⁺-N, NO₃^--N, and DP) in rivers were primarily impacted by the land use characteristic at catchment and riparian scales. Our results suggested that multi-scale explorations would help to achieve a fully understanding on the impacts of land use on river water quality.

Effects of landscape characteristics, anthropogenic factors, and seasonality on water quality in Portland, Oregon

Urban areas often struggle with deteriorated water quality because of complex interactions between landscape factors and climatic variables. However, few studies have considered the effects of landscape variables on water quality at a sub-500-m scale. We conducted a spatial statistical analysis of six pollutants for 128 water quality stations in four watersheds around Portland, Oregon, using data from 2015 to 2021 for the wet season at two microscales (100 m and 250 m buffers). E. coli was associated with land cover, soil type, topography, and pipe length, while lead variations were best explained by topographic variables. Developed land cover and impervious surface explained variations in nitrate, while orthophosphate was associated with mean elevation. Models for zinc included land cover and topographic variables in addition to pipe length. Spatial regression models better explain variations in water quality than ordinary least squares models, indicating strong spatial autocorrelation for some variables. Our findings provide valuable insights to city planners and researchers seeking to improve water quality in metropolitan areas by manipulating city landscapes.

A holistic framework of water quality evaluation using water quality index (WQI) in the Yihe River (China)

The Yihe River is an important river in Shandong Province, China. It is a catchment river for the South-to-North Water Diversion Project (SNWDP-ER), providing a variety of benefits and ecosystem services, such as flood and drought regulation, fishery and aquaculture, drinking water sources, and biodiversity conservation. In order to objectively reflect the status and changing trend of water environmental quality of the Yihe River, reduce the cost of detection, and improve the efficiency of water quality evaluation, samples were collected at 8 sampling sites in the 220 km main stream of the Yihe River from 2009 to 2019. The spatiotemporal variations of 10 water quality indicators were analyzed, including pH, water temperature (WT), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total phosphorus (TP), ammonia nitrogen (NH3-N), nitrate (NO3-N), fluoride (F-), and sulphate (SO42-). The water quality index (WQI) was used to evaluate the spatiotemporal water quality changes, and the minimum WQI (WQImin) model consisting of five key indicators, i.e., NH3-N, BOD5, DO, SO42-, and WT, was built by using stepwise multiple linear regression analysis. The results indicated that the water quality indicators in the Yihe River showed significant spatiotemporal variations. With the exception of the COD and TP, the other water quality indicators conformed to the Class I or II standards of China, indicating that the water quality of the Yihe River was better than most natural water bodies. Seasonally, the WQI was better in the autumn and higher in the upstream area compared to the downstream. The water quality remained at the "good" level. The weighted WQImin model performed well in evaluating water quality, with coefficient of determination (R2), mean square error (MSE), and percentage error (PE) values of 0.903, 3.05, and 1.70%, respectively.

Multi-spatial scale effects of multidimensional landscape pattern on stream water nitrogen pollution in a subtropical agricultural watershed

Multidimensional (coupled land use, soil properties, and topography) landscape effects on stream water nitrogen (N) are complex and scale-dependent. However, studies that identify critical buffer zones that explain large variations in riverine N, and estimate specific thresholds of multidimensional landscape patterns at the class level, result in a sudden changes in riverine N pollution, are still limited. Here, a new multidimensional landscape metric that combined land use, soil properties, and topography effects was applied to various riparian buffer zones and sub-watershed scales, and their relationships to riverine N levels were investigated. We used stream water ammonium-N, nitrate-N, and total-N concentrations datasets, from 2010 to 2017, in the nine subtropical sub-watersheds in China. The results of model selection and model averaging in ordinary least squares regressions, indicated that the riparian buffer zone with widths of 400 m, had more pronounced influence on water NH₄-N and TN levels than at other scales. Within the 400 m buffer zone, the key landscape metrics for NH₄-N, NO₃-N and TN concentrations in stream water were different, and explained up to 43.35%-76.55% (adjusted R²) of the total variation in river N levels. When ENN_MN_Class17 below 39-56 m, PD_Class8 above 4.63-6.55 n/km², PLAND_Class27 above 23-29%, and CONTIG_MN_Class42 below 0.35-0.37% within the 400 m buffer zone, riverine NH₄-N and TN would be abruptly increased. This study provided practical ideas for regulation regarding landscape management linked to watershed structure, and identified reference thresholds for multidimensional landscape metrics, which should help reduce riverine N pollution in subtropical China.

Spatial heterogeneity of chemistry of the Small Aral Sea and the Syr Darya River and its impact on plankton communities

The shrinking of the Aral Sea represents one of the greatest ecological disasters of modern time. The data on the surviving northern part (Small Aral) is scarce and requires an update. This study aimed to analyze the chemistry, phyto- and zooplankton composition, and their relation in the waters of the Small Aral and its tributary, Syr Darya River. The chemistry of both ecosystems was significantly different. Small Aral was characterized by higher ionic concentrations, salinity, and electric conductivity and more significant spatial variation of chemical properties. The area near the river mouth was more pristine, while the ions concentration and salinity in the distant bays were much higher (>10‰). The highest concentrations of nitrates and total phosphorus in the Syr Darya were observed near Kyzylorda, indicating urban pollution. Overall, 109 phytoplankton taxa were identified in both ecosystems, with diatoms, green algae, and cyanobacteria being most abundantly represented. Oligohalobes dominated, but no polyhalobes and euhalobes algal species were identified. In total, 27 taxa of zooplankton were identified in both studied ecosystems, with the domination of rotifers over microcrustaceans. An exceptionally high level of dominance (65-91%) of rotifer Keratella cochlearis in the Syr Darya was found. The phyto- and zooplankton species richness was higher in the Syr Darya. Plankton communities of the Small Aral reflected horizontal variability of chemical properties. The total phosphorus promoted the prevalence of diatoms, rotifers, and crustaceans. Increased nitrogen concentration promoted cyanobacteria, chlorophytes, cryptophytes and chrysophytes, and rotifers Keratella cochlearis and K. quadrata. The abundance of dinophytes, diatoms Navicula cryptotenella and Cocconeis placentula, green algae Mychonastes jurisii and rotifer Keratella tecta was driven by the higher alkalinity and conductivity/salinity levels. The results represent a reference point for future monitoring of the area and add to understanding the complexity of biological transformations in the Aral Sea and its tributary.

Spatial Differences in Zooplankton Community Structure between Two Fluvial Lakes in the Middle and Lower Reaches of the Yangtze River: Effects of Land Use Patterns and Physicochemical Factors

The zooplankton community composition in shallow lakes is influenced by numerous factors, such as environmental factors and the land use patterns around the lake. To investigate the interaction between the spatial differences in the zooplankton community structure, aquatic parameters, and land use patterns in the Lake Chen Yao complex (Lake Chen Yao and Lake Feng Sha), we assessed them in four seasons from October 2020 to August 2021. The results showed that the zooplankton density and biomass of Lake Chen Yao were higher than the latter. The results of Pearson correlation and RDA analysis revealed that electrical conductivity (EC), Chlorophyll a (Chl.a), dissolved oxygen (DO), and pH were the main environmental factors affecting the zooplankton community structure in the two lakes. The nutrient content of nitrogen (N) and phosphorus (P) were significantly higher in Lake Chen Yao, and there was a considerable relationship with the distribution of land use patterns around the two lakes. The land use patterns were the main reason for the difference in water quality and thus the spatial variation in the characteristics of the zooplankton communities in the two lakes.

Improved Daily Spatial Precipitation Estimation by Merging Multi-Source Precipitation Data Based on the Geographically Weighted Regression Method: A Case Study of Taihu Lake Basin, China

Accurately estimating the spatial and temporal distribution of precipitation is crucial for hydrological modeling. However, precipitation products based on a single source have their advantages and disadvantages. How to effectively combine the advantages of different precipitation datasets has become an important topic in developing high-quality precipitation products internationally in recent years. This paper uses the measured precipitation data of Multi-Source Weighted-Ensemble Precipitation (MSWEP) and in situ rainfall observation in the Taihu Lake Basin, as well as the longitude, latitude, elevation, slope, aspect, surface roughness, distance to the coastline, and land use and land cover data, and adopts a two-step method to achieve precipitation fusion: (1) downscaling the MSWEP source precipitation field using the bilinear interpolation method and (2) using the geographically weighted regression (GWR) method and tri-cube function weighting method to achieve fusion. Considering geographical and human activities factors, the spatial and temporal distribution of precipitation errors in MSWEP is detected. The fusion of MSWEP and gauge observation precipitation is realized. The results show that the method in this paper significantly improves the spatial resolution and accuracy of precipitation data in the Taihu Lake Basin.

Associations among drinking water quality, dyslipidemia, and cognitive function for older adults in China: evidence from CHARLS

BACKGROUND

The current study aimed to examine the association between drinking water quality and cognitive function and to identify the direct and indirect effects of drinking water quality and dyslipidemia on cognitive function among older adults in China.

METHODS

Primary data for the study were selected from China Health and Retirement Longitudinal Study (CHARLS, 2015) and 4,951 respondents aged 60 and above were included. Data on drinking water quality were selected from the 2015 prefectural water quality data from the Institute of Public and Environment Affairs in China and measured by the Blue City Water Quality Index. Dyslipidemia was measured by self-reported dyslipidemia diagnosis and lipid panel. Three composite measures of cognitive function included mental status, episodic memory, and global cognition. Mixed effects models were conducted to assess the associations between drinking water quality or dyslipidemia and cognitive function. The mediation effects of dyslipidemia were examined by path analyses.

RESULTS

Exposure to high quality drinking water was significantly associated with higher scores in mental status, episodic memory, and global cognition (β = 0.34, p < 0.001 for mental status; β = 0.24, p < 0.05 for episodic memory; β = 0.58, p < 0.01 for global cognition). Respondents who reported dyslipidemia diagnosis had higher scores in the three composite measures of cognitive function (β = 0.39, p < 0.001 for mental status; β = 0.27 p < 0.05 for episodic memory; β = 0.66, p < 0.001 for global cognition). An elevated blood triglycerides was only associated with higher scores in mental status (β = 0.21, p < 0.05). Self-reported dyslipidemia diagnosis was a suppressor, which increased the magnitude of the direct effect of drinking water quality on mental status, episodic memory, and global cognition.

CONCLUSION

Drinking water quality was associated with cognitive function in older Chinese and the relationship was independent of natural or socioeconomic variations in neighborhood environments. Improving drinking water quality could be a potential public health effort to delay the onset of cognitive impairment and prevent the dementia pandemic in older people.

Spatial and temporal variations of microplastic concentrations in Portland's freshwater ecosystems

While microplastics are a pollutant of growing concern in various environmental compartments, less is known regarding the sources and delivery pathways of microplastics in urban rivers. We investigated the relationship between microplastic concentrations and various spatiotemporal factors (e.g., land use, arterial road length, water velocity, precipitation) in two watersheds along an urban-rural gradient in the Portland metropolitan area. Samples were collected in August, September, and February and were analyzed for total microplastic count and type. Nonparametric statistics were used to evaluate potential relationships with the explanatory variables, derived at both the subwatershed and near stream scales. In August, microplastic concentrations were significantly higher than in February. August concentrations also negatively correlated with flow rate, suggesting that lower flow rates may have facilitated the accumulation of microplastics. Smaller size microplastic particles (< 100 μm) were found more in August than September and February, while larger size particles were more dominant in February than the other months. Microplastic concentrations were positively related to 24-h antecedent precipitation in February. Negative correlations existed between wet season microplastic concentrations and agricultural lands at the near stream level. The results indicate that near stream variables may more strongly influence the presence and abundance of microplastics in Portland's waterways than subwatershed-scale variables. Fragments were the most commonly observed microplastic morphology, with a dominance of gray particles and the polymer polyethylene. The findings of this study can inform management decisions regarding microplastic waste and identify hotspots of microplastic pollution that may benefit from remediation.

Spatial correlation among cultivated land intensive use and carbon emission efficiency: A case study in the Yellow River Basin, China

Considering the current global goal of carbon neutrality, the relationship between cultivated land intensive use (CLIU) and carbon emission efficiency (CEE) should be explored to address the global climate crisis and move toward a low-carbon future. However, previous work in this has been conducted at provincial/regional scales and few have identified the spatial correlation between CLIU and CEE at the scale of large river basins. Therefore, this study explored the spatiotemporal characteristics of CLIU, cultivated land carbon emissions (CLCE), and CEE, as well as the spatial correlation between CLIU and CEE in the Yellow River Basin (YRB), China. A comprehensive evaluation model, the Intergovernmental Panel on Climate Change (IPCC) coefficient methodology, existing data envelopment analysis model, and bivariate spatial autocorrelation models were used to analyze statistical data from 2005 to 2017. We found that the overall CLIU and CLCE values in the YRB exhibited a continuous increase; the average carbon emission total efficiency and carbon emission scale efficiency first decreased and then increased, and the average carbon emission pure technical efficiency gradually decreased. Areas of high CLCE were concentrated in eastern areas of the YRB, whereas those of high CLIU, carbon emission total efficiency, carbon emission scale efficiency, and carbon emission pure technical efficiency predominantly appeared in the eastern areas, followed by central and western areas of the YRB. Spatial analysis revealed a significant spatial dependence of CLIU on CEE. From a global perspective, the spatial correlations between CLIU and CEE changed from positive to negative with time. Moreover, the aggregation degree between CLIU and CEE gradually decreases with time, while the dispersion degree increases with time, and the spatial correlation gradually weakens. The local spatial autocorrelation further demonstrates that the number of high-low and low-high clusters between CLIU and CEE gradually increases over time, while the number of high-high and low-low clusters gradually decreased over time. Collectively, these findings can help policymakers formulate feasible low-carbon and efficient CLIU policies to promote win-win cooperation among regions.

Flow Cytometry and Fecal Indicator Bacteria Analyses for Fingerprinting Microbial Pollution in Karst Aquifer Systems

Microbial pollution of aquifers is a persistent water quality problem globally which poses significant risks to public health. Karst aquifer systems are exceptionally vulnerable to pollution from fecal contamination sources as a result of rapid recharge of water from the surface via discrete pathways linked to highly conductive, solutionally enlarged conduits alongside strong aquifer heterogeneity. Consequently, rapid changes in microbial water quality, which are difficult to monitor with expensive and time-consuming conventional microbiological methods, are a major concern in karst environments. This study examined flow cytometric (FCM) fingerprinting of bacterial cells in groundwater together with fecal indicator bacteria (FIB) at nine separate karst springs of varying catchment size over a 14 month period in order to assess whether such a technique can provide faster and more descriptive information about microbial pollution through such karst aquifer systems. Moreover, the data have also been evaluated with respect to the potential of using turbidity as an easy-to-measure proxy indicator of microbial pollution in a novel way. We argue that FCM provides additional data from which enhanced insights into fecal pollution sources and its fate and transport in such karst catchments can be gained. We also present valuable new information on the potential and limitations of turbidity as an indicator of fecal groundwater contamination in karst. FCM has the potential to become a more widely used tool in the field of contaminant hydrogeology.

Temporal and spatial variability of water quality in an urban wetland and the effects of season and rainfall: a case study in the Daguan Wetland, China

Urban wetlands provide multiple functions including water treatment, recreation, and education, but they are also highly vulnerable, so it is important to monitor wetland water quality to ensure wetland health. In this study, water quality parameters of an urban wetland and rainfall were monitored at 6 sites for 1 year. The correlation analysis of water quality parameters and spatial-temporal variability analysis of water quality were carried out. Besides, the effects of season and rainfall on the wetland water quality were evaluated by the comprehensive water quality identification index (CWQII). These results have shown that there is a significant correlation between nutrient pollutants and Chl-a. Wetland water quality changed with the seasons, but it also varied due to changes in rainfall and location. The water quality of the shallow areas both had high susceptibility and response to seasonal changes and rainfall, but the water quality of the deepwater area was relatively stable. The CWQIIs in different seasons were ranked: Winter (5.98) > spring (4.67) > autumn (4.66) > summer (4.26), and the CWQIIs of different rainfall intensities were ranked: torrential rain (5.09) > heavy rain (4.88) > light rain (4.50) > no rain (4.39) > moderate rain (3.95). The results of this study distinctly explained the effects of season and rainfall on water quality in an urban wetland in a subtropical monsoon climate zone and would be helpful to the policymakers and concerned authorities in developing better water quality management strategies for these wetlands.

Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches

Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian approaches such as Bayesian generalized linear model (BGLM), Bayesian regularized neural network (BRNN), Bayesian additive regression tree (BART), and Bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling. The results showed that the Bayesian models used in this study were all competent to model groundwater nitrate and the BART model with R2 = 0.83 was more efficient than the other models. The result of variable importance showed that potassium (K) has the highest importance in the models followed by rainfall, altitude, groundwater depth, and distance from the residential area. The results of the study can support the decision-making process to control and reduce the sources of nitrate pollution.

Development of a Landscape-Based Multi-Metric Index to Assess Wetland Health of the Poyang Lake

Human-induced changes in landscapes are one of the major drivers of wetland loss and degradation. The Poyang Lake wetland in China has been experiencing severe degradation due to human disturbance and landscape modification. Indicators to assess the condition of this wetland are thus needed urgently. Here, a landscape-based multi-metric index (LMI) is developed to evaluate the condition of the Poyang Lake wetland. Twenty-three candidate metrics that have been applied to wetland health assessment in published studies were tested. Metrics that show strong discriminative power to identify reference and impaired sites, having significant correlations with either benthic macroinvertebrate- or vegetation-based indices of biotic integrity (B-IBI or V-IBI), were chosen to form the LMI index. Five of these metrics (largest patch index, modified normalized differential built-up index, Shannon’s diversity index, connectance index, and cultivated land stress index) were selected as our LMI metrics. A 2 km buffer zone around sample sites had the strongest explanatory power of any spatial scale on IBIs, suggesting that protecting landscapes at local scales is essential for wetland conservation. The LMI scores ranged between 1.05 and 5.00, with a mean of 3.25, suggesting that the condition of the Poyang Lake wetland is currently in the “fair” category. The areas along lakeshores were mainly in poor or very poor conditions, while the less accessible inner areas were in better conditions. This study demonstrates significant links between landscape characteristics and wetland biotic integrity, which validates the utility of satellite imagery-derived data in assessing wetland health. The LMI method developed in this study can be used by land managers to quickly assess broad regions of the Poyang Lake wetland.

Determination of the Connectedness of Land Use, Land Cover Change to Water Quality Status of a Shallow Lake: A Case of Lake Kyoga Basin, Uganda

Catchments for aquatic ecosystems connect to the water quality of those waterbodies. Land use land cover change activities in the catchments, therefore, play a significant role in determining the water quality of the waterbodies. Research on the relationship between land use and land cover changes and water quality has gained global prominence. Therefore, this study aimed at determining land use, land cover changes in the catchments of L. Kyoga basin, and assessing their connectedness to the lake’s water quality. The GIS software was used to determine eight major land use and land cover changes for 2000, 2010, and 2020. Meanwhile, water quality data was obtained through both secondary and primary sources. Spearman correlation statistical tool in SPSS was used to correlate the land use, land cover changes, and water quality changes over the two-decade study period. The results showed that different land use and land cover activities strongly correlated with particular water quality parameters. For example, agriculture correlated strongly with nutrients like TP, TN, and nitrates and turbidity, TSS, BOD, and temp. The correlation with nitrates was statistically significant at 0.01 confidence limit. The findings of this study agreed with what other authors had found in different parts of the world. The results show that to manage the water quality of L. Kyoga, management of land use, land cover activities in the catchment should be prioritized. Therefore, the results are helpful to decision and policy makers and relevant stakeholders responsible for water management.

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Managers need to know how to mitigate rising stream water temperature (WT) due to climate change. This requires identifying the environmental drivers that influence thermal regime and determining the spatial area where interventions are most effective. We hypothesized that (i) extreme thermal events can be influenced by a set of environmental factors that reduce thermal sensitivity and (ii) the role played by those factors varies spatially. To test these hypotheses, we (i) determined which of the environmental variables reported to be the most influential affected WT and (ii)identified the spatial scales over which those environmental variables influenced WT. To this end, the influence of multi-scale environmental variables, namely land cover, topography (channel slope, elevation), hydromorphology (channel sinuosity, water level, watershed area, baseflow index) and shade conditions, was analyzed on the three model variables (day thermal sensitivity, night thermal sensitivity, and non-convective thermal flux) in the model developed by Georges et al. (2021) of the temporal thermal dynamics of daily maximum WT during extreme events. Values were calculated on six spatial scales (the entire upstream catchment and the associated 1 km and 2 km circular buffer, and 50 m wide corridors on each side of the stream with the associated 1 km and 2 km circular buffer). The period considered was 17 extreme days during the summer identified by Georges et al. (2021) based on WT data measured every 10 min for 7 years (2012-2018) at 92 measurement sites. Sites were located evenly throughout the Wallonia (southern Belgium) hydrological network. Results showed that shade, baseflow index (a proxy of the influence of groundwater), water level and watershed area were the most significant variables influencing thermal sensitivity. Since managers with finite financial and human resources can act on only a few environmental variables, we advocate restoring and preserving the vegetation cover that limits solar radiation on the watercourse as a cost-effective solution to reduce thermal sensitivity. Moreover, management at small spatial scale (50 m riparian buffer) should be strategically promoted (for finance and staffing) as our results show that a larger management scale is not more effective in reducing thermal sensitivity to extreme events.

A Study on Comprehensive Evaluation Methods for Coordinated Development of Water Diversion Projects Based on Advanced SWOT Analysis and Coupling Coordination Model

The implementation of water diversion projects will exert different influences on upstream water offering areas and the downstream water receiving areas. In order to effectively promote the coordinated development of the two regions, a comprehensive evaluation system for the coordinated development of water transfer projects has been proposed with the Middle Route of the South-to-North Water Transfer Project as the research object. The system conducts a multidimensional evaluation of social development, economic development, and ecological environmental impact, and builds a comprehensive evaluation index system with fifteen evaluation indexes at three levels, with the indexes weighted through the comprehensive weighting method based on the combination of the G1 method and the entropy weight method. Based on the degree of coordinated development among various systems, the coordinated development of the Middle Route of the South–North Water Transfer Project is graded through a comprehensive evaluation. This method is tested in the decision support system of the Middle Route Construction and Administration Bureau, China. The results show that: (1) The coupling coordination degree value of the middle route of the South-to-North Water Diversion Project is 0.8912, which shows that the regional development of the water transfer project is high coupled coordination. (2) The coordination between the economic system and the ecological environment system is weaker than the coordination between the economic system and the social service system, and the coordination between the ecological and social services is the best. Finally, based on an advanced SWOT analysis of the future development of the middle route of the South-to-North Water Diversion Project, effective suggestions for regional development are provided. It provides reference or guidance for the competent authority to manage the water diversion project and the central government to comprehensively evaluate the effectiveness of the water diversion project.

Relating Land Use/Cover and Landscape Pattern to the Water Quality under the Simulation of SWAT in a Reservoir Basin, Southeast China

Understanding the relationship between land use/cover pattern and water quality could provide guidelines for non-point source pollution and facilitate sustainable development. The previous studies mainly relate the land use/cover of the entire region to the water quality at the monitoring sites, but the water quality at monitoring sites did not totally reflect the water environment of the entire basin. In this study, the land use/cover was monitored on Google Earth Engine in Tang-Pu Reservoir basin, China. In order to reflect the water quality of the whole study area, the spatial distribution of the determinants for water quality there, i.e., the total nitrogen and total phosphorus (TN&TP), were simulated by the Soil and Water Assessment Tool (SWAT). The redundancy analysis explored the correlations between land use/cover pattern and simulated TN&TP. The results showed that: (1) From 2009 to 2019, forest was the dominant land cover, and there was little land use/cover change. The landscape fragmentation increased, and the connectivity decreased. (2) About 25% TP concentrations and nearly all the TN concentrations at the monitoring points did not reach drinking water standard, which means nitrogen and phosphorus pollution were the most serious problems. The highest output per unit TN&TP simulated by SWAT were 44.50 kg/hm2 and 9.51 kg/hm2 and occurred in areas with highly fragile landscape patterns. (3) TN&TP correlated positively with cultivated and construction land but negatively with forest. The correlation between forest and TN&TP summited at 500–700-m buffer and construction land at 100-m buffer. As the buffer size increased, the correlation between the cultivated land, and the TN weakened, while the correlation with the TP increased. TN&TP correlated positively with the Shannon’s Diversity Index and negatively with the Contagion Index. This study provides a new perspective for exporting the impact of land use/cover pattern on water quality.

Understanding the spatiotemporal pollution dynamics of highly fragile montane watersheds of Kashmir Himalaya, India

Pollution of riverine ecosystems through the multidimensional impact of human footprints around the world poses a serious challenge. Research studies that communicate potential repercussions of landscape structure metrics on snowmelt riverine water quality particularly, in climatically fragile Himalayan watersheds are very scarce. Though, worldwide, grasping the influence of land-use practices on water quality (WQ) has received renewed attention yet, the relevance of spatial scale linked to landscape pattern is still elusive due to its heterogenic nature across diverse geomorphic regions. In this work, therefore, we tried to capture the insights on landscape-aquascape interface by juxtapositioning the impacts of landscape structure pattern on snowmelt stream WQ of the whole Jhelum River Basin (JRB) under three varying spatial scales viz., watershed scale, riparian corridor (1000 m wide) and reach buffer (500 m wide). The percentage of landscape pattern composition and configuration metrics in the JRB were computed in GIS utilizing Landsat-8 OLI/TIRS satellite image having 30 m resolution. To better explicate the influence of land-use metrics on riverine WQ with space and time, we used Redundancy analysis (RDA) and multilinear regression (MLR) modeling. MLR selected land-use structure metrics revealed the varied response of WQ parameters to multi-scale factors except for total faecal coliform bacteria (TC) which showed perpetual presence. The reach-scale explained slightly better (76%) variations in WQ than riparian (75%) and watershed (70%) scales. Likewise, across seasonal scale, autumn (75%), winter (83%), and summer (77%) captured the most WQ variation at catchment, riparian, and reach scales respectively. We observed impairing WQ linkages with agriculture, built-up and barren rocky areas across watersheds, besides, pastures in riparian buffer areas, and fragmentation of landscape patches at the reach scale. Due to little appearance of spatial scale differences, a multi scale perspective landscape planning is emphasized to ensure future sustainability of Kashmir Himalayan water resources.

Agricultural impacts drive longitudinal variations of riverine water quality of the Aral Sea basin (Amu Darya and Syr Darya Rivers), Central Asia

River ecosystems are under increasing stress in the background of global change and ever-growing anthropogenic impacts in Central Asia. However, available water quality data in this region are insufficient for a reliable assessment of the current status, which come as no surprise that the limited knowledge of regulating processes for further prediction of solute variations hinders the development of sustainable management strategies. Here, we analyzed a dataset of various water quality variables from two sampling campaigns in 2019 in the catchments of two major rivers in Central Asia-the Amu Darya and Syr Darya Rivers. Our results suggested high spatial heterogeneity of salinity and major ion components along the longitudinal directions in both river catchments, pointing to an increasing influence of human activities toward downstream areas. We linked the modeling outputs from the global nutrient model (IMAGE-GNM) to riverine nutrients to elucidate the effect of different natural and anthropogenic sources in dictating the longitudinal variations of the riverine nutrient concentrations (N and P). Diffuse nutrient loadings dominated the export flux into the rivers, whereas leaching and surface runoff constituted the major fractions for N and P, respectively. Discharge of agricultural irrigation water into the rivers was the major cause of the increases in nutrients and salinity. Given that the conditions in Central Asia are highly susceptible to climate change, our findings call for more efforts to establish holistic management of water quality.

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.

Using citizen science to understand river water quality while filling data gaps to meet United Nations Sustainable Development Goal 6 objectives

This study investigates water quality along the river Liffey in Dublin city with the help of citizen scientists, including the community of river users such as paddle boarders and those accessing the river from the bank. The primary objective was to evaluate water quality near sources of pollution observed by citizens, while filling data gaps for the United Nations (UN) Sustainable Development Goal (SDG) 6, Indicator 6.3.2. The participants used field chemistry kits to measure nitrate (NO₃-N) and phosphate (PO₄-P) at 19 locations on a monthly basis over the course of nine months, recording the results on a smartphone app. 10% of nitrate samples were indicative of low quality water values while 35.6% of phosphate samples were indicative of low quality water. Rainfall over the study period was analysed to investigate the impact of run-off from rainwater on the river. Results indicated that excessive rainfall was not a factor in lower water quality in this area. Citizen scientists' observational notes and photographs entered onto the database, with accompanying test results were key to highlighting pollution sources at specific locations which correlated with high levels of nitrate and phosphate resulting in low quality water. Land use was a factor in these areas of recent housing development indicating possible domestic misconnections. Citizen scientist data has the potential to fulfil UN SDG 6, in contributing to Indicator 6.3.2 while detecting contamination.

Linking hydraulic geometry, land use, and stream water quality in the Taihu Basin, China

Understanding the complexity of catchment-scale human activities, natural factors, and stream water quality is particularly important for basin water resources management. Thorough investigations on how multiple environmental factors quantitatively and simultaneously affect water quality are limited. This study employed Spearman's correlation and ridge regression analysis (RRA) to disentangle the hydraulic geometry and land use contributions to water quality variables (WQVs). Nine and six indicators were used to describe the hydraulic geometry and land use characteristics, respectively, in the Taihu Basin. The results revealed significant correlations between the land use, hydraulic geometry, and stream water quality. Built-up land and cropland negatively impacted the stream water quality, while woodland had the opposite trend. The structure and morphological connectivity of the river network were associated with most WQVs. The hydrologic connectivity characteristics strongly influenced ammonia-nitrogen (NH₃-N), permanganate index (COD_Mn), and dissolved oxygen (DO). Six equations that estimated the stream water quality were established through RRA. Human factors impose a greater impact on the stream water quality than natural factors in the Taihu Basin. Our findings provide quantitative insights to mitigate water pollution via reasonable management and control of the river structure and connectivity and land-use patterns.

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.

Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach

This paper aims to assess the influence of land use and land cover (LULC) indicators and population density on water quality parameters during dry and rainy seasons in a tourism area in Indonesia. This study applies least squares regression (OLS) and Pearson correlation analysis to see the relationship among factors, and all LULC and population density were significantly correlated with most of water quality parameter with P values of 0.01 and 0.05. For example, DO shows high correlation with population density, farm, and built-up in dry season; however, each observation point has different percentages of LULC and population density. The concentration value should be different over space since watershed characteristics and pollutions sources are not the same in the diverse locations. The geographically weighted regression (GWR) analyze the spatially varying relationships among population density, LULC categories (i.e., built-up areas, rice fields, farms, and forests), and 11 water quality indicators across three selected rivers (Ayung, Badung, and Mati) with different levels of tourism urbanization in Bali Province, Indonesia. The results explore that compared with OLS estimates, GWR performed well in terms of their R2 values and the Akaike information criterion (AIC) in all the parameters and seasons. Further, the findings exhibit population density as a critical indicator having a highly significant association with BOD and E. Coli parameters. Moreover, the built-up area has correlated positively to the water quality parameters (Ni, Pb, KMnO4 and TSS). The parameter DO is associated negatively with the built-up area, which indicates increasing built-up area tends to deteriorate the water quality. Hence, our findings can be used as input to provide a reference to the local governments and stakeholders for issuing policy on water and LULC for achieving a sustainable water environment in this region.

Predicting stream water quality under different urban development pattern scenarios with an interpretable machine learning approach

Urban development pattern significantly impacts stream water quality by influencing pollutant generation, build-up, and wash-off processes. It is thus necessary to understand and predict stream water quality in accordance with different urban development patterns to effectively advise urban growth planning and policies. To do so, we collected pollutant concentration data on nitrate (NO₃^--N), total phosphate (TP), and Escherichia coli (E. coli) from 1047 sampling stations in the Texas Gulf Region. We utilized a Random Forest (RF) machine learning model to predict stream water quality under four planning scenarios with different urban densities and configurations. SHapley Additive exPlanations (SHAP) was used to prove the importance of urban development pattern in influencing stream water quality. The spatial variations of the impact of these patterns were explored with Geographically Weighted Regression (GWR). SHAP results indicated that Largest Patch Index (LPI), Patch Cohesion Index (COHESION), Splitting Index (SPLIT), and Landscape Division Index (DIVISION) were the most important urban development pattern metrics affecting stream water quality. The spatial variations of such patterns were shown to impact stream water quality depending on pollutants, seasonality, climate, and urbanization level. RF prediction results suggested that high density aggregated development was more effective in reducing TP and NO₃^--N concentrations than the current sprawl development, but had the potential risk of increasing E. coli pollution in the wet season. The results of this study provide empirical evidence and a potential mechanistic explanation that stream water quality degradation is a consequence of urban sprawl. Lastly, machine learning is a powerful tool for scenario prediction in land use planning to forecast environmental impacts under different urban development pattern scenarios.

Possibility of using multiscale normalized difference vegetation index data for the assessment of total suspended solids (TSS) concentrations in surface water: A specific case of scale issues in remote sensing

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R² = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions.