Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Impact of Landscape Patterns on Water Quality in Urbanized Rivers at Characteristic Scale: A Case of Pearl River Delta, China

The impacts of landscape patterns on river water quality are commonly acknowledged, but understanding the complex processes by which landscape patterns affect water quality is still limited, especially in densely populated urban areas. Exploring the mechanisms through which landscape characteristics influence water quality changes in urbanized rivers will benefit regional water resource protection and landscape-scale resource development and utilization. Utilizing daily water quality monitoring data from rivers in the urbanized area of the Pearl River Delta in 2020, our research employed canonical analysis and partial least squares structural equation modeling (PLS-SEM) to explore the processes and mechanisms of the influence of urbanized river landscape patterns on surface water quality. The results indicated that total nitrogen (TN) was the critical indicator limiting the water quality of rivers in the Pearl River Delta. The landscape composition and configuration indexes exhibited non-linear variations with scale, and the landscape fragmentation was higher closer to the river. Landscape patterns had the most significant influence on water quality under the characteristic scale of a 5.50 km circular buffer zone, and landscape composition dominated the change of water quality of urbanized rivers, among which 30.64% of the percentage patch area of construction (C_PLAND) contributed 46.40% to the explanation rate of water quality change, which was the key landscape index affecting water quality. Moreover, landscape patterns had a higher interpretive rate of 39.29% on water quality in the wet season compared to 36.62% in the dry season. Landscape composition had an indirect negative impact on water quality, with a value of 0.47, by affecting the processes of runoff and nutrient migration driven by human activities, while landscape configuration had an indirect negative impact on water quality, with a value of 0.11. Our research quantified the impacts of landscape patterns driven by human activities on surface water quality and proposed management measures to optimize the allocation of landscape resources in riparian zones of urbanized rivers. The results provide a scientific basis for water quality management and protection in urbanized rivers.

Assessment of water quality and identification of priority areas for intervention in Guanabara Bay basin, Rio de Janeiro, Brazil, using nonparametric and multivariate statistical methods

The Guanabara Bay hydrographic region (GBHR) has served as a central hub for human settlement and resource utilization throughout Brazil's history. However, the region's high population density and intense industrial activity have come at a cost, leading to a significant decline in water quality. This work aimed to identify homogeneous regions in GBHR according to water quality parameters in dry and rainy periods. The following water quality monitoring variables were monitored at 49 gauge stations: total phosphorus (TP), nitrate (NO3-), dissolved oxygen (DO), hydrogenionic potential (pH), turbidity (Turb), thermotolerant coliforms (TCol), total dissolved solids (TDS), biochemical oxygen demand (BOD), water temperature (Tw), and air temperature (Ta). The statistical analysis consisted of determining principal components, cluster analysis, seasonal differences, and Spearman's correlation. The water quality parameter correlations were not expressively influenced by seasonality, but there are differences in the concentrations of these parameters in the dry and rainy periods. In the dry period, urban pressure on water quality is mainly due to fecal coliforms. The resulting clusters delimited areas under urban, agricultural, and forestry influence. Clusters located in areas with high demographic density showed high concentrations of TCol and TP, while clusters influenced by forestry and agriculture had better water quality. In the rainy season, clusters with urban influence showed problems with TCol and TP, in addition to some characteristics in each group, such as high TDS, NO3-, and BOD. Forested areas showed high DO, and clusters under agricultural influence had higher concentrations of TCol, BOD, and NO3- concerning forested regions. The troubling state of sanitation in GBHR occurs in metropolitan regions due to lack of a formal sanitation system.

Multi-evidences investigation into spatiotemporal variety, sources tracing, and health risk assessment of surface water nitrogen contamination in China

A comprehensive understanding of nitrogen pollution status, especially the identification of sources and fate of nitrate is essential for effective water quality management at the local scale. However, the nitrogen contamination of surface water across China was poorly understood at the national scale. A dataset related to nitrogen was established based on 111 pieces of literature from 2000 to 2020 in this study. The spatiotemporal variability, source tracing, health risk assessment, and drivers of China's surface water nitrogen pollution were analyzed by integrating multiple methods. These results revealed a significant spatiotemporal heterogeneity in the nitrogen concentration of surface water across China. Spatially, the Haihe River Basin and Yellow River Basin were the basins where surface water was seriously contaminated by nitrogen in China, while the surface water of Southwest Basin was less affected. Temporally, significant differences were observed in the nitrogen content of surface water in the Songhua and Liaohe River Basin, Pearl River Basin, Southeast Basin, and Yellow River Basin. There were 1%, 1%, 12%, and 46% probability exceeding the unacceptable risk level (HI＞1) for children in the Songhua and Liaohe River Basin, Pearl River Basin, Haihe River Basin, and Yellow River Basin, respectively. The primary sources of surface water nitrate in China were found to be domestic sewage and manure (37.7%), soil nitrogen (31.7%), and chemical fertilizer (26.9%), with a limited contribution from atmospheric precipitation (3.7%). Human activities determined the current spatiotemporal distribution of nitrogen contamination in China as well as the future development trend. This research could provide scientifically reasonable recommendations for the containment of surface water nitrogen contamination in China and even globally.

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.

Effects of landscape changes on water quality: A global meta-analysis

Landscape changes resulting from anthropogenic activities and climate changes severely impact surface water quality. A global perspective on understanding their relationship is a prerequisite for pursuing equity in water security and sustainable development. A sequent meta-analysis synthesizing 625 regional studies from 63 countries worldwide was conducted to analyze the impacts on water quality from changing landscape compositions in the catchment and explore the moderating factors and temporal evolution. Results exhibit that total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) in water are mostly concerned and highly responsive to landscape changes. Expansion of urban lands fundamentally degraded worldwide water quality over the past 20 years, of which the arid areas tended to suffer more harsh deterioration. Increasing forest cover, particularly low-latitude forests, significantly decreased the risk of water pollution, especially biological and heavy metal contamination, suggesting the importance of forest restoration in global urbanization. The effect size of agricultural land changes on water quality was spatially scale-dependent, decreasing and then increasing with the buffer radius expanding. Wetland coverage positively correlated with organic matter in water typified by COD, and the correlation coefficient peaked in the boreal areas (r=0.82, p<0.01). Overall, the global impacts of landscape changes on water quality have been intensifying since the 1990s. Nevertheless, knowledge gaps still exist in developing areas, especially in Africa and South America, where the water quality is sensitive to landscape changes and is expected to experience dramatic shifts in foreseeable future development. Our study revealed the worldwide consistency and heterogeneity between regions, thus serving as a research roadmap to address the quality-induced global water scarcity under landscape changes and to direct the management of land and water.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45-59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83-87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89-91) and fallow land (CN = 88-93). The period 1985-1995 experienced an increased ET scenario (911-1050 mm), while the recent period (2005-2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Effect of the molecular weight of DOM on the indirect photodegradation of fluoroquinolone antibiotics

Dissolved organic matter (DOM) is ubiquitous and widespread in natural water and influences the transformation and removal of antibiotics. Nevertheless, the influence of DOM molecular weight (MW) on the indirect photodegradation of antibiotics has rarely been reported. This study attempted to explore the influence of the molecular weight of DOM on the indirect photodegradation of two fluoroquinolone antibiotics (FQs), ofloxacin (OFL) and norfloxacin (NOR), by using UV-vis absorption and fluorescence spectroscopy. The results showed that indirect photodegradation was considered the main photodegradation pathway of FQs in DOM fractions. Triplet-state excited organic matter (3DOM*) and singlet oxygen (1O2) were the main reactive intermediates (RIs) that affected the indirect photodegradation of FQs. The indirect photodegradation rate of FQs was significantly promoted in DOM fractions, especially in the low molecular weight DOM fractions (L-MW DOM, MW < 10 kDa). The results of excitation-emission matrix spectroscopy combined with parallel factor analysis (EEM-PARAFAC) showed that terrestrial humic-like substances had a higher humification degree and fluorophore content in L- MW DOM fractions, which could produce more 3DOM* and 1O2 to promote the indirect photodegradation of FQs. This study provided new insight into the effects of DOM at the molecular weight level on the indirect photodegradation of antibiotics in natural water.

Multi-spatial-scale land/use land cover influences on seasonally dominant water quality along Middle Ganga Basin

Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO2 + NO3, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model's predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales.

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.

Water quality assessment for organic matter load in urban rivers considering land cover dynamics

The strategy of considering a model that is comparable to the Soil Conservation Service Curve-Number (SCS-CN) method that employs land use maps to estimate the effects of land use on the water quality has considerable potential for application. This paper presents the LUPC (Land Use Pollutant Contribution) Model to estimate water pollution from the watershed land use obtained by satellite image classification (Sentinel-2). It defines that each land use produces a specific pollutant load per unit area, called Pollutant Standard Index (PSI), which undergoes degradation and/or retention until it reaches the river. This decay estimate is based on a Kernel Function. Organic matter (OM) was the pollutant chosen for the definition of the LUPC model and fractions of labile and refractory organic matter (LOM, ROM). The model was applied to the Barigüi River basin, and five samples were collected at 12 points along the river. Water quality parameters such as dissolved organic carbon (DOC) and UV-Visible absorbance in addition to chemical and biological oxygen demand (COD and BOD), dissolved oxygen (DO), and nitrogen and phosphorus fractions were the reference for modeling purposes. The results indicate that organic loads can be estimated from watershed characteristics, despite influence from seasonal influences captured by the PSI values and the basin shape parameter. Considering its versatile response, the LUPC model can be used for integrated water resources and land use planning and management and be indicator of the potential pollution of rivers by OM.

Urbanization and agriculture intensification jointly enlarge the spatial inequality of river water quality

Rivers are severely polluted by multiple anthropogenic stressors. An unevenly distributed landscape pattern can aggravate the deterioration of water quality in rivers. Identifying the impacts of landscape patterns on the spatial characteristics of water quality is helpful for river management and water sustainability. Herein we quantified the nationwide water quality degradation in China's rivers and analyzed its responses to spatial patterns of anthropogenic landscapes. The results showed that the spatial patterns of river water quality degradation had a strong spatial inequality and worsened severely in eastern and northern China. The spatial aggregation of agricultural/urban landscape and the water quality degradation exhibits high consistency. Our findings suggested that river water quality would further deteriorate from high spatial aggregation of cities and agricultures, which reminded us that the dispersion of anthropogenic landscape patterns might effectively alleviate water quality pressures.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

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.

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

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

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

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

The Effect of Rainfall on Aquatic Nitrogen and Phosphorus in a Semi-Humid Area Catchment, Northern China

The impact of rainfall on water quality may be more important in semi-arid regions, where rainfall is concentrated over a couple of months. To explore the impact of rainfall changes on water quality, e.g., nitrogen (TN) and phosphorous (TP), the diversion from Luan River to Tianjin Watershed in the northern semi-humid area was selected as the study area. TN and TP concentrations in rivers and the Yuqiao Reservoir during the three-year high-flow season (2019-2021) were analyzed. The response relationship and influencing factors among the watershed's biogeochemical process, rainfall, and water quality were clarified. The results showed that rainfall in the high flow season mainly controlled the river flow. The concentration of TN and TP in the inflow rivers is regulated by rainfall/flow, while the concentration of TN and TP in the water diversion river has different variation characteristics in the water diversion period and other periods. The lowest annual concentrations of TN and TP were observed in the normal year, while the highest annual concentration was observed in the wet year, indicating that the hydrological process drove the nutrient transport in the watershed. For the tributaries, the Li River catchment contributed a large amount of N and P to the aquatic environment. For the reservoir, the extreme TN concentrations were the same as the tributaries, while the extremes of TP concentrations decreased from the dry year to wet year, which was in contrast to the tributaries. The spatial variation of TN and TP concentrations in the reservoir showed that the concentration decreased following the flow direction from the river estuary to the reservoir outlet. Considering climate change, with the increase of rainfall in North China in the future, the TN and TP transport fluxes in the watershed may continue to increase, leading to the nitrogen and phosphorus load of the downstream reservoir. To ensure the impact of the increase of potential N and P output fluxes in the watershed on the water quality of the reservoir area, it is necessary to strengthen the effective prevention and control of non-point source pollution in the watershed.

Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River

Assessment of river water quality is very important for understanding the impact of human activities on aquatic ecosystems. As the second-largest river in China, the Yellow River's water environment is closely related to the social development and water security of northern China. The Huangshui River is a major tributary of the upper Yellow River, and it supplies water to cities in the lower reaches. In this study, a Takagi-Sugeno (T-S) fuzzy neural network was used to evaluate water quality of the Huangshui River, and pollutant sources were analyzed. The heavy metal pollution index (HPI) was calculated to assess the heavy metal pollution level, and the health risks posed by heavy metal elements were assessed. The results indicated that the main contaminants in the Huangshui River were ammonia nitrogen (NH₃-N) and total phosphorus (TP), which was affected by various activities of industry, agriculture, and urbanization, and the maximum concentration of NH₃-N and TP was 5.90 mg/L and 0.36 mg/L, respectively. The T-S evaluation results of some points in the middle reaches were 3.317 and 3.197, which belonged to Level Ⅳ and the water quality was poor. The concentrations of Cu, Zn and Cr in the river were 0.57-44.58 μg/L, 10-122.50 μg/L and 2-28.67 μg/L, respectively, and they were relatively large. The T-S fuzzy neural network could evaluate water quality, avoiding extreme evaluation results by using fuzzy rules to reduce the influence of pollutant concentrations that are too high or too low. In addition to qualitative categorization of water quality, this approach can also quantitatively assess water quality within a single category. The results of water quality assessment could provide a scientific data support for river management.

Temporal and Spatial Characteristics of River Water Quality and Its Influence Factors in the TAIHU Basin Plains, Lower Yangtze River, China

Water quality pollution has been a serious problem in the Taihu Basin plains, which is a highly urbanized area in China. This study aims to detect the interannual and seasonal changes and spatial patterns of water quality in this region. Based on cluster analysis, Moran’s I, and standard deviational ellipses, the site clusters, spatial heterogeneity of water quality characteristics and identified polluted regions were clarified. Results showed that (1) water quality improved since 2002, and nutrient concentrations were lower in summer and autumn than in winter and spring. (2) The monitoring sites were divided into six clusters according to the water quality during the period from 2010 to 2014. Water quality worsened from Cluster 1 to Cluster 4. Cluster 1 sites were mostly distributed beside the Yangtze River and Taihu Lake. Cluster 4 sites were mainly located along the southeast border near Shanghai, while the remaining sites were separately distributed in the main cities. (3) A polluted region of both total nitrogen (TN) and total phosphorus (TP) was present in the southeastern part of the study area near the border from 2010 to 2014. In addition, polluted regions were most likely to form near the junctions of main cities. (4) Anthropogenic factors had greater impacts on water quality than natural factors. More attention should be given to water quality protection around impervious surface areas due to the greatest considerable effect.

Trends and influential factors of high ecological status mobility in Irish Rivers

The decline in high ecological water status in rivers is a significant concern in European countries. It is thus important to investigate the factors that cause sites to lose high status in order to undertake measures to protect and restore high status water quality. Analysis of 20 years of water quality data reveals strong mobility between high status and non-high status (especially good status) rivers. Associations between this mobility and socio-economic and physical environmental variables were estimated by multinomial logistic regression at national scale and regional scale. Based on reported changes in water quality status cross across 1990, 2000 and 2010, four classes of the mobility of high status were defined in this study: those sites that maintain high status (maintain), enter high status (enter), fluctuate between high and non-high status (fluctuate) and exit from high status (exit). The national results indicate that agricultural activity as indicated by variables representing intensity of livestock farming (organic nitrogen) and tillage farming (cereal share) and elevation had significant negative impacts on high status rivers. Meanwhile, significant differences in population density and septic tank density between 'exit', 'maintain', 'fluctuate' and 'enter' classes indicate that these factors played important roles in the stability of high status rivers. The regional outcomes reveal differential significant pressures across regions. For example, rainfall and elevation had positive impacts on high status rivers in the north-west region, while organic nitrogen had a negative effect in the south-west. This paper demonstrates the challenge in achieving the Water Framework Directive goal of maintaining high status rivers, given the sensitive and highly differentiated nature of areas that have lost high status or fluctuated in and out of high status. This paper also suggests the necessity for localised policies and mitigation measures.

Spatial optimization of the water quality monitoring network in São Paulo State (Brazil) to improve sampling efficiency and reduce bias in a developing sub-tropical region

Water quality monitoring networks (WQMNs) are essential to provide good data for management decisions. Nevertheless, some WQMNs may not appropriately reflect the conditions of the water bodies and their temporal/spatial dimensions, more particularly in developing countries. Also, some WQMNs may use more resources to attain management goals than necessary and can be improved. Here we analyzed the São Paulo State (Brazil) WQMN design in order to evaluate and increase its spatial representativeness based on cluster analysis and stratified sampling strategy focused on clear monitoring goals. We selected water resources management units (UGRHIs) representative of contrasting land uses in the state, with bimonthly data from 2004 to 2018 in 160 river/stream sites. Cluster analysis indicated monitoring site redundancy above 20% in most of the UGRHIs. We identified heterogeneous spatial strata based on land use, hydrological, and geological features through a stratified sampling strategy. We identified that monitoring sites overrepresented more impacted areas. Thus, the network is biased against determination of baseline conditions and towards highly modified aquatic systems. Our proposed spatial strategy suggested the reduction of the number of sites up to 12% in the UGRHIs with the highest population densities, while others would need expansions based on their environmental heterogeneity. The final densities ranged from 1.6 to 13.4 sites/1,000km². Our results illustrate a successful approach to be considered in the São Paulo WQMN strategy, as well as providing a methodology that can be broadly applied in other developing countries.

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.

Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation

A systematic understanding of the spatial distribution of water quality is critical for successful watershed management; however, the limited number of physical monitoring stations has restricted the evaluation of spatial water quality distribution and the identification of features impacting the water quality. To fill this gap, we developed a modeling process that employed the random forest regression (RFR) to model the water quality distribution for the Taihu Lake basin in Zhejiang Province, China, and adopted the Shapley Additive exPlanations (SHAP) method to interpret the underlying driving forces. We first used RFR to model three water quality parameters: permanganate index (COD_Mn), total phosphorus (TP), and total nitrogen (TN), based on 16 watershed features. We then applied the built models to generate water quality distribution maps for the basin, with the COD_Mn ranging from 1.39 to 6.40 mg/L, TP from 0.02 to 0.23 mg/L, and TN from 1.43 to 4.27 mg/L. These maps showed generally consistent patterns among the COD_Mn, TN, and TP with minor differences in the spatial distribution. The SHAP analysis showed that the TN was mainly affected by agricultural non-point sources, while the COD_Mn and TP were affected by agricultural and domestic sources. Due to differences in sewage collection and treatment between urban and rural areas, the water quality in highly populated urban areas was better than that in rural areas, which led to an unexpected positive relationship between water quality and population density. Overall, with the RFR models and SHAP interpretation, we obtained a continuous distribution pattern of the water quality and identified its driving forces in the basin. These findings provided important information to assist water quality restoration projects.

Assessment of global hydro-social indicators in water resources management

Water is a vital element that plays a central role in human life. This study assesses the status of indicators based on water resources availability relying on hydro-social analysis. The assessment involves countries exhibiting decreasing trends in per capita renewable water during 2005–2017. Africa, America, Asia, Europe, and Oceania encompass respectively 48, 35, 43, 20, and 5 countries with distinct climatic conditions. Four hydro-social indicators associated with rural society, urban society, technology and communication, and knowledge were estimated with soft-computing methods [i.e., artificial neural networks, adaptive neuro-fuzzy inference system, and gene expression programming (GEP)] for the world’s continents. The GEP model’s performance was the best among the computing methods in estimating hydro-social indicators for all the world’s continents based on statistical criteria [correlation coefficient (R), root mean square error (RMSE), and mean absolute error]. The values of RMSE for GEP models for the ratio of rural to urban population (PRUP), population density, number of internet users and education index parameters equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe and Oceania, respectively. Scalable equations for hydro-social indicators are developed with applicability at variable spatial and temporal scales worldwide. This paper’s results show the patterns of association between social parameters and water resources vary across continents. This study’s findings contribute to improving water-resources planning and management considering hydro-social indicators.

Microbial Find, Inform, and Test Model for Identifying Spatially Distributed Contamination Sources: Framework Foundation and Demonstration of Ruminant Bacteroides Abundance in River Sediments

Microbial pollution in rivers poses known ecological and health risks, yet causal and mechanistic linkages to sources remain difficult to establish. Host-associated microbial source tracking (MST) markers help to assess the microbial risks by linking hosts to contamination but do not identify the source locations. Land-use regression (LUR) models have been used to screen the source locations using spatial predictors but could be improved by characterizing transport (i.e., hauling, decay overland, and downstream). We introduce the microbial Find, Inform, and Test (FIT) framework, which expands previous LUR approaches and develops novel spatial predictor models to characterize the transported contributions. We applied FIT to characterize the sources of BoBac, a ruminant Bacteroides MST marker, quantified in riverbed sediment samples from Kewaunee County, Wisconsin. A 1 standard deviation increase in contributions from land-applied manure hauled from animal feeding operations (AFOs) was associated with a 77% (p-value <0.05) increase in the relative abundance of ruminant Bacteroides (BoBac-copies-per-16S-rRNA-copies) in the sediment. This is the first work finding an association between the upstream land-applied manure and the offsite bovine-associated fecal markers. These findings have implications for the sediment as a reservoir for microbial pollution associated with AFOs (e.g., pathogens and antibiotic-resistant bacteria). This framework and application advance statistical analysis in MST and water quality modeling more broadly.

Effects of Urbanization on Ecosystem Services in the Shandong Peninsula Urban Agglomeration, in China: The Case of Weifang City

Ecosystem services are the material basis of economic and social development, and play essential roles in the sustainable development of ecosystems. Urbanization can remarkably alter the provision of ecosystem services. Most studies in this area have focused on densely populated metropolises with poor ecological environments, while comparatively few studies have focused on cities with low ecological pressures. Therefore, to avoid continuing to engage in the repetitive pattern of destroying first and rehabilitating later, quantitative analyses of urbanization and ecosystem services should be carried out in representative cities. In this study, based on partial least squares-discriminant analysis, kernel density estimation, and correlation analysis, we quantitatively evaluated the impact of urbanization on ecosystem services in Weifang city. The Data Center for Resources and Environmental Sciences at the Chinese Academy of Sciences and the Institute of Geographic Sciences and Natural Resources Research provided remote sensing data on land use, the gross domestic production (GDP), population data, and ecosystem services. The results were as follows: (1) The variation in population, GDP, and built-up areas consistently increased throughout the study period, whereas the ecosystem service values (ESVs) decreased; (2) food production, raw material production, nutrient cycle maintenance, and soil conservation were decisive ecosystem services that led to vast reductions in ESVs during the process of urbanization; and (3) the negative correlation coefficient between built-up areas and ecosystem services was greater than that between the population or GDP and ecosystem services, which indicated that the impacts of population and economic urbanization on ecosystem services lagged behind the impact of land urbanization. This study provides references for fully recognizing the ecological effects of urbanization, and make suggestions regarding the application of ecosystem services in sustainable development.

Assessing riparian zone changes under the influence of stress factors in higher-order streams and tributaries: Implications for the management of massive dams and reservoirs

Riparian ecosystem services along higher-order streams and connected tributaries may change over time as disturbances continuously increase, resulting in diverse deterioration of buffer zones. How habitat, plant cover, regeneration, erosion, and exotic parameters (riparian health conditions) change within huge dams and reservoirs worldwide is an unanswered question. We used multivariate statistical techniques to assess changes in riparian health parameters affected by disturbances identified in 304 transects within the Three Gorges Dam Reservoir, China, and associated tributaries. Kruskal-Wallis tests (p < 0.01) revealed high diversity in habitat, plant cover, regeneration, erosion, and overall stream condition. There was also notable variance relating to exotic and pressure parameters. The critical variables of riparian health indicators and stress factors identified by principal component analysis explained 58.40% and 74.6% (in the main waterway) and 53.23% and 71.0% (in the tributaries) of the total variance. Among riparian health indicators, one habitat parameter (riparian vegetation width) in the main waterway and one regeneration parameter (tree size classes) in tributaries contributed greatly, along with other specified parameters. Furthermore, stress factors such as farming systems, land-use types, and pollutant activity variables had the highest impact on these water bodies. In comparison, counting stress factors alone showed more deterioration in the main waterway with a range of (r = -0.527- 0.493), as determined using Pearson correlation (p < 0.05). Furthermore, after indexing, the parameters exhibited weaker coefficient values in tributaries, where exotic correlated negatively with other indexed values. These findings are relevant for managers of massive dam and reservoir ecosystems seeking to mitigate environmental and socioeconomic losses.

Socioeconomic Risks and Their Impacts on Ecological River Health in South Korea: An Application of the Analytic Hierarchy Process

It is imperative to develop a methodology to identify river impairment sources, particularly the relative impact of socioeconomic sources, to enhance the efficiency of various river restoration schemes and policies and to have an internal diagnosis system in place. This study, therefore, aims to identify and analyze the relative importance of the socioeconomic factors affecting river ecosystem impairment in South Korea. To achieve this goal, we applied the Analytical Hierarchy Process (AHP) to evaluate expert judgement of the relative importance of different socioeconomic factors influencing river ecosystem impairment. Based on a list of socioeconomic factors influencing stream health, an AHP questionnaire was prepared and administered to experts in aquatic ecology. Our analysis reveals that secondary industries form the most significant source of stream ecosystem impairment. Moreover, the most critical socioeconomic factors affecting stream impairment are direct inflow pollution, policy implementation, and industrial wastewater. The results also suggest that the AHP is a rapid and robust approach to assessing the relative importance of different socioeconomic factors that affect river ecosystem health. The results can be used to assist decision makers in focusing on actions to improve river ecosystem health.

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.

Spatiotemporal variation of nitrogen and phosphorus and its main influencing factors in Huangshui River basin

The foundation of managing excess nutrients in river is the identification of key physical processes and the control of decisive influencing factors. The existing studies seldom consider the influence of rainfall-runoff relationship and only focus on a few anthropogenic activities and natural attributes factors. To address this issue, a comprehensive set of influencing factors including rainfall-runoff relationship (represented by runoff coefficient), basic physical and chemical parameters of water quality, land use types, landscape patterns, topography, and socioeconomic development was constructed in this study. M-K test and cluster analysis were conducted to identify the temporal mutation and spatial clustering characteristics of NH₃-N and TP in Huangshui River basin, respectively. Partial least squares regression was used to elucidate the linkages between water contaminants and the factors. As shown in the results, the temporal mutations of NH₃-N and TP were obvious in the middle reaches, with 4 out of 7 catchments in the middle reaches have a larger number of mutations of NH₃-N than other catchments. The cluster analysis results of NH₃-N and TP among catchments were similar. This study also indicated that although the Huangshui River basin was located in the upper reaches of the Yellow River, the influences of rainfall-runoff relationship on spatiotemporal changes of NH₃-N and TP in its sub-basins were limited. Only the temporal change of NH₃-N in Jintan catchment in the upstream area was significantly affected by runoff coefficient. The indexes of proportion of water area (PWA), proportion of impervious area (PIA), and proportion of primary industry (PPI) were the top three influencing factors of temporal variation of NH₃-N and TP for most catchments in the middle reaches. The temporal change of NH₃-N in Jintan catchment in the upstream area was obviously affected by runoff coefficient. The spatial variation of NH₃-N and TP were all affected by PWA and proportion of secondary industry significantly. The results of this study can provide theoretical basis and technical support for the control and management of nitrogen and phosphorus pollution in upper reaches of rivers.

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.

Retrospective analysis of estrogenic endocrine disruption and land-use influences in the Chesapeake Bay watershed

The Chesapeake Bay is the largest estuary in the United States and its watershed includes river drainages in six states and the District of Columbia. Sportfishing is of major economic interest, however, the rivers within the watershed provide numerous other ecological, recreational, cultural and economic benefits, as well as serving as a drinking water source for millions of people. Consequently, major fish kills and the subsequent finding of estrogenic endocrine disruption (intersex or testicular oocytes and plasma vitellogenin in male fishes) raised public and management concerns. Studies have occurred at various sites within the Bay watershed to document the extent and severity of endocrine disruption, identify risk factors and document temporal and spatial variability. Data from these focal studies, which began in 2004, were used in CART (classification and regression trees) analyses to better identify land use associations and potential management practices that influence estrogenic endocrine disruption. These analyses emphasized the importance of scale (immediate versus upstream catchment) and the complex mixtures of stressors which can contribute to surface water estrogenicity and the associated adverse effects of exposure. Both agricultural (percent cultivated, pesticide application, phytoestrogen cover crops) and developed (population density, road density, impervious surface) land cover showed positive relationships to estrogenic indicators, while percent forest and shrubs generally had a negative association. The findings can serve as a baseline for assessing ongoing restoration and management practices.

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

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

Comparing Trace Elements (As, Cu, Ni, Pb, and Zn) in Soils and Surface Waters among Montane, Upland Watersheds and Lowland, Urban Watersheds in New England, USA

Trace element biogeochemistry from soils to rivers is important for toxicity to aquatic ecosystems. The objective of this study was to determine whether trace element exports in contrasting watersheds are controlled by their abundance in soil, current land uses in the watershed, or geologic processes. Upland soils and river water samples were collected throughout the Deerfield watershed in southern Vermont and western Massachusetts and in the Quinebaug and Shetucket watersheds of eastern Connecticut. Soil concentrations were only an important predictor for dissolved Fe export, but no other trace element. Soil pH was not correlated with normalized dissolved exports of trace elements, but DOC was correlated with normalized dissolved Pb and Ni exports. The limited spatial and depth of soil sampling may have contributed to the poor correlation. Surprisingly, linear regressions and principal component analysis showed that human development was associated with higher soil trace metal concentrations but not significantly correlated with dissolved trace elements export. Instead, forest abundance was a strong predictor for lower Cu, Pb, and Zn soil concentrations and lower As, Fe, Ni and Pb dissolved exports across the watersheds. Dissolved exports of Al, K, and Si suggest that enhanced mineral dissolution in the montane watersheds was likely an important factor for matching or exceeding normalized pollutant trace element exports in more urbanized watersheds. Further studies are needed to evaluate subsurface/hyporheic controls as well as soil–surface water interface to quantify exchange and transport.

Effect of Urbanization on Ecosystem Service Values in the Beijing-Tianjin-Hebei Urban Agglomeration of China from 2000 to 2014

Rapid urbanization has led to the continuous deterioration of the surrounding natural ecosystem. It is important to identify the key urbanization factors that affect ecosystem services and analyze the potential effects of these factors on the ecosystem. We selected the Beijing, Tianjin, and Hebei (BTH) urban agglomeration to investigate these effects, and designed three indicators to map the urbanization level: Population density, gross domestic product (GDP) density, and the construction land proportion. Four indicators were chosen to quantify ecosystem services: Food production, carbon sequestration and oxygen production, water conservation, and soil conservation. To handle the nonlinear interactions, we used a random forest (RF) method to assess the effect of urbanization on ecosystem services in the BTH area from 2000 to 2014. Our study demonstrated that population density and economic growth were the internal driving forces affecting ecosystem services. We observed changing trends in the effect of urbanization: The effect of population density on ecosystem services increased, the effect of the proportion of construction land was consistent with population density, and the effect of GDP density on ecosystem services decreased. Our results suggest that controlling the population and GDP would significantly influence the sustainable development in large urban areas.

Seasonal occurrence and removal of organophosphate esters in conventional and advanced drinking water treatment plants

In this study, the fate of organophosphate esters (OPEs) in conventional and advanced drinking water treatment plants (DWTPs) was investigated in field scale. In addition, the risk of OPEs by drinking water was assessed. The average total OPE concentrations in raw and treated water were lower in the rainy season (94.3 and 57.1 ng/L, respectively) than dry season (163 and 84.2 ng/L, respectively). Advanced DWTPs showed better removal efficiencies of major OPEs rather than those in conventional DWTPs. The average removal rates for two chlorinated OPEs, including tris(2-chloroethyl)phosphate (TCEP) and tris(1-chloro-2-propyl)phosphate (TCIPP), were negative (TCEP: -87%, TCIPP: -41%) for a conventional DWTP but positive (TCEP: 46%, TCIPP: 49%) for advanced DWTPs using granular activated carbon filtration. The average removal rates for advanced DWTPs were statistically higher for the alkyl/aryl OPEs, tri-n-butyl phosphate (TNBP: 67%) and tris(2-butoxyethyl) phosphate (TBOEP: 63%), than those for the conventional DWTPs (TNBP: 21%, TBOEP: 25%). The hazardous quotient (HQ) of major OPEs were lower for advanced DWTPs and water irrigated from upstream sties/reservoir compared to that of conventional DWTPs and water irrigated from downstream sites. We believe that this is the first comparison of OPE removal efficiencies achieved in conventional and advanced DWTPs.

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

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

Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and Google Earth Engine

Spatial and temporal changes in land cover have direct impacts on the hydrological cycle and stream quality. Techniques for accurately and efficiently mapping these changes are evolving quickly, and it is important to evaluate how useful these techniques are to address the environmental impact of land cover on riparian buffer areas. The objectives of this study were to: (1) determine the classes and distribution of land cover in the riparian areas of streams; (2) examine the discrepancies within the existing land cover data from National Land Cover Database (NLCD) using high-resolution imagery of the National Agriculture Imagery Program (NAIP) and a LiDAR canopy height model; and (3) develop a technique using LiDAR data to help characterize riparian buffers over large spatial extents. One-meter canopy height models were constructed in a high-throughput computing environment. The machine learning algorithm Support Vector Machine (SVM) was trained to perform supervised land cover classification at a 1-m resolution on the Google Earth Engine (GEE) platform using NAIP imagery and LiDAR-derived canopy height models. This integrated approach to land cover classification provided a substantial improvement in the resolution and accuracy of classifications with F1 Score of each land cover classification ranging from 64.88 to 95.32%. The resulting 1-m land cover map is a highly detailed representation of land cover in the study area. Forests (evergreen and deciduous) and wetlands are by far the dominant land cover classes in riparian zones of the Lower Savannah River Basin, followed by cultivated crops and pasture/hay. Stress from urbanization in the riparian zones appears to be localized. This study demonstrates a method to create accurate high-resolution riparian buffer maps which can be used to improve water management and provide future prospects for improving buffer zones monitoring to assess stream health.

Application of geographically weighted regression models to predict spatial characteristics of nitrate contamination: Implications for an effective groundwater management strategy

Groundwater nitrate contamination has been the main water quality problem threatening the sustainable utilization of water resources in Jeju Island, South Korea. The spatially varying distribution of nitrate levels associated with complex environmental and anthropogenic factors has been a major challenge restricting improved groundwater management. In this study, we applied ordinary least squares (OLS) regression and geographically weighted regression (GWR) models to determine the relationships between the NO₃-N concentration and various parameters (topography, hydrology and land use) across the island. A comparison between the OLS regression and GWR prediction models showed that the GWR models outperformed the OLS regression models, with a higher R² and a lower corrected Akaike Information Criterion (AICc) value than the OLS regression models. Interestingly, the GWR model was able to provide undiscovered information that was not revealed in the OLS regression models. For example, the GWR model found that orchards (OR) and urban (UR) variables significantly contributed to nitrate enrichment in the certain parts of the island, whereas these variables were ignored as a statistically insignificant factor in the OLS regression model. Our study highlighted that GWR models are a useful tool for investigating spatially varying relationships between groundwater quality and environmental factors; therefore, it can be applied to establish advanced groundwater management plans by reflecting the spatial heterogeneity associated with environmental and anthropogenic conditions.

Assessment of intensive agriculture on water quality in the Culiacan River basin, Sinaloa, Mexico

The percentage of agricultural land cover effect on water quality in Culiacan River basin is studied in this research. The basin contains only intensive cropland as primary economic activity with 60% of the total area. Mathematical relationships between percentages of cropland and total phosphorus (TP) and total nitrogen (TN) concentrations were established. Sampling sites in middle and lower basin and water quality information during 2013-2018 were considered, and percentages of cropland were obtained by geospatial methods including variable area buffers. During rainy season, coefficients of determination were less than 0.2, although quantified nutrient concentration was higher, related to point sources of pollution in the basin. During dry season, coefficients of determination were higher than 0.76 and 0.90 for TN and TP, respectively, with an exponential mathematical trend. Results suggest that intensive agriculture practices generate accelerated loss of soil consolidation, which is transported to water bodies. These soils are in continuous contact with fertilizers and pesticides, mostly organophosphates which have been transported by runoff and underground flows. Using the information generated will help to establish environmental management plans, and to improve environmental diagnosis and effect in countries where there is not enough historical cartographic information and/or water quality data.

Investigation of the spatially varying relationships of PM2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression

PM_2.5 pollution is caused by multiple factors and determining how these factors affect PM_2.5 pollution is important for haze control. In this study, we modified the geographically weighted regression (GWR) model and investigated the relationships between PM_2.5 and its influencing factors. Experiments covering 368 cities and 9 urban agglomerations were conducted in China in 2015 and more than 20 factors were considered. The modified GWR coefficients (MGCs) were calculated for six variables, including two emission factors (SO₂ and NO₂ concentrations), two meteorological factors (relative humidity and lifted index), and two topographical factors (woodland percentage and elevation). Then the spatial distribution of MGCs was analyzed at city, cluster, and region scales. Results showed that the relationships between PM_2.5 and the different factors varied with location. SO₂ emission positively affected PM_2.5, and the impact was the strongest in the Beijing-Tianjin-Hebei (BTH) region. The impact of NO₂ was generally smaller than that of SO₂ and could be important in coastal areas. The impact of meteorological factors on PM_2.5 was complicated in terms of spatial variations, with relative humidity and lifted index exerting a strong positive impact on PM_2.5 in Pearl River Delta and Central China, respectively. Woodland percentage mainly influenced PM_2.5 in regions of or near deserts, and elevation was important in BTH and Sichuan. The findings of this study can improve our understanding of haze formation and provide useful information for policy-making.

The spatial non-stationary effect of urban landscape pattern on urban waterlogging: a case study of Shenzhen City

The problem of urban waterlogging has consistently affected areas of southern China, and has generated widespread concerns among the public and professionals. The geographically weighted regression model (GWR) is widely used to reflect the spatial non-stationarity of parameters in different locations, with the relationship between variables able to change with spatial position. In this research, Shenzhen City, which has a serious waterlogging problem, was used as a case study. Several key results were obtained. (1) The spatial autocorrelation of flood spot density in Shenzhen was significant at the 5% level, but because the Z value was not large it was not very obvious. (2) The degree of impact on flood disasters from large to small was: Built up_ DIVISION > SHDI > Built up_ COHESION > CONTAG > Built up_ LPI. (3) The degree of waterlogging disasters in higher altitude regions was less affected by the landscape pattern. The results of this study highlight the important role of the landscape pattern on waterlogging disasters and also indicate the different impacts of different regional landscape patterns on waterlogging disasters, which provides useful information for planning the landscape pattern and controlling waterlogging.

Effects of Land Use on Stream Water Quality in the Rapidly Urbanized Areas: A Multiscale Analysis

The land use and land cover changes in rapidly urbanized regions is one of the main causes of water quality deterioration. However, due to the heterogeneity of urban land use patterns and spatial scale effects, a clear understanding of the relationships between land use and water quality remains elusive. The primary purpose of this study is to investigate the effects of land use on water quality across multi scales in a rapidly urbanized region in Hangzhou City, China. The results showed that the response characteristics of stream water quality to land use were spatial scale-dependent. The total nitrogen (TN) was more closely related with land use at the circular buffer scale, whilst stronger correlations could be found between land use and algae biomass at the riparian buffer scales. Under the circular buffer scale, the forest and urban greenspace were more influential to the TN at small buffer scales, whilst significant positive or negative correlations could be found between the TN and the areas of industrial land or the wetland and river as the buffer scales increased. The redundancy analysis (RDA) showed that more than 40% variations in water quality could be explained by the landscape metrics at all circular and riparian buffer scales, and this suggests that land use pattern was an important factor influencing water quality. The variation in water quality explained by landscape metrics increased with the increase of buffer size, and this implies that land use pattern could have a closer correlation with water quality at larger spatial scales.

Hydrochemical Characteristics and Water Quality Evaluation of Rivers in Different Regions of Cities: A Case Study of Suzhou City in Northern Anhui Province, China

To study the disparity of river hydrochemical characteristics and water quality in different regions of the city, this paper took the Tuo River in the center of Suzhou, Northern Anhui, China and the Bian River on the edge of the urban area as the research objects, used Piper trigram, Gibbs diagram, and hydrogen and oxygen isotope content characteristics to analyze the geochemical characteristics of surface water in the study area, and then the improved fuzzy comprehensive evaluation method was used to evaluate the water quality. The results showed that the hydrochemical types of the two rivers were SO4-Cl-Na type, and the contents of Na+, K+, SO42−, Cl−, Ca2+, total phosphorus (TP) in the Bian River at the edge of the city were much higher than those in the Tuo River at the center of the city (ANOVA, p < 0.001). Gibbs diagram showed that the ion composition of the two rivers was mainly affected by rock weathering. The results of correlation analysis and water quality evaluation showed that Bian River was greatly affected by agricultural non-point source pollution, and its water quality was poor, class IV and class V water account for 95%, while, for Tuo River, due to the strong artificial protection, class II and class III accounted for 40.74% and 59.26%, respectively, and the overall water quality was better than that of Bian River. The evaluation results of irrigation water quality showed that the samples from Tuo River were high in salt and low in alkali, which could be used for irrigation when the soil leaching conditions were good, while Bian River water samples were high in salt and medium in alkali, which was suitable for irrigation of plants with strong salt tolerance.

Spatially Varying and Scale-Dependent Relationships of Land Use Types with Stream Water Quality

Understanding the complex relationships between land use and stream water quality is critical for water pollution control and watershed management. This study aimed to investigate the relationship between land use types and water quality indicators at multiple spatial scales, namely, the watershed and riparian scales, using the ordinary least squares (OLS) and geographically weighted regression (GWR) models. GWR extended traditional regression models, such as OLS to address the spatial variations among variables. Our results indicated that the water quality indicators were significantly affected by agricultural and forested areas at both scales. We found that extensive agricultural land use had negative effects on water quality indicators, whereas, forested areas had positive effects on these indicators. The results also indicated that the watershed scale is effective for management and regulation of watershed land use, as the predictive power of the models is much greater at the watershed scale. The maps of estimated local parameters and local R² in GWR models showcased the spatially varying relationships and indicated that the effects of land use on water quality varied over space. The results of this study reinforced the importance of watershed management in the planning, restoration, and management of stream water quality. It is also suggested that planners and managers may need to adopt different strategies, considering watershed characteristics—such as topographic features and meteorological conditions—and the source of pollutants, in managing stream water quality.

Influences of Land-Use Dynamics and Surface Water Systems Interactions on Water-Related Infectious Diseases—A Systematic Review

Human interactions with surface water systems, through land-use dynamics, can influence the transmission of infectious water-related diseases. As a result, the aim of our study was to explore and examine the state of scientific evidence on the influences of these interactions on water-related infectious disease outcomes from a global perspective. A systematic review was conducted, using 54 peer-reviewed research articles published between 1995 and August 2019. The study revealed that there has been an increase in the number of publications since 2009; however, few of these publications (n = 6) made explicit linkages to the topic. It was found that urban and agricultural land-use changes had relatively high adverse impacts on water quality, due to high concentrations of fecal matter, heavy metals, and nutrients in surface water systems. Water systems were found as the common “vehicle” for infectious disease transmission, which in turn had linkages to sanitation and hygiene conditions. The study found explicit linkages between human–surface water interaction patterns and the transmission of water-based disease. However, weak and complex linkages were found between land-use change and the transmission of water-borne disease, due to multiple pathways and the dynamics of the other determinants of the disease. Therefore, further research studies, using interdisciplinary and transdisciplinary approaches to investigate and enhance a deeper understanding of these complexities and linkages among land use, surface water quality, and water-related infectious diseases, is crucial in developing integrated measures for sustainable water quality monitoring and diseases prevention.

Spatiotemporal Changes in Ecosystem Services along a Urban-Rural-Natural Gradient: A Case Study of Xi’an, China

Urban areas are the areas that are most strongly affected by human activities, which presents many challenges to the ecosystem and human well-being. Ecosystem services (ES) are a comprehensive indicator to measure the ecological effects of urbanization. To effectively identify and evaluate the impact of urbanization on ES, the spatial-temporal pattern of ES should be considered. According to the level of urbanization, Xi’an city is divided into four regions: the urban core area, the urban extended area, the rural area, and the ecological conservation area, then, five comprehensive ecosystem services (CES) are evaluated by In VEST model. The results showed the following: (1) There is an obvious spatial heterogeneity in the distribution of ES. The ecological conservation area is the hot spot of ES supply, and the low value is mostly distributed in the urban core area. (2) The CES in the urban extended area that has undergone the greatest change between 2000 and 2015, and the rates of change in the ecological conservation area are the smallest. (3) There is a significant correlation between urbanization and ES, and the correction between landscape urbanization and ES is the most significant. (4) The agglomeration relationship between urbanization and ES in different regions is not consistent. Regional division provides a new way to understand the interaction between urbanization and ES in time and space, so as to provide better guidance for policy makers in formulating sustainable development policies to alleviate the loss of ES caused by the process of urbanization.

Assessing Anthropogenic Impacts on Chemical and Biochemical Oxygen Demand in Different Spatial Scales with Bayesian Networks

In order to protect the water environment in seriously polluted basins, the impacts of anthropogenic activities (sewage outfalls and land use) on water quality should be assessed. The Bayesian network (BN) provides a convenient way to model these complex processes. In this study, anthropogenic impacts on chemical oxygen demand (COD) and biochemical oxygen demand (BOD) were evaluated in the Huaihe River basin (HRB) considering dry and wet seasons and different spatial scales. The results showed that anthropogenic activities had the most significant impacts on COD and BOD at the catchment scale. In dry seasons, sewage outfalls played an important role in organic pollution. Farmland became the most important source in wet seasons although it had a “sink” process in dry seasons. Intensive human activities in urban made significant contributions to increased COD levels. Grassland had a negative relationship with organic pollution, especially in dry seasons. Therefore, governments should implement strategies to control organic matters transported from urban and farmland regions. Increasing the efficiency of wastewater treatments and the percentage of grassland in the riparian zone could improve water quality. These results can enhance understanding of anthropogenic impacts on water quality and contribute to efficient management for river basins.

Land use/cover and eco-toxicity indices for identifying metal contamination in sediments of drains, Manzala Lake, Egypt

Six heavy metals in three main drains along the East Nile Delta were estimated to assess the environmental risk and employ land use/cover map of each drain. Composite sediment samples (n = 3) were collected from each drain. The elements were analyzed by Atomic Absorption Spectrophotometer. The order of metal ions in the sediments of three drains of Manzala lake take the following order: Fe > Co > Ni > Cr > Cd > Pb in El-Serw drain, Fe > Ni > Co > Cd > Cr > Pb in Hadous drain and Fe > Cd > Ni > Co > Pb > Cr in Bahr El-Baqar drain. Studied Pollution indices indicate that drains discharged into Manzala Lake are mostly contaminated by metals. Geo-accumulation index showed contamination by Cd in all sites especially in site 13 of Bahr El-Baqar drain and low values to others. The mean probable effect level quotient showed percent of 21% in Hadous and El-Serw drains and 73% probability of being toxic in Bahr El-Baqar drain. The mean effect range median quotient also showed 21% in Hadous and El-Serw to 49% probability of being toxic in Bahr El-Baqar drains. Index of anthropogenicity impact indicate that the man-made activity either agricultural, industrial or fisheries impacted in the appearance of metal ions in the following sequence; Cd > Co > Pb > Ni > Cr. Hazard severity according to hazard quotient and modified hazard quotient of Ni and Cd take the following sequence; El-Serw < Hadous < Bahr El-Baqar drains. For Cr is; Hadous < Bahr El-Baqar < El-Serw and Pb is; Hadous < Elserw < Bahr El-Baqar drains. According to contamination severity index showed low for Pb, Ni and Cr and severe for Co and Cd which take the sequence of; Bahr El-Baqar > El-Serw > Hadous.

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

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